Climate Change Models Research Articles

Sustainable watersheds management in Peru: Challenges and perspectives

Sustainable watershed management stands at the forefront of global efforts to tackle pressing environmental and socio-economic challenges. This study offers a pioneering assessment of the state of research on Peru's watersheds, integrating geographical, environmental, and socio-economic dimensions. By harnessing cutting-edge bibliometric tools, including Bibliometrix and VOSviewer, and adhering to PRISMA guidelines for a systematic review, this work maps the scientific landscape of watershed management in Peru. Utilizing comprehensive databases such as Scopus and Web of Science, the study identifies and analyses key thematic clusters, revealing an escalating focus on the urgent issues of climate change and urbanization, particularly within the critical Rimac River Basin. This basin, vital to the water supply of Lima, the world's second-largest desert city, faces severe threats from pollution and climate instability. Our findings not only chart the evolution of research in this domain but also spotlight emerging opportunities to modernize water management practices through the integration of climate change models, advanced data monitoring, and artificial intelligence. The study makes a compelling case for a collaborative approach, urging stronger alliances among local communities, research institutions, and international stakeholders to foster more resilient and sustainable watershed management strategies in Peru. This paper provides actionable insights, making it an indispensable resource for policymakers and practitioners aiming to secure Peru's water future.

Climate Change Impact on Photovoltaic Production From the Perspective of Climate Models: a Systematic Review

Objective: This study investigates how the researchers approached the theme the climate change impacts on photovoltaic production (PVP) from the perspective of simulations using climate models. Methodology: A comprehensive literature review was conducted to establish correlations between future PV production and climate change susceptibility on regional and global scales. Keywords related to the theme were used in the Web of Science database. Inclusion criteria selected only complete, free, English scientific articles related to the research theme, published between 1965 and 2023. Results and analysis: As a result, it generated a sample consisted of 58 articles on the impact of climate change on photovoltaic production, with 14 included in the final quantitative synthesis. A wide range of results was presented, stemming from both climate change models and models assessing the influence of weather-related factors on PV production. Patterns in the impacts of climate change on photovoltaic production were observed, and the main climate models used to estimate PVP were recognized, grouped, and discussed. Key databases, commonly used simulation periods, and equations linking meteorological variables to PVP were identified and examined. Conclusion: Numerous publications have emerged on the impacts of climate change on PVP, focusing on empirical equations that relate meteorological variables, cell temperature, and cell efficiency. Most studies adopted a forecast period until the end of the 21st century. The majority of the studies focused on Europe and Asia, with little research on impacts of climate changes on PVP in South and Central America.

The Germination Performance After Dormancy Breaking of Leucaena diversifolia (Schltdl.) Benth. Seeds in a Thermal Gradient and Its Distribution Under Climate Change Scenarios.

Climate change models predict temperature increases, which may affect germination, an important stage in the recruitment of individuals in agroecosystems. Therefore, it is crucial to conduct research on how temperature will impact the germination of multipurpose native species. Leucaena diversifolia (Schltdl.) Benth. is native to America and is commonly cultivated around the world due to having a high protein content in seeds, and their trees are used in agrosilvopastoral systems because they fix nitrogen and provide shade and cattle feed. However, climate change affects the critical phases of its life cycle and influences its growth, reproduction, phenology, and distribution. To assess the germination performance of Leucaena diversifolia under different temperatures throughout thermal times, we estimated germination variables and determined cardinal temperatures and thermal time; we also analysed germination and potential distribution under two climate change scenarios. We found significant variations in seed germination (78-98%) and differences in cardinal temperatures (Tb = 5.17 and 7.6 °C, To = 29.42 and 29.54 °C, and Tc = 39.45 and 39.76 °C). On the other hand, the sub-optimal and supra-optimal temperature values showed little differences: 51.34 and 55.57 °Cd. The models used showed variations in germination time for the analysed scenarios and the potential distribution. We confirm that the populations and distribution of L. diversifolia will be altered due to climate changes, but the species retains the ability to germinate under warmer conditions.

