Search Results (5,827)

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

Background: Population ageing, increasing care demands, and rapid advances in artificial intelligence and robotics have intensified interest in care robots as potential tools to support independent living and complement human caregiving. However, the successful implementation of robotic technologies depends largely on public acceptance, which is influenced by functional, psychological, ethical, cultural, and generational factors. Objective: This study examined generational differences in the acceptance of care robots among Portuguese adults by integrating the Almere Model of technology acceptance with the Katz Index of Activities of Daily Living (ADL) and the Lawton–Brody Instrumental Activities of Daily Living (IADL) Scale. The research sought to determine whether acceptance varies according to generation and the type of caregiving activity performed by the robot. Methods: A cross-sectional quantitative study was conducted using an online questionnaire administered to a purposive sample of 235 adults residing primarily in the Lisbon Metropolitan Area, Portugal. The questionnaire combined constructs from the Almere Model with perceptions of robotic assistance for ADLs and IADLs. Principal Component Analysis, reliability analysis, descriptive statistics, and inferential analyses were performed to examine differences across generational groups. Results: Acceptance of care robots was strongly task-dependent. Participants expressed significantly greater acceptance of robots assisting with instrumental activities, including housekeeping, shopping, transportation, meal preparation, and medication management, than with intimate personal care activities such as bathing, dressing, toileting, feeding, and continence care. Contrary to common assumptions regarding digital natives, Generation Z reported higher levels of fear, discomfort, and perceived intimidation than Generation X and Baby Boomers. Older generations generally demonstrated more pragmatic acceptance of robotic assistance, particularly regarding future support needs associated with ageing. Across generations, respondents preferred robots with more human-like appearances; however, emotional trust remained substantially lower than perceived functional usefulness. Conclusions: The findings suggest that acceptance of care robots is conditional rather than universal and is shaped by the nature of the caregiving task, generational differences, and broader emotional and cultural perceptions of care. Integrating the Almere Model with established ADL and IADL frameworks provides a novel perspective by linking technology acceptance to specific functional domains of caregiving. The results support the view that care robots are more likely to be accepted as complementary tools that enhance human-centred care rather than as substitutes for professional or family caregivers. Given the purposive and geographically limited sample, the findings should be interpreted cautiously and not generalised to the wider Portuguese population. They nevertheless provide valuable implications for the design of socially assistive robots, healthcare practice, and public policy in ageing societies. Full article

The Ganjiang River–Poyang Lake Basin is a typical river–lake composite water-system region and a major concentration area of intangible cultural heritage (ICH) in Jiangxi Province. However, officially listed ICH does not simply represent the natural distribution of cultural practices but reflects the combined effects of historical accumulation, environmental contexts, and institutional recognition. Based on 616 national- and provincial-level ICH items, this study employs the nearest neighbor index, kernel density analysis, Lorenz curve, standard deviational ellipse, and Geodetector to examine spatial patterns and associated factors. The results reveal significant spatial clustering (NNI = 0.31, Z = −39.16), characterized by riverine concentration, lakeside distribution, and polycentric development. Traditional craftsmanship and folk customs cluster around Poyang Lake, while traditional drama, folk literature, and quyi extend along the Ganjiang River. Distance to major water systems (q = 0.821), policy support (q = 0.813), and inheritors (q = 0.806) show the highest explanatory power. The findings reveal a spatial process of lake-area accumulation, river-channel diffusion, and nodal support. Full article

