Search Results (294)

Future climate projections suggest an increase in heat-related mortality and a decrease in cold-related deaths under warming scenarios. Understanding the health impacts of extreme heat, and their implications for healthcare demand is essential for assessing the future burden of climate-related illnesses. In this study, we examined the relationship between extreme heat events and Emergency Department Visits (EDV) for heat-related illnesses (HRIs) across the United States from 2018 to 2025. Using data from the Centers for Disease Control and Prevention (CDC) Heat and Health Tracker and other relevant sources, we analyzed EDV rates standardized to 100,000 population. We aggregated daily into the 10 U.S. Health and Human Services (HHS) Regions. We used 0.5° × 0.5° gridded maximum daily temperature data (aggregated to HHS regions with proportional area weighting) and daily maximum heat index extracted from the CDC data portal (estimated using the US National Weather Service methodology and aggregated to HHS regions using total population weighting) to characterize seasonal patterns and regional variability. The association between peak heat events and EDV time series was explored using log-linear mixed-effects models, which accounted for seasonal trends, climate variables, and their regional variability. Random effects were used to capture regional heterogeneity in predictor-response relationships, accommodating variation in associations across regions. Model performance was evaluated using prediction error metrics and goodness-of-fit assessments. Maximum temperature and heat index were both significant predictors, with the heat index offering a slightly better fit. Associations were largely contemporaneous, with peak correlations at lag zero, underscoring the need for real-time response. EDV increased several days before peak environmental conditions, consistent with early exposure effects. While temperature-EDV relationships varied regionally, heat index associations were more stable. This work underscores the urgent need for regionally adaptive public health strategies in the face of intensifying climate extremes and outlines future directions for research and policy to strengthen health systems’ preparedness in a warming world. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article

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

The construction of the Hainan Free Trade Port (FTP) is guided by the core philosophy of “ecological priority and green development.” To meet the practical requirements of building a “Green and Beautiful FTP,” this study constructed a kernel Normalized Difference Vegetation Index (kNDVI) dataset using Landsat series satellite imagery. By integrating methods including the Mann–Kendall (MK) trend test, Hurst exponent, and coefficient of variation (CV), an in-depth analysis was conducted on the spatiotemporal evolution characteristics and trends of growing-season vegetation in Hainan Island from 1994 to 2023. Additionally, the Extreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) interpretation method were employed to quantitatively unravel the driving mechanisms of climatic factors and human activities on kNDVI variations. The results indicate that: (1) Over the past three decades, the overall kNDVI of Hainan Island has exhibited a significant upward trend, characterized spatially by an evolution pattern of “stable recovery in the central region and localized degradation along the coast.” (2) The vegetation evolution demonstrates strong persistence and is highly consistent with community stability. The high stability in the central mountainous areas stems from a superior natural background and strict ecological protection; the transition zone is jointly influenced by vegetation types and anthropogenic management; meanwhile the coastal areas exhibit significant degradation characteristics driven by high-intensity human disturbances. (3) The analysis of driving mechanisms reveals a complex control logic of “topographical foundation—human reshaping—extreme climate triggering.” Static topographical factors, such as elevation and slope, occupy an absolute dominant position; human activities, represented by rubber plantation expansion and urbanization, exert a bidirectional reshaping effect characterized by “inland greening and coastal suppression”; furthermore, extreme drought and high temperatures in the later stages of the study demonstrated a significant pulse-like impact, exacerbating the risks of short-term climatic stress. This study clarifies the core patterns of vegetation evolution in Hainan Island, validates the effectiveness of ecological policies, and provides a quantitative scientific basis for ecological conservation and sustainable development in tropical island regions. Full article

