Table of contents

Scaling up electric vehicles (EVs) provides an avenue to mitigate both carbon emissions and air pollution from road transport. The benefits of EV adoption for climate, air quality, and health have been widely documented. Yet, evidence on the distribution of these impacts has not been systematically reviewed, despite its central importance to ensure a just and equitable transition. Here, we perform a systematic review of recent EV studies that have examined the spatial distribution of the emissions, air pollution, and health impacts, as an important aspect of the equity implications. Using the Context-Interventions-Mechanisms-Outcome framework with a two-step search strategy, we narrowed down to 47 papers that met our inclusion criteria for detailed review and synthesis. We identified two key factors that have been found to influence spatial distributions. First, the cross-sectoral linkages may result in unintended impacts elsewhere. For instance, the generation of electricity to charge EVs, and the production of batteries and other materials to manufacture EVs could increase the emissions and pollution in locations other than where EVs are adopted. Second, since air pollution and health are local issues, additional location-specific factors may play a role in determining the spatial distribution, such as the wind transport of pollution, and the size and vulnerability of the exposed populations. Based on our synthesis of existing evidence, we highlight two important areas for further research: (1) fine-scale pollution and health impact assessment to better characterize exposure and health disparities across regions and population groups; and (2) a systematic representation of the EV value chain that captures the linkages between the transport, power and manufacturing sectors as well as the regionally-varying activities and impacts.

Climate change by its very nature epitomizes the necessity and usefulness of the global-to-local-to-global (GLG) paradigm. It is a global problem with the potential to affect local communities and ecosystems. Accumulation of local impacts and responses to climate change feeds back to regional and global systems creating feedback loops. Understanding these complex impacts and interactions is key to developing more resilient adaptation measures and designing more efficient mitigation policies. To this date, however, GLG interactions have not yet been an integrative part of the decision-support toolkit. The typical approach either traces the impacts of global action on the local level or estimates the implications of local policies at the global scale. The first approach misses cumulative feedback of local responses that can have regional, national or global impacts. In the second case, one undermines a global context of the local actions most likely misrepresenting the complexity of the local decision-making process. Potential interactions across scales are further complicated by the presence of cascading impacts, connected risks and tipping points. Capturing these dimensions is not always a straightforward task and often requires a departure from conventional modeling approaches. In this paper, we review the state-of-the-art approaches to modeling GLG interactions in the context of climate change. We further identify key limitations that drive the lack of GLG coupling cases and discuss what could be done to address these challenges.

The current drought plaguing the Southwest US (SWUS) underscores the need for long-term precipitation predictability to inform sustainable planning of future ecological and economic systems. Precipitation predictability requires understanding the teleconnections and intercorrelations of a suite of climate indices that are known to impact the SWUS. However, decision criteria about the selection of El Niño and southern oscillation (ENSO) and non-ENSO indices, definition of winter months, geographical extent, temporal scale, computation of what constitutes a long-term mean, and determination of the study period, have not been systematically examined, yet have important consequences on the appropriate characterization of SWUS winter precipitation predictability. Here, we used Pearson's correlations, Mann–Kendall tests, descriptive statistics, and principal component analyses to explore the statistical relationships between natural modes of climate variability and observed SWUS precipitation. We found no statistically significant persistent changes in the patterns of precipitation for a suite of SWUS geographical designations. Our results show that the choice of the temporal scale has an important impact on the determination of the strength of the climate signal. We show that ENSO indices were the primary determinants of SWUS precipitation, although inconsistencies persisted depending on the choice of ENSO index, the selection of SWUS geographical designation, and the chosen winter month combination. Non-ENSO indices in isolation were found inadequate to explain SWUS precipitation outcomes. Our analysis also indicates the predictability of SWUS precipitation must consider neutral ENSO events when non-ENSO modes are found to play an important role. We recommend the undertaking of a coordinated multi-decadal suite of numerical modeling experiments that systematically account for the individual and total impacts of this critical set of climate indices to improve understanding of past precipitation outcomes and by extension, improve predictability for a future for which tens of millions of people will require advanced planning.

As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to this literature, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects of power outages, how power outage impacts seasonally vary, and the different durations of power outage impacts.