This study employs panel data from 11 provinces and municipalities along the Yangtze River Economic Belt spanning 2010–2021. It measures green transport efficiency (GTE) using principal component analysis (PCA) and the undesirable Super-SBM model while constructing an economic high-quality development index (HQEDI) through an entropy-weighted CRITIC approach. The study combines coupling coordination degree modeling with spatial autocorrelation analysis (Global Moran’s I, LISA, hotspot/coldspot detection) to empirically investigate their synergistic evolution mechanism. The findings indicate the following: (1) Multidimensional policy combinations exhibit a nonlinear threshold effect on enhancing green transport efficiency, with efficiency significantly rebounding post-2015 as low-carbon policies deepened. (2) High-quality economic development displays a dual-stage ‘convergence-divergence’ pattern, where downstream regions lead in HQEDI but mid- and upstream regions show faster growth in coordination and green dimensions. (3) The coupling coordination degree exhibits pronounced spatial spillover effects, with the global Moran’s I mean reaching 0.485. High-value clusters form in downstream regions, while upstream areas predominantly exhibit low-value clusters, revealing an ‘east-high, west-low’ regional differentiation pattern. (4) The gradient divergence mechanism stems from heterogeneity in infrastructure density, industrial structure, and policy responsiveness elasticity. Accordingly, it is recommended to establish a multi-level governance mechanism to dismantle administrative barriers and to construct a tripartite policy package integrating ‘digital transport, ecological compensation, and industrial radiation’ to advance coordinated basin development. Full article

Environmental, Social, and Governance practices have emerged as a critical mechanism for mitigating extreme climate risks and achieving Sustainable Development Goals (SDGs). Against this backdrop, this study focuses on Chinese listed manufacturing enterprises from 2015 to 2023. By incorporating the city-level climate risk expressions of public views index, we examine the impact of public concern and negative sentiment regarding climate risk (CR-PCNS) on corporate ESG performance. Our baseline findings indicate that elevated CR-PCNS significantly enhances corporate ESG performance, particularly within the environmental and social pillars, while exerting no significant effect on the governance dimension. Heterogeneity analysis reveals that this promotional effect is more pronounced among non-state-owned enterprises and firms located in the eastern region, with the most noticeable improvements manifested in their environmental performance. Furthermore, the moderation analysis demonstrates that higher executive educational attainment significantly amplifies the positive impact of CR-PCNS on corporate ESG performance, whereas local protectionism severely attenuates this promotional effect. These findings offer crucial policy implications for how to effectively harness public involvement to incentivize greater corporate engagement in ESG initiatives. Full article

The spatial allocation of urban road infrastructure directly affects urban operational efficiency, social equity, and ecological environmental quality. Taking 36 major Chinese cities as the research subjects and drawing on data from municipal statistical yearbooks, this study employs a composite index method, Theil index decomposition, and correlation analysis to reveal spatial differentiation patterns, imbalances, and driving factors of urban road infrastructure levels, based on both aggregate and average indicators. The results indicate that the aggregate road infrastructure level exhibits a “high in the southeast, low in the northwest” pattern along the Hu Huanyong Line, while the average road infrastructure level reveals relatively lower performance in some first-tier cities. Imbalances exist in both aggregate and average dimensions, with Theil indices of 0.231 and 0.059, respectively; intra-regional disparities contribute more to total inequality than inter-regional disparities. Urban permanent population (ridge regression coefficient: 0.1991) and fiscal revenue (ridge regression coefficient: −0.1087) are the core driving factors among the four influencing factors of aggregate road infrastructure level spatial differentiation, whereas GDP (−0.0318) and built-up area (0.0703) exert only marginal effects. This suggests that current aggregate urban road infrastructure levels are shaped by the interplay of urbanization stage, economic development level, fiscal system, and spatial planning policies, all operating within the constraints imposed by the city’s natural geographical conditions, and have not yet adequately addressed residents’ demand for spatial equity. The study recommends establishing differentiated investment mechanisms, optimizing road network density in developed cities, and constructing a spatial matching early-warning system to promote people-oriented new urbanization and coordinated regional sustainable development. Full article