Coordinated regional development plays an increasingly important role in promoting low-carbon transitions across China’s urban agglomerations. This study examines the effect of the Beijing–Tianjin–Hebei Coordinated Development Strategy, elevated to major national strategic status in 2014, on urban carbon emissions. Using an unbalanced panel of 294 prefecture-level and higher-level cities from 2010 to 2023, we employ a two-way fixed-effects difference-in-differences model to evaluate the strategy’s overall carbon-mitigation effect, potential transmission pathways, and spatial heterogeneity. The baseline DID coefficient is −0.052, corresponding to a 5.0% decline in city-level total CO₂ emissions in the 13 treated cities relative to the national comparison group after policy implementation. Within the same comparison framework, further analysis documents significant positive post-policy differentials in both the industrial-structure-upgrading index and green invention-patent applications; the industrial-structure-upgrading results are consistent with a potential emissions-reduction pathway. The heterogeneity analysis indicates a statistically significant relative carbon-mitigation effect for the prefecture-level cities of Hebei compared with the national comparison group, with a larger relative effect in Beijing and Tianjin. This spatial pattern is consistent with differences in industrial structure, innovation capacity, and environmental governance capacity across cities. The findings provide city-level quasi-experimental evidence that coordinated regional development can promote urban carbon mitigation and support differentiated carbon governance, coordinated industrial upgrading, and cross-regional low-carbon policy design. Full article

The post-pandemic acceleration of digital tools has reshaped how small and medium-sized enterprises (SMEs) in Latin America compete, yet the resulting maturity gains remain poorly understood in individual emerging-market economies. Ecuador exemplifies this gap: SMEs account for over ninety per cent of the formal business register but appear in few econometric studies, most based on convenience samples. This study asks how strongly digital adoption is associated with the digital maturity of Ecuadorian SMEs and which tools best predict e-commerce engagement. The analysis draws on the National Digital Skills Survey (ENHD, Encuesta Nacional de Habilidades Digitales), administered by Ecuador’s Ministry of Telecommunications (MINTEL), covering 847 SMEs in 2021. A ten-indicator Digital Adoption Index (DAI) is related to the ENHD maturity score through least squares regression with robust standard errors, and a binary logit model with multicollinearity diagnostics identifies the tools that predict e-commerce adoption. The DAI explains over half of the variance in digital maturity, robust to firm-size and sector controls. Website presence exerts the largest marginal effect on e-commerce adoption, followed by cloud services. Top adopters score twice as high as non-adopters, consistent with cumulative advantage. Policy implications include subsidised digital-foundations bundles for laggard micro-enterprises. Full article

Large Language Model (LLM) agents in long-horizon planning often exhibit Semantic-Execution Drift (SED), where executed actions progressively deviate from original language constraints. To formalize this phenomenon, we model SED as a stochastic drift process, expressed by the recurrence D(t+1) = alpha D(t) + epsilon(t) + beta P(t), and show that policies with an alpha below one induce semantic contraction. We propose ReflectiChain, a framework integrating Retrospective Reflection, a Latent World Model, and Double-Loop Policy Adaptation to preserve semantic consistency during long trajectories. To evaluate SED, we introduce Sema-Sim, a multi-agent supply chain benchmark containing 10 policy constraints, six adversarial perturbations, and 30-step planning horizons. We further propose the Semantic Fidelity Index (SFI) for measuring instruction adherence. Experiments on DeepSeek-V3.2 across seven reasoning strategies show that ReflectiChain achieves the highest SFI (88.7) and stable semantic contraction (alpha = 0.823, below one). The results are consistently validated on Qwen3.5-122B and Qwen2.5-72B. Ablation studies demonstrate that Retrospective Reflection contributes most to performance gains. Additional analyses on scalability, failure modes, and cost efficiency further verify the robustness and practicality of the proposed framework. All code and evaluation resources are publicly released. Full article

