Projections of macroeconomic damage from future climate change tend to suggest mild to moderate impacts. This can lead to welfare-optimal climate policies in integrated assessment models (IAMs) that recommend very slow emissions reductions over the coming decades, in sharp contrast with the ambitions of the Paris Agreement. These econometric models assume that weather impacting a single country is all that affects the economy of that country. We examine whether the addition of global weather conditions in the empirical modelling of economic growth affects the projections of the impact of climate change on global gross domestic product (GDP). In effect, we explore whether the interconnectedness of the global economy makes individual countries vulnerable to weather changes that impact other countries. Using three influential econometric models we add global weather to the regressions. We find that this leads to significant worsening of the projections of macroeconomic damage for given future emissions scenarios. Damage to world GDP in 2100 under SSP5-8.5, averaged across both econometric models and climate models increases from ∼11% under models without global weather to ∼40% if global weather is included. Further, we demonstrate that when the damage function used in a recent IAM is estimated from empirical models augmented with global weather conditions, they reduce the welfare-optimal amount of climate change from ∼2.7$\,^{\circ}$C to ∼1.7$\,^{\circ}$C which is consistent with the Paris Agreement targets. Our results highlight the need for econometric modelling and climate science's understanding of extreme events to be integrated much more consistently to ensure the costs of climate change are not underestimated.

In light of uncertainties regarding climate sensitivity and future anthropogenic greenhouse gas emissions, we explore the plausibility of global warming over the next millennium which is significantly higher than what is usually expected. Although efforts to decarbonize the global economy have significantly shifted global anthropogenic emissions away from the most extreme emission scenarios, intermediate emission scenarios are still plausible. Significant warming in these scenarios cannot be ruled out as uncertainties in equilibrium climate sensitivity (ECS) remain very large. Until now, long-term climate change projections and their uncertainties for such scenarios have not been investigated using Earth system models (ESMs) that account for all major carbon cycle feedbacks. Using the fast ESM CLIMBER-X with interactive CO₂ and CH₄ (the latter typically not included in most models), we performed simulations for the next millennium under extended SSP1-2.6, SSP4-3.4 and SSP2-4.5 scenarios. These scenarios are usually associated with peak global warming levels of 1.5 ^∘C, 2 ^∘C and 3 ^∘C, respectively, for an ECS of ∼3 ^∘C, considered the best estimate in the latest Intergovernmental Panel on Climate Change (IPCC) report. As ECS values lower or higher than this estimate cannot be ruled out, we emulate a wide range of ECS from 2 ^∘C to 5 ^∘C, defined as the 'very likely' range by the IPCC. Our results show that achieving the Paris Agreement goal of a 2 ^∘C temperature increase is only feasible for low emission scenarios and if ECS is lower than 3.5 ^∘C. With an ECS of 5 ^∘C, peak warming in all considered scenarios more than doubles compared to an ECS of 3 ^∘C. Approximately 50% of this additional warming is attributed to positive climate–carbon cycle feedbacks with comparable contributions from CO₂ and CH₄. The interplay between potentially high ECS and carbon cycle feedbacks could drastically enhance future warming, demonstrating the importance of properly accounting for all major climate feedbacks and associated uncertainties in projecting future climate change.

While controls over the Earth's climate system have undergone rigorous hypothesis-testing since the 1800s, questions over the scientific consensus of the role of human activities in modern climate change continue to arise in public settings. We update previous efforts to quantify the scientific consensus on climate change by searching the recent literature for papers sceptical of anthropogenic-caused global warming. From a dataset of 88125 climate-related papers published since 2012, when this question was last addressed comprehensively, we examine a randomized subset of 3000 such publications. We also use a second sample-weighted approach that was specifically biased with keywords to help identify any sceptical peer-reviewed papers in the whole dataset. We identify four sceptical papers out of the sub-set of 3000, as evidenced by abstracts that were rated as implicitly or explicitly sceptical of human-caused global warming. In our sample utilizing pre-identified sceptical keywords we found 28 papers that were implicitly or explicitly sceptical. We conclude with high statistical confidence that the scientific consensus on human-caused contemporary climate change—expressed as a proportion of the total publications—exceeds 99% in the peer reviewed scientific literature.