To effectively reduce illegal timber trade, law enforcers need forensic methods to independently verify claims of wood origin. Multi-element analysis of traded plant material has the potential to be used to trace the origin of commodities, but for timber it has not been tested at relevant large scales. Here we put this method to the test, by evaluating its tracing accuracy for three economically important tropical timbers: Azobé and Tali in Central Africa (22 sites) and Red Meranti on Borneo (9 sites). Wood samples from 991 trees were measured using Inductively Coupled Plasma Mass Spectrometry and element concentrations were analysed to chemically group similar sites (clustering) and assess accuracy of tracing samples to their origin (Random Forest models). For all three timbers, we found distinct spatial differences in chemical composition. In Central Africa, tracing accuracy was 86%–98% for regional clusters of chemically similar sites, with accuracy depending on the tracing question. These clusters were 50–800 km apart and tracing accuracy was highest when combining the two timbers. Tracing accuracy of Red Meranti on Borneo was 88% at the site level. This high accuracy at a small scale may be related to the short distances at which differences in soil type occur on Borneo. A blind sample analysis of 46 African timber samples correctly identified the origin of 70%–72% of the samples, but failed to exclude 70% of the samples obtained from different species or outside the study area. Overall, these results illustrate a high potential for multi-element analysis to be developed into a timber tracing tool which can identify origin for multiple species and can do so at a within-country scale. To reach this potential, reference databases need to cover wider geographic areas and represent more timbers.

The annual minimum Antarctic sea ice extent (SIE) in February 2022 hits a record low in the satellite era, with less than 2 million square kilometres observed on 25 February 2022, contrasting with the slightly positive trend in the Antarctic SIE prior to 2014. However, the preceding Amundsen Sea Low (ASL) in austral spring 2021 was the deepest since 1950. According to a linear regression model, the very low ASL contributed about 60% to the record low SIE in 2022. This study further investigates the underlying mechanism. The investigation of the lagged impact of the ASL on Antarctic SIE is based on observational data and state-of-the-art simulations. We found that (a) the deepened ASL associated with strengthened southerly winds accelerates the sea ice export away from the western Antarctic continent in spring, leading to the expansion of coastal polynyas (open water areas); (b) through the positive ice-ocean albedo feedback, the lack of the sea ice off the coastline enhances solar heating in the upper ocean and further sea ice melting in summer can occur. Specifically, in spring 2021, the deepest ASL is accompanied by a large sea-ice area flux of about 17.6 × 10³ km² across 70° S over the Ross Sea in October and November, contributing to a significant increase in net surface radiation of 20–30 W m⁻² and upper ocean warming of about 0.5 K in summer. Therefore, the deepened ASL in spring 2021 plays a crucial role for the record low Antarctic SIE in February 2022. In addition, it is found that both the La Niña conditions and the strong stratospheric polar vortex contributed significantly to the very strong ASL in 2021. Currently, nearly 2/3 of Earth system models in the Coupled Model Intercomparison Project Phase 6 have difficulties capturing the relationship between the ASL and the Antarctic SIE.

Cooperative management of shared natural resources is one of the most urgent challenges the world is facing today. While there have been advances in understanding institutional design features that enable sustainable management, there are few field studies that provide theory-based insights into social psychological predictors of willingness to cooperate around shared resources. Here, we address this issue in the context of shared land management in pastoralist community conservancies in Kenya. In a large survey of individuals from different conservancies, we test a path model that links willingness to cooperate to how strongly respondents identified with their conservancy. This relationship is mediated by how efficient conservancies were perceived to be, and to what extent other people in the community were perceived to share cooperative norms. The results also point towards several parameters that may be conducive to developing stronger conservancy identification: transparency of purpose, motive alignment, sense of ownership, and demonstrating benefit. The findings provide insights into the social psychological processes that impact whether cooperative outcomes can be achieved in real-world shared resource settings, and offer practical implications for strengthening governance within pastoralist conservancies and related shared natural resource management contexts.

