South Asia’s rich biodiversity, shaped by its complex biogeographic history, faces increasing threats from climate change, land-use change, and habitat fragmentation. Impacts of these drivers of biodiversity loss on species distributions, habitat dynamics, and protected area effectiveness remain poorly understood. We integrated MaxEnt-based species distribution modelling, habitat fragmentation analysis, and spatial conservation prioritization to assess current and future conservation priorities for 127 threatened plant species (33 Near Threatened, 41 Vulnerable, 38 Endangered, and 15 Critically Endangered) across South Asia. We compiled 8,503 georeferenced occurrence records and 24 environmental predictors to model current distributions and projected suitability for the years 2050 and 2070 under SSP126, SSP245, and SSP585 scenarios using the GISS-E2-1-G climate model. Habitat fragmentation and connectivity were quantified using FRAGSTATS, and zonation was applied to identify high-priority conservation areas. Overlay analysis was performed to quantify the spatial overlap between high-priority conservation areas identified through zonation and the existing protected area network. MaxEnt revealed that under current climatic scenarios, only 6.05 % of the total area of interest was predicted to be highly suitable for these species. The most suitable habitats were identified in the Himalayan mountain range, including its western, central, and eastern parts, and the Western Ghats of southern India, with moderate patch density (0.0037 patches/km²), and high connectivity (proximity index 69,473; aggregation index 89.92). Species’ habitat suitability was predicted to undergo significant changes under future climatic scenarios. Under most of the climatic scenarios across time periods, highly suitable habitats were found to decline considerably with only 9.39 % to 14.33 % of highly suitable habitat overlapping with existing protected area networks. Species-wise analysis revealed that a considerable percentage of species ranging from 44.40 % under SSP126 (2050) to 83.3 % under SSP585 (2070) are predicted to lose suitable habitats. Further, future climate projections indicate increased fragmentation from 29.66 % to 36.39 % and declining connectivity across all scenarios. Spatial prioritization identified 912,037 km² as high-priority zone; 85.01 % of which lies outside the existing protected area networks. These results demonstrate the need to expand and connect protected areas and implement adaptive, climate-informed conservation strategies to safeguard South Asia’s threatened flora under rapid environmental change.

The photovoltaic industry plays a crucial role in addressing the global challenge of climate change. In recent years, China’s photovoltaic industry has developed rapidly and maintained international leadership, requiring a deeper systematic understanding of its future carbon reduction potential and spatial layout optimization strategies. This study constructed a decision-making framework for photovoltaic carbon reduction potential analysis and spatial layout optimization, coupling future development scenarios, and conducted a case study in Inner Mongolia Autonomous Region, China. The research integrated the pvlib python function package with life cycle assessment methods to optimize the estimation of annual power generation and carbon reduction benefits of solar farms. Combined with regional policies and development plans, multiple photovoltaic development scenarios for 2030 and 2060 were constructed to characterize the distribution patterns of solar farms in the study area under different development scenarios. On this basis, the carbon reduction potential was quantified, and layout optimization strategies were explored. The results showed that photovoltaic development in the study area was rapid from 2013 to 2023, bringing nearly 25-fold growth in annual power generation and carbon reduction benefits. Photovoltaic facilities deployed in high-suitability areas will bring significant carbon reduction potential, while improving the installed capacity of the entire study area is more effective for enhancing carbon reduction potential compared to changing the proportion of installed capacity in different regions. The annual carbon reduction potential in the study area could reach up to seven times the current level by 2060, with the desert regions contributing 78 % of this increase. The study area still has considerable photovoltaic development potential, and it is recommended to prioritize the development of high-suitability areas in the western sand desert, gravel desert, and other desert regions while accelerating the configuration of the entire photovoltaic industry chain. The research results can provide decision support for regional photovoltaic industry development planning, and the research framework has important reference value for national and even global-scale energy transition planning.

