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THE BENEFITS OF AN ONLINE JOURNAL FOCUSED ON ENVIRONMENTAL CASE STUDIES

The case for case studies in confronting environmental issues.

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Wil Burns; The Case for Case Studies in Confronting Environmental Issues. Case Studies in the Environment 31 December 2017; 1 (1): 1–4. doi: https://doi.org/10.1525/cse.2017.sc.burns01

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In its most distilled form, a “case study” involves investigation of “real-life phenomenon through detailed contextual analysis of a limited number of events or conditions, and their relationships” [ 1 , 2 ]. The “case” may focus upon an individual, organization, event, or project, anchored in a specific time and place. Most cases are based on real events, or a plausible construction of events, and tell a story, often involving issues or conflicts which require resolution [ 3 ]. They also frequently include central characters and quotations and dialogue [ 4 ]. Often the objective of a case study approach is to develop a theory regarding the nature and causes of similarities between instances of a class of events [ 5 ]. More broadly, case studies seek to illustrate broader, overarching principles or theses.

In recent years, researchers have increasingly embraced the study method in recognition of the limitations of quantitative methods to provide in-depth and holistic explanations of social problems [ 6 ]. A case study, in the context of environmental issues, usually involves the focus on an actual environmental situation, commonly involving a decision, an issue, a challenge, or an opportunity faced by a group of individuals, an organization, or a society.

Case studies enjoy a natural advantage in research of an exploratory nature. As Yin concludes, case studies allow a researcher to “reveal the multiplicity of factors [which] have interacted to produce the unique character of the entity that is the subject of study” [ 7 ]. Explanatory case studies can facilitate conducting causal studies, and in extremely complex and multivariate cases, help to structure analyses that employ pattern-matching techniques [ 8 ]. Descriptive case studies help researchers to formulate hypotheses of cause-effect relationships from descriptive theories [ 8 ].

Case studies have been employed throughout history to facilitate the pursuit of knowledge and its dissemination. The Hippocratic Corpus in the fifth century BC employed case studies to develop insights into medicine that stimulated discoveries for centuries to come [ 9 ]. The case study approach also informed the work of Darwin, Freud, and Piaget.

The formal use of case studies in academia began at Harvard Law School at the turn of the twentieth century [ 10 ]. In recent years, empirical research has demonstrated the value of the case study method as a pedagogical tool in the classroom, with case studies employed in the humanities, social sciences, engineering, law, medicine, and business [ 11 , 12 ]. Case studies have also been used by practitioners in a wide array of fields, including medicine, law, and business [ 13 , 14 , 15 , 16 , 17 ]. In environmental science and policy sectors, case studies are particularly salutary in providing practitioners with examples of best practices [ 18 ], and to assist them in developing effective recommendations and policy prescriptions [ 8 , 19 , 20 ].

Many learners are more inductive than deductive reasoners. Case studies can help to facilitate learning by helping them to reason from examples, analogies, and models, as well as from basic principles [ 21 , 22 , 23 ]. Studies surveying faculty and student learning results associated with the use of case studies demonstrate significant increases in student critical thinking skills and knowledge acquisition, as well as enhanced ability to make connections between multiple content areas and to view issues from different perspectives [ 24 , 25 , 26 ]. Case studies also promote active learning, which has been proven to enhance learning outcomes [ 21 , 27 , 28 , 29 ]. Through careful examination and discussion of various cases, “students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem” [ 30 , 31 , 32 ]. Moreover, cases can serve as models or paradigms that facilitate understanding similar cases [ 33 ].

Additionally, case-based instructional methods usually employ empirical or realistic narratives to afford students the opportunity to integrate multiple sources of information in real-world contexts in ways that might not be captured through experimental or survey research methods [ 6 ]. Studies have indicated that this can increase student motivation to participate in class activities, promoting learning and increasing performance on assessments [ 34 ]. It also often affords students the opportunities to engage with ethical and societal issues related to their disciplines [ 35 ], as well as facilitating interdisciplinary learning [ 34 ]. The fostering of effective integrative learning experiences in the classroom was identified as one of the four essential learning outcomes in the Learning for the New Global Century report of the Association of American Colleges and Universities [ 36 ].

Case studies have proven to be a valuable component of teaching environmental studies and science by fostering critical transdisciplinary perspectives conductive to addressing environmental issues [ 37 ], as contributing to efforts to “flip the curriculum” in an effort to foster engaged learning in environmental studies and science courses [ 38 ]. Case studies are also a valuable tool for environmental practitioners. They can provide guidelines for best practices [ 15 , 39 ], as well as lessons learned by others in any given professional sector, including in the environmental arena [ 40 , 41 ]. The case study method has proven to be an effective tool to assist environmental professional in developing effective recommendations and policy prescriptions [ 19 , 20 ]. Also pertinent to the environmental sector, case study research can also help to identify relevant variables to facilitate subsequent statistical research [ 42 ]. Moreover, case studies can be employed in organizations for training purposes to foster problem-based learning and the ability to formulate solutions [ 43 ].

Most instructors and environmental professionals that have utilized case studies in the classroom, or in their work, have found them to be a valuable tool [ 6 ]. However, within the classroom environment one of the main obstacles to using case-based instructional method is lack of preparation time, with most instructors currently preparing their own case studies [ 35 ]. The imposing nature of case study construction, as well as the imposing cost of developing cases internally [ 38 ], ensures that many instructors eschew this teaching method.

