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Associations between Nature Exposure and Health: A Review of the Evidence

Marcia p. jimenez.

1 Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02215, USA

2 Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA 02215, USA; ude.dravrah.hpsh@semajp

Nicole V. DeVille

3 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA; ude.dravrah.hpsh@ttoillee (E.G.E.); ude.dravrah.gninnahc@hcjer (J.E.H.)

Elise G. Elliott

4 Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02215, USA; ude.dravrah.hpsh@ffihcsj (J.E.S.); ude.dravrah.g@tliwg (G.E.W.)

Jessica E. Schiff

Grete e. wilt, jaime e. hart, peter james.

There is extensive empirical literature on the association between exposure to nature and health. In this narrative review, we discuss the strength of evidence from recent (i.e., the last decade) experimental and observational studies on nature exposure and health, highlighting research on children and youth where possible. We found evidence for associations between nature exposure and improved cognitive function, brain activity, blood pressure, mental health, physical activity, and sleep. Results from experimental studies provide evidence of protective effects of exposure to natural environments on mental health outcomes and cognitive function. Cross-sectional observational studies provide evidence of positive associations between nature exposure and increased levels of physical activity and decreased risk of cardiovascular disease, and longitudinal observational studies are beginning to assess long-term effects of nature exposure on depression, anxiety, cognitive function, and chronic disease. Limitations of current knowledge include inconsistent measures of exposure to nature, the impacts of the type and quality of green space, and health effects of duration and frequency of exposure. Future directions include incorporation of more rigorous study designs, investigation of the underlying mechanisms of the association between green space and health, advancement of exposure assessment, and evaluation of sensitive periods in the early life-course.

1. Introduction

The “biophilia hypothesis” posits that humans have evolved with nature to have an affinity for nature [ 1 ]. Building on this concept, two major theories—Attention Restoration Theory and Stress Reduction Theory—have provided insight into the mechanisms through which spending time in nature might affect human health. Attention Restoration Theory (ART) posits that the mental fatigue associated with modern life is associated with a depleted capacity to direct attention [ 2 ]. According to this theory, spending time in natural environments enables people to overcome this mental fatigue and to restore the capacity to direct attention [ 3 ]. The Stress Reduction Theory (SRT) describes how spending time in nature might influence feelings or emotions by activating the parasympathetic nervous system to reduce stress and autonomic arousal because of people’s innate connection to the natural world [ 4 , 5 ]. Further, proponents of the biophilia hypothesis postulate that green spaces provide children with opportunities such as discovery, creativity, risk taking, mastery, and control, which positively influence different aspects of brain development [ 6 ]. Beyond the biophilia hypothesis, there are a number of other pathways through which nature may affect health, including but not limited to increasing opportunities for social engagement and space for physical activity, while mitigating harmful environmental exposures (e.g., air pollution, noise, heat) [ 7 , 8 , 9 , 10 ]. Though evidence is inconsistent, physical activity may serve as an important mechanistic pathway to beneficial health outcomes by providing increased opportunities for outdoor exercise (e.g., walking) and play [ 7 , 8 , 9 ]. Facilitation of social contact is a promising mechanism emerging from recent literature, where natural environments and green space provide an avenue for increased contact with others and a greater sense of community [ 9 , 10 ]. The mechanism’s underlying associations between nature exposure and health outcomes are many, not completely understood, and could act in isolation or synergistically [ 11 ].

While the study of exposure to nature and health outcomes has expanded substantially over recent years, there remain many understudied relationships, mechanisms, and populations. For instance, there is a much more expansive evidence base for associations between nature and health, particularly with experimental studies, in adults than in children. This narrative review synthesizes recent scientific literature on associations between nature and health, highlighting studies conducted among children and youth where possible, published throughout August 2020 and based on: (1) randomized experimental studies of short-term exposure to nature and acute responses; and (2) observational studies of exposure to nature.

A narrative review synthesizes the results of quantitative studies that employ diverse methodologies and/or theoretical frameworks without a focus on the statistical significance of the studies’ results [ 12 , 13 ]. We conducted a keyword search-based review using PubMed Advanced Search on 31 August 2020 for studies published in the last ten years with titles or abstracts containing “greenness”, “green space” or “NDVI” (i.e., normalized difference vegetation index) as the exposure, and “health, “children’s health” or “youth health” as the outcome (National Library of Medicine, Bethesda, MD, USA). Using World Health Organization definitions, we categorized a child as a person younger than 10 years and youth from 10 to 24 years inclusive [ 14 ]. We limited this narrative review to research on human subjects only and included English-language-based, international peer-reviewed articles (e.g., primary research, reviews), online reports, electronic books, and press releases. We included both experimental and observational studies and applied snowballing search methodology using the references cited in the articles identified in the literature search. Each identified item was assessed for relevance by a member of the study team. This review is not comprehensive but is intended to summarize recent literature on nature exposure and health.

In retrieving literature on associations of nature and health, we reviewed a range of research from multiple health-related disciplines, geographic regions, and study populations. Evidence from the experimental and observational studies presented below represents more recent literature (e.g., the last decade) on nature exposure and health, primarily from Western countries.

3.1. Experimental Studies

We found a substantial body of research on natural environment interventions to evaluate the effects of nature on health from an experimental approach. The interventions consisted of active engagement in the natural environment (e.g., walking, running, or other activities), passive engagement (e.g., resting outside or living with a view), or virtual exposure (e.g., watching videos or viewing images of nature) [ 15 , 16 ]. The majority of experimental studies assessed mental health and neurologic outcomes. Results from experimental studies suggested a protective effect of exposure to natural environments on mental health outcomes and cognitive function.

3.1.1. Stress

Several experimental studies have examined perceived stress and other subjective measures of stress, such as sleep quality. A recent systematic review of more than 40 experimental studies indicates that measures of heart rate, blood pressure, and perceived stress provide the most convincing evidence that exposure to nature or outdoor environments may reduce the negative effects of stress [ 17 ]. The results from perceived or reported stress after exposure to natural environments were more consistent than findings from studies using physiological stress measurements (e.g., cortisol levels) among adults. A recent meta-analysis found evidence suggesting that exposure to natural environments may reduce cortisol levels, one of the most frequently studied biological markers of stress. Song et al. [ 18 ] reviewed 52 articles from Japan that examined the physiological effects of nature therapy. There was overwhelming evidence that cortisol levels decreased when participants were exposed to a natural environment. In numerous studies, salivary cortisol levels decreased after mild to moderate exercise in a natural environment compared with an urban environment [ 18 ].

Although many studies have observed significant decreases in measured salivary cortisol levels after exposure to natural environments, others have not observed any significant differences in salivary cortisol levels before and after exposure to natural environments [ 17 , 19 ]. However, a key limitation of using cortisol as a biomarker of stress in experimental studies is the fluctuation of cortisol over a 24-h period. Diurnal cortisol levels need to be taken into account in order to make a fair comparison, and most of the literature on exposure to nature and stress have only studied cortisol levels before and after exposure [ 17 ].

Experimental studies focusing on children or youth are sparse [ 20 , 21 ]. One quasi-experimental study conducted in 10–12 year-olds in a school setting examined the influence of natural environments on stress response [ 22 ]. The researchers observed higher tonic vagal tone, a measure of heart rate variability, in natural environments but found no associations with event or phasic vagal tone.

3.1.2. Affective State

Exposure to natural environments has also been studied in relation to the self-reported affective state, or the underlying experience of feeling, emotion or mood. Although study measures vary, studies among adults have generally observed relationships between exposure to natural environments and affective state, with positive associations with positive emotions and negative associations with negative emotions [ 16 , 22 , 23 ]. A study randomly assigned sixty adults to a 50-min walk in either a natural or an urban environment in Palo Alto, California, and found that compared to urban experience, nature experience led to affective benefits (decreased anxiety, rumination, and negative affect, and preservation of positive affect) as well as cognitive benefits (increased working memory performance) [ 23 ]. In a study investigating forest bathing, or shinrin-yoku, researchers found that time spent in forests was associated with a reduction in reported feelings of hostility, depression, and anxiety among adults with acute and chronic stress [ 24 ]. Another study examining walking in different environments observed the largest and most consistent improvements in psychological states associated with forest walks [ 25 ]. Forest bathing may play an important role in health promotion and disease prevention. However, the lack of studies focused on children or youth limits the generalizability of these findings across a wide age range [ 26 ].

3.1.3. Anxiety and Depressive Mood

Exposure to natural environments has been linked with decreases in anxiety and rumination, which are associated with negative mental health outcomes, such as depression and anxiety [ 23 , 27 ]. Nature-based health interventions (NBI) are interventions that aim to engage people in nature-based experiences with the goal of improving health and wellness outcomes [ 28 ]. One study evaluated a wetland NBI in Gloucestershire, UK, that was designed to facilitate engagement with nature as a treatment for individuals diagnosed with anxiety and/or depression. The study found that the wetland site provided a sense of escape from participants’ everyday environments, facilitating relaxation and reductions in stress [ 27 ]. A recent systematic review and meta-analysis found a reduction in depressive mood following short-term exposure to natural environments [ 21 ]. However, the authors noted that the reviewed studies were generally of low quality due to a lack of blinding of study participants and a lack of information on randomization quality among randomized trials.

3.1.4. Cognitive Function

Experimental studies have examined the impact of brief nature experiences and cognition among adults, investigating cognitive function related to exposure to natural environments, and are consistent with the results from studies among school-aged children. A growing number of studies have found that exposure to natural environments compared with urban environments is associated with improved attention, executive function, and perceived restorativeness [ 16 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. These studies have found statistically significant associations with positive cognitive outcomes, even after short periods of time spent in natural environments. Additionally, an emerging area of research is virtual reality (VR), using eye-tracking and wearable biomonitoring sensors to measure short-term physiological and cognitive responses to different biophilic indoor environments. These studies have found consistent physiological and cognitive benefits in indoor environments with diverse biophilic design features [ 38 , 39 ].

3.1.5. Brain Activity

Exposure to nature has been associated with alterations in brain activity in the prefrontal cortex, an area of the brain that plays an important role in emotional regulation [ 18 , 19 ]. One experimental study among female university students in Japan investigated physiological and psychological responses to looking at real plants compared with images of the same plants [ 40 ]. Although participants reported feelings of comfort and relaxation after seeing either real plants or images of the same plants, a physiological response was observed only after seeing real plants. Seeing real plants was associated with increased oxy-hemoglobin concentrations in the prefrontal cortex, suggesting that real plants may have physiological benefits for brain activity not replicated by images of plants.

3.1.6. Blood Pressure

Two meta-analyses [ 18 , 41 ] found evidence suggesting that exposure to a natural environment reduced blood pressure. Song et al. [ 18 ] reviewed the research in Japan from 52 studies on the physiological effects of nature therapy and found overwhelming evidence that blood pressure levels decreased when participants were exposed to a natural environment. Decreases in both systolic and diastolic blood pressure levels were observed across young healthy populations, as well as populations with hypertension. This suggests that forest walking may lead to a state of physiological relaxation [ 18 ]. Ideno et al. [ 41 ] conducted another systematic review and meta-analysis to synthesize the effects of forest bathing on blood pressure, including 20 trials involving 732 participants including high-school and college-aged youth. The authors found that both systolic and diastolic blood pressure taken in the forest environment were significantly lower than in non-forest environments [ 41 ].

3.1.7. Immune Function

In Japan, forest bathing has been positively associated with human immune function [ 42 ]. A study was conducted in which subjects experienced a 3-day/2-night bathing trip to forest areas, and blood and urine were sampled on days 2 and 3 of the trip. On days 7 and 30 after the trip, it was found that the mean values of natural killer (NK) cells (which play a major role in the immune system) and NK activity were higher on forest bathing days compared with control days [ 43 ]. This effect persisted for 30 days after the trip. A potential pathway for improved immune function is exposure to phytoncides (a substance emitted by plants and trees to protect themselves from harmful insects and germs), which could decrease stress hormones in the human body and increase NK cell activity. Additionally, the findings indicated that a day trip to a forest park also increased the levels of intracellular anti-cancer proteins [ 43 ].

3.1.8. Postoperative Recovery

While there is limited research on the effect of nature on postoperative recovery, a seminal study by Ulrich [ 4 ] investigated recovery after a cholecystectomy on patients with and without a room with a window view of a natural setting. Patients with a view of a natural setting had shorter hospital stays, received fewer negative evaluative comments in the nurse’s notes section of their charts, and took fewer potent analgesics (e.g., opiates) than those patients whose windows faced a brick building wall [ 4 ]. More recent research has successfully replicated the concept that plants and foliage in the hospital environment may have beneficial impacts on surgical recovery in randomized trials [ 44 , 45 ].

3.2. Observational Studies

Cross-sectional observational studies have shown evidence of positive associations between exposure to nature, higher levels of physical activity, and lower levels of cardiovascular disease. Increasingly, longitudinal observational studies have started to examine the long-term effects of exposure to nature on depression, anxiety, cognitive function, and chronic disease. Below, we summarize the key findings on mental health, physical activity, obesity, sleep, cardiovascular disease, diabetes, cancer, mortality, birth outcomes, asthma and allergies, and immune function.

3.2.1. Mental Health

A recent systematic review found limited evidence suggesting a beneficial association with mental well-being in children and depressive symptoms in adolescents and young adults [ 21 ]. However, access to green space has been linked with improved mental well-being, overall health, cognitive development in children [ 46 ], and lower psychological distress in teens [ 47 ]. A study that examined the restorative benefits associated with frequency of use of different types of green space among US-based students found that students who engaged with green spaces in active ways ≥15 min four or more times per week reported a higher quality of life, better overall mood, and lower perceived stress [ 48 ]. Research in the U.S.-based Growing Up Today Study (GUTS) found that increased exposure to greenness measured around the home was associated with a lower risk of high depressive symptoms cross-sectionally (as measured with the McKnight Risk Factor Survey) and a lower incidence of depression longitudinally [ 49 ]. The investigators observed stronger associations in more densely populated areas and among younger adolescents [ 49 ]. Similarly, a study in four European cities (Barcelona, Spain; Doetinchem, The Netherlands; Kaunas, Lithuania; and Stoke-on-Trent, UK) that evaluated childhood nature exposure and mental health in adulthood showed that adults with low levels of childhood nature exposure had, when compared with adults with high levels of childhood nature exposure, significantly worse mental health, assessed through self-reports of nervousness or depression [ 50 ]. Another study of approximately one million Danes over 28 years of follow-up found that high levels of continuous green space presence during childhood were associated with lower risk of a wide spectrum of psychiatric disorders later in life [ 51 ]. A study based in the UK tracked individuals’ residential trajectories for five consecutive years and showed that individuals who moved to greener areas had better mental health than before moving [ 52 ]. Collectively, these studies suggest that implementation of environmental policies to increase urban green space may have sustainable public health benefits.

Novel research has examined green outdoor settings as potential treatment for mental and behavioral disorders, such as attention-deficit/hyperactivity disorder (ADHD). One study demonstrated associations between green space exposure and improvement in behaviors and symptoms of ADHD and higher standardized test scores [ 46 ]. A recent systematic review found significant evidence for an inverse relationship between green space exposure and emotional and behavioral problems in children and adolescents [ 21 ]. Research has also shown that more and better quality residential green spaces are favorable for children’s well-being [ 53 ] and health-related quality of life [ 54 ]. Furthermore, the quality of green space appears to be more important as children age, as associations between green space quality and well-being are stronger in 12–13 year-olds compared with 4–5 year-olds [ 53 ]. In addition, natural features near schools, including forests, grasslands, and tree canopies, are associated with early childhood development, preschoolers’ improvement in socio-emotional competencies [ 55 ], and a decrease in autism prevalence [ 56 ].

Exposure to nature during adulthood also appears to be important for mental health. A study of 94,879 UK adults indicated a consistent protective effect of greenness on depression risk that was more pronounced among women, participants younger than 60 years, and participants residing in areas with low neighborhood socioeconomic status or high urbanicity [ 57 ]. Other innovative studies are starting to examine quantifiable time of exposure to evaluate the duration of time spent in nature that is associated with mental health benefits. For example, using a nationally representative sample of American adults, Beyer et al. [ 58 ] found that individuals who spent 5–6 or 6–8 h outdoors during weekends had lower odds of being at least mildly depressed, compared with individuals who spent less than 30 min outdoors on weekends. Another study from the UK suggested that lower levels of depression were associated with spending five hours or more weekly in a private garden [ 59 ]. Other studies are focused on uncovering which characteristics of green space are the determinants of mental health benefits. A UK study examined neighborhood bird abundance during the day and found inverse associations with prevalence of depression, anxiety, and stress [ 60 ].

The collective results from these studies suggest that nearby nature is associated with quantifiable mental health benefits, with the potential for lowering the physical and financial costs related to poor mental health. Most of these studies are cross-sectional, and reverse causation is possible. However, researchers are employing novel designs to examine the relationship between green space and mental health. For example, in a study of twins enrolled in the University of Washington Twin Registry, increased greenness was associated with decreased risk of self-reported depression, stress, or anxiety; however, only the results for depression were robust in within-twin pair analyses, suggesting the effect of green space on depression cannot be explained by genetics alone [ 61 ]. Finally, it is important to note that technological advancements have yielded improvements in assessments of exposure to nature and mental health. For instance, one study among adults 18–75 years of age used smartphones equipped with ecological momentary assessment applications to track location, physical activity, and mood for consecutive days, and found positive associations with feeling happy and restored or relaxed within 10 min of exposure to natural outdoor environments [ 62 ]. More novel studies such as these will bolster the evidence behind exposure to nature and mental health among children and/or youth.

3.2.2. Physical Activity

An extensive body of literature documents the impacts of access to green spaces or surrounding greenness on physical activity in children and adults. Proximity to green spaces may promote physical activity by providing a space for walking, running, cycling, and other activities. Although the bulk of the literature is cross-sectional, most studies (in both children and adults) have observed higher levels of physical activity in areas with more access to green space. For example, a study in Bristol, UK, evaluated associations between accessibility to green space and the odds of respondents achieving a recommended 30 min or more of moderate activity five times a week; respondents who lived closest to the type of green space classified as a formal park were more likely to achieve the physical activity recommendation [ 63 ]. Another study of adults in the UK found that people living in greenest compared with least-green areas were more likely to meet recommended daily physical activity guidelines [ 64 ]. However, another UK-based study did not find associations between road distance to nearest green space, number of green spaces, area of green space within a 2-km radius of residence, or green space quality and physical activity [ 65 ].

Almanza et al. [ 66 ] used GPS and accelerometry data among 208 children in California and found that greenness was associated with higher odds of moderate to vigorous physical activity, when comparing those in the 90th and 10th percentiles of greenness. Additionally, they found that children with >20 min daily green space exposure had nearly 5 times the daily rate of moderate to vigorous physical activity compared with those with nearly zero daily exposure [ 66 ]. Another study of Australian children illustrated that time spent outdoors at baseline positively predicted the amount of physical activity three years later [ 67 ]. In a review of youth health outcomes related to exercising in nature (i.e., “green exercise”), the results of fourteen studies (5 in the UK, 5 in the U.S., 2 in Australia, and 1 in Japan) indicated little evidence that green exercise is more beneficial than physical activity conducted in other locations, although any physical activity was beneficial across settings [ 68 ].

More recent studies have employed more sophisticated study designs to determine whether exposure to greenness increases physical activity. In studies that objectively assessed physical activity via accelerometers, individuals exposed to more greenness tended to be more physically active. For example, in a study of 15-year-olds in Germany, increases in greenness around the home address were associated with increased moderate-to-vigorous physical activity among youth in rural, but not urban, areas [ 69 ]. Another study of children in the UK evaluated momentary green space exposure based on GPS-derived location and contemporaneous physical activity measured by an accelerometer and found higher odds of physical activity in green space (versus outdoor non-green space) for boys but not girls [ 70 ].

3.2.3. Obesity

Green space may influence overweight or obesity through a physical activity pathway [ 71 ]. Some studies have shown that exposure to green space is associated with lower rates of obesity in children [ 67 ] and adults [ 72 ]; however, the results are conflicting. As with physical activity, many early studies were cross-sectional, and findings were more mixed for children than for adults. Some studies reported U-shaped associations with obesity [ 73 ], while other studies reported no association after adjustment for respondent characteristics [ 63 ] or neighborhood socioeconomic status [ 74 ]. Some studies demonstrate effect modification by gender [ 72 ]. Further, one cross-sectional UK-based study found that living in the greenest areas was associated with an increase in risk of being overweight and obese [ 75 ].

In one study of U.S. children, increasing greenness was associated with lower BMI z-scores and lower odds of increasing BMI z-scores between two follow-up times [ 76 ]. Another study of schoolchildren in Spain found that greenness and forest proximity were associated with lower prevalence of being overweight or obese [ 77 ]. One study found that street tree density was associated with lower obesity prevalence in New York City (U.S.) children; however, no association was found with park areas [ 78 ]. In an Australian study, the prevalence of being overweight was 27–41% lower in girls and boys who spent more time outdoors at the study baseline than those who spent less time outdoors [ 67 ]. Another study found that greenness was associated with decreased risk of being overweight but only among those in areas with a greater population density [ 79 ].

