Alleviating the imbalance between urban and rural areas for regional coordinated development is an imperative response to the Sustainable Development Goal 10 of the United Nations. To track China’s urban-rural integration progress and address the uneven issues in specific fields, this study constructed a novel seven-dimension index system of urban-rural integration, comprising free population mobility, efficient land transfer, interactive economic growth, highly-linked transportation, equal public services, joint environmental governance and unimpeded informatization between urban and rural areas. Based on a comprehensive measurement framework and multi-source panel data, we uncovered the spatial-temporal evolution of urban-rural integration in China’s 367 prefecture-level administrative units from 1980 to 2022. The results demonstrated that China’s urban-rural integration steadily increased from 27.51 to 57.35 with an average annual growth rate of 3.40 %. Whereas, the overall urban-rural integration was relatively inferior in 2022, at the level of moderate integration whose proportion of China’s land area was 88.08 %. The urban-rural integration level in eastern region and urban agglomerations was higher than that in mid-west and non-urban agglomerations. From the perspective of seven dimensions, interactive economic growth, joint environmental governance and unimpeded informatization made an obvious improvement and reached higher integration, while free population mobility, efficient land transfer, highly-linked transportation and equal public services maintained the stage of moderate integration in 2022. In the future, China should make targeted efforts for urban-rural integration in terms of population, land use, transportation and public services, and accelerate urban-rural common prosperity in the mid-west and economically underdeveloped areas.

Low-carbon development in poverty-stricken areas is crucial for the Sustainable Development Goals. However, the relationship between poverty alleviation and carbon emissions in impoverished regions remains unclear. This study calculated land-use carbon emissions of 505 counties in China’s contiguous poor areas, which suffer from extreme poverty and are the most difficult to lift out of poverty, established evaluation frameworks for carbon emissions efficiency and multidimensional poverty alleviation, and employed an improved coupling coordination model to assess the relationship. Results showed that all counties achieved remarkable multidimensional poverty alleviation from 2000 to 2020 while the speed varied among regions. After initial deterioration, emissions efficiency exhibited significant improvements during 2010–2020. This reversal was mainly driven by reduced carbon intensity and carbon emissions per unit of construction land. Analysis revealed 505 counties’ transformation from an uncoordinated “low development-low carbon” state toward a sustainable “high development-low carbon” state. These findings evidence that poverty alleviation development and low-carbon target can be compatible, offering valuable insights for sustainable development in global underdeveloped regions.

Worldwide crises related to water, energy, food, and ecosystems are expected in the future. Although a number of studies have focused on the water-energy-food-ecosystem (WEFE) nexus, a comprehensive review that integrates bibliometric patterns, methodological approaches, and multi-scale applications within a unified WEFE framework remains lacking. To address this gap, this study applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review protocol and conducts bibliometric and content analyses of publications from 2010 to 2024. The results show a steady increase in WEFE-related studies and a paradigm shift from the traditional water-energy-food (WEF) triad toward a four-component framework that explicitly incorporates ecosystems. Moreover, the WEF nexus remains a central research theme. Methodological approaches can be categorized into quantitative assessment, simulation and prediction, and integrated management; however, most rely on static analysis and therefore cannot sufficiently capture dynamic feedbacks. Scale-based analysis indicates that regional and urban studies dominate the field and focus on resource integration and internal resource management optimisation, respectively, whereas transboundary-scale research remains limited. This review is the first to systematically synthesize the conceptual evolution, methodological pathways, and scale-specific challenges of the WEFE nexus within a unified paradigm. Moreover, this review identifies key directions for future research, including framework standardization, multi-source data integration, and scale-appropriate governance strategies, thereby providing theoretical insights and empirical support for advancing sustainable WEFE nexus research.