Moreover, there is imposing challenge of developing effective discussion questions to scaffold case-based learning exercises [ 4 , 44 ]. Case studies also are often not subjected to sufficient academic rigor, undermining their effectiveness and credibility [ 45 , 46 ]. Finally, many instructors are intimidated by the challenge of student evaluation when case studies are incorporated into the educational process [ 47 ].

Case Studies in the Environment hopes to address all of these challenges. It will seek to develop a substantial compendium of case studies in the following categories in field of environmental science and studies:

Ecology and Biodiversity Conservation

Climate Change Mitigation and Adaptation

Environmental Law, Policy and Management

Energy and the Environment

Water Management, Science and Technology

Sustainability

Each case study will be 1,500–3,000 words, and will be subject to peer-review by experts in the field of both environmental studies and science and case studies. Moreover, each case study will be accompanied by a set of suggested discussion questions to help scaffold their use in the classroom, 1 as well as a set of Power Point slides for lectures or presentations in professional environments. It is our hope to ultimately develop a community of academics and practitioners around case studies through workshops, conference panels and online interaction.

Recipient(s) will receive an email with a link to 'The Case for Case Studies in Confronting Environmental Issues' and will not need an account to access the content.

Subject: The Case for Case Studies in Confronting Environmental Issues

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Environmental Justice Research: Contemporary Issues and Emerging Topics

Environmental justice (EJ) research seeks to document and redress the disproportionate environmental burdens and benefits associated with social inequalities. Although its initial focus was on disparities in exposure to anthropogenic pollution, the scope of EJ research has expanded. In the context of intensifying social inequalities and environmental problems, there is a need to further strengthen the EJ research framework and diversify its application. This Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) incorporates 19 articles that broaden EJ research by considering emerging topics such as energy, food, drinking water, flooding, sustainability, and gender dynamics, including issues in Canada, the UK, and Eastern Europe. Additionally, the articles contribute to three research themes: (1) documenting connections between unjust environmental exposures and health impacts by examining unsafe infrastructure, substance use, and children’s obesity and academic performance; (2) promoting and achieving EJ by implementing interventions to improve environmental knowledge and health, identifying avenues for sustainable community change, and incorporating EJ metrics in government programs; and (3) clarifying stakeholder perceptions of EJ issues to extend research beyond the documentation of unjust conditions and processes. Collectively, the articles highlight potentially compounding injustices and an array of approaches being employed to achieve EJ.

Environmental justice (EJ) research seeks to document and redress the disproportionate environmental burdens and benefits associated with social inequalities. Although its initial focus was on anthropogenic pollution, the scope of EJ research has expanded significantly in recent years to encompass other phenomena—for example, access to healthful food and climate change—with disparate negative impacts on particular social groups. Dimensions of social inequality examined have expanded beyond race and socioeconomic status to focus to some degree on ethnicity, immigration status, gender, sexual orientation, age, as well as intersections between dimensions of inequality. In the context of intensifying social inequalities and environmental problems, there is a need to further strengthen the EJ research framework and diversify its application. This Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) incorporates 19 articles that collectively advance EJ scholarship in conceptual, methodological, and empirical terms.

These articles demonstrate how the scope and purpose of EJ research have broadened significantly in recent years and continue to expand in new directions, both topically and geographically. Several articles in this Special Issue break new ground by extending the EJ research framework to consider emerging issues such as energy [ 1 , 2 ], food [ 3 ], drinking water [ 4 , 5 ], flooding [ 6 , 7 ], sustainability initiatives [ 8 , 9 ], and gender dynamics [ 10 ], including EJ concerns in Canada [ 5 , 11 ], the UK [ 12 ], and Eastern Europe [ 13 ]. Finley-Brook and Holloman [ 1 ] explore the EJ implications of energy production in the U.S. Their study demonstrates how the transition from high carbon energy sources such as coal and oil contribute to environmental injustices, and proposes priorities for a new energy justice research agenda that combines advocacy, activism, and academics. Kyne and Bolin [ 2 ] focus on nuclear hazards associated with both the U.S. weapons programs and civilian nuclear power. Their article argues that nuclear power plants, uranium mining, and waste disposal raise a variety of EJ issues that encompass distributive, procedural, recognition, and intergenerational justice. Carrel et al. [ 3 ] examine the EJ impacts of animal feeding operations in Iowa, USA. Their findings underscore the need to understand the structural, political, and economic factors that create an environmentally unjust landscape for swine production in the U.S. Midwest. Galway [ 4 ] investigates access to safe and reliable drinking water in First Nations communities in Ontario, Canada, based on drinking water advisory data. The study highlights the prevalence of drinking water advisories as a growing problem that needs to be addressed. Campbell et al. [ 5 ] focus on the governmental failures in treating the municipal water system that led to the poisoning of hundreds of children and adults in Flint, Michigan, USA, and discuss how such tragic events can be prevented in the future. Maldonado et al. [ 6 ] examine if Hispanic immigrants are disproportionately exposed to flood hazards compared to other racial/ethnic groups in the Houston and Miami metropolitan areas, USA, based on household-level survey data. Their divergent findings for these two urban areas suggest that future EJ research on flooding should distinguish between Hispanic subgroups based on nativity status and other local contextual factors. Muñoz and Tate [ 7 ] focus on the EJ consequences of disaster recovery, based on a case study of three communities in Iowa, USA, that were affected by severe flooding in 2008. Their analysis of the two federal programs that funded property acquisitions indicated that households in socially vulnerable areas were less likely to obtain full financial compensation and endured longer waiting periods before receiving acquisition funds. Jennings et al. [ 8 ] examine another emerging issue in EJ research: advancing sustainability by ensuring that urban ecosystem services and related health benefits are equally distributed across all population groups. Their article integrates complementary concepts from multiple disciplines to illustrate how cultural ecosystem services from urban green spaces are associated with equity and social determinants of health. Hornik et al. [ 9 ] explore how people conceptualize the connection between EJ and sustainability, based on analyzing stakeholder perspectives in Milwaukee, WI, USA. Bell [ 10 ] addresses an important gap in prior EJ research by providing a gender perspective and exploring women’s experience of EJ, based on a review of the existing literature and her own prior experiences as a scholar and activist. Bell’s analysis confirms that women tend to experience inequitable environmental burdens and are less likely than men to have control over environmental decisions, both of which lead to disproportionate health impacts.