The meridional current in the southern Bay of Bengal (BOB) exhibits prominent intraseasonal variability (ISV), which exerts a critical influence on meridional mass and energy exchange. However, its relationship with the northward propagating monsoon intraseasonal oscillation (MISO), which is the predominant variability in the tropical Indian Ocean during summer, is not well understood. Using a one-year mooring deployed at 5.5 °N, 90 °E, a strong ISV of the meridional current is observed between 150 and 250 m during summer, exhibiting amplitudes exceeding 0.4 ms⁻¹ and periods of 30–70 d. Further analysis shows that the summer ISV is forced by a strong MISO event with the following dynamic processes. The MISO first drives the zonal wind stress at the equator, leading to the equatorial Kelvin wave and the reflected equatorial Rossby wave at 5 °N. Then, the MISO propagates northward and generates local wind stress curl-induced Ekman pumping near the mooring site, leading to the subsequent off-equatorial Rossby wave at 8 °N. As a result, the synergy of the remotely- and locally-forced Rossby wave causes strong subsurface ISV in southern BOB. This study provides a new insight into the dynamic relationship between the MISO and the meridional current in the southern BOB, which has important implications for regional climate research and prediction.

Atmospheric carbon dioxide (CO₂) concentrations have increased as a direct result of human activity and are at their highest level over the last 2 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO₂, the fate of anthropogenic CO₂ over time, and the likelihood of a trend in the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model with the novel ability to explicitly track carbon as it flows through the Earth system. We use key model parameters in a Monte Carlo analysis of 15 000 model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much anthropogenic carbon ends up in the atmosphere. Modeled airborne fraction was roughly 52%, consistent with observational studies. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960–2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. However, the percentage of atmospheric CO₂ derived from anthropogenic origins can be much higher because of Earth system feedbacks. We find it peaks at over 90% between 2010–2050. Moreover, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics of anthropogenic CO₂ in the Earth system.

Winter cold extreme events have been observed to frequently take place over North America mainly over its east side, which show significant interannual and decadal variability and cause huge economic losses in the United States. However, it is unclear what leads to the interannual-decadal variability of winter cold extremes over the eastern North America. In this study, we indicate that the decadal variability of winter cold extremes over the eastern North America, whose period is shortened in the recent decades, is mainly tied to Pacific decadal oscillation (PDO), whereas their interannual variability is mainly regulated by Victoria mode (VM). A positive PDO promotes cold extremes in the lower latitudes of the eastern North America mainly owing to the presence of positive Pacific North American (PNA⁺) patterns, whereas a positive VM is favorable for intense cold extremes in the higher latitudes of the eastern North America mainly due to the occurrence of negative North Pacific oscillation (NPO⁻) patterns. Thus, the positive VM and PDO combine to significantly contribute to the interannual-to-decadal variability of winter cold extremes over the eastern North America through changes in the winter NPO⁻ and PNA⁺ patterns due to the variations of meridional background potential vorticity gradient and basic zonal winds. These new findings can help us understand what are the origins of the interannual-decadal variability of winter cold extremes over the eastern North America.

Considering the anticipated rise in wet extremes due to climate change, effective management of flood risks in global agriculture necessitates an initial assessment of the impact of floods on crop production. Such estimation can inform the development of strategies to enhance the resilience of the global agricultural system against floods, particularly in the face of growing demand for food. To this end, a worldwide calculation of inundation areas' return periods was conducted using a global river and inundation model output. This information was then linked to a global historical yield map, allowing for the identification of flood-induced crop yield changes. The findings revealed that for return periods over ten years, global average yield losses were estimated to be 4% for soy, 3% for rice, 2% for wheat, and 1% for maize. These losses amounted to a total production loss of 5.5 billion United States dollars during the 1982–2016 period. This first global estimation of flood impacts on crop production contributes to the advancement of flood risk management in agriculture, although the limitations identified in this study need to be addressed in future research.