3.2.4. Sleep

Exposure to green space may influence sleep duration and quality. For instance, surrounding greenness may serve as a buffer for noise, which would disturb sleep. To date, only a handful of studies have examined these associations, and to our knowledge, even fewer have explored this association in children. A recent systematic review provided evidence of an association between green space exposure and improved sleep quality among adults [ 80 ]. A study of Australian adults who lived in areas with greater than 80% green space demonstrated lower risk of short sleep duration, even after adjustment for other predictors of sleep [ 81 ]. Among U.S. adults participating in the Behavioral Risk Factor Surveillance System survey, natural amenities (e.g., green space, lakes, and oceans) were associated with lower reporting of insufficient sleep, and greenness was especially protective among men and individuals over 65 years of age [ 82 ]. In the Survey of Health in Wisconsin Study, increased tree canopy at the Census block group level was associated with lower odds of short sleep duration on weekdays and suggestive of an association with lower odds of short sleep duration on weekends, although there was no association between tree canopy and self-reported sleep quality [ 83 ]. A nationally representative study of Australian and German children and adolescents found no evidence of significant associations between residential green space and insufficient sleep or poor sleep quality [ 84 ].

3.2.5. Cardiovascular Disease

Exposure to green space may affect levels of physical activity, stress, and high blood pressure that drive cardiovascular disease risk. Recent reviews have found consistent evidence that exposure to residential green space is associated with decreased cardiovascular disease incidence [ 85 , 86 ]. Participants living in areas with lower greenness have higher levels of mortality following a stroke [ 87 ], higher cardiovascular disease mortality [ 88 , 89 ], and higher coronary heart disease [ 90 ]. A study from the UK found that associations between exposure to nature and cardiovascular outcomes differed by gender, where male cardiovascular disease and respiratory disease mortality rates decreased with increasing green space, and no associations were found for women [ 88 ]. Furthermore, the relationships between exposure to greenspace and cardiovascular outcomes may be modified by urbanicity. A recent Australian study showed significantly lower odds of high blood pressure among adults in an urban population when reported green space visits were an average of 30 min or more [ 91 ].

3.2.6. Diabetes

Although limited, evidence regarding the association between green space and type 2 diabetes highlights green space as a possible route for diabetes prevention. There are a few cross-sectional studies that have reported that green space is inversely related to type 2 diabetes among adults [ 92 , 93 ]. Few studies have examined the relationship between green space and diabetes in children. Cross-sectional studies of children found inverse associations between time spent in green spaces and fasting blood glucose levels [ 77 ] and insulin resistance [ 94 ]. A recent longitudinal study conducted on US children found no associations between residential exposure to green space and insulin resistance [ 95 ].

3.2.7. Cancer

Research on the link between green space and cancer is limited and may vary depending on the type of cancer. A recent case-control study examined whether residential green space exposure was related to prostate cancer incidence and found that higher residential greenness was associated with lower risk of prostate cancer [ 96 ], and a separate study of U.S. men demonstrated inverse associations between neighborhood greenness and lethal prostate cancer [ 97 ]. Another study examined the association between green space and several cancer types and found that green space was protective for mouth, throat, and non-melanoma skin cancers but was not associated with colorectal cancer [ 98 ]. A U.S.-based nationwide study of nurses found that residential greenness was inversely associated with breast cancer mortality [ 99 ]. Conversely, another systematic review that evaluated evidence on the association between residential green spaces and lung cancer mortality found no benefits of residential greenness [ 85 ].

3.2.8. Mortality

Many early mortality studies relied on cross-sectional data and could not estimate nature exposure over time [ 100 ], whereas others could not account for important potential confounding by race/ethnicity, individual-level smoking, and area-level socioeconomic factors, such as median home value [ 101 , 102 ]. A UK-wide ecological study found that all-cause mortality was higher in greener cities [ 89 ]. An analysis of greenness and mortality in male and female stroke survivors living in Boston (U.S.) found that greater exposure to greenness was associated with higher survival rates [ 87 ]. Another U.S.-based nationwide study of nurses found a consistent protective relationship between residential greenness and non-accidental mortality [ 103 ]. The greenness–mortality relationship was explained primarily by a mental health pathway, and the relationship was strongest among those who had high levels of physical activity [ 103 ]. A study of 4.2 million adults in the Swiss National Cohort assessed the relationship between residential greenness and mortality, while mutually considering socioeconomic status, air pollution, and transportation noise exposure, and found that higher exposure to green space was associated with lower rates of death from natural causes, respiratory disease, and cardiovascular disease [ 104 ]. Protective effects were stronger in younger individuals and in women and, for most outcomes, in urban (versus rural) and in the highest (versus lowest) socioeconomic quartile. Effect estimates did not change after adjustment for air pollution and transportation noise, suggesting that the protective effect of exposure to nature persists in the absence of pollution sources. Finally, a systematic review and meta-analysis of cohort studies on green space and mortality assessed findings from nine studies, comprising 8.3 million individuals from seven countries across the globe [ 105 ]. Seven of the nine studies demonstrated an inverse relationship between green space exposure and mortality, and the authors recommended wide-scale interventions to increase and manage green spaces in order to improve public health outcomes.

3.2.9. Birth Outcomes

The relationship between exposure to nature and birth outcomes has been studied extensively in analyses across multiple countries. Findings of positive associations between greenness and birth weight and decreased risk of low birth weight are consistent, with stronger associations observed among those of a lower socioeconomic status [ 106 ]. Banay et al. [ 107 ] reviewed studies that examined the association between greenness and maternal or infant health. While the majority of studies were cross-sectional, many studies found evidence for positive associations between greenness and birth weight. Fewer studies demonstrated consistent evidence for an association between greenness and gestational age, preeclampsia, or gestational diabetes. These studies also found that effects were stronger among those of a lower socioeconomic status. A more recent review highlighted the evolving literature showing that higher levels of residential greenness were associated with lower risk of preterm birth, low birth weight, and small gestational-age babies [ 108 ]. Akaraci et al. [ 109 ] conducted a systematic review and meta-analysis of 37 studies on residential green and blue spaces and pregnancy outcomes. Increases in residential greenness were associated with higher birthweight and lower odds of being small for gestational age; however, no significant associations between residential blue spaces and birth outcomes were found.

3.2.10. Asthma/Allergies

Several studies have examined the relationship between greenspace and atopic outcomes, including asthma and allergies. Mechanistically, trees and plants are a source of allergens and respiratory irritants [ 110 ]. However, the biodiversity created by green space could be protective against inflammatory conditions [ 111 , 112 ]. The literature reflects these contrasting hypotheses with mixed findings. Some studies have shown no association between the normalized difference vegetation index (NDVI) or tree canopy cover and asthma [ 113 ], while other studies have shown that living close to forests and parks was positively associated with allergic rhinoconjunctivitis and asthma [ 77 ]. Another study of greenspace and allergies in Germany demonstrated positive associations in urban areas and negative associations in rural areas [ 114 ]. The same investigators examined data from seven birth cohorts across Sweden, Australia, the Netherlands, Canada, and Germany and found that the relationship between residential NDVI and allergic disease was positive in some countries and negative in others [ 115 ]. A study in Spain found proximity to residential greenness to be protective of bronchitis in the Mediterranean region of Spain and protective of wheezing for children in the Euro-Siberian region of Spain [ 116 ]. One study conducted in China examined the relationship between exposure to greenness and parks and asthma and allergies among middle-school-aged children [ 117 ]. The researchers observed no associations between residential greenness exposure and self-reported doctor-diagnosed asthma, pneumonia, rhinitis, and eczema; however, living farther away from a park was associated with decreased odds of currently or ever having asthma. In sum, the relationship between exposure to nature and asthma and allergies is inconsistent, with associations varying in magnitude and direction by geography. One review of fourteen studies suggested an association between early life exposure to urban greenness and allergic respiratory diseases (e.g., asthma, bronchitis, allergic symptoms) in childhood; however, there were inconsistencies among study results, likely due to variability in study design, exposure assessment, outcome ascertainment, and geographic region [ 118 ].

3.3. Natural Experiments/Randomized Controlled Trials of Chronic Outcomes

Beyond smaller experimental studies of short-term outcomes and observational epidemiologic studies of chronic outcomes, there are a few natural experiments and randomized controlled trials that add substantial evidence to the relationship between exposure to nature and health. These quasi-experimental and randomized trials have lower potential for confounding bias to explain observed associations between nature and health. One important study capitalized on a natural experiment when an invasive tree pest, the emerald ash borer, killed over 100 million ash trees in the Midwestern United States [ 119 ]. The investigators found that living in a county infested with the emerald ash borer was associated with a 41% increased risk of cardiovascular disease, and these results were only consistent when looking in metropolitan areas where they could adjust for socioeconomic status. Another innovative study examined the greening of vacant lots in Philadelphia [ 120 ]. This citywide study used a three-arm randomized trial approach to randomize 110 vacant lots to either no intervention, cleaning but no greening, or cleaning and greening. The study found that those living around lots that were greened had substantial decreases in reports of depression, poor mental health, and feelings of worthlessness compared with lots that had no intervention. Those living around lots that were cleaned but not greened showed no difference compared with no intervention. Another ongoing longitudinal study in Sydney, Australia, is evaluating the effects of large-scale investment in green space (e.g., public access points, advertising billboards, walking and cycle tracks, BBQ stations, and children’s playgrounds) on physical activity, mental health, and cardiometabolic outcomes [ 121 ]. This natural experiment utilizes proximity to different areas of the Western Sydney Parklands to define treatment and control groups.

Looking to the future, there are a few randomized trials in progress that will provide fundamental evidence to understand whether adding green pace to cities benefits health. The Green Heart Project in Louisville, Kentucky, will assess risk of diabetes and heart disease, stress levels, and the strength of social ties in 700 participants [ 122 ]. The team will take baseline measurements of air pollution levels and will plant as many as 8000 trees, plants, and shrubs throughout Louisville neighborhoods to create an urban ecosystem that promotes physical activity while simultaneously decreasing noise, stress, and air pollution. During five years of follow-up, participants will receive annual check-ups to evaluate how the increasing greenery has affected their physical and mental health and social ties. A second randomized trial is the ‘Productive Green Infrastructure for Post-industrial Urban Regeneration’ or ProGIreg, a multi-city study examining the potential effects of green infrastructure [ 123 ]. This project is based in Dortmund (Germany), Turin (Italy), Zagreb (Croatia) and Ningbo (China) where Living Labs are hosted and nature-based solutions are developed, tested, and implemented. Although health is not the main focus of this study, researchers are hoping to incorporate health metrics into the study design to examine pre- and post-intervention outcome data. Collectively, these randomized trials, natural experiments, and pre-post study designs will establish crucial data on whether interventions to incorporate nature into cities can measurably improve health.

3.4. Effect Modification/Susceptible Populations

Inequitable distribution of green spaces could exacerbate health inequalities if people who are already at greater health risks (e.g., people with lower socioeconomic status) have limited access. Many studies have indicated that disadvantaged populations have decreased access to nature and greenspace [ 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 ]. At the same time, evidence suggests that exposure to nature disproportionately benefits disadvantaged populations, a phenomenon known as the equigenic effect of green space, which upends the expected association between lower socioeconomic status and greater risk of poor health outcomes [ 133 ]. Based on the theory of equigenic environments, one study showed that populations exposed to the greenest environments also had the lowest levels of health inequality related to income deprivation, suggesting that green space might be an important factor in reducing socioeconomic health disparities [ 89 ]. A review of 90 studies on green space and health outcomes demonstrated that individuals of lower socioeconomic status showed more beneficial effects than those of higher socioeconomic status; the authors found no significant differences in the protective effects of green space on health outcomes among different racial/ethnic groups [ 134 ]. The evidence is inconsistent, and more work is needed to elucidate potential mechanisms.

Conversely, improvements in access to green space may lead to “green gentrification,” an increase in property values that displaces low-income residents from their neighborhoods [ 129 , 135 , 136 , 137 ]. This process needs to be studied and understood so that its adverse effects can be prevented. Other cultural and contextual factors may affect nature preferences and experience of nature. For instance, there is evidence that the legacy of forced labor, lynching, and other violence may evoke deeply disturbing associations with trees, fields, and forests among some African Americans [ 138 , 139 ]. Similarly, some people may prefer open fields for sports, while others prefer picnic facilities for socializing.

4. Discussion

The purpose of this narrative review was to summarize recent experimental and observational literature on associations between nature exposure and health in adults and children/youth. While some associations between nature and health outcomes are well-studied, our review highlights the lack of studies, particularly experimental, among child/youth and other susceptible populations. We found evidence for associations between exposure to nature and improved cognitive function, brain activity, blood pressure, mental health, physical activity, and sleep. Results from experimental studies indicated protective effects of nature exposure on mental health and cognitive function. Cross-sectional observational studies provide evidence of positive associations between nature exposure, higher levels of physical activity and lower levels of cardiovascular disease. Observational studies, natural experiments, and randomized controlled trials are starting to assess the longitudinal effects of exposure to nature on depression, anxiety, cognitive function, chronic disease, and other health outcomes. Our review synthesizes recent literature, primarily from Western countries; thus, a limitation of this review is that we may not have captured all relevant literature from outside our publication range or across all geographic regions.

4.1. Data Gaps and Limitations

There are several limitations in the literature on exposure to nature and health. First, definitions of nature are inconsistent across studies. Further, the impacts of the quality of green space, duration of exposure to nature, frequency of exposure, or type of nature exposure on health outcomes are not well understood. Second, methods for measuring exposure to nature (e.g., percentage of residential greenness versus distance to the closest park) or defining the relevant geographic area of exposure (e.g., 500 m away from our home versus 1 km or 10 km) are inconsistent [ 140 , 141 ]. We must also develop methods to elucidate thresholds for dose and duration of nature exposure to achieve a given health effect. Although some studies have determined potential estimates of relevant doses [ 142 ], this area of research is nascent. In addition, standard approaches towards nature exposure assessment do not capture the variations in how people experience nature differentially (e.g., smell, touch, etc.) and have low reproducibility across studies (e.g., inconsistent land-use measures). Third, critical time windows of exposure during the life course that might have the greatest impact on health are also understudied (e.g., early life exposure, childhood exposure). Fourth, mechanistic pathways are understudied. Further, the dynamic relationship between green space, air pollution, noise, temperature, and neighborhood walkability also warrant further exploration, as these factors could be both mediators or moderators of the nature–health relationship [ 143 , 144 ]. We also know little about the potential harms of exposure to nature, most commonly observed in studies of asthma and allergies.

4.2. Future Directions

There are ample promising future directions for nature and health research. First, future research should employ rigorous study designs (e.g., longitudinal studies, randomized controlled trials) and investigate the underlying mechanisms of observed associations between exposure to nature and health outcomes. Although cross-sectional studies dominate the literature, there is increasing evidence emerging from prospective studies, which are essential to investigating causal relationships [ 108 ]. Novel designs, such as quasi-experimental studies and randomized trials, will provide further detail on how nature influences health [ 119 ]. Furthermore, studies should thoroughly evaluate potential biases, such as confounding by socioeconomic status, that may threaten the validity of studies on nature and health. Researchers should rigorously examine factors that may modify the effects of exposure to nature (e.g., socioeconomic status, gender, or race) to determine the subpopulations that might benefit most from exposure to nature. A life-course approach to examining associations between green space and health is also essential. We need to better understand vulnerable time windows in the early life-course where access or exposure to nature may have stronger impacts on health than in other time periods. Similarly, additional research assessing dose-response relationships (e.g., duration of time in nature or quantity of vegetation) is crucial to determine the minimum amount of exposure to green space needed to yield health benefits or if the relevant dosage varies across the life-course or across different countries/settings [ 142 ].

Second, future studies should make use of novel datasets and computational approaches that may provide rapid advances in exposure assessment. Emergence of advanced satellite and aerial photos combined with machine learning to develop tree canopy measures and other more specific metrics of nature provide information on specific species on the ground. Google Street View and other ubiquitous geocoded imagery, when combined with machine learning, also provide scalable approaches to estimate specific natural features from the on the ground perspective as human beings experience them [ 145 ]. Combined with geocoded residential addresses or GPS data and health or behavioral data, these approaches may unveil novel insights on how nature exposure affects health. Leveraging smartphones with GPS and accelerometry enable fine-scale information on exposure and physical activity. Ecological momentary assessment (EMA) or micro-surveys administered through smartphones can be used to ask about processes for how and why people interact with nature [ 62 ]. EMA can also be applied to estimate mental health outcomes in real time, and these responses can be geo-tagged and linked to spatial measures of natural environments. In addition, consumer wearable devices (e.g., FitBit) provide objective information on physical activity patterns, heart rate, sleep, and other biometrics down to the second level [ 146 ]. These data will prove crucial to better understand the behavioral mechanisms through which nature exposure impacts health. We should also capitalize on geo-located social media data (Flickr, Twitter, Facebook) and other data sources to understand exposure to nature [ 147 ]. Innovative metrics of mental health, such as skin conductivity, cortisol (stress), heart rate variability, brain activity through EEG, and functional MRI, can also provide information on stress processes when individuals encounter natural environments [ 148 ]. Such measures of nature exposure and time spent in nature should be incorporated into large federal data collection efforts, such as the Behavioral Risk Factor Surveillance System (BRFSS), National Health Interview Survey (NHIS), and National Health and Nutrition Examination Survey (NHANES) in the United States or the Health Survey for England (HSE) in the United Kingdom. These recommendations cannot be accomplished without also considering the impacts climate change is currently having and will have on exposures to nature, and how climate change may alter the relationship individuals have with nature.

Third, future studies on nature and mental health should focus more on positive health—happiness, purpose, flourishing—instead of just the absence of negative mental health outcomes. Further, more research is required on natural water features, or blue space [ 149 ], as well as other natural environments.

Fourth, the overwhelming majority of research on nature and health is on urban study populations in North America, Europe, and Australia. Researchers should also focus on different geographic areas, low-income and middle-income settings, and vulnerable or historically marginalized populations where nature benefits might be greatest. Researchers should also work together with communities as they conduct their research to ensure their work addresses the needs of community members.

Finally, we must also recognize the potential unintended consequences of adding green infrastructure in cities. Adding green amenities to cities may entice high-income populations, and the resulting increased property values shape a new conundrum, embodied in the exclusion and displacement associated with so-called green gentrification [ 135 ]. Results from this type of research should also be considered for policies, urban planning, and designing cities.

5. Conclusions

The purpose of this review was to examine recent literature on exposure to nature and health, highlighting studies on children and youth where possible. We assessed the strength of evidence from experimental and observational studies and found evidence for associations between exposure to nature and improved cognitive function, brain activity, blood pressure, mental health, physical activity, and sleep. Evidence from experimental studies suggested protective effects of exposure to natural environments on mental health outcomes and cognitive function. Cross-sectional observational studies provide evidence of positive associations between exposure to nature, higher levels of physical activity and lower levels of cardiovascular disease. Longitudinal observational studies are starting to assess the long-term effects of exposure to nature on depression, anxiety, cognitive function, and chronic disease. Limitations and gaps in studies of nature exposure and health include inconsistent measures of exposure to nature, knowledge of the impacts of the type and quality of green space, and the health effects of the duration and frequency of exposure among different populations (e.g., adults, children, historically marginalized). Future research should incorporate more rigorous study designs, investigate the underlying mechanisms of the association between green space and health, advance exposure assessment, and evaluate sensitive periods throughout the life-course.

Author Contributions

Conceptualization, M.P.J., N.V.D., J.E.H. and P.J.; methodology, M.P.J., N.V.D., J.E.H. and P.J.; writing—original draft preparation, M.P.J., N.V.D., E.G.E., J.E.S., G.E.W., J.E.H. and P.J.; writing—review and editing, M.P.J., N.V.D., E.G.E., J.E.S., G.E.W., J.E.H. and P.J.; supervision, J.E.H. and P.J.; project administration, N.V.D.; funding acquisition, P.J. All authors have read and agreed to the published version of the manuscript.

This research was funded by The National Geographic Society, and NIH grants R00 CA201542, R01 HL150119, T32 {"type":"entrez-nucleotide","attrs":{"text":"ES007069","term_id":"164015192","term_text":"ES007069"}} ES007069 , K99 AG066949, R01 ES028712 and P30 ES000002.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Original research article, data science of the natural environment: a research roadmap.

• 1 Data Science Institute, Lancaster University, Lancaster, United Kingdom
• 2 Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, United Kingdom
• 3 Pentland Centre for Sustainability in Business, Lancaster University, Lancaster, United Kingdom

Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health, and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting 10 top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems.

Introduction

Data science is emerging as a major new area of study, having significant impacts on areas as diverse as eCommerce and marketing, smart cities, logistics and transport, and health and well-being ( Dhar, 2013 ; Provost and Fawcett, 2013 ). To date, there has been little work on data science applied to the understanding and management of the natural environment. This is surprising for two reasons. Firstly, studies of the natural environment are increasingly data rich with a pressing need for new techniques to make sense of the accelerating amount of data being captured about environmental facets and processes. Secondly, climate change is such a major challenge and one would anticipate that data science researchers would be drawn toward this area and the many rich data challenges. However, this is not yet happening.

This paper examines the potential of data science for the natural environment. More specifically, the paper has the following main objectives:

1. To define and map out the emerging field of environmental data science;

2. To systematically discuss the major data challenges in environmental science;

3. To draw up a research roadmap, highlighting the most significant areas requiring further research and collaboration.

As an additional objective, the paper aims to draw others to this field to create a worldwide, cross-disciplinary community to progress the field of environmental data science.

The paper draws on experience from a strategic partnership between the Data Science Institute (DSI) at Lancaster University and the Centre of Ecology and Hydrology (CEH) to create a world-leading Centre of Excellence in Environmental Data Science (CEEDS). This partnership builds on excellence in (cross-disciplinary) environmental science in both CEH and Lancaster, the national capability offered by CEH in terms of data sets and modeling capabilities, and the wide range of cross-disciplinary methodological and computational skills that are present in the Data Science Institute.

Note that our scope is deliberately broad in considering all areas of environmental science, including but not limited the geosphere, hydrosphere, biosphere, and atmosphere; our experiences indicate that there are many more commonalities than differences when considering the data science challenges in the various aspects of the natural environment.