In addition to broadening the scope of EJ scholarship by exploring these new frontiers, our Special Issue contributes to three specific research themes: (a) documenting connections between unjust environmental exposures and health impacts; (b) promoting and achieving EJ; and (c) clarifying stakeholder perceptions of EJ issues. These themes and related articles are described below.

Documenting connections between unjust environmental exposures and health impacts : As the EJ framework has expanded in new directions, recent research has emphasized the need to examine health outcomes and health disparities associated with exposure to environmental hazards, thus extending EJ to environmental health justice. Several articles in this Special Issue advance environmental health justice scholarship by documenting linkages between unequal environmental exposure and adverse health impacts associated with unsafe infrastructure and homes [ 5 , 14 ], substance use and addiction [ 15 ], and children’s obesity and academic performance [ 16 ]. Campbell et al. [ 5 ] provide a detailed assessment of the recent drinking water crisis and lead poisoning in Flint, USA. In addition to describing how this tragedy happened and why socially disadvantaged populations are at particularly high risk for lead exposure, Campbell et al. discuss how childhood lead exposure and Flint-like events can be prevented from occurring in the future. Mankikar et al. [ 14 ] examine whether participation in a two-month long environmental education intervention program reduces exposure to homebased environmental health hazards and asthma-related medical visits. Their home intervention program in southeastern Pennsylvania, USA, focused on low-income households where children had asthma, were at risk for lead poisoning, or faced multiple unsafe housing conditions. Cleaning supplies (e.g., a microfiber cloth, soap), safety supplies (e.g., CO detector, fire alarm) and pest management tools (e.g., caulk, roach bait) were provided along with educational materials and face-to-face instruction. Their findings indicate that low-cost comprehensive home interventions are effective in reducing environmental home hazards and improve the health of asthmatic children in the short term. Mennis et al.’s [ 15 ] review article seeks to extend EJ research by including environmental factors influencing substance use disorders—one of the most pressing global public health problems. They demonstrate why inequities in risky substance use environments should be considered as an EJ issue and conclude that future research needs to examine where, why, and how inequities in risky substance use environments occur, the implications of such inequities for disparities in substance use disorders and treatment outcomes, and the implications for tobacco, alcohol, and drug policies as well as prevention and treatment programs. Clark-Reyna et al. [ 16 ] focus on chemicals known as metabolic disruptors that are of specific concern to children’s health and development. Their article examines the effect of residential concentrations of metabolic disrupting chemicals on children’s school performance in El Paso, Texas, USA. Results indicate that concentrations of metabolic disruptors are significantly associated with lower grade point averages directly and indirectly through body mass index. Findings from this study have important implications for future EJ research and chemical policy reform in the U.S.

Promoting and achieving EJ : While EJ scholars often focus on describing the injustices experienced by socially disadvantaged communities, several articles in this Special Issue direct attention toward efforts to achieve EJ through implementation of interventions to improve environmental knowledge and health [ 14 , 17 ], identification of avenues for sustainable and just community and societal change [ 1 , 8 , 9 , 13 ], and incorporation of EJ metrics in government programs [ 12 ]. In the area of interventions, Ramirez-Andreotta et al. [ 17 ] examine parental perceptions of the “report back” process after an exposure assessment. Results showed that parents coped with their challenging circumstances using data and that they made changes to reduce children’s exposure to contaminants. The findings suggest that providing information to EJ community members could be an effective strategy to reduce exposure, when immediate wider scale remediation is not possible. While Mankikar et al. [ 14 ] was summarized above, what is relevant here is that low income communities disproportionately face challenges from poor quality housing, especially renters. The promise of the type of intervention conducted by Mankikar et al. for achieving EJ is that it works to improve the environmental health of children. In terms of identifying avenues for change, Hornik et al. [ 9 ] examine stakeholder beliefs about how positive change should be made to ameliorate injustices related to water pollution in Milwaukee, WI, USA. In order to work towards EJ, the authors argue that is important to build mutual understanding among stakeholders and acknowledge the potential for complex interactions across scales of governance in order to mitigate conflicts. Related to avenues for achieving EJ, Finley-Brook and Holloman [ 1 ] emphasize the importance of involving communities in the participatory design of solutions and fairly distributing benefits. The energy case studies they review suggest that empowering approaches are feasible, but also highlight the potential for conflict between what is “green” and what is “just”. Petrescu-Mag et al. [ 13 ] explore EJ issues in a Roma community in Romania beset by environmental challenges associated with a landfill. Researchers engaged community residents in discussions about potential action options, and residents strongly preferred improving local on-site living opportunities at the dump. An examination of the process of selecting this option suggests that negotiations among stakeholders are required in order to begin to address environmental injustices. Jennings et al. [ 8 ] argue that it is critical for all communities to have access to cultural ecosystem services that influence social determinants of health in order to achieve health equity and promote physical and psychological well-being. Taking a different approach, Fairburn et al. [ 12 ] trace the development and diffusion of indices of multiple deprivation (IMD). EJ scholars have impacted public policy through the incorporation of environmental data into IMD in England, Wales, and Northern Ireland, and evidence suggests that IMD are potential catalysts for EJ as they enable decision-makers to make more equitable decisions.