Ecological resilience -defined as the capacity of ecosystems to withstand perturbations -is an important dimension of ecosystem stability. However, the spatial patterns and underlying drivers of resilience in forests relative to grasslands remain poorly understood. Using a temporal autocorrelation index (AC1) derived from satellite-based vegetation indices, we assessed high-resolution (250 m) vegetation resilience from 2000 to 2020 across China’s natural terrestrial vegetation. We found that vegetation in forest-dominated regions was more resilient than in grassland-dominated regions, with divergent associations to climate, vegetation, soil, topography, and human drivers. Specifically, forest resilience was primarily associated with climate variables, with path analysis indicating a potential indirect link through vegetation characteristics. By contrast, grassland resilience was strongly related to soil and topographic properties. Particularly, among the climate variables examined, long-term warm drought condition, represented by the negative of Standardized Precipitation Evapotranspiration Index (SPEI), generally showed spatial negative associations with resilience, especially in temperate steppe and subtropical forest regions. Notably, the resilience of cold forests and high-altitude grasslands showed weaker or even positive relationship with warm droughts. These findings highlight the threats posed by ongoing climate change to vegetation resilience, and emphasize the need for ecosystem-specific strategies to enhance resilience in the future.

China’s large-scale ecological restoration programs have markedly increased vegetation cover, but their ecological suitability and long-term sustainability remain insufficiently evaluated. To support the development of science-based restoration strategies, this study introduced potential natural vegetation (PNV) as a reference and developed an innovative hybrid model combining random forest algorithm and process-based model (i.e., LPJ-GUESS) to map PNV distributions. Results showed that the hybrid model achieved high predictive accuracy, with an overall classification accuracy of 0.89 and kappa coefficient of 0.87. A comparative analysis of the spatial distribution of the restored (and existing) vegetation and that of the PNV under both the current and the future period was conducted. It revealed that 6.7% of restored forests and 48.8 % of restored grasslands were mismatched with PNV, indicating widespread misallocation of restoration efforts under current ecological restoration programs. Moreover, 2.5 % of existing forests and 34.2 % of existing grasslands were mismatched with PNV in the current period (1993–2022), while 0.5 %–1.4 % of existing forests and 50.8 %–70.4 % of existing grasslands are projected to be mismatched with PNV in the future period (2071–2100) under different SSP scenarios. About 32.4 %–41.5 % of China’s land area was identified as unstable PNV region under future climate change, within which various vegetation transitions are likely to occur. These findings highlight the necessity to align ecological restoration programs with ecological suitability and sustainability criteria to achieve long-term resilience and cost-effectiveness. The hybrid modeling framework offers a robust tool for guiding adaptive restoration planning across China.

The Heilong-Amur River Basin (HARB), the largest transboundary basin in northeastern Asia, is increasingly vulnerable to water-security risks arising from climate change and human activity, yet robust quantitative spatiotemporal attribution remains challenging. In this study, we developed a Grid-based Hydrological Response Attribution (GHRA) framework that integrates the Variable Infiltration Capacity (VIC) model with a cumulative slope change approach. By coupling a distributed hydrological model with attribution analysis and incorporating a physically based correction coefficient, the framework enables spatially explicit and physically consistent streamflow attribution. The GHRA was applied to the upper HARB for historical (1992–2017) and future periods (2025–2099). Results show that climate change and human activity contributed 49.57 % and 50.43 % to historical streamflow changes, respectively. Climate change is projected to dominate during the future period, contributing 71.11 % under SSP2–4.5 and 76.91 % under SSP5–8.5. Compared with uncorrected results, the GHRA reduced the standard deviation of attribution values by 11.33 % and 20.74 % under SSP2–4.5 and SSP5–8.5, respectively. Regionally, climate change dominates the Kherlen and Argun Rivers, whereas human activity -particularly land-cover change -remains the primary driver in the Zeya and Shilka Rivers. Projections based on CMIP6 data indicate that Russia contributes the largest proportion of water resources (63 % and 67 % under SSP2–4.5 and SSP5–8.5, respectively), followed by China (26 % and 21 %) and Mongolia (11 % and 12 %). Climate-driven stress increases spring floods along the Argun, Heilong-Amur, and Zeya Rivers and intensifies summer flooding in the Shilka and Zeya Rivers, exacerbating transboundary water security challenges.