We analyze the evolution of the scientific consensus on anthropogenic global warming (AGW) in the peer-reviewed scientific literature, examining 11 944 climate abstracts from 1991–2011 matching the topics 'global climate change' or 'global warming'. We find that 66.4% of abstracts expressed no position on AGW, 32.6% endorsed AGW, 0.7% rejected AGW and 0.3% were uncertain about the cause of global warming. Among abstracts expressing a position on AGW, 97.1% endorsed the consensus position that humans are causing global warming. In a second phase of this study, we invited authors to rate their own papers. Compared to abstract ratings, a smaller percentage of self-rated papers expressed no position on AGW (35.5%). Among self-rated papers expressing a position on AGW, 97.2% endorsed the consensus. For both abstract ratings and authors' self-ratings, the percentage of endorsements among papers expressing a position on AGW marginally increased over time. Our analysis indicates that the number of papers rejecting the consensus on AGW is a vanishingly small proportion of the published research.

The Antarctic Circumpolar Current (ACC) is the world's strongest ocean current and plays a disproportionate role in the climate system due to its role as a conduit for major ocean basins. This current system is linked to the ocean's vertical overturning circulation, and is thus pivotal to the uptake of heat and CO₂ in the ocean. The strength of the ACC has varied substantially across warm and cold climates in Earth's past, but the exact dynamical drivers of this change remain elusive. This is in part because ocean models have historically been unable to adequately resolve the small-scale processes that control current strength. Here, we assess a global ocean model simulation which resolves such processes to diagnose the impact of changing thermal, haline and wind conditions on the strength of the ACC. Our results show that, by 2050, the strength of the ACC declines by ∼20% for a high-emissions scenario. This decline is driven by meltwater from ice shelves around Antarctica, which is exported to lower latitudes via the Antarctic Intermediate Water. This process weakens the zonal density stratification historically supported by surface temperature gradients, resulting in a slowdown of sub-surface zonal currents. Such a decline in transport, if realised, would have major implications on the global ocean circulation.

Current anthropogenic climate change is the result of greenhouse gas accumulation in the atmosphere, which records the aggregation of billions of individual decisions. Here we consider a broad range of individual lifestyle choices and calculate their potential to reduce greenhouse gas emissions in developed countries, based on 148 scenarios from 39 sources. We recommend four widely applicable high-impact (i.e. low emissions) actions with the potential to contribute to systemic change and substantially reduce annual personal emissions: having one fewer child (an average for developed countries of 58.6 tonnes CO₂-equivalent (tCO₂e) emission reductions per year), living car-free (2.4 tCO₂e saved per year), avoiding airplane travel (1.6 tCO₂e saved per roundtrip transatlantic flight) and eating a plant-based diet (0.8 tCO₂e saved per year). These actions have much greater potential to reduce emissions than commonly promoted strategies like comprehensive recycling (four times less effective than a plant-based diet) or changing household lightbulbs (eight times less). Though adolescents poised to establish lifelong patterns are an important target group for promoting high-impact actions, we find that ten high school science textbooks from Canada largely fail to mention these actions (they account for 4% of their recommended actions), instead focusing on incremental changes with much smaller potential emissions reductions. Government resources on climate change from the EU, USA, Canada, and Australia also focus recommendations on lower-impact actions. We conclude that there are opportunities to improve existing educational and communication structures to promote the most effective emission-reduction strategies and close this mitigation gap.

Rising greenhouse gas concentrations and declining global aerosol emissions are causing energy to accumulate in Earth's climate system at an increasing rate. Incomplete understanding of increases in Earth's energy imbalance and ocean warming reduces the capability to accurately prepare for near term climate change and associated impacts. Here, satellite-based observations of Earth's energy budget and ocean surface temperature are combined with the ERA5 atmospheric reanalysis over 1985–2024 to improve physical understanding of changes in Earth's net energy imbalance and resulting ocean surface warming. A doubling of Earth's energy imbalance from 0.6±0.2 Wm⁻² in 2001–2014 to 1.2±0.2 Wm⁻² in 2015–2023 is primarily explained by increases in absorbed sunlight related to cloud-radiative effects over the oceans. Observed increases in absorbed sunlight are not fully captured by ERA5 and determined by widespread decreases in reflected sunlight by cloud over the global ocean. Strongly contributing to reduced reflection of sunlight are the Californian and Namibian stratocumulus cloud regimes, but also recent Antarctic sea ice decline in the Weddell Sea and Ross Sea. An observed increase in near-global ocean annual warming by 0.1 $^\circ\mathrm{C}\,\mathrm{yr}^{-1}$ for each 1 Wm⁻² increase in Earth's energy imbalance is identified over an interannual time-scale (2000–2023). This is understood in terms of a simple ocean mixed layer energy budget only when assuming no concurrent response in heat flux below the mixed layer. Based on this simple energy balance approach and observational evidence, the large observed near-global ocean surface warming of 0.27 ${\,}^\circ\mathrm{C}$ from 2022 to 2023 is found to be physically consistent with the large energy imbalance of 1.85±0.2 Wm⁻² from August 2022 to July 2023 but only if (1) a reduced depth of the mixed layer is experiencing the heating or (2) there is a reversal in the direction of heat flux beneath the mixed layer associated with the transition from La Niña to El Niño conditions. This new interpretation of the drivers of Earth's energy budget changes and their links to ocean warming can improve confidence in near term warming and climate projections.

