environment research study

Environmental Studies: An Introduction

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Research Topics & Ideas: Environment

100+ Environmental Science Research Topics & Ideas

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. Here, we’ll explore a variety research ideas and topic thought-starters related to various environmental science disciplines, including ecology, oceanography, hydrology, geology, soil science, environmental chemistry, environmental economics, and environmental ethics.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the environmental sciences. This is the starting point though. To develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. Also be sure to also sign up for our free webinar that explores how to develop a high-quality research topic from scratch.

Overview: Environmental Topics

• Ecology /ecological science
• Atmospheric science
• Oceanography
• Soil science
• Environmental chemistry
• Environmental economics
• Environmental ethics
• Examples  of dissertations and theses

Topics & Ideas: Ecological Science

• The impact of land-use change on species diversity and ecosystem functioning in agricultural landscapes
• The role of disturbances such as fire and drought in shaping arid ecosystems
• The impact of climate change on the distribution of migratory marine species
• Investigating the role of mutualistic plant-insect relationships in maintaining ecosystem stability
• The effects of invasive plant species on ecosystem structure and function
• The impact of habitat fragmentation caused by road construction on species diversity and population dynamics in the tropics
• The role of ecosystem services in urban areas and their economic value to a developing nation
• The effectiveness of different grassland restoration techniques in degraded ecosystems
• The impact of land-use change through agriculture and urbanisation on soil microbial communities in a temperate environment
• The role of microbial diversity in ecosystem health and nutrient cycling in an African savannah

Topics & Ideas: Atmospheric Science

• The impact of climate change on atmospheric circulation patterns above tropical rainforests
• The role of atmospheric aerosols in cloud formation and precipitation above cities with high pollution levels
• The impact of agricultural land-use change on global atmospheric composition
• Investigating the role of atmospheric convection in severe weather events in the tropics
• The impact of urbanisation on regional and global atmospheric ozone levels
• The impact of sea surface temperature on atmospheric circulation and tropical cyclones
• The impact of solar flares on the Earth’s atmospheric composition
• The impact of climate change on atmospheric turbulence and air transportation safety
• The impact of stratospheric ozone depletion on atmospheric circulation and climate change
• The role of atmospheric rivers in global water supply and sea-ice formation

Topics & Ideas: Oceanography

• The impact of ocean acidification on kelp forests and biogeochemical cycles
• The role of ocean currents in distributing heat and regulating desert rain
• The impact of carbon monoxide pollution on ocean chemistry and biogeochemical cycles
• Investigating the role of ocean mixing in regulating coastal climates
• The impact of sea level rise on the resource availability of low-income coastal communities
• The impact of ocean warming on the distribution and migration patterns of marine mammals
• The impact of ocean deoxygenation on biogeochemical cycles in the arctic
• The role of ocean-atmosphere interactions in regulating rainfall in arid regions
• The impact of ocean eddies on global ocean circulation and plankton distribution
• The role of ocean-ice interactions in regulating the Earth’s climate and sea level

Tops & Ideas: Hydrology

• The impact of agricultural land-use change on water resources and hydrologic cycles in temperate regions
• The impact of agricultural groundwater availability on irrigation practices in the global south
• The impact of rising sea-surface temperatures on global precipitation patterns and water availability
• Investigating the role of wetlands in regulating water resources for riparian forests
• The impact of tropical ranches on river and stream ecosystems and water quality
• The impact of urbanisation on regional and local hydrologic cycles and water resources for agriculture
• The role of snow cover and mountain hydrology in regulating regional agricultural water resources
• The impact of drought on food security in arid and semi-arid regions
• The role of groundwater recharge in sustaining water resources in arid and semi-arid environments
• The impact of sea level rise on coastal hydrology and the quality of water resources

Topics & Ideas: Geology

• The impact of tectonic activity on the East African rift valley
• The role of mineral deposits in shaping ancient human societies
• The impact of sea-level rise on coastal geomorphology and shoreline evolution
• Investigating the role of erosion in shaping the landscape and impacting desertification
• The impact of mining on soil stability and landslide potential
• The impact of volcanic activity on incoming solar radiation and climate
• The role of geothermal energy in decarbonising the energy mix of megacities
• The impact of Earth’s magnetic field on geological processes and solar wind
• The impact of plate tectonics on the evolution of mammals
• The role of the distribution of mineral resources in shaping human societies and economies, with emphasis on sustainability

Topics & Ideas: Soil Science

• The impact of dam building on soil quality and fertility
• The role of soil organic matter in regulating nutrient cycles in agricultural land
• The impact of climate change on soil erosion and soil organic carbon storage in peatlands
• Investigating the role of above-below-ground interactions in nutrient cycling and soil health
• The impact of deforestation on soil degradation and soil fertility
• The role of soil texture and structure in regulating water and nutrient availability in boreal forests
• The impact of sustainable land management practices on soil health and soil organic matter
• The impact of wetland modification on soil structure and function
• The role of soil-atmosphere exchange and carbon sequestration in regulating regional and global climate
• The impact of salinization on soil health and crop productivity in coastal communities

Topics & Ideas: Environmental Chemistry

• The impact of cobalt mining on water quality and the fate of contaminants in the environment
• The role of atmospheric chemistry in shaping air quality and climate change
• The impact of soil chemistry on nutrient availability and plant growth in wheat monoculture
• Investigating the fate and transport of heavy metal contaminants in the environment
• The impact of climate change on biochemical cycling in tropical rainforests
• The impact of various types of land-use change on biochemical cycling
• The role of soil microbes in mediating contaminant degradation in the environment
• The impact of chemical and oil spills on freshwater and soil chemistry
• The role of atmospheric nitrogen deposition in shaping water and soil chemistry
• The impact of over-irrigation on the cycling and fate of persistent organic pollutants in the environment

Topics & Ideas: Environmental Economics

• The impact of climate change on the economies of developing nations
• The role of market-based mechanisms in promoting sustainable use of forest resources
• The impact of environmental regulations on economic growth and competitiveness
• Investigating the economic benefits and costs of ecosystem services for African countries
• The impact of renewable energy policies on regional and global energy markets
• The role of water markets in promoting sustainable water use in southern Africa
• The impact of land-use change in rural areas on regional and global economies
• The impact of environmental disasters on local and national economies
• The role of green technologies and innovation in shaping the zero-carbon transition and the knock-on effects for local economies
• The impact of environmental and natural resource policies on income distribution and poverty of rural communities

Topics & Ideas: Environmental Ethics

• The ethical foundations of environmentalism and the environmental movement regarding renewable energy
• The role of values and ethics in shaping environmental policy and decision-making in the mining industry
• The impact of cultural and religious beliefs on environmental attitudes and behaviours in first world countries
• Investigating the ethics of biodiversity conservation and the protection of endangered species in palm oil plantations
• The ethical implications of sea-level rise for future generations and vulnerable coastal populations
• The role of ethical considerations in shaping sustainable use of natural forest resources
• The impact of environmental justice on marginalized communities and environmental policies in Asia
• The ethical implications of environmental risks and decision-making under uncertainty
• The role of ethics in shaping the transition to a low-carbon, sustainable future for the construction industry
• The impact of environmental values on consumer behaviour and the marketplace: a case study of the ‘bring your own shopping bag’ policy

Examples: Real Dissertation & Thesis Topics

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various environmental science-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• The physiology of microorganisms in enhanced biological phosphorous removal (Saunders, 2014)
• The influence of the coastal front on heavy rainfall events along the east coast (Henson, 2019)
• Forage production and diversification for climate-smart tropical and temperate silvopastures (Dibala, 2019)
• Advancing spectral induced polarization for near surface geophysical characterization (Wang, 2021)
• Assessment of Chromophoric Dissolved Organic Matter and Thamnocephalus platyurus as Tools to Monitor Cyanobacterial Bloom Development and Toxicity (Hipsher, 2019)
• Evaluating the Removal of Microcystin Variants with Powdered Activated Carbon (Juang, 2020)
• The effect of hydrological restoration on nutrient concentrations, macroinvertebrate communities, and amphibian populations in Lake Erie coastal wetlands (Berg, 2019)
• Utilizing hydrologic soil grouping to estimate corn nitrogen rate recommendations (Bean, 2019)
• Fungal Function in House Dust and Dust from the International Space Station (Bope, 2021)
• Assessing Vulnerability and the Potential for Ecosystem-based Adaptation (EbA) in Sudan’s Blue Nile Basin (Mohamed, 2022)
• A Microbial Water Quality Analysis of the Recreational Zones in the Los Angeles River of Elysian Valley, CA (Nguyen, 2019)
• Dry Season Water Quality Study on Three Recreational Sites in the San Gabriel Mountains (Vallejo, 2019)
• Wastewater Treatment Plan for Unix Packaging Adjustment of the Potential Hydrogen (PH) Evaluation of Enzymatic Activity After the Addition of Cycle Disgestase Enzyme (Miessi, 2020)
• Laying the Genetic Foundation for the Conservation of Longhorn Fairy Shrimp (Kyle, 2021).

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. To create a top-notch research topic, you will need to be precise and target a specific context with specific variables of interest . In other words, you’ll need to identify a clear, well-justified research gap.

Need more help?

If you’re still feeling a bit unsure about how to find a research topic for your environmental science dissertation or research project, be sure to check out our private coaching services below, as well as our Research Topic Kickstarter .

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12 Comments

research topics on climate change and environment

Researched PhD topics on environmental chemistry involving dust and water

I wish to learn things in a more advanced but simple way and with the hopes that I am in the right place.

Thank so much for the research topics. It really helped

the guides were really helpful

Research topics on environmental geology

Thanks for the research topics….I need a research topic on Geography

hi I need research questions ideas

Implications of climate variability on wildlife conservation on the west coast of Cameroon

I want the research on environmental planning and management

I want a topic on environmental sustainability

It good coaching

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Explore the foundations of environmental studies and the unique interdisciplinary methods used to address challenging environmental issues. As students, researchers, and educators take a close look at this field of study, they learn how human actions can impact planet Earth, including plants, animals, and entire ecosystems. While taking a global perspective, environmental studies also analyzes what occurs on local, regional, and international levels.

This broad field of study examines how human activity has affected the natural environment, which many scientists have linked to complex environmental problems, including major natural disasters such as hurricanes, earthquakes, and volcanic eruptions as well as long-term issues like climate change. Research has shown that human activities like cutting down forests and damming rivers can significantly impact environmental health and society.   

Environmental studies focuses on the scientific and humanitarian components of these issues. Drawing from the principles of the physical sciences, economics, and social sciences, researchers examine the complexities of contemporary environmental issues such as global warming, pollution, sustainability, renewable energy, and more. This field of study also considers how ecology and environmental science intersect with other subjects like ethics, anthropology, politics, urban planning, and social justice. 

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Environmental studies resources.

Gale provides environmental studies resources that support research, including  databases ,  primary source archives , and eBooks .

Gale databases  offer environmental studies journals, environmental studies articles, and more to support research.

Gale In Context: Environmental Studies

Focuses on the area of sustainability and the environment. Topics include ecosystems, biodiversity, global warming, food safety, and environmental disasters.

Gale OneFile: Environmental Studies and Policy

Addresses environmental concerns and research with coverage from more than 300 environmental studies journals and book reference content.

Gale In Context: Opposing Viewpoints

Supports science, current events, and language arts and presents all sides of important issues, empowering learners to develop information literacy and critical-thinking skills.

Primary Source Archives

Gale Primary Sources  contains archives and collections that provide firsthand content, including historical documents, archives, journals, and news articles that examine and analyze environmental concerns.

Environmental History

Explore the long history behind these contests that join advocacy to action and pit public policy against human behavior, including the development of environmental protections, disaster response strategies, and international trade rules. Follow the rise of the modern conservation movement in Gale’s  Environmental History  series.

Clean Air Act Amendments of 1990: Reduction of Acid Rain, Urban Air Pollution, and Environmental Policy

Archives Unbound presents topically focused digital collections of historical documents that support the research and study needs of scholars and students at the college and university level.

Development of Environmental Health Policy: Pope A. Lawrence Papers 1924‒1983

Correspondence, reports, scientific data, photographs, and maps all document the varied research and policy-making career of Pope A. Lawrence, an environmental health scientist with the Public Health Service (PHS) and the Environmental Protection Agency (EPA).

Punch Historical Archive, 1841-1992

The fully text-searchable online archive Punch Historical Archive, 1841‒1992 is available for scholars, students, and the general researcher to explore. The archive is an unrivaled tool for researching and teaching 19 th - and 20 th -century political and social history on key themes such as World War I and World War II; colonialism, imperialism and End of Empire; impact of new technology and modernity; public health, conservation, and environmentalism; social change; and the role of women.

Gale eBooks

Gale offers a variety of eBooks covering a wide range of environmental topics, including climate change, pollution, climate issues, technology, and more. Users can add  Gale eBooks  to a customized collection and cross-search to pinpoint relevant content.  Workflow tools  help users easily share, save, and download content.

California Greenin': How the Golden State Became an Environmental Leader, 1 st Edition

Princeton University Press | 2018 | ISBN-13: 9781400889594

This book offers a political history of environmental policy and regulation in California, from the Gold Rush to the present. Over its 150-year history, California has successfully protected its scenic wilderness areas, restricted coastal oil drilling, regulated automobile emissions, preserved coastal access, improved energy efficiency, and, most recently, addressed global climate change. How has this state, more than any other, enacted so many innovative and stringent environmental regulations over such a long period of time? The first comprehensive look at California’s history of environmental leadership, this book shows why the Golden State has been at the forefront in setting new environmental standards, often leading the rest of the nation. It traces California’s remarkable environmental policy trajectory and explains why the state took a leading role—it had more to lose from environmental deterioration and more to gain from preserving its stunning natural geography.

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Environmental Carbon Footprints: Industrial Case Studies, 1 st Edition

Butterworth-Heinemann | 2018 | ISBN-13: 9780128128503

This book provides a wide range of industrial cases, beginning with textiles, energy systems, and biofuels. Each footprint is associated with background information, scientific consensus and the reason behind its invention, methodological framework, assessment checklist, calculation tool/technique, applications, challenges, and limitations. More importantly, applications of each indicator/framework in various industrial sectors and their associated challenges are presented. As case studies are the most flexible of all research designs, this book allows researchers to retain the holistic characteristics of real-life events while investigating empirical events.

Environmental Engineering: Review for the Professional Engineering Examination, 1 st Edition

Springer | 2013 | ISBN-13: 9780387499307

This book will help the reader expand into chemical engineering and become a licensed professional engineer (PE), which can offer a tremendous boost to one’s career as there are certain career opportunities available only to licensed engineers. Licensure demonstrates high standards of professionalism, knowledge, and ability. Because of the work experience requirement, PE examinees generally have been out of school for some time. This book summarizes the theoretical background of topics covered in the exam, which will help examinees refresh their memories on subjects they may not have been exposed to since their undergraduate classes. Another advantage of using this book to prepare for the PE exam is that two or three “logical distractors” (answers that result from common mistakes) are included among the answer choices for each problem. The solutions to the problems also explain why the logical distractors are incorrect. Research has shown that this is an efficient teaching tool. Thus, the inclusion of these logical distractors and their explanations will give individuals a better understanding of the subject matter in a shorter period of time. Although this book is intended primarily to help engineers prepare for the PE environmental engineering examination, it’s also useful in undergraduate engineering courses that cover environmental engineering topics.  