Desertification is a critical global challenge, driving complex evolutionary shifts and vulnerabilities within the ecology–economy–society composite system (EES system) in desertified regions. Quantifying these vulnerabilities is essential for the United Nation’s 2030 Agenda for sustainable development but remains technically challenging. Consequently, this study designs an integrated model coupling climate, vegetation and soil, land use, society and economy, and water resources to simulate the regional vulnerability of the EES system. Applied to Inner Mongolia, China, the model demonstrated high reliability, with land use area under the curve (AUC) values above 0.8, the coefficient of determination (R²) over 0.9 between simulated and MODIS net primary productivity, and socioeconomic and water resource errors generally within 10 %. The spatial pattern of the vulnerability of the EES system in Inner Mongolia exhibits significant heterogeneity, with high vulnerable regions located in Ulanqab, Chifeng, and western Alxa. Regional vulnerability was lowest under the ecological protection scenario, and higher under the economic and balanced development scenarios. The vulnerability trend during 2021–2030 varies across different scenarios, and generally depicted a decreasing trend in most regions, with the most rapid rate of decline under the SSP5–RCP8.5 and economic development priority scenarios. The developed integrated model will help to understand the evolutionary trend and mechanism of the vulnerability of the EES system, and the simulated results could be used to assist local governments in improving desertification control and sustainable development strategies.

The Arctic has experienced rapid and profound changes due to its heightened sensitivity to global warming and growing regional human pressures. While past research has advanced our understanding of these transformations, a comprehensive assessment within a unified analytical framework is still needed to quantify the ecological impacts of human activity across this fragile region. In this study, we systematically assessed the expansion of human activity and its ecological effects across Arctic and sub-Arctic regions from 2000 to 2020. We combined satellite-based land-cover datasets, vegetation resilience indicator (i.e., lag-1 month temporal autocorrelation of remotely sensed greenness), and species distribution data to track and analyze these changes and impacts. Our findings show that areas affected by human activity -mainly cultivated lands and artificial surfaces -expanded by nearly 13,000 km², equivalent to a rate of 1.8 % per decade. This growth was largely driven by the increase in artificial surfaces (~77.2 %) and extended to higher latitude. As a result, natural habitats became increasingly fragmented, vegetation resilience declined, and risks of ecological tipping points rose. These impacts threatened the habitats of approximately 97.5 % of Arctic species, including 111 species listed as vulnerable or endangered. Our results highlight that, beyond the effects of climate change, the continued expansion of human activity is intensifying ecological risks in the Arctic. This underscores an urgent need for enhanced ecological protection and transformative social strategies to safeguard the region’s future.

Achieving sustainable agricultural production is a critical global challenge, yet the spatial inequalities in greenhouse gas (GHG) emissions and their drivers remain poorly understood. Here, we developed a comprehensive provincial-level assessment of China’s agricultural GHG emissions and inequality from 2000 to 2019, integrating carbon dioxide (CO₂) and non-CO₂ gases using region-specific activity data and emission factors. We quantify spatial heterogeneity in emissions by examining per capita, per agricultural value added, and per unit land area emissions, and apply population-, economy-, and land-based Gini coefficients to systematically evaluate emission inequalities. Our results show that China’s agricultural emissions remained relatively stable over the study period, fluctuating around approximately 0.94–1.06 gigatons CO₂-equivalent, with non-CO₂ gases accounting for 86 %–93 % of total emissions. Substantial spatial disparities persist across provinces. Per capita emissions are highest in sparsely populated western and northeastern regions, while per unit area emissions are concentrated in eastern provinces. Emission intensity per unit of agricultural value added declined markedly nationwide, indicating significant efficiency gains. Inequality analysis reveals a continuous increase in the population-based Gini coefficient (from 0.20 to 0.35), a comparatively stable economy-based Gini coefficient (from 0.23 to 0.28), and a declining land-based Gini coefficient (from 0.51 to 0.42), highlighting divergent equity dynamics depending on the metric used. These findings reveal that while emission intensity has improved, substantial regional heterogeneity persists. This study provides a nuanced understanding of agricultural emission inequalities and offers valuable insights for policymakers to design tailored mitigation strategies.