We develop a tensor-based dynamical framework for monetary flows in multi-sector, multi-agent economies and quantify the information destroyed when the monetary state is reduced to a scalar aggregate. The state is a third-order tensor encoding capital flows across sectors, agent classes, and time; deviations from equilibrium obey a tensor-indexed Langevin (multivariate Ornstein–Uhlenbeck) equation with a coupling operator and channel-specific friction rates. Using standard Lyapunov theory, we assemble a stability and convergence framework for the induced vectorized system, with a bound stated so as to remain valid for the non-normal system matrices generated by asymmetric economic coupling, and characterize the stochastically forced case in the mean-square sense. Shannon entropy, Kullback–Leibler divergence, and sector–agent mutual information measure the structural information discarded by scalar aggregation. We then study a stylized, heuristically calibrated $3\times 3$ economy subject to a shock inspired by the 2007–2009 crisis; we emphasize at the outset that the figures reported below are properties of that calibration and are not empirical estimates. In this scenario Finance absorbs an 18.9% peak capital loss while Manufacturing and Services suffer 5.8% and 3.9% secondary drops, against an aggregate contraction of only 8.6%; the Kullback–Leibler divergence of the sector–agent flow distribution recovers systematically later than the aggregate signal, a lag that is positive in 96.6% of a 1000-draw Monte Carlo ensemble, although its magnitude is calibration-dependent. Under a symmetric exit rule, a deficit-targeted stimulus restores equilibrium substantially faster than a share-weighted uniform stimulus in 100% of the ensemble while spending strictly less—its realized expenditure saturates below the uniform budget because it self-terminates as deficits close—and attains integrated disequilibrium within 18% of the exact linear-quadratic optimum at equal control effort while requiring no knowledge of the system matrix. The ordinal conclusions—aggregation masks the epicenter, structure lags the aggregate, and deficit targeting dominates uniformity—are robust across a wide neighborhood of the calibration, and identify the disaggregated state as the object that stabilization policy needs and that scalar aggregation destroys. Full article

Blockchain-based peer-to-peer (P2P) energy trading enables prosumers to sell surplus electricity directly to one another without a central intermediary, but its open ledger reveals each participant’s bid price to every blockchain node, including the distribution system operator (DSO) that settles the trades. Prior work has addressed this concern by using inner product encryption (IPE) to encode each bid as a vector and perform matching directly over ciphertexts, yet the resulting pairing-based comparison and heavy on-chain heap restructuring still incur substantial gas costs. To resolve this issue, this paper proposes two orthogonal optimization methods. First, we replace the pairing-based IPE of the prior baseline with a matrix-based IPE, which substitutes pairing operations with matrix multiplication and a trace evaluation. This allows compact ternary encoding of integers and enables ciphertext entries to be packed into narrower Solidity integer types. Second, we introduce two heap-management policies: (i) the index-based heap, which exchanges integer indices instead of full ciphertexts during swaps, and (ii) the root-retention heap, which eliminates redundant heap restructuring under residual rebidding. Our performance analysis shows that the proposed optimization strategies substantially reduce the total gas consumption of the energy trading system compared with the pairing-based baseline, and the heap-management policies deliver consistent savings regardless of the underlying cryptographic primitive. Full article

Improving natural resource total factor productivity (NTFP) is sential for easing resource and environmental constraints, advancing high-quality development, and supporting China’s dual-carbon goals. This study aims to evaluate the static efficiency, intertemporal productivity change, regional heterogeneity, and hierarchical frontier differences in environmentally adjusted natural resource total factor productivity in China’s aggregated agriculture, forestry, animal husbandry, and fishery sector from 2008 to 2020. To achieve this aim, we apply a non-radial, non-oriented undesirable-output SBM-DEA model under constant returns to scale, national, regional, and province-specific frontier comparisons. A Global Malmquist–Luenberger index is further used to decompose productivity change into efficiency change and technical change. The analysis shows an overall upward trajectory in national mean NTFP, a persistent eastern advantage, and co-movement among provincial, regional, and national frontier indices, while the dynamic productivity change indicates that productivity improvement was driven mainly by technical change rather than efficiency change. Sensitivity analyses using alternative undesirable-output specifications and cluster-based groupings support the robustness of the main conclusions and inform more targeted discussion and policy implications. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

