Search Results (280)

Assessing climate performance through emission-centric metrics provides an incomplete picture of countries’ progress toward integrated climate objectives. This study proposes the Climate Integrated Performance Index (CIPI), a multidimensional and explainable composite indicator for 27 European countries during 2015–2023. CIPI integrates six thematic dimensions: emissions, energy systems, mitigation capacity, transport, agriculture, and waste–land-use interactions, using robust normalization, a policy-informed weighting framework, and formal statistical validation. Based on 243 country–year observations, the results indicate that CIPI is non-redundant. Pearson correlations reveal strong positive associations with the Energy Index (r = 0.899) and Mitigation Index (r = 0.894), alongside a significant negative association with the Agriculture Index (r = −0.659), highlighting sectoral trade-offs. Variance decomposition further shows that energy and mitigation dimensions jointly account for approximately 87% of explained variance, whereas agriculture exerts a systematic counterbalancing influence. To support structural interpretation, an explainable machine learning framework combining XGBoost and SHAP was implemented as a diagnostic layer. Renewable-energy capacity emerged as the dominant structural driver of integrated climate performance, and SHAP-based analyses revealed a nonlinear threshold effect, with positive contributions accelerating beyond a normalized renewable-capacity level of approximately 0.58 (95% bootstrap confidence interval: 0.54–0.62), particularly under low fossil-fuel dependency conditions. Because the machine learning models use indicators that also contribute to index construction, the results are interpreted as evidence of structural consistency and diagnostic interpretability rather than independent predictive discovery. To address this limitation, repeated cross-validation, subsample validation, benchmark comparisons, and weighting-sensitivity analyses were conducted. Ranking robustness remained high under alternative weighting schemes (Spearman ρ > 0.96), while comparison with an emission-centric benchmark demonstrated substantial rank reversals, indicating that broader sectoral and policy dimensions influence climate-performance assessment. Overall, CIPI functions not only as a benchmarking tool but also as a transparent diagnostic framework for identifying structural trade-offs, nonlinear relationships, and policy-relevant climate-transition dynamics. Full article

Achieving environmental sustainability, particularly the targets outlined in Sustainable Development Goal 13 (Climate Action), is a critical global imperative. This investigation analyzes the heterogeneous effects of artificial intelligence (AI), tourism intensity, tourism expenditure, the Gross Domestic Product (GDP) share contributed by tourism, natural resource rents, and environmental policy stringency on the ecological footprints of advanced countries from 2000 to 2022. Using a robust analytical framework featuring advanced econometric methods, specifically the Method of Moments Quantile Regression (MMQR) and an innovative machine learning approach—Panel Quantile-on-Quantile Kernel-Based Regularized Least Squares (PQQKRLS)—the research elucidates complex, nonlinear interdependencies. Key empirical results show that AI adoption significantly mitigates ecological footprints across all quantile distributions. Conversely, heightened tourism intensity and increased tourism expenditure are associated with greater environmental degradation. The analysis further indicates a U-shaped tourism–ecological footprint relationship, suggesting that tourism’s economic contribution may initially reduce ecological pressure but may increase it again beyond a certain expansion threshold. These conclusions underscore the necessity for advanced nations to adopt synergistic policy frameworks that strategically leverage AI technologies, promote sustainable tourism practices, and reinforce rigorous environmental governance to advance climate action and ecological sustainability. Full article

