Search Results (1,589)

Exploring the spatial correlation network (SCN) characteristics and influencing factors of the eco-efficiency of cultivated land use (ECLU) in Xinjiang’s counties is crucial for clarifying inter-regional spatial mechanisms and supporting cross-regional collaborative governance. Using a panel dataset covering 85 counties across Xinjiang, this study adopts the super-efficiency SBM model, revised gravity model, social network analysis and QAP model to quantify ECLU, and further investigate its spatial network features as well as driving mechanisms. The results showed that: (1) ECLU exhibited a fluctuating upward trend with significant regional differentiation, and northern Xinjiang performed notably better than southern Xinjiang. (2) SCN remained connected overall, but network density was low, average path length was long, and spatial transmission efficiency was relatively low. (3) Regional differences in economic levels, labor productivity, and industrial structure all had positive effects on the formation of the SCN throughout the study period. Regional differences in fiscal support for agriculture had positive effects only in 2014 and 2017, while differences in the soil and water coordination ratio had a negative effect in 2021. Future policies for sustainable cultivated land use should be differentiated and zone-specific, based on each county’s role within the correlation network, to promote coordinated improvement of ECLU across counties. Full article

Against China’s dual carbon peaking and carbon neutrality strategic goals, nationwide digital transformation brings both carbon abatement dividends and potential energy rebound risks, and its full low-carbon potential is constrained by local industrial foundations and regional economic development stages. Most existing studies merely treat industrial upgrading as an intermediate transmission channel, with little discussion of its moderating influence. Moreover, few threshold analyses take the comprehensive level of regional economic development as the core threshold variable to capture the boundary conditions of digital decarbonization effects. Based on balanced panel data covering 30 provincial-level regions of China from 2011 to 2023, this paper constructs a multi-dimensional digital economy index via the entropy weight method. Prior to formal regression, we conduct Pearson correlation analysis and mean-centered VIF multicollinearity diagnostics to avoid biased estimation. Two-way fixed-effects regression, moderation tests, Bootstrap-based regional heterogeneity comparison and Hansen’s single threshold model are adopted for empirical analysis. The results show that digital economy development significantly curbs carbon emission intensity; a one-standard-deviation increase in the digital economy composite index is associated with an approximately 9.7% decline in carbon emission intensity. The mean-centered interaction term DIG × UIS is significantly negative at the 1% level, proving that service-oriented industrial upgrading strengthens the carbon reduction effect of digitalization. The mitigation effect displays distinct spatial divergence: the estimated coefficient equals −2.638 for eastern provinces, −3.585 for central regions and −1.700 for western areas. Bootstrap inter-group coefficient tests confirm statistically significant gaps between east–west and central–western subgroups. Threshold regression identifies a single threshold of logarithmic per capita GDP at 11.94. After crossing this economic development threshold, the inhibitory coefficient of the digital economy rises markedly from −0.844 to −1.473. This study enriches the theoretical system of digital low-carbon transition by jointly uncovering the moderating role of industrial upgrading and the stage threshold constraint of economic development and offers differentiated digital low-carbon policy guidance for provincial governments. Full article

The core predicament of energy transition lies not in the availability of clean technologies, but in whether an economy possesses the institutional capacity and social foundation to systematically regulate its carbon-energy metabolic processes. Drawing upon co-evolutionary theory from evolutionary economics, this paper constructs a composite indicator of Low-Carbon Green Energy Eco-Co-evolution (LCEE) encompassing three functional dimensions: efficiency advancement, kinetic energy replacement, and boundary adherence. Concurrently, by integrating innovation ecosystem theory with inclusive development theory, we propose the concept of “Inclusive Potential of Regional Innovation Ecosystems” (IEP), characterizing the systemic potential for transforming innovation outcomes into social welfare across four dimensions: Knowledge Matrix Abundance (KMF), Cultural Capillary Permeation (CCP), Technological Community Succession (TCS), and Social Root Nourishment (SRN). Taking China’s 2016 intellectual property (IP) powerhouse construction pilot as the institutional prototype of Industrial Intellectual Property Upgrading Reform (IPR), we incorporate IPR, IEP, and LCEE into a unified causal analytical framework, proposing a testable transmission logic of ‘institutional supply → ecological development → co-evolutionary synergy. Using panel data from 30 Chinese provincial-level administrative regions over 2010–2022, we employ a Spatial Durbin Difference-in-Differences (SDM-DID) model to identify the direct and spatial spillover effects of IPR on LCEE, and embed a Double Machine Learning (DML) framework to test the mediating mechanism of IEP while controlling for high-dimensional nonlinear interference. The findings reveal that IPR exerts a significant and robust direct promoting effect on LCEE, generating positive spatial spillovers to neighboring regions through the public disclosure of patent information. IEP significantly promotes local LCEE, yet its spatial spillover lacks statistical support due to structural conflicts in inter-dimensional transmission attributes. IEP plays a significant partial mediating role between IPR and LCEE, with the indirect effect accounting for over one-third of the total effect, a finding robust to alternative machine learning algorithms, sample split adjustments, and exclusion of contemporaneous competing policies. Sub-path tests reveal that KMF bears the strongest mediating efficacy, serving as the primary transmission channel, while CCP exhibits full mediation—the institutional effect on LCEE in the cultural dimension depends almost entirely on the mediating transformation through the public cultural service system. Heterogeneity analysis further demonstrates full mediation in the Low-Carbon Green Energy Eco-Kinetic Replacement (KER) dimension, indicating that the institutional catalytic effect on clean energy substitution must be realized through IEP transformation. This paper provides empirical evidence for the proposed causal pathway through which institutional public goods indirectly enhance the synergistic quality of carbon-energy transition via the inclusive potential of innovation ecosystems, providing theoretical foundations and policy implications that, while grounded in China’s institutional context, may offer valuable reference points for emerging market economies facing similar dual pressures of technological constraints and green transition. Full article