Much of the world's data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center's water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.

Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

The consensus that humans are causing recent global warming is shared by 90%–100% of publishing climate scientists according to six independent studies by co-authors of this paper. Those results are consistent with the 97% consensus reported by Cook et al (Environ. Res. Lett. 8 024024) based on 11 944 abstracts of research papers, of which 4014 took a position on the cause of recent global warming. A survey of authors of those papers (N = 2412 papers) also supported a 97% consensus. Tol (2016 Environ. Res. Lett. 11 048001) comes to a different conclusion using results from surveys of non-experts such as economic geologists and a self-selected group of those who reject the consensus. We demonstrate that this outcome is not unexpected because the level of consensus correlates with expertise in climate science. At one point, Tol also reduces the apparent consensus by assuming that abstracts that do not explicitly state the cause of global warming ('no position') represent non-endorsement, an approach that if applied elsewhere would reject consensus on well-established theories such as plate tectonics. We examine the available studies and conclude that the finding of 97% consensus in published climate research is robust and consistent with other surveys of climate scientists and peer-reviewed studies.

Targeting urban air quality improvements in India, the National Clean Air Program (NCAP) was launched in 2019 to reduce PM_2.5 concentrations by 20%–30% in 122 initial cities over a 7 year period (2017–2024). However, considering the regional nature of air pollution nationwide and the significant emission load from rural areas, a potentially large fraction of urban PM_2.5 might originate from emissions outside of a city's boundary, i.e. transboundary emissions. Here, we couple top-down (STILT-PM_2.5) and bottom-up (WRF-Chem) modeling approaches with a new, nationwide, monthly-resolved, and fine-scale (5 km × 5 km) anthropogenic emission inventory to assess the impact of transboundary emissions to urban PM_2.5 concentrations across 143 cities (122 NCAP and 67 million plus -cities with population >1 million, among which 46 cities are also NCAP cities). We find that, on average, ∼85% (STILT-PM_2.5: 82% [95% CI: 80–85%; IQR: 77%–94%] & WRF-Chem: 89% [95% CI: 87%–91%; IQR: 88%–96%]) of urban PM_2.5 across the 143 cities originates from transboundary emissions, with domestic biomass burning (32%), energy generation (16%) and industry (15%) being the leading average emission sources to the transboundary contribution. In addition, 107 of the 122 NCAP cities from both modeling approaches have annual transboundary PM_2.5 contributions exceeding 80%, indicating that an entire mitigation of within-boundary emissions alone in these cities will not achieve the most conservative targets outlined as part of NCAP. Our findings underscore the need for multi-scale, regional action planning and implementation to achieve PM_2.5-air quality targets throughout India.

Severe ozone pollution persists during summertime in 60 cities in the North China Plain and Fenwei Plain, which requires a fundamental change in the current mitigation strategy. Herein, we investigate how city-level ozone pollution would be affected by self- and collaborative mitigation actions among and beyond the 60 cities, by a modeling analysis of the daily maximum 8 h average ozone for summer (June–July–August). We find that a local uniform 20% cut in anthropogenic emissions would decrease ozone by a mere 2.6% and even worsen ozone over two cities. Due to cross-city ozone transport, the implementation of a range of city-specific emission cuts from 10% to 30% resulted in ozone changes that were essentially the same as those obtained from a uniform 20% cut. By contrast, a 20% emission cut across the entire country would decrease ozone in the 60 cities by 4.5% with no ozone deterioration in any city. Furthermore, owing to the transitioned ozone chemical regime and extended ozone chemical lifetime, the transboundary ozone from outside the two plains would be enhanced by emission reductions in the 60 cities (e.g. an increase by 68% with complete removal of emissions), leading to a significant suppression (about 23%) on the expected benefit. Nationwide collaborative action is essential for more effective city-level ozone mitigation.