Idiot’s Guides: Environmental Science, 1 st Edition

Alpha Books | 2014 | ISBN-13: 9781615643738

Environmental science is an integrated interdisciplinary field that combines the study of ecology, physics, chemistry, biology, soil science, geology, atmospheric science, and geography. It is among the top 10 most popular Advanced Placement examinations taken by high school seniors to receive postsecondary college credit.  Idiot’s Guide ® to Environmental Science  provides a step-by-step review of the disciplines that comprise environmental science, helping students grasp the basic concepts, internalize the information, and prepare for exams.

Oil Spill Environmental Forensics Case Studies, 1 st Edition

Butterworth-Heinemann | 2018 | ISBN-13: 9780128044353

This book includes 34 chapters presenting various aspects of environmental forensics in relation to “real-world” oil spill cases from around the globe. Authors representing academic, government, and private researcher groups from 14 countries bring a diverse and global perspective to this volume

Science and the Global Environment: Case Studies for Integrating Science and the Global Environment, 1 st Edition

Elsevier Inc. | 2017 | ISBN-13: 9780128018088

This book provides data-rich cases focused on complex global environmental issues in four major content areas: water resources, the atmosphere and air quality, ecosystem alteration, and global resources and human needs. It’s designed to help students of the environment and natural resources make the connections between their training in science and math and today’s complex environmental issues.

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Annual Review of Environment and Resources

Volume 48, 2023, review article, open access, advances in qualitative methods in environmental research.

• Holly Caggiano 1 , and Elke U. Weber 2
• View Affiliations Hide Affiliations Affiliations: 1 Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey, USA; email: [email protected] 2 Department of Psychology and Public Affairs, Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey, USA; email: [email protected]
• Vol. 48:793-811 (Volume publication date November 2023) https://doi.org/10.1146/annurev-environ-112321-080106
• First published as a Review in Advance on April 11, 2023
• Copyright © 2023 by the author(s). This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information

Qualitative research methods examine a wide range of topics in the study of environment and resource management. This first review on the topic highlights innovative and impactful research over the past few decades, drawing from social science disciplines that include sociology, geography, anthropology, political science, public policy, and psychology. We describe qualitative research methods that have addressed five scientific goals: ( a ) describing what the world is like, ( b ) predicting what the world can be like, ( c ) acknowl-edging researcher positionality and reflexivity and diversifying ways of knowing in theorizing and research designs, ( d ) integrating imaginaries into empirical research and building narratives to make sense of possible futures and to broaden our view of scientific inquiry, and ( e ) helping scholars grapple with the deep complexity of socioecological systems. As we explore these themes, we explain foundational qualitative approaches and highlight examples of environmental qualitative research that apply them.

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Research Methods for Environmental Studies

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The methodological needs of environmental studies are unique in the breadth of research questions that can be posed, calling for a textbook that covers a broad swath of approaches to conducting research with potentially many different kinds of evidence.　　

Written specifically for social science-based research into the environment, this book covers the best-practice research methods most commonly used to study the environment and its connections to societal and economic activities and objectives. Over five key parts, Kanazawa introduces quantitative and qualitative approaches, mixed methods, and the special requirements of interdisciplinary research, emphasizing that methodological practice should be tailored to the specific needs of the project. Within these parts, detailed coverage is provided on key topics including the identification of a research project; spatial analysis; ethnography approaches; interview technique; and ethical issues in environmental research.　

Drawing on a variety of extended examples to encourage problem-based learning and fully addressing the challenges associated with interdisciplinary investigation, this book will be an essential resource for students embarking on courses exploring research methods in environmental studies.

TABLE OF CONTENTS

Chapter | 14  pages, introduction to research methods in environmental studies, chapter | 25  pages, a brief history of knowledge and argumentation, chapter | 20  pages, general research design principles, chapter | 12  pages, general principles of quantitative research, chapter | 22  pages, quantitative data and sampling, chapter | 24  pages, basic quantitative methods and analysis, chapter | 28  pages, more advanced methods of quantitative analysis, chapter | 17  pages, spatial analysis and gis, chapter | 19  pages, general principles of qualitative research, the case study method, the ethnographic approach, chapter | 15  pages, actor-network theory, chapter | 18  pages, environmental discourse analysis, chapter | 13  pages, action research, mixed methods, data collection i, data collection ii, ethical issues in environmental research, chapter | 23  pages, writing a research proposal.

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Ben Clarke et al 2022 Environ. Res.: Climate 1 012001

Extreme event attribution aims to elucidate the link between global climate change, extreme weather events, and the harms experienced on the ground by people, property, and nature. It therefore allows the disentangling of different drivers of extreme weather from human-induced climate change and hence provides valuable information to adapt to climate change and to assess loss and damage. However, providing such assessments systematically is currently out of reach. This is due to limitations in attribution science, including the capacity for studying different types of events, as well as the geographical heterogeneity of both climate and impact data availability. Here, we review current knowledge of the influences of climate change on five different extreme weather hazards (extreme temperatures, heavy rainfall, drought, wildfire, tropical cyclones), the impacts of recent extreme weather events of each type, and thus the degree to which various impacts are attributable to climate change. For instance, heat extremes have increased in likelihood and intensity worldwide due to climate change, with tens of thousands of deaths directly attributable. This is likely a significant underestimate due to the limited availability of impact information in lower- and middle-income countries. Meanwhile, tropical cyclone rainfall and storm surge height have increased for individual events and across all basins. In the North Atlantic basin, climate change amplified the rainfall of events that, combined, caused half a trillion USD in damages. At the same time, severe droughts in many parts of the world are not attributable to climate change. To advance our understanding of present-day extreme weather impacts due to climate change developments on several levels are required. These include improving the recording of extreme weather impacts around the world, improving the coverage of attribution studies across different events and regions, and using attribution studies to explore the contributions of both climate and non-climate drivers of impacts.

Friederike E L Otto et al 2023 Environ. Res.: Climate 2 025001

As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

A J Pitman et al 2022 Environ. Res.: Climate 1 025002

Efforts to assess risks to the financial system associated with climate change are growing. These commonly combine the use of integrated assessment models to obtain possible changes in global mean temperature (GMT) and then use coupled climate models to map those changes onto finer spatial scales to estimate changes in other variables. Other methods use data mined from 'ensembles of opportunity' such as the Coupled Model Intercomparison Project (CMIP). Several challenges with current approaches have been identified. Here, we focus on demonstrating the issues inherent in applying global 'top-down' climate scenarios to explore financial risks at geographical scales of relevance to financial institutions (e.g. city-scale). We use data mined from the CMIP to determine the degree to which estimates of GMT can be used to estimate changes in the annual extremes of temperature and rainfall, two compound events (heatwaves and drought, and extreme rain and strong winds), and whether the emission scenario provides insights into the change in the 20, 50 and 100 year return values for temperature and rainfall. We show that GMT provides little insight on how acute risks likely material to the financial sector ('material extremes') will change at a city-scale. We conclude that 'top-down' approaches are likely to be flawed when applied at a granular scale, and that there are risks in employing the approaches used by, for example, the Network of Central Banks and Supervisors for Greening the Financial System. Most fundamental, uncertainty associated with projections of future climate extremes must be propagated through to estimating risk. We strongly encourage a review of existing top-down approaches before they develop into de facto standards and note that existing approaches that use a 'bottom-up' strategy (e.g. catastrophe modelling and storylines) are more likely to enable a robust assessment of material risk.

Arlene M Fiore et al 2022 Environ. Res.: Climate 1 025008

Observational records of meteorological and chemical variables are imprinted by an unknown combination of anthropogenic activity, natural forcings, and internal variability. With a 15-member initial-condition ensemble generated from the CESM2-WACCM6 chemistry-climate model for 1950–2014, we extract signals of anthropogenic ('forced') change from the noise of internally arising climate variability on observed tropospheric ozone trends. Positive trends in free tropospheric ozone measured at long-term surface observatories, by commercial aircraft, and retrieved from satellite instruments generally fall within the ensemble range. CESM2-WACCM6 tropospheric ozone trends are also bracketed by those in a larger ensemble constructed from five additional chemistry-climate models. Comparison of the multi-model ensemble with observed tropospheric column ozone trends in the northern tropics implies an underestimate in regional precursor emission growth over recent decades. Positive tropospheric ozone trends clearly emerge from 1950 to 2014, exceeding 0.2 DU yr −1 at 20–40 N in all CESM2-WACCM6 ensemble members. Tropospheric ozone observations are often only available for recent decades, and we show that even a two-decade record length is insufficient to eliminate the role of internal variability, which can produce regional tropospheric ozone trends oppositely signed from ensemble mean (forced) changes. By identifying regions and seasons with strong anthropogenic change signals relative to internal variability, initial-condition ensembles can guide future observing systems seeking to detect anthropogenic change. For example, analysis of the CESM2-WACCM6 ensemble reveals year-round upper tropospheric ozone increases from 1995 to 2014, largest at 30 S–40 N during boreal summer. Lower tropospheric ozone increases most strongly in the winter hemisphere, and internal variability leads to trends of opposite sign (ensemble overlaps zero) north of 40 N during boreal summer. This decoupling of ozone trends in the upper and lower troposphere suggests a growing prominence for tropospheric ozone as a greenhouse gas despite regional efforts to abate warm season ground-level ozone.

Isamar M Cortés et al 2024 Environ. Res.: Climate 3 045004

Although mangroves provide many beneficial ecosystem services, such as blue carbon storage and coastal protection, they are currently under threat due to changes in climate conditions, such as prolonged drought exposure. Under drought conditions, evaporation exceeds precipitation and high soil salinities can lead to stunted growth and die-back. To quantify this interplay, we developed a database for low-lying and uninhabited mangrove islands in the Caribbean under various evaporation and precipitation regimes. We extracted physical and biological information from each island using remote sensing techniques and coupled it with a process-based model. We used this database to develop a model that explains both the spatial variability in vegetated area across the Caribbean—as a function of rates of evaporation and precipitation—and porewater salinity concentration and dispersion from island edge towards the interior of mangrove islands. We then used this validated model to predict mangrove area loss associated with increases in evaporation to precipitation rates by 2100 for different Shared Socioeconomic Pathways (SSP). Less wealthy Caribbean regions such as Belize, Puerto Rico, and Venezuela are disproportionally affected, with mangrove area losses ranging from 3%–7% for SSP 2.6 and 13%–21% for SSP 7.0. Furthermore, foregone carbon sequestration in lost biomass under SSP 4.5 and 7.0 scenarios could compromise the ability of low-lying Caribbean mangrove islands to vertically adjust to sea level rise.

Sylvia Dee et al 2023 Environ. Res.: Climate 2 022002

The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a 'common currency' that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

Joseph Giguere et al 2024 Environ. Res.: Climate 3 035003

Nilofer K Bharwani et al 2024 Environ. Res.: Climate 3 045008

As environmental disasters disproportionately affect women, international guidelines to address the disparity focus on the inclusion of women in disaster risk reduction programs. While inclusion is a noble pursuit, it may fall short of equitable outcomes and instead lead to tokenization. Drawing on standpoint feminism and intersectionality, the goal of this study was to investigate tensions between inclusion and equity for women working in disaster risk reduction. We interviewed women from Ecuador and Tajikistan and asked them about the ways that gender shapes their experiences working in disaster risk reduction. We found that the women reflect upon themselves as agents with various forms of privilege and disadvantage, based on their multifaceted identities and differing levels of perceived discrimination within their professions. The findings suggest that inclusion, while necessary, can fall short of its promises. We recommend that organizational guidelines make everyone responsible for fostering equitable workplaces for disaster risk reduction.

Toyin Adekanmbi et al 2024 Environ. Res.: Climate 3 012001

Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO 2 ), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.

Andrés Navarro and Francisco J Tapiador 2024 Environ. Res.: Climate 3 042001

Climate classification systems (CCSs) were developed in the 19th century to explain the global distribution of plants and animals, but were given a new life in the Earth physics community in the 21st century as a means to validate Earth System Models (ESMs). The purpose of this paper is to critically review the evolution of this approach to validation and to discuss the current status. It is shown that CCSs can not only measure model performance, but also help identify and deconstruct systematic model biases. CCSs provide a rational and comprehensive zonation of the global environment using quantitative estimates of physical variables such as precipitation, temperature, and humidity. They offer several advantages over single variables due to the high sensitivity of the resulting classes to small changes in input variables and dimensionality reduction. CCSs encompass model behavior on both energy and water balances, allowing for a more comprehensive assessment of ESM performance.

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Kelvin S Ng et al 2024 Environ. Res.: Climate 3 041001

While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

Zachary I Glaser et al 2024 Environ. Res.: Climate 3 045007

Internal climate variability (ICV) often violates the assumptions of statistical methods, and the climate research community does not have an established approach for addressing resulting biases. Here we argue for a technique we call climate model Large-Ensemble Monte-Carlo (LENS-MC) to inform the selection of statistical methods for real-world application. Until now, scientists have often made best efforts to select methods based on assumptions about the mathematical properties of ICV. LENS-MC relaxes these assumptions and justifies method selection, potentially for a wide range of statistical analyses. We demonstrate LENS-MC using a case study of statistical errors in 20 year trends in global temperature and top-of-atmosphere flux series, comparing results with standard ordinary least squares (OLS). OLS commonly underestimates trend uncertainties, resulting in a higher likelihood of falsely reporting statistically significant trends or changes in trends, for example reporting p < 0.05 in 20 year temperature trends when the statistics are actually equivalent to p < 0.56. LENS-MC tests result in the selection of methods that almost eliminate the low bias in OLS trend standard errors. Using the suggested methods, researchers are less likely to mistakenly report significant trends, and LENS-MC could be widely applied to statistical climate analysis for which model output is available, provided that model ICV displays similar statistical structure, such as in autocorrelation, to observed ICV.

Ashbin Jaison et al 2024 Environ. Res.: Climate 3 045006

Windstorms cause severe damages and are the dominant natural hazard responsible for insurance payouts in Norway. Using future wind speeds from twenty bias corrected regional climate model simulations from EURO-CORDEX and four damage functions, fitted on municipality level to observed insurance losses, we estimate storm related losses to increase in all Norwegian counties due to climate change. At national level, there is an increase in storm losses in the far future, but some counties and municipalities can still exhibit a decrease in losses. Under a changing climate, despite a decrease in the 98th percentile of the wind speed, the most extreme winds increase, and this drives an increase in the windstorm damages. We also show that the wind load design values may need to be re-evaluated to take into account the changing wind speeds. In addition to climate change, the impact of future exposure changes in the form of population changes using municipality-level population projections is shown to be important for determining the level of future windstorm damage risk. Overall, the loss projections show a large spread, highlighting their sensitivity to the choice of the storm-damage function and to the choice of the simulation.