Rates and drivers of natural forest regeneration are areas of uncertainty for policy, forest management and climate change mitigation. In this study, the rate of deforestation and the rate and drivers of natural regeneration are described for 56 million hectares of village land in Tanzania, a country undergoing rapid deforestation. To determine the regeneration and deforestation rates, remote sensing (RS) data for 500 randomly selected points were reviewed for a 34 year period from 1987 to 2021 using Google Earth Engine. Over this period, regeneration, involving a transition from forest to non-forest and back to forest was detected on 4.8% of village land (95% CI: 3.1%–7.1%), while 0.8% of land transitioned from non-forest to forest (95% CI: 0.2%–2.04%). 22% of village land was deforested (95% CI: 18.6%–26.1%), equivalent to a mean annual net loss of 0.35 million hectares of forest. Using a combination of RS data, field plots and structured interviews, the land cover change trajectories of 180 regenerating plots, in 10 sampling clusters, were assessed to identify regeneration drivers and assess biomass and tree species accumulation rates. Agricultural fallows are the regeneration driver in 47% of plots (95% CI: 39.8%–54.8%). Other common regeneration drivers include abandonment of cultivated areas for reasons apart from fallowing, conservation and post wood-extraction abandonment in 19% (95% CI: 13.9%–26%), 18.3% (95% CI: 13%–24.8%) and 12.8% (95% CI: 8.3%–18.6%) of plots, respectively. The mean carbon sequestration rate was 1.4 Mg C ha⁻¹ y⁻¹, equivalent to 4.3 Tg C y⁻¹ (95% CI: 3.9–4.7 Tg C y⁻¹) across the 3.15 million hectares of regenerating village land forest. The mean species accumulation rate was 1.08 species y⁻¹ (95% CI: 1.0–1.2). Regeneration time, location and precipitation have the greatest influence on biomass and species richness. The study highlights the potential for natural regeneration to contribute to global and national climate and biodiversity goals and to sustainable, productive forest management. The importance of cooperation and policy-alignment between the forest, agriculture and land sectors are under-scored.

Household access to clean energy is a priority for public health and the environment in low- and middle-income countries. However, past illustrative studies have explored benefits of replacing all polluting energy sources, a transition that is only theoretically possible. Factors that limit achievement of the entire theoretical reduction potential should be explored to inform programmatic decision making. We propose a hierarchy of reduction potentials for emissions from household energy, representing different implementation barriers. Following similar work in renewable energy, we propose four categories of reduction potentials beyond the theoretical maximum: distributional, technical, economic, and market. We apply this framework to household energy emissions using a high-resolution spatiotemporal emission inventory of India, a country chosen for its data availability and level of interest in mitigation. We explore distributional potential using distance from urban areas, technical potential by attributing emissions to energy services, and economic potential with a village- level proxy for likelihood of program success. For distributional potential (spatial accessibility), we find that applying reduction programs within 5 km of urban centers would achieve 36%–78% of the theoretical potential across seven regions in India; extension to 10 km yields reductions of 63%–90%. Technical and economic reduction potentials differ most greatly from theoretical potential in regions that contribute the most to national emissions. Even if some of the relationships underlying emission causes are not completely known, reflecting the factors that affect transitions can inform practitioners and programs seeking to scale and deliver clean energy solutions. We assert that including these important influences should be a goal of emission inventory development, beyond the simple quantification of baseline emissions.

Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,...etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.

Protected areas (PAs) are the foremost policy tool for biodiversity conservation internationally. In order for PAs to deliver desired conservation outcomes effectively, equitably, and for the long-term, they require a high level of support from local communities. A mosaic of factors have been proposed aiming to explain the level of support for PAs focusing mainly on governance indicators, perceived social impacts and social attributes, such as personal norms and values. However, these factors are often explored in isolation and in studies of small scale within the scientific literature. To cover this research gap we run a structural equation model exploring how the interconnections between these factors can lead to higher levels of support for PAs using data from 3239 local residents in 10 PAs in Europe. Our analysis shows that the mediation effects of governance indicators, such as trust in institutions and level of public engagement, are important in explaining associations between an individual's social profile and their perceived social impacts with public support. Our findings also provide a useful and operational framework for PA practitioners and researchers illuminating pathways to increase the level of public support for a PA.