Microfibers (MFs) are released into the environment during the entire life of textile materials, from manufacturing to disposal. It is evident that micro-sized wastes are just as significant as macro-sized ones, and this issue should be prioritized. The use of textile waste in sound insulation materials is increasingly gaining attention. However, conventional sound absorption materials, such as fiberglass, polyurethane, and melamine foams, offer high-performance acoustic properties, but are derived from non-renewable resources and contribute to environmental degradation. This study aims to develop environmentally friendly fibrous sound absorption panels by reusing MF waste generated during textile finishing processes. Waste MFs used within the scope of the study were collected from a textile finishing process' wastewater containing a variety of fibers, including wool, cotton, acrylic, polyamide, polyester, polypropylene, and viscose by filtration method and blended with polyester fiber as a binder. Then, acoustic panels were produced using a hot press technique by varying the panel thickness, density, and binder fiber ratio, and the physical, chemical, morphological, and acoustic properties of these panels were tested. Findings revealed that thickness emerged as a critical factor, with the thickest samples exhibiting the highest sound absorption coefficient (0.9 at 3000 Hz). Moreover, an increase in sample density correlated positively with enhanced sound absorption values, while the binder fiber ratio demonstrated a negative impact. Additionally, all samples exhibited hydrophobic characteristics, showcasing water resistance. The statistical analysis of sound absorption performance was conducted using one-way ANOVA and Tukey's honestly significant difference test, with the results visualized through boxplots. Compared to conventional materials, the developed MF-based panels provide an eco-friendly alternative by reducing reliance on virgin synthetic materials while achieving competitive sound absorption properties. This study enables sustainable waste management in the textile industry and the reuse of MF waste, providing alternative and environmentally friendly solutions to currently used sound absorption materials. While the recyclability and reuse potential of these panels remain promising, further research is needed to evaluate their long-term mechanical performance, resistance to environmental degradation, and practical implementation in real-world applications. Future investigations should focus on optimizing large-scale production processes and assessing the environmental footprint of these materials throughout their lifecycle.

Despite growing evidence of health risks posed by animal feeding operations (AFOs) including concentrated AFOs (CAFOs), few studies have explored the associated disproportionate health burdens. We investigated risk of cause-specific mortality associated with AFO/CAFOs and related disparities for North Carolina, Pennsylvania, and Virginia (2000–2020). We estimated associations between AFO/CAFO exposure and mortality (anemia, asthma, COPD, diabetes mellitus, cerebrovascular disease, and kidney disease) using logistic regression. For each participant, we applied two exposure metrics based on buffers around population-weighted ZIP-code centroids: (1) binary exposure based on presence or absence of AFOs/CAFOs, and (2) exposure intensity (no exposure, low, medium, and high). We investigated health disparities by individual-level (sex, race/ethnicity, age, education, marital status) and community-level (race, income, poverty, education, racial isolation, educational isolation) characteristics. Presence of AFO/CAFOs was associated with higher risks of cause-specific mortality, particularly for diabetes mellitus or cerebrovascular disease, across all states. People in ZIP codes within ⩽10 km of AFO/CAFO were 1.028 (95% Confidence Interval 1.014, 1.042), 1.039 (1.025, 1.053), and 1.053 (1.031, 1.075) times more likely to die from cerebrovascular disease compared to those in ZIP codes without AFO/CAFO exposure for North Carolina, Pennsylvania, and Virginia, respectively. We found disproportionate health burden associated with AFO/CAFO exposure in some subpopulations, however results varied by state. Our findings provide evidence of higher mortality risk with high AFO/CAFO exposure, with some populations facing disproportionate health burden, although such relationships differed by location.