The blueprint for feeding the world’s population within the planetary boundaries is to build “capacity of dietary sustainability”-everyone can access and afford a healthy and environmentally friendly diet with relatively greater stability under global shocks such as climate change. But the global capacity of dietary sustainability to deal with climate change remains unknown. Here we developed a novel framework to evaluate dietary sustainability and assess capacity of dietary sustainability to deal with climate change in nutritional security (Capacity^nut), social equity (Capacity^soc), and environmental protection (Capacity^env) dimensions, based on publicly available dietary sustainability data and climate change data from websites. Descriptive statistics results showed that the average score for the three capacities in rural areas was 7.32 % higher than in urban areas, and that the Gini coefficient for the three capacities in urban areas was 28 % higher than in rural areas. Urban areas showed stronger synergy between Capacity^nut and Capacity^soc than rural areas, with a Pearson coefficient of 0.622 in urban areas and 0.436 in rural areas. Education was the most important factor for Capacity^soc, with a standardized coefficient of 0.401; distance food trade was the most important factor for Capacity^env, with a standardized coefficient of 0.500. By 2100, the average score for the three capacities was expected to increase by 39.36 % to 63.64 %. Our results demonstrate the essence of targeted efforts in urban areas and stable international food trade for enhancing capacity of dietary sustainability to deal with climate change globally.

Alpine grasslands are highly sensitive to environmental changes, with leaf longevity crucially modulating carbon cycling. Addressing uncertainties in long-term leaf longevity dynamics, driving mechanisms and its interplay with net primary productivity (NPP), we analyzed the spatiotemporal changes in leaf longevity and NPP of the Three-Rivers-Source Region (TRSR) from 2003 to 2022 using multi-source remote sensing data. Key drivers of leaf longevity were identified using XGBoost-SHAP algorithm and lasso regression, while a causality-based model projected future trajectories. Results showed that over 81 % of the study area exhibited a significant leaf longevity extension (9.32 days decade⁻¹), mainly due to delayed leaf senescence date. Concurrently, regional NPP increases were dominated by summer gains. There was a non-linear positive correlation between leaf longevity and NPP, confirming that longer leaf longevity enhanced carbon uptake by prolonging photosynthesis. However, this marginal gain declined once leaf longevity surpassed the ecological threshold (about 150 days), indicating that after summer vegetation activity peaks, relying solely on extending the growing season does not lead to substantial net carbon gains, and the carbon sink becomes saturated. Temperature consistently drove leaf longevity variation, while enhanced solar radiation exerted increasing influence, highlighting the greater importance of photothermal resources for foliar phenology. Projections suggested continued leaf longevity extension under SSP245 and SSP585 climate scenarios, with short-term NPP increasing but long-term stagnating or declining. These findings emphasize that alpine grassland management should prioritize ecosystem sustainability and adaptive resilience over maximizing leaf longevity, especially under extreme climate stresses, offering key insights for carbon sequestration optimization and restoration strategies in global alpine ecosystems.