Increasing climate aridity and drought, exacerbated by global warming, are increasing risks for western United States of America (U.S.A.) rainfed farming, and challenging producers' capacity to maintain production and profitability. With agricultural water demand in the region exceeding limited supplies and fewer opportunities to develop new water sources, rainfed agriculture is under increasing pressure to meet the nation's growing food demands. This study examines three major western U.S.A. rainfed crops: barley, spring wheat, and winter wheat. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal climatic water deficit (CWD). To isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. We used historical climate and agricultural data between 1958 and 2020 to model crop repurposing (e.g. forage) across the observed CWD record using a fixed effect model. Our methodology is applicable for any region and incorporates regional differences in farming and economic drivers. Our results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. Of the major winter wheat growing regions, only the Northern High Plains in Texas showed a trend of decreasing grain harvest during high CWD. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. Interestingly, winter wheat repurposing is significantly higher in the southern Great Plains (∼50% harvested for grain) compared to the rest of the West (∼90%). Our results highlight that the major barley and spring wheat regions' grain harvests are vulnerable to high spring CWD and low summer CWD, while winter wheat grain harvest is unaffected by variable CWD in most of the West.

To avoid dangerous climate change, the global community has committed to phasing down coal at COP26 in Glasgow. Since policies and pledges currently implemented in the power sector are not sufficient to meet mitigation targets, countries are expected to increase their ambition over time within the UNFCCC process. Furthermore, some countries are bilaterally negotiating support packages to speed up the phase-out of coal through 'Just Energy Transition Partnerships'. Yet, to assess those ratcheting up efforts an understanding of the current baseline is pivotal. Here, we quantify the probability that currently planned coal plants will be built, based on an in-depth expert elicitation consisting of interviews with 29 experts from ten countries. We analyze the most important factors influencing the realization of the coal pipeline, isolate the effect of the COVID-19 pandemic, and compare the experts' forecasts with mitigation pathways scenarios. We find that globally 170 GW–270 GW of new coal-fired power plants are likely to be built in the upcoming years. Future negotiations for joint partnerships can use the results of this elicitation as a baseline to determine ambitious coal phase-out plans.

Increasingly severe droughts are straining municipal water resources and jeopardizing urban water security, but uncertainty in their duration, frequency, and intensity challenges drought planning and response. We develop the Drought Resilient Interscale Portfolio Planning model (DRIPP) to generate optimal planning responses to urban drought. DRIPP is a generalizable multi-scale framework for optimizing dynamic planning strategies of long-term infrastructure deployment and short-term drought response. It integrates climate and hydrological variability with high-fidelity representations of urban water distribution, available technology options, and demand reduction measures to yield robust and cost-effective water supply portfolios that are location-specific. We apply DRIPP in Santa Barbara, California to assess how least cost water supply portfolios vary under different drought scenarios and identify portfolios that are robust across drought scenarios. In Santa Barbara, we find that drought intensity, not duration or frequency, drives cost increases, reliability risk, and regret of overbuilding infrastructure. Under uncertain drought conditions, a diversified technology portfolio that includes both rapidly deployable, decentralized technologies alongside larger centralized technologies minimizes water supply cost while maintaining high robustness to climate uncertainty.

Temperature targets of the Paris Agreement limit global net cumulative emissions to very tight carbon budgets. The possibility to overshoot the budget and offset near-term excess emissions by net-negative emissions is considered economically attractive as it eases near-term mitigation pressure. While potential side effects of carbon removal deployment are discussed extensively, the additional climate risks and the impacts and damages have attracted less attention. We link six models for an integrative analysis of the climatic, environmental and socio-economic consequences of temporarily overshooting a carbon budget consistent with the 1.5 °C temperature target along the cause-effect chain from emissions and carbon removals to climate risks and impact. Global climatic indicators such as CO₂-concentration and mean temperature closely follow the carbon budget overshoot with mid-century peaks of 50 ppmv and 0.35 °C, respectively. Our findings highlight that investigating overshoot scenarios requires temporally and spatially differentiated analysis of climate, environmental and socioeconomic systems. We find persistent and spatially heterogeneous differences in the distribution of carbon across various pools, ocean heat content, sea-level rise as well as economic damages. Moreover, we find that key impacts, including degradation of marine ecosystem, heat wave exposure and economic damages, are more severe in equatorial areas than in higher latitudes, although absolute temperature changes being stronger in higher latitudes. The detrimental effects of a 1.5 °C warming and the additional effects due to overshoots are strongest in non-OECD countries (Organization for Economic Cooperation and Development). Constraining the overshoot inflates CO₂ prices, thus shifting carbon removal towards early afforestation while reducing the total cumulative deployment only slightly, while mitigation costs increase sharply in developing countries. Thus, scenarios with carbon budget overshoots can reverse global mean temperature increase but imply more persistent and geographically heterogeneous impacts. Overall, the decision about overshooting implies more severe trade-offs between mitigation and impacts in developing countries.