Physically-based modelling of UK river flows under climate change

This study presents the model setup and results from the first calibrated, physically-based, spatially-distributed hydrological modelling of combined land cover and climate change impacts on a large sample of UK river catchments. The SHETRAN hydrological model was automatically calibrated for 698 UK catchments then driven by the 12 regional climate model projections from UKCP18, combined with urban development and natural flood management scenarios. The automatic calibration of SHETRAN produces a median Nash-Sutcliffe efficiency value of 0.82 with 581 catchments having a value greater than 0.7. 24 summary metrics were calculated to capture changes to important aspects of the flow regime. The UKCP18 realisations in SHETRAN indicate that a warming climate will cause river flows, on average, to decrease. These decreases are simulated to be greatest in the south and east of the UK, with droughts becoming longer and more severe. While high flows also decrease on average, an increased number of extremes are exhibited, implying a greater number of extreme flood events in the future, particularly in the north and west of the UK. In the urban development scenarios, for flood events there is an increase in flow with the increased urbanization, with the 1 in 3-year peak flow event showing the greatest increase. The natural flood management scenarios consider the effect of increasing woodland and adding surface water storage ponds. The inclusion of these features produces a complex response but overall, the modelling shows a reduction in low, median, and high flows, although the more extreme the flow event the smaller the percentage change in flow. Simulated timeseries and summary metric datasets are freely available on the CEDA archive.

Changing Climate Change Mental Models Through Game-Based Learning: A Controlled Experiment Involving Cognitive Mapping

Climate change is one of humankind's grand challenges, and how we think about it influences how we perceive its risks and act. Understanding people's mental models of climate change (that is, how they internally represent it) and potentially challenging and changing them can be a fruitful avenue towards engagement. However, our understanding of how games can affect climate change mental models is limited, especially in the lack of control conditions with comparable content. In this experiment, participants created cognitive maps of climate change before and after experiencing an immersive VR game, a desktop PC game, or a text with graphs. We show that all conditions improved the quality, focus, and systems orientation of cognitive maps, but no significant differences were found between groups. The intervention effectively increased the salience of issues such as health and biodiversity impacts, even though changes were limited in scope. The game was on average more enjoyable than the text, with the VR version significantly more so than the other two conditions. The findings suggest that games can be a viable and attractive communication and learning method where engagement is a struggle. We provide avenues for future research and, especially, game design in this area.

How Climate Change Will Shape Pesticide Application in Quebec’s Golf Courses: Insights with Deep Learning Based on Assessing CMIP5 and CMIP6

The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec’s golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%.

Scenario analysis of supply‐ and demand‐side solutions for circular economy and climate change mitigation in the global building sector

AbstractResidential and non‐residential buildings are a major contributor to human well‐being. At the same time, buildings cause 30% of final energy use, 18% of greenhouse gas emissions (GHGE), and about 65% of material accumulation globally. With electrification and higher energy efficiency of buildings, material‐related emissions gain relevance. The circular economy (CE) strategies, narrow, slow, and close, together with wooden buildings, can reduce material‐related emissions. We provide a comprehensive set of building stock transformation scenarios for 10 world regions until 2060, using the resource efficiency climate change model of the stock–flow–service nexus and including the full CE spectrum plus wood‐intensive buildings. The 2020–2050 global cumulative new construction ranges from 150 to 280 billion m2 for residential and 70‐120 billion m2 for non‐residential buildings. Ambitious CE reduces cumulative 2020–2050 primary material demand from 80 to 30 gigatons (Gt) for cement and from 35 to 15 Gt for steel. Lowering floor space demand by 1 m2 per capita leads to global savings of 800‐2500 megatons (Mt) of cement, 300‐1000 Mt of steel, and 3‐10 Gt CO2‐eq, depending on industry decarbonization and CE roll‐out. Each additional Mt of structural timber leads to savings of 0.4‐0.55 Mt of cement, 0.6‐0.85 Mt of steel, and 0.8‐1.8 Mt CO2‐eq of system‐wide GHGE. CE reduces 2020–2050 cumulative GHGE by up to 44%, where the highest contribution comes from the narrow CE strategies, that is, lower floorspace and lightweight buildings. Very low carbon emission trajectories are possible only when combining supply‐ and demand‐side strategies. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Spatial Optimization of Land Use and Carbon Storage Prediction in Urban Agglomerations under Climate Change: Different Scenarios and Multiscale Perspectives of CMIP6

Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Effects of climate change and species distribution modeling of Loxosceles (Araneae: Sicariidae) from Central Mexico

With 40 of the 149 described species, Mexico harbors the highest diversity of the spider genus Loxosceles. However,knowledge about these spiders’ distribution patterns in a climate change (CC) context is poorly known. In this study,the distributions of 4 species from Central Mexico, Loxosceles malintzi, L. misteca, L. tenochtitlan and L. zapoteca, were estimated and evaluated based on species distribution modeling (SDM) and the possible effects of CC. Two future scenarios were simulated (years 2050 and 2080) to show possible increases or reductions in species distributions. The most important variables that influence the distribution of the species were: isothermality, seasonality of temperature, and precipitation. In the CC scenarios, some species showed a possible increase, specifically, Loxosceles malintzi with an increase in its distribution of 79% by 2050 and 66% by 2080, whereas L. misteca was projected to increase its distribution by 28% for 2050 and 38% for 2080. However, a decrease in the distribution of L. tenochtitlan by 51% for 2050 and 38% for 2080 was projected, as well as a 45% decrease by 2050 and a 40% decrease by 2080 for L. zapoteca.