Clarifying stakeholder perceptions of EJ issues : The EJ research framework has focused on objectively documenting conditions and processes that constitute environmental injustices. Based on this materialist foundation, less emphasis in EJ research has been placed on people’s subjectivities. Several articles in this Special Issue advance EJ research by examining and clarifying stakeholder subjectivities regarding EJ issues [ 9 , 11 , 18 , 19 ], which extends the research framework beyond the documentation of unjust conditions and processes. In Hornik et al.’s [ 9 ] study, which clarifies community group perceptions of EJ in the context of water sustainability initiatives in Milwaukee, WI, USA, stakeholders shared similar perspectives on environmental injustice as an everyday experience. However, they had divergent perspectives on how environmental injustices are produced and most effectively redressed, which has implications for promoting initiatives for EJ and sustainability. Teixeira and Zuberi [ 18 ] examine neighborhood perceptions of environmental health hazards among black youth in Pittsburgh, PA, USA. Youth identified the intersection of race and poverty, poor waste management, housing abandonment, and crime as salient neighborhood environmental concerns, and understood correctly (based on the authors’ analysis of secondary spatial data) that black vs. white neighborhoods in the city are characterized by unequal environments. Findings suggest that environmental conditions provide clearly recognizable indicators of injustice for youth, and, furthermore, that youth interpret the lack of response to unjust conditions to imply that no one cares. Songsore and Buzzelli [ 11 ] examine the role of Ontario, Canada media in amplifying people’s perceptions of wind energy development (WED) health risks and injustices. Scientific evidence for negative health effects of wind turbines is contested, yet provincial media legitimated concerns about serious health impacts, which amplified public health risk perceptions and aroused claims of procedural injustice regarding the lack of community participation in Ontario’s WED process. Findings highlight the importance of media in shaping perceptions of environmental injustice, and reveal how public perceptions of injustice may be cultivated to impede societal transitions toward renewable energy sources. Ard et al. [ 19 ] use multilevel models in a US national study of the roles of neighborhood social capital and exposure to industrial air pollution in explaining the racial gap in self-rated health between black, Hispanic, and white individuals. They found that individuals’ feelings of trust in neighbors of different social standing and perceptions of political empowerment largely accounted for lower self-rated health among African Americans (and partially accounted for it among Hispanics) relative to whites, while exposure to industrial air pollution was statistically irrelevant. Results suggest that people’s perceptions of well-being may be shaped largely by their social contexts, and that harmful environmental exposures may not always be of paramount importance in shaping those perceptions. Taken together, these articles underscore how people’s subjectivities deeply matter: they influence which phenomena are contested as EJ issues and condition possibilities for redressing environmentally unjust arrangements.

The wide array of environmental health hazards, communities, and countries represented in this Special Issue reflect the expanding scope and purpose of EJ research, which has broadened and transformed significantly in recent years. The articles cover topics ranging from energy, food, water, obesogenic chemicals, landfills, and greenspace. They document connections between unjust environmental exposures and health impacts; provide ideas for how to promote and achieve EJ; and clarify stakeholder perceptions of EJ issues. In doing so, the Special Issue illustrates the existence of multiple and compounding marginalities, but also the wide variety of approaches being employed to achieve EJ, in its many diverse forms.

Author Contributions

All three authors contributed to the organization, writing, and editing of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Diachronic study of coastline behavior using remote sensing: a case study of Korba beach, Tunis

• Original Paper
• Published: 24 April 2024

Cite this article

• Rebai Noamen 1 ,
• Mejdoub El Fehri Rihem 1 ,
• Yahyaoui Zouhour 1 ,
• El Mokh Riadh 1 &
• Gannouni Sonia 2  

This study analyzed the spatiotemporal changes of the Korba coastline over 18 years using LANDSAT 5 and 7 and Sentinel 2A imagery. The research, focused on 2004–2022, used consistent spring tide conditions for image selection and processing. Following radiometric and geometric corrections, the LANDSAT imagery employed the normalized differential water index (NDWI) for land–water differentiation, while Sentinel 2A imagery used histogram-based pixel classification. Digital Shoreline Analysis System (DSAS) software validated the shoreline position changes. Using Earth Observation Point Reference (EPR) techniques, both regression and transgression in the study area were identified. In subsequent stages, comparisons were based on the 2004 topographic survey data and Landsat 7 imagery, emphasizing the consistent spring tide phase. Preliminary results from the study showcased minor variations in the coastal landscape, thus affirming the efficacy of high-resolution remote sensing methodologies for coastline monitoring. The synchronization of data acquisition with consistent tidal phases emerged as a crucial factor in ensuring the accuracy and reliability of the findings. This research not only contributes valuable insights into the spatiotemporal dynamics of the Korba coastline but also underscores the significance of methodological rigor in remote sensing applications along tidal phases for environmental monitoring.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

I would like to extend my heartfelt gratitude to all those who contributed to the success of this study. Firstly, to the entire research team, whose diligence, expertise, and unwavering commitment were invaluable. I am particularly thankful to the local communities of Korba, who generously shared their insights and experiences, grounding our research in the realities of the coastline they call home. The contributions from the technical staff, who ensured the seamless processing and analysis of the remote sensing data, deserve special mention. Furthermore, the authors would like to mention that the maps used in this research (which were prepared by the authors) are based on Open Street Maps, USGS data, and official regional data. Finally yet importantly, I am grateful to our peers who provided critical reviews and constructive feedback at various stages of the research.