Decarbonization of global energy supply requires among others the development and deployment of unconventional energy technologies, which can overcome certain barriers to the deployment of conventional technologies. In addition, it is necessary to clarify the amount of energy that can be potentially produced from these novel technologies. This study presents a global assessment of the energy potential of an unconventional wind energy technology called airborne wind energy system (AWES) for onshore applications. This technology has a considerably small material footprint and visual impact compared to the conventional wind turbines. The target technology is a system currently available in the market that generates electricity based on a soft kite connected by a tether to a generator. It was found that globally, this technology can theoretically deliver 38.5 PWh yr⁻¹. After considering topographic and land suitability restrictions the energy potential decreases to 12.5 PWh yr⁻¹ (equivalent to around half of 2022 electricity consumption). The high-grade energy potential (annual average capacity factor >32%) constituted around three quarters of the global total. Further analyses will clarify the effect of uncertainties involved in the assessment (such as the height of operation and the land suitability constraints, among others), and the conditions under which AWES outperform conventional wind turbines. Also, the assessment can be extended to offshore applications and to include the economic evaluation of the energy potential. This study is a first attempt to assess the global potential of an unconventional wind energy technology which can be considered in the analysis of future decarbonization scenarios.

This paper presents a comprehensive survey of the process-based models currently available for blue-green infrastructure for the assessment of cooling potential, stormwater and pollution control, carbon sequestration, and water provision. The assessment of the modelling tools for blue-green elements (BGEs) documents that currently there is no process-based model for the simultaneous evaluation and optimisation of multiple ecosystem services of BGEs. To evaluate coupling options, this study conducted a meta-analysis on model interoperability by assessing the model scales, drivers, overlaps, gaps, and interfaces of these models for BGEs. Model meta-analysis points out the conceptual and constructual constraints preventing easy model coupling, and thus, an integrated assessment of ecosystem services. Constraints arise due to very different disciplinary approaches from different scientific communities involved in model development, differences in the simulation of transformation and transport processes at urban interfaces relevant for BGEs, and fundamental divergences in spatial and temporal scales and time steps of existing models for single ecosystem services. In particular, the lack of vegetation models tailored for BGEs hinders current model developments towards developing a process-based tool for multiple ecosystem services, which would be able to handle nonstationary climate conditions, including feedback assessments of drought and heatwave impacts on the functioning of BGEs.

A quarter of the deforested Amazon has regrown as secondary tropical forest and yet the climatic importance of these complex regenerating landscapes is only beginning to be recognised. Advances in satellite remote-sensing have transformed our ability to detect and map changes in forest cover, while detailed ground-based measurements from permanent monitoring plots and eddy-covariance flux towers are providing new insights into the role of secondary forests in the climate system. This review summarises how progress in data availability on Amazonian secondary forests has led to better understanding of their influence on global, regional and local climate through carbon and non-carbon climate benefits. We discuss the climate implications of secondary forest disturbance and the progress in representing forest regrowth in climate models. Much remains to be learned about how secondary forests function and interact with climate, how these processes change with forest age, and the resilience of secondary forest ecosystems faced with increasing anthropogenic disturbance. Secondary forests face numerous threats: half of secondary forests in the Brazilian legal Amazon were 11 years old or younger in 2023. On average, 1%–2% of Amazon secondary forests burn each year, threatening the permanence of sequestered carbon. The forests that burn are predominantly young (in 2023, 55% of burned secondary forests were <6 years old, <4% were over 30 years old). In the context of legally binding international climate treaties and a rapidly changing political backdrop, we discuss the opportunities and challenges of encouraging tropical forest restoration to mitigate anthropogenic climate change. Amazon secondary forests could make a valuable contribution to Brazil's Nationally Determined Contribution provided there are robust systems in place to ensure permanence. We consider how to improve communication between scientists and decision-makers and identify pressing areas of future research.