A Fuzzy Logic Framework for Modeling Climate Change Impacts on Ecosystems

Climate change poses significant challenges to ecosystems, necessitating robust models to predict and manage its impacts. This paper presents a novel fuzzy logic framework designed to model the complex and uncertain interactions between climate change variables and ecosystem responses. The proposed framework leverages fuzzy logic's ability to handle imprecise and ambiguous data, providing a more nuanced understanding of how temperature fluctuations, precipitation changes, and extreme weather events affect biodiversity, species distribution, and ecosystem services. By integrating ecological knowledge with fuzzy inference systems, the model offers a flexible tool for simulating various climate scenarios and their potential effects on ecosystems. Case studies demonstrate the framework's applicability across different ecosystems, highlighting its potential to inform conservation strategies and policy- making. This work contributes to the growing body of research on climate change modeling, offering a powerful approach to anticipating and mitigating the adverse effects of environmental changes on natural habitats.

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.

Warming decreased plant litter decomposition by modulating soil fauna interactions in a Tibetan alpine meadow

Litter decomposition is a vital process for maintaining ecosystem carbon cycling. It is affected by soil fauna which are predators and decomposers of litter. However, how the interactions of soil fauna communities affect litter decomposition remains unclear under warming. Here, we conducted a five-year in-situ manipulative warming experiment by Open-Top Chamber (OTC) in an alpine meadow on the Tibetan Plateau to reveal how warming affects litter decomposition. The results demonstrated that warming decreased the litter decomposition rate by 29 %, the soil collembola abundance by 25 %, and the nematode abundance by 27 %. Nematode ecological indices remain stable but a shift in the decomposition of litter to the fungivores pathway under warming. The piecewise structural equation modelling result revealed that the combined reduction in soil collembola and nematodes synergistically leads to a massive decline in litter decomposition rate under warming. Our results highlight that the interactions of soil fauna can regulate litter decomposition under warming, and collembola abundance as the “speed-limiter” of litter decomposition. Therefore, the response of changes in soil fauna relationships to warming should be completely considered in future climate change modelling of the grassland carbon cycle.

Flooding and dam operations facilitate rapid upstream migrations of native and invasive fish species on a regulated large river

Dams commonly restrict fish movements in large rivers but can also help curtail the spread of invasive species, such as invasive bigheaded carps (Hypophthalmichthys spp). To determine how dams in the upper Mississippi River (UMR) affect large-scale invasive and native fish migrations, we tracked American paddlefish (Polyodon spathula) and bigheaded carp across > 600 river km (rkm) and 16 navigation locks and dams (LD) of the UMR during 2 years with contrasting water levels. In 2022, a low-water year, both native paddlefish and invasive bigheaded carp had low passage rates (4% and 0.6% respectively) through LD15, a movement bottleneck being studied for invasive carp control. In contrast, flooding in 2023 led to open-river conditions across multiple dams simultaneously, allowing 53% of paddlefish and 46% of bigheaded carp detected in Pool 16 to move upstream through LD15. Bigheaded carp passed upstream through LD15 rapidly (μ = 32 rkm per day) a maximum of 381 rkm, whereas paddlefish moved an average of 9 upstream rkm per day (maximum of 337 rkm). Our results can inform managers examining trade-offs between actions that enhance native fish passage or deter movements of invasive species. This understanding is critical because current climate change models project increases in flooding events like that observed during 2023.

Analysis of Agricultural Water Security Based on Network Invulnerability: A Case Study in China's Virtual Water Trade Networks

Abstract“Invulnerability” of complex network was firstly introduced to virtual water (VW) research, aiming to broaden the scope of studies on water use and management. Beginning with the construction of China's virtual water trade networks (VWTNs) of major grain crops, Node Degree (K) and Betweenness Centrality (B) are employed to evaluate and rank the importance of China's 31 regions. Regions with high values for both indicators are identified as playing pivotal roles in the VWTNs: Jiangsu (ranking 1st for both K and B), Hubei (2nd for K, 3rd for B), Henan (3rd for K, 6th for B), Hebei (4th for K, 4th for B), Hunan (4th for K, 5th for B). Using this ranking to simulate the invulnerability of VWTNs under random and intentional attacks. The results reveal a rapid decrease in both Network Efficiency (E) and Maximum Connectivity (C) under intentional attack. In comparison to seven random attacks, E falls below 0.1 and C drops below 0.5 after only three intentional attacks, and the network completely collapsed after 10 intentional attacks. This highlights the VWTN's vulnerability in maintaining food supply and agricultural water security when key regions are subjected to man‐made destruction, such as military blockades or occupations. Future work should include integrating climate change models, crops yield models, and water resource allocation models to protect the key areas. Furthermore, interdisciplinary approaches are crucial for overcoming the limitations of VW research and these findings will provide valuable insights to enhance the optimal regulation of VWTNs.