Background and Objective: Glucagon-like peptide-1 (GLP-1) receptor agonist therapy has intensified the policy debate over whether obesity pharmacotherapy increases short-term healthcare expenditures or reduces downstream medical costs (i.e., non-drug, acute-care expenditures). This study estimated same-year associations between GLP-1 use and medical spending among adults with observed obesity. Methods: Analyses used Medical Expenditure Panel Survey (MEPS) person-year files compiled from 2018 to 2022, with body mass index (BMI)-defined analyses restricted to 2019 and 2021. The primary analytic sample included 7144 person-years, of which 275 represented sustained GLP-1 users. Overlap-weighted Poisson pseudo-maximum likelihood models, utilization regressions, two-part decompositions, and a layered diagnostic framework were applied to assess spending patterns and same-year cost implications. Robustness was evaluated through sensitivity analyses across all-obesity, severe-obesity, and diabetes-excluded subsamples, as well as exploratory molecule-specific specifications. Results: Findings showed no evidence of same-year cost savings, but rather a distinct shift in spending composition. In fully adjusted primary models, GLP-1 use was associated with reduced non-drug medical spending (beta = −0.2494; marginal effect = −$2586 per person-year) and lower acute-care spending (beta = −0.5856; marginal effect = −$2019), while total medical spending was higher (beta = 0.3182; marginal effect = +$6483). Point E values of 1.89 (non-drug) and 2.99 (acute-care) indicated moderate-to-strong resilience to unmeasured confounding. Negative control models using dental spending and visits showed significant positive associations. Since this residual confounding operates in the direction opposite to the negative acute-care association, the headline compositional pattern is unlikely to be an artifact of upward health-seeking bias. Utilization models indicated fewer inpatient discharges (IRR = 0.686) and inpatient nights (IRR = 0.524). Two-part models further suggested that the reduction in acute-care spending was concentrated on the intensive margin among individuals with positive expenditures. Conclusions: The use of GLP-1 is not associated with overall short-term cost savings. This usage is related to a shift in the composition of observed medical spending during the same year. This change is leading to a decline in hospital admissions and acute care costs, in parallel with the rise in pharmaceutical spending. Non-drug and acute-care costs were lower among regular users. However, total spending was at a higher level, consistent with a trend in the budget records from that period in which drug costs were the dominant factor. These associations are consistent with a potential reallocation of healthcare resources from inpatient acute care toward pharmacy and ambulatory services, with corresponding planning implications for payers and possible workforce reallocation of nursing capacity from acute to outpatient settings, contingent on replication in post-2022 data. Full article

Enhancing supply chain resilience has become crucial for sustainable manufacturing development as firms face repeated disruptions from pandemics, geopolitical shocks, logistics bottlenecks, and climate-related uncertainty. As digital finance alleviates corporate financing constraints and improves information transmission across supply chains, it may strengthen supply chain resilience, thereby supporting the sustainable development of manufacturing firms. Accordingly, this paper examines this effect using panel data consisting of 21,060 firm-year observations of Chinese A-share listed manufacturing firms spanning the period 2011–2023. It combines the proxy of digital finance, which is the city-level Baidu search index for digital finance, with the entropy-weighted and firm-level supply chain resilience index to assess its sustainability. Based on the fixed-effects model, digital finance positively affects the resilience of manufacturing companies’ supply chains and, by extension, promotes the sustainable development of manufacturing supply chains. We also find that digital finance improves manufacturing supply chain resilience by enhancing information transparency, resolving maturity mismatches between investment and financing, and mitigating financial risks. This impact is larger in poorly developed traditional financial regions, firms with poor governance, and in growing companies. Policy recommendations center on advancing digital supply chain finance, strengthening data governance, and improving risk management systems to reinforce supply chain resilience and promote the long-term sustainable development of manufacturing firms. Full article