Extreme heat is an important public health concern, and heat stress exposure and related adaptive capacity are not equally distributed across social groups. We conducted a systematic review to answer the question: What is the effect of social disadvantage on exposure to subjective and objective heat stress and related adaptive capacity to prevent or reduce exposure to heat stress in the general population? We systematically searched for peer-reviewed journal articles that assessed differences in heat stress exposure and related adaptive capacity by social factors that were published between 2005 and 2024. One author screened all records and extracted data; a second author screened and extracted 10% for validation. Synthesis included the identification and description of specific social groups unequally exposed to heat stress and with lower adaptive capacity. We assessed European studies for the potential risk of bias in their assessment. We identified 123 relevant publications. Subjective heat stress appeared in 18.7% of articles, objective heat stress in 54.5%, and adaptive capacity in 54.5%. Nearly half came from North America (47.2%), 22.8% from Asia, and 17.1% from Europe. Publishing increased from zero articles in 2005 to 21 in 2023. Most studies considered socioeconomic status (SES) (78.8%), and many considered age (50.4%), race/ethnicity (42.3%), and sex/gender (30.1%). The identified studies show that lower-SES populations, young people, immigrants, unemployed people, those working in outdoor and manual occupations, and racial/ethnic minorities are generally more exposed to heat stress and have lower adaptive capacity. Most studies of objective heat stress use inadequate measures which are not representative of experienced temperatures. European studies generally have a low or moderate risk of bias in their assessments. Social inequalities in heat stress exposure and related adaptive capacity have been documented globally. In general, socially disadvantaged populations are more exposed to heat stress and have lower adaptive capacity. These social inequalities are context-dependent, dynamic, multi-dimensional, and intersectional. It is essential to consider social inequalities during heat-health action planning and when developing and implementing climate change adaptation policies and interventions.

Approaches to defining a heat wave vary globally. While they are mostly meteorology-centric, there is an increasing need to consider their health implications. Our methodology involved a review of biometeorological indices, followed by a systematic policy search of country-level heat wave definitions to explore the variability of heat protection mechanisms. We analyzed the regional coverage of heat wave definitions and warnings by examining the diversity of variables and threshold limits for 112 countries/territories. We identified the upper-most heat stress limits of biometeorological indices that trigger illness or death. The findings highlight that a large proportion of countries define heat waves based solely on maximum temperature, while only a few countries combine them with minimum temperature and/or humidity. We also find significant geographical variability in the incorporation of temperature limits with most countries in northern latitudes defining heat waves at lower thresholds. We highlight the need for policy reforms towards adjustment of heat warning thresholds to regionally appropriate levels considering rising extreme heat conditions. Given the predominance of maximum temperature-centric approaches, we argue that the focus of heat protection at the policy level must shift beyond projecting heat wave episodes and consider broader heat-health associations beyond mortality.

The increasing impact of global climate change on hydrogeological and hydrological systems presents substantial challenges to the sustainable management of groundwater quality (GWQ). Changes in precipitation regimes, temperature fluctuations, and the frequency of extreme hydro-climatic events driven by climate change accelerate the deterioration of GWQ, thereby threatening ecosystems and human health. In response to these challenges, recent research has increasingly focused on developing and refining analytical models (AM) and machine learning (ML) techniques to understand better and predict the impacts of climate change on GWQ. This systematic literature review critically examines the current state of knowledge on applying AM and ML models in the context of GWQ assessment under climate-induced stressors. By synthesizing findings from a comprehensive review of existing studies, this paper discusses the capabilities, limitations, and future directions of hybrid ML and traditional AM in GWQ prediction, vulnerability, and threshold estimation. The review reveals that while ML approaches significantly enhance predictive accuracy and model robustness, there remain substantial challenges in their application due to the complexity of climate-induced variables and the scarcity of high-resolution data. This paper aims to provide GWQ researchers, water resource managers, and policymakers with an advanced understanding of the interactions between climate change and GWQ and the innovative AM and ML modelling approaches available to address these challenges. By highlighting the potential and limitations of current models, this review offers insights into developing more effective and adaptive management strategies for safeguarding GWQ in an era of rapid climatic change.

Nature-based solutions (NbS), such as green stormwater infrastructure (GSI) have numerous co-benefits alongside stormwater management, including reduced environmental contamination, improved urban aesthetics, and enhanced community health. However, the implementation of GSI often does not benefit marginalized communities and can exacerbate existing inequities through green gentrification. Here, we propose the utilization of a community-based participatory research (CBPR) approach, which highlights procedural equity issues in communities that have historically been excluded from environmental decision-making and benefits through current stormwater management practices. The study outlines the methodological procedure for building strong relationships with communities through partnerships with well-established local non-profit organizations, working to improve the accessibility of research efforts for participants. Through increased accessibility, we gather detailed accounts of community perceptions of GSI among underserved populations in Northeast Houston and Baytown, Texas. Our results show that community members are broadly in favor of implementing GSI and see it as a means to alleviating the effects of environmental degradation. However, there remains distrust of governmental, developmental, and industrial organizations from past experiences during major flooding events. These insights support the need to actively incorporate place-based expertise and insights throughout the process of stormwater management.