High temperatures negatively impact thermal comfort and public health. Understanding the relationship between urban morphology and thermal variation is crucial for mitigating excessive heat. In this study, we propose a framework that uses the sky view factor (SVF), building view factor (BVF), and green view factor (GVF) to represent street-level spatial morphology, computed from Baidu street view images using semantic segmentation and fisheye transformation. The framework also integrates local climate zones (LCZs) classification with summer Landsat thermal data to explore the relationships between SVF, BVF, GVF, and intra-urban thermal variation. An empirical study is conducted in the core built-up area of Fuzhou. Key findings include: (1) Strong urban thermal intensity is characterized by high openness (SVF_mean = 0.60) and low vegetation (GVF_mean = 0.15). (2) In general, SVF (r = 0.27) is positively associated with street thermal intensity, whereas BVF (r = -0.15) and GVF (r = -0.21) exhibit negative correlations, based on the entire study area. (3) Within LCZ classes (e.g., LCZs 1–5), SVF (54.57% of the grids) is mostly positively correlated with temperature difference, while GVF (61.30% of the grids) is predominantly negatively correlated with temperature difference. (4) BVF shows the greatest uncertainty in its relationship with thermal variation, as it effectively quantifies exposure to heat-trapping building surfaces that dominate the local energy balance. The above findings imply that there is spatial heterogeneity in the relationship between view factors and intra-urban thermal variation. These results aid in developing street renewal strategies for urban heat mitigation.

Regional soil erosion mapping is crucial for understanding erosion patterns and guiding conservation planning. The ridge–furrow direction is an important factor influencing soil erosion and can be quantified using the oblique cross-slope tillage subfactor (T_o). However, T_o has often been overlooked in regional assessments because of data and computational challenges. In this study, a novel methodology was introduced for calculatingT_o on a regional scale, including sample unit design, parcel-by-parcel furrow direction surveys, grid-by-grid T_o calculations, and interpolation to obtain regional T_o values. Using Northeast China (1.432 million km²) as a case study, 945 sample units (small watersheds of about 1 km² each) were designed based on a stratified systematic area sampling method. In total, 6,679 erosion parcels were delineated, and 7,283 tillage lines were digitized to calculate T_o. Soil erosion rates were assessed using the proposed method and compared with regional assessments that neglected T_o, with detailed survey-based T_o estimates within the sample units used as the reference values. The results showed that neglecting T_o led to a 30.1 % overestimation, whereas the proposed regional method reduced this overestimation to 6.3 %. The average T_o value across the study region was 0.805, and its distribution was closely related to topography, with higher values in gentler areas and lower values in steeper areas. These findings provide valuable guidance for soil conservation planning: maintaining north–south tillage in areas with slopes < 0.5°, adopting more contour tillage for slopes between 0.5° and 5°, and implementing terracing or additional measures on slopes &gt; 5°. The incorporation of T_o into regional soil erosion assessments is crucial, and the proposed method provides an effective and practical solution.

Grassland ecosystems provide vital ecological and cultural services. However, their phenological dynamics are becoming increasingly vulnerable to climate change, directly threatening the seasonal availability of recreational opportunities. This study investigated climate–phenology relationships at a regional scale across the grasslands of Northwest China and further linked phenological dynamics to recreational demand in seven representative grassland destinations. Drawing on multi-decadal satellite observations of vegetation indices, including the normalized difference vegetation index and the green vegetation index, climate reanalysis data, and online search–derived recreational demand indices, we identified a significant contraction of the growing season (−1.316 ± 0.761 days decade⁻¹, P < 0.1) between 1982 and 2022, primarily due to delayed green-up after 2013. Ridge regression analysis revealed a shift in climatic control from precipitation-dominated to temperature-dominated regimes, with warming and intensified drought stress emerging as the dominant drivers of phenological change. Recreational demand exhibited moderate-to-strong correlations with vegetation greenness (r = 0.461–0.701), although this coupling weakened after 2020 owing to pandemic-related disruptions. The shortening of the growing season compresses the temporal window of scenic attractiveness and exacerbates spatial mismatches between ecological supply and urban demand. Our findings demonstrate that climate-induced phenological shifts directly constrain recreational services, with far-reaching implications for tourism sustainability, regional economies, and ecosystem resilience. We developed a coupled climate–phenology–recreation framework to guide adaptive governance and integrated management strategies, ensuring that grassland ecosystems can sustain their ecological and societal functions amid accelerating climate change.