Previous studies have drawn attention to racial and socioeconomic disparities in exposures associated with flood events at varying spatial scales, but most of these studies have not differentiated flood risk. Assessing flood risk without differentiating floods by their characteristics (e.g. duration and intensity of precipitation leading to flooding) may lead to less accurate estimates of the most vulnerable locations and populations. In this study, we compare the spatial patterning of social vulnerability, types of housing, and housing tenure (i.e. rented vs. owned) between two specific flood types used operationally by the National Weather Service—flash floods and slow-rise floods—in the floodplains across the Contiguous United States (CONUS). We synthesized several datasets, including established distributions of flood hazards and flooding characteristics, indicators of socioeconomic status, social vulnerability, and housing characteristics, and used generalized estimating equations to examine the proportion of socially vulnerable populations and housing types and tenure residing in the flash and slow-rise flood extents. Our statistical findings show that the proportion of the slow-rise flooded area in the floodplains is significantly greater in tracts characterized by higher percentages of socially vulnerable. However, the results could not confirm the hypothesis that they are exposed considerably more than less vulnerable in the flash flooded floodplains. Considering housing-occupancy vulnerability, the percentage of renter-occupancies are greater in the flash flood floodplains compared to slow-rise, especially in areas with high rainfall accumulation producing storms (e.g. in the Southeast). This assessment contributes insights into how specific flood types could impact different populations and housing tenure across the CONUS and informs strategies to support urban and rural community resilience and planning at local and state levels.

Most procedures for redressing systematic bias in climate modeling are calibrated using current climate observations, and perform well. However, their performance in the future climate remains uncertain as no observations exist to compare against. In this context, we use the current and future climate outputs of an ultra-high resolution of Community Earth System Model (UHR-CESM) as the representative truth and bias correct monthly sea surface temperature (SST) simulations of eight Coupled Model Intercomparison Project 6 models over the Niño 3.4 region. A time-frequency bias correction approach is used to correct for bias in distributional, trend, and spectral attributes present in the models. This results in a near perfect power spectrum of the bias corrected current climate model simulations. Considering all correction procedures remain unchanged into the future, the overall representation of the corrected SST simulations shows improvement with consistency across models for the doubled CO₂ scenario, but higher variability and lower consistency in the quadrupled CO₂ concentration scenario.

Though the Sustainable Development Goals (SDGs) were crafted through an inclusive process, research on the relationship between greater female or younger legislative representation and SDG performance has been lacking. This article employs a linear mixed effects modeling approach to shed light on this relationship. Controlling for economic and democracy levels and population, the modeling reveals a positive correlation between female and youth legislative representation and SDG performance. Additional analyses, however, suggest the strength of the relationships with female and youth legislative representation vary between the socioeconomic and environmental SDGs. Female and youth representation are strongly correlated with the socioeconomic SDG index; they improve the fit of the model for the environmental SDG index. This result may stem from a tendency in developed countries to trade off the environmental SDGs for the socioeconomic SDGs. It may also imply that greater legislative representation is not sufficient to overcome constraints in energy and consumption and production systems that often lead to those trade-offs. Rather bringing women and younger people into legislatures may need to be combined with institutional and policy reforms that turn socioeconomic and environmental trade-offs into synergies.

Oil palm cultivation has become one of the world's most important drivers of land use change in the tropics causing biodiversity loss and greenhouse gas emissions. The impact of climate change and rising carbon dioxide (CO₂) concentrations in the atmosphere on oil palm productivity is not well understood. If environmental change leads to declining palm oil yields in existing cultivation areas, cultivation areas may expand or shift to other regions. Here we assess climate change impacts on palm oil production using an extended version of the dynamic global vegetation model with managed land, LPJmL4, and a range of climate scenarios from the inter-sectoral impact model intercomparison project. We find increasing average yields under all future climate scenarios. This contradicts earlier studies, which did not consider the potential positive effect of CO₂ fertilization. If we do not account for CO₂ fertilization, future yields also decrease in our simulations. Our results indicate the potentially large role of rising CO₂ levels on oil palm cultivation. This highlights the importance of further applied plant science to better understand the impact of climate change and elevated CO₂ levels on oil palm growth and productivity.

