Despite the need to limit climate change and transition to low-carbon energy, there is disagreement about how to share the burden of reducing CO emissions. We assess different approaches to global mitigation, accounting for three key factors: avoided climate harms, health (co)benefits from improved air quality, and the economic cost of CO policies. We then rank the approaches according to different preferences for inter-generational and intra-generational equity. We compare a reference scenario to three scenarios that limit warming to 2°C: one through least cost, one that shifts mitigation burden towards higher-income countries (referred to as the international equity scenario), and a third that is identical to international equity, but within which low-income and middle-income countries (LMICs) also adopt air quality policies to reduce air pollution to the levels that occur in least-cost. Emissions and policy costs are modelled with Global Change Analysis Model, air quality with GEOS-Chem, health impacts with the Global Exposure Mortality Model, and climate benefits with Greenhouse Gas Impact Value Estimator. Climate action to limit global warming to 2°C results in more than 13·5 million avoided premature deaths from air pollution between 2020 and 2050, mostly in middle-income countries. Opting for the least-cost scenario rather than international equity reduces the mitigation burden for LMICs but also reduces their health co-benefits by several million deaths, highlighting a trade-off between mitigation effort (an important component of climate justice) and the urgent need to reduce environmental health burdens in LMICs. The extent to which equity is prioritised determines what to do about that trade-off; as more priority is given to lower-income countries, the international equity scenario is preferred. The most favourable scenario is the combined international equity and air quality scenario, whereby higher-income countries pay more climate mitigation costs, and LMICs use the cost savings to implement conventional air quality controls that offset foregone health co-benefits. Justice-centred climate mitigation strategies must ensure that LMICs do not miss an opportunity to realise transformative reductions in air pollution. United States National Science Foundation.

Climate change is a substantial global health threat in the 21st century, disproportionately affecting low-income and middle-income countries (LMICs), which face significant climate risks, pre-existing vulnerabilities, and have relatively few interventions in place. With a scarcity of research in LMICs, and diminishing development assistance, setting priorities to address climate change-related health impacts on women and children is both urgent and prudent. We consulted 88 climate and health researchers between 2022 and 2024 to generate relevant questions regarding climate change impacts on women and children's health and potential solutions. A diverse group of 52 experts prioritised a shortlist of 70 questions using the Child Health and Nutrition Research Initiative method. The top three priorities included vulnerability mapping, integrating climate metrics into surveillance, and long-term heat exposure effects. This Health Policy underscores key knowledge gaps in climate-related health outcomes affecting women and children in LMICs, and suggests a focused research agenda for guiding global investments in resilience and adaptation.

Implementation climate is a key organizational determinant of whether evidence-based interventions (EBIs) are adopted, delivered with fidelity, and sustained in healthcare. Despite its importance, the concept is inconsistently defined, often conflated with culture or readiness, and assessed with measures of unclear scope. We aimed to clarify how implementation climate is defined and used in healthcare, identify its defining attributes, antecedents, and consequences, delineate boundaries with related constructs, and synthesize quantitative and qualitative approaches to measurement. We combined a Joanna Briggs Institute (JBI) scoping review with Walker and Avant's eight-step concept analysis method. CINAHL, Embase, MEDLINE, and PsycINFO were searched in November 2024, supplemented by citation tracking and grey literature searching. Empirical and theoretical articles that defined, discussed or measured implementation climate in healthcare were eligible. Data were charted in Excel and synthesised inductively to derive definitions, attributes, antecedents, consequences, conceptual boundaries, and measurement indicators; attributes were mapped to the Consolidated Framework for Implementation Research (CFIR). We included 39 sources (24 quantitative, 7 qualitative, 5 theoretical/review, 3 mixed-methods). We propose a synthesized definition: implementation climate is the shared perception among staff that using a specific EBI is an organizational priority, actively supported through resources and HR processes, reinforced by incentives or recognition, and aligned with everyday values and workflows. Seven recurring attributes emerged: clear expectations for EBI use; tangible organizational support (e.g., protected time, training, leadership); incentives and recognition; compatibility with workflow and values; high relative priority; tension for change; and HR practices that select and socialize staff for EBI openness. Transformational leadership and deliberate resource allocation surfaced as dominant antecedents. Stronger implementation climates were associated with improved implementation outcomes (e.g., reach and fidelity) and workforce outcomes (e.g., retention, lower burnout). Measurement options include the Implementation Climate Scale (ICS), the briefer Implementation Climate Measure (ICM), the Equity-Oriented Implementation Climate tool, and qualitative assessments. Implementation climate is a modifiable, measurable organizational lever for strengthening quality improvement and implementation efforts. Leaders can strengthen it by clarifying expectations, protecting time and training, aligning incentives, and ensuring workflow fit. Researchers should refine and adapt measures across contexts and test objective indicators alongside staff perceptions.

The resilience of electric power grids is threatened by natural hazards. Climate-related hazards are becoming more frequent and intense due to climate change. Statistical analyses clearly demonstrate a rise in the number of incidents (power failures) and their consequences in recent years. Therefore, it is of utmost importance to understand and quantify the resilience of the infrastructure to external stressors, which is essential for developing efficient climate change adaptation strategies. To accomplish this, robust fragility and other vulnerability models are necessary. These models are employed to assess the level of asset damage and to quantify losses for given hazard intensity measures. In this context, a comprehensive literature review is carried out to shed light on existing fragility models specific to the transmission network, distribution network, and substations. The review is organized into three main sections: damage assessment, fragility curves, and recommendations for climate change adaptation. The first section provides a comprehensive review of past incidents, their causes, and failure modes. The second section reviews analytical and empirical fragility models, emphasizing the need for further research on compound and non-compound hazards, especially windstorms, floods, lightning, and wildfires. Finally, the third section examines risk mitigation and adaptation strategies in the context of climate change. This review aims to improve the understanding of approaches to enhance the resilience of power grid assets in the face of climate change. These insights are valuable to various stakeholders, including risk analysts and policymakers, who are involved in risk modeling and developing adaptation strategies.

The Working Group I contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive assessment of the physical science basis of climate change. It considers in situ and remote observations; paleoclimate information; understanding of climate drivers and physical, chemical, and biological processes and feedbacks; global and regional climate modelling; advances in methods of analyses; and insights from climate services. It assesses the current state of the climate; human influence on climate in all regions; future climate change including sea level rise; global warming effects including extremes; climate information for risk assessment and regional adaptation; limiting climate change by reaching net zero carbon dioxide emissions and reducing other greenhouse gas emissions; and benefits for air quality. The report serves policymakers, decision makers, stakeholders, and all interested parties with the latest policy-relevant information on climate change. Available as Open Access on Cambridge Core.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems. The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.

Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.

The Synthesis Report (SYR) distils and integrates the findings of the three Working Group contributions to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the most comprehensive assessment of climate change undertaken thus far by the IPCC: Climate Change 2013: The Physical Science Basis; Climate Change 2014: Impacts, Adaptation, and Vulnerability; and Climate Change 2014: Mitigation of Climate Change. The SYR also incorporates the findings of two Special Reports on Renewable Energy Sources and Climate Change Mitigation (2011) and on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (2011).