Félix Sebastián Riera et al 2024 Environ. Res.: Climate 3 045005

Stakeholder adaptation is a critical strategy to overcome changing climate patterns worldwide. Still it relies on the speed and effectiveness of information flow to end-users. Research shows that the loss of information in several stages of its spread and learning from peers is more important than the knowledge circulated by extension services. Women's participation and contribution are supportive and strategic, depending on the level of agreement and the interplay of trust variables within the network. In the arid Andes, agriculture is central and dependent on water management and macroeconomic conditions that shape market prospects, irrigation practices, and stakeholder behavior. Data were collected using the platform of a capacity-building program for organisations of water users in the Diamante and Atuel River basins in Mendoza, Argentina. Social Network Analysis (SNA) contributes to unveiling the cornerstones of information flow by identifying group structures, strong bonds, and bottlenecks in water management systems. In the first step, we evaluated the characteristics (density, centrality, average shortest path, and degree) of the pre-existing relationships and five sub-topic networks. Second, we compare networks containing pre-existing links only with those formed during the lecture. Emphasizing adaptation practices to cope with climate change impacts, the results provide valuable insights into the intricate interplay of gender dynamics, trust, expertise recognition, and discussion patterns within water and agricultural extension groups in Argentina. These insights highlight the ongoing need to promote gender equity, address biases in expertise recognition, and leverage trust for meaningful knowledge exchanges within evolving social contexts. It also reveals the alignment of Argentina's gender performance with similar production setups in Southern America or the Global North, highlighting the universality of challenges and opportunities in fostering inclusive and equitable participation. Our findings indicate that each group within the two river basins exhibits numerous pre-existing links and tends to be less accessible to newcomers, resulting in a shorter average path. Thus, information can spread faster. Trust is an underlying facilitator for sensible topics and a catalyzer for communication.

Review articles

Claude-Michel Nzotungicimpaye and H Damon Matthews 2024 Environ. Res.: Climate 3 032001

Anthropogenic CO 2 emissions are causing climate change, and impacts of climate change are already affecting every region on Earth. The purpose of this review is to investigate climate impacts that can be linked quantitatively to cumulative CO 2 emissions (CE), with a focus on impacts scaling linearly with CE. The reviewed studies indicate a proportionality between CE and various observable climate impacts such as regional warming, extreme daily temperatures, heavy precipitation events, seasonal changes in temperature and precipitation, global mean precipitation increase over ocean, sea ice decline in September across the Arctic Ocean, surface ocean acidification, global mean sea level rise, different marine heatwave characteristics, changes in habitat viability for non-human primates, as well as labour productivity loss due to extreme heat exposure. From the reviewed literature, we report estimates of these climate impacts resulting from one trillion tonne of CE (1 Tt C). These estimates are highly relevant for climate policy as they provide a way for assessing climate impacts associated with every amount of CO 2 emitted by human activities. With the goal of expanding the number of climate impacts that could be linked quantitatively to CE, we propose a framework for estimating additional climate impacts resulting from CE. This framework builds on the transient climate response to cumulative emissions (TCRE), and it is applicable to climate impacts that scale linearly with global warming. We illustrate how the framework can be applied to quantify physical, biological, and societal climate impacts resulting from CE. With this review, we highlight that each tonne of CO 2 emissions matters in terms of resulting impacts on natural and human systems.

Fengfei Song et al 2023 Environ. Res.: Climate 2 042001

Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, pattern shift, phase and wet/dry season length changes. In a warming climate, the amplitude of tropical rainfall annual cycle is enhanced, more evidently over ocean, while the phase of tropical rainfall annual cycle is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The pattern shift under warming is marked by two features: equatorward shift of the inter-tropical convergence zone throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter is suggested to be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress of the last decade, many gaps remain in understanding the mechanism, quantifying and attributing the emergence, narrowing the inter-model uncertainty, and evaluating the impact of tropical rainfall annual cycle changes, motivating future work guided by some directions proposed in this review.

G Persad et al 2023 Environ. Res.: Climate 2 032001

Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols' climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols' complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society's ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

Accepted manuscripts

Ivanova et al 

Human populations are migrating as a result of climate change. We conducted a systematic literature review examining the relationship between several climate hazards and migration flows. We reviewed 58 empirical studies between 2010 and 2023 that examined current and future migration related to acute and long-term climate hazards. The results indicate that the relationship between climate hazards and migration is diverse and depends on a range of socio-economic factors. Droughts and rising temperatures have a major impact on both international and domestic migration. Our review also reveals that multiple climate hazards collectively tend to intensify domestic migration toward urban areas, especially in agriculturally dependent regions. A better understanding of migration patterns and the climate hazards that drive them can inform global policy and preparedness for climate migration.

Danilenko et al 

This paper is the first to analyse the role of women authors in fostering justice-relevant topics in climate adaptation research. As representation, citation and payment patterns remain gender-biased across scientific disciplines, we explore the case of climate science, particularly adaptation, as its most human-oriented facet. In climate research and policy, there has been a recent surge of interest in climate justice topics: mentions of justice have increased almost tenfold in IPCC Working Group 2 reports between the latest assessment cycles (AR5 and AR6). We conduct a systematic examination of the topic space in the adaptation policy scholarship. As it is a vast and rapidly growing field, we use topic modelling, an unsupervised machine learning method, to identify the literature on climate justice and related fields, as well as to examine the relationship between topic prevalence and the gender of the authors. We find climate change adaptation policy research to be male dominated, with women holding 38.8% of first and 28.8% of last authorships. However, we observe topic-specific variability, whereby the share of female authors is higher among publications on justice-relevant topics. Female authorship is highly linked to topics such as Community, Local Knowledge, and Governance, but less to Food Security and Climate Finance. Our findings corroborate the evidence that female authors play a significant role in advancing the research and dialogue on the relationship between climate change and areas that have meaningful impact on lives of women and other marginalised groups.

Kalik et al 

While observational records provide evidence for strengthening of the Pacific Walker circulation and the boreal winter Hadley cell (HC) over the last few decades, the underlying causes are not well understood. This study investigates the radiative effects of CO 2 and anthropogenic aerosols on regional variations in the intensity of tropical meridional (Hadley-like) and zonal (Walker-like) overturning circulations, based on a suite of experiments using the IITM Earth System Model (IITM-ESM) along with supplementary analysis of observed and reanalysis datasets. Two key findings emerge from our study (i) strengthening of the Pacific Walker circulation with enhanced zonal sea surface temperature (SST) gradients and precipitation increase over the tropical Indo-Pacific, in response to global warming via ocean-atmosphere coupled feedbacks (ii) aerosol-induced intensification of regional meridional overturning circulation over the (0 − 120 ◦ E) longitudinal domain resulting from inter hemispheric energy imbalance and southward shift of the southern hemispheric (SH) rainfall belt extending eastward from Africa across the Indian Ocean. Our results suggest that the combined radiative influence of increased CO2 and northern hemispheric (NH) anthropogenic aerosols reinforces the regional meridional overturning circulation, by enhancing convection over the SH and promoting widespread descent over the NH subtropics and mid-latitudes covering northern Africa, Mediterranean, parts of middle-East, West and South Asia. The present findings have important implications for the regional water resources, agriculture and environment.

Klug et al 

In June 2017, the Economic Community of West African States (ECOWAS) adopted the Policy for Gender Mainstreaming in Energy Access to integrate gender equity in West Africa's energy and climate policy making. The policy represents the first regional gender and energy policy in the world, establishing a framework to enhance understanding at the nexus of gender and energy, improve the inclusivity of policies, programs, and initiatives, and increase women's participation in the field of energy in the public and private sectors. Each member state developed a National Action Plan to operationalize these goals and align implementation with national contexts and policy priorities. This study examines the experience of Senegal's National Action Plan development, from 13 semi-structured interviews of key stakeholders and document review. We use the Actor, Objective, Context political economy framework to understand how Senegal's economic and political structures, historical and social context, and influence of key stakeholders shaped their efforts to mainstream gender in the country's energy sector. We then describe four main objectives of actors in government, civil society, and the development space involved in policy development, summarizing these as: electricity access, economic development, access to clean fuels and technologies for cooking, and gender equity. We find that the objectives of government and development actors are reflected in the emphasis on energy access in the National Action Plan and the government's long-term development agenda, though a critical lack of engagement with the private sector has limited the policy's effectiveness in mainstreaming women across energy sub-sectors beyond clean cooking. Women's civil society organizations, historically less engaged with energy issues, have nonetheless achieved considerable success in advocating for increased women's political participation. This advocacy has set a precedent for policies promoting "positive discrimination" for women and marginalized groups, evident in the policy's activities aimed at electrifying women's economic sectors.

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• 2022-present Environmental Research: Climate doi: 10.1088/issn.2752-5295 Online ISSN: 2752-5295

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50 Best Environmental Science Research Topics

May 31, 2023

Environmental science is a varied discipline that encompasses a variety of subjects, including ecology, atmospheric science, and geology among others. Professionals within this field can pursue many occupations from lab technicians and agricultural engineers to park rangers and environmental lawyers. However, what unites these careers is their focus on how the natural world and the human world interact and impact the surrounding environment. There is also one other significant commonality among environmental science careers: virtually all of them either engage in or rely on research on environmental science topics to ensure their work is accurate and up to date.

In this post, we’ll outline some of the best environmental science research topics to help you explore disciplines within environmental science and kickstart your own research. If you are considering majoring in environmental science or perhaps just need help brainstorming for a research paper, this post will give you a broad sense of timely environmental science research topics.

What makes a research topic good?

Before we dive into specific environmental science research topics, let’s first cover the basics: what qualities make for a viable research topic. Research is the process of collecting information to make discoveries and reach new conclusions. We often think of research as something that occurs in academic or scientific settings. However, everyone engages in informal research in everyday life, from reading product reviews to investigating statistics for admitted students at prospective colleges . While we all conduct research in our day-to-day lives, formal academic research is necessary to advance discoveries and scholarly discourses. Therefore, in this setting, good research hinges on a topic in which there are unanswered questions or ongoing debates. In other words, meaningful research focuses on topics where you can say something new.

However, identifying an interesting research topic is only the first step in the research process. Research topics tend to be broad in scope. Strong research is dependent on developing a specific research question, meaning the query your project will seek to answer. While there are no comprehensive guidelines for research questions, most scholars agree that research questions should be:

1) Specific

Research questions need to clearly identify and define the focus of your research. Without sufficient detail, your research will likely be too broad or imprecise in focus to yield meaningful insights. For example, you might initially be interested in addressing this question: How should governments address the effects of climate change? While that is a worthwhile question to investigate, it’s not clear enough to facilitate meaningful research. What level of government is this question referring to? And what specific effects of global warming will this research focus on? You would need to revise this question to provide a clearer focus for your research. A revised version of this question might look like this: How can state government officials in Florida best mitigate the effects of sea-level rise?

 2) Narrow

Our interest in a given topic often starts quite broad. However, it is difficult to produce meaningful, thorough research on a broad topic. For that reason, it is important that research questions be narrow in scope, focusing on a specific issue or subtopic. For example, one of the more timely environmental science topics is renewable energy. A student who is just learning about this topic might wish to write a research paper on the following question: Which form of renewable energy is best? However, that would be a difficult question to answer in one paper given the various ways in which an energy source could be “best.” Instead, this student might narrow their focus, assessing renewable energy sources through a more specific lens: Which form of renewable energy is best for job creation?

 3) Complex

As we previously discussed, good research leads to new discoveries. These lines of inquiry typically require a complicated and open-ended research question. A question that can be answered with just a “yes” or “no” (or a quick Google search) is likely indicative of a topic in which additional research is unnecessary (i.e. there is no ongoing debate) or a topic that is not well defined. For example, the following question would likely be too simple for academic research: What is environmental justice? You can look up a definition of environmental justice online. You would need to ask a more complex question to sustain a meaningful research project. Instead, you might conduct research on the following query: Which environmental issue(s) disproportionately impact impoverished communities in the Pacific Northwest? This question is narrower and more specific, while also requiring more complex thought and analysis to answer.

4) Debatable

Again, strong research provides new answers and information, which means that they must be situated within topics or discourses where there is ongoing debate. If a research question can only lead to one natural conclusion, that may indicate that it has already been sufficiently addressed in prior research or that the question is leading. For example, Are invasive species bad? is not a very debatable question (the answer is in the term “invasive species”!). A paper that focused on this question would essentially define and provide examples of invasive species (i.e. information that is already well documented). Instead, a researcher might investigate the effects of a specific invasive species. For example: How have Burmese pythons impacted ecosystems in the Everglades, and what mitigation strategies are most effective to reduce Burmese python populations?

Therefore, research topics, including environmental science topics, are those about which there are ample questions yet to be definitively answered. Taking time to develop a thoughtful research question will provide the necessary focus and structure to facilitate meaningful research.

10 Great Environmental Science Research Topics (With Explanations!)

Now that we have a basic understanding of what qualities can make or break a research topic, we can return to our focus on environmental science topics. Although “great” research topics are somewhat subjective, we believe the following topics provide excellent foundations for research due to ongoing debates in these areas, as well as the urgency of the challenges they seek to address.

1) Climate Change Adaptation and Mitigation

Although climate change is now a well-known concept , there is still much to be learned about how humans can best mitigate and adapt to its effects. Mitigation involves reducing the severity of climate change. However, there are a variety of ways mitigation can occur, from switching to electric vehicles to enforcing carbon taxes on corporations that produce the highest carbon emission levels. Many of these environmental science topics intersect with issues of public policy and economics, making them very nuanced and versatile.

In comparison, climate change adaptation considers how humans can adjust to life in an evolving climate where issues such as food insecurity, floods, droughts, and other severe weather events are more frequent. Research on climate change adaptation is particularly fascinating due to the various levels at which it occurs, from federal down to local governments, to help communities anticipate and adjust to the effects of climate change.

Both climate change mitigation and adaptation represent excellent environmental science research topics as there is still much to be learned to address this issue and its varied effects.

2) Renewable Energy

Renewable energy is another fairly mainstream topic in which there is much to learn and research. Although scientists have identified many forms of sustainable energy, such as wind, solar, and hydroelectric power, questions remain about how to best implement these energy sources. How can politicians, world leaders, and communities advance renewable energy through public policy? What impact will renewable energy have on local and national economies? And how can we minimize the environmental impact of renewable energy technologies? While we have identified alternatives to fossil fuels, questions persist about the best way to utilize these technologies, making renewable energy one of the best environmental science topics to research.

3) Conservation

Conservation is a broad topic within environmental science, focusing on issues such as preserving environments and protecting endangered species. However, conservation efforts are more challenging than ever in the face of a growing world population and climate change. In fact, some scientists theorize that we are currently in the middle of a sixth mass extinction event. While these issues might seem dire, we need scientists to conduct research on conservation efforts for specific species, as well as entire ecosystems, to help combat these challenges and preserve the planet’s biodiversity.

4) Deforestation

The Save the Rainforest movement of the 1980s and 90s introduced many people to the issue of deforestation. Today, the problems associated with deforestation, such as reduced biodiversity and soil erosion, are fairly common knowledge. However, these challenges persist due, in part, to construction and agricultural development projects. While we know the effects of deforestation, it is more difficult to identify and implement feasible solutions. This is particularly true in developing countries where deforestation is often more prevalent due to political, environmental, and economic factors. Environmental science research can help reduce deforestation by identifying strategies to help countries sustainably manage their natural resources.

Environmental Science Topics (Continued)

5) urban ecology.

When we think of “the environment,” our brains often conjure up images of majestic mountain ranges and lush green forests. However, less “natural” environments also warrant study: this is where urban ecology comes in. Urban ecology is the study of how organisms interact with one another and their environment in urban settings. Through urban ecology, researchers can address topics such as how greenspaces in cities can reduce air pollution, or how local governments can adopt more effective waste management practices. As one of the newer environmental science topics, urban ecology represents an exciting research area that can help humans live more sustainably.