Australia is a world leader in habitat loss and species extinction, and for many species, ecological restoration will be necessary for continued persistence. Between 2014 and 2018, the Australian federal government allocated a substantial portion of funding for threatened species recovery to a nation-wide ecological restoration program called '20 Million Trees Land-care Program', which included a competitive grant round. By comparing successful and unsuccessful grant applications, we were able to identify factors associated with restoration funding allocation. We then assessed the Program's ability to provide benefits to threatened species by analyzing the overlap between restoration projects and threatened species habitat. We found that funding allocation under the 20 Million Trees Program was primarily driven by 'value for money' factors, specifically 'cost per tree' and number of trees planted. Additionally, projects were more likely to be funded if they mentioned threatened species in the description, but less likely to be funded if they actually overlapped with areas of high threatened species richness. Of the 1960 threatened species assessed, we found that only 9 received funding for restoration projects covering more than 1% of their range. Conversely, we found that utilizing alternative project selection schemes, such as alternative 'value for money' metrics or spatial planning methods, could have delivered better outcomes for some of the threatened species most impacted by habitat loss. Our results show that inopportune selection criteria for awarding of funding for ecological restoration can significantly reduce the benefits delivered by programs.

Numerous countries plan to promote increased levels of vehicle electrification. This study demonstrates that, when considering the life cycle of automobiles, higher levels of vehicle electrification over the next 30 years in Japan would generate higher carbon emissions, preventing the country from meeting carbon reduction and neutrality targets in 2030 and 2050, respectively. In 2020, 2030, and 2050, domestic emissions could decrease to 92.5, 72.9, and 49.6 Mt, respectively, while emission transfers to other countries could reach 12.1 Mt (11.5% of the global carbon footprint), 10.4 Mt (12.5%), and 8.7 Mt (14.9%). The results indicate that even if the widespread use of alternative fuel vehicles could reduce domestic emissions, such emissions might be transferred to foreign countries, and blanket promotion of automobile electrification should be avoided. Instead, governments and the automotive industry should take responsibility for global and whole life-cycle emissions of vehicles, not only domestic tank-to-wheel emissions. These results provide baseline information for use in the recommendation measures and international rules to be adopted by the automobile industry stakeholders and policymakers.

It is assumed that extreme precipitation (P) increases with air temperature (T) by a scaling rate close to 7%/°C without moisture limitation according to the Clausius-Clapeyron (C-C) relationship. However, the spatial distribution of the P-T relationship in China is subject to divergent conclusions including both sub-C-C (<7%/°C) and super-C-C (>7%/°C) scaling with reasons yet to be examined. Based on the long-term observations, here we show that P-T relationships with peak structure exist in most regions across China. The scaling rate in the wet season shows a decreasing spatial pattern from the southeast to the northwest, while sub-C-C scaling in the dry season dominates most regions across China. Mixing precipitation events from different seasons could lead to miscalculation of the P-T scaling rate. Furthermore, significant increases in peak precipitation at high percentiles have been observed in southern regions of China during the historical period, indicating that the peak structure does not imply a potential upper limit for precipitation extremes. Our results highlight the importance of considering seasonal characteristics in analyzing the extreme precipitation-temperature relationship in a changing climate.

Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities.

Drylands are serviced as an essential component of the earth's ecosystem. The potential changes in dryland areas are of great importance to the environment, but various debates remain as to whether and to what extent drylands are expected to expand. Here we employ a physically-based potential evapotranspiration (E_P) model accounting for vegetation response to climate change to quantify potential changes in dryland areas, on the basis of a commonly used indicator, aridity index (multiyear mean E_P over precipitation). Results show that by the end of this century, drylands will expand slightly by ∼5%, while vegetation productivity will increase by ∼50%. Elevated CO₂ slows down the increase rate of E_P that impedes the expansion of drylands, but greatly promotes vegetation growth with increases in both leaf assimilation and canopy foliage. These findings improve our understanding of the potential changes in dryland and their ecological impacts in a warmer climate.