Urbanization has accelerated rapidly in recent decades, with nearly 70 % of the global population projected to live in cities by 2050. As urban areas confront growing challenges, including climate change, housing shortages, and informal growth, urban research has diversified in scope and intensified in focus. However, comprehensive assessments of the field remain limited. This study fills that gap through a scientometric analysis of 14,282 publications indexed in Scopus between 1970 and 2024. It examines three distinct phases of urbanization research (1970–1990, 1991–2010, and 2011–2024) and employs VOSviewer, CiteSpace, and Bibliometrix (RStudio) to map publication trends, thematic evolution, and collaboration networks. Results reveal an exponential growth in urban research output after 2010, reflecting a growing focus on smart cities alongside the rising urgency of climate-related challenges. Five dominant thematic clusters were identified, focusing on urban planning and governance, environmental challenges, sustainability, socio-spatial transformations, and technological innovation. The analysis also highlights the rise of new research frontiers such as smart urbanism, climate adaptation, and resilience, alongside persistent geographic disparities in the Global South. These findings deepen understanding of the intellectual evolution of urbanization research and provide insights to guide future scholarship and sustainable urban policy.

The distinctive geography and climate of the Lancang–Mekong River Basin (LMRB) have promoted rich species diversity, making it one of the world’s most important biodiversity hotspots. However, rapid increases in human activity intensity (HAI) have led to significant habitat loss, posing biodiversity conservation challenges across this transboundary basin. To address this gap, this study explored spatiotemporal variations in HAI and habitat quality (HQ) in the LMRB from 2000 to 2020, analyzed their spatial relationships, and applied the four-quadrant model to delineate four ecological management zones. The empirical results show that HAI in the LMRB increased by 14.15 % over the past 20 years, while HQ declined by 1.84 %. Thailand, Vietnam, and Cambodia exhibited high HAI over the studied period, while Laos, Myanmar and China maintained high HQ. Bivariate local autocorrelation analysis revealed predominantly “low HAI–high HQ” and “high HAI–low HQ” spatial clustering patterns. The spatial relationship between HAI and HQ showed a significant negative correlation and uneven distribution over the studied period, indicating that increasing human activity has degraded habitats. Using the Geodetector method, land use and land cover, net primary productivity, and temperature were identified as primary HQ change drivers in the LMRB. Additionally, the interactions between factor pairs exerted a significantly stronger influence than individual factors. Overall, this study’s findings offer new insights into biodiversity conservation in the LMRB, and the proposed ecological zoning based on HAI and HQ provides support for habitat protection and sustainable development in this transboundary basin.

Human settlement quality (HSQ) is a critical component of sustainable urban development, directly affecting residents’ health, well-being, and quality of life. However, most existing studies rely on expert-defined service radii and indicator weights at the city scale, overlooking residents’ perceptions and failing to capture fine-grained variations at the community level. This study proposes a Perceptual-Scale Optimized Random Forest (PSO-RF) to enable human-centered, community-scale HSQ evaluation by integrating subjective satisfaction data with objective environmental indicators. The framework captures multi-scale perceptual differences in environmental features and determines the optimal measurement scale for each indicator, leading to more accurate HSQ assessments. Five case cities in China and Germany -Beijing, Changsha, Shenzhen, Berlin, and Munich -were selected to reflect diverse regional, socio-economic, and developmental contexts, based on multi-source spatiotemporal data from 2010, 2015, and 2020. The findings reveal that: (1) Residents perceive HSQ across two dominant spatial scales: local (860 m) and accessible (2,050 m); (2) Chinese communities emphasize socio-economic conditions within close proximity, while German communities prioritize broader natural environmental factors; (3) The PSO-RF model reduces evaluation error by 9.6 % compared to fixed-radius approaches by identifying indicator-specific perceptual scales; (4) The generated HSQ and shortcoming maps uncover localized human settlement challenges and offer practical guidance for targeted urban planning. This study advances the methodological foundation for perception-driven livability research and provides actionable insights for precision urban governance.