6) Environmental Justice

While environmental issues such as climate change impact people on a global scale, not all communities are affected equally. For example, wealthy nations tend to contribute more to greenhouse-gas emissions. However, less developed nations are disproportionately bearing the brunt of climate change . Studies within the field of environmental justice seek to understand how issues such as race, national origin, and income impact the degree to which people experience hardships from environmental issues. Researchers in this field not only document these inequities, but also identify ways in which environmental justice can be achieved. As a result, their work helps communities have access to clean, safe environments in which they can thrive.

7) Water Management

Water is, of course, necessary for life, which is why water management is so important within environmental science research topics. Water management research ensures that water resources are appropriately identified and maintained to meet demand. However, climate change has heightened the need for water management research, due to the occurrence of more severe droughts and wildfires. As a result, water management research is necessary to ensure water is clean and accessible.

8) Pollution and Bioremediation

Another impact of the increase in human population and development is heightened air, water, and soil pollution. Environmental scientists study pollutants to understand how they work and where they originate. Through their research, they can identify solutions to help address pollution, such as bioremediation, which is the use of microorganisms to consume and break down pollutants. Collectively, research on pollution and bioremediation helps us restore environments so they are sufficient for human, animal, and plant life.

9) Disease Ecology

While environmental science topics impact the health of humans, we don’t always think of this discipline as intersecting with medicine. But, believe it or not, they can sometimes overlap! Disease ecology examines how ecological processes and interactions impact disease evolution. For example, malaria is a disease that is highly dependent on ecological variables, such as temperature and precipitation. Both of these factors can help or hinder the breeding of mosquitoes and, therefore, the transmission of malaria. The risk of infectious diseases is likely to increase due to climate change , making disease ecology an important research topic.

10) Ecosystems Ecology

If nothing else, the aforementioned topics and their related debates showcase just how interconnected the world is. None of us live in a vacuum: our environment affects us just as we affect it. That makes ecosystems ecology, which examines how ecosystems operate and interact, an evergreen research topic within environmental science.

40 More Environmental Science Research Topics

Still haven’t stumbled upon the right environmental science research topic? The following ideas may help spark some inspiration:

• The effects of agricultural land use on biodiversity and ecosystems.
• The impact of invasive plant species on ecosystems.
• How wildfires and droughts shape ecosystems.
• The role of fire ecology in addressing wildfire threats.
• The impact of coral bleaching on biodiversity.
• Ways to minimize the environmental impact of clean energies.
• The effects of climate change on ocean currents and migration patterns of marine species.

Environmental Justice and Public Policy

• Opportunities to equalize the benefits of greenspaces for impoverished and marginalized communities.
• The impact of natural disasters on human migration patterns.
• The role of national parks and nature reserves in human health.
• How to address inequalities in the impact of air pollution.
• How to prevent and address the looming climate refugee crisis.
• Environmentally and economically sustainable alternatives to deforestation in less developed countries.
• Effects of environmental policies and regulations on impoverished communities.
• The role of pollutants in endocrine disruption.
• The effects of climate change on the emergence of infectious diseases.

AP Environmental Science Research Topics (Continued)

Soil science.

• Effects of climate change on soil erosion.
• The role of land management in maintaining soil health.
• Agricultural effects of salinization in coastal areas.
• The effects of climate change on agriculture.

Urban Ecology

• How road construction impacts biodiversity and ecosystems.
• The effects of urbanization and city planning on water cycles.
• Impacts of noise pollution on human health.
• The role of city planning in reducing light pollution.

Pollution and Bioremediation

• The role of bioremediation in removing “forever” chemicals from the environment.
• Impacts of air pollution on maternal health.
• How to improve plastic recycling processes.
• Individual measures to reduce consumption and creation of microplastics.
• Environmental impacts of and alternatives to fracking.

Environmental Law and Ethics

• Ethical implications of human intervention in the preservation of endangered species.
• The efficacy and impact of single-use plastic laws.
• Effects of religious and cultural values in environmental beliefs.
• The ethics of climate change policy for future generations.
• Ethical implications of international environmental regulations for less developed countries.
• The impact and efficacy of corporate carbon taxes.
• Ethical and environmental implications of fast fashion.
• The ethics and efficacy of green consumerism.
• Impacts of the hospitality and travel industries on pollution and emissions.
• The ethical implications of greenwashing in marketing.
• Effects of “Right to Repair” laws on pollution.

Final Thoughts: Environmental Science Research Topics

Environmental science is a diverse and very important area of study that impacts all aspects of life on Earth. If you’ve found a topic you’d like to pursue, it’s time to hit the books (or online databases)! Begin reading broadly on your chosen topic so you can define a specific research question. If you’re unsure where to begin, contact a research librarian who can connect you with pertinent resources. As you familiarize yourself with the discourse surrounding your topic, consider what questions spring to mind. Those questions may represent gaps around which you can craft a research question.

Interested in conducting academic research? Check out the following resources for information on research opportunities and programs:

• Research Opportunities for High School Students
• Colleges with the Best Undergraduate Research Programs
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Emily Smith

Emily earned a BA in English and Communication Studies from UNC Chapel Hill and an MA in English from Wake Forest University. While at UNC and Wake Forest, she served as a tutor and graduate assistant in each school’s writing center, where she worked with undergraduate and graduate students from all academic backgrounds. She also worked as an editorial intern for the Wake Forest University Press as well as a visiting lecturer in the Department of English at WFU, and currently works as a writing center director in western North Carolina.

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What really matters for successful research environments? A realist synthesis

Rola ajjawi.

1 Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Geelong, Victoria, Australia

Paul E S Crampton

2 Research Department of Medical Education, University College London, London, UK

3 Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia

Charlotte E Rees

Associated data.

Table S2. MeSH terms and a selection of key terms utilised in the database searches.

Table S3. Inclusion and exclusion criteria with respect to topic, recentness and type of article.

Table S4. Refined inclusion and exclusion criteria to include contextual parameters.

Table S5. Studies by type: qualitative, quantitative and mixed‐methods.

Research environments, or cultures, are thought to be the most influential predictors of research productivity. Although several narrative and systematic reviews have begun to identify the characteristics of research‐favourable environments, these reviews have ignored the contextual complexities and multiplicity of environmental characteristics.

The current synthesis adopts a realist approach to explore what interventions work for whom and under what circumstances.

We conducted a realist synthesis of the international literature in medical education, education and medicine from 1992 to 2016, following five stages: (i) clarifying the scope; (ii) searching for evidence; (iii) assessing quality; (iv) extracting data, and (v) synthesising data.

We identified numerous interventions relating to research strategy, people, income, infrastructure and facilities (IIF), and collaboration. These interventions resulted in positive or negative outcomes depending on the context and mechanisms fired. We identified diverse contexts at the individual and institutional levels, but found that disciplinary contexts were less influential. There were a multiplicity of positive and negative mechanisms, along with three cross‐cutting mechanisms that regularly intersected: time; identity, and relationships. Outcomes varied widely and included both positive and negative outcomes across subjective (e.g. researcher identity) and objective (e.g. research quantity and quality) domains.

Conclusions

The interplay among mechanisms and contexts is central to understanding the outcomes of specific interventions, bringing novel insights to the literature. Researchers, research leaders and research organisations should prioritise the protection of time for research, enculturate researcher identities, and develop collaborative relationships to better foster successful research environments. Future research should further explore the interplay among time, identity and relationships.

Short abstract

This realist review shows when and why interventions related to research strategy; people; income, infrastructure and facilities; and collaboration result in positive or negative research environments. Findings indicate that protected time, researcher identities and collaborative relationships are important for fostering successful research environments.

Introduction

Research environments matter. Environmental considerations such as robust cultures of research quality and support for researchers are thought to be the most influential predictors of research productivity. 1 , 2 Over 25 years ago, Bland and Ruffin 1 identified 12 characteristics of research‐favourable environments in the international academic medicine literature spanning the period from the mid‐1960s to 1990 (Box 1 ). Although these characteristics are aspirational in flavour, how they interplay to influence research productivity within increasingly complex institutional structures is not yet known. Indeed, although existing reviews have begun to help us better understand what makes for successful research environments, this research has typically ignored the contextual complexities and multiplicity of environmental characteristics 1 , 3 , 4 , 5 , 6 , 7 and has focused on narrow markers of productivity such as the quantity of research outputs (e.g. ref. 7 ) The current realist synthesis, therefore, aims to address this gap in the research literature by reviewing more recent literature ( 1992–2016 ) and exploring the features of successful research environments in terms of which interventions work, for whom, how and in what circumstances.

Characteristics of successful research environments 1

• Clear organisational research goals
• Research productivity as a priority and at least equal priority to other activities
• A robust research culture with shared research values
• A positive group climate
• Participative governance structures
• Non‐hierarchical and decentralised structures
• Good communication and professionally meaningful relationships between team members
• Decent resources such as people, funding, research facilities and time
• Larger group size, moderately established teams and diversity
• Rewards for research success
• Recruitment and selection of talented researchers
• Research‐oriented leaders with research expertise and skill

The contextual background for understanding successful research environments

Against a backdrop of the mass production of education, reduced government funding for research and ‘new managerialist’ cultures in higher education, 8 , 9 increased scrutiny of the quantity and quality of research, the research environments in which research is produced and the impacts of research has become inevitable. 10 Indeed, in higher education institutions (HEIs) globally, research productivity is being measured as part of individual researcher and research group key performance indicators. 7 In many countries, such as Australia, Hong Kong, New Zealand and the UK, 11 HEI research is measured on a national scale through government‐led research assessments. Such research measurement has contributed to the allocation of funding to universities and differentiation of universities in the competitive marketplace, with some solidifying their institutional identities as ‘research‐intensive’ and others emphasising their relative ‘newcomer‐to‐research’ status (e.g. previously ‘teaching‐intensive’ universities). 9 , 12 , 13 Such institutional differentiation also parallels that of individual academics within universities, who are increasingly encouraged to take either ‘research‐active’ or ‘education‐focused’ career pathways. 8 , 9 It is these broader national and institutional constraints that inevitably impact on research environments at the level of units, centres, departments and schools within universities (the level of ‘research environment’ that we focus on in this paper). Table S1 provides definitions of key terms.

Key features of research environments identified in previous reviews

Evans defines a research environment as including: ‘shared values, assumptions, beliefs, rituals and other forms of behaviour whose central focus is the acceptance and recognition of research practice and output as valued, worthwhile and pre‐eminent activity.’ 14 Previous reviews have tended to focus on interventions aimed at individual researchers, such as research capacity building, 4 , 5 , 7 and with individual‐level outcomes, such as increased numbers of grants or publications. 4 , 5 , 7 These reviews have typically concluded that research capacity‐building interventions lead to positive research outcomes. 4 , 5 , 7 Furthermore, the reviews have identified both individual and institutional enablers to research. Individual enablers included researchers’ intrinsic motivation to conduct research. 6 , 7 Institutional enablers included peer support, encouragement and review, 7 mentoring and collaboration, 4 , 5 research leadership, 5 , 6 institutional structures, processes and systems supporting research, such as clear strategy, 5 , 6 protected time and financial support. 5 Although these reviews have begun to shed light on the features of successful research environments, they have significant limitations: (i) they either include studies of low to moderate quality 4 , 5 or fail to check the quality of studies included, 7 and (ii) they do not explore what works for whom and under what circumstances, but instead focus on what works and ignore the influence of the context in which interventions are implemented and ‘how’ outcomes come about. Indeed, Mazmanian et al. 4 concluded in their review: ‘…little is known about what works best and in what situations.’

Conceptual framework: a realist approach

Given the gaps in the research literature and the importance of promoting successful research environments for individuals’ careers, institutional prestige and the knowledge base of the community, we thought a realist synthesis would be most likely to elucidate how multiple complex interventions can influence success. Realism assumes the existence of an external reality (a real world), but one that is filtered (i.e. perceived, interpreted and responded to) through human senses, volitions, language and culture. 15 A realist approach enables the development and testing of theory for why interventions may or may not work, for whom and under what circumstances. 16 It does this through recognising that interventions do not directly cause outcomes; instead, participants’ reactions and responses to the opportunities provided by the intervention trigger outcomes. This approach can allow researchers to identify causal links in complex situations, such as those between interventions and the contexts in which they work, how they work (mechanisms) and their outcomes. 17 Although the context–mechanism–outcome (CMO) approach is not necessarily linear, it can help to provide explanations that privilege contextual variability. 18

Aligned with the goals of realist research, this synthesis aims to address the following research question: What are the features of successful research environments, for whom, how and in what circumstances?

We followed five stages of realist synthesis: (i) clarifying scope; (ii) searching for evidence; (iii) assessing quality; (iv) extracting data, and (v) synthesising data. 19 Our methods also follow the RAMESES ( r ealist a nd m eta‐narrative e vidence s ynthesis: e volving s tandards) reporting guidelines. 20

Clarifying the scope

We first clarified the scope of our realist synthesis by identifying relevant interventions based on the Research Excellence Framework (REF) 2014 environment assessment criteria. The REF is a national exercise assessing the quality of research produced by UK HEIs, its impact beyond academia, and the environment that supports research. The assessment criteria indicated in the REF2014 environment template included the unit's research strategy , its people (including staffing strategy, staff development and research students), its income, infrastructure and facilities (IIF), as well as features of collaboration . 21 These guided our search terms (see stage 2 below). We chose to use these quality markers as they informed the UK national assessment exercise, upon which other national exercises are often based. In addition, these criteria were explicit, considered and implementable, and were developed through consensus. Like other realist syntheses, 18 , 22 , 23 ours considered a multiplicity of different interventions rather than just one and some of the papers we reviewed combined multiple interventions.

Based on previous reviews, 1 , 4 , 5 , 7 our initial programme theory speculated that interventions aligned to having an explicit research strategy, staff development opportunities, funding and establishing research networks would be effective for creating successful research environments (Fig. ​ (Fig.1 1 gives further details of our initial programme theory).

Initial programme theory

Searching for empirical evidence

We devised search terms as a team and refined these iteratively with the help of a health librarian experienced in searching. We split the research question into three key concepts: (i) research environment; (ii) discipline, and (iii) research indicator (i.e. positive or negative). We then used variations of these terms to search the most relevant databases including MEDLINE, ProQuest, Scopus, CINAHL (Cumulative Index to Nursing and Allied Health Literature) and Web of Science. Table S2 illustrates the MeSH terms and provides a selection of key terms utilised in the database searches.

We were interested in comparing research cultures across the disciplines of medical education, education and medicine for two key reasons. Firstly, the discipline of medical education consists of a rich tapestry of epistemological approaches including biomedical sciences, social sciences and education, and medicine. 24 , 25 Secondly, there have been disciplinary arguments in the literature about whether medical education should be constructed as medicine or social science. 24 , 26

We agreed various inclusion and exclusion criteria with respect to topic, recentness and type of article (Table S3 ), as well as refined criteria to include contextual parameters (Table S4 ). We chose 1992 as the start date for our search period as 1992 saw the first published literature review about productive research environments in the academic medicine literature. 1

Study selection

The first top‐level search elicited 8527 journal articles across all databases. Once duplicate results had been removed, and ‘topic’ and ‘recentness’ study parameters reinforced, 420 articles remained. The searching and selection process is summarised in a PRISMA ( p referred r eporting i tems for s ystematic reviews and m eta‐ a nalyses) diagram (Fig. ​ (Fig.2). 2 ). Three research assistants and one of the authors (PESC) initially assessed relevance by reviewing abstracts using preliminary inclusion criteria. If any ambiguities were found by any of the reviewers, abstracts were checked by one of the other two researchers (RA and CER). Where divergent views existed, researchers discussed the reasons why and agreed on whether to include or exclude. A 10% sample of these 420 abstracts were double‐checked by an additional two researchers, including a number of articles previously excluded, for quality control purposes.