The paper is structured as follows. Section Data Science of the Natural Environment looks more closely at the motivation for a data science of the natural environment. Section Challenges then examines the core challenges associated with environmental science arguing that the challenges are both unique and significant. Following this, section Data Science of the Natural Environment: Revisited provides a more refined statement of the nature and scope of environmental data science. Section Case Study: Modeling Extreme Melt Events on the Greenland Ice Sheet presents a case study of environmental data science in practice, highlighting the potential in this area. The paper then concludes with a series of overall observations culminating in a research roadmap—highlighting the top 10 research challenges of environmental data science.

Data Science of the Natural Environment

What is data science.

Data science is the science of extracting meaning from complex data , hence supporting decision-making in an increasingly complex world ( Baesens, 2014 ). Many commentators use the term “big data” ( Mayer-Schonberger and Cukier, 2013 ; Jagadish et al., 2014 ; Reed and Dongarra, 2015 ) as a synonym for data science. We avoid this term as it emphasizes the “big” whereas the real challenges lie in the complexity and heterogeneity of the underlying data sources—discussed further below.

Researchers agree that data science is an interdisciplinary challenge and a series of data science research institutes have been created, drawing on statistics, computer science, artificial intelligence (AI), social sciences, psychology, economics, health, and so on. These include the Alan Turing Institute in London and Data Science Institutes in Berkeley and Columbia. For some, the emphasis is on algorithmics and computation. We argue that data science research should be problem-driven to ensure that algorithmic and computational breakthroughs are targeted toward real-world problems; and that the most significant and transformational breakthroughs will emerge from research where the disciplinary boundaries become permeable and a range of researchers work together on problems situated in the real world. Furthermore, researchers working on the problem domain should not just be end users but should be first class citizens in the resultant collaborations. This situated philosophy is at the heart of data science research at Lancaster.

We argue that the potential for environmental data science is enormous and indeed understanding, and managing the impact of, environmental change is a grand challenge for the emerging subject of data science. Before developing this argument further, we look more closely at the nature of the environmental sciences.

A Focus on the Natural Environment

Understanding of the natural environment is increasingly important as society struggles to respond to the implications of a changing climate and anthropogenic pressures on finite natural resources, and their impacts on water, energy and food security, infrastructure, human health, natural hazards, and biodiversity. This is also a major cross-disciplinary challenge involving, for example, ecologists, hydrologists, soil scientists, biologists, chemists, physicists, and statisticians. With the need to influence policy and derive well-founded adaptation and mitigation strategies, there is also an increasing emphasis on social science and communication of science.

More generally, it is possible to observe a significant shift in this area toward a “ big” science , which is a science that is more integrative and collaborative. This represents a cultural shift away from individual scientists working within their own (siloed) discipline, with the emphasis now on understanding the full complexities of the natural environment in all its facets. The prime example of this is the move toward natural capital and ecosystem services ( Helm, 2015 ; Potschin et al., 2016 ). Natural capital is concerned with the world's stocks of natural assets, including its soil, water, air, energy sources, and all living entities on the planet. The study of ecosystem services then investigates the sustainable and integrated management of complex ecosystems in the support of the services we need to live, hence reifying the complexity of this management in all its facets including environmental, social, health, and economic considerations ( Muller et al., 2010 ). Future Earth is a further example of a major initiative seeking cross-disciplinary insights (in their case around global sustainability) 1 .

The environmental and earth sciences, as with other areas of science, are also increasingly data-driven ( Hey et al., 2009 ). In parallel, there is a move toward more open data, leading to an open science , and a science that is more transparent and potentially repeatable and/or reproducible 2 .

A Data Science of the Natural Environment?

It is clear that, given the challenges outlined above, earth and environmental sciences should be fully embracing data science and should be at the forefront of this initiative. There are pockets of excellent data science work being carried out in the environmental community, and we reference several examples in throughout the paper, but it will become clear that this work needs to be significantly extended. Similarly, data scientists should be reaching out to this community to offer support. In reality, again, this is not happening to the extent it should. Other areas of science are much further on in embracing data science, most notably physics ( Philip Chen and Zhang, 2014 ) and life sciences ( Marx, 2013 ) (including a wealth of work in bioinformatics; Greene et al., 2014 ; Wang et al., 2015 ). This leaves a significant semantic gap in: (i) the integration of highly complex data sets, (ii) transforming this underlying data into new knowledge, for example around ecosystem services, and (iii) informing policy around, for example, appropriate mitigation and adaptation strategies in the face of climate change.

In summary, there should be a strong symbiotic relationship between data science and the earth and environmental sciences. Earth and environmental sciences need data science, and data science should be responding to the intellectual challenges associated with complex and heterogeneous data. More profoundly, data science should be woven into the very fabric of earth and environmental sciences as we seek a new kind of science and subsequently intellectual breakthroughs that can transform society. Finally, we note that while data science can have a significant impact on the earth and environmental sciences, we qualify this by stating that it is clearly not a “silver bullet” in terms of understanding and responding to environmental change; it must sit alongside other initiatives in the spheres of politics, economics, and so on.

The Data Challenge

Data is central to earth and environmental sciences with significant investments in techniques for managing a wide range of environmental data. The data challenge is quite distinct from many fields of science with the most striking factor being the heterogeneity of the underlying data sources and types of data, hence the inappropriateness of the term “big data” in this field (as discussed in section What Is Data Science? above). More specifically, data science is often annotated using the four “V”s of data: volume, velocity, variety, and veracity ( Jagadish et al., 2014 ). While in many areas of data science, consideration of volume and velocity dominate, in the environment variety and veracity (accuracy/precision) are the most important characteristics. This is not to diminish the first two properties as there are areas where there are very large data sets and where the processing of such data sets can be challenging, e.g., in climate science ( Schnase, 2017 ), but this only helps to exacerbate the issue of variety when considered alongside other data sources. We look at the issues of variety and veracity in more detail below.

Environmental data comes from a wide variety of sources and this is increasingly rapidly with new innovations in data capture:

1. Large volumes of data are collected via remote sensing where environmental phenomena are observed without contact with the phenomena, typically from satellite sensing or aircraft-borne sensing devices, including an increasing use of drones. This includes passive sensing, such as photography or infra-red imagery, and active sensing, e.g., RADAR/LIDAR. The increasing availability of open satellite data, in particular, is a major trend in earth and environmental sciences. For example, the EU Copernicus programme and the associated Sentinel missions, or NASA's LandSat archive are regularly mined for data for a variety of applications (e.g., Langley et al., 2016 ).

2. Other data are collected via earth monitoring systems , which consist of a range of sensor technologies more typically in close proximity with the observed phenomena. Such sensors will monitor a range of parameters around the atmosphere, lithosphere, biosphere, hydrosphere, and cryosphere. Examples include weather stations and monitoring systems for water quality. Historically, such sensing technologies would be placed in the field and visited to periodically download data. It is more common now to have telemetered data providing real-time access to such data streams. Developments around the Internet of Things (IoT) also have the potential to dramatically increase the level of monitoring in the natural environment through real-time access to dense deployments of a wide variety of sensors ( Atzori et al., 2010 ; Nundloll et al., 2019 ).

3. Significant quantities of data are collected through field campaigns involving manual observation and measurement of a range of environmental phenomena and these are increasingly supplemented by citizen science data collected by enthusiasts with strong exemplars in the areas of soils data (e.g., through the use of a mobile application called MySoil; Shelley et al., 2013 ) and biodiversity (e.g., RSPB's Big Garden Bird Watch; Godard et al., 2010 ).

4. There are large quantities of historical records that are crucial to the field. Many of these are digitized but, equally, significant quantities of potentially important information are not, particularly at a local level. Important examples of historical records in the UK context, for example, include: geological survey data and samples, managed by the British Geological Survey (BGS), and meteorological and ice observation records going back to the 1800s as managed by the British Antarctic Survey (BAS).

5. Model output is also a significant generator of environmental data with results from previous model runs often stored for subsequent analysis (see section The Spatial/Temporal Challenge for a more in-depth consideration of modeling).

6. Significantly, there is growing interest (as in many fields) of exploiting data mining , discovering data, and data patterns from the web and social media platforms, such as seeking images showing localized water levels during periods of flood ( Cervone et al., 2016 ) or seeking evidence of air quality problems and impacts on human health ( Mei et al., 2014 ). This area is in its infancy but is likely to grow massively over the next few years.

Together, this adds up to the potential for having environmental data at an unprecedented scale, hence providing major opportunities for science but also key challenges. In particular, it should now be very apparent that variety is a crucial and central issue in environmental data. Data is captured from a wide variety of sources about a wide variety of natural phenomena. The underlying data are highly heterogeneous in terms of how the data are stored and exchanged. Some of the data will be structured, others unstructured (structured data is highly organized with the structure captured by a data model or scheme, whereas unstructured is not). The majority of the data will be quantitative but important qualitative data will also be present. Some of the data will be lodged in an environmental data center and decorated with appropriate meta-data to enhance discovery and interpretation. Other data will be held on individual scientists' PCs. Researchers at the University of Chicago's Computation Institute refer to this latter phenomenon as the “long tail of science,” whereby vast amounts of data are not available for sharing or community analyses due to lack of resources and tools to make them accessible 3 . The data will also cover different geographical regions and be at different spatial scales, which can impact on integration (and ditto with the temporal dimension)—see also section The Uncertainty Challenge below. Some advances have been made around managing variety, particularly around interoperability standards for environmental data, e.g., the Inspire Directive 2007/2/EC, which covers a wide range of sources of spatial data, and also more domain specific proposals such as Water ML from the OGC. In addition, many researchers see linked data as a promising technology to capture the complex interrelationships between different data sets in the natural environment ( Bizer et al., 2010 ; Hitzler and Janowicz, 2013 ). Similarly, a number of initiatives are looking at the semantic web , and the development of ontologies as a means of describing and subsequently supporting the integration of disparate data sets ( Raskin and Pan, 2005 ; Compton et al., 2012 ). Linked data is a “set of best practices for publishing, sharing, and interlinking structured data on the Web [and] its main objective is to liberate data from silos” 4 . Berners-Lee et al. (2001) define the semantic web as “a web of data that can be processed directly and indirectly by computers,” and ontologies then have the important role of capturing the meaning of data, including complex relationships across data.

Veracity is also increasingly important, particularly given new developments alluded to above. For example, how reliable is data emanating from citizen science collection methods? (The answer will also vary significantly depending on the level of expertise of the citizen.) Satellite observations may be of lower fidelity when compared to in situ observations. Similarly, with the growth of the Internet of Things it is likely that expensive and hence almost certainly more accurate instruments may co-exist with dense deployments of cheaper, less reliable, sensors and hence the provenance of data sources must be both stored and factored into data analyses.

Arguably the major trend is to creatively bring together different data sources in terms of understanding relationships across phenomena and also to constrain uncertainty by linking different observed data readings. To be effective though, the data science issues around the four “V”s need to be addressed, especially around variety, and veracity.

Summary of data challenges

Managing the variety and heterogeneity in underlying sources of data, including achieving interoperability across data sets;

Reducing the long tail of science and making all data open and accessible through environmental data centers;

Ensuring all data are enhanced with appropriate semantic meta-data capturing rich semantic information about the data and inter-relationships;

Ensuring mechanisms are in place to both record and reason about the veracity of data;

Finding appropriate mechanisms and techniques to support integration of different data sets to enhance scientific discovery and constrain uncertainty.

The Modeling Challenge

Modeling is the principal tool for understanding the environment and forecasting or projecting environmental change. Modeling enables us to make sense of the data that emanates from the various observation and monitoring techniques described above and, from this, to make predictions about the future and analyze “what-if” scenarios. Models can usefully be classified as either process models , which attempt to capture and/or abstract over the underlying physical processes being considered, or data-driven models , which are based on empirical statistical fits to observations or data derived from more complex models. Many environmental process models are often heavily parameterized, where complex phenomena, or phenomena acting at small scales, are captured by semi-empirical methods and approaches.

Model simulations are often combined to form ensembles in order to explore uncertainty and sensitivities. Ensembles may consist of a number of single model runs or of a number of different models. Single model ensembles may be used to explore the sensitivity to initial starting conditions (e.g., Kay et al., 2015 ) or to investigate the uncertainties associated with the model structure (e.g., perturbed physics ensembles , where parameters are varied within their uncertainty; e.g., Beven and Binley, 1992 ; Carslaw et al., 2013 ). Multi-model ensembles often include individual models run with the same input data in order to compare predicted outcomes, such as the global climate model intercomparisons conducted in support of the IPCC process (CMIP5; Taylor et al., 2012 ). These are referred to as “ ensembles of opportunity ” to emphasize that they are not full explorations of uncertainty.

There is also significant interest in integrated modeling, where multiple environmental and impact models may be combined to address complex real-world problems, particularly problems requiring higher-order systems thinking and holistic solutions ( Laniak et al., 2013 ). Global climate (or Earth system) models can be viewed as integrated modeling systems, where different aspects of the environment (atmosphere, land, ice, oceans etc.) are simulated by individual model components or sub-models. These models often have sophisticated software architectures to manage the associated couplings between the model components ( Alexander and Easterbrook, 2015 ), as illustrated, for example, by the Community Earth System Model (CESM) developed at NCAR, shown in Figure 1 . As can be seen, CESM is made up of different model components, namely CAM4, POP2, CLM4, and CICE4 (representing the atmosphere, oceans, land, and sea ice resp.), interconnected by a coupler, MCT. Each of these model components is a complex model in its own right, perhaps integrating several other smaller model components (e.g., clouds, chemistry, and radiative transfer in the atmospheric model).

Figure 1 . The software architecture of the Community Earth System Model (CESM), taken from Alexander and Easterbrook (2015) and reproduced with the permission of the authors.

There is also interest in integrated modeling in other areas of earth and environmental sciences ranging from combining a small number of models to investigate inter-relationships between phenomena ( Thackeray, 2016 ), through to complex integrated modeling for an understanding of nature's contribution to people (hence requiring models of economic and social factors, for example; Alvarez et al., 2015 ; Harrison et al., 2015 ). This is a very demanding area featuring a number of data science challenges, such as quantifying and propagating uncertainty, and dealing with complex phenomena, interdependent variables, and feedback loops. These integrated modeling systems will often include complex process models coupled with data-driven models, perhaps relying on archived output of process models rather than coupling components online.

One key challenges for integrated environmental modeling is variety , with models being developed by different groups for a wide range of environmental phenomena, operating at a wide range of scales and temporal/spatial resolutions, and perhaps with different representations and data types for the same phenomena. At the software level, models are often written in different languages, with Fortran featuring heavily as a legacy language in many process models, and specialized environments such as R, Matlab, Python, or Julia being used for data-driven models. There is some support for integrating models together, including the Earth System Modeling Framework (ESMF) or the OGC standard OpenMI (Open Modeling Interface). However, major interoperability challenges remain, amplified by the level of heterogeneity in the data being exchanged (as discussed in section The Data Challenge above).

One method to promote model integration and shared access is by taking advantage of the cloud and cloud standards, such as Web Services. Such an approach would also enable modelers to take advantage to the elastic storage and computational resources available in the cloud, permitting management of large ensemble simulations and their analysis with cloud machine learning tool kits for instance. However, existing cloud services are not yet suited to supporting the execution of environmental models and ensemble or integrated model runs. For example, popular Platform as a Service (PaaS) offerings such as MapReduce and Apache Spark ( Dean and Ghemawat, 2004 ; Zaharia et al., 2016 ) have a simple computational model whereby the same computation is carried out on different partitions of large data sets. This is quite different from the requirements of complex models, where different parts of the data might be tightly coupled (e.g., atmospheric and ocean circulation in climate models).

There are also significant issues around the veracity of models and, given unknowns in the accuracy and precision of different models, understanding the impacts when they are combined (e.g., Wilby and Dessai, 2010 ). Models are mostly trained, calibrated, or evaluated against historical data, while recognizing that the past is not necessarily a good indicator of the future, especially given that climate change may take the environment to states outside observations. There has been a long-standing interest in combining models with observations to partially address this problem, a field known as data assimilation (e.g., Lahoz et al., 2010 ). Historically, most work in this area has been carried out in the context of numerical weather prediction, but data assimilation has also been applied to ecology ( Niu et al., 2014 ), the carbon cycle ( Williams et al., 2005 ), and flood forecasting ( Yucel et al., 2015 ; see also Park and Xu, 2017 ). More generally, there is huge potential in combining process models with data-driven models to achieve deeper understanding on environmental change and the uncertainties associated with such change.

Summary of modeling challenges

Moving models to the cloud to support open and shared access to a range of environmental models;

Providing interoperability between the full range models, including process and data-driven models;

Supporting the construction of a range of possible ensemble models;

Supporting integrated modeling including potentially highly complex and multi-faceted models for natural capital assessment;

Reasoning about and managing uncertainty in model runs, including in ensembles and across integrated modeling frameworks.

The Complexity Challenge

The earth is a complex system, even more so when considerations of the earth are folded together with economic and social concerns. Dealing with this inherent complexity is a major challenge for data science. Complexity is itself a major area of study, reflected in the emergence of complexity science as a subject in its own right. Kastens et al. (2009) usefully define a complex system as one that “exhibits the following characteristics:

- feedback loops where change in a variable results in either an amplification (positive feedback) or a dampening (negative feedback) of that change;

- many strongly interconnected variables, with multiple inputs contributing to observed outputs;

- chaotic behavior, i.e., extreme sensitivity to initial conditions, fractal geometry, and self-organized criticality;

- multiple (meta)stable states, where a small change in conditions may precipitate a major change in the system;

- a non-Gaussian distribution of outputs, often where outcomes that are far away from the average are more likely than you might think.”

Of the many definitions of complex systems, this really resonates with studies of the earth and the environment. For example, an analysis of feedback loops has been shown to be core to understanding rebound effects in climate change where technological innovations have failed to slow the rate of emission of greenhouse gasses ( Greening et al., 2000 ; Jarvis et al., 2012 ). Similarly, it is well-understood that in the natural environment everything is interconnected and hence the second bullet strongly aligns with research in this area. As a final example, extreme value theory has emerged to explain phenomena that are far removed from the average and associated with rare events that may otherwise be regarded as outliers. Unsurprisingly, extreme value theory has been applied successfully to environmental science, including in flood prediction ( Tawn, 1988 ).

Dealing with this complexity represents a major challenge for earth and environmental sciences and folding in new methods to deal more explicitly with feedback loops and interconnected variables across spatial scales, would represent a significant breakthrough in many areas of environmental science. Complexity also represents a major challenge for data science with data science also offering interesting perspectives on how to handle complexity, for example the role of machine learning in dealing with and responding to emergent phenomena and in dealing with surprises in complex systems.

Summary of complexity challenges

Managing the complexity of the underlying phenomena, particularly in terms of understanding feedback loops and inter-dependent behaviors, chaotic behaviors, and also extremes;

Developing new data science techniques to deal with and respond to emergent behavior and other complex phenomena.

The Spatial/Temporal Challenge

Studies of the environment are often related to reasoning about natural phenomena across space and time. Estimating spatial or temporal patterns in data and deciphering the effects of covariates across the domain of interest is key to this. Many environmental processes exhibit spatial and/or temporal structure and describing and quantifying such structure is important for enabling robust inference, in terms of patterns and covariate effects, to be drawn. Evaluating such structure within a modeling framework means having to account for second order properties, that is to say the dependence between observations, rather than simple mean effects, which poses many practical modeling challenges. In many instances the dependence structure is captured through a covariance matrix, but in other cases alternative measures may be more appropriate, see for instance Coles et al. (1999) and Davison et al. (2012) for an introduction to tail dependence measures used in extreme value analysis. Inclusion of such second order properties involves additional stochastic processes within the model and standard likelihood based approaches are unsuitable. There has been a large body of work over many years in both time series analyses and spatial statistics where the goal has been to estimate inherent spatial/temporal structure within the observed data and to exploit this for predictive purposes. Traditional examples include ARIMA models, Kriging, Gaussian processes, and spatial point processes. Such approaches have been continuously developed, modified, and improved over many years to overcome limiting assumptions or allow for greater flexibility. Excellent summaries of these approaches are provided in Brockwell et al. (2002) for time series methods and Cressie (1993) and Gelfand et al. (2010) for spatial methods.

Technical developments in several fields have created the opportunity to observe, simulate, and forecast our environment at unprecedented scales of space, time, and complexity. This has led to deluge of spatially and temporally referenced data. Traditional methodological approaches are, however, not well-suited for the era of big data and most scale poorly to handling large spatio-temporal data sets. The computational demands become limiting as the need to handle increasingly large covariance matrices becomes infeasible. There is therefore an increasing challenge to develop approaches that can handle large spatio-temporal data sets and to estimate the fine scale structure within. Heaton et al. (2018) provide a nice summary of the state of the art and comparison of approaches to handle large spatial data, but admit that further research is required for the spatio-temporal setting and for optimizing computational run times. There is therefore a real opportunity here for data science to significantly enhance the state-of-the-art and provide intellectual breakthroughs in capabilities for spatial/temporal reason across large-scale environmental data sets.

A further key challenge is to increase the spatial and temporal resolution of predictions. For example, global climate models typically operate at a resolution of 2 degree grids (equivalent to 200 km at the equator) and there is a desire in the community to significantly increase the resolution, for example to 5 km grids or even 1 km grids in the longer term. Similarly, researchers wish to develop air quality predictions at the level of street “canyons” ( Reis et al., 2015 ) offering more localized warnings of health risk and richer mitigation and/or adaptation strategies. Such analyses typically exploit the availability of multiple data sources via statistical downscaling or data fusion approach. However, challenges remain since there are often mismatches in terms of either, or both of, spatial or temporal scale, and spatial and temporal overlap or representativeness. It is also sometimes the case that different data sources do not measure the same processes. For all of these reasons, it can be very hard to achieve integration across different data sets and models. These are largely unresolved issues in earth and environmental sciences and a key challenge for environmental data science.