Existing research on global grain trade has largely neglected the micro-level drivers embedded in rural human-land systems. Addressing this gap, this study develops a cross-scale causal framework to examine how the symbiosis degree between rural settlements and cropland at China’s county level cascades through cropping structure adjustments and domestic supply-demand imbalances, ultimately producing spillover effects across the global grain trade network. The results indicated a significant chain mediation effect: a higher symbiosis degree between rural settlements and cropland is associated with a lower non-grain level, which in turn increases the grain supply-demand ratio, and ultimately reduces China’s grain imports. Specifically, the direct effect of the symbiosis degree on grain imports was −0.190 (p < 0.05), the indirect effect was −0.013 (p < 0.01), and the total effect was −0.203 (p < 0.01). From 2000 to 2020, the symbiosis degree in rural China exhibited a distinct spatial differentiation pattern, characterized by higher values in the three major plains compared to other regions. This divergence has led to a grain conversion production in the three major plains, while other areas have shown a shift toward non-grain conversion production. Consequently, the respective grain supply patterns in these regions reflect situations of excess supply and insufficient supply, respectively. Under scenarios of 30% and 50% increase in symbiosis degree, grain imports are projected to decline by 7.99% and 13.31%, respectively, attributable to the enhanced optimization of human-land systems in rural China. Driven by this contraction in Chinese imports, grain exports from key partner countries (including the United States, Australia, and Canada) are increasingly redirected toward East and Southeast Asia. By revealing the transnational spillover effects of rural system optimization in China on global grain trade, this study offers valuable insights for other import-dependent nations.

During the energy transition, solar power is increasingly replacing traditional energy sources, contributing to carbon-neutral commitments and Sustainable Development Goals (SDGs). Unlike the pollutant-dominant environmental impacts of fossil fuels, solar power exerts climate- and ecology-dominant influences on the entire environment. By changing land surface radiative properties, solar photovoltaic (PV) systems create new energy interaction interfaces with original ecosystems, thereby modifying land surface processes and associated climate variables. While many studies have monitored, measured, or simulated climate variables of PV systems, a comprehensive systematic summary remains lacking. This study synthesizes 147 studies to conduct a systematic review and Meta-analysis. We found that studies on land surface process alterations of PV systems employed research methods of field observation, remote sensing, and numerical simulations across multiple scales, including microsite, solar plant, landscape, as well as continent and globe. The impacts of PV systems on wind speed (–29.96 %), albedo (–17.49 %), daily (–0.44 °C) and daytime (–0.90 °C) land surface temperature, soil temperature (–2.42 °C), and soil water content (+ 38.60 %) were significant. Additionally, heterogeneous responses across different underlying surface types highlight various interactions of PV systems with ecological processes. Finally, a future-oriented framework integrating “underlying surface–research method–climate variable–land surface process–research scale” is proposed to promote interdisciplinary research on land surface process responses of PV systems. This systematic review offers valuable insights into the environmental impact assessment of solar power-integrated ecosystems, providing scientific support for climate actions and facilitating a high-quality energy transition.