Digital Twin technologies hold significant promise for advancing smart campus initiatives by enabling data-driven management of facilities, sustainability planning, and safety monitoring. Despite this potential, affordability remains a critical barrier to widespread adoption across higher education institutions. This study introduces an initial exploratory scenario-based affordability index for campus digital twin and maps the affordability of implementing campus digital twin across 1872 U.S. higher education institutions using spatial analysis techniques. Sensitivity analysis is also conducted to evaluate the robustness of the results. Our analysis yields three key findings: (1) Under the baseline scenario, most campuses show moderate-to-high affordability with spatial clusters concentrated in coastal and metropolitan regions, while nearly one-quarter remain unaffordable or marginally affordable, with clusters located in the Midwest. (2) Private institutions demonstrate relatively greater affordability than public institutions, with for-profit private institutions exhibiting higher affordability than their non-profit counterparts. Spatial aggregation patterns further reveal heterogeneity in affordability between these sectors. (3) States with strong economic and educational infrastructures, such as California, contain a greater number of affordable campuses for digital twin implementation, while resource-constrained states face significant barriers. These findings remain generally robust and consistent across the baseline and sensitivity scenarios. The results underscore the need for standardized cost models, public cost databases, targeted policy guidance, and multi-stakeholder collaboration to promote equitable adoption. By positioning digital twins as strategic tools for campus resilience, efficiency, and innovation, this study advances the conceptual understanding of their role in higher education and provides exploratory yet actionable insights for institutional leaders and policymakers to support inclusive digital transformation. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

Rapid urbanization has generated substantial economic growth but has also intensified pressure on the environment. China’s New-Type Urbanization Policy (NTU) was designed to promote people-centered, smart governance, and green development. However, whether the NTU policy has improved the coordination between urbanization and environmental quality remains insufficiently examined. Using panel data in China from 2010 to 2021, this study constructs an urbanization–environment coupling coordination degree index based on satellite-derived China’s High-resolution Eco-Environmental Quality dataset and applies a continuous difference-in-differences model to evaluate the impact of NTU policy intensity. The results show that greater NTU policy intensity is associated with significant improvements in urbanization–environment coordination after controlling for pre-existing socioeconomic, ecological, and geographical differences across cities. This finding is consistent with a corrective effect: rather than simply expanding urban scale, NTU appears to help offset the misalignment between urban development and environmental quality. Conditional analyses further suggest that the estimated policy effect is more readily observed where industrial structures are more advanced and local attention to ecological governance is stronger, indicating that these conditions shape the extent to which NTU can be translated into coordination gains. Heterogeneity analyses show that the estimates are statistically more robust in eastern, coastal, and non-resource-based cities, while the comparison across green-coverage groups remains inconclusive because the low-coverage group has a larger but less precisely estimated coefficient. Overall, the findings suggest that NTU has corrective potential, but that its effect is conditional rather than uniform across cities and depends on local governance, industrial, and ecological circumstances. Full article

The transition toward Agriculture 4.0 aims to improve farm performance and sustainability; however, existing macroeconomic indicators do not fully capture the depth of farm-level digital adoption. This study proposes the Digital Farm Index (DFI) as a tool for assessing digital maturity and regional disparities in European agriculture. The research combines bibliometric mapping of the scientific literature with an empirical DFI assessment for 18 European Union Member States using Eurostat data. The empirical assessment utilizes multiple linear regression, log-linear scale modeling, and hierarchical clustering to analyze adoption patterns and structural determinants. The index integrates four dimensions: connectivity, precision agriculture, robotics, and farm management information systems (FMIS). Results indicate that adoption is concentrated in larger farms, as area-weighted digital maturity (DFI-Hectares) consistently exceeds farm-level adoption (DFI-Farms). CAPEX-based cost modeling suggests the existence of a technological indivisibility threshold, whereby digitalization may become an entry barrier for fragmented farms with limited economies of scale. Multiple linear regression suggests an East–West structural trend: while farm size influences the territorial diffusion of technology, a more consolidated regional innovation ecosystem appears more relevant for farm-level adoption. Findings also highlight a hardware–software imbalance and limited use of data-driven managerial tools. Support policies should therefore move beyond equipment subsidies and include technology transfer networks, digital skills, and managerial data-integration tools. Full article

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article