Search Results (153)

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article

While the association between criteria air pollutants and sleep duration is well-documented, evidence on the impact of fine particulate matter (PM_2.5) chemical components on sleep remains limited. This study investigated the effects of intermediate- (6-month) and long-term (2-year) exposure to PM_2.5 and its five major components—black carbon (BC), organic matter (OM), sulfate (SO₄²⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺)—on nocturnal sleep and daytime napping duration. We included 19,505 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018). Residential PM_2.5 and component concentrations were estimated via the Tracking Air Pollution in China dataset, and sleep data were collected through self-reported questionnaires. Linear mixed-effects models and quantile-based g-computation (qgcomp) were used to assess single- and multi-pollutant effects. Results showed that both intermediate- and long-term exposure to PM_2.5 components was associated with shorter nocturnal sleep and longer daytime napping. Subgroup analyses revealed greater susceptibility among rural residents, solid fuel users, and individuals without pensions. These findings emphasize the need for component-specific PM_2.5 control strategies and targeted public health interventions to reduce sleep-related health inequalities, especially in socioeconomically disadvantaged populations. Full article

With a modest estimate of 11 million workers and high greenhouse gas emissions, the Indian brick sector is a relevant study for understanding how low-carbon energy transition impacts justice for the society, environment, and livelihoods. This empirical article provides an analysis of the ongoing policy-driven energy efficiency transition and justice trade-offs and benefits in the brick production sector in the state of Bihar. The transition is explored in a larger framework of power relations and vulnerability to determine whether the policies enable or challenge transformative justice for the labour force, nature and future generations. Present policies focus on regulations and financial incentives relevant for entrepreneurs with pre-existing skills, network and financial resources. Further, present policy narratives lack attention to mechanisms that reproduce the socio-economic inequality of the brick labour force, and implications for balancing different livelihood and environmental objectives. We conclude that the findings emphasise the need for integrating a wider variety of social dimensions and relevant support schemes to overcome inequality barriers and safeguard the environment for future generations. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article

Life expectancy (LE) varies widely across Organization of Islamic Cooperation (OIC) countries, reflecting differences in economic, social, environmental, and health-system conditions. This review aimed to synthesize quantitative evidence on determinants of LE at birth in OIC member countries. The study was conducted in accordance with the PRISMA guidelines, and a systematic search of electronic databases was performed up to September 2025. After screening 5312 records and assessing full texts, studies were appraised using the Joanna Briggs Institute checklists, with an inclusion threshold of ≥80%. A total of 54 studies, mainly ecological, time-series, and panel analyses using national-level data, were included. Higher gross domestic product per capita, education, employment, and health expenditure were consistently associated with longer LE. In contrast, poverty, income inequality, air pollution, and carbon dioxide emissions were associated with shorter LE. Clear differences were observed across World Bank income groups, with LE being lowest in low-income OIC countries and highest in high-income Gulf Cooperation Council states, where gains were driven more by health-system resources than by income growth. Improving LE in OIC countries requires integrated economic, social, environmental, and health-system policies. Full article

The countries in the Belt and Road Initiative (BRI) significantly influence global carbon emissions, and the spatial correlation and driving mechanisms of their emissions are crucial for regional emission reduction and global climate governance. This study constructs a carbon emission spatial correlation network, where links represent pairwise spatial correlations derived from a modified gravity model, using data from 54 BRI countries (2011–2020). It applies social network analysis (SNA) to examine the network structure and uses the Temporal Exponential Random Graph Model (TERGM) to identify influencing factors. The main findings are as follows: (1) The BRI carbon emission network has become more interconnected and cohesive, with stronger regional connectivity and reduced inequality. (2) The network shows a core–periphery structure with notable spatial association patterns. Countries like Qatar, Israel, India, China, and the UAE have rapidly established carbon emission links, positioning them at the core due to their high connectivity and influence. (3) The network displays temporal dependence, with reciprocity associated with stronger mutual connections and transitivity associated with more cohesive network structures. Technological innovation and industrial structure optimization are positively associated with the formation of carbon emission connections, while energy structure and foreign investment are negatively associated with it. Economic development and technological innovation are associated with a country’s greater involvement in carbon emission connections, and countries with similar urbanization rates, energy, and industrial structures, but large economic disparities are more likely to form carbon emission associations, reflecting potential complementarities in the network structure. Full article