Wind power is central to decarbonizing China's power sector, yet its CO₂ reduction benefits are often evaluated using static assumptions that overlook the dynamic evolution of the energy system. To address this limitation, we established a dynamic assessment framework taking two distinct approaches: the Dynamic High-Carbon Displacement Emission Factor (DHC-DEF), which assumes the priority displacement of high-carbon baseloads, and the Dynamic All Mix Energy Displacement Emission Factor (DAM-DEF), which reflects the system-wide average emission intensity of the evolving power mix. Leveraging this framework, we quantified wind power's mitigation benefits and conducted a monetized assessment of its climate-economic value in different Social Cost of Carbon (SCC) scenarios. The results show that, owing to wind power's leapfrog development over the past two decades, China has achieved a cumulative carbon dioxide (CO₂) reduction of 3,672.41 Mt in the DAM-DEF case, generating around \$273.26 billion in climate benefits. By 2060, an annual wind generation of 6,660 TWh is expected to abate at least 931.25 Mt of CO₂, achieving a climate-economic value of around \$33.52 billion. Cumulative reductions over the entire period are forecasted to reach 50,101.21 Mt, delivering economic returns of around $1,803.64 billion. Spatially, the mitigation contributions have been largely concentrated in North (45.7 %) and Northwest (15.8 %) China. Notably, our analysis results show that the strategic prioritization of displacing highcarbon or coal-fired technologies holds the potential to boost mitigation benefits by over 50 %. Moreover, given the high sensitivity of economic valuation to SCC, policymakers should incorporate SCC into dynamic shadow pricing mechanisms and energy planning to support high-quality wind power development.

Reconciling the conflict between rigid socio-economic demand and escalating scarcity is a critical challenge for sustainable water management in drylands. Conventional models are often limited in navigating high-dimensional sectoral trade-offs and nonlinear dynamics within the “water–ecology–economy” nexus. This study proposes RF-NSGA-III-Ada, a machine learning–enhanced framework that synergizes data-driven prediction with evolutionary optimization. Uniquely, the RF module quantifies socio-hydrological inertia to delineate a physically meaningful decision space, while the NSGA-III-Ada employs adaptive reference-point mechanisms to resolve the diversity-convergence dilemma. Applied to the prototypical Heihe River Basin, the framework optimizes the coupled allocation of surface, groundwater, and unconventional water sources. Results reveal a “stability-constrained efficiency” pathway: total water consumption decreases by 0.553% (2025) and 1.172% (2030), achieved through the framework’s precise structural optimization and marginal substitution strategies. Crucially, the optimized schemes effectively navigate the trade-offs between economic efficiency and social stability, achieving emission reductions without compromising the continuity of historical usage patterns. This framework offers a scalable paradigm for resolving the deadlock between structural rigidity and development in water-scarce regions globally.

Climate and social systems are intrinsically coupled. While this interconnectedness is widely recognized theoretically, operational frameworks in research and governance frequently compartmentalize them. This disconnection obscures critical feedback loops and impedes effective climate action. To address this, we introduce the Möbius-based Coupling Climate-Social System, which is structured around three core principles: human-Earth coupling, telecoupling, and strategic coupling. Effective climate governance requires the active integration of these three coupling dimensions. This entails fostering a balance between human activities and the environment, promoting cross-scale climate-social governance, and ensuring strategic coupling through the coordination of policies, planning, and actions.

While the United Nations Sustainable Development Goals (SDGs) embody humanity’s ambitious commitment to building a resilient and sustainable future for our planet, little is known about how the interactions among SDGs affect their progress and synergistic relations. In this study, we developed a system dynamics model to explore the progress of China’s SDGs and their interactions under different development scenarios by 2030. Results showed that the overall progress of SDGs could increase by 7.0 % under the scenario of sustainable transition compared to the baseline scenario. Network analysis revealed a strong synergy among SDG 2, SDG 6 and SDG 7, suggesting a potential enhancement of water-energy-food nexus through collaboration among departments. The differences in the progress of SDGs under different scenarios and the synergies and trade-offs among SDGs can largely be explained by the complex interactions among SDG indicators. The superior performance of sustainable transition scenario stemmed from the increases in educational investment and water-use efficiency, which could promote the decoupling of economic development from environmental pressure. The synergy among water, energy and food systems was attributed to the alleviation of water stress, which could promote agricultural productivity and support energy uses by expanding power capacity. In addition, educational attainment contributing to the achievement of multiple SDGs (e.g., SDG 7, SDG 9, SDG 12) through promoting technological progress and decreasing energy intensity served an important bridging role in the interactions among SDGs. This study highlights the importance of incorporating interactions into systematic management through cross-sectoral coordination strategies.