Finally, we see that there is great potential in incorporating spatial dependence structures within analytical frameworks that are traditionally focused at single sites and hence evaluating marginal effects. Examples here include extreme value analysis where return levels are typically estimated for each site independently and changepoint analyses where changes are identified on a site-by-site basis. Incorporation of spatial structure into models of this type not only results in more physically realistic models, but also enables a better understanding of the underlying processes and allows sharing of information between sites; the latter is particularly helpful for sites for which there may be little, or no, data. This is a highly active area for research and an area where novel data science insights can provide a significant step forward. Such insights might contribute both to the development of completely novel modeling approaches or, as in the case of extreme value analysis, to the development of a more accessible implementations of existing multivariate and/or spatial methodology.

Summary of spatial/temporal challenges

Providing a range of data science techniques to support sophisticated reasoning across space and time, including areas such as clustering, propagation, and extrapolation, particularly for big data;

Develop data science techniques to achieve the required level of spatial and temporal resolution in scientific studies;

Support the integration of data and models that operate at different spatial and temporal scales;Support the extension of typically marginal analyses to incorporate spatial and/or temporal structure.

The Uncertainty Challenge

This challenge is arguably the defining one for environmental data science. Uncertainty can emanate from a wide variety of sources, including:

- Uncertainty (or veracity) of the underlying data sources/observations, with this becoming even more significant given the newer sources of data discussed in section The Data Challenge;

- Uncertainty related to the choice of model(s) used in experiments;

- Uncertainty related to model structure, including consideration of inter-dependent variables, parameterizations of unresolved processes (e.g., clouds in global climate models), and altogether missing processes and feedback loops;

- Uncertainty related to the initial conditions and assumptions for model runs and the potential sensitivities to small changes in these parameters;

- Uncertainty in the scenarios used for projection (e.g., we do not know what will the greenhouse gas emissions will be in 25 years);

- Uncertainties related to the accuracy of the data used to calibrate or train the models.

Crucially, these all need to be made explicit and folded into a reasoning framework to assess uncertainty. This is an area that is not well-developed in the earth and environmental sciences. Notable counter-examples include the work on UncertWeb ( Bastin et al., 2013 ), which offers a set of mechanisms and tools to represent and support reasoning about uncertainty in modeling scenarios (including the use of UncertML to capture meta-data related to uncertainty), EQUIP 5 and QUMP 6 . In hydrology, GLUE (generalized likelihood uncertainty estimation) has been developed to reason about uncertainties in hydrological modeling ( Beven and Binley, 2014 ). Other approaches to managing uncertainty include Differential Adaptive DREAM ( Vrugt et al., 2009 ) and Bayesian Total Error Analysis ( Kavetski et al., 2005 ). Beven and Lamb (2014) also discuss the important aspect of reasoning about cascading uncertainties in integrated modeling. These are examples of good environmental data science being carried out, but not necessarily rolled out across the sub-disciplines of environmental science (another example being data assimilation in numerical weather prediction as mentioned above).

Uncertainty can also usefully be divided into aleatory and epistemic sources of uncertainty ( Beven and Young, 2013 ). The word aleatory is derived from the Latin word for die or a game of dice and hence represents random variability that derives from “irreducible natural variability” ( Beven, 2015 ). In contrast, epistemic uncertainty arises from lack of knowledge and hence the uncertainties can be reduced by the availability of new knowledge. In other words, aleatory uncertainty can be captured stochastically through “odds” whereas for epistemic uncertainty additional information is always required to assess the level of uncertainty. If this information becomes available, then epistemic uncertainties can become aleatory in nature but the danger is that such uncertainties may be irreducible. Such uncertainties are very hard to deal with through stochastic means. They may then appear “rather arbitrary in their occurrence” and equate to “surprises,” which must then be dealt with in associated model structures ( Beven, 2015 ).

To deal with such uncertainties, Beven (2007) has long argued for new approaches to modeling. His hypothesis is that such epistemic uncertainties will never be accurately captured by probabilistic models and he proposes an approach to models which they deem models of everywhere . In this approach, models operate at very fine spatial resolution, associated with particular places, and many such models co-exist. With this approach, it is then possible to collect local data including data from local historic records and derived from local knowledge (for example from farmers), and this knowledge can help resolve both styles of uncertainty and, in particular, deal with surprises. While derived for hydrology, such an approach has promise for other fields of environmental modeling in managing different sources and styles of uncertainty. Other approaches may also be applicable in this context. For example, machine learning may have a role in dealing with emergent properties and surprises emanating from models and their interactions.

Another interesting possibility is to consider adaptive strategies in response to estimates of uncertainty and indeed in response to more general contextual information around model execution and environmental observation. There is a strong literature in Computer Science around adaptive/self-adaptive/autonomic computing ( Kephart and Chess, 2003 ; McKinley et al., 2004 ; Cheng et al., 2009 ) and there is potential to apply this work in the area of environmental understanding. One example would be to link environmental models and Internet of Things technology whereby the uncertainty in models is used to drive the volume and velocity of the sampled data from the Internet of Things infrastructure. There has been some work in adaptive environmental modeling, for example the use of adaptive mesh refinement to adapt the resolution of model execution in response to the sensitivity or turbulence of the area being modeled ( Cornford et al., 2013 ). There is the potential to go much further though in terms of self-organizing environmental modeling frameworks that adjust their modeling strategies and approaches to reduce uncertainty and more generally achieve the overall goals of the modeling experiment. Once again, there is strong potential to employ machine learning and other related data science methods in this context.

Summary of uncertainty challenges

Reifying uncertainty as a first class entity in all aspects of environmental science related to data and models;

Providing a framework to support reasoning about uncertainty;

Developing data science techniques to deal with epistemic uncertainties including emergent events and surprises emanating from the underlying complexity of the systems being observed or modeled;

Based on uncertainty and other contextual information, seek adaptive strategies for sampling and model execution.

The Cross-Disciplinary Challenge

The cross-disciplinary space associated with environmental data science is shown pictorially in Figure 2 below.

Figure 2 . The cross-disciplinary nature of environmental data science.

This is not just a matter of bringing researchers together from different disciplines. We argue that this requires new means of organization, new methods and indeed a fundamentally new culture of working; and that this is at the heart of the promise of data science as an emerging area of study. This contrasts significantly with current modes of organization and working in universities, research labs and funding councils where research is often categorized and, by implication, siloed.

It is also important, as discussed above, that data science is situated in the problem domain and that we have a data science of the natural environment and not data science for the natural environment. This also poses significant challenges for the modes or organization discussed above.

Summary of cross-disciplinary challenges

Bringing together of a wide variety of disciplines in new data science initiatives targeting the natural environment;

The identification and discovery of new means of organization and fundamentally new modes of working to expedite and maximize innovation at the interface between the many disciplines involved;

To ensure the resultant data science is embedded in the problem domain.

Data Science of the Natural Environment: Revisited

Building on the discussion above, environmental data science is concerned with the development of data science principles and techniques for sense making and decision support related to the natural environment . From the discussion in section Challenges, it is apparent that environmental data science is distinctive with a set of challenges that are unique to this area (particularly when considered collectively). For example, very few other application domains have the same rich legacy of process models, which then must be combined with a data models to develop a more complete understanding; the spatial and temporal dimensions are highly distinctive; the level of heterogeneity across data and process is high; when coupled with issue around uncertainty and complexity, this is a uniquely challenging but exciting field.

One of the over-arching themes that comes across from considering the challenges is that of integration : of a rich variety of data sources, of models, of data with models, and most profoundly of disciplines to work together in interpreting the associated data and models to achieve new scientific insights through a new integrative science. Some of the building blocks of this are more obvious and straightforward such as the role of cloud computing in providing a common platform for the technological aspects of integration, complemented by emerging standards to ensure interoperability. Such platforms also enable a more open and collaborative approach to science, providing a catalyst and common focus for the necessary cross-disciplinary collaboration. Other aspects are more challenging, particularly the challenges of meaningful cross-disciplinary collaboration, and this requires profound shifts in culture, method, and organization to be effective.

The second key over-arching theme is that of maintaining broad vision and not getting too narrow in the definition of data science. This means embracing a rich set of potential data sources, new methods of modeling and data interpretation and of course the breadth and richness that comes from multiple disciplinary perspectives on key scientific problems. Environmental data science is not about better process modeling for earth and environmental science. Nor is it about deep learning on unstructured environmental data. It is about the possibilities of transformation and intellectual breakthrough when we embrace the full breadth and diversity that should be apparent from the discussions above and seek innovations at the interfaces between disciplinary perspectives as well as learning from best practices in other areas, for example the deep understanding of integrated modeling in the climate science community or the insights into data assimilation in weather prediction.

Data science is a new and emerging area of study and environmental data science is in its infancy. The topic can usefully be broken down into a series of over-lapping and mutually supporting themes as shown in Figure 3 .

Figure 3 . Key data science themes.

The first area is data acquisition , embracing the breadth of existing and emerging techniques to provide a significant step change in the observation and monitoring of the natural environment (see section The Data Challenge).

Building on this, there is a need to provide appropriate data science infrastructure supporting data storage, discovery, and processing capabilities . This builds on innovation in the area of cloud computing, but many research challenges remain before this infrastructure is fit for purpose for the challenges of environmental data science ( Elkhatib et al., 2013 ).

Continuing upwards in this diagram, there is a need to provide appropriate data science methods to help make sense of the plethora of environmental data. This is arguably the biggest area of potential innovation. The core of environmental data science is providing novel methods and combinations of methods to solve particular scientific challenges and problems. This includes combinations of process models and data-driven models, with the latter drawing on areas such as spatial and temporal statistics, machine learning, deep learning, extreme value theory, changepoint analysis, and optimization, offering a rich “playground” for innovation and offering new tools to scientists in responding to the challenges alluded to above (An example of such a combination is provided in section Case Study: Modeling Extreme Melt Events on the Greenland Ice Sheet below).

The final area is that of supporting decision making in an uncertain and complex world , and this involves the development of new methods of decision support aligned with ways of communication of data-driven scientific output and its translation into new understanding and policy development. This can also draw on new developments in visualization to aid the interpretation and understanding of the underlying complex and inevitably messy data. This is arguably the most important area but also the most complex and under-developed.

Case Study: MODELING Extreme Melt Events on the Greenland Ice Sheet

Purpose of the study.

Modeling Extreme Melt Events on the Greenland ice sheet (MEMOG) was originally a feasibility study supported through the EPSRC funded SECURE network. The aim of the project was to assess the potential for integrating process and stochastic models to improve forecasts of future Greenland ice sheet melting, working at the boundary between data science and environmental science. We found that process-based models that are currently used to simulate future Greenland ice sheet melting (Regional Climate Models—RCMs), and the associated contribution to global sea level rise, underestimate present-day melting because they do not capture extremely high temperatures ( Figure 4 ). Preliminary investigations (unpublished) also suggested that statistical models associated with Extreme Value Analysis can potentially be used to downscale RCM predictions of temperature to give better agreement with observed behavior. This has led to two main research priorities: (1) improving the representation of processes in RCMs such that they simulate extreme temperatures with greater fidelity and (2) developing data-driven models of extreme melting on Greenland to support work conducted with the process-based RCM. The latter is a key focus of current research in the EPSRC funded Data Science for the Natural Environment (DSNE) project in which we aim to use such models to (a) quantify the spatial/temporal structure of extreme melt events (b) make predictions on the risk of future extremes and (c) downscale RCM predictions to improve their fidelity with respect to observed behavior.

Figure 4 . Median frequency, duration, and magnitude of extreme temperature events simulated by four model variants at 13 locations. Frequency is denoted by the height of each box, duration is indicated by the width of each box, and observed values are given by the dashed black boxes. Box colors indicate the departure of the modeled magnitude from the observed value, blue colors indicate an underestimate, and red colors indicate an over estimate ( Leeson et al., 2017 ).

Data Science Perspective

Extreme value analysis (EVA) provides a tool-kit of asymptotically motivated statistical methods that can be used to statistically model the extreme values of a data set and predict the size and frequency of unusually large (or small) events in the future. In an environmental context this could include modeling extreme wind speeds, temperatures, droughts, precipitation, wave heights etc. It has previously been used with great success in many environmental applications, e.g., flood forecasting, however it has never been considered in estimates of cryospheric change. Historically, EVA models made the assumption of independent extreme events and had limited capacity to account for temporal and spatial trends. For many climate-driven processes, such limitations are a major weakness as they limit the ability to account for climate change, which has a strong signal in Greenland ( Hanna et al., 2012 ). In recent and ongoing research, several methods have been developed to deal with these issues by the use of covariates, random effects (also known as latent processes) or multivariate methods ( Eastoe and Tawn, 2009 , 2012 ). Using a subset of these state-of-the-art techniques, in the MEMOG project we modeled the frequency, distribution and magnitude of statistically extreme temperature events in in-situ observations and contemporaneous RCM predictions at 13 sites. We then used these data to (1) develop a climatology of extreme temperature events in the observational record, (2) analyse the performance of the RCM in terms of reproducing these extremes, and (3) make preliminary (unpublished) investigations into developing a method by which EVA can be used to downscale RCM output to reproduces extreme events with greater fidelity.

This work is proving to be an excellent platform to explore the potential for an integrated modeling approach to ice melt prediction. We have achieved success with our marginal (site-wise) approach and the next step is to incorporate spatial elements. In order to do this however, there are a number of issues that our current efforts aim to overcome. These include:

1. Sparsity of in-situ observations . In order to independently model extreme events, i.e., without using the spurious RCM output, one would ideally want direct observations. The Greenland ice sheet is 1.71 million km 2 and yet there are only ~20 weather stations on its surface from which it is possible to acquire temperature and melting data. As such, it is not yet possible to model the spatial dependence of extreme melt events using these data.

2. Data heterogeneity . While gridded satellite-derived observations of temperature and melting covering the entire ice sheet do exist, these data are a derived product that suffer from heterogeneity. For example, neighboring pixels in the dataset may have been acquired at different times of day and thus are not directly comparable. In addition, there are no observations during periods of cloud cover, and since these periods tend to be associated with higher temperatures than usual it is not possible to assume these data are “missing at random” for statistical modeling purposes.

3. High volumes of data . While observational data are sparse, RCM output is abundant (order of Tb) and continuous in both time and space. This provides an opportunity in that it enables us to explore the spatio-temporal dependence of extreme events (albeit in the model space only) however it also presents additional challenges in terms of both necessary computational power, and devising meaningful data-reduction techniques (e.g., clustering) in order to enable useful inference from the data.

Lessons Learned for Environmental Science

While this work is focused on Greenland ice sheet melting, lessons learned during this process are eminently transferable to other areas of Environmental Science.

1. It is insufficient to test process model fidelity against aggregated data such as annual, or even seasonal, means; “outliers” are important when it comes to overall model performance. Here, we were able to perform a more robust assessment using EVA to compare modeled vs. observed extreme events and found that the RCM misses 16–41% of melt energy at selected locations, largely due to poor representation of temperature extremes.

2. The heterogeneity inherent in environmental data requires a high degree of innovation in applying data science methods. For example, in this study we found that the strength of extremal dependence between observations and climate model output varied between sites. This necessitated the use of a sufficiently flexible bivariate EVA model that could then be applied across a number of heterogeneous locations regardless of the type of extremal dependence. Studying the spatial dependencies in extremal behavior revealed by this study is now a key part of ongoing work.

3. Understanding the physical drivers of why RCMs may not represent extreme events is difficult as they are extremely large and complex models comprising many interconnected processes. However, by using EVA we may be able to correct for this at least. This is important because assessments of the ice sheet contribution to sea level rise (i.e., total melting) are used for policy and decision-making.

4. Integration of process and statistical models presents in itself a novel research challenge and further effort is needed in order to determine principled ways of using the EVA model to drive the RCM output into states that do not naturally arise from integrations of model physics.

This case study, on combining process and stochastic models, demonstrates how models of different kinds can usefully be combined to better represent the reality, in this case, of extreme events. We see many other innovative combinations of process models and data-driven or stochastic models, for example the use of changepoint analysis or machine learning alongside process models (a couple of studies have also recently used machine learning in this way to attempt to derive patterns that are indicative of El Niño occurrences, a complex phenomenon that has so far eluded traditional process-based analyses; Lima et al., 2015 ; Chalupka et al., 2016 ). We also see data science methods being usefully combined in different ways to create hybrid approaches, for example the use of changepoint analysis with machine learning to discover patterns of higher-level events resulting from fundamental change in the environment. This is core to our vision of a future environmental data science—that is, by enabling innovation at the interfaces between disciplines and approaches, through bringing the different groups of researchers together in multi-disciplinary teams.

A Research Roadmap

Building on our analyses and experiences documented above, we present a research roadmap for data science for the natural environment in terms of a top 10 set of research challenges 7 . This is not necessarily intended to be complete but rather to highlight from our perspective some of the key challenges that must be addressed to achieve a form of maturity in this area.

Challenge 1 : To encourage and enable a cultural shift toward open science , that is toward a science that is more collaborative and integrative through open approaches to data, models and knowledge formation, and also toward a science that is more transparent, repeatable and reproducible.

Challenge 2 : To build on the benefits of cloud computing, but offer levels of abstraction (and associated services) that are much better suited to the domain of science, including high-level support for running complex, integrated modeling in the cloud.

Challenge 3 : To address complexity more fundamentally and explicitly, in particular, seeking data science techniques that recognize and resolve key issues around feedback loops, inter-dependent variables, extremes and reasoning about emergent behavior.

Challenge 4 : To provide techniques and frameworks to both reify uncertainty in scientific studies and also reason about the cascading uncertainties across complex experiments, e.g., in integrated modeling frameworks.

Challenge 5 : To seek adaptive techniques driven by considerations of uncertainty and also the goals of a scientific study, including adaptive approaches to sampling or gathering of data and adaptive modeling.

Challenge 6 : To seek approaches that deal with epistemic uncertainty in environmental modeling, noting the important links with dealing with emergent behavior in complex and irreducible phenomena.

Challenge 7 : To seek novel data science techniques and, in particular, innovative combinations of data science techniques that can make sense of the increasing complexity, variety and veracity of underlying environmental data, exploiting also multiple data sets including real-time streaming data.

Challenge 8 : To seek innovations in modeling by combining process models with data-driven or stochastic modeling techniques and also seeking ways of assimilating a range of data sources more generally into steering model executions.

Challenge 9 : To incorporate sophisticated spatial and temporal reasoning , including reasoning across scales, as an integral aspect of environmental data science and not something that is just provided through separate tools such as GIS tools.

Challenge 10 : To discover new modes of working, methods and means of organization that enable the required level of cross-disciplinary collaboration as required to address the grand challenges of earth and environmental sciences and, more specifically, environmental data science in its contribution to these grand challenges.

These research challenges cross-cut the themes of data acquisition, infrastructure, methods and policy making as illustrated in Figure 3 . The overarching challenge is then to overcome the 10 challenges above in an end-to-end environmental data science (from acquisition right through to policy and strategy) and to apply such techniques in responding to the many problems around the management of the natural environment.

Concluding Remarks

This paper has discussed the emergent area of environmental data science arguing that there is an important symbiotic relationship between the fields of data science and earth/environmental sciences: data science has a lot to offer in terms of a deeper understanding the natural environment and in informing mitigation and adaptation strategies in the face of climate change; this domain of application has much to offer in terms of data science with its unique combination of challenges, challenges that require significant breakthrough and innovation in data science methods.

The contributions of the paper are: (i) a definition of the field of environmental data science; (ii) a systematic analysis of the range of challenges in environmental data science; (iii) a research roadmap in the form of 10 key research challenges that, if addressed, would lead to significant progress in environmental data science.

The paper sets out with the additional objective of reaching out to researchers working in this space to create an international community to address the very significant challenges in this area. In retrospect, the creation of such an international community would dwarf the other contributions in terms of long-term significance. We invite you to this international effort.

Author Contributions

GB was the principal author for the text, and responsible for editing the whole manuscript together. Other authors contributed to the underlying research and to its analysis, and also contributed specific sections of text. AL and EE were the main authors for section Case Study: Modeling Extreme Melt Events on the Greenland Ice Sheet, while PY and AL led the writing of The Modeling Challenge and PH and EE led on The Spatial/Temporal Challenge.

This work was partially supported by the following grants: DT/LWEC Senior Fellowship (awarded to GB) on the Role of Digital Technology in Understanding, Mitigating, and Adapting to Environmental Change, EPSRC: EP/P002285/1; Models in the Cloud: Generative Software Frameworks to Support the Execution of Environmental Models in the Cloud, EPSRC: EP/N027736/1; Data Science of the Natural Environment, EPSRC: EP/R01860X/1; Modeling extreme melt events on the Greenland ice sheet, SECURE Network, EPSRC: EP/M008347/1 (FP2016008AL); NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to acknowledge our colleagues in the Centre of Excellence in Environmental Data Science, the Data Science Institute at Lancaster, and the Ensemble research group for providing such a stimulating cross-disciplinary environment and context for this research.