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

Planning capacity for wind–photovoltaic (PV)–thermal–storage systems with high renewable penetration requires models that address investment costs, operational feasibility, and strict carbon limits under uncertainty. This paper presents a two-stage robust optimization model for integrated wind–PV–thermal–storage capacity expansion that guarantees carbon compliance under worst-case renewable realizations. Unlike conventional approaches that relax carbon constraints through price penalties, we enforce the annual carbon emission cap as a hard operational constraint, ensuring candidate portfolios remain feasible even under adverse renewable conditions. To reflect practical storage design, a fixed energy-to-power (E/P) ratio couples storage energy capacity with power converter ratings, preventing unrealistic storage expansions. Renewable uncertainty is captured through a Bertsimas–Sim budgeted polyhedral set defined over representative days, balancing robustness with computational tractability. A tailored decomposition framework integrates economic dispatch and carbon-compliance verification within an outer column-and-constraint generation (C&CG) loop, simultaneously certifying worst-case operating cost and minimum achievable emissions. By exploiting strong duality, we generate two families of valid inequalities iteratively: economic cuts from the Economic subproblem (Economic-SP) and carbon-feasibility cuts from the Carbon subproblem (Carbon-SP). This dual-certification approach ensures capacity plans remain both economically optimal and carbon-compliant across all uncertainty realizations. Case studies on a realistic wind–PV–thermal–storage system demonstrate that the method produces carbon-compliant, robust capacity plans with manageable computational effort, converging in 10–15 iterations. The model explicitly captures operational coupling among renewables, thermal generation, and storage, providing a decision-support tool for low-carbon power systems under deep decarbonization targets. Full article

Globally, diverse agricultural production strategies have been implemented to address the impacts of climate change, with sustainable farming models emerging as key approaches, particularly in regions affected by environmental degradation. Latin America is especially vulnerable due to its strong dependence on agriculture, pressure on natural resources, and persistent socioeconomic inequalities in rural areas. This study presents a review of sustainable agricultural practices, with particular attention to evidence from Latin America on sustainable agricultural practices as effective strategies for climate change adaptation and mitigation, natural resource conservation, and food security enhancement. Special emphasis is placed on the role of the bioeconomy and the integration of traditional knowledge with modern agricultural management, highlighting their combined contribution to agroecosystem resilience. The review critically examines how sustainable agricultural practices influence soil health, agroecosystem resilience, and the long-term sustainability of agricultural production within a circular economy framework. The findings indicate that practices such as no-till farming, crop rotation, organic fertilization, and integrated soil management significantly improve soil structure, nutrient retention, organic matter content, and soil biodiversity. These practices also reduce soil degradation, enhance resource-use efficiency, and promote carbon sequestration, thereby contributing directly to climate change mitigation. Overall, the results underscore the importance of holistic approaches that integrate traditional practices with technological innovations and highlight the need for further applied research across diverse environmental and socioeconomic contexts, particularly to address adoption barriers among smallholder farmers and to optimize sustainable agricultural strategies at local and regional scales. Full article

Fluoride (F⁻) contamination in deep groundwater threatens drinking water security, yet its enrichment is commonly governed by coupled nonlinear hydrogeochemical feedbacks that are difficult to resolve with linear diagnostics alone. Here, we integrate an explainable deep learning framework (HydroAttentionNet + SHAP) with thermodynamic and mass-conservative inverse modeling (PHREEQC) to quantitatively link data-driven thresholds to mineral water processes in a multi-aquifer system. Using 258 deep-well samples, we delineate a robust evolution pathway from background to ultra-high-fluoride (Ultra-High F⁻, ≥1.5 mg/L) waters. HydroAttentionNet achieves strong predictive skill (R² = 0.77) and reveals a clear mechanistic tipping behavior: alkalinity (HCO₃⁻/CO₃²⁻) is the primary trigger for F⁻ activation, while progressive Na+ enrichment and Ca²⁺ depletion act as amplifiers by suppressing a(Ca²⁺) and weakening fluorite precipitation capacity. PHREEQC simulations confirm a coupled “salinization–decalcification–fluoridation” loop in which (i) evaporite dissolution elevates ionic strength (salt effect) and supplies Na⁺ to promote Na–Ca exchange, and (ii) carbonate re-equilibration drives calcite precipitation as an efficient Ca sink, offsetting ~45.8% of Ca²⁺ inputs; together, these processes maintain fluorite undersaturation and sustain net fluorite dissolution, contributing 56.6% of newly added dissolved F⁻ in evolved end-members. Monte Carlo health risk assessment (10,000 iterations) indicates substantial intergenerational inequity: 67.9% of children exceed the non-carcinogenic risk threshold (HQ > 1), compared with 29.3% of adults. Sensitivity analysis identifies source-water fluoride concentration as the dominant driver (Spearman r = 0.93), implying that supply-side interventions (defluoridation, well-screen optimization, and blending with low-F sources) are substantially more effective than behavioral measures. Full article