As highly sensitive geographical units, deserts require timely monitoring of landscape pattern evolution. This study employed the Google Earth Engine (GEE) platform and Landsat imagery, combined with deep learning, to analyse the spatiotemporal changes in China’s desert landscapes from 1980 to 2024. Findings reveal that over the 45-year period, desert landscapes exhibited a coexistence of expansion and partial reversal. Sandy area increased by 38,332.60 km², rising from 62.74 % to 67.42 % of the study region, while grassland area decreased by 42,537.31 km², representing a net loss of 5.57 %. The period 2000–2010 was marked by accelerated desertification, with an annual sandy area expansion rate of 0.725 % -45 times the rate before 2000. However, from 2010 to 2024, sandy area decreased for the first time, by 0.21 %, and the proportion of semi-fixed and fixed dunes increased to 6.61 %, indicating that ecological restoration has gradually become effective. Spatially, western and central deserts remain fragile and are dominated by mobile dunes, while eastern sandy lands show higher vegetation coverage due to engineering and agricultural interventions. Human activities (54.46 %) contributed more to landscape changes than climatic factors (45.54 %), though seasonal cropland may cause “false greening”, potentially overstating restoration success. Future desert management presents opportunities through solar energy and characteristic industries, but caution is needed to avoid secondary risks from photovoltaic projects. Sustainable development in China’s desert regions should therefore be guided by water availability and differentiated zonal strategies -for instance, balancing solar energy and specialty industry expansion with safeguards against photovoltaic-related risks -to reconcile ecological restoration with economic needs.

Sustainable rural development in China is challenged by rapid urbanization and the resulting fragmentation of settlement landscapes. A critical gap persists in quantifying this fragmentation with integrated metrics that capture its nonlinear dynamics and multifactorial drivers. To address this, we developed a composite Rural Landscape Index (RLI), integrating four fundamental landscape metrics to assess fragmentation across structural and functional dimensions. We applied the RLI to a comprehensive county-level dataset covering China from 1980 to 2020 and used Structural Equation Modelling to elucidate driving mechanisms. Large Language Models (LLMs) were subsequently employed to generate context-aware policy pathways grounded in the empirical results. Spatial analysis indicates that high fragmentation (RLI > 0.8) is concentrated in eastern coastal provinces, particularly northeastern Zhejiang and southern Anhui, whereas the northwestern and southwestern plateaus are characterized by high landscape integrity (RLI < 0.2). Our results further demonstrate that in coastal regions, a 10 % expansion of settlements was associated with a 23.5 % loss in ecological connectivity, representing a nonlinear threshold effect undetectable by conventional indices. The mechanistic model identified key natural and socioeconomic drivers and delineated distinct regional resilience regimes, indicating that moderately fragmented regions hold significant potential for eco-efficiency gains through spatial recomposition. The derived policy pathways, including strategies for settlement clustering and tenure reform, offer actionable, evidence-based insights for mitigating fragmentation. This study provides a transparent, scalable framework that directly connects landscape pattern analysis to actionable policy formulation for sustainable land governance.

Great variability among regions in advancing the Sustainable Development Goals (SDGs) highlights the necessity of adopting a multidimensional regional classification perspective. Existing studies tend to overlook structural differences among regions in their social, economic, and environmental foundations, making it difficult to systematically identify the processes and interactions among SDGs across regions with similar characteristics. Using a systematic classification approach based on social, economic, and environmental dimensions, this study classifies 31 provinces of China into seven types and evaluates their SDG progress, interactions, and priority goals from 2000 to 2023. The study found that a high development foundation does not ensure faster SDG progress or more synergistic interactions. Although provinces with high performance in all three dimensions (social, economic, and environmental) have the highest average SDG scores (66), they show the slowest growth (0.52) and the lowest share of synergistic interactions (60.0 %). By contrast, provinces with different combinations of social, economic, and environmental advantages and disadvantages show distinct patterns of growth, synergies, trade-offs, and priority goals. In some cases, provinces that are not the most developed display even stronger synergistic tendencies than both highly developed and severely disadvantaged regions. Different region types also prioritize different SDGs, with SDG 3, SDG 7, SDG 11, and SDG 17 commonly emerging as key goals across multiple region types. This study offers a new perspective on understanding regional variations and interaction mechanisms in SDG progress, providing practical guidance for tailored policy formulation and collaborative sustainable development among similar regions.