PRISMA flow diagram of the selection process

Assessment of quality

We assessed the journal articles for relevance and rigour. 20 We defined an article's relevance according to ‘whether it can contribute to theory building and/or testing’. 20 Following the relevance check and ‘type’ exclusions to original research papers, 100 articles remained, which were then assessed for rigour. Although we chose to narrow down to original research, we kept relevant articles such as systematic reviews and opinion pieces to inform the introduction and discussion sections of this paper.

We defined rigour as determining ‘whether the method used to generate the particular piece of data is credible and trustworthy’. 20 We used two pre‐validated tools to assess study quality: the Medical Education Research Study Quality Instrument (MERSQI) to assess the quality of quantitative research, 27 , 28 and the Critical Appraisal Skills Programme (CASP) qualitative checklist for qualitative and mixed‐method studies. 29 Both tools are used to consider the rigour of study design, sampling, type of data, data analysis and outcomes/findings, and have been employed in previous reviews. 23 , 30

Following the quality assessment, 47 articles remained and were then subjected to data extraction and synthesis. Five papers were excluded as they did not contribute to our theory building or lacked CMO configurations (CMOCs). We kept notes of the reasons for excluding studies and resolved doubts through discussion (Fig. ​ (Fig.2 2 ).

Data extraction

Two data‐rich articles containing multiple CMOCs were inductively and deductively (based on the initial programme theory) coded by all of us to ensure consistency. We then discussed any similarities and differences in our coding. As is inherent in the challenges of realist approaches, we found differences in our identifications of CMOCs, which often related to how one particular component (e.g. time) could be an outcome at one moment and a mechanism the next. This alerted us to overlapping constructs, which we then explored as we coded remaining papers. To collect data across all remaining papers, we extracted information relating to: study design, methods and sample size; study setting; intervention focus; contexts of the intervention; mechanisms generated in the results, and outcomes. The key CMOCs in all 42 articles were identified primarily from the results sections of the papers. The process of data extraction and analysis was iterative with repeated discussion among the researchers of the demi‐regularities (i.e. patterns of CMOCs) in relation to the initial programme theory and negotiations of any differences of opinion.

Data synthesis

Finally, we interrogated our data extraction to look for patterns across our data/papers. We used an interpretative approach to consider how our data compared with our initial programme theory in order to develop our modified programme theory.

Characteristics of the studies

The 42 papers represented the following disciplines: medical education ( n = 4, 10%); 31 , 32 , 33 , 34 education ( n = 18, 43%), 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 and medicine ( n = 20, 48%). 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 There were 26 (62%) qualitative studies, 11 (26%) quantitative studies and five (12%) mixed‐methods studies (Table S5 ). The studies were from countries across the globe, including Australia ( n = 10, 24%), the USA ( n = 7, 17%), the UK ( n = 6, 14%), Canada ( n = 4, 10%), South Africa ( n = 4, 10%), Denmark ( n = 2, 5%), Turkey ( n = 2, 5%) and others ( n = 7, 17%) (e.g. Belgium, China, Germany, New Zealand and the Philippines). The research designs varied but common approaches included qualitative interviews, surveys, documentary/bibliographic analysis, case studies and mixed‐methods studies. Study participants included academics, teachers, health care professionals, senior directors, PhD students, early‐career researchers (ECRs) and senior researchers. Table S6 lists the individual contexts, interventions, mechanisms and outcomes identified from individual papers.

Extending our initial programme theory

A key finding from our realist synthesis was that the same interventions fired either positive or negative mechanisms leading to positive or negative outcomes, respectively, depending on context. Surprisingly, the CMOCs were mostly consistent across the three disciplines (i.e. medical education, education and medicine) with local contexts seemingly interplaying more strongly with outcomes. Therefore, we present these disciplinary contexts here as merged, but we highlight any differences by disciplinary context where relevant.

Having a research strategy promoted a successful research environment when it enabled appropriate resources (including time) and valuing of research; however, it had negative consequences when it too narrowly focused on outputs, incentives and rewards. In terms of people , individual researchers needed to be internally motivated and to have a sense of belonging, and protected time and access to capacity‐building activities in order to produce research. Lack of knowledge, researcher identity, networks and time, plus limited leadership support, acted as mechanisms leading to negative research outcomes. The presence of IIF was overwhelmingly indicated as necessary for successful research environments and their absence was typically detrimental. Interestingly, a few papers reported that external funding could have negative consequences because short‐term contracts, reduced job security and the use of temporary junior staff can lead to weak research environments. 40 , 67 , 71 Finally, collaboration was crucial for successful research mediated through trusting respectful relationships, supportive leadership and belongingness. Poor communication and competitive cultures, however, worked to undermine collaboration, leading to isolation and low self‐esteem, plus decreased research engagement and productivity. Table ​ Table1 1 highlights illustrative CMOCs for each intervention extending our initial programme theory.

Positive and negative context–mechanism–outcome configurations (CMOCs) for each intervention

CMOCs indicated in bold highlight the three cross‐cutting themes of time, identity and relationships.

ECRs = early‐career researchers.

Key cross‐cutting mechanisms: time, identity and relationships

As Table ​ Table1 1 shows, the same intervention can lead to positive or negative outcomes depending on the particular contexts and mechanisms triggered. This highlights greater complexity than is evident at first glance. Cross‐cutting these four interventions were three mechanisms that were regularly identified as critical to the success (or not) of a research environment: time; researcher identities, and relationships. We now present key findings for each of these cross‐cutting mechanisms and discuss how their inter‐relations lead to our modified programme theory (Fig. ​ (Fig.3). 3 ). Note that although we have tried to separate these three mechanisms for ease of reading, they were often messily entangled. Table ​ Table2 2 presents quotes illustrating the way in which each mechanism mediates outcomes within particular circumstances.

Modified programme theory. ECR = early‐career researcher

Time, identity and relationships as cross‐cutting mechanisms mediating successful research environments

Time was identified as an important mechanism for mobilising research outcomes across our three disciplines. Time was conceptualised severally including as: protected time; workload pressures influencing time available; efficient use of time; flexible use of time; making time, and time in career. The two most commonly considered aspects were protected time and workload implications. Protected time was largely talked about in the negative across a variety of contexts and disciplines, with lack of protected time leading to lack of researcher engagement or inactivity and reduced research productivity. 32 , 35 , 37 , 41 , 44 , 47 , 49 , 61 , 62 , 63 , 67 Also across a variety of contexts and disciplines, and acting as a positive mechanism, available protected time was found to lead to increased research productivity and active research engagement. 31 , 36 , 40 , 48 , 49 , 63 , 65 With regard to workload, limitations on the time available for research imposed by excessive other workloads led to reduced research activity, lower research productivity, poor‐quality research and reduced opportunity to attend research training. 40 , 41 , 47 , 49 , 60 , 67 Juggling of multiple responsibilities, such as clinical, teaching, administrative and leadership roles, also inhibited research productivity by diminishing the time available for research. 35 , 40 , 49 The alignment of research with other non‐research work was described as driving efficiencies in the use of time leading to greater research productivity (Table ​ (Table2, 2 , quote 1).

Identity was also an important mechanism for mobilising research outcomes across our three disciplines. Interpretations included personal identities (e.g. gender), professional identity (e.g. as a primary practitioner or a primary researcher), and social identity (e.g. sense of belongingness). Researcher identity was often referred to in relation to first‐career practitioners (and therefore second‐career researchers). Sharp et al. 48 defined these as participants recruited into higher education not directly from doctoral study but on the basis of their extensive ‘first‐order’ knowledge and pedagogical expertise. These were also practitioners conducting research in schools or hospitals. Identities were also referenced in relation to early, mid‐career or senior researchers. Academic staff working in academic institutions needed to develop a sense of researcher identity, belongingness, self‐efficacy for research and autonomy to increase their satisfaction, competence and research activity. 39 , 40 , 44 , 46 , 51 , 67 For first‐career practitioners (i.e. teachers, doctors), the research needed to be highly relevant and aligned to their primary identity work in order to motivate them. 53 , 59 , 62 , 65 This alignment was described as having a strong research–teaching nexus. 40 , 48 Linked to this concept was the need for first‐career practitioners to see the impact of research in relation to their primary work (e.g. patient‐ or student‐oriented) to facilitate motivation and to develop a researcher identity (Table ​ (Table2, 2 , quote 2). 36 , 37 , 41 , 49 , 53 , 54 , 67 Where research was seen as irrelevant to primary identity work (e.g. English language teaching, general practice), there was research disengagement. 37 , 48 , 52 , 59 , 67

Relationships

For all researchers and across our three disciplines, relationships were important in the mediating of successful research environments. 31 , 34 , 38 , 39 , 41 , 44 , 57 , 60 , 66 , 67 Positive research relationships were characterised by mutual trust and respect, 40 , 41 , 42 , 43 , 54 , 66 , 72 whereas others described them as friendships that take time to develop. 51 Mutually supportive relationships seemed to be particularly relevant to ECRs in terms of developing confidence, self‐esteem and research capacity and making identity transitions. 35 , 43 , 48 , 58 , 67 Relationships in the form of networks were considered to improve the quality of research through multicentre research and improved collaboration. 33 , 60 Supportive leadership as a particular form of relationship was an important mechanism in promoting a successful research environment. Supportive leaders needed to monitor workloads, set the vision, raise awareness of the value of research, and provide positive role‐modelling, thereby leading to increased productivity, promoting researcher identities and creating thriving research environments (Table ​ (Table2, 2 , quote 3). 31 , 34 , 37 , 38 , 40 , 41 , 43 , 44 , 46 , 48 , 49 , 53 , 55 , 62 Research leadership, however, could be influenced negatively by the context of compliance and counting in current university cultures damaging relationships, creating a loss of motivation, and raising feelings of devalue. Indeed, the failure of leaders to recognise researcher identities led to negative research productivity. 36 , 37 , 38 , 43 , 46 , 48 , 49

Intersections between time, identity and relationships within successful research environments

Time and identity.

Time and identity intersected in interesting ways. Firstly, time was a necessary enabler for the development of a researcher identity. 37 , 38 , 41 , 48 , 49 , 54 , 59 , 61 , 63 , 65 , 67 , 69 Secondly, those who identified as researchers (thus holding primary researcher identities) used their time efficiently to favour research activity outcomes despite a lack of protected time. 35 , 43 Conversely, for other professors who lacked personal determination and resilience for research, having protected time did not lead to better research activity. 43 This highlights the fact that time alone is insufficient to support a successful research environment, and that it is how time is utilised and prioritised by researchers that really matters (Table ​ (Table2, 2 , quote 4).

Identity and relationships

Interventions aimed at developing researcher identity consistently focused on relationship building across the three disciplines. The interventions that supported identity transitions into research included formal research training, 44 , 48 , 52 , 68 mentoring, 41 , 48 , 57 , 65 , 72 writing groups, 72 and collaboration with peers and other researchers, 39 , 41 , 43 operating through multiple mechanisms including relationships. The mechanisms included self‐esteem/confidence, increased networks, external recognition as a researcher, belongingness, and self‐efficacy. 35 , 41 , 43 , 44 , 45 , 52 , 57 Furthermore, our data suggest that leadership can be an enabler to the development of a researcher identity. In particular, leadership enabled research autonomy, recognition and empowerment, and fostered supportive mentoring environments, leading to researcher identity development and research productivity (Table ​ (Table2, 2 , quote 5). 34 , 38 , 46 , 48

Time and relationships

Relationships were developed and sustained over time (Table ​ (Table2, 2 , quote 6). Across the three disciplines, the role of leaders (managers, directors, deans) was to acknowledge and raise awareness of research, and then to prioritise time for research against competing demands, leading to effective research networks, cohesion and collaboration. 31 , 34 , 38 , 43 , 46 , 48 , 49 , 50 , 53 , 55 , 70 Second‐career PhD students who did not invest time in establishing relationships with researchers in their new disciplines (as they already had strong supportive networks in their original disciplines) found that they had limited research networks following graduation. 48

Summary of key findings

Our initial programme theory was based on previous literature reviews 1 , 4 , 5 , 6 , 7 and on the REF2014 criteria. 10 , 21 However, we were able to develop a modified programme theory on the basis of our realist synthesis, which highlights novel findings in terms of what really matters for successful research environments. Firstly, we found that key interventions led to both positive (subjective and objective) and negative (subjective and objective) outcomes in various contexts. Interestingly, we did not identify any outcomes relating to research impact despite impact nowadays being considered a prominent marker of research success, alongside quantitative metrics such as number of publications, grant income and h‐indices. 21 Secondly, we found that disciplinary contexts appeared to be less influential than individual, local and institutional contexts. Finally, our modified programme theory demonstrates a complex interplay among three cross‐cutting mechanisms (time, researcher identity and relationships) as mechanisms underpinning both successful and unsuccessful research environments.

Key findings and comparisons with the existing literature

Our research supports the findings of earlier reviews 1 , 5 , 6 , 7 regarding the importance of having a clear research strategy, an organisation that values research, research‐oriented leadership, access to resources (such as people, funding, research facilities and time), and meaningful relationships. However, our research extends these findings considerably by flagging up the indication that a clear linear relationship, whereby the presence of these interventions will necessarily result in a successful research environment, does not exist. For example, instituting a research strategy can have negative effects if the indicators are seen as overly narrow in focus or output‐oriented. 38 , 40 , 46 , 47 , 64 Similarly, project money can lead to the employment of more part‐time staff on fixed‐term contracts, which results in instability, turnover and lack of research team expertise. 40 , 67 , 71

Our findings indicate that the interplays among time, identity and relationships are important considerations when implementing interventions promoting research environments. Although time was identified as an important mechanism affecting research outcomes within the majority of papers, researcher identity positively affected research outcomes even in time‐poor situations. Indeed, we found that identity acted as a mechanism for research productivity that could overcome limited time through individuals efficiently finding time to prioritise research through their motivation and resilience. 35 , 43 Time was therefore more than just time spent doing research, but also included investment in developing a researcher identity and relationships with other researchers over time. 37 , 38 , 41 , 48 , 49 , 54 , 59 , 61 , 63 , 67 , 69 Relationship‐building interventions were also found to be effective in supporting difficult identity transitions into research faced by ECRs and those with first‐career practitioner backgrounds. Supportive leadership, as a particular form of relationship, could be seen as an enabler to the provision of protected time and a reasonable workload, allowing time for research and for researcher identity formation. 34 , 38 , 46 , 48 Indeed, our realist synthesis findings highlight the central importance of researcher identity and thus offer a novel explanation for why research environments may not flourish even in the presence of a research strategy, resources (e.g. time) and valuing of research.