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls. Full article

China possesses 400 million hectares of grasslands that provide regulating ecosystem services (ESs), including wind erosion control, water conservation, and carbon sequestration. The central government implemented the Grassland Ecological Protection Subsidy and Reward Policy (GERCP) in 2011, allocating 150 billion yuan (approximately $23 billion) through 2020, while national vegetation coverage increased from 51.0% in 2011 to 56.1% in 2020. Existing valuation studies emphasize total economic value but rarely quantify the concentration of ES values across space or their alignment with fiscal allocation. We compiled 734 grassland ES valuation observations from 186 studies published between 2000 and 2024, and estimated a multi-level mixed-effects meta-regression model for benefit transfer. We projected standardized county-level ES values, decomposed spatial inequality using the Gini coefficient and Theil index, and assessed the mismatch between value-informed allocation weights and observed GERCP transfers. Predicted values exhibit high concentration (Gini coefficient = 0.58), and between-zone differences explain 52% of total Theil inequality. The mismatch analysis identifies 94 high-value and low-compensation counties concentrated in southern Qinghai and northern Tibet, where per-hectare values are 180 to 240% above national medians, and compensation is 35 to 55% below the median. The results support value-informed targeting and redistribution of fiscal weights across regions, while payment levels require pricing benchmarks based on opportunity cost or conservation cost rather than total economic value. We propose calibrating compensation rates through a tiered schedule based on ESV quantiles or standardized ecosystem-service bundles, and implementing county-level differentiated payments with periodic updating tied to monitoring and evaluation. As a minimum viable step, we recommend piloting this scheme in counties with high ESV yet low current compensation, and integrating it into existing ecological compensation funding channels to reduce administrative frictions. Full article

We evaluated the effectiveness and long-term sustainability of China’s low-carbon policies using a comprehensive policy intensity index and satellite-based CO₂ emissions. We found that both command-and-control and market-based measures have significantly reduced emissions across China but mainly via scale effects (i.e., contraction of industrial activity) rather than technique effects (i.e., more green invention patents granted and an increase in carbon total factor productivity) or composition effects (i.e., industrial upgrading and clean energy transition). Furthermore, command-and-control policies are associated with less green innovation, while market-based policies lead to limited gains in industrial restructuring and, unexpectedly, also show a negative association with clean energy adoption. Using a unique dataset of millions of business registration records and county-level CO₂ emissions, we also uncovered substantial intra-national carbon leakage at the city level, with emissions relocating to provincial border areas where enforcement is weaker, thus exacerbating emission inequality among jurisdictions. Furthermore, our novel transfer learning projections indicate that current policies may lose their efficacy in nearly 47% of cities under foreseeable economic and structural changes, exposing the fragility of contraction-led carbon abatement. These results underscore the need to move beyond the short-term suppression of outputs toward a durable, innovation-driven pathway of decarbonization. Full article

The global energy transition (ET) is widely portrayed as a technological shift toward low-carbon systems; however, it also entails profound geopolitical and socio-environmental transformations. While energy justice (EJ) has become a key framework for assessing fairness in energy systems, it seldom incorporates the geopolitical restructuring of material, energy, and economic flows that underpin contemporary transitions. This article develops a geopolitically informed approach to EJ, trying to capture how the new flows of energy, matter, and power shape—and are shaped by—enduring centre–periphery inequalities. Using a guided literature synthesis that combines EJ, political ecology, decolonial critiques, and green extractivism, the study enhances classical EJ tenets by incorporating transboundary flows, ecological unequal exchange, ontological plurality, and local self-determination. An illustrative application to copper extraction in Chile and Peru demonstrates how critical-mineral supply chains reproduce new sacrifice zones within emerging geopolitical configurations. By connecting local socio-environmental conflicts to global energy dynamics, the framework advances a more comprehensive, multidimensional approach to justice in the ET. The findings offer conceptual and practical insights for designing more equitable and geopolitically aware sustainability policies. Full article