Integrated Assessment Models (IAMs) have become indispensable tools for exploring strategies to mitigate climate change while achieving broader social and environmental goals. However, most modelled pathways assume continued economic growth throughout the century, even for high-income nations. This has sparked calls for modellers to expand their visions of sustainable futures. One suggested approach is post-growth, which shifts the focus of the economy from economic growth to ecological stability, equality, human well-being and enhanced democracy. In this review, we examine current post-growth scenario modelling approaches, spanning national to global scales and single-sector to whole-economy approaches, to identify best practices and key gaps in representing a post-growth transition. We develop a framework for evaluating these scenarios along five key dimensions of post-growth theorisation: feasible technological change, scale-down of harmful production, good life for all, wealth redistribution, and international justice. We then explore current approaches to post-growth scenario modelling, focusing on the types of models used, the mechanisms employed to simulate post-growth scenarios and the representation of post-growth policies. Finally, drawing on the wider post-growth literature, we offer recommendations for improving post-growth model representation, focusing on five main areas: the energy-economy connection, spatial differentiation, sectoral differentiation, the inclusion of different provisioning systems and feasibility considerations.

The net effect of warmer temperatures for cold-climate agriculture remains unknown: cold temperatures disrupt production, but warmer temperatures can also bring yield-reducing extreme heat. We introduce an approach to measure cold-temperature exposure using sinusoidal degree days, an exact analog to the approach widely used to measure heat exposure. Applying this approach to model the yields of six Canadian crops, we find yield penalties to cold-temperature exposure mirror those from extreme heat. While average yields under low- and high-emission scenarios increase significantly when warmer days mitigate cold damage, these gains are too small to offset additional extreme-heat damage, and the net effect remains broadly negative. For barley, canola, oats and wheat, damages are severely negative (-28.4\% to -57.8\% loss). For {maize and soybean}, outcomes range from no distinguishable change to moderate losses (-5.2\%). Additionally, yield risk increases, with higher coefficients of variation and low-yield probabilities across all scenarios.

We analyze changes in the maximum annual transient snowline elevation (M_ATSL) of glaciers for two regions in western North America from 1984 to 2024 using five satellite remote sensing datasets. M_ATSL reached its highest elevations in 2019 (155 m above the long-term average) for Alaska (Region 1) and in 2023 (148 m) for Western Canada and USA (Region 2). The rate of M_ATSL rise accelerated fourfold, increasing from 2.1 ± 0.8 m a^-1 in 1984–2010 (r² = 0.1, p < 0.01) to 8.9 ± 1.7 m a^-1 in 2010–2024 (r² = 0.5, p < 0.01). In 2019, 91 glaciers exceeded the 95^th percentile M_ATSL elevation, a threshold indicative of complete loss of the accumulation area, in Region 1. In Region 2, 149 glaciers exceeded this threshold in 2023. Year-to-year variability in M_ATSL was strongly influenced by mean summer air temperature (MSAT) with sensitivities of +46 m °C^-1 (r² = 0.54) and +23 m °C^-1 (r² = 0.30) for Regions 1 and 2, respectively. Mean Spring Snow Water Equivalent (MSSWE) also played an important role with sensitivities of -351 m m.w.e.^-1 (r² = 0.48) and -155 m m.w.e.^-1 (r² = 0.37), respectively. Per-glacier analysis revealed that south-facing slopes experienced the largest MATSL increases. Terrain attributes, including slope, aspect, hypsometry, and elevation, enhanced M_ATSL prediction models compared to those using only climate vari-ables. The pronounced rise in M_ATSL underscores a critical glacier melt feedback mechanism, warranting further investigation. This study highlights the utility of automated M_ATSL-time-series mapping for regional-scale anal-yses and identifies key limitations and opportunities for future research.

The large-scale exploitation of offshore renewable energy in floating platforms will increase the use of synthetic mooring cables to secure them to the sea-bottom, because of the need to employ low-cost and lightweight materials to ensure economic viability. The degradation of these cables will release microplastic particles to the ocean, causing environmental impacts that have so far received little attention. Here, we try to raise awareness to this potential problem, by explaining the fundamental differences between offshore renewable energy structures and traditional ones, such as oil platforms, in what concerns their economics and layout at sea, listing the most relevant materials for mooring cables, and discussing potential problems and solutions. These impacts have not yet materialised because offshore renewable energy technology is only now reaching commercial viability, but are likely to become an issue in the future.&#xD;