Climate Change Prediction Model using MCDM Technique based on Neutrosophic Soft Functions with Aggregate Operators

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such as neural networks and fuzzy systems, this research demonstrates significant improvements in forecast precision, particularly in scenarios with limited or noisy data. Additionally, the paper discusses the integration of NSFs with advanced machine learning algorithms for climate pattern recognition and anomaly detection, enabling timely identification of climate change indicators and facilitating proactive measures for adaptation and mitigation. Through empirical validation using real-world climate datasets, this study underscores the efficacy of NSFs in enhancing the predictive capabilities of climate change models, thereby contributing to more informed decision-making in climate-related policies and strategies.

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

The role of climate change and corrosion modeling strategy in dynamic response of pile-supported wharves

This paper explores the fragility of pile-supported wharves to environmental hazards, notably climate change and corrosion, and underscores the critical need to understand the interplay between these factors when assessing structural safety. The research advocates for comprehensive methodologies that encompass climate change effects, aging, and time-dependent deterioration in evaluating the seismic fragility functions of pile-supported wharves. An examination of aging and seismic effects is performed on a representative pile-supported wharf at designated time intervals. This study highlights the pronounced impacts of climate change and corrosion on the structural integrity of concrete and steel in marine environments. Specifically, it considers effects such as sea level rise, increased temperatures, and heightened relative humidity on pile-supported wharves. Additionally, three corrosion pitting configurations in prestressed strands with and without climate change considerations are analyzed to determine their influence on the strength and ductility of materials, limit states, and ultimately, on the fragility curves. The findings indicate that climate change significantly exacerbates the corrosion of materials in pile-supported wharves, and increases failure probability. The relative increase in corrosion rate after 50 years due to climate change is found to be 94%.

Mapping Ocean Gateways from 3D Seismic Data Using Color Processing and Analogues from Seismic Data

Mapping sedimentary features to delineate ancient ocean gateways from 3D seismic data has long been a challenge due to their limited vertical resolution. We present the technique of color processing of seismic data which improves the vertical resolution to very few seismic samples corresponding to 5–10 m. The result is a representation of the seismic impedance contrast correlation in Red-Green-Blue (RGB) colors, which creates images similar to satellite images that assist the geological interpretation because the RGB colors can be associated with mud-rich and sand-rich sediments. Using this approach, we have extracted six different sedimentary bedforms from different bottom flow settings and correlated these with the bedform-velocity matrix by Stow et al. (2009). Then we studied bedform assemblies in their regional context for the area covered by the 3D seismic data set. We conclude with comparing a modern and an ancient analogue – both from 3D seismic data – and infer paleo flow direction and velocity using oceanographic measurements available for the modern analogue. The method provides insights into ocean bottom currents and their impact on the formation of contourite depositional systems in modern and ancient ocean gateways which contribute to the reconstruction of the paleoclimate as an input to present day climate change models.

Can today’s and tomorrow’s world uniformly gain from carbon taxation?

Climate change will impact current and future generations in different regions very differently. This paper develops the first large-scale, annually calibrated, multi-region, overlapping generations model of climate change and carbon policy. It features region-specific temperature and damage functions with the phased impact of emissions on global and regional temperature calibrated to the latest scientific evidence. Absent policy, climate change may, under high-damage scenarios, dramatically reduce GDP in most regions, with India, Brazil, and the South Asian Pacific suffering long-term catastrophic damages. Carbon taxation, coupled with region- and generation-specific transfers, can both correct the carbon externality and raise the welfare of all current and future agents across all regions by 4.3 percent. The impact on the use and duration of fossil fuels is dramatic as is the reduction in the path of global emissions. However, achieving completely uniform welfare gains leaves future generations in particular regions facing exceptionally high compensatory payments. Fortunately, a carbon tax-cum redistribution policy that limits this burden for any generation in any region to less than 10 percent, measured on a consumption-equivalent basis, can deliver a 4.0 percent or higher welfare gain for all peoplekind — present and future. However, carbon taxes set through time, at carbon’s marginal social cost, do far too little to mitigate climate change unless all major emitters, particularly China, adopt them and do so immediately.