Understanding the specific contributions of ecosystem services (ES) to the Sustainable Development Goals (SDGs) is crucial, yet challenging, given their complex interrelationships. This study develops an integrated approach, combining network analysis and structural equation modeling, to elucidate how ESs related to food, water, and carbon interact with and influence the achievement of SDGs 2, 6, 7, 11, 12, 13, and 15 in the Pearl River Basin (PRB) from 2000 to 2019. We independently evaluated the supply, demand, and flow of ESs, along with SDG scores. Our results show that despite an overall increase in the average SDG score in the PRB from 44.07 to 55.84 over the two decades, spatial mismatches between ES supply and demand across cities intensified, primarily driven by ES flows. Notably, ES demand emerged as a dominant factor shaping SDG scores, particularly for SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land). Food-related ES flows exhibited the most significant positive impact on regional SDG performance. Furthermore, synergistic effects between ES supply (food, water, carbon) and SDGs strengthened over time, especially for SDG 12 (Responsible Consumption and Production). Among the three ESs, the food ES has the highest impact on the SDGs, with coefficients of 0.28, 0.47, and 0.24 for the ES supply, demand, and flow, respectively. This study introduces a novel framework for the basin scale that simultaneously quantifies both the ES supply-demand flows and their causal pathways to SDGs. These findings indicate that optimizing ES flows through improved resource allocation and cross-sectoral governance is a key mechanism for enhancing regional sustainability.

The rapid development of wind energy in China since 2000 has raised concerns about its impacts on local climate and vegetation. Despite regional and local studies, a comprehensive national assessment is lacking. Here, we analyzed the effects of 675 onshore wind farms, representing &gt; 90,000 identified wind turbines in China, on land surface temperature (LST) and vegetation using Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data from 2003 to 2022. We found a daytime cooling effect of -0.05 ± 0.48 °C (mean ± STD) and a nighttime warming effect of 0.06 ± 0.28 °C across all wind farms. The construction of wind farm infrastructure initially reduced peak normalized difference vegetation index (NDVI) by -0.006 ± 0.036, and this adverse impact weakened over time (-0.004 after 7 years), indicating vegetation recovery. The wind farm impacts varied by land cover type. The nighttime warming was largest for barren lands (0.19 °C), followed by croplands (0.10 °C), grasslands (0.07 °C), and forests (0.01 °C). These differences contributed to increasing night warming from southern to northern China. The adverse vegetation impacts were largest for forests (-0.010), followed by grasslands (-0.008) and barren lands (-0.003), with croplands (0.001) being almost unaffected. Correlation analysis identified precipitation and mean LST as significant factors influencing spatial variations in nighttime LST impact, with greater vegetation decline reinforcing night warming. Our large-scale analysis provides comprehensive evidence of the heterogeneous environmental impacts of wind farms across China, informing the sustainable development of wind energy.

Fisheries carbon sinks are being increasingly recognized as emerging components of blue carbon and nature-based climate solutions. However, their roles extend far beyond carbon sequestration alone, encompassing ecosystem functioning, food systems, livelihoods, and social equity. Existing research remains fragmented across biogeochemical, ecological, socioeconomic, and governance domains, limiting a coherent understanding of how fisheries carbon sinks contribute to sustainable development. In this study, an integrated Fisheries Carbon Sinks–Ecosystem Services–Human Well-Being (FCS–ESS–HWB) framework is developed, and a systematic synthesis of evidence from 399 peer-reviewed studies is conducted following the RepOrting standards for Systematic Evidence Syntheses (ROSES) protocol. By coupling a theoretical analysis of ocean carbon pumps with a multidimensional bibliometric review, we examine the mechanisms, system types, intervention pathways, indicator systems, and governance structures that shape the FCS–ESS–HWB nexus. The results reveal that fisheries carbon sinks-across seaweed farming, shellfish aquaculture, integrated multi-trophic aquaculture, and animal-mediated biological pumps-simultaneously influence climate regulation, water purification, coastal protection, biodiversity maintenance, food security, livelihoods, and social equity. However, major uncertainties persist regarding long-term carbon fate, accounting boundaries, spatial representativeness, and the quantitative role of governance. Governance regimes emerge as decisive in determining whether fisheries carbon sinks generate synergistic sustainability outcomes or reinforce ecological and social trade-offs. By explicitly embedding fisheries carbon sinks within a social–ecological–governance systems perspective, this study advances a sustainability-oriented understanding of marine carbon sequestration and highlights key priorities for integrating fisheries carbon sinks into climate policy and sustainable ocean governance.