Forest regeneration can be a low-cost solution to mitigate climate change, and mapping its extent can support global goals such as the Bonn Challenge, which set a goal to put 350 million hectares of degraded forests and landscapes into restoration by 2030. Our study combined multiple remote sensing datasets and expert surveys, identifying $55.7 \pm 6.2$ million hectares of likely regenerated forests between 2000 and 2015 across areas that were not forested before 2000 and have remained forested from 2015 to 2018. The identified forest regeneration could potentially represent 22–25 billion young trees and a total biomass of about 3.2 billion tonnes. Forest regeneration took place in sites with less opportunity cost for agriculture for every country, but in more developed regions, forest regeneration took place in sites with higher suitability for cultivation. Expert feedback associated agricultural land use transitions and the establishment of protected areas, coupled with effective management and local support, as the key factors leading to successful forest regeneration. The results, publicly available, can facilitate discussions and help identify strategic locations to foster forest regeneration to achieve the global goals of mitigating climate change and restoring biodiversity.

Tracking progress towards the Paris Agreement climate goal requires understanding the 2030 emission levels implied by countries' National Determined Contributions (NDCs). However, key uncertainties and assumptions impact greenhouse gas (GHG) emission projections implied by the NDCs. This study analyses this impact, both globally and for major emitting countries. We find that the assessed uncertainties markedly affect global GHG emission projections. Full achievement of NDC targets is estimated to result in a range of 46–60 GtCO₂eq by 2030 (median estimate: 53 GtCO₂eq). The uncertainty in measuring historical emissions, including land-use, as reflected by different datasets is the most important contributing factor. This is followed by two equally important factors globally: socio-economic baseline uncertainty and uncertainty about the emissions implied by current policies in case NDCs are less ambitious than these. Overall, the impact of policy uncertainty (i.e. uncertainty resulting from conditionality of or ranges in NDC targets and uncertainty in emissions resulting from current policies) is about equally important as model/technical uncertainty (i.e. uncertainty in historical emissions and socio-economic baseline variations). This new insight is important for decision makers and researchers because a larger share of the total uncertainty is now attributable to aspects that can be influenced by policy decisions compared to previous analyses of NDC uncertainty.

The utilization of cropland and rooftops for solar photovoltaics (PVs) installation holds significant potential for enhancing global renewable energy capacity with the advantage of dual land-use. This study focuses on estimating the global area suitable for agrivoltaics (PV over crops) and rooftop PVs by employing open-access data, existing literature and simple numerical methods in a high spatial resolution of 10 km × 10 km. For agrivoltaics, the suitability is assessed with a systematic literature review on crop-dependent feasibility and profitability, especially for 18 major crops of the world. For rooftop PV, a non-linear curve-fitting method is developed, using the urban land cover to calculate the PV-suitable built-up areas. This method is then verified by comparing the results with open-access building footprints. The spatially resolved suitability assessment unveils 4.64 million km² of global PV-usable cropland corresponding to a geographic potential of about 217 Terawatts (TW) in an optimistic scenario and 0.21 million km² of rooftop-PV suitable area accounting for about 30.5 TW maximum installable power capacity. The estimated suitable area offers a vast playground for energy system analysts to undertake techno-economic assessments, and for technology modellers and policy makers to promote PV implementation globally with the vision of net-zero emissions in the future.

Understanding how soil microbes respond to permafrost thaw is critical to predicting the implications of climate change for soil processes. However, our knowledge of microbial responses to warming is mainly based on laboratory thaw experiments, and field sampling in warmer months when sites are more accessible. In this study, we sampled a depth profile through seasonally thawed active layer and permafrost in the Imnavait Creek Watershed, Alaska, USA over the growing season from summer to late fall. Amplicon sequencing showed that bacterial and fungal communities differed in composition across both sampling depths and sampling months. Surface communities were most variable while those from the deepest samples, which remained frozen throughout our sampling period, showed little to no variation over time. However, community variation was not explained by trace metal concentrations, soil nutrient content, pH, or soil condition (frozen/thawed), except insofar as those measurements were correlated with depth. Our results highlight the importance of collecting samples at multiple times throughout the year to capture temporal variation, and suggest that data from across the annual freeze-thaw cycle might help predict microbial responses to permafrost thaw.