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Reliable high-resolution precipitation is essential for monitoring hydrologic extremes and informing climate-risk decisions, yet satellite precipitation products often show biases and remain too coarse (5–25 km) to resolve localized processes. Conventional downscaling also tends to overlook dynamic moisture–cloud mechanisms that drive precipitation variability. We develop a Physics-Informed Geospatial Machine-Learning Downscaling rainfall model (PIGMLD) to produce 1-km daily precipitation over China (2000–2020) by combining the 10-km Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) (GPM IMERG) product with rare ground observations, ERA5-Land precipitation, and physically traceable covariates describing moisture, clouds, and land–atmosphere coupling. Evaluation against independent gauges across China and nine major river basins shows broad skill gains: 84.1 % of stations achieve KGE > 0.60, with improved event detection and reduced systematic bias. Gains are constrained by complex topography and sparse in-situ stations in the Northwest and Southwest basins (median RMSE = 1.18 mm; KGE = 0.45). For heavy and torrential rainfall, RMSE decreases by 36.1 % and 28.3 %, respectively. Relative threshold assessments indicate robust corrections under dry and wet extremes: for events below the 10th percentile, BIAS is reduced by ∼66.7 % at > 90 % of stations; for events ≥ 90th percentile, underestimation is substantially alleviated, with BIAS typically reduced by ∼50.1 %. XGBoost–SHAP attribution reveals scale-dependent controls: 10-km estimates are dominated by cloud and column moisture, whereas 1-km estimates are more sensitive to near-surface humidity and land-surface states, and heavy rainfall reflects coupled moisture–dynamics–thermodynamics interactions. Overall, PIGMLD provides a mechanism-aware pathway for producing and interpreting 1-km precipitation fields and clarifies when finer-scale information improves extreme-event characterization.

Zero Hunger (Sustainable Development Goal 2, SDG 2) serves as a cornerstone for achieving global sustainable development, and is intricately linked with other SDGs exhibiting complex and multifaceted synergies and trade-offs. While the interconnections among indicators referring to food system within environmental domain have been widely investigated, interactions among indicators of all three pillars (social, economic, and environmental) remain under-researched. This study leverages the 2020 Sustainable Development Solutions Network (SDSN) assessment data to construct a global SDG 2-related network comprising 38 targets and 61 indicators, and examine how this network’s structure varies across income levels. The results reveal high-income countries (HICs) have achieved notable advancements in eradicating hunger and improving agricultural productivity, while facing unique challenges of overnutrition. Low-income countries (LICs), by contrast, face persistent constraints in agricultural productivity, infrastructure, and resource access. Across the global SDG 2-related network, SDG 2 targets show direct synergies with 31 targets in other SDGs, covering all studied economic targets, whereas 10 targets exhibit direct trade-offs, all of which are related to the environment. The share of trade-offs declines as income rises, from 28 % in LICs to 13 % in HICs. Synergies mainly occur between economic targets in LICs, while they often occur between economic and social targets in HICs. Trade-offs linked to environmental targets indicate LICs rely more on natural resources, whereas HICs face environmental spillovers. These findings underscore the need for tailored strategies, with LICs prioritizing agricultural productivity and infrastructure, while HICs addressing social equity, social distribution, and environmental sustainability.