Sustainable food systems are increasingly challenged by climate change, resource constraints, market volatility, and growing food demand, making accurate forecasting of food production essential for food security and long-term sustainability. Despite the growing use of machine learning in agricultural forecasting, studies directly modeling the Food Production Index (FPI) within a sustainable food systems framework remain limited, particularly in emerging economies. This study addresses this gap by forecasting Türkiye’s Food Production Index using agricultural, macroeconomic, and trade-related indicators covering the period 1962–2023. Seven predictive approaches, including Multiple Linear Regression (MLR), Bayesian Ridge Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), and K-Nearest Neighbors (KNN), were comparatively evaluated using R², RMSE, and MAE metrics. The results demonstrate that Bayesian Ridge Regression (R² = 0.968) and MLR (R² = 0.918) significantly outperform more complex machine learning algorithms, indicating that model–data compatibility is more critical than algorithmic complexity in long-term food production forecasting. The findings reveal that economic growth, agricultural inputs, and structural transformation processes play a decisive role in shaping food production dynamics. By integrating machine learning with sustainability-oriented food system analysis, this study provides a robust evidence base for supporting food security strategies, resource-efficient agricultural planning, and resilient food system governance. The proposed framework offers macro-level decision-support insights for policymakers engaged in long-term food system planning, strategic risk monitoring, and evidence-based policy evaluation. Full article

Against the backdrop of deepening global climate governance and the ongoing advancement of the dual carbon goals, clarifying whether climate risk management can effectively drive improvements in urban green energy efficiency holds significant importance for synergistically advancing climate adaptation and green transition. This study employs data from 284 prefecture-level and above cities in China spanning 2012–2023, utilising the climate-resilient city pilot policy as a quasi-natural experiment. By integrating dual machine learning models with spatial lag models, it systematically examines the causal effects, transmission mechanisms, and spatial spillover characteristics of climate risk governance on urban green energy efficiency. Findings reveal: ① Climate-resilient city development significantly enhances local green energy efficiency through three pathways: green finance development, green technological innovation, and industrial structure upgrading; ② The impact exhibits pronounced spatio-temporal heterogeneity, characterised by delayed and amplified effects, particularly pronounced in inland, central cities, and non-resource-based cities; ③ The impact exhibits a pronounced positive spatial correlation, not only catalysing local green transitions but also generating positive spatial spillovers to neighbouring regions, indicating potential for regional collaborative development. Additionally, green finance itself possesses positive cross-regional spillover effects. This study provides empirical evidence and policy recommendations for optimising climate adaptation policy design, strengthening green finance coordination, and advancing regionally linked green transitions. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article

The innovation-driven transformation of carbon-intensive industries is central to global climate governance and sustainable industrial specialization. Integrating Global Value Chain (GVC) embedding, external environmental dynamics, and industrial innovation, this study utilizes micro-to-macro extracted panel data from China’s carbon-intensive industries from 2007 to 2023 to construct a double debiased machine learning (DDML) model. We identify a robust innovation-promoting effect driven by both GVC participation and the GVC Domestic Content Ratio (DCR). Employing a causal mediation framework, we reveal that GVC embedding indirectly drives innovation by reshaping four functional dimensions: upgrading intermediate export quality, stimulating technology market activity, internalizing climate policy uncertainty, and aligning environmental violation disclosure. Furthermore, structural heterogeneity tests demonstrate that mature resource-based cities and backward-linked industries benefit substantially from learning-by-doing effects. In contrast, forward-linked industries and non-mature cities face an elevated risk of a positioning paradox and comparative advantage lock-in. This study provides rigorous empirical evidence for advancing SDG 9 in developing economies seeking to reconcile deeper global economic integration with carbon neutrality objectives. Full article

