Author Correction: Atmospheric warming contributions from airborne microplastics and nanoplastics
Yu Liu, Hongbo Fu, Hongliang Zhang, Yunhang Wang, Rajan K. Chakrabarty, Xiang Tu, Xu Tang, Alexander Laskin, Gregory R. Carmichael, Jianmin Chen, Joseph S. Francisco, Drew T. Shindell
Charlotte A. Kukowski, Kristian S. Nielsen, Swen J. Kühne, Clover Hogan, Sander van der Linden, Lorraine Whitmarsh, Grit Zwingenberger, Felix Creutzig, Kimberly A. Nicholas
A multidimensional assessment of systemic cooling poverty in the global south
Giacomo Falchetta, Antonella Mazzone, Shikha Bhasin, Marinella Davide, Paula Bezerra, Kristian Fabbri, Gaia Bertarelli, Anna Pistorio, Ilaria Dal Barco, Enrica De Cian
Toward climate-smart rewilding: An integrated framework for biodiversity, climate change, and society
Gavin Stark, Magali Weissgerber, Néstor Fernández, Laura C. Quintero-Uribe, Marek Giergiczny, Nikolaj Rauff Poulsen, Nacho Villar, Bjorn Mols, Elisabeth S. Bakker, Angus Monro Smith, Georg Winkel, Diogo Alagador, José M. Rey-Benayas, Josep Maria Espelta, Miriam Selwyn, Lluís Brotons, Tatiana Kluvankova, Stanislava Brnkalakova, Judith Kloibhofer, Reinhard Prestele, Henrik G. Smith, Alba Lázaro-González, Robert Buitenwerf, Elena A. Pearce, Jens-Christian Svenning, Joana Santana, Pedro Beja, Francisco Moreira, Sven Wunder, Miroslav Svoboda, Vlado Vancura, Almut Arneth, Arndt Hampe, Henrique M. Pereira
Health Impact of Climate Change on Older Adults Living With Dementia: A Scoping Review
Sachit Gurung, Camila Astolphi Lima, David B. Hogan, Nandia Shirchindorj, Samuel A. J. Lowe, Liz Dennett, Stuart M. Evans, C. Allyson Jones, Shelby S. Yamamoto
Artificial intelligence (AI), and especially generative AI (GenAI), is rapidly reshaping climate change communication (CCC). Once dominated by news coverage and public campaigns, CCC now extends across scientists, NGOs, corporations, journalists, influencers, and citizens—all increasingly encountering and adopting AI tools. This article provides a comprehensive review of scholarship on the nexus of AI and CCC, synthesizing insights scattered across disciplines from social and computer science, and interdisciplinary fields like environmental and science studies. It identifies robust patterns alongside significant gaps, highlighting areas where future research is needed. Based on existing evidence, it shows that AI—as of now—functions less as a disruptive replacement of established communication and information‐seeking practices rather than as an assistive layer in CCC: accelerating routine newsroom tasks, enabling personalized and multilingual outreach, and generating new textual, visual, and multimodal representations of climate change. Stakeholders use AI to monitor discourse, expose greenwashing, and broaden access to climate information, though systematic research on uptake and effects remains limited. Journalists experiment cautiously with AI, emphasizing human oversight, while influencers and content creators are understudied despite their growing role. The potential of AI‐driven systems for fact‐checking, policy analysis, and creative engagement has been explored, yet studies remain heavily English‐centric and focused on text. Citizen studies reveal promises and risks: generative dialogues can reduce skepticism and foster engagement, but biases, misinformation, and equity concerns persist. Advancing the field requires comparative and interdisciplinary agendas that integrate computational and traditional methods, foreground transparency and inclusion, and address how AI can equitably support awareness, trust, and climate action. This article is categorized under: Perceptions, Behavior, and Communication of Climate Change > Communication
Social Inequality Effects of Climate‐Change‐Related Disasters: A Systematic Literature Review in the Context of Developing Countries
The impacts of climate change‐related disasters have garnered significant attention in policy discourse and scientific research, particularly in low‐ and middle‐income countries (LMICs). However, the impacts of climate change‐induced disaster shocks on social structure and equity (i.e., social inequality effects) in LMICs are under‐researched and poorly understood. We conduct a systematic literature review to better understand how climate‐change‐related disasters affect the distribution of income, wealth, and well‐being in agrarian LMICs. Using a conceptual approach that focuses on the disproportionality in climate‐related disaster risks, as well as their impacts and burdens, we analyze the literature by retrieving research papers from the Scopus ( n = 596), ScienceDirect ( n = 644), and Web of Science ( n = 583) databases. Most reviewed studies suggest that climate change‐related disasters tend to exacerbate socioeconomic and sociodemographic inequalities, particularly between small and large landholders in agrarian societies in LMICs, though only a few document reductions in these inequalities. The opportunity to reduce and transform climate‐related risks into adaptive capacity, enabling marginalized groups to equalize their share of climate burdens, is being taken up by only a few farmers. We posit that, without appropriate interventions for equity and social justice, climate change impacts are likely to generate, ceteris paribus, cascading adverse impacts on social equality. Our findings underscore the need to adopt an equity and social justice approach in climate change research, and we call for the adoption of a Social Inequality‐lens‐based policy approach, along with the formulation of social equity‐ and climate justice‐based adaptation programming. This article is categorized under: Assessing Impacts of Climate Change > Observed Impacts of Climate Change Climate and Development > Social Justice and the Politics of Development Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
Global Environmental Change: Human and Policy Dimensions
“But they hardly ever freeze now”: Exploring weather-heritage, memory, and change in southeastern England
George Adamson, Jessica Rapson, Anna Woodham, Taylor Annabell, Lauren Cantillon
This article summarizes methods, prospects, and methodological questions for water quality benefit transfer. Benefit transfer is the use of welfare estimates from settings where original valuation research has been conducted to predict similar estimates for other settings, allowing quantification of benefits and costs when original valuation studies are infeasible. Water quality has been among the most researched areas of benefit transfer, in part due to requirements for benefit-cost analysis within policies and programs that affect surface and ground water. The article begins with a review of benefit transfer methods and characteristics of water quality that challenge transfer validity. We then explore alternative approaches for meta-regression modeling and value prediction to support these transfers, focusing on emerging methods that may afford greater flexibility than traditional parametric approaches and enable gains in transfer accuracy, while maintaining desirable theoretical properties. The article concludes with a discussion of unresolved questions and research needs.
npj Urban Sustainability
OpenAlex: Domain is not Social
Sciences
The future of nature-based recreation in warming tropical cities
Perrine Hamel, Emma E. Ramsay, Shawnda A. Morrison, Moshe Mandelmilch, Su Li Heng, Beatrice H. Ho, Pearl Min Sze Tan, Lancy Sim, Jason Kai Wei Lee, Winston T. L. Chow