Researcher identity is complex and intersects with other identities such as those of practitioner, teacher, leader and so on. Brew et al. 39 , 73 , 74 explored researcher identification and productivity by asking researchers if they considered themselves to be ‘research‐active’ and part of a research team. Those who identified as researchers prioritised their work differently: those who were highly productive prioritised research, whereas those in the low‐productivity group prioritised teaching. 73 Interestingly, highly productive researchers tended to view research as a social phenomenon with publications, presentations and grants being ‘traded’ in academic networks. Brew et al. 39 explain that: ‘…the trading view relates to a self‐generating researcher identity. Researcher identity develops in the act of publication, networks, collaborations and peer review. These activities support a person's identification as a researcher. They also, in turn, influence performance measures and metrics.’ Although the relationships among identity, identification and productivity are clearly complex, we explored a broader range of metrics in our realist synthesis than just productivity.

Methodological strengths and limitations

This is the first study to explore this important topic using realist synthesis to better understand the influence of context and how particular interventions lead to outcomes. We followed RAMESES 20 guidelines and adopted a rigorous team‐based approach to each analytic stage, conducting regular quality checks. The search was not exhaustive as we could have ‘exploded’ the interventions and performed a comprehensive review of each in its own right (e.g. mentoring). However, for pragmatic reasons and to answer our broad research questions, we chose not to do this, as suggested by Wong et al. 20 Although all members of the team had been involved in realist syntheses previously, the process remained messy as we dealt with complex phenomena. The messiness often lies in untangling CMOCs and identifying recurrent patterns in the large amounts of literature reviewed.

Implications for education and research

Our findings suggest that interventions related to research strategy, people, IIF and collaboration are supported under the ‘right’ conditions. We need to focus on time, identity and relationships (including leadership) in order to better mobilise the interventions to promote successful research environments.

Individuals need to reflect on how and why they identify as researchers, including their conceptions of research and their working towards the development of a researcher identity such that research is internally motivated rather than just externally driven. Those who are second‐career researchers or those with significant teaching or practitioner roles could seek to align research with their practice while they establish wider research networks.

We recommend that research leaders support individuals to develop their researcher identity, be seen to value research, recognise that research takes time, and provide access to opportunities promoting research capacity building, strong relationships and collaboration. Leaders, for example, may introduce interventions that promote researcher identities and build research relationships (e.g. collaborations, networking, mentoring, research groups etc.), paying attention to the ways in which competitive or collaborative cultures are fostered. Browne et al. 75 recently recommended discussions around four categories for promoting identity transition: reflection on self (values, experiences and expectations); consideration of the situation (circumstances, concerns); support (what is available and what is needed), and strategies (personal strategies to cope with change and thrive). With the professionalisation of medical education, 76 research units are increasingly likely to contain a mixture of first‐ and second‐career researchers, and our review suggests that discussions about conceptions of research and researcher identity would be valuable.

Finally, organisations need to value research and provide access to resources and research capacity‐building activities. Within the managerialist cultures of HEIs, compliance and counting have already become dominant discourses in terms of promotion and success. Policymakers should therefore consider ways in which HEIs recognise, incentivise and reward research in all its forms (including subjective and objective measures of quantity, quality and impact) to determine the full effects of their policies on research environments.

Future research would benefit from further exploration of the interplay among time, identities and relationships (including leadership) in different contexts using realist evaluation. 77 Specifically, as part of realist approaches, longitudinal audio‐diaries 78 could be employed to explore researcher identity transitions over time, particularly for first‐career practitioners transitioning into second‐career researchers.

Contributors

RA and CER were responsible for the conception of the synthesis. All authors contributed to the protocol development. RA and PESC carried out the database searches. All authors sifted for relevance and rigour, analysed the papers and contributed to the writing of the article. All authors approved the final manuscript for publication.

Conflicts of interest

Ethical approval.

not required.

Supporting information

Table S1. Definitions of key terms.

Table S6. Contexts, interventions, mechanisms and outcomes identified in individual studies.

Acknowledgements

we thank Andy Jackson, Learning and Teaching Librarian, University of Dundee, Dundee, UK, for his advice and help in developing our literature searches. We also thank Laura McDonald, Paul McLean and Eilidh Dear, who were medical students at the University of Dundee, for their help with database searches and with sifting papers for relevance and rigour. We would also like to thank Chau Khuong, Australian Regenerative Medicine Institute, Monash University, Melbourne, Victoria, Australia, for her work in designing Figs ​ Figs1 1 and ​ and3 3 .

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• Open access
• Published: 16 August 2024

Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

• Xiao Xiao 1 ,
• Qijin He 1 , 2 ,
• Selimai Ma 1 ,
• Jiahong Liu 1 ,
• Weiwei Sun 1 ,
• Yujing Lin 1 &

Scientific Reports volume  14 , Article number:  18964 ( 2024 ) Cite this article

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• Carbon cycle
• Sustainability

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R 2  = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

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Introduction.

Soil is the largest reservoir of organic carbon in terrestrial ecosystems 1 , 2 , storing twice the amount of organic carbon in the atmosphere and three times as much as that in vegetation 3 , 4 , 5 . Therefore, soil organic carbon (SOC) is an important component of the global carbon cycle 6 and is a fundamental basis for investigating atmospheric CO 2 content and climate change. Moreover, SOC can effectively improve soil fertility by sequestering carbon and participating in soil nutrient cycling 7 , 8 . It improved soil nutrient and water retention capacity, alongside promoting soil respiration, thereby slowing down land degradation and ensuring food production security 9 , 10 , 11 . As a result, accurate and rapid monitoring of SOC content is essential for global carbon pool monitoring, climate change research, and national agricultural management policy formulation.

Traditional methods for estimating SOC content rely on field sampling and laboratory physicochemical analyses. The results of these methods are accurate and reliable, although the process of collecting sampling points is time-consuming and labor-intensive. Besides, it can cause some damage to the surface of the area where the points were collected 12 , 13 . Furthermore, it is economically expensive and difficult to monitor the spatial distribution of SOC over a large region 14 . Therefore, the key issue to be addressed is how to estimate the SOC content of larger regions in a non-destructive, rapid, and accurate. Recently, the development of remote sensing has provided new methods for the non-destructive monitoring of SOC content in a larger region. Many scholars have constructed simple linear estimation models for the SOC content using spectral bands or their combinations based on the spectral response characteristics of soils with different SOC contents. 13 , 15 , 16 . For example, Zhou et al. 17 compared the differences in SOC response between different optical and radar sensors and used their response characteristics to predict SOC content in Spain based on RF. Allory et al. 15 discussed the differences between soil spectral profiles of urban and rural areas and predicted SOC stocks for two cities in France. However, these methods mostly use the original spectral reflectance of each band and their combinations as variables, and their prediction accuracy is not high despite their simple calculation process and strong interpretability. Therefore, it is important to research models that incorporate environmental factors (e.g., climate, terrain, soil, and vegetation cover) to improve the accuracy of SOC content estimation models based on optical remote sensing.

Many studies have demonstrated that SOC exhibits significant spatial heterogeneity 18 , 19 and its content is strongly influenced by environmental variables, including climate, terrain, and soil, particularly precipitation 8 , 20 , 21 , 22 , 23 . For example, Luo et al. 24 constructed a prediction model for the soil organic matter content in the Songnen Plain of China based on random forest and found that the model prediction accuracy was significantly affected by precipitation. Meanwhile, Luo et al. 25 examined the best variable for forecasting the soil organic matter during the bare soil period in Northeast China and concluded that climatic factors can effectively improve the predictive accuracy. These studies showed that incorporating environmental variables as auxiliary variables to the SOC content estimation model, especially those that have a more significant impact on SOC changes, can improve the accurate representation of the spatial distribution of SOC, and it is an important approach to enhance the current estimation model. However, adding more variables can easily lead to problems such as multicollinearity, the number of variables is larger than the samples, and it requires a high level of predictive model arithmetic power 26 . Among the modeling methods for estimating SOC content, PLSR resolves the problem of variable multicollinearity and works with a smaller sample size than MLR, while machine learning regression models like RF and SVM provide great computational ability for complex models 27 , 28 , 29 , 30 , 31 . At the same time, most of the current studies primarily concentrate on the impact of individual environmental factors or modeling methods on the estimation accuracy of SOC content, and there is a lack of comparing the effects of various environmental variables on the estimation accuracy using different methods. Therefore, it is crucial to ascertain the optimal environmental variables and modeling methods for models estimating the SOC content.

The terrestrial transect has already become an important approach for the International Geosphere-Biosphere Programme (IGBP) to study global change. Its aim is to understand the response of terrestrial ecosystems to global change, and to predict and evaluate the potential impacts of global change on terrestrial ecosystems 32 . The Northeast China Transect (NECT) was one of the first IGBP set up for studying global change study in 1993. It is a gradient affected mainly by precipitation 32 . The NECT follows an evident precipitation gradient from east to west, creating various vegetation types and soil characteristics that reflect the most significant and critical climate changes in the mid-latitude temperate zone of East Asia. Therefore, non-destructive monitoring of SOC content in NECT can effectively demonstrate the changes to environmental variables that affect SOC content. This is crucial for the rapid and effective estimation of SOC content over a large region.

Derivative processing is an effective method for eliminating information interference, yet it is rarely applied to Landsat 5 image. In this study, we focused on using Landsat 5 images processed in various ways as the primary data, with environmental variables serving as auxiliary factors, to develop a model for rapidly estimating soil organic carbon content in the Northeast Transect in 2001. We hypothesized that Landsat 5 images could indirectly estimate SOC content, and that derivative processing could effectively remove background interference. However, we also posited that second-order derivation might inhibit the expression of information regarding SOC content, potentially reducing the accuracy of the model's estimation. The specific objectives of this study are: (1) To compare the effects of different environmental variables on the accuracy of the SOC estimation model. (2) To evaluate the SOC estimation capability of Landsat 5 TM images before and after derivative processing. (3) To analyze the differences in optimal variables for constructing SOC estimation models using MLP, PLSR, RF, and SVM methods. (4) To identify the best variables and methods for SOC estimation and map the spatial distribution of SOC content in the study area.

Materials and methods

The Northeast China Transect (NECT) (112°–130° 30′ E), with a total length of about 1600 km, is located in the mid-latitude semiarid region along 43° 30′ N (Fig.  1 ) and serves as a land sample zone for global change research wherein precipitation is the main driver. The NECT represents a gradual transition from an oceanic humid climate to a continental arid climate, extending from east to west, and characterized by three climatic zones, namely temperate humid zone, temperate humid semiarid zone, and temperate semiarid and arid zone. Meanwhile, the moisture gradient in the study area is extremely decreased from east to west. In 2001, the annual precipitation in the Changbai Mountains in the east was as high as 800 mm, while that in the central agricultural area was about 550 mm, that in the steppe zone in the center-west ranged from 350 to 500 mm, and that in the western desert steppe was less than 200 mm ( https://data.cma.cn/ ). The average annual temperature ranged from − 3.86 to 7.28 °C in the region ( https://data.cma.cn/ ), and the differences in heat across the region are mainly influenced by the topography with the central plains having the highest average annual temperature and the mountainous or plateau areas at the east and west ends being cooler. Besides, the land use pattern and intensity in the NECT exhibit distinct spatial variations, and there is a complete sequence and transition of forest-agriculture-pasture areas from east to west.

Climatic and geographic of the study area and spatial distribution of sampling points.

Collection and laboratory determination of soil samples

The field trip and soil samples collection took place from July 26 to August 8, 2001. The route began from the Chunhua Forest Farm in Hunchun, Jilin, China, and proceeded westward along 43° 30′ N. During this route, a total of 25 soil samples were collected. Their distribution of sampling points is shown in Fig.  1 . In the process of sampling, one soil sample was collected using the five-point sampling method at a depth of 0–20 cm within 30 × 30 m. Each soil sample weighed about 1 kg. At the same time, to locate the sampling points and determine their corresponding positions in Landsat 5 TM images, the coordinates of the sampling points were established using the Magellan GPS Field PROVTM (California, U.S.A.), which is a Global Positioning System (GPS).

In addition, during the collection of soil samples, we removed coarse stones, plant fragments, and roots, placed them in self-sealing bags, and brought them back to the laboratory for analysis. The soil samples were air-dried, ground, filtered (2 mm), and analyzed indoors. Afterward, the SOC content was determined using external heating and the potassium dichromate volumetric method 33 , while soil pH was determined using a pH meter with a soil–water ratio of 1:5.

Landsat 5 TM image acquiring and preprocessing

28 Landsat 5 TM images covering the study area were collected and screened from the Geospatial Data Cloud website ( http://www.gscloud.cn/ ) based on the sampling time and cloud conditions of the soil sample sites in this study. The images were pre-processed using ENVI (The Environment for Visualizing Images, Version 5.3, http://www.harrisgeospatial.com/ ) software 34 , specifically radiometric calibration, atmospheric correction, image stitching, and clipping. During the radiometric calibration process, the thermal infrared bands were not included in the calculations due to their different resolutions. Following radiometric calibration, only six bands of images remain (bands 1–5 and 7), all of which have a spatial resolution of 30 m 35 . Moreover, the preprocessed original spectral reflectance was subjected to first-order derivation (FD) and second-order derivation (SD) through ENVI 5.3 software.

Data sources and processes for environmental variables

Surface reflectance results from the interaction of various surface factors 13 . The SOC content estimation model based on spectral reflectance lacks the influence of other surface factors. Consequently, its prediction fails to reflect actual scenarios, leading to significant prediction errors. In order to make the estimation results better match actual conditions, this study considered multiple environmental factors, such as climatic conditions, topographic differences, soil physicochemical properties, and vegetation cover on soil spectral reflectance.

Climate factors (CF)

Average annual precipitation and average annual temperature were chosen to characterise the climate of the study area. The two variables have a crucial contribution to the input and decomposition of SOC, and they significantly affect SOC storage 25 , 31 . The climate factors in this study were obtained from the National Earth System Science Data Centre ( http://www.geodata.cn/ ), and its spatial distribution in the study area was mapped by ArcGIS (Geographic Information System, Version 10.2, http://www.esri.com/software/arcgis ) software 36 , with a sample resolution of up to 30 m using the resampling method (Fig.  2 a,b).

The environmental variables selected for this study, 2001. [( a ) is the spatial distribution of average annual precipitation. ( b ) shows average annual temperature. ( c ) shows the spatial distribution of elevation. ( d ) shows the spatial distribution of slope. ( e ) is the spatial distribution of slope orientation. ( f ) shows soil pH. ( g ) is the spatial distribution of NDVI. While ( h ) shows EVI)].

Terrain factors (TF)

Terrain factors have a significant impact on hydrological and ecological mechanisms, like surface runoff, plant growth, and distribution, which in turn directly impacts the SOC content 37 , 38 . Among them, elevation, slope, and slope orientation are frequently utilized to indicate the topographical condition of a region. Therefore, these factors were selected to accurately represent the topographic features of the study area. The terrain factors of the study area were obtained by using the spatial analysis tool of ENVI 5.3 software to extract data from ASTERGDEM ( http://earthexplorer.usgs.gov/ ), and the results are shown in Fig.  2 c,e.