No funds, grants, or other support was received.

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Research Unit of Geotechnical and Geo-Risk Engineering LR14ES03, National School of Engineers of Tunis (ENIT), Tunis, Tunisia

Rebai Noamen, Mejdoub El Fehri Rihem, Yahyaoui Zouhour & El Mokh Riadh

Georesources Laboratory, Water Research and Technologies Center (CERTE), Technopark of Borj Cedria, Tourist Route of Soliman Nabeul, P.O. Box No. 273, 8020, Soliman, Tunisia

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Noamen, R., Rihem, M.E., Zouhour, Y. et al. Diachronic study of coastline behavior using remote sensing: a case study of Korba beach, Tunis. Euro-Mediterr J Environ Integr (2024). https://doi.org/10.1007/s41207-024-00478-3

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DOI : https://doi.org/10.1007/s41207-024-00478-3

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Life and Environmental Science Ethics: Case Studies

This collection of cases covers topics related to Life and Environmental Science ethics including, agriculture ethics, bioethics, environmental ethics, and more. Cases come from a variety of online educational sources, ethics centers, and ethics programs.

Ethics Unwrapped. “Arctic Offshore Drilling.” 2021. https://ethicsunwrapped.utexas.edu/case-study/arctic-offshore-drilling.

• Offshore oil and gas reserves, primarily along coastlines in Alaska, California, Louisiana, and Texas, account for a large proportion of the oil and gas supply in the United States. In August 2015, President Obama authorized Royal Dutch Shell to expand drilling off Alaska’s northwest coast. His decision brought into sharp relief the different, oftentimes competing views on the expansion of offshore drilling.

Ethics Unwrapped. “Climate Change & the Paris Deal.” 2021. https://ethicsunwrapped.utexas.edu/case-study/climate-change-paris-deal.

• In December 2015, representatives from 195 nations gathered in Paris and signed an international agreement to address climate change, which many observers called a breakthrough for several reasons. First, the fact that a deal was struck at all was a major accomplishment, given the failure of previous climate change talks. Second, unlike previous climate change accords that focused exclusively on developed countries, this pact committed both developed and developing countries to reduce greenhouse gas emissions. However, the voluntary targets established by nations in the Paris climate deal fall considerably short of what many scientists deem necessary to achieve the stated goal of the negotiations: limiting the global temperature increase to 2 degrees Celsius. Furthermore, since the established targets are voluntary, they may be lowered or abandoned due to political resistance, short-term economic crises, or simply social fatigue or disinterest.

Ethics Unwrapped. “Patient Autonomy & Informed Consent - Ethics Unwrapped.” 2021. https://ethicsunwrapped.utexas.edu/case-study/patient-autonomy-informed-consent.

• In the context of health care in the United States, the value on autonomy and liberty was cogently expressed by Justice Benjamin Cardozo in Schloendorff v. Society of New York Hospitals (1914), when he wrote, “Every human being of adult years and sound mind has a right to determine what shall be done with his own body.” This case established the principle of informed consent and has become central to modern medical practice ethics . However, a number of events since 1914 have illustrated how the autonomy of patients may be overridden. In Buck v. Bell (1927), Justice Oliver Wendell Holmes wrote that the involuntary sterilization of “mental defectives,” then a widespread practice in the U.S., was justified, stating, “Three generations of imbeciles are enough.” Another example, the Tuskegee Syphilis Study, in which African-American males were denied life-saving treatment for syphilis as part of a scientific study of the natural course of the disease, began in 1932 and was not stopped until 1972.

Ethics Unwrapped. “Prenatal Diagnosis & Parental Choice.” 2021. https://ethicsunwrapped.utexas.edu/case-study/prenatal-diagnosis-parental-choice.

• In the United States, many citizens agree that the government may impose limits on the freedom of individuals when individuals interfere with the rights of others, but the extent of these limits is often a topic of debate. Among the most debated of bioethical issues is the issue of abortion, which hinges on whether the fetus is a person with rights, notably the right to life.

Ethics Unwrapped. “Retracting Research: The Case of Chandok v. Klessig.” 2021. https://ethicsunwrapped.utexas.edu/case-study/retracting-research-case-chandok-v-klessig.

• In 2003, a research team from prominent laboratory the Boyce Thompson Institute (BTI) for Plant Research in Ithaca, New York published an article in the prestigious academic journal Cell. It was considered a breakthrough paper in that it answered a major question in the field of plant cell biology. The first author of this paper was postdoctoral researcher Meena Chandok, working under her supervisor Daniel Klessig, president of BTI at the time.

International Dimensions of Ethics Education in Science & Engineering. “IDEESE Case: GMOs.” University of Massachusetts Amherst, 2009. https://www.umass.edu/sts/ethics/online/cases/GMO/case.html.