The establishment of a sustainable land use system integrating the mitigation capacity of wetlands is crucial in the plains of the temperate zone. Following the approaches of green infrastructure development programs and the spatial planning framework, two zones with potential for wetland conservation and restoration were selected in the Hungarian Plain (Central and Eastern Europe) covering an estimated 21 % of the landscape. Then, linking hydrological and ecosystem modelling tools, we estimated the groundwater recharge that would result from a hydrological restoration and its impact on surrounding maize fields with different groundwater levels and soil conditions. The amount of groundwater recharge ranged between 367 m³ ha^-1 and 552 m³ ha^-1; thus, the horizontal impact of the hydrological restoration as indicated by the ratio of the extension of the groundwater impact area and surface water in the May–August period, would be almost 2:1. Croplands in transition zones between high terraces and wetlands may reap the greatest benefit from the hydrological restoration of wetlands due to its edge effect, which results in more balanced year-to-year yields on fine-grained soils. This is what has led to an estimated 20 %–28 % increase in maize yields in such zones. The results of the investigation confirm the hypothesis that the hydrological restoration of wetlands may effectively mitigate increasing climate risk in drought vulnerable plains. These findings, complemented by the abundance of other services (e.g., flood regulation) provided by restored wetlands shed light on the intersectoral prospects of the hydrological restoration of wetlands in Europe.

Grain imports are an important means of ensuring China’s food security, but they also create dual global challenges in terms of environmental spillover effects and supply risks. It is crucial to balance the environmental sustainability with supply stability in grain import strategies. Therefore, based on environmental footprint, country-specific risks, and market concentration, this study assessed the global environmental impacts and potential supply risks of China’s grain imports, and used a multi-objective optimization model to identify improved import strategies. The results showed that from 2000 to 2020, the global cultivated land savings caused by China’s grain imports increased from 1,859.70 kha to 18,209.11 kha, water saving increased from 3,665.43 Mm³ to 63,572.45 Mm³, and GHG emission reduction increased from 1,282.30 kt to 13,811.94 kt, but these effects varied substantially across crops. Overall, China’s grain import risk (CGIR) followed a rise-then-decline pattern, with periodic certain cyclical fluctuations. Import risks differed significantly across crops, with maize (mainly from the United States and Ukraine) and soybeans (primarily from the United States and Brazil) remaining at elevated risk levels. It was difficult to reconcile environmental impacts and import risk through adjustments to grain import patterns in a single-objective setting. Under dual environmental and risk constraints, adjusting China’s grain import strategy reduced cultivated land consumption by 4.92 %, GHG emissions by 5.02 %, and import risk by 36.55 %, despite increasing water consumption by 5.41 %. This study offers policy-relevant recommendations to help alleviate the tension between grain import security and sustainable development.