The escalating pressure of global warming necessitates practical and effective climate governance policies to meet the urgent goals of theParis Agreement. However, for conventional policy simulation models like integrated assessment models (IAMs), their reliance on predetermined scenarios makes it challenging to adequately address the substantial uncertainties inherent in climate-social system evolution. This study proposes an adaptive decision-making framework that integrates deep reinforcement learning (DRL) with a climate–social system model to identify governance strategies that can reduce the risk of transgressing planetary boundaries. In this framework, we operationalize Social Tipping Elements (STEs) as targeted actions. A reward function provides feedback to optimize these interventions, ensuring the system remains within critical planetary boundaries. Our results demonstrate that, compared to conventional static models, the framework discovers adaptive policies through dynamic learning, enabling real-time responses to evolving climate-social conditions. These adaptive policies exhibit an “early-stage intensive intervention, mid-term moderation, and late-stage reinforcement” pattern that reduces planetary boundary overshoot time by 55 years while stabilizing global warming below 1.5 °C by 2100. By varying governance objectives, we further reveal the trade-off mechanisms between competing climate and socioeconomic goals. Notably, the designed multi-objective reward function enables a synergistic balance across competing objectives, resulting in the shortest planetary boundary overshoot duration (15 years) among all evaluated scenarios. This framework overcomes the limitations of static simulations, offering a robust and interpretable tool to design adaptive strategies crucial for navigating the competing objectives of time-sensitive climate governance.

Artificial Intelligence (AI) is increasingly recognized as both an enabler of and a risk to sustainable development. Yet research remains dominated by expert-driven assessments, with little attention to how the public perceives and discusses AI’s sustainability implications. This study examines concern-oriented public discourse about AI in relation to the United Nations Sustainable Development Goals (SDGs) by analyzing social media posts and developing expert-derived solutions. We collected and analyzed over 700,000 posts from X (formerly Twitter) spanning three years (2022–2025), applying natural language processing techniques including Fine-tuned BERT, Zero-Shot Classification, and Pre-trained XLNet. The analysis reveals pronounced imbalances in public attention across the 17 SDGs, with 52.8 % of AI-related discussions concentrated on SDG 9 (Industry, Innovation, and Infrastructure) and 11.8 % on SDG 16 (Peace, Justice, and Strong Institutions), while critical goals such as SDG 1 (No Poverty) and SDG 2 (Zero Hunger) receive minimal attention (1.0 % and 0.3 %, respectively). To address these gaps, we convened a focus group of nine experts from academia, industry, and policy sectors to develop actionable solutions for each SDG, including AI-driven financial inclusion tools, precision agriculture models, and energy-efficient “Green AI” technologies. Our findings suggest a notable misalignment between AI’s potential to address pressing humanitarian challenges and the patterns of concern-oriented public discourse captured in this study. These findings point to the need for broader public awareness, robust ethical governance, and interdisciplinary collaboration if AI is to advance sustainability goals equitably and reach underserved communities.

The 15-minute city concept, which promotes urban liveability by ensuring that essential services are accessible within a short walking distance, has gained global attention as a sustainable alternative to car-dependent urban sprawl. While existing accessibility studies predominantly rely on map-based methods and non-pedestrian-focused mobility assessments, significant gaps remain in understanding pedestrian accessibility from a perceptual and experiential standpoint. This study addresses these gaps through a systematic review of 53 scholarly articles sourced from Web of Science, Scopus, and Google Scholar, critically examining the methodologies, metrics, techniques, and data inputs used to assess pedestrian accessibility in 15-minute city models. The review particularly focuses on data acquisition, tool digitalization, and methodological refinement. Findings highlight global trends in the implementation of pedestrian accessibility assessment, revealing variations in demographic representation, data diversity, and scale of analysis. The results show a strong reliance on geospatial data-driven approaches but a limited incorporation of perceived urban attributes, such as visual environments, travel impedance, and street-level experiences, in pedestrian-focused accessibility models. Furthermore, the study underscores the potential of integrating perceptual indicators into accessibility assessments to bridge the gap between urban infrastructure and pedestrian experience. By synthesizing advancements in data-driven urban analytics, this review contributes to the refinement of pedestrian accessibility measurement frameworks, advocating for more inclusive, perception-based urban planning strategies.