• High ethical concern about GM organisms has two sources: concerns for the integrity and sustainability of the natural environment and concern about the social consequences of allowing the supply of seeds or breeding stock to be controlled by developers (mainly though not exclusively large multinational corporations) having 20-year monopolies over the distribution of any particular genetic material as a consequence of patent rights.

International Dimensions of Ethics Education in Science & Engineering. “IDEESE Case: Stem Cell.” University of Massachusetts Amherst, 2009. https://www.umass.edu/sts/ethics/online/cases/StemCell/case.html.

• Stem cells are undifferentiated cells in the human body which are able to replenish themselves by dividing. Under particular natural or medically induced circumstances, they are able to develop into more specialized cells for forming bones, nerves, body tissue, brains, muscles, and blood. Stem cell research has provoked considerable ethical concern; while many welcome the prospect of more effective treatments of birth defects or diseases, using human embryonic stem cells for such treatments, or even in scientific research, is very controversial. The embryo must be destroyed to secure its stem cells, and anyone who believes that human life begins at the moment of conception equates destroying embryos with committing murder. Excitement generated by the first acquisition of human embryonic stem cells in 1998 spread around the world. In South Korea, where scientists and the government had been attuned to advances in genetics, bioscience, and biotechnology since the mid-1980s, there was strong interest in taking up the new possibilities. Four years earlier, the South Korean government had adopted an ambitious "Plan 2000" intended to make South Korea one of the leading sites of bioscience and biotechnology research in the world. In 1990 it provided its national Genetics Research Institute with ample facilities in the new Taedok Science town just outside Seoul; in 1995 it expanded the Institute and renamed it the Korean Research Institute for Bioscience and Biotechnology to better reflect its expanded areas of work.

Iowa State University. “Case Studies.” Bioethics Program, 2021. https://bioethics.las.iastate.edu/a-note-about-case-studies-for-the-classroom/.

• The following are helpful for introducing real-life ethics situations to students. These case studies are designed for teaching purposes, to help students develop critical responses to ethical issues, taking into account a multitude of viewpoints. Please feel free to use these case studies in your classrooms, or modify as necessary for your purposes. Please give credit where credit is due.

Teach the Earth. “Case of GMOs in Environmental Cleanup.” Across the Geoscience Currirulum, 2019. https://serc.carleton.edu/geoethics/activities/84049.html.

• This case represents various agendas, hidden and otherwise, that can come into play during environmental remediation.

Teach the Earth. “Does A River Have Rights?” Across the Geoscience Curriculum, 2019. https://serc.carleton.edu/geoethics/activities/84031.html.

• Individual students have different ethical "lines." This class discussion proceeds with a series of prompts that presents a set of scenarios that explores ethical boundaries. Students discuss right and wrong actions with respect to a river and discuss why those actions are "right" or "wrong" as well as how their ethical viewpoints vary.

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Study reveals more than half of branded global plastic waste linked to just 56 companies

Dal researcher co-authors paper on five-year international effort.

Alison Auld - April 24, 2024

For more than five years, citizen scientists in dozens of countries combed beaches, waterways, parks, busy city streets and other public areas in an ambitious bid to quantify the amount of plastic waste in the environment and track its source.

They carefully recorded the brand or trademark on each plastic item and the number of items with those brands wherever possible, also noting the location, date, type of plastic, type of item, number of plastic layers and time of each audit event, which ran from 2018 to 2022. 

Now, researchers have synthesized those results in a new paper that found a clear link between plastic production and plastic pollution, such that a one-per-cent increase in plastic production was associated with a one-per-cent increase in plastic pollution in the environment. 

The team, including co-author Dr. Tony Walker of Dal's School for Resource and Environmental Studies , also determined that companies producing single-use consumer goods disproportionately contributed to the problem more than household and retail companies, and that most collected items had no discernible brand. 

"We were surprised to find that the direct relationship between plastic production and plastic pollution was consistent around the world, irrespective of whether the litter audits were conducted in the global north or global south," says Dr. Walker, noting that plastic production doubled to about 400 metric tons from 2000 to 2019. 

"This confirms that companies responsible for omnipresent plastic pollution is consistent no matter where you live." 

Data 'speaks for itself'

The study, published Wednesday (April 24) in Science Advances , marks the first robust quantification of the global relationship between production and pollution, and comes at a time when world leaders are meeting in Ottawa to hammer out a Global Plastics Treaty at the fourth annual International Negotiating Committee, or INC-4. 

They also discovered that about 52 per cent of the more than two million inventoried plastic items had no identifiable brand, highlighting the need for better transparency about production and labeling of plastic products to enhance traceability and accountability. The researchers suggest creating an international, open-access database into which companies are obliged to quantitatively track and report their products, packaging and brands. 

"When I first saw the relationship between production and pollution, I was shocked," says co-author Win Cowger of the Moore Institute for Plastic Pollution Research. "Despite all the things big brands say they are doing, we see no positive impact from their efforts. But on the other hand, it gives me hope that reducing plastic production by fast-moving consumer goods companies will have a strong positive impact on the environment.” 

The research, led by scientists at Dalhousie and a dozen different universities in the United States, Australia, the Philippines, New Zealand, Estonia, Chile, Sweden and the U.K., found that 56 global companies are responsible for more than half of all branded plastic pollution. The paper states that the top five producers of branded plastic pollution were Coca-Cola Company, which was responsible for 11 per cent of roughly 910,000 branded items, followed by PepsiCo (5%), Nestlé (3%), Danone (3%), and Altria/Philip Morris International (2%). The top companies produce food, beverage or tobacco products. 