Soil factor (SF)

The SOC content is affected at the local scale by soil physicochemical properties 37 . Soil pH is one of these properties and influences the rate of the SOC decomposition process by shaping the behavior of soil microorganisms, thus modifying its speed 39 . In acidic soils, microbial species and numbers are constrained, leading to a slower SOC decomposition rate, particularly when the pH is out of the optimal range, that is, below 5.5 or above 8.5, since these values are highly detrimental to microbial growth and activity 40 , 41 . The spatial distribution of soil pH in the study area was mapped using the Inverse Distance Weight interpolation (IDW) method of ArcGIS 10.2 software that was based on the sampling points. The spatial resolution was sampled up to 30 m using the resampling method. The processed spatial distribution of soil pH is shown in Fig.  2 f.

Vegetation cover factor (VF)

The Normalised Difference Vegetation Index (NDVI) is the most commonly used vegetation index to reflect vegetation cover. The concept of ratio calculation can significantly reduce noise, which effectively represents the plant growth status and the degree of vegetation cover 42 . The greater the plant cover, the more abundant the apoplastic material and leaves, while the residual roots and leaves remain in the soil to be decomposed by microorganisms, thereby increasing the SOC content 43 . The Enhanced Vegetation Index (EVI) is an improvement of NDVI by adding blue light to correct for atmospheric aerosol scattering and soil background. It reduces the effects of background noise and stably characterises the vegetation condition in high biomass areas 44 . We calculated the NDVI and EVI of the study area using ENVI 5.3 software, based on the preprocessed Landsat 5 TM images (Fig.  2 g,h). The specific calculation formulas are described below.

where Band1, Band3, and Band4 represent the original reflectance of the corresponding bands in the preprocessed Landsat 5 TM images, respectively.

Modeling methods

The multiple linear regression model is widely used in remote sensing inversion studies. In this study, we constructed the SOC content estimation model by employing MLR and PLSR models. Additionally, to address the saturation issue in non-linear fitting, we employed random forest and support vector machines models. The details are presented below.

Multiple linear regression (MLR): MLR is a traditional method for exploring the relationship between dependent and independent variables while effectively determining the influence of multiple independent variables on the dependent variable. In particular, by entering distinct arrangements of independent variables, examining and discerning the deviation between the anticipated and actual values of the dependent variables, and choosing the combination of independent variables with the least prognostication error, the most outstanding combination of regression independent variables under the approach is determined. In this study, the MLR modeling process was performed based on the “stats” package of R 4.2.3 45 .

Partial least squares regression (PLSR): PLSR is a commonly used multivariate linear regression modeling method that analyzes the interdependence of predictor and response variables 46 . PLSR concentrates on principal component analysis, linear regression analysis, and typical correlation analysis, and is suitable for studies with multiple sets of predictor variables and small sample sizes. In this study, the parameter selection and modeling process of PLSR were performed based on the “pls” package of R 4.2.3 45 , 47 .

Random forest (RF): RF is a machine learning model in which the set of predictor variables is randomly constrained in each split point, which effectively avoids the problem of tree-to-tree correlation in the computation, and thus achieves highly accurate predictions 11 , 48 . Compared with traditional decision tree construction methods, RF can effectively deal with the problem of insensitivity to the multicollinearity of predictor variables. Furthermore, the ease of parameter tuning is another important advantage of RF, in which the number of spanning trees (ntree), the minimum number of leaves (nodesize), and the number of predictor variables used to split the nodes at each node (mtry) are the key parameters to improve the accuracy of the model. In this study, the estimated model ntree was taken as 500, nodesize was set to 5, and mtry was 3. The parameter selection and modeling process were carried out using the “randomForest” package of R 4.2.3 45 , 49 .

Support Vector Machines (SVM): In the early days, SVM analysis was mainly applied to classification problems. The algorithm is flexible and efficient by inputting data from multiple variables and creating multiple planes to classify the variables into different categories 50 . Recently, many scholars demonstrated that SVM exhibits good modeling stability and high prediction accuracy 28 , 51 , 52 . Therefore, this study constructed the SOC estimation model based on the “e1071” package of R 4.2.3 45 , 53 .

Model accuracy evaluation methods

To assess the accuracy of the model, we used the model decision coefficients (R 2 ), root mean square error (RMSE), and mean absolute error (MAE). The formulas for R 2 , RMSE, and MAE are described in Eqs. ( 3 – 5 ). Among these, the range of R 2 is between 0 and 1, and a higher value of R 2 indicates a stronger correlation between the measured and the predicted values. In contrast, RMSE and MAE are both in the range of [0, + ∞]. The lower the values of RMSE and MAE, the closer the measured values are to the predicted values. On the other hand, larger values of RMSE and MAE indicate higher prediction errors. In this study, the three indicators were calculated based on the “modelr” package of R 4.2.3 45 , 54 .

where y i and \({\text{y}}_{{\text{i}}}^{\prime }\) represent the measured and predicted SOC content, respectively, \({\bar{\text{y}}}\) is the average of the measured SOC content, and n is the number of samples.

Descriptive statistics of SOC content and environmental variables at sample sites

SOC primarily originates from animal, plant, and microbial residues and their secretions, and undergoes a constant process of decomposition and formation. The SOC content represents the current dynamic equilibrium state of the ecosystem to a certain extent 55 , 56 , 57 . According to Table  2 , the overall SOC content was high in the study area, attaining a mean value of about 22.90 g kg −1 . However, The spatial variation of the SOC content was considerably different based on various environmental factors such as climate, vegetation, and soil, ranging from 2.06 to 107.04 g kg −1 . The standard deviation of 27.04 g kg −1 and the coefficient of variation (CV) was 118.10%. The regions towards the east of the study area were identified by dense vegetation, high accumulation of apomixis, and gradual breakdown of SOC, leading to an increase in SOC content. Meanwhile, the regions towards the west had reduced SOC content, which could be due to the hastened rate of SOC decomposition caused by the climatic circumstances (Fig.  3 ). In addition, 25 samples were randomly divided into a training dataset and a validation dataset in this study, with the former including 20 samples and the latter including 5 samples. The statistical characteristics of both datasets were similar (Table  2 ). It was therefore feasible to apply the estimation model in this study.

Distribution of SOC content in soil sample sites collected in the study area.

The spatial distribution of SOC content was impacted by environmental variables, however, the effects varied among different classes of environmental variables. Table  1 shows that the terrain in the study area significantly varies. Specifically, the sampling points had an elevation range of 120.00–1580.00 m, with a mean of 722.56 m and a standard deviation of 410.70 m. The slope ranged from 1.01 to 43.39°, with a CV of 104.76%, while the standard deviation of the slope orientation was 107.16%. Furthermore, the NDVI ranged from 0.08 to 0.88, with a mean value of 0.46 and CV of 59.31%, while EVI ranged from 0.13 to 1.00 with a mean value of 0.67 and CV of 47.54%. These observations indicate that the vegetation cover in the study area was high and unevenly distributed. Climatic resources varied significantly, especially the variation of average annual precipitation, ranging from 101.04 to 906.59 mm, with an average of 388.48 mm and CV of 67.34%. The average temperature was about 3.67 °C, with a CV of 51.60%.

Association analysis of spectral reflectance with SOC content

Defining the spectral reflectance characteristics of SOC is an important basis for estimating SOC content. Figure  4 shows the spectral reflectance characteristics of soils at five different SOC levels. In OSR, the spectral characteristics of different SOC contents showed significant variance, with spectral differences mainly occurring in Band1-3, 5, and 7, especially in the lower SOC levels. In the lower level range, the increase in SOC contents led to a significant decrease in reflection across the five bands, consistent with previous research 16 , 58 , 59 . With higher SOC content, the spectral curve differences primarily occurred in Band4, with fewer variations in the other bands. In FD, the spectral differences of lower SOC contents mainly occurred in Band4, Band5, and Band7, while the differences of spectral curves in the bands of higher SOC contents were small. In addition, the differences in different SOC contents are mainly observed in Band7 in SD.

Spectral characteristics of Landsat 5 TM with different SOC contents.

Furthermore, association analysis was used to investigate the relationship between SOC content and spectral reflectance. As shown in Table  2 , except for Band1, which was processed by the first-order derivation and had a weak correlation with SOC content, all the other bands had high correlations. Specifically, Band1-3, Band5, and Band7 showed a significant negative correlation with SOC content in OSR, especially Band7 with a correlation coefficient as high as − 0.523, while Band4 was significantly positively correlated with SOC content (0.409). After the first-order derivation transformation, a significant increase in the correlation between the SOC content and Band2, Band3, and Band5 was noted. Specifically, Band3 depicted a significant positive correlation (0.594), whereas Band5 showed a highly significant negative correlation (− 0.625). After the second-order derivation transformation, a significant decrease was observed in the correlation of Band5 with SOC content. Moreover, the correlation between SOC content and the remaining bands was comparatively weaker, especially Band5. In summary, although there was a high correlation between reflectance and SOC content in the original bands, the first-order derivation improved this correlation to some extent. However, the second-order derivation did not demonstrate this ability, which may be related to the lack of information caused by the amplified noise effect after the second-order derivation.

Comparative results of estimation models

Derivative processing.

The training and validation results of the 72 estimation models for SOC content are shown in Table  3 . The results showed that derivative processing did not significantly improve the estimation accuracy. Moreover, the second-order derivative led to a significant decrease in the estimation ability of the model. In particular, the derivative processing reduced the estimation accuracy for the model constructed using a training dataset. Taking the model without environmental variables as an example, the accuracy of “FD + MLR” decreased by 0.1395 compared to “OSR + MLR”. Also, the accuracy of “SD + MLR” decreased by 0.1708 compared to “OSR + MLR”. Similarly, machine learning methods exhibited the same trend, with “FD + Band + SVM” decreasing the accuracy by 0.0069 compared to “OSR + Band + SVM”, and “SD + Band + SVM” decreasing the estimation accuracy by 0.0828 compared to “OSR + Band + SVM”. Moreover, environmental variables alleviated the negative influence of derivative processing to some extent, while this phenomenon still existed. Consider the model with four environmental variables, where the accuracy of “FD + EV + SVM” decreased by 0.0328 compared to “OSR + EV + SVM”, while “SD + EV + SVM” was 0.0112 less accurate than “OSR + EV + SVM”. It is worth mentioning that terrain factors and derivative processing improved the estimation accuracy of the MLR method. Regarding the “MLR + TF” context, derivative processing improved by 0.0204 (FD) and 0.0882 (SD) respectively.

• Environmental variables

The modeling accuracy was significantly higher for the model which takes environmental variables into account in comparison to the spectral model that only considers spectral bands. The addition of VF, CF, TF, and SF individually can improve the accuracy of the model, especially of CF and SF (Table  3 ). Specifically, the estimation models constructed using the training dataset exhibited better simulation accuracies under various derivative treatments when they incorporated CF, TF, and SF independently. For example, under different derivative processing, the SVM method improved the model with only CF added by 0.1499 (OSR), 0.0839 (FD), and 0.1854 (SD) respectively, when compared to the model with no environmental variables added. In contrast, the accuracy of the model with only TF added improved by 0.1451 (OSR), 0.1305 (FD), and 0.1829 (SD) respectively. By adding only SF, the model accuracy improved by 0.1504 (OSR), 0.0846 (FD), and 0.1888 (SD) respectively. Furthermore, although adding only VF improved the model accuracy, it had less influence compared to the three other types of variables. For example, in OSR, under different modeling methods, adding only VF improved the model accuracy by 0.1202 (PLSR), 0.0211 (RF), and 0.0054 (SVM) compared to the model with no environmental variables, respectively. In addition, there was no significant difference in the accuracy precision of the MLSR method. It is worth mentioning that adding only the TF in the model constructed based on PLSR led to a significant decrease of 0.6123 (OSR), 0.3174 (FD), and 0.3364 (SD) in the accuracy of model estimation in the context of derivative processing. However, this phenomenon was not present in the other modeling methods. Furthermore, adding environmental variables, especially VF and CF, significantly improves the accuracy of the model constructed from the validation dataset, compared to the model without these variables. However, including only TF in “OSR + PLSR” lowers its accuracy (R 2  = 0.0812), which is notably inferior to that of the other models.

Although these environmental variables impacted the estimation model differently, adding them at the same time can significantly improve its fitting accuracy. Taking the model constructed based on the training dataset as an example, the accuracy of “OSR + EV + SVM” was 0.1702 higher than that of “OSR + Band + SVM”, 0.1756 higher than that of “OSR + VF + SVM”, 0.0203 higher than that of “OSR + CF + SVM”, 0.0250 higher than that of “OSR + TF + SVM”, and 0.0198 higher than that of “OSR + SF + SVM”. In summary, the combination of VF, CF, TF, and SF can significantly enhance SOC estimation accuracy within the confines of the study area. This was especially evident when the study covered a vast region and displayed substantial spatial variations in diverse environmental variables.

Comparing the four modeling methods, it can be found that the conclusions of the models constructed based on the training and validation datasets were consistent. The results showed that under different derivative processing, both RF and SVM methods have higher accuracy in fitting and estimating SOC content compared with traditional regression analysis methods (MLR and PLSR). Specifically, the model based on the SVM method, with the original spectral bands and the four types of environmental variables achieved the highest accuracy (Training dataset: R 2  = 0.9590, RMSE = 13.9887, MAE = 10.8075; Validation dataset: R 2  = 0.9220, RMSE = 11.6165, MAE = 10.8075), followed by the “OSR + EV + RF” model. Specifically, considering the model developed based on the training dataset under identical derivative processing conditions, the accuracy of the “OSR + EV + SVM” model was 0.0856 greater than that of the “OSR + EV + RF” model. It exceeds the “OSR + EV + MLR” model by 0.1050 (RMSE = 11.0773, MAE = 9.2049) and the “OSR + EV + PLSR” model (RMSE = 21.6330, MAE = 15.0098) by 0.5531. Therefore, including environmental variables can effectively improve SOC accuracy, and the SVM method presents the best estimation accuracy, while the PLSR method is unsuitable for the study area. Besides, this study plotted the plots of the estimated and measured values of the sample points based on the SVM method (Fig.  5 ). The above results showed that including environmental variables significantly improved the estimation of SOC content, and the predicted values were in good agreement with measured values. Moreover, the “OSR + EV + SVM” model (R 2  = 0.9387) showed the best fit between the predicted and measured values (Fig.  5 d). These results confirm that the “OSR + EV + SVM” model was the optimal SOC estimation model for the study area.

Comparison of measured and predicted SOC content among different SVM models.

In general, environmental variables significantly increased the accuracy of the SOC content estimation model, and the best model was the “OSR + SVM” model combined with environmental variables, which had the best estimation performance. Derivative processing did not yield better estimation results. Among them, the effects of FD and OSR processing were similar, while the prediction effect of SD was weaker and significantly worse than that of the case without derivative processing. As for the choice of modeling methods, machine learning prediction has a higher accuracy than traditional methods. Among them, the SVM model has the most prediction ability, followed by RF, while the PLSR model is not suitable for fitting and predicting.