1. ^ www.futureearth.org

2. ^ http://royalsociety.org/uploadedFiles/Royal_Society_Content/policy/projects/sape/2012-06-20-SAOE.pdf

3. ^ https://voices.uchicago.edu/compinst/blog/unwinding-long-tail-science/

4. ^ https://www.coar-repositories.org/community/events/archive/repository-observatory-third-edition/coar-talks-ir-cris-interoperability/second-edition-linked-open-data/7-things-you-should-know-about-open-data/

5. ^ www.equip.leeds.ac.uk

6. ^ https://www.metoffice.gov.uk/research/applied/international/precis/qump

7. ^ It is important to stress that there is excellent work in many of these areas in the environmental sciences but this work is rather fragmented and it is clear that a more integrated approach is required.

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Keywords: data science, earth and environmental sciences, complex systems, uncertainty, spatial and temporal reasoning

Citation: Blair GS, Henrys P, Leeson A, Watkins J, Eastoe E, Jarvis S and Young PJ (2019) Data Science of the Natural Environment: A Research Roadmap. Front. Environ. Sci. 7:121. doi: 10.3389/fenvs.2019.00121

Received: 19 April 2018; Accepted: 24 July 2019; Published: 14 August 2019.

Reviewed by:

Copyright © 2019 Blair, Henrys, Leeson, Watkins, Eastoe, Jarvis and Young. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gordon S. Blair, g.blair@lancaster.ac.uk

2019 Best Papers published in the Environmental Science journals of the Royal Society of Chemistry

In 2019, the Royal Society of Chemistry published 180, 196 and 293 papers in Environmental Science: Processes & Impacts , Environmental Science: Water Research & Technology , and Environmental Science: Nano , respectively. These papers covered a wide range of topics in environmental science, from biogeochemical cycling to water reuse to nanomaterial toxicity. And, yes, we also published papers on the topic of the environmental fate, behavior, and inactivation of viruses. 1–10 We are extremely grateful that so many authors have chosen our journals as outlets for publishing their research and are equally delighted at the high quality of the papers that we have had the privilege to publish.

Our Associate Editors, Editorial Boards, and Advisory Boards were enlisted to nominate and select the best papers from 2019. From this list, the three Editors-in-Chief selected an overall best paper from the entire Environmental Science portfolio. It is our pleasure to present the winners of the Best Papers in 2019 to you, our readers.

Overall Best Paper

In this paper, Johansson et al. examine sea spray aerosol as a potential transport vehicle for perfluoroalkyl carboxylic and sulfonic acids. The surfactant properties of these compounds are well known and, in fact, key to many of the technical applications for which they are used. The fact that these compounds are enriched at the air–water interface makes enrichment in sea spray aerosols seem reasonable. Johansson et al. systematically tested various perfluoroalkyl acids enrichment in aerosols under conditions relevant to sea spray formation, finding that longer chain lengths lead to higher aerosol enrichment factors. They augmented their experimental work with a global model, which further bolstered the conclusion that global transport of perfluoroalkyl acids by sea spray aerosol is and will continue to be an important process in determining the global distribution of these compounds.

Journal Best Papers

Environmental Science: Processes & Impacts

First Runner-up Best Paper: Yamakawa, Takami, Takeda, Kato, Kajii, Emerging investigator series: investigation of mercury emission sources using Hg isotopic compositions of atmospheric mercury at the Cape Hedo Atmosphere and Aerosol Monitoring Station (CHAAMS), Japan , Environ. Sci.: Processes Impacts , 2019, 21 , 809–818, DOI: 10.1039/C8EM00590G .

Second Runner-up Best Paper: Avery, Waring, DeCarlo, Seasonal variation in aerosol composition and concentration upon transport from the outdoor to indoor environment , Environ. Sci.: Processes Impacts , 2019, 21 , 528–547, DOI: 10.1039/C8EM00471D .

Best Review Article: Cousins, Ng, Wang, Scheringer, Why is high persistence alone a major cause of concern? Environ. Sci.: Processes Impacts , 2019, 21 , 781–792, DOI: 10.1039/C8EM00515J .

Environmental Science: Water Research & Technology

First Runner-up Best Paper: Yang, Lin, Tse, Dong, Yu, Hoffmann, Membrane-separated electrochemical latrine wastewater treatment , Environ. Sci.: Water Res. Technol. , 2019, 5 , 51–59, DOI: 10.1039/C8EW00698A .

Second Runner-up Best Paper: Genter, Marks, Clair-Caliot, Mugume, Johnston, Bain, Julian, Evaluation of the novel substrate RUG™ for the detection of Escherichia coli in water from temperate (Zurich, Switzerland) and tropical (Bushenyi, Uganda) field sites , Environ. Sci.: Water Res. Technol. , 2019, 5 , 1082–1091, DOI: 10.1039/C9EW00138G .

Best Review Article: Okoffo, O’Brien, O’Brien, Tscharke, Thomas, Wastewater treatment plants as a source of plastics in the environment: a review of occurrence, methods for identification, quantification and fate , Environ. Sci.: Water Res. Technol. , 2019, 5 , 1908–1931, DOI: 10.1039/C9EW00428A .

Environmental Science: Nano

First Runner-up Best Paper: Janković, Plata, Engineered nanomaterials in the context of global element cycles , Environ. Sci.: Nano , 2019, 6 , 2697–2711, DOI: 10.1039/C9EN00322C .

Second Runner-up Best Paper: González-Pleiter, Tamayo-Belda, Pulido-Reyes, Amariei, Leganés, Rosal, Fernández-Piñas, Secondary nanoplastics released from a biodegradable microplastic severely impact freshwater environments , Environ. Sci.: Nano , 2019, 6 , 1382–1392, DOI: 10.1039/C8EN01427B .

Best Review Article: Lv, Christie, Zhang, Uptake, translocation, and transformation of metal-based nanoparticles in plants: recent advances and methodological challenges , Environ. Sci.: Nano , 2019, 6 , 41–59, DOI: 10.1039/C8EN00645H .

Congratulations to the authors of these papers and a hearty thanks to all of our authors. As one can clearly see from the papers listed above, environmental science is a global effort and we are thrilled to have contributions from around the world. In these challenging times, we are proud to publish research that is not only great science, but also relevant to the health of the environment and the public. Finally, we also wish to extend our thanks to our community of editors, reviewers, and readers. We look forward to another outstanding year of Environmental Science , reading the work generated not just from our offices at home, but also from back in our laboratories and the field.

Kris McNeill, Editor-in-Chief

Paige Novak, Editor-in-Chief

Peter Vikesland, Editor-in-Chief

• A. B Boehm, Risk-based water quality thresholds for coliphages in surface waters: effect of temperature and contamination aging, Environ. Sci.: Processes Impacts , 2019, 21 , 2031–2041,   10.1039/C9EM00376B .
• L. Cai, C. Liu, G. Fan, C Liu and X. Sun, Preventing viral disease by ZnONPs through directly deactivating TMV and activating plant immunity in Nicotiana benthamiana , Environ. Sci.: Nano , 2019, 6 , 3653–3669,   10.1039/C9EN00850K .
• L. W. Gassie, J. D. Englehardt, N. E. Brinkman, J. Garland and M. K. Perera, Ozone-UV net-zero water wash station for remote emergency response healthcare units: design, operation, and results, Environ. Sci.: Water Res. Technol. , 2019, 5 , 1971–1984,   10.1039/C9EW00126C .
• L. M. Hornstra, T. Rodrigues da Silva, B. Blankert, L. Heijnen, E. Beerendonk, E. R. Cornelissen and G. Medema, Monitoring the integrity of reverse osmosis membranes using novel indigenous freshwater viruses and bacteriophages, Environ. Sci.: Water Res. Technol. , 2019, 5 , 1535–1544,   10.1039/C9EW00318E .
• A. H. Hassaballah, J. Nyitrai, C. H. Hart, N. Dai and L. M. Sassoubre, A pilot-scale study of peracetic acid and ultraviolet light for wastewater disinfection, Environ. Sci.: Water Res. Technol. , 2019, 5 , 1453–1463,   10.1039/C9EW00341J .
• W. Khan, J.-Y. Nam, H. Woo, H. Ryu, S. Kim, S. K. Maeng and H.-C. Kim, A proof of concept study for wastewater reuse using bioelectrochemical processes combined with complementary post-treatment technologies, Environ. Sci.: Water Res. Technol. , 2019, 5 , 1489–1498,   10.1039/C9EW00358D .
• J. Heffron, B. McDermid and B. K. Mayer, Bacteriophage inactivation as a function of ferrous iron oxidation, Environ. Sci.: Water Res. Technol. , 2019, 5 , 1309–1317,   10.1039/C9EW00190E .
• S. Torii, T. Hashimoto, A. T. Do, H. Furumai and H. Katayama, Impact of repeated pressurization on virus removal by reverse osmosis membranes for household water treatment, Environ. Sci.: Water Res. Technol. , 2019, 5 , 910–919,   10.1039/C8EW00944A .
• J. Miao, H.-J. Jiang, Z.-W. Yang, D.-y. Shi, D. Yang, Z.-Q. Shen, J. Yin, Z.-G. Qiu, H.-R. Wang, J.-W. Li and M. Jin, Assessment of an electropositive granule media filter for concentrating viruses from large volumes of coastal water, Environ. Sci.: Water Res. Technol. , 2019, 5 , 325–333,   10.1039/C8EW00699G .
• K. L. Nelson, A. B. Boehm, R. J. Davies-Colley, M. C. Dodd, T. Kohn, K. G. Linden, Y. Liu, P. A. Maraccini, K. McNeill, W. A. Mitch, T. H. Nguyen, K. M. Parker, R. A. Rodriguez, L. M. Sassoubre, A. I. Silverman, K. R. Wigginton and R. G. Zepp, Sunlight mediated inactivation of health relevant microorganisms in water: a review of mechanisms and modeling approaches, Environ. Sci.: Processes Impacts , 2018, 20 , 1089–1122,   10.1039/C8EM00047F .

Climate change and ecosystems: threats, opportunities and solutions

Affiliations.

• 1 Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK.
• 2 Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
• 3 Nature-based Solutions Initiative, Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK.
• 4 School of Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Waterfront Campus, European Way, Southampton SO14 3ZH, UK.
• 5 Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.
• 6 Stanford Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA.
• 7 National Museum of Natural History, Smithsonian, MRC 163, PO Box 37012, Washington, DC 20013-7012, USA.
• PMID: 31983329
• PMCID: PMC7017779
• DOI: 10.1098/rstb.2019.0104

The rapid anthropogenic climate change that is being experienced in the early twenty-first century is intimately entwined with the health and functioning of the biosphere. Climate change is impacting ecosystems through changes in mean conditions and in climate variability, coupled with other associated changes such as increased ocean acidification and atmospheric carbon dioxide concentrations. It also interacts with other pressures on ecosystems, including degradation, defaunation and fragmentation. There is a need to understand the ecological dynamics of these climate impacts, to identify hotspots of vulnerability and resilience and to identify management interventions that may assist biosphere resilience to climate change. At the same time, ecosystems can also assist in the mitigation of, and adaptation to, climate change. The mechanisms, potential and limits of such nature-based solutions to climate change need to be explored and quantified. This paper introduces a thematic issue dedicated to the interaction between climate change and the biosphere. It explores novel perspectives on how ecosystems respond to climate change, how ecosystem resilience can be enhanced and how ecosystems can assist in addressing the challenge of a changing climate. It draws on a Royal Society-National Academy of Sciences Forum held in Washington DC in November 2018, where these themes and issues were discussed. We conclude by identifying some priorities for academic research and practical implementation, in order to maximize the potential for maintaining a diverse, resilient and well-functioning biosphere under the challenging conditions of the twenty-first century. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Keywords: adaptation; biosphere; climate change impacts; mitigation; nature-based solutions; resilience.

Publication types

• Introductory Journal Article
• Research Support, Non-U.S. Gov't
• Climate Change*
• Conservation of Natural Resources*

Twenty Key Challenges in Environmental and Resource Economics

• Open access
• Published: 16 October 2020
• volume  77 ,  pages 725–750 ( 2020 )

You have full access to this open access article

• Lucas Bretschger 1 &
• Karen Pittel 2  

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Economic and ecological systems are closely interlinked at a global and a regional level, offering a broad variety of important research topics in environmental and resource economics. The successful identification of key challenges for current and future research supports development of novel theories, empirical applications, and appropriate policy designs. It allows establishing a future-oriented research agenda whose ultimate goal is an efficient, equitable, and sustainable use of natural resources. Based on a normative foundation, the paper aims to identify fundamental topics, current trends, and major research gaps to motivate further development of academic work in the field.

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1 introduction, 1.1 research frontier.

The research agenda in environmental and resource economics has always been very broad and dynamic, reflecting the ways our economies interact with the natural environment. While in classical economics of the eighteenth century the factor land played a dominant role, the effects of pollution externalities, resource scarcities, ecosystem services, and sustainability became important in subsequent time periods. These issues have triggered different waves of research with very prominent results, specifically on optimal policies in the presence of externalities (Pigou 1920 ), optimal extraction of non-renewable resources (Hotelling 1931 ), optimal capital accumulation in the presence of resource scarcities (Dasgupta and Heal 1974 ), and sustainable development (Hartwick 1977 ; Pearce et al. 1994 ). Of course, the list of topics has already been very diverse in the past but has increasingly become so with recent global environmental problems challenging the functioning of a world economy which is growing at a high rate and heavily relies on an international division of labour and trade.

In the past, new research challenges emerged and manifested in different ways: Some topical fields became increasingly relevant due to new technological developments, new ecological or societal challenges or new political agendas. Others arose in fields that were already well researched but rose in importance. Not all challenges were of a topical nature. In some fields, we found our methodological tool-kit not equipped to deal with new problems or in need of extension to find new (and better) answers to old questions. At the same time, it has become increasingly clear that we have to reach out to other disciplines to meet new and often immense challenges. In environmental economics it is key to seek a good balance between disciplinary excellence, interdisciplinary collaboration, and political impact.

Environmental and resource economics is a dynamic field, in which new key topics emerge frequently. So, while the topical and methodological challenges that the paper identifies will be important for some time to come, they will and should also be subject to further development over the next years and decades. The paper aims to identify and address the variety of new complex problems generated by humans when they exploit natural resources and the environment. We specifically identify Twenty Challenges that we feel will be important for environmental and resource economists to address. We are aware that such a list will never be unanimously agreed upon and we do not even lay claim on the list being complete; the next section provides a background to the compilation of the list. Nevertheless, we feel it to be important to (at best) point researchers in directions important to work in in the future or (at least) to launch a new—controversial but productive—discussion on the development of our field. In any case, the paper should support the profession to operate at the research frontier generating novel theories, empirical designs, and workable policies. But, before we turn to the Twenty Challenges , we aim to motivate the framing of research in our field—past, present and future.

1.2 Identification of Research Challenges

To provide a normative foundation for our research agenda we characterize our underlying assumptions and generalized views on the nature of research in the field. This set of basic assumptions motivates the criteria of importance, activeness, and distinction of the selected topics as well as our choices with respect to design, methodology and research methods. Identifying the relevant issues, i.e. the mere choice of what to study in environmental economics imposes specific values on the subjects. In our view, the guiding principle in the normative framework is that environmental economics differs from general economics by its ontology, i.e. the system of belief that reflects the interpretation of what constitutes an important fact. It is a deep and serious concern about the state of the natural environment that drives the economic analysis of ecological processes. Nature is not simply part of the economic system but a different system with its own very complex regularities and dynamics; ecosystem values are not reducible to market exchange values. The task to integrate the ecological and economic systems to a holistic framework in an appropriate manner and to derive valid guidelines for the economy under the restrictions imposed by the environment lies at the heart of our research. Central parts of the ontology are the valuation of ecosystems, the increasing scarcities in natural resources and sinks, the effects of environmental externalities, the long-term orientation of planning, an important role of uncertainty, and the existence of irreversible processes. The anthropocentric view and the use of utilitarianism do not imply that individuals are purely self-centered and narrowly selfish. It highlights the indistinguishable role of human decision making for the future of the planet and aims at decision making that cares for efficiency, equity, and posterity. Based on a broad utilitarian setup, growth is not valued in terms of material consumption but in terms of wellbeing, which includes elements like social preferences, work-life balance, appreciation of nature etc. Posterity reflects our care for future generations, whose welfare should not be harmed by the activities of current generations. Fundamental changes of the economy e.g. the phase-out of fossil fuels, includes policy-induced decrease of activities, a role for technology, substitutability in production and consumption, a decoupling from natural resource use, and internalizing cost to correct market failures. Substantive transitions are very difficult to implement, as important lock-in mechanisms such as habit persistence, built infrastructure, and supporting policies such as subsidies stabilize current practices. To achieve a change of mindset in politics to achieve a transition to a green economy is a difficult task. A fundamental systems change, as discussed by many these days, is undoubtedly much more complex to accomplish; its impacts are uncertain and may delay the necessary steps which are important to rapidly improve the state of our ecosystems.

We acknowledge that one can always challenge an ontological position because it reflects ethical principles. In our research agenda there is no external reality, independent of what we may think or understand it to be. We reduce economic and ecological complexity through our personal system of belief to design our preferred map, which by definition is not the territory. In his survey of ecological research issues for the economists, Ehrlich ( 2008 ) refers to his ”own mental meta-analysis” to motivate his choices and to alert us to the importance of research on big issues like the meaning of life, mortality, and death. At the same time, he acknowledges that the emergence of pervasive new environmental problems, such as climate change and biodiversity loss, requires to flexibly adjust research programs to societal demand. Adjustments of the agenda may also be supply driven, when new methods allow for more effective engagement with important issues like risk and uncertainty or assessment of empirical regularities with superior estimation methods.

1.3 Forming a Research Agenda

Environmental economics is closely linked to general economics in its epistemology, i.e. the validity, scope and methods of acquiring knowledge by using models, distinguishing between positive and normative models, and testing hypotheses with empirical methods and experiments. An important cornerstone for economic research has always been the analysis of economic efficiency. Since the early days of environmental economics research, this has also held for our field whether it concerned the efficiency in the use of natural resources or the design of policies. Although research in our field has become much more interdisciplinary and policy-oriented, this still constitutes common ground. It is still a prime duty of the economist to point at the potentially vast allocative inefficiencies of the use of natural resources in pure market economies. Efficiency is a necessary condition for optimal states of the economic-ecological system and the foundation for policies maximizing social welfare.

The pursuit of optimality has to be complemented by a requirement to take care of equity and posterity enabling sustainability of development. In this long-run perspective, economics has to highlight the substitution effect as a powerful mechanism establishing consistency between humanity and its natural environment. Substitution comes in many guises, e.g. as substitution between clean and dirty production, renewable and exhaustible resources, extractive and conservationist attitude, pollution intensive and extensive consumption, etc. This dynamic analysis is crucial in many respects. It has recently been included at all levels of research in the fields. The same holds for the issue of risk and uncertainty, a pervasive topic when dealing with the environment.

In many cases, there has been a significant discrepancy between the theoretical derivation of social optima in academia and the attempts to foster their implementation under realistic policy conditions. As a consequence, policies dealing with environmental issues have been of very different quality and effectiveness. The reduction of acid rains, the protection of the ozone layer, and cutbacks of particulate matter emissions in many world regions were among the prominent successes. Global warming, extraction of rare earth elements, and loss of biodiversity are not yet addressed in a comprehensive manner. Political resistance against the protection of nature often refers to the economic costs of policies, including the concerns of growth reduction, employment loss, and adverse effect on income distribution. The lack of success in many policy areas has led to reformulation and extension of the research agenda. In the future, research should focus more on strengthening the links between theory and policy.

Our selection of the Twenty Challenges is also based on the potential of research in these areas to contribute and leverage social welfare and sustainable development. We specifically look for areas that are either inherently new to the research agenda in environmental and resource economics or in which research stagnates. We present the challenges in a specific order and like to highlight the links between them before we enter into the details. The aim of net zero carbon emission by the mid of the century dominates current policy debates and unites basically all important elements of our discipline; it thus constitutes a good starting point. Decarbonization necessarily involves a deep understanding of systems dynamics and of risk and resilience, which are presented next. An important and not sufficiently addressed research issue is the emergence of disruptive development during a substantive transition, the next challenge for our research. Extending the scope, we then address human and government behaviour. In the context of environmental policy, the popular and sometimes underrated request of an equitable use of the environment has emerged as a dominant topic, a next issue for further research. As natural capital involves many more elements than the climate, biodiversity and general ecosystem services are included in the sequence. Broadening the scope to the big problems of human behaviour with natural resources we then turn to political conflicts, population development and conflicting land use. Shifting the focus on induced movements of the labour force we go on by dealing with environmental migration and urbanization. These affect welfare of the individuals in a major way, like health and the epidemiological environment as a next research challenge. In terms of the reorganization of the transition to a green economy we highlight the central role of finance and the implementation of new measures in the dominant energy sector. The final three research challenges are motivated by advances in the methodology. Big data and machine learning offer new perspectives in sustainability research, refined methods and increasing experience improve our simulation models and structural assessment modelling, which forms the last three challenges of our list.

1.4 Links to Current Research

In order to put our agenda into a broader perspective and to concretize the selected challenges, we believe it is important to show the relationship between our research agenda and the priorities in current literature and policy debates. We have considered three main links. First, we conducted a quantitative and qualitative literature review and analyzed current research as presented at international conferences (World Conference of Environmental and Resource Economics in 2018, the SURED conference in 2018, Meetings of the American, European, and Asian Associations of Environmental and Resource Economics in 2019). The aim of this analysis was to see where our profession moves and which of the currently hotly debated topics offers a high potential for future research. Second, we took the discussions in interdisciplinary research fora into consideration to identify further fields that are of high importance for future resource use, sustainable development and environmental outcomes but have so far not been adequately addressed from an economics perspective. Information on this research was gained through interdisciplinary research initiatives (for example The Belmont Forum, Future Earth and National Research Funding Activities). Involvement in interdisciplinary and globally oriented research councils provided further access to the discussions in other disciplines. Third, we draw conclusions from current policies and news as well as our involvement in the policy arena. The authors are involved in a number of institutionalized policy-oriented activities on the regional, national and international level (Regional Climate Councils, National Climate Policy Platforms as well as the UN climate negotiations).