The land sector has significant potential to contribute to net-zero greenhouse gas (GHG) emissions commitments via changes in land-use and management. Land-use change however, can have spillover effects for a range of other socio-economic and environmental indicators. In this study, we identified net-zero pathways which mitigate trade-offs and capture co-benefits for the Australian land sector using a combination of exploratory scenario analysis and scenario discovery. Using machine learning-based surrogate models trained on the Land-Use Trade-Offs (LUTO) model, we identified many net-zero compliant scenarios at three different estimates of emissions abatement (low, medium, and high) requirement for the land sector and quantified their potential spillover effects. We found that significant land-use change may be required for Australia’s land sector to contribute to net-zero and that this has both trade-offs and co-benefits for economic returns, food production, biodiversity, and water resources. Crucially, the impacts of achieving net-zero on land-use change and spillover effects varied widely with both the level of land sector emissions abatement required and the policy settings within each abatement level. To illustrate, compared to the worst performing Medium abatement level pathway, the best performing pathway achieved 23.04 \$B yr^-1 more in economic returns, produced 28.76 \$B yr^-1 more food/fibre, and generated 23.24 % greater biodiversity services, but with a trade-off of 2.82 million ML yr^-1 more water use. We outline the specific policy settings required for a sustainable net-zero transition for the land sector (i.e., which avoid trade-offs and capture co-benefits) for the three abatement levels including carbon pricing, agricultural productivity gains, reduced barriers to adoption, and incentives to support biodiversity. The results illustrate the importance of a portfolio of well-designed, evidence-based policy interventions for supporting a sustainable transition to net-zero emissions.

The effects of climate change, such as increasing temperatures and changing precipitation patterns, significantly affect road infrastructure’s structural integrity and longevity, requiring a reevaluation of maintenance standards. Previous research on pavement performance did not incorporate climate adaptation into maintenance standards. This study develops a scenario-based road infrastructure maintenance cost prediction approach integrating climate and traffic scenarios. Using high-resolution data on pavement depth and materials, it evaluates future impacts on maintenance costs in Western Australia, and advances sustainable spatial planning under climate change through geospatial, decision-oriented analysis. The results show that without intervention, traffic and climate change could significantly increase road maintenance costs by 2050. Under an equivalent service-improvement target of a 1.0-point reduction in network-average International Roughness Index, adopting climate-adaptive pavement standards reduces life-cycle maintenance costs by 16.1 % relative to the baseline scenario, while traffic-management-based redistribution reduces costs by 17.0 % relative to the same baseline scenario. Integrating climate adaptation into road maintenance standards provides more accurate, actionable recommendations for policymakers and engineers to enhance infrastructure resilience, reduce long-term costs, and safeguard against the adverse climate change impacts on transportation networks.

A rapid transition to renewable energy is necessary for achieving global decarbonization targets, but siting conflicts, particularly beyond the built environment, remain a key barrier to sustainable development. At the same time, climate-induced pressures on biodiversity intensify the socio-ecological trade-offs within the energy-agriculture-biodiversity nexus. Using New York State as a case study, we assess the geographic implications of utility-scale solar energy development under competing land-use priorities. We apply a mixed-integer linear programming (MILP) optimization model to evaluate solar buildout across three distinct scenarios: minimizing cost, prioritizing agricultural preservation, and conserving biodiversity, employing a lexicographic hierarchy to enforce a strict ordering of stakeholder priorities. Results indicate that New York can meet its mid-century decarbonization goals by deploying 46,216 MW _dc of solar energy, however, achieving this goal involves considerable land-use trade-offs. A cost-minimizing scenario disproportionately targets pasture and hay lands (&gt; 40,000 ha), nearly half of which overlap with grassland bird habitat and broader biodiversity areas. Prioritizing agriculture spares ~80 % of farmland but creates potential for deforestation of over 41,000 ha. Biodiversity-conscious siting avoids ecologically sensitive areas and increases the annualized total costs by 0.17 %, indicating economic feasibility. Our findings highlight the need for spatially informed, integrative land-use strategies that reconcile climate goals with ecological and agricultural values. By linking geospatial optimization with socio-ecological criteria, this work contributes a transferable framework to inform just and ecologically responsible energy transitions in multifunctional landscapes, offering new insights into how geography can advance sustainable development.