European urban policy increasingly requires city benchmarks that are people-centred, multidimensional and methodologically defensible, yet perception-based environmental evidence is rarely aggregated without arbitrary weighting. Accordingly, this paper develops and justifies a synthetic index of perceived urban environmental performance (UEP) for European cities. Specifically, using the environmental module of the 2023 Eurostat Urban Audit Perception Survey (UAPS) for 83 Functional Urban Areas (FUAs), four satisfaction indicators (air quality, noise, cleanliness and green spaces) are aggregated with a benefit-of-the-doubt (BoD) composite indicator that assigns each city endogenous, self-favouring weights. Standard, weight-restricted and cross-efficiency variants are estimated, benchmarked against an equal-weight comparator, and embedded in an exploratory spatial data analysis. The results demonstrate that nine cities form the efficient frontier, led by Oulu, Luxembourg and Zurich, while Skopje, Naples and Athens anchor the lower tail. Additionally, a robust North–South gradient emerges, and green space satisfaction is the dominant structural driver of composite scores, partially compensating weak air quality in many cities. The study addresses perception-based BoD benchmarking—uncovering dimension-specific environmental governance deficits masked by national indicators—and complements objective environmental monitoring for European Union (EU) cohesion and climate-neutrality policy. 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

Residential electricity tariff structures in emerging economies are often highly complex, dynamically combining regional climatic variables and multi-tiered price adjustments. In Mexico, despite a preferential scheme designed to mitigate seasonal expenditure fluctuations, baseline energy subsidies frequently fail to protect low-income households due to structural targeting inefficiencies based strictly on regional temperature thresholds rather than socioeconomic status. This study addresses this methodological and regulatory gap by developing the Energy-Economic Impact Index (EEII), a novel mathematical and heuristic framework that couples complex Increasing Block Tariffs (IBT) architecture with localized household income dynamics at the state level. The proposed methodology was comprehensively validated using synchronized biennial empirical datasets from all 32 Mexican states, combining Federal Electricity Commission (CFE) billing data and National Surveys of Household Income and Expenditures (ENIGH) spanning the 2018–2024 period. Quantitative results reveal a severe, non-linear escalation of the financial energy burden across the territory, demonstrating that rising residential electricity costs significantly outpaced domestic income growth trajectories. Notably, households situated within high-temperature geographic regimes (Tariffs 1D, 1E, and 1F) exhibited the most critical economic vulnerability, with the calculated energy cost impact absorbing up to 14.90% of the real monthly household income in 2024. Ultimately, the EEII framework proves to be a robust predictive and decision-support tool for energy policy planners aiming to optimize fiscal subsidy allocation, reduce energy poverty gaps, and mitigate socioeconomic risks in transition economies. 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

Green Production and Lifestyles (GPL) are essential for advancing green development and sustainable development. However, limited attention has been paid to their integrated transformation and the combined effects of social governance-oriented and environmental technology-oriented policies. This study examines the effects of the Dual Pilot Policy (DP) of the National Civilized City Program and the Low-Carbon City Pilot Policy on urban GPL transition. Using balanced panel data from 276 prefecture-level cities in China during 2002–2023, we employ a staggered difference-in-differences (DID) model. The results show that the DP significantly increases the GPL index by 0.0367 units, equivalent to a 3.67% improvement on a 0–1 scale. The DP generates a stronger additional combined effect than individual policies, while “Civilized City first, followed by Low-Carbon City” produces a stronger effect. The effects are more pronounced in non-resource-based cities, non-old industrial base cities, cities outside the Yangtze River Basin, and coastal cities. Mediation analysis identifies government support, industrial structure upgrading, and public GPL awareness as important channels, while significant spatial spillover effects are also observed. These findings provide empirical evidence for optimizing policy combinations. Full article