The paper relates to similar contributions in recent literature. Based on citation data Auffhammer ( 2009 ) identifies important topics and scholars and provides a brief historical overview of the discipline from exhaustible and renewable resources to sustainability, pollution control, development, international trade, climate change, international agreements, and non-market valuation. Polyakov et al. ( 2018 ) analyze authorship patterns using text analysis for classification of articles in Environmental and Resource Economics. Based on 1630 articles published in the Journal from 1991 to 2015 they document the importance of applied and policy-oriented content in the field. They identify non-market valuation, recreation and amenity, and conservation, as popular topics and growing when measured by both number of articles and citations. Costanza et al. ( 2016 ) investigate the most influential publications of Ecological Economics in terms of citation counts both within the journal itself and elsewhere. Important topics turn out to be social aspects of environmental economics and policy, valuation of environmental policy, governance, technical change, happiness and poverty, and ecosystem services. A contemporary analysis of how research issues have developed in the Journal of Environmental Economics and Management in the time of its existence is provided by Kubea et al. ( 2018 ). These authors show that the sample of topics has broadened from the core issues of non-market valuation, cost-benefit analysis, natural resource economics, and environmental policy instruments to a more diversified array of research areas, with climate change and energy issues finding their way into the journal. In addition, increasing methodological plurality becomes apparent. They conclude that energy, development, and health are on the rise and that natural resources, instrument choice, and non-market valuation will endure; multidisciplinary work will be increasingly important. An excellent survey on research in the central field of sustainable development is provided in Polasky et al. ( 2019 ), which explicitly shows where the collaboration between economists and the other disciplines is currently insufficient and how it should be intensified in the future.

Regarding the literature that we connect our Twenty Challenges to, we naturally face the problem that some challenges have so far not been addressed adequately in the (economics) literature. In these cases we also reference papers from other disciplines. We, however, also take basic literature and recent research in environmental and resource economics into account. As we often deal with emerging topics, we cite some of this work even when not yet published. In other cases, where future research can build on or learn from past research, we also go back in time and reference older papers. Ultimately, neither our list of challenges nor the literature we base our analysis on will be satisfying to everybody. Our selection cannot be comprehensive and does not claim to be. But the specific task to identify future-oriented topics ultimately lasts on a subjective individual assessment of the authors. Nevertheless, hopefully it imparts impulses for future research in the different subfields of environmental and resource economics.

2 Twenty Challenges

The ordering of the following challenges should not be understood to perfectly reflect their individual importance (beyond what we explained in the previous sections). Also, many of the fields discussed are inherently related, creating some unavoidable overlap. We feel that efforts to bring the challenges into some complete ’natural order’ are not only doomed to fail but also would not do them justice as they relate to very different areas and can/should not be weighed against each other. Also, attempting to show their interrelations would result in a 20-by-20 matrix that would not provide more clarity.

Deep decarbonization and climate neutrality To limit global warming to a maximum of 1.5 degrees Celsius, a state of net zero greenhouse gas emissions—i.e. climate neutrality—should be reached by the mid of the century (IPCC 2018 ). The directly following and unprecedented challenge is to decarbonize the global economy in very a narrow time window (Hainsch et al. 2018 ). This holds especially as the threshold for 1.5 degrees is expected to be passed around 2040 (IPCC 2018 ). Countries must increase their NDC ambitions of the Paris Agreement more than fivefold to achieve the 1.5 degree goal (UN - United Nations 2019 ). The time window for necessary decisions is closing fast. Infrastructure that is installed today often has a life span that reaches until and beyond 2050. Decisions on investments today therefore affect the ability to reach climate targets not only in 2030 but also 2050 and beyond. And while the necessity of reaching net zero emissions by mid century is reflected by, e.g., the European Commission’ Green Deal, much uncertainty remains regarding its implementation. This holds to an even larger extent with respect to other countries and regions. The fundamental challenge is to better understand economically viable deep decarbonization paths and then to implement incentives for input substitution, technology development, and structural change. More specifically, the vision of these policies has to be long-term and reach beyond phasing out coal and increasing energy efficiency. However, despite recent research efforts in climate economics, many issues around decarbonization, negative emissions and economic development are still controversial or insufficiently understood by economists. Specifically, industry applications for which alternative technologies are not available yet as well as agricultural emissions will have to be addressed. Also, the later greenhouse gas emissions start to fall, the faster their decline will have to ultimately be in order not to overshoot temperature targets (Agliardi and Xepapadeas 2018 ), leading to an increased need for negative emissions. However, potential trade-offs and synergies in the use of land for negative emission technologies, food production and biodiversity are still underresearched. Identifying technologies today that are the most promising in the very long run is subject to high uncertainty. Yet, while investing too early might be costly, delaying investment might cost even more or might lead to a weakening of future climate targets (Gerlagh and Michielsen 2015 ). Also, transition processes may involve strong scale effects implying nonlinear development of abatement cost. Once certain thresholds are reached, lower abatement cost or even disruptive development completely altering the production process could emerge in a later phase of decarbonization. Given the dramatic increase needed in mitigation efforts to reach the 1.5 or even 2 degree target, more attention also has to be devoted to the question of adaptation. Until today, the focus of research as well as policy has been primarily on mitigation rather than adaptation, partially because of expected substitution effects between mitigation and adaptation and partially because adaptation was taken to be automatic (Fankhauser 2017 ). However, as Fankhauser lays out “knowledge gaps, behavioral barriers, and market failures that hold back effective adaptation and require policy intervention”. All of these topics present a wide scope for substantial further research.

Dynamics of the economic-ecological system Depletion of exhaustible resources, harvesting of renewable resources, recycling of raw materials, and accumulation of pollution stocks require basic societal decisions which are of an inherently dynamic nature. Whether the world society will be able to enjoy constant or increasing living standards under such dynamic natural constraints depends on another dynamic process, which is the accumulation of man-made capital. To derive the precise laws of motion in all the stock variables is challenging because general solutions of dynamic systems with several states are usually hard to obtain. An adequate procedure to obtain closed-form solutions may be to link several stocks in a reasonable way, e.g. when simultaneously dealing with resource, pollution, and capital stocks (Peretto 2017 ; Bretschger 2017b ). The specific challenge is then to find the best possible economic justification to motivate the links. One may also focus on a few stocks which are considered the main drivers of economic development and sustainable growth on a global scale (Marin and Vona 2019 ; Borissov et al. 2019 ). When resorting to numerical simulation methods it is a main challenge to provide basic economic results which are sufficiently robust and supported by ample economic intuition. Social-ecological systems are increasingly understood as complex adaptive systems. Essential features of these systems - such as nonlinear feedbacks, strategic interactions, individual and spatial heterogeneity, and varying time scales—pose another set of substantial challenges for modeling in a dynamic framework. A main challenge is the characterization and selection of dynamic paths with multiple equilibria and the overall tractablility of the models, given the diversity of interlinkages and nonlinear relationships. The complexity of economic-ecological systems lead to a main challenge for designing effective policies is taking account of network effects, strategic interaction, sectoral change, path dependencies, varying time lags, and nonlinear feedbacks have to be considered as well as different regional and temporal scales, interdependencies between ecosystems, institutional restrictions and distributional implications (see, e.g., Engel et al. 2008 ; Levin et al. 2013 ; Vatn 2010 ). Optimal policies should also acknowledge the balance between the preservation of the ecology and the development of the economy especially for countries growing out of poverty. Setting a price for ecosystem services and natural capital via policy is important for preventing innovation incentives from being skewed against maintaining natural capital and ecosystem services.

Risk, uncertainty, and resilience The vast majority of contributions in environmental economics use models with a purely deterministic structure. However, large negative environmental events require a completely different framework, which poses specific challenges for modelling. Heatwaves, floods, droughts, and hurricanes are shocks that are very uncertain, arriving at irregular times and with varying intensity. Also, risk and uncertainty about socio-economic impacts and technological development affect the optimal design of policies (see, e.g., Jensen and Traeger 2014 ). Moreover, uncertainty changes the political economy of climate policy and, finally, regulatory and policy uncertainty might create obstacles to reach climate targets through, for example, distortions of investment decisions (Pommeret and Schubert 2018 ; Bretschger and Soretz 2018 ). Stern ( 2016 ) argued forcefully that climate economics research needs to better integrate risk and uncertainty. Bigger disasters or so-called ”tipping points” such as the melting of the Greenland ice sheet, the collapse of Atlantic thermohaline circulation, and the dieback of Amazon rainforest involve an even higher level of uncertainty (Lenton and Ciscar 2013 ) with implications for optimal policy design and capital accumulation (Van der Ploeg and de Zeeuw 2018 ). Understanding the implications of tipping points is further complicated as the different tipping points are not independent of each other (Cai et al. 2016 ). The Economy and the Earth system both form non-deterministic systems; combining the two in an overarching framework and adding institutions for decision making multiplies the degree of complexity for adequate modelling and methods (Athanassoglou and Xepapadeas 2012 ). It is thus a main challenge for further research to provide analytic foundations and policy rules for rational societal decision-making under the conditions of risk and uncertainty up to deep uncertainty (Brock and Xepapadeas 1903 ; Baumgärtner and Engler 2018 ). Future work on policy design under deep uncertainty can build on a wide range of literature ranging from the assessment of the precautionary principle in this context to the fundamental contributions by Hansen and Sargent ( 2001 ) and Klibanoff et al. ( 2005 ) as well as on more recent analyses in the context of environmental and resource economics, e.g. Manoussi et al. ( 2018 ). An important challenge of the environmental discipline is to provide a framework for the global economy providing the conditions for resilience against major shocks and negative environmental events (Bretschger and Vinogradova 2018 ). With deep uncertainty one has to generate rules for deep resilience. Including uncertainty is especially important when environmental events do not occur constantly but cause the crossing of tipping points involving large and sudden shifts. Economic modeling needs to increasingly incorporate tipping points and the value of resilience in theory and to generate and use data supporting the empirical validity. The combination of uncertainty and potential irreversible outcomes (e.g., species extinction) is another big challenge for research.

Disruptive development and path dependencies Substantial and sometimes disruptive changes in behavioral patterns, economic structure and technologies will be required if net zero GHG emissions and the UN sustainable development goals are to be reached. On the bright side, development may exhibit favorable disruptions. Consumers’ preferences and political pressure coupled with new technology achievements may alter certain sectors in a short period of time. Similar to the communication industry which has completely changed, transportation and heat generation could and mst probably will undergo fundamental changes in the near future. The research challenge here is to provide adequate models predicting and adequately analyzing such important transitions and to highlight resisting forces at the same time. In fact, the change of trajectories in development is often hampered by technological, economic and behavioral lock-ins, resulting in path dependencies and inertia. In such situations, history influences current development through, for example, past investment in R&D, the size of established markets, increasing returns or habits acquired (Aghion et al. 2016 ; Barnes et al. 2004 ; Arthur 1989 ). Behavioral path dependencies affect acceptance and adoption of new technologies, hinder social innovation and might render policies aimed at marginal changes ineffective. They can thus postpone the transition to a low-carbon economy, harm efforts in biodiversity conservation and prolong unsustainable resource use patterns and lifestyles, even if they are welfare enhancing in the long-run (e.g. Acemoglu et al. 2012 ; Kalkuhl et al. 2012 ). Inertia and lock-ins may also be policy driven with, for example, political or economics elites trying to block change (Acemoglu and Robinson 2006 ) or clean energy support schemes fostering new technology lock-ins. Whether disruption or a lock-in emerges depends, for example, on expectations determining the steady state of an economy (Bretschger and Schaefer 2017 ). This requires nonlinearities e.g. in capital return, generating overlap regions in which the growth path is indeterminate and could be either driven by history or by expectations. The challenge is to add more substantial research into system dynamics and the political economy of change, to gain a better understanding of the different mechanisms responsible for inertia and disruptive change. So far, the role of path dependencies has often been neglected in empirical as well as theoretical analyses (Calel and Dechezlepretre 2016 ). Also, understanding the triggers or tipping points for disruptive change can help to identify policies that have a big environmental impact with moderate costs in terms of environmental policy.

Behavioral environmental economics Traditionally, economics focuses predominantly on the supply side when analyzing potentials and challenges for environmental policies. Preferences of individuals are mostly assumed to be given with economic analysis confining itself to studying the effects of changing incentives and altering constraints. The change and development of preferences over time plays only a comparative minor role for economic research. Also, the follow-up question whether policies should be allowed to tamper with preferences is rarely discussed with nudging being one big exception to this rule (e.g. Strassheim and Beck 2019 ). While the traditional, supply-side oriented analysis has provided powerful results in positive analysis, it proves to be limited in a field which inherently includes normative conclusions like environmental economics. The path toward sustainable development requires behavioral changes and political actions changing our relationship to the environment. Ultimately, environmental policies have to be decided by the same people overusing the environment in the absence of a policy. In situations where outcomes are inefficient because individuals and political actors follow their own self-interest and ignore external costs and benefits of their actions, it is clearly not sufficient for economists to advocate the implementation of environmental policies. It is crucial to understand under what conditions preferences change and agents support green policies (Casari and Luini 2009 ). So, the challenge to economic research is to better understand the evolution of green attitudes, the emergence of preferences for a clean environment, and expectations in the case of multiple equilibria (Cerda Planas 2018 ). The formation and development of preferences is also not independent from cultural, regional and community aspects. Research that ignores heterogeneity among actors or the role of social and group dynamics and only relies on the traditional, isolated analysis of individual preferences is likely to lead to an incomplete understanding of preference dynamics. As the example of discounting shows, the social context has an impact on myopic attitudes and the motivation to undertake sacrifices for a cleaner future (Galor and Özak 2016 ). Also, attention to behavioral details, that economists might find rather uninteresting from a research perspective, might influence effectiveness of policies tremendously (Duflo 2017 ). Especially with the natural environment, the choice and guise of policy instruments should take these mechanisms into account.

Institutional analysis of environmental policy Virtually every contribution to the environmental and resource economics literature culminates in one or several policy conclusions. However, these results are often received with skepticism from industry and public. Therefore, a continuing key challenge for our profession is a thorough understanding of environmental policy institutions, processes and decision-making; this task has become even more important given the enormous scale and global nature of future policies. Research in this area has, however, the advantage of already looking back on a long tradition (see e.g. the body of work by Daniel Bromley, e.g. Bromley 1989 ). Well-designed institutions support and create incentives to drive development toward a welfare-improving state. Absent, weak, inefficient, or even corrupt governments and institutions are detrimental to successful environmental policy (Pellegrini and Gerlagh 2008 ; Dasgupta and De Cian 2016 ) or might lead to detrimental effects of resource wealth (see Badeeb et al. 2017 for an overview of the related literature). To effectively increase social welfare by, for example, conservation of ecological services, one has to design policies in a way that allow implementation under realistic policy conditions (Rodrik 2008 ). Pure reference to the construct of a social planner is not sufficient. For increasing efficiency in problem solving, the ex-post evaluation of policies has to be expanded and improved. Policy evaluation should not only analyze if regulatory objectives have been reached but also which side-effects arise (OECD 2017 ). Moreover, the comparison with alternative measures and a continuous international exchange of best practices have to be supported by science. A proactive environmental policy analysis should furthermore include studying vested interests, lobbying, political power, policy communication, and voting behavior. Especially insights from behavioral economics may add to our understanding of a proper design of environmental institutions. On the international level, the adequate institutional design for global environmental policy still poses great challenges. Beyond traditional research fields like international environmental agreements in specific areas like climate change, the multi-dimensionality of the sustainable development goals (SDGs) and potential trade-offs between different goals need to be explored further. This holds especially given the vast differences in income, vulnerability, and resilience between countries, as well as the need for unanimity and voluntary contributions on the UN level. Relating national to international policies has the potential to be especially rewarding in this context given the SDGs relevance for and acceptance in national as well as international politics. Insights from the analysis of institutions in traditional economic sectors (e.g. on the efficiency of capital markets) should be transferred and applied to the global level (e.g. with respect to investment in the world’s natural capital stock).

Equitable use of the environment We place equity and fairness in dealing with the natural environment on the priority list of our challenges because first and foremost equity is a central requirement for sustainability of development. By definition, sustainable development seeks an equitable treatment across different generations as well as agents living today. We also believe that for successful environmental policies, equity and fairness are crucial complements to the dominant efficiency requirement (Sterner 2011 ). It is a specific challenge of our field to study equity in an economic context and to demonstrate its importance for sustainability to mainstream economics and the public. The first aspect of the problem is the aforementioned unequal vulnerability of countries to environmental changes such as global warming. If vulnerability is higher in less developed countries, the equity perspective is especially striking. As a matter of fact, most of the climate vulnerable countries have a low average income. Global environmental policy is then motivated not only by efficiency but also by the aim of preventing increasing inequalities (Bretschger 2017a ). Global efforts are also indicated to avoid adverse feedback effects of induced inequalities like environmental migration. The second aspect is that acceptance of public policies sharply increases with the perceived fairness of the measure (Pittel and Rübbelke 2011 ; IPCC 2018 ). In the past, economists have often underestimated political resistance against efficient environmental protection, which was mostly related to negative impacts on income distribution. Take carbon pricing and emission regulation as a current example. Although evidence from cross-country studies suggests that regressivity of carbon pricing is much less frequent than often assumed in the public (Parry 2015 ), the perceived distributional impact is often very different (Beck et al. 2016 ). Therefore the impact of environmental policies on income groups, regions, and countries should be better integrated in our analysis and policy recommendations. Where efficient policies are regressive, economists have to evaluate and propose alternative or complementary policy designs. Benefits and costs need to be disaggregated by group (country) with a special attention on the poorest members of society (countries). Internationally, equity concerns need to be addressed especially in situations where the entire world benefits from the protection of natural capital and ecosystem services in poor countries (e.g., of carbon sinks and biodiversity hubs like tropical rain forests). The experience with the REDD+ process shows the complexity of designing such international approaches to incentivize and enable developing countries to protect these global public goods. More economic analysis is needed on all of the above aspects, giving rise to a rich research agenda in theory and applied work.

Loss of biodiversity and natural capital The rate of species extinction today is estimated to be up to 1000 times higher than without human interference (Rockstrom 2009 ). Human activities impact biodiversity through land use change, pollution, habit fragmentation and the introduction of non-native species but also increasingly through climate change and its interaction with already existing drivers of biodiversity change (IPCC 2002 ). In view of this, biodiversity conservation has long been a focus of politics. In 1992, the United Nations Convention on Biological Diversity main objectives were stated as ”the conservation of biological diversity, the sustainable use of its components and the fair and equitable sharing of the benefits arising out of the utilization of genetic resources” (UN - United Nations 1992 ). Yet, although economists have developed conceptual and theoretical frameworks addressing the valuation of biodiversity (Weitzman 1998 ; Brock and Xepapadeas 2003 ) and despite data on valuation having become increasingly available (see, e.g. TEEB 2020 ), Weitzman ( 2014 ) points out, that an objective or even widely agreed measure of biodiversity and its value is still missing. The same holds for an underlying theory framework and a comprehensive measure of natural capital that not only includes biodiversity but also its links to regulating services (e.g., pollution abatement, land protection), material provisioning services (e.g., food, energy, materials), and nonmaterial services (e.g., aesthetics, experience, learning, physical and mental health, recreation). How biodiversity and natural capital should be measured, which societal, political and economic values underlie different measures and valuation and how ecological and economical trade-offs should be dealt with are big challenges left for future research. In order to address these issues, not only do we need to develop appropriate assessment methods, but we also need to disclose the theoretical basics of this assessment and which trade-offs go hand in hand with different assessments (Brei et al. 2020 ; Antoci et al. 2019 ; Drupp 2018 ). Completely new issues for the valuation of biodiversity and natural capital arise with the development of new technologies. Take DSI (digital sequence information), for example. DSI are digital images of genetic resources (DNA) that can be stored in databases. This gives rise not only to new challenges regarding their valuation but also about the fair and equitable sharing of the benefits arising out of the utilization of these resources.

Valuing and paying for ecosystem services Related to the question of biodiversity valuation is the market and non-market valuation of ecosystem services in general and the adequate design of payment for ecosystem services (PES). Overall, research on ecosystem services valuation has made significant progress in the last decades. Nevertheless, challenges remain even in traditional valuation fields (for example, valuation of non-use or interconnected ecosystems). Other, so far underresearched areas that constitute promising fields for future research are health-related valuation aspects (Bratman et al. 2019 ) and nonmaterial ecosystem services, such as amenities of landscapes or cultural ecosystem services (Small et al. 2017 ; James 2015 ). Also, data availability remains a problem in many valuation areas. Although digitized observation and information systems offer large potentials for previously unknown data access, they also raise a whole slew of new ethical, privacy as well as economic questions, especially in areas like health. While a lot of progress has been made in the valuation of ecosystem services, their impact on decision making still lags behind. One factor contributing to this disconnect are prevalent mismatches between regional and temporal scales of economic, institutional and ecological systems that make valuation and policy design complex (Schirpke et al. 2019 ). The challenge is to develop combined natural science-economic models that allow better insights into how changes in economic systems lead to changes in the flows of ecosystem services and vice versa (Verburg et al. 2016 ). This requires a deep understanding of ecological and economic systems as well as other aspects like technologies, regional heterogeneity and system boundaries, i.e. catastrophic events. It also raises classic economic problems, such as choosing an appropriate discount rate and degree of risk aversion. Regarding tools to include ecosystem services in economic decision making, PES are a, by now, well-established (Salzman et al. 2018 ) and also quite well-researched approach for promoting environmental outcomes. Still, the literature has identified a number of aspects to be addressed in the design of PES to make them more effective as well as efficient and to simultaneously improve social outcomes (Wunder et al. 2018 ; Chan et al. 2017 ). A promising area of research rarely addressed are PES to preserve transboundary or global ecosystem services through international payment schemes (for example, in tropical forest preservation). While some work has been done on the conceptual level (e.g. Harstad 2012 ), the REDD+ process (Maniatis et al. 2019 ) and the failure of the Yasuni initiative (Sovacool and Scarpaci 2016 ) show the complexity of such approaches for which a thorough economics analysis is still missing.