"This global branded plastic pollution data speaks for itself and demonstrates unequivocally that the world's top global producers are the biggest plastics polluters,” says Dr. Walker.

Paradigm shift needed

The five-year analysis used data from 1,576 audit events in 84 countries. Brand audits are citizen science initiatives in which volunteers conduct waste cleanups and document the brands collected. More than 100,000 volunteers submitted data through Break Free from Plastic or the 5 Gyres’ TrashBlitz app. 

The authors state that the strong relationship between plastic production and pollution, across geographies and different waste management systems, suggests that reducing the production of single-use plastic consumer goods could curb global plastic pollution. 

"Findings from this study suggest we need a paradigm shift in how we regulate plastic producers, especially the top branded producers that are responsible for half of branded plastic pollution," says Dr. Walker. 

For world leaders, this research serves as a tool to support a legally binding treaty that includes provisions on corporate accountability, prioritizing plastic production reduction measures, and promoting reuse and refill systems. 

"Our study underscores the critical role of corporate accountability in tackling plastic pollution," says Dr. Lisa Erdle, director of Science and Innovation at the 5 Gyres Institute . "I urge world leaders at INC-4 to listen to the science, and to consider the clear link between plastic production and pollution during negotiations for a Global Plastics Treaty." 

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Home / 2024 / April / Center for Critical Urban and Environmental Studies launches at UC Santa Cruz

New research center studies interconnections between urbanism and the environment, with a focus on lessons from the Santa Cruz region

April 22, 2024

By Allison Arteaga Soergel

One of UC Santa Cruz’s newest research centers, the Center for Critical Urban and Environmental Studies (CUES) , builds upon a long history of UC Santa Cruz thought leadership in urban-environmental politics to tackle converging 21st Century crises, like climate change and housing affordability. 

The center focuses on the interconnections between urbanism and the environment and the social and political underpinnings of environmental issues. The researchers aim to overcome disciplinary divisions between urban and environmental studies, and to show how the pursuit of sustainability and social justice are often intertwined. And they’re using the broader Santa Cruz region as a living laboratory, identifying important lessons from our area that may be applicable across the state, country, and world. 

“There aren’t many research centers that combine urban and environmental research in this way, and we want to foreground the importance of thinking of them together,” said Associate Professor of Sociology Hillary Angelo, co-founder and founding director of the center. 

“Research on the environment tends to be very positivist, technocratic, and scientific in orientation, but our study of these issues is different in that it’s very politically engaged,” she added. “It’s explicitly about mixing social scientific and technical approaches to these questions to pursue social justice and build a better and more just world.”

Angelo joined UC Santa Cruz’s sociology department in 2015 and is an urban and environmental sociologist who studies understandings of the environment and their relationship to large-scale spatial and social transformations. The center’s assistant director and co-founder, Sociology Professor Miriam Greenberg, studies the intersection of cultural, environmental, and critical urban studies, with particular focus on the temporality and politics of crisis. 

The center aims to support related research at UC Santa Cruz through initiatives like funding for graduate student researchers, hosting events , leading a working group where graduate students can learn together and discuss their current projects, and serving as a hub for related faculty research. 

One example of the center’s research is a project  funded by the California Climate Action Grants program. Eighteen UC Santa Cruz faculty, staff, and students are working with local partners to study how the state’s housing crisis may be increasing climate change risk by driving homeowners, renters, and informally housed people into fire-prone wildland-urban interface areas in search of affordable housing. 

The center’s overall approach to research will combine a variety of techniques, from deep dives into theory to historical and archival work to community-engaged research and observation of current trends. The center will also aim to document and draw upon UC Santa Cruz’s history of innovation in critical urban and environmental studies, dating back to the 1970s, including path-breaking work by sociology faculty members like Jim O’Connor and Bill Friedman and the development of UCSC’s interdisciplinary Environmental Studies Department. 

Meanwhile, the center’s regional focus will draw attention to what makes the Santa Cruz area unique, as well as the challenges we face that may be more universal. 

“There are so many interesting social and environmental dynamics concentrated in our region—between the Monterey Bay, the Santa Cruz Mountains, agricultural areas, areas stewarded by Native tribes, and the increasing expansion of Silicon Valley into our region—and all of these dynamics are so intensely interconnected in our area,” said Miriam Greenberg. “This center will help to advance the unique contributions to research that our region can make and to put them in dialogue with what’s happening globally.”

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Can climate change accelerate transmission of malaria? Pioneering research sheds light on impacts of temperature

Malaria is a mosquito-borne disease caused by a parasite that spreads from bites of infected female Anopheles mosquitoes. If left untreated in humans, malaria can cause severe symptoms, health complications and even death.

In tropical and subtropical regions where malaria is prevalent, scientists are concerned that climate warming might increase the risk of malaria transmission in certain areas and contribute to further spread. However, there is still much to learn about the relationship between temperature and the mosquito and parasite traits that influence malaria transmission.

In "Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum," a groundbreaking study published in the journal Nature Communications , researchers at the University of Florida, Pennsylvania State University and Imperial College, combined novel experimental data within an innovative modeling framework to examine how temperature might affect transmission risk in different environments in Africa.