Best estimate modeling and spatial mapping of SOC content in the study area

Since the “OSR + EV + SVM” model had a higher prediction accuracy than other models, we used this model to estimate the SOC content in the study area (Fig.  6 ). Spatially, the SOC content in the study area in 2001 was highly spatially variable, with a mean value of 31.74 g kg −1 and a standard deviation of 28.31 g kg −1 . The central and western parts of the NECT had low SOC content while the eastern part had high SOC content. This trend was consistent with environmental variables, particularly elevation, slope, and precipitation. Statistical analysis of the SOC content indicated high SOC content in the entire study area. About 57.50% of the area had a SOC content higher than 20.00 g kg −1 . 23.7% of the area had SOC content higher than 50.00 g kg −1 , and 33.80% of the covered area had a SOC content between 20.00 and 50.00 g kg −1 . In addition, areas with SOC content above 100.00 g kg −1 and below 5.00 g kg −1 accounted for 2.30% and 19.90% of the total area, respectively.

The SOC content in the study area based on the best model, 2001.

Accurate quantification of soil organic carbon content is crucial for soil safety assessment, carbon cycling, and climate change research. Currently, constructing models to estimate SOC content based on optical remote sensing has become a pivotal research approach. However, the choice of different image processing methods, modeling techniques, and selection of environmental variables significantly influences model accuracy. Derivative processing aids in smoothing background information and reducing noise interference 27 , 29 , 30 , widely applied in hyperspectral studies. We hypothesized that integrating Landsat 5 with environmental factors can effectively estimate SOC content, while derivative processing suppresses information expression in Landsat 5 images. Model comparison results validate our hypothesis that Landsat 5 images effectively convey SOC information, with derivative processing showing no significant improvement in SOC content estimation accuracy, consistent with previous studies 13 , 60 . This may be attributed to the large coverage area, high variability in soil physicochemical properties, significant climate differences, and complex terrain and vegetation cover variations in the study area. Regarding modeling methods, studies by Bao et al. 61 and Sabetizade et al. 62 demonstrate that machine learning methods better handle the non-linear relationship between SOC content and spectral features, enhancing prediction reliability and predictability. Our findings support these conclusions. Additionally, our results indicate that Random Forest (RF) and Support Vector Machine (SVM) exhibit significant modeling advantages in estimating SOC content based on small sample datasets, particularly with SVM demonstrating stable modeling capabilities.

Our study reveals that adding environmental factors can effectively enhance SOC content estimation accuracy. Among these factors, mean annual precipitation and soil pH are the primary determinants influencing SOC content estimation accuracy, followed by elevation, slope, and aspect. This aligns with existing research 4 , 28 , 31 . Temperature influences SOC content significantly through its effects on soil microbial activity and soil respiration 63 , 64 . Soil pH affects soil chemical reactions and ion exchange properties, with acidic soils typically limiting microbial activity and organic matter decomposition, thereby inhibiting SOC accumulation 65 . Precipitation indirectly affects SOC content by influencing soil moisture status. These findings confirm the significant impact of environmental factors on the spatial distribution of SOC content, with varied effects observed among different environmental variables.

In our study, integrating soil organic carbon data with Landsat 5 elucidates the influence of environmental variables on SOC content estimation. While our conclusions are insightful, they come with limitations. Firstly, the factors influencing spatial distribution of soil organic carbon content are complex, and this study only discusses common environmental variables. Future research will incorporate additional factors influencing spatial distribution of soil organic carbon content and analyze the differential importance of various environmental variables. For instance, soil clay particles adsorb and protect soil organic matter, preventing microbial decomposition or hydraulic erosion, often correlating with higher soil organic carbon content, a factor worth considering in future studies. Secondly, anthropogenic management practices also significantly influence soil organic carbon content. Subsequent studies should adequately characterize factors such as land use types and management practices (e.g., land use intensity) to enhance SOC content estimation capability. Lastly, our study covers a broad area with diverse land cover types, necessitating the collection of more sample points and the use of machine learning algorithms and feature selection suitable for big data to further analyze the impact of different environmental factors on soil organic carbon content across different regions, thereby improving SOC mapping. In the next step, we will integrate the latest satellite data with the results of a new round of large-scale sampling. This approach will enable us to include additional environmental variables and spectral bands for SOC mapping, thereby refining our methods and enhancing the accuracy of SOC content detection.

Through comparative analysis of derivative processing and four different modeling methods, our study generated soil organic carbon content maps for the surface layer of the Northeast Transect at a spatial resolution of 30 m. Results demonstrate that adding environmental variables enhances the accuracy of SOC content estimation across various models. Concerning derivative processing, the performance of OR and FD modeling methods showed minimal difference, while SD modeling exhibited poorer capabilities. In terms of modeling method selection, SVM models constructed with the inclusion of four categories of environmental factors consistently displayed strong predictive abilities, whereas Partial Least Squares Regression (PLSR) performed less effectively. Therefore, “OR + environmental variables + SVM” represents the optimal model for estimating SOC content in the study area (R 2  = 0.9590, RMSE = 13.9887, MAE = 10.8075). Our research highlights the importance of key environmental variables in monitoring SOC content. This study provides theoretical reference for the rapid estimation of large-area soil organic carbon content using remote sensing images combined with environmental factors. It also offers technical support for more precise SOC content detection in the future, which is crucial for soil conservation and sustainable development in Northeast China.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Acknowledgement for the data support from “National Earth System Science Data Center, National Science & Technology Infrastructure of China ( http://www.geodata.cn )”.

This research was funded by the National Natural Science Foundation of China (No. 42141007).

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Xiao Xiao, Qijin He, Selimai Ma, Jiahong Liu, Weiwei Sun, Yujing Lin & Rui Yi

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China

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X.X.: Formal analysis, Investigation, Methodology, Writing-original draft, Visualization. Q.H.: Conceptualization, Resources, Writing-review and editing, Validation, Supervision, Project administration, Funding acquisition. S.M.: Data collection, Data curation, Validation. J.L.: Software, Writing-review and editing. W.S.: Software. Y.L.: Data collection, Data curation. R.Y.: Data collection, Data curation. All authors have read and agreed to the published version of the manuscript. All the authors approved the final article.

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Xiao, X., He, Q., Ma, S. et al. Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content. Sci Rep 14 , 18964 (2024). https://doi.org/10.1038/s41598-024-68424-5

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Environmental sustainability of research and innovation

• Related content
• Peer review
• Jonathan E Slutzman , assistant professor of emergency medicine 1 ,
• Alexandra Barratt , professor of public health 2 ,
• Jodi D Sherman , associate professor of anesthesiology 3
• 1 Center for the Environment and Health Massachusetts General Hospital Harvard Medical School
• 2 Wiser Healthcare University of Sydney
• 3 Yale Center on Climate Change and Health Yale School of Public Health
• Corrrespondence to: J Sherman jodi.sherman{at}yale.edu

New concordat is necessary but no match for the scale of the challenge

The newly released Concordat for the Environmental Sustainability of Research and Innovation Practice, co-developed by the UK research and innovation community—including universities, research organisations, funders, and their partners—represents a broad ambition for the sector to transition to a sustainable future. 1 Concordat signatories are asked to prioritise action on leadership and system change, sustainable infrastructure, sustainable procurement, emissions from business and academic travel, collaborations and partnerships, and reporting data on the environmental impact of their activities.

We commend the developers of the concordat for encouraging the advancement of environmental sustainability in research and innovation across all sectors, including healthcare, but it does not go far enough given the scale of the challenge. Important limitations include the voluntary nature of the concordat, lack of clarity in reporting standards and guidance, lack of verification, and a limited capacity to address the environmental impact of supply chains.

First, action stronger than voluntary participation is needed to achieve the scale and speed of the changes required. The clock is ticking, and the global carbon budget is dwindling. 2 The concordat emphasises the “need to act now” with measures in the next 5-10 years that include “deep, rapid, and sustained reductions in greenhouse gas emissions” and “actions to address unsustainable resource consumption.” But to really drive change, public and private research funders, including the National Institute for Health and Care Research (NIHR) and Wellcome, should consider making funding contingent on a commitment to the concordat. This would create the strongest possible incentive for all stakeholders.

Accountability

Second, although the concordat calls on its signatories to publicly disclose their commitments and report progress, there is no verification requirement and the consequences of failure to follow through are unclear. Verification is essential and could be achieved through the EU Corporate Sustainability Reporting Directive (CSRD) or a similar authority. The directive requires large and listed companies, including some independent research organisations, to measure, track, and disclose direct and indirect emissions, along with their efforts to operationalise sustainability. 3 Assurances are assessed through transparent third party verification, to avert greenwashing and reduce the risk of conflicts of interest.

Clear reporting standards are also important for ensuring accountability, but the concordat provides limited guidance on emissions accounting. Consistent with guidance from the Environmental Association for Universities and Colleges, 4 the concordat recommends including direct greenhouse gas emissions, indirect energy emissions, and other indirect emissions that are material to an organisation’s activities. The first two are well defined and quantifiable but usually contribute only a minority (15-35%) of an organisation’s emissions. Other indirect emissions, such as those arising from supply chains, business travel, and waste management, comprise a much larger proportion of an organisation’s carbon footprint but are harder to measure. Accuracy is particularly important when comparing emissions across different organisations and when tracking emissions over time.

Measuring greenhouse gas emissions associated with procurement and supply chains is critical since purchased goods and services often comprise at least 50% of an organisation’s emissions. The concordat encourages life cycle assessment (LCA) where possible, as this is the most accurate way to quantify these emissions. However, LCAs require expertise and resources unavailable to many organisations. Furthermore, each analysis has a specific scope and goals, limiting generalisability, and the quality of existing assessments is heterogeneous.

In response to the mixed quality of existing LCAs, proposed guidelines for assessing the environmental consequences of healthcare (Ecohealth) aim to provide a reporting standard for analyses relevant to healthcare, including research and innovation. 5 This will improve both the quality and the comparability of sustainability reports.

Ultimately, though, industry partners in research and innovation will have to use their own knowledge of materials, production methods, and energy sources to report product level emissions in a standardised and verifiable way that enables downstream organisations to report supply chain emissions accurately. This would be better than the spend based models suggested by the concordat.

Finally, the concordat asks institutions to establish “sustainable procurement policy … that prioritise[s] more environmentally sustainable options” but does not indicate how. Manufacturers make numerous claims about the environmental credentials of their products and services, but without evidence based on standardised product level LCAs and independent verification of claims, purchasers may make incorrect choices based on inaccurate or misleading environmental information.

Consistent with the EU’s CSRD, NHS England is phasing in a requirement for vendors of healthcare products and services to report emissions in a standardised, transparent, verified manner along with decarbonisation plans consistent with the Paris agreement. Product level disclosures will be required by 2028. 6 The concordat could help create collective purchasing power to drive down embodied emissions by directing signatories to require standardised and verified product level environmental disclosures in all their purchasing processes. This would also improve environmental accounting and procurement decisions.

We support the concordat’s vision and aspiration but call for more, to enable the research and innovation sector to truly lead change. This means mandatory, verified reporting of emissions by all stakeholders using accurate, comparable methods to help organisations make better environmental choices.

Competing interests: The BMJ has judged that there are no disqualifying financial ties to commercial companies. The authors declare the following other interests: JES has received consulting fees from Teleflex, AstraZeneca, and AlphaSights; honorariums for speaking on healthcare sustainability from the University of New Mexico, Columbia University, the University of Colorado, Harvard University, and the Institute for Healthcare Improvement; and travel reimbursements to speak on healthcare sustainability from the Canadian Anesthesiologists’ Society, Vizient, University of Colorado, and the Institute for Healthcare Improvement. Further details of The BMJ policy on financial interests are here: https://www.bmj.com/sites/default/files/attachments/resources/2016/03/16-current-bmj-education-coi-form.pdf .

Provenance and peer review: Commissioned; not externally peer reviewed.

• Concordat for the Environmental Sustainability of Research and Innovation Practice. https://wellcome.org/what-we-do/our-work/environmental-sustainability-concordat
• Friedlingstein P ,
• O’Sullivan M ,
• European Commission. Corporate sustainability reporting. https://finance.ec.europa.eu/capital-markets-union-and-financial-markets/company-reporting-and-auditing/company-reporting/corporate-sustainability-reporting_en
• EUAC. Standardised Carbon Emissions Framework. [REMOVED IF= FIELD] https://www.eauc.org.uk/scef
• Slutzman J ,
• Barratt A ,
• Eckelman M ,
• McAlister S ,
• MacNeil A ,
• NHS England. Greener NHS: suppliers. https://www.england.nhs.uk/greenernhs/get-involved/suppliers/

2023 Theses Doctoral

Can a Changing Food Environment Tip the Scale? A Mixed-Methods Study of Food Habitus and Obesity in a Neighborhood Undergoing Gentrification

Rhodes-Bratton, Brennan

The disproportionate concentration of unhealthy food in communities of color in the United States may contribute to health inequities and food insecurity. Gentrification has been associated with residents’ increased adverse health outcomes in its early and rapid phases. This study adds to the growing body of research by examining the relationship between gentrification, the food environment, food habits (the interplay between food chances and food choices), and health in New York City. I used a mixed methods approach to assess the food landscape in NYC between 1990 and 2014, using group-based trajectory modeling, the National Establishments Time-Series database, census data, and in-depth interviews with mothers from the Columbia Center for Children’s Environmental Health study. I found that the growth in the food environment was unevenly distributed. While healthy food chances declined across all examined neighborhoods, unhealthy food chances quickly grew, commanding dominance. It was gentrifying neighborhoods; however, that surprisingly experienced the most remarkable growth in unhealthy food chances compared to other neighborhoods. A cross-tabulation of the food chance trajectories of New York City census tracts indicated the presence of food ecologies that exhibit both healthy and unhealthy food chances. There was a strong association between the type of food ecology and gentrification status (p < 0.001). The in-depth interviews corroborated these findings and revealed that food insecurity is a by-product of gentrification in two ways. First, neighborhoods in the early stages of gentrification are inundated with unhealthy food chances, such as fast-food chains, without adequate access to quality, fresh, healthy foods. Secondly, when healthy food chances finally arrive in resource-deprived areas through gentrification, families are forced to relocate to areas without access to fresh, affordable, healthy foods due to the increased cost of living. This cycle of food insecurity is inequitable due to historical racial segregation, exploitative capitalistic markets, and racist stereotypes. Speculators invest in unhealthy food chances aligned with pre-existing stereotypes, assumptions, and beliefs that such communities do not or will not consume healthier foods. Therefore, a cycle of structural racism reinvents itself through this investment in unhealthy food chances, constructing food deserts and swamps bestowed upon communities experiencing poverty and disproportionate adverse cardiovascular health conditions. Strengthening policy focused on the relationship between gentrification mitigation and health outcomes is needed.

Geographic Areas

• New York (State)--New York
• Public health
• Obesity--Epidemiology
• Obesity in children
• Food habits--Health aspects
• Gentrification
• Environmental health

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The study, published in July in Environmental Science and Technology , looked at citywide data for every six-by-six-inch square of tree canopy across the five boroughs. The researchers analyzed the types of trees and modeled what could happen to ground-level ozone pollution if the city grew enough trees to cover 40 percent of its land.

In one scenario, if the city only planted oaks, ground-level ozone in Manhattan could increase more than 30 percent. In a second scenario, continuing to plant a mixed canopy like the one that exists today could increase peak ground-level ozone by about 10 percent, depending on the borough.

New York City repeatedly violates federal health-based air quality standards for ozone on hot days, according to the study. Every year more than 400 New Yorkers are estimated to die from ground-level ozone pollution , and more than 4,300 adults and children in the city visit emergency rooms for ozone-related asthma.

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