Conflicts over natural resources Climate change and decarbonization transform regional and global geopolitical landscapes and might give rise to future domestic as well as international conflicts (Mach et al. 2019 ; Carleton and Hsiang 2016 ). First, decarbonization changes the role of resources and of resource- and energy-related infrastructures. Climate policies affect the rent allocation between different fossil fuels like, for example, coal and natural gas, but might also change the overall rent level (Kalkuhl and Brecha 2013 ). Asset stranding can endanger stability in resource (rent) dependent countries. Conflicts may also arise over materials critical to new, low-carbon energy technologies like rare earth elements but also over access to sustainable energy (Goldthau et al. 2019 ; O’Sullivan et al. 2017 ). Further research is needed to design policies that are better equipped to reduce the vulnerability of economies to changes in resource availability and resource rents. This opens up challenges for future research, especially as restrictions from very diverse institutional capacities have to be considered to render policies efficient and effective. Second, climate change will affect the ability to meet basic human needs through food, land and water. Sulemanaa et al. ( 2019 ) find a positive effect of the occurrence of temperature extremes on conflict incidence. They stress the need for more advanced spatial econometric models to identify effects that are transmitted across space. More research is also needed on the role of institutions and interaction with other phenomena like population dynamics, migration, and environmental degradation. Currently, the role of climate for conflict is still small compared to other causes, many linkages between conflicts and climate change as well as other factors promoting conflict are still uncertain (Mach et al. 2019 ). The challenge to economic research is to get early insights into the nexus of historical and cultural factors, vested interests, population dynamics and climate change in order to help to prevent resource-related conflicts.

Population development and use of the environment Already since antiquity, demographic analysis has been a central topic of human thinking. With the Malthusian predictions of catastrophes caused by population growth, the topic is firmly related to the natural environment and the limits of planet Earth. While limited food production was the dominant topic in the 18th century, the impact of world population on global commons, availability of renewable and exhaustible resources, and ecosystem services have been dominant topics in the last decades. Still, while it is often argued in the public and in natural sciences that world population size should be a concern because of ecological constraints, economics has largely left the topic on the side; the few exceptions (Peretto and Valente 2015 ) and (Bretschger 2013 , 2020 ) point in a different direction, namely the compatibility of population growth and sustainable development under very general conditions. Current trends of demographic transition show significant signs of population degrowth for leading economies while trends for developing countries vary substantially (UN - United Nations 2019 ). Population is forecasted to expand especially in Africa, accounting for more than half of the world’s population growth over the coming decades, raising questions about the effect of this population increase on fragile ecosystems, resource use and ultimately the potential for sustainable growth (African Development Bank 2015 ). Population growth will also promote further urbanization and migration triggered by environmental and resource depletion but also giving rise to new environmental problems (Awumbila 2017 ). Challenges from population development and environment are thus closely linked to the other research topics highlighted in this article. However, population growth is not exogenously given but determined by economic, social as well as environmental factors. Education and income or economic development have long been established as crucial for fertility (see e.g. the reviews of the literature provided by Kan and Lee 2018 ; Fox et al. 2019 ). To integrate these findings into a holistic approach is a mediating challenge for future research. Climate change might affect these channels in different ways, potentially exacerbating global inequality (Casey et al. 2019 ). However, population development, fertility, and mortality are not only affected by climate change but also by other environmental stresses like air pollution (Conforti et al. 2018 ). A successful combination of endogenous fertility and mortality with natural resource scarcity, agricultural production, and pollution accumulation as well as capital and knowledge build-up in a comprehensive framework is a respectable challenge for an economic modeller; we suggest that in the future it should be considered by economists more intensively.

Land use and soil degradation The terrestrial biosphere with its products, functions and ecosystem services is the foundation of human existence, not only for food security but far beyond. Currently, about a quarter of ice-free land area is degraded by human impacts (IPCC 2019 ). The optimal use of scarce land resources becomes an even more urgent topic in the face of the biodiversity crisis and the onset of climate change. This holds especially as the physical and economic access to sufficient, safe and nutritious food is the basic precondition for human existence. Climate change challenges this access on different levels. On the one hand, climate change increases the pressure on productive land areas (due to extreme weather events such as droughts, floods, forest fires or the shifting of climatic zones). On the other hand, land plays a major role in many climate protection scenarios by reducing emissions from land use and land use change, protecting carbon stocks in soils and ecosystems, and conserving and expanding natural carbon sinks. Also, the capture and storage of CO 2 through carbon dioxide removal technologies plays an increasing role for reaching the Paris climate goals (IPCC 2018 ). The induced increase in the demand for the different services from land inevitably implies trade-offs. However, neither the trade-offs nor the potentials for synergic uses are, as of now, comprehensively understood from an economic point of view and thus pose a challenge for future research. While there is a growing literature on negative emission technologies, their costs, potentials and side effects (Fuss et al. 2019 and references within) as well as on the interaction between climate goals and other SGDs on the global level (von Stechow et al. 2016 ), many research questions still remain to be addressed (Minx et al. 2018 ). This concerns especially a better understanding of opportunity costs, governance requirements, regional and distributional effects as well as of acceptance and ethical considerations. With respect to land degradation and land use for food production, changing climate and weather conditions as well as regional population pressure may raise the rate of land degradation (Fezzi and Bateman 2015 ), hurting food security and calling for preservation policies (Brausmann and Bretschger 2018 ). The overuse of ecosystems like forests and water, which protect and complement land, can accelerate the risk of adverse shocks and thus lower soil fertility, which reveals the close link between the different research subjects. However, much of the agricultural research in this field is still quite distant from mainstream environmental economics which can harm research productivity substantially. It remains a challenge to integrate agricultural and environmental research better, for example by bringing together food production, population, and the environment into a macrodynamic framework (Lanz et al. 2017 ).

Environmental migration Migration in times of climate change is an extraordinarily complex, multicausal and controversial challenge (Adger et al. 2014 ). Heatwaves, droughts, hurricanes, and rising sea levels are likely to motivate or even force a growing number of people to leave their homes moving to presumably safer places. Climate-related migration can take a variety of different forms (Warner 2011) from voluntary to involuntary, from short- to long-distance and from temporary to permanent. Migration decisions are usually based on different motives and personal circumstances (climatically, politically, economically, socially), leading to heterogeneous reactions to climate events and making it often problematic to identify and delineate climate-induced migration. Due to these and other methodological difficulties and the small number of studies so far, no globally reliable forecasts for climate induced migration exist (WBGU - German Advisory Council on Global Change 2018a , b ). At present, the forecasted magnitude of the phenomenon ranges from 25 million up to 1 billion people by 2050 (Ionesco et al. 2017 ). Much of this migration can be expected to take place within countries, for example, from rural to urban areas or from drylands to coastal zones (Henderson et al. 2014 ) with environmental migration being one possible adaptation and survivor strategy in the face of climate change (Millock 2015 ). Given the uncertainty in future migration projections, the challenge is to improve migration models (Cattaneo et al. 2019 ) which includes a better understanding and integration of the microfoundation of agents’ migration decisions. Migration, and especially mass-migration, can have a profound impact on the environment of the new as well as the old settlement location and on their economic structure. Labor and commodities markets will be affected the most, with challenges arising also for education and health systems, government budgets and public spending. By affecting public institutions and the skill-mix of the labor force, migration alters economic development both in the sending and in the receiving countries or regions. More research is needed on these impacts. The influx of environmental migrants to new settlement locations may also trigger hostile attitudes and lead to clashes and even armed conflicts. The migrants may be perceived as rivals for scarce resources (land, clean water) or jobs. The situation may be aggravated by lack of political stability and poor-quality political institutions. Dealing with these aspects gives rise to new challenges in environment and resource economics. Traditional analysis of economic costs and benefits of migration have to be complemented by behavioral economic and political economy analyses.

Urbanization as a key for environmental development In the last 70 years, the urban population has increased fivefold with more than half of the world’s population living in cities today and forecasts projecting the share of urban population to rise to almost 70% in 2050 (UN - United Nations 2018 ). Cities are responsible for about 70% of the world energy use and global CO \(_{2}\) -emissions (Seto et al. 2014 ) and ecological footprints are positively correlated to the degree of urbanization (WBGU - German Advisory Council on Global Change 2016 ). In 2014, about 880 million people were living in slums (UN - United Nations 2016 ) elucidating the problems to make urban development environmentally as well as economically and socially sustainable. The speed of urbanization is projected to be the fastest in low and middle income countries, especially in Africa and Asia (UN - United Nations 2018 ), leading to new challenges for the provision of infrastructure, housing, energy supply, transport and even health care. Climate change can be expected to not only foster urbanization trends (Henderson et al. 2017 ) but also increase the magnitude of urbanization-related challenges. Urban areas are often located close to the coast or rivers basins, making them susceptible to rising sea levels and impacts of extreme weather events. Risks can be expected to be higher for poor households due to settlement in less safe areas and poorer housing (Barata et al. 2011 ), potentially perpetuating existing inequalities. On the other hand, cities might offer more efficient adaptation potentials. To date the consequences of climate change for cities and urbanization are still to be determined in detail but depend heavily on factors like location, size and level of development as well as governance capacities. Making cities, their population and their infrastructure resilient to climate change will be decisive for future development. The main challenge here is to better connect the research fields of environmental and urban economics to understand the drivers and dynamic effects of climate change on urbanization and resulting economic development, on adaptation costs and benefits and on the role of institutions. Insights from regional, political and behavioral economics can help shape effective governance to enhance resilience of cities to climate change.

Health and epidemiological environment Environmental degradation can have profound implications for human health. These implications lead to direct as well as indirect challenges for economic decision making, economic development and thus economic research. While many of these challenges might not be new per se, they can be severely exacerbated by, for example, climate change. Economic implications of long-term increases in vector-borne diseases and heat stress as well as pandemics like the COVID-19 and ozone formation still remain to be analyzed in depth, as do the costs and benefits of adaptation measures dedicated to mitigating these effects (Mendelsohn 2012 ). Climate change also affects human health indirectly through impacts on economic development, land use, and biodiversity - and vice versa. Failed emission reductions and bad environmental management especially impact developing countries negatively through direct effects on health but also through health effects of delayed poverty reduction (Fankhauser and Stern 2020 ). Exposure to diseases or epidemics can increase the risk of civil conflicts and violence (Cervellati et al. 2016 , 2018 ). While research has addressed effects of life-expectancy, diseases and premature mortality on long-run economic development (e.g. Ebenstein et al. 2015 ; Acemoglu and Johnson 2007 ), a thorough analysis of the climate-health-development nexus is still missing. Overall, most research carried out on the interaction between environment, climate and human health has focused on physical health and mortality. The effects of air pollution from the burning of fossil fuels or agriculture on premature deaths, cardiac conditions and respiratory diseases, for example, received not only renewed interest in the wake of recent scandals (see e.g. Alexander and Schwandt 2019 ) but have been an active field of research for a number of years (Schlenker and Walker 2016 ; Tschofen et al. 2019 ). Mental health implications like stress, anxiety or depression on the other hand have received much less attention although, for example, Chen et al. ( 2018 ) in a study on air pollution in China estimate these effects to be on a similar scale to costs arising from impacts on physical health. Also, Danzer and Danzer ( 2016 ) find substantial effects of a large energy-related disaster (the Chernobyl catastrophe) on subjective well-being and mental health. Economic research should take up the challenge and put more effort into the economic evaluation of mental health related effects of climate change and environmental degradation in general. Potential to analyze these and other health-related questions have risen substantially in the last years, method-wise as well as topical, with new large data sets becoming available. Big data from insurance companies, satellite imagery on pollution dispersion and effects of draughts, for example, can provide new insights into the dynamics between environmental changes and health. But digital technologies themselves also generate new research questions addressing, for example, risks, costs and benefits of these new technologies.

Carbon exposure and green finance The impact of climate change and of climate policy on the financial system is a topic of increasing public concern. The transition to a low-carbon economy poses a lot of challenges not only from physical risks and damages but also from transition risks. These accrue in such different areas as climate-related policy making, altered market behavior, changes in international trade patterns, technology development, and consumer behavior. To support a safe and gradual transition to a low-carbon economy, the financial sector needs to evaluate and eventually address the new risks associated with climate change and decarbonization in an efficient manner. There is widespread concern that financial markets currently lack sufficient information about the carbon exposure of assets, resulting in risks from climate change and climate policy for investments (Karydas and Xepapadeas 2018 ). If not anticipated by the markets, climate shocks also cause asset stranding, i.e. unanticipated and premature capital write-offs, downward revaluations, and conversion of assets to liabilities (Rozenberg et al. 2020 ; Bretschger and Soretz 2018 ). The same holds true for climate policies which are not or cannot be correctly anticipated by investors (Dietz et al. 2016 ; Stolbova et al. 2018 ; Sen and von Schickfus 2020 ). The growing awareness of these risks is reflected in the attention that policy makers have devoted to the development of transparency improving information systems and indicators in recent years. However, challenges related the design of these systems and indicators, e.g. with respect to an accurate and encompassing risk assessment, still remain. The importance of addressing these challenges is excerbated by prevalent network effects and counterparty risks that transmit climate-induced financial shocks from individual firms to the broad public holding their capital in stocks of fossil-fuel-related firms, investment funds, and pension funds, which all could suffer from stranded assets (Battiston et al. 2017 ). Divestment campaigns, shareholder engagement, and mandatory disclosure of climate-relevant financial information by companies and investors warrant further theoretical and empirical analysis. Also, a better understanding of the economics behind financing instruments like green bonds is only recently emerging (Agliardi and Agliardi 2019 ). Despite some early studies there is a knowledge gap with respect to the extent of climate and policy risks for central banks and regarding the potential significance of different channels connecting the risks in the real economy with monetary policy. Given the environmental and international policy perspective of the climate problem, the specific contribution of the financial sector and the central banks in the architecture of global climate policy has to be subject to further investigation.

Energy system transformation The transition from a fossil-based to a green economy is needed to combat climate change but requires a thorough transformation of energy systems (Pommeret and Schubert 2019 ) in developed as well as in developing countries. In industrialized countries, challenges arise from the structural transformation of highly complex energy systems and their linkage with other economic sectors. While one hundred years ago, it was the rapid dissemination of fossil-based industrial processes, transportation, and heating that resulted in wide-spread sectoral change, similar adjustments can be expected with the increasing importance of electricity for decarbonization. However, changing the use of energy technologies in practice involves decisions on different levels and constitutes a highly nonlinear process. Future power generation in many countries will increasingly rely on renewable energies like wind and solar energy. To offset intermittent power generation, more and better storage capacities of batteries or pumped hydropower will be needed (Ambec and Crampes 2019 ). Synthetic fuels, heat pumps, fuel cells and e-mobility will increasingly use electricity to replace fossil fuels not only in the power sector but also in traffic and heat generation. While the adoption of renewable technologies like wind and solar was often much faster than predicted in the past, the critical mass of market penetration has still to be reached in other areas to benefit from potential scale effects and cost decreases. Shape and speed of the energy transition are, however, highly dependent on a political process which is hard to predict for market participants. Policy and ecological risks, together with the long-run character of the energy and related infrastructure investments, pose a big challenge for research and practice. In this context, it is especially the economic potential of green hydrogen and/or synthetic fuels that is controversially discussed at present. As production costs are expected to fall (Glenk and Reichelstein 2019 ), interest in hydrogen is increasing sharply (IEA 2019 ) and new research questions arise. For developing countries, clean and decentralized renewable energy technologies offer big potentials for electrification and economic development. However, despite the potential for decarbonization and the reduction of other externalities and health hazards and despite the fact that more than 90% of the annual increase in power generation comes from emerging economies, research on the development and adoption of clean energy technologies still focuses mainly on the developed world. More research on the barriers and challenges for adoption in developing countries is needed, including sustainable financing, institutional framing and the design of regionally tailored policies.

Sustainability perspective on digitalization Digitalization and artificial intelligence are often seen as opportunities for enhancing the efficiency of energy and resource use. They offer new opportunities for circular economy, agriculture, monitoring of ecosystems and biodiversity, sustainable finance and decarbonization (see WBGU 2019 and literature within). However, they may also accelerate energy and resource use, increase inequality between regions and income groups and endanger sustainable development. Digitalization offers new access to markets, impacts market forms and shapes consumer behavior all of which can have extensive implications for the ecological, social and economic dimensions of sustainable development. Digitalization is a cross-cutting theme that reaches across spatial scales (from regional development to globalization) as well as temporal scales (from short-run impacts on energy systems to long-run adaptation to climate change). So far, the potentials and challenges for sustainable development that are associated with digital technologies have mostly been addressed outside of environmental and resource economics. The focus has been on topics such as data security and privacy or, for example, on the implications of the ”fourth industrial revolution” on employment and labor markets. Costs and benefits of digitization, the design and effectiveness of policies in industrialized as well as developing countries have garnered much less attention in the context of environmental, resource, energy and climate economics. Also, impacts of digitization on the behavior of economic agents resulting in, for example, rebound effects or changes in consumption patterns and environmental awareness, have not been addressed comprehensively (Gossar 2015 ). In all of these areas, our limited knowledge base creates opportunities and challenges for future research in the field. But, digitalization not only creates new research questions, it also provides new means to answer them. It has led to new developments in data science, big data analysis, machine learning and artificial intelligence that allow new insights into, for example, material flows, emission patterns and technology diffusion as well as the optimal design, implementation and effectiveness of regulation (Fowlie et al. 2019 ; Weersink et al. 2018 ; Graziano and Gillingham 2015 ).

Quantitative analysis of environmental use Recently, there has been a significant shift in the empirical methods used in economics from traditional regression analysis to random assignment and quasi-experiments. Arguably this can improve the capturing of causal relationships and reduce the biases of traditional study designs. In environmental economics, experimental and quasi-experimental approaches have been applied mainly for capturing individuals’ or firms’ decisions on the use of land, water, resources, and energy (e.g. Allcott 2011 ; Duflo et al. 2013 ; Deschenes et al. 2017 ). Wider applications of these rigorous methods in environmental economics and well-suited empirical designs are desirable but certainly challenging e.g. when assessing aggregate environmental costs from climate change or biodiversity loss. An important but underrated field in applied environmental economics is the ex-post empirical assessment of environmental policies. The challenge is not only to identify environmental externalities, causalities, and impact intensities but also to provide an accurate valuation of the cost of policies, because they vary widely especially in environmental economics. The traditional empirical methods remain to be important and are not simply replaced. The same holds true for empirical designs in a time, cross-country, or panel structure. The increasing availability of large or very large datasets with observations varying widely across time and space offers a different set of options to provide evidence on the impact of environmental damages or policies to abate them (e.g. Currie and Walker 2011 ; Martin et al. 2014 ; Zhang et al. 2018 ). Fast-growing computational power and machine learning provide even more avenues for fruitful applications in environmental economics (see e.g. Abrell et al. 2019 ) but the challenge to use computer power wisely and to derive results which are sufficiently robust remains demanding .

Structural assessment modelling and modelling transparency In an effort to better understand the ramifications of political decisions and technological developments on climate change, energy supply and resource extraction (to name but a few examples), increasingly sophisticated numerical models have been developed in recent decades. It is evident that quantitative economics analysis is important for policy advice. Yet despite their complexity, these models usually still adopt some very simplifying and sometimes ad-hoc assumptions. In particular assumptions used in integrated valuation models have come under heavy criticism in recent years (Stern 2013 ; Pindyck 2013 ). Simplifications concern market structures and market failures, the integration of risk and uncertainty as well as societal, institutional and cultural detail. Also, manifestations of climate change and damages come at very different regional and temporal scales, making a truly integrated assessment of the climate-ecosystem-economy nexus next to impossible. We see it as a major challenge for future research to provide more accurate foundations for integrated assessment models. While simplifications are needed to reduce computational complexity, they raise the question to which extent the results obtained render reliable insights into future developments. Asking for models that are detailed in every dimension and can answer every question resembles of course the search for the holy grail. However, the need for a better understanding of the model dynamics has already led to the development of a new generation of models which have a stronger foundation in theory (Golosov et al. 2014 , Bretschger and Karydas 2019 ). A better understanding of the limits of models and of the questions specific models can and cannot address is still needed as well as transparency in model development. More applied studies, assessments of global environmental trends under different economic assumptions often use ”scenarios” to describe future trajectories. The scenarios are mostly based on expert opinion and do not rely on estimates about the likelihood that such a trajectory will occur. It is also critical that the economics behind the scenarios is often neglected. Prominently, per capita income can be projected using endogenous growth theory, while population development can be evaluated using state-of-the-art theories on fertility and morbidity.

3 Conclusions

This article set out to highlight a number of challenges that are highly relevant for future research in the field of environmental and resource economics. The focus was mainly, although not exclusively, on topical issues. We only briefly touched upon on some methodological advancements that might have the power to further parts of our field. Big data, machine learning and artificial intelligence hold high promise in this regard but their limits and potentials for environment, climate and resource economics have yet to be fully understood.