"In broad terms, scientists know that temperature affects key traits such as mosquito longevity, the time it takes for a mosquito to become infectious after feeding on an infected host, and the overall ability of the mosquito to transmit the disease" said Matthew Thomas, a UF/IFAS professor and UF/IFAS Invasion Science Research Institute (ISRI) director. "But what might seem surprising is that these temperature dependencies have not been properly measured for any of the primary malaria vectors in Africa."

"Our findings provide novel insights into the effects of temperature on the ability of Anopheles gambiae mosquitoes -- arguably the most important malaria mosquito in Africa -- to transmit Plasmodium falciparum, the most prevalent species of human malaria in Africa," said Eunho Suh, joint first-author with Isaac Stopard at Imperial College, and assistant research professor at Penn State, who conducted the empirical research as a post-doctoral student in Thomas' previous lab.

The study involved several detailed laboratory experiments in which hundreds of mosquitoes were fed with Plasmodium falciparum-infected blood and then exposed at different temperatures to examine the progress of infection and development rate within the mosquitoes, as well as the survival of the mosquitoes themselves.

"The novel data were then used to explore the implications of temperature on malaria transmission potential across four locations in Kenya that represent diverse current environments with different intensities of baseline transmission, and that are predicted to experience different patterns of warming under climate change," explained Thomas.

The study supports previous research results in demonstrating that various mosquito and parasite traits exhibit intermittent relationships with temperature and that under future warming temperatures, transmission potential is likely to increase in some environments but could reduce in others. However, the new data suggest that parasites can develop more quickly at cooler temperatures and that the rate of parasite development might be less sensitive to changes in temperature, than previously thought.

The data also indicate that the successful development of parasites in the mosquito, declines at thermal extremes, contributing to the upper and lower environmental bounds for transmission.

Combining these results into a simple transmission model suggests that contrary to earlier predictions, the anticipated surge in malaria transmission, attributed to climate warming, may be less severe than feared, particularly in cooler regions like the Kenyan Highlands.

"Some of the current assumptions on mosquito ecology and malaria transmission derive from work done in the early part of the last century. Our study is significant in highlighting the need to revisit some of this conventional understanding," said Thomas.

"While the time it takes for a mosquito to become infectious is strongly dependent on environmental temperature, it also depends on the species and possibly strain of malaria and mosquito," said Suh.

The comprehensive study and findings represent a significant step forward in understanding the intricacies of malaria transmission and paves the way for future research aimed at controlling malaria on a global scale.

"Our work focused on the malaria parasite Plasmodium falciparum in the African malaria vector, Anopheles gambiae. However, Plasmodium vivaxis another important parasite species responsible for most malaria in Asia, as well as the recently reported malaria cases in the U.S.," said Suh. "Like Plasmodium falciparum, the established model describing the effects of temperature on development of Plasmodium vivaxremains poorly validated."

The same is true for other vector-borne diseases, such as dengue or Zika virus, added Suh.

"We need more work of the type we present in the current paper, ideally using local mosquito and parasite or pathogen strains, to better understand the effects of climate and climate change on transmission risk," he said.

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Materials provided by University of Florida . Note: Content may be edited for style and length.

Journal Reference :

• Eunho Suh, Isaac J. Stopard, Ben Lambert, Jessica L. Waite, Nina L. Dennington, Thomas S. Churcher, Matthew B. Thomas. Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-47265-w

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Maximilian Kotz, Anders Levermann & Leonie Wenz

Institute of Physics, Potsdam University, Potsdam, Germany

Maximilian Kotz & Anders Levermann

Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Correspondence to Leonie Wenz .

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Received : 25 January 2023

Accepted : 21 February 2024

Published : 17 April 2024

Issue Date : 18 April 2024

DOI : https://doi.org/10.1038/s41586-024-07219-0

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DES’ Slawomir Lomnicki and LSU Superfund Research Center receive more than $400K to study air pollution

April 25, 2024

BATON ROUGE - A grant from the National Institute of Environmental Health Sciences, or NIEHS, has presented LSU researchers and their colleagues with an unusual opportunity – to monitor changes in air quality following operational changes at the site.

The NIEHS is giving $426,808 to researchers from the LSU Superfund Research Center , including Slawomir Lomnicki of the Department of Environmental Sciences, to monitor air pollution in the small town of Colfax, in Grant Parish, Louisiana.

The grant comes at a crucial time for the area. Colfax has been home to the nation’s only commercially-operating open burn/open detonation hazardous waste thermal treatment facility. It is now transitioning to treating the waste in an enclosed site, and the NIEHS grant will allow the researchers to work with the local community to track local air quality during the transition.

“Studies of impact of Superfund waste treatment technologies on the local population’s health and well-being are typically associated with large uncertainty due to the lack of a reference point, instead relying on statistical comparisons with similar populations,” said Lomnicki, a co-Principal Investigator on the project. “Here, we have an opportunity to directly observe how the regulations and technology changes can affect exposure to pollutants.”

The Colfax facility has treated contaminated soils from other areas, spent military munitions and other explosives, while the Louisiana Department of Environmental Quality has asked the operator to construct a closed burn facility to continue operation.

“Colfax is at an inflection point in terms of how waste management proceeds there,” said Jennifer Richmond-Bryant, a professor at North Carolina State who works with the LSU Superfund Research Center. “Hopefully, documenting conditions at this time will help illustrate how operational changes can make a difference in air quality.”

Learn more about the work of the Superfund Research Center:

Bobbi Parry

College of the Coast & Environment (225) 578 - 6534

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