Agricultural carbon emissions represent a major source of greenhouse gases and play a critical role in achieving global climate mitigation and sustainable agricultural development targets. In China, the rapid transformation of agricultural production systems has led to substantial spatial heterogeneity in emission patterns and driving mechanisms of agricultural carbon emissions, while the underlying processes at the county scale remain insufficiently understood. This study investigated the spatiotemporal evolution and potential influencing factors of agricultural carbon emissions at the county level from 2000 to 2022 in Guangdong Province, China. First, agricultural carbon emissions were estimated based on a multi-source accounting framework covering land management, crop cultivation, animal production, and straw burning based on internationally recognized emission accounting methods and IPCC global warming potentials. Then, spatial clustering characteristics were analyzed using local spatial autocorrelation (LISA) to identify heterogeneous emission patterns. Finally, an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) was employed to quantify the nonlinear effects and relative contributions of multiple socioeconomic and agricultural drivers. The results showed that agricultural carbon emissions in Guangdong Province exhibited a fluctuating but overall decreasing trend, declining from 50.89 Mt CO₂-eq in 2000 to 39.24 Mt CO₂-eq in 2022, with an overall reduction of 22.9%. High-emission clusters were primarily concentrated in western and northern Guangdong, while low-emission areas were mainly located in the Pearl River Delta (PRD). The RF models demonstrated satisfactory predictive performance, with spatial cross-validated R² values ranging from 0.75 to 0.91 across different years. SHAP analysis suggested that ploughing area, fertilizer and pesticide usage, agricultural machinery power, and primary industry GDP were the dominant factors associated with agricultural carbon emissions, whereas urbanization consistently showed a negative association. Furthermore, these drivers exhibited pronounced nonlinear responses and distinct regional heterogeneity, particularly between the PRD and the western and northern parts of Guangdong Province. These findings suggested that agricultural carbon emissions are jointly influenced by agricultural production intensity, mechanization, and socioeconomic transition and can provide a scientific basis for developing region-specific low-carbon agricultural policies and promoting the sustainable transformation of agricultural systems. Full article

Flooding is a destructive natural hazard intensified by climate change, posing challenges to sustainable disaster risk management. This study developed and evaluated machine learning models for flood severity prediction within a hexagonal grid system (H3, resolution 8) under rainy season conditions in Maha Sarakham, Thailand. Four models Random Forest (RF), XGBoost, Gradient Boosting (GB), and Support Vector Machine (SVM) were trained using 11 environmental variables across historical years (2018, 2021, 2022) and tested on a projected year (2025) under SSP scenarios. XGBoost demonstrated the most stable performance (accuracy > 0.95 across all years), while SVM achieved high historical accuracy (0.970 average) but failed to detect positive flood cases in 2025 (recall = 0), highlighting the importance of temporal validation. Topographic variables were the most consistent predictors, but NSMI (soil moisture) emerged as the top SHAP predictor in 2025 (r = 0.52), suggesting a shift in flood-generating mechanisms under climate change. A polarization pattern was observed: flood-affected area declined to 6.8% in 2025 (79% reduction from 2022), yet maximum flood point counts remained high at 14.0, indicating more concentrated but intense flooding. Under SSP projections, using the historical baseline (27.5%), SSP1-2.6 (45.2%) and SSP2-4.5 (45.0%) indicate increased flood risk relative to the historical baseline through 2040. The SSP5-8.5 projection (3.4%) is identified as a model extrapolation artifact through formal out-of-distribution assessment (Mahalanobis distance = 8.72, p < 0.001) and is therefore excluded from policy recommendations. Although GRU and LSTM achieved marginally higher AUC values in retrospective validation, we recommend XGBoost for operational forecasting due to its temporal stability, computational efficiency, and interpretability. We further recommend integrating real-time soil moisture monitoring into early warning systems and shifting to hotspot-targeted adaptation strategies. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Accurate forecasting of country-level temperature anomalies is increasingly important for climate monitoring, policy planning, and environmental risk assessment. However, the trade-off between predictive performance, model complexity, and computational cost remains insufficiently explored, particularly across multiple countries using compact and interpretable feature representations. This study presents a comprehensive comparative evaluation of eight forecasting approaches for annual temperature anomaly prediction using country-level observations from the FAOSTAT Temperature Change dataset. The evaluated methods comprise a Persistence baseline, Ordinary Least Squares (OLS), Ridge regression, Support Vector Regression (SVR), Random Forest, a multilayer perceptron (MLP), and the classical time-series models ARIMA and ETS. Annual temperature anomalies were modeled using lagged observations, a temporal trend, and a trailing moving average under a temporally ordered 80/20 train–test split. Model performance was assessed using RMSE, MAE, $R^2$, per-country win-rate, computational runtime, and pairwise statistical comparisons based on the Wilcoxon signed-rank test with Holm correction. Hyperparameters were optimized through expanding-window temporal cross-validation, and an ablation study was conducted to quantify feature contributions. Results indicate that the ETS model achieved the best overall predictive performance, obtaining the lowest median RMSE (0.3388 °C), the lowest MAE (0.2792 °C), and the highest per-country win-rate (40.07%). ARIMA provided competitive forecasting accuracy but incurred substantially higher computational cost, whereas OLS and Ridge offered an attractive compromise between predictive performance, robustness, interpretability, and computational efficiency. In contrast, the more flexible machine learning models (SVR, Random Forest, and MLP) did not consistently outperform the simpler approaches despite their higher complexity. Overall, the results demonstrate that classical statistical forecasting methods remain highly competitive for annual country-level temperature anomaly prediction and that increasing model complexity does not necessarily translate into improved predictive performance. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