It should have become clear, that a number of the challenges presented can only be addressed adequately by interdisciplinary research teams with relevant disciplines ranging from climate science, (computer) engineering, sociology, virology to soil sciences. In many cases, economists’ analysis and the derivation of sound policy recommendations require the knowledge available in these fields. However, such research cooperations are by no means one-way streets: Other disciplines need the input of economists in order to assess future development scenarios and implementability of solutions. The knowledge and data required for economics analysis does not always exist yet, but interdisciplinary cooperation can help to identify and close these gaps. Overall, the less economists have already worked on specific challenges, the harder it is to assess best research strategies and the potential for success. Take the digitization-sustainable-development-nexus as an example: best research strategies and success are extremely difficult to predict as not only is the related economics research still in its infancy but also the field itself is extremely dynamic.

As already pointed out in the beginning: We are aware that our selection is bound to create discontent and disagreement. Having said this, it should also be stated that we expect some of our challenges to be more or less universally agreed upon. This holds especially for the broader topics: for example, how to accomplish deep decarbonization; how to deal with risk and uncertainty; or how to assess the role of disruptive development. One reason for this lies in the encompassing nature of these topics. They are relevant for many of the other fields that we have pointed out: For behavioral analyses, the capacity to deal with disruptive change in the face of risk and uncertainty are essential. Loss of biodiversity and natural capital, land degradation, conflicts over resources and migration are exacerbated by climate change. The potential of digitization for sustainable development constitutes disruptive change in itself. Yet, all of these fields are not merely subfields of the more overarching themes, they raise important research questions in their own right.

Nevertheless, it is to be expected that it will be the more specific fields over which disagreement will arise: Are ‘land use and soil degradation’ more important than ‘fisheries’? Is the ‘institutional analysis of environmental policies’ of higher relevance than the ‘development of alternative welfare concepts’ (to pick out some random examples). Of course, there are more fields that could have been included and also, of course, there is no objective criterion for the inclusion or exclusion of fields. The selection of the challenges is based on the analysis and criteria presented in the first section but it is ultimately ours; we are happy if this paper contributes to a lively and constructive discussion about the future of our field.

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Top 100 Environmental Research Paper Topics for Your Inspiration

Why taking care of our environment is essential.

Ever since humans first appeared, our planet has provided us with all basic needs like food, air, water, and energy. However, our environment can sometimes harm us too, with natural disasters like droughts, earthquakes, landslides, and floods. In such cases, we give our best to try and figure out the leading causes for their occurrence. 

The more we learn about why something happens in nature, the better we can understand how to enhance or prevent it. As human beings, we all have some basic knowledge of the environment we live in. However, it’s essential to broaden those understandings daily and learn more about ecology and the environment. It’s the only way for us to stop the hazardous side-effects of our industrial growth from damaging the planet we live on.

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We can also help prevent the extinction of endangered or endemic species, which are an essential part of our ecosystem and could cause a disbalance in their local environment if they were to disappear. Environmental research is not just a technique for survival – it’s much more.

It is now a separate field of study led by environmental sciences to positively impact the world around us. This type of research is extensive, and there are numerous environmental research paper topics college students can choose from and explore.

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Environmental research paper topics cover numerous issues which usually overlap with chemistry, biology, oceanography, civil engineering, water resources engineering, zoology, and the gas and oil industry. Simply put, there’s a great variety of topics you can choose from.

What makes one topic better than the other, though?

• First of all, it’s always better to choose a topic from an area of research you’re particularly interested in. For example, if you are more of a biologist, you should opt for topics covering plants, e.g., deforestation and afforestation.
• Secondly, that would always be an advantage if you’re able to reflect on a topic from a bystander perspective. 
• And last but not least, a powerful topic should offer solutions to a particular modern-day problem in our environment. That way, your topic will have a clear purpose.

List of the top 100 environment research paper topics

The following list of 100 environment research topics will help you find inspiration, so you’ll be able to design your topic faster and start writing your paper without further delays. 

The topics have been divided into groups for you to narrow down your search easier.

Environmental Health Topics

• Endemic wildlife – their unique importance for nature as a whole
• National parks and their significance for our health 
• The global impact of tectonic movements on the world’s ecosystems
• Lung cancer and radon – analysis and potential solutions
• Acid rain and the harmful effects on aquatic life
• Killing wildlife with acid rain – what can we do to prevent it?
• How vital was prehistoric wildlife for the ecosystems we have today?
• Air pollution and its destructive impact on health
• Can recycling help improve the health of people worldwide?
• What can we do to minimize the depletion of the ozone layer?
• The depletion of the ozone layer and its harmful impacts on health?
• GMOs, herbicides, and pesticides in food and their impact on health

Environmental Debates Topics

• Can life on Earth co-exist with radiation? Artificial vs. natural radioactivity
• How essential is oil for the ecosystem? Oil pollution and the oil industry
• Is there anything we can do to reverse the ozone layer depletion?
• Will using red lights make a significant difference in our environment?
• When we say green energy, what do we mean? Is it green?
• Can we redeem our planet with the use of green energy?
• How far should humans go into meddling with extinction? Is it a natural cause?

Environmental Justice Topics

• Does the government have the most significant impact on the recycling effort of the country?
• Do we use the total capacity of science to impact climate change?
• Nuclear power – the importance for the environment and its role in foreign policies
• Freight transport is a major cause of greenhouse gases emission – how can we reduce it?
• The hospitality industry and the environmental management

Environmental Science Topics

• Industrial plants and their connection to water resources – are they a great cause for human diseases?
• Switching to hydrogen from fossil fuels – why is it beneficial for the world?
• How can we stop the destruction of coral reefs?
• The contamination of our soil – to what extent are wastes and pesticides responsible for it?
• The acidification of the ocean – how big a problem is it?
• The melting of permafrost and its impact on climate change
• Global warming – busting all myths about it
• The increased concentration of CO2 in our atmosphere – downsides
• Small water resources and their importance for the environment
• Acid rains and industrialization – what’s the link?

Environmental Controversial Topics

• The impact of toxic waste on our environment
• The causes and effects of global warming – what can we expect in the next decade?
• Can people make use of the greenhouse effect?
• The depletion of the ozone layer, the current situation, and prospects
• If all ice glaciers in the world melt from global warming – what can we expect?
• How important is recycling? Is it a safety strategy or a business?

Environmental Persuasive Speech Topics

• What strategic actions can we implement to save our environment?
• Conservation – an analysis
• How can Donald Trump help save our planet?
• To what extent should humans be concerned about endangered species, and how can they help stop their extinction?
• Deforestation – causes, dangers, and effects on our modern world
• The destruction of wildlife in the Amazon forest – impacts
• Afforestation – is it possible? Can it help save a dying planet?

Environmental Biology Topics

• Asthma attacks and the environmental influence on them
• The effects of genetic diseases on humans
• Roots of plants – a comparative study
• Photosynthesis is different in some plants – a comparative study
• Crustaceans and their importance for the environment
• Why do we call Earth a living organism?
• Invasive species and their impact on the environment
• Soil composition – is it the same everywhere, and why not?
• Viruses in nature – an analysis of how they work
• The different types of trees in your local area
• If honey bees become extinct, what would the effects on nature be? 

Environmental Chemistry Topics

• The scientific standpoint for climate change 
• Scientific examination and critic reviews on climate change
• The spread of harmful and dangerous microorganisms and farm chemicals
• How does farming affect the environment? Are there dangers to it?
• The contamination of groundwater – causes and risks
• The destruction of the forest ecosystem and its coping mechanisms
• Bush burning – the hazardous effects on the environment
• GMOs, pesticides, and herbicides – how do they impact our lives
• Spraying vegetables with chemicals – pros and cons
• The oil pollution and the dangers for wildlife 

Environmental Economic Topics

• Air pollution and urban migration – is there a link?
• Modernization and noise pollution
• If we harness solar energy, will we make a good impact on the environment?
• The Gulfstream and its importance in the world’s economy
• The impact of the technological advancements on the environment
• Technology and the environment – benefits & downsides
• Ecology in the world today and prospects for the next decade

Environmental Argument Topics

• The impact of the environmental issues on the world as a whole 
• Our planet Earth and its desertification – causes & effects
• Can we make a significant change in the environment with sustainable consumption?
• The implementation of sustainable consumption and prospects
• PET bottles – what’s unsafe about them? Can they kill you?
• The parameters for the quality of the sol and the impact of drought on it
• Cattle grazing and GMOs – their effect on the production of greenhouse gas

Environmental History Topics

• EPA – the hazardous waste
• Exxon Valdez and Santa Barbara oil spills
• The Love Canal Case and the Eastman Kodak Case
• The 1978 Three Mile Island
• A comparative analysis of the most prominent earthquakes throughout history
• A comparative analysis of the most prominent floods throughout history
• A comparative study of the most prominent landslides throughout history
• Norman Borlaug and the Rockefeller Foundation in the Green Revolution
• The SARE/LISA and the USDA programs on sustainable agriculture
• The emergence of agricultural biotechnology

Environmental Law Topics 

• Human vs. animal rights
• Would implementing tax payments for carbon emissions help minimize them?
• Making vegetarianism mandatory – pros and cons
• If governments ban GMOs, what can we expect?
• The future of agriculture and organic farming
• Exports of animals – should governments ban them?
• Zoos – should governments ban them?
• Selling fur – should the government of each country ban it?
• Should we make the selling of plastic bags illegal?
• The impacts of tourism on our environment

We hope that these classified lists of environment project topics will help you find your most suitable pick. Whichever option you choose from, be it from the group of environmental science research topics or a research connected to environmental justice, you should always present both the supporting and opposing views.

Note that you have to set an academic goal before you start writing an essay. Therefore, make sure that the topic you choose can accomplish it. If you need help with research paper choice, writing, or else, you can always consult our experts. 

If you think that would be too strenuous, buying research paper is another option many people resort to.

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Top 50 environment research topics for your paper.

The planet Earth has been home to humans for years and years. The earth has been a source of sustenance by providing food and most of our basic needs. However, our environment has also been unkind to us as species sometimes. Floods, draughts and a host of other disasters have been the end to many humans, causing humans to research into their causes. In essence, environment research by man spans through human history and in a way, has been more of a survival technique.

Today, man has evolved, so has his understanding of the environment. Environment and ecology research, as of today is not just a survival technique. Instead, it has grown into a field of study under environmental sciences. The research so far in environmental science is very wide, seeing environment research topics even overlap with some other disciplines.

Environment Research Topics Cover Different Fields

Environment research topics in the areas covering water environment research could overlap with disciplines like biology, chemistry, civil engineering, oceanography, zoology, water resources engineering, and even oil and gas. This implies that there are a whole lot of environment-related research topics that you can write about. Since time and resources are very limited, you must be wise in writing your environment research paper.

The first and arguably the most important step in writing environment research papers has to be coming up with research paper topics about environment. In coming up with the research topic, you have to decide what area of research you are going into. For instance, are you conducting marine environment research or is it just about the terrestrial environment?

You can go further to capture the dangers and effects of some ecological problems in the research environment in your research paper topic. Your topic may also reflect a by-standers assessment of how the earth and man have evolved with time. Better still, but more difficult, your topic can be about proffering solutions to the modern-day environmental problems.

50 Environment Research Paper Topics

The following is a list of environment topics that will help you to write your paper faster . The environment paper topics have been arranged into groups to make your search easier.

Most Interesting Topics About The Environment

• Possibilities of new ecosystems in the future
• Coral reef destruction: impacts, way forward.
• Fixing the environment by power: switching to cleaner vehicles and fuels
• The relationship between acid rains and industrialization
• Small water bodies and their importance in the environment
• Paleoecology and its importance in environmental research

Environment Debate Topics

• Natural and artificial radioactivity: will life on earth adapt to radiation?
• The oil industry and oil pollution: of what use is oil in the ecosystem?
• Can the depletions in the ozone layer be reversed?
• Can led lights make real difference
• Is green energy really green?
• How far can green energy help to redeem the planet?
• Is extinction natural? To what extent should humans interfere?

Environment Safety Topics

• Toxic waste and its impacts on the environment
• Global warming in the world today; causes, effects, and the way forward
• How humanity can harness the greenhouse effect
• Ozone layer: depletion, present status, and the way forward
• Global warming: what could happen if ice glaciers melt?
• Recycling: business or safety strategy

Good Environment Speech Topics

• Environmental issues and their impacts on the world today
• Desertification in the World Today
• Sustainable consumption; implementation and possibilities for the future
• How PET bottles can kill you
• Drought and its effects on soil quality parameters
• GMOs in cattle grazing and its effects on greenhouse gas production

Environment Persuasive Speech Topics

• Conservation: strategic actions to preserve the environment
• Saving the planet; how can Trump help
• Endangered species: why humans should be concerned
• The dangers of deforestation in the world today
• Impacts of wildfire destruction in the amazon forest
• Afforestation in a dying planet

Environment Project Topics For College

• Scientific examination of the scientific consensus on climate change, with particular emphasis on critic reviews
• Animal grazing and spread of harmful micro-organisms
• Farm chemicals and soil contamination
• Farming and how it affects the environment
• Causes and risks of ground water contamination
• Bush burning: coping mechanisms of forest ecosystem

Environment Essay Topics

• Urban migration and air pollution
• Noise pollution and modernization
• Harnessing solar energy for the good of the environment
• How important is the gulfstream
• Technology and its impact on the environment
• Ecology prospects in the next century

Living Environment Topics

• Importance of the unique endemic wildlife in nature
• Importance of national parks on a global scale
• Impacts of tectonic movements on ecosystems
• Radon and lung cancer
• How nature is killed: acid rain and its harmful impacts on wildlife and aquatic life
• Worldwide invasion by black rats
• Prehistoric wildlife and its importance in future eco-systems

How Can I Help Change The World? Check Out Environment Research Jobs

Finding a job in environment research could be very exciting and rewarding, especially if you have formal education in the field of environmental science. As a student, finding an internship in any of these places could help with learning environment research.

• Conservation ScientistThese scientists conduct field experiments to determine how natural resources are used in a bid to protect these natural resources. They are concerned with how farmers can use the soil safely for continued use. As a researcher working on conservation, you could also conduct experiments and collect data about groundwater contamination and proffer solutions.
• Environmental ChemistThese are special scientists that research in a bid to understand the impacts of chemicals in the environment. They travel around areas that have been contaminated, collecting data in the form of water, soil, plant life, and other materials. It is from these data that they determine which chemical or chemicals are causing the problems. The results of their findings are used in giving recommendations.
• Research AssistantYou could also work as an environmental research assistant as well. Research assistants provide support to professionals conducting research, gathering data or analyzing information. As a research assistant in the field of environmental science, you could work with think tanks. A very good example of such think thank is the Property and Environment Research Center.
• Academic Journal EditorAcademic journal editors review research papers that are to be published in academic journals. To become an environment research journal editor has to do with a lot of work. You have to write a lot of environment research papers and have them published in the journal of your choice. If your work is good enough, chances are that you might be called up to be the editor of the journal. For those who are looking for academic paper writing help, we advise to check our custom paper service .

Deciding to go into environment research is just as exciting as it sounds. The truth is, the environment we live in is very important. All life-forms are inter-connected and inter-related and our existence is dependent on the well-being of every organism on the planet. Deciding to pursue a career in environment research makes you understand this interconnectivity better. It also puts you in the best position to give informed recommendations on how to make the world a better place.

20 Environmental Science Research Paper Topics: Explore Like an Expert

Writing papers in college is a great deal different than writing one in high school. Firstly, it has to acknowledge issues that are of a significantly advanced level on more special subjects. Secondly, it has to discuss matters that haven’t been deeply-investigated yet but are better to be recognized by fellow peers like you or your groupmates. Thirdly, it has to stick to particular strict paper requirements concerning an academic style, writing tone and formatting (for example, in APA, MLA, or Harvard). When a perfect writing and formatting style and a good research topic pair up, it creates not only remarkable results for a field of study but high grades for each student who puts all possible efforts to perform this task.

And as an additional benefit, when your research topic is the best in the class, you even get the bragging rights (the biggest reward among all possible). So read this article attentively to draw inspiration from some good environmental science research paper topics you can explore on your own. Besides, you’ll know how to do research as a real expert does. Apply all the steps in writing a research paper on Environment and enjoy the final results when your instructor will say, “Oh! Your paper is a joy to read. It is music to my ears.”

Table of Contents

Choose a Research-Worthy Environmental Topic

Choosing an issue to cover in a research paper is not so easy especially for environmental science research papers. But the selection of related research paper topics can simplify the process of choosing an interesting research idea in such an important field of study as the natural environment. There are so many environmental concerns humans face today and can face in the future. The consequences can be exceedingly impressive – no clear air, soil, and water. Consequently, it results in ill health among living beings, including human beings who you belong to as well. You can start to care about it if you take a look at the list of research-worthy topics on Environment. Choose one of them for your own environmental research paper.

You’ll definitely end up astounding your audience with the help of a topic from the following list of research topics:

• The Black Rat – A Worldwide Invasive Species
• The Destruction of the World’s Coral Reefs
• Examining the Scientific Consensus on Climate Change – Why the Critics Are Complaining
• Can the Use of LED Lights Make a Difference? If Yes, Name Them
• How to Sparingly Use Wind, Solar, and Other Alternative Energy Sources?
• The Ozone Hole Is Finally Healing – or Is It?
• Waste and Pesticides Are Contaminating Soil
• How to Stop Coral Reef Destruction
• Radon Is a Naturally Occurring Radioactive Gas That Can Cause Lung Cancer
• Animal Grazing: Spreading Deadly Viruses and Bacteria That Gets into the Country’s Rivers, Streams and Lakes
• What Kills Nature: Effects of Acid Rain on Fish and Wildlife
• The Ecological Significance of Exchange Processes between Rivers and Groundwater
• What Trump Can Do to Save the Environment
• Preserving the Vital Biodiversity of Gabon’s Wetlands
• Hoasjoe: The Mystery Sister of the Crystal Cave in Bermuda
• The Sightings of the Endangered Species of the World
• The Ecological Integrity of Groundwater and Fluvial Systems Os Often Threatened by Human Activities
• Tracking the Recovery of a Keystone Urchin Species and Its Role in Reef Restoration
• What Is in Our Power: The Transition to Cleaner Vehicles and Fuels
• Everyday Harm: Why Is the Use of PET Bottles Harmful for You

To be honest, environmental science is a vast field and there are loads of eager environmentalists who will really appreciate if you take on one of these grown-up research topics. As a matter of fact, there are so many concerns about the environment that dwelling and focusing on them will give you a lot of ideas to write about. Moreover, using these topics will come in handy to catch the attention of your target audience. We all live on Earth and we have to take care of it to make our lives better. Use these suggested ideas to get inspired to craft a useful paper that will call everyone to action. Let’s find out how it is better to do it so that you can be proud of the fact, “I managed to write my research paper to influence the current environmental situation positively!”

Do In-Depth Research on an Environment Issue

Indeed, it is difficult to write a good research paper if you do not read about the chosen topic enough to have something valuable to state about it especially when it concerns environmental issues. If you want to avoid any pitfalls, you should read about your subject a lot, end of! What is more important in proper academic research is that you should read credible sources of information, at least use them as references in your work. It means that Wikipedia isn’t appropriate in the academic field. If you are used to applying the Internet to research, do it properly for your essays.

There are so many websites on the Internet where you can take numerous data and information on the environment. Aware of how to assess online sources ? Look at the picture with the questions to answer when you see an unknown source. But before you start answering them, pay attention to the URL that stands for Uniform Resource Locator as a protocol for specifying addresses on the Internet. In other words, look at the web address of the website. The domain name is exactly that can help you decide whether the information is published by a credible source. Feel free to use sites with URLs such as “.edu”, “.gov” or “.org”. The domain suffix gives you a clue about the type of organization the site is linked to – “.edu” corresponds to educational platforms on which you can access various research studies, “.gov” belongs to the government sites that give access to reports and their findings, “.org” refer to non-profit organizations that also conduct surveys and provide the research results. However, there is one more domain suffix that also can indicate a reliable source – “.com”, for example, in the name of the website Encyclopedia.com where you also can find some useful information about environmental situations.

Structure the Main Research Ideas

Follow All the Paper Requirements

After you are given a writing assignment, examine it carefully paying close attention to every single detail concerning a word/page count, structure, references, format and so on. All that plays a huge role in the final assessment of your paper. So don’t neglect it! Keep all the paper instructions in front of your eyes so that you won’t miss any point. Don’t think if you write more that is required, it will be highly assessed. It doesn’t work in this way. Your instructor expects you to express the main ideas in the limited number of words or pages. It is vital to stick to the point while speaking about research problems. Environmental issues are no exception. If you are required to use 5-7 references, do it exactly as required. Luckily, you are provided with the practical tips on how to find reliable sources of information without going to a library. Just open Google and find those necessary 5-7 references to cite in your research papers.

Edit Your Research Paper Accordingly

Managed to choose a topic for a research paper, carry out research, structure the key research ideas, follow all the instructions and think that you are free now? Stop, stop, stop! This isn’t a final of your struggle. There is one more step to take at the end of the writing process – editing. Every academic paper should go through the editing process so that it won’t have any defect in writing and formatting. Keep in mind that it is better to put the final touches on the content with the help of fresh eyes. You can have such a critical eye only after break time. Rushing into editing immediately after you finish writing is a bad idea. As a result, you won’t notice any possible typos or will make more mistakes in formatting references or in-text citations. Alternatively, you can find someone to read out your research paper, but this ‘someone’ must specialize in the subject and be keen-eyed to observe all ambiguities.

Now, you have more chances to write a perfect research paper as all the essentials are explained in the simplest terms, It is your turn to take all the practical steps to write an environmental science research paper. Get on with it!

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