Amidst booming digital economy and tightening climate governance, enhancing green total-factor energy efficiency has become pivotal for socioeconomic transformation. Whether digital economy drives urban green energy transition remains unresolved, particularly regarding factor mobility mechanisms and spatial spillovers. Using panel data of 281 Chinese cities from 2011 to 2022, this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy’s impact on urban green energy efficiency. Findings indicate that (i) digital economy significantly enhances local green energy efficiency through green technological innovation, green finance development, and industrial upgrading; (ii) it facilitates talent and capital agglomeration toward digitally advanced regions, with innovation factor mobility serving as a crucial mediator; and (iii) significant spatial positive correlation exists, where digital economy generates pronounced spillovers to neighboring cities that exceed direct effects, fostering regional synergies. This research overcomes conventional methodological limitations and pioneers integrating innovation factor mobility into digital economy-green transition analysis, revealing factor reconfiguration as the core mechanism. Findings provide policy implications for cross-regional digital-energy coordination, factor marketization reforms, and differentiated green strategies. Full article

Climate change threatens global food security through shifts in temperature and precipitation regimes, greater frequency of extreme weather events, and cascading effects on agricultural input markets and institutional capacity. Policymakers require nationally comparable diagnostic tools that are reproducible, transparent, and grounded in open data. This study presents an interpretable machine learning framework for country-level crop yield prediction and climate sensitivity screening, using publicly available FAOSTAT-derived panel data spanning 101 countries and 10 staple crop types over 1990–2013. A gradient-boosted decision tree model (XGBoost 2.1.4) is trained on observations from 1990 to 2008 and evaluated on a strictly held-out temporal window (2009–2013), using annual mean temperature, annual precipitation, total pesticide use (as a rough proxy for agricultural input management intensity), crop type and country identifiers, and temporally lagged yield values as predictive features. The optimized model yields high predictive accuracy on held-out data (R² = 0.982; RMSE = 11,183 hg/ha; MAE = 4396 hg/ha). Ablation analysis reveals that model performance depends primarily on temporal yield persistence and crop identity, with performance declining to R² = 0.940 when lagged features are omitted, while climate-only variables explain limited variation (R² = 0.119). Notably, an ordinary least squares (OLS) baseline achieves comparable performance (R² = 0.984), suggesting that the dominant predictive signals arise from a stable temporal–cross-sectional structure rather than nonlinear modeling flexibility. SHAP-based feature attribution identifies regime-dependent temperature effects, with larger (more negative) marginal contributions under high-temperature conditions. Stylized sensitivity perturbations (+1 °C, +2 °C, −20% pesticide inputs) indicate modest mean yield changes (−1.3% to −1.6%) but substantial cross-national heterogeneity. Systematic residual analysis identifies countries exhibiting consistent over- or under-prediction patterns, offering diagnostic signals for further institutional investigation. This framework is designed as a transparent, scalable screening tool for evidence-based prioritization rather than a validated causal or forecasting instrument. It complements localized agronomic expertise and supports SDG 2 (Zero Hunger) and SDG 13 (Climate Action). Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article