Coordinating air pollution control with carbon mitigation is a central challenge for urban sustainability. However, there is currently a lack of sufficient research on whether digital infrastructure can complement environmental regulations to enhance the synergy of pollution and carbon emission reduction (SPCER). Using the overlapping implementation of China’s Key Air Pollution Control Zones (KAPCZs) and Broadband China (BC) policies as an empirical policy setting, this study applies a partially linear Double Machine Learning framework to panel data for 282 Chinese cities from 2006 to 2023. The results show that the dual-policy pilots significantly improve urban SPCER, while a unified interaction test further provides statistical evidence of a positive synergistic effect between the two policies. Mediation analyses provide evidence consistent with Green Technological Innovation, industrial structure upgrading, and green finance development as potential transmission mechanisms. Formal cross-group tests reveal significant heterogeneity across regions and city characteristics, with larger effects in highly urbanized, low industrial development, non-old industrial base, central, and non-resource-based cities. Spatial Durbin Model estimates further indicate positive spillovers to neighboring cities. These findings demonstrate the potential value of coordinating environmental regulation with digital infrastructure and provide evidence for differentiated policy design and cross-regional collaboration in urban digital–green transitions. Full article

Urban well-being in China emerges from spatially uneven configurations of development intensity, environmental pressure, governance input, public-service capacity, land-use transformation, and urban–rural conditions. Using a balanced panel of 294 Chinese prefecture-level cities from 2004 to 2023 (5880 city–year observations), this study combines spatial mapping, Local Moran’s I, Getis–Ord Gi*, and two-way fixed-effects (TWFE) models to examine how governance-related conditions and ecological and environmental pressure are associated with resident well-being. Prefecture-level cities are treated as urban–rural territorial governance units encompassing urban cores, peri-urban areas, and surrounding county-level jurisdictions. Spatial diagnostics reveal non-identical clustering of resource and environmental intensity (REI), government regulation and investment (GRI), ecological and environmental pressure (EEP), and resident well-being (RWB). REI is positively associated with EEP, and this association remains positive after excluding observations containing ordinary statistical completion. EEP is negatively associated with RWB in the full-sample TWFE and one-year-lagged specifications, but the association weakens in the 2014–2023 and restricted samples, indicating temporal and sample boundaries rather than a stable mediating mechanism. GRI shows contrasting cross-city and within-city patterns, consistent with a distinction between governance capacity and pressure-responsive adjustment. Dimension-level results further show that EEP is negatively associated with rural disposable income, whereas medical service capacity is positively associated with rural disposable income. Overall, the study provides a spatially grounded account of heterogeneous governance–environment–welfare relationships across Chinese prefecture-level territories. Full article

Against the dual strategic backdrop of carbon peaking and carbon neutrality goals and high-quality urbanization development, extant literature exhibits four prominent research gaps: oversimplified evaluation indicator systems, exclusive exploration of the unidirectional carbon impacts exerted by urbanization, a scarcity of long-time-series coupling analyses targeting the Yangtze River Economic Belt (YEB), and functional fragmentation between coupling coordination assessment and predictive simulation tools. Drawing on panel data covering 11 provinces and municipalities within the YEB spanning 2000 to 2021, this study constructs a comprehensive urbanization evaluation framework encompassing four dimensions: population, economy, society, and spatial layout. Meanwhile, an integrated carbon emission assessment system is established from the perspectives of population, economy, energy consumption, and carbon sinks. The entropy-weight method is adopted to assign indicator weights, and a combination of the coupling coordination degree model and system dynamics (SD) model is employed to analyze spatiotemporal evolutionary characteristics and simulate development trends from 2022 to 2032. By organically integrating the coupling coordination model and the SD model, this study establishes an integrated analytical framework that unifies static comprehensive evaluation and driving-mechanism decomposition, thereby compensating for the limitations of time-series forecasting models such as the grey prediction model and ARIMA, which only fit trends from historical data. Empirical results reveal that regional urbanization levels witnessed sustained growth across 2000–2021, with spatial urbanization acting as the core driving pillar. The overall coupling coordination degree maintained a steady upward trajectory, while the east–west regional disparity gradually narrowed. The simulation projections for 2022–2032 demonstrate continuous improvements in coordinated development across the entire basin: the coupling coordination degree ranges from 0.788 to 0.954 for the eastern region, 0.810 to 0.859 for the central region, and 0.752 to 0.865 for the western region. Such spatial differentiation corresponds to distinct practical development pathways: low-carbon stock optimization in the east, low-carbon industrial undertaking in the central zone, and clean energy transition acceleration in the west. All provincial-level administrative regions are projected to achieve an upgrade in their coupling coordination grades by 2032. This study acknowledges several limitations: missing raw data are supplemented via interpolation, only a single baseline scenario is simulated, predictive uncertainty is not quantitatively measured, and subjectivity persists in the weight assignment of coupling subsystems. Ultimately, differentiated low-carbon urbanization governance strategies are proposed for the three sub-regions, offering empirical references for the coordinated realization of dual carbon targets throughout the Yangtze River basin. Full article

Urban low-carbon transition is increasingly shaped by the interaction between digital infrastructure, intelligent applications, land-space allocation, and regional governance. Existing studies have mainly examined whether the digital economy or smart-city development can reduce emissions, but less attention has been paid to the coordination between digitalization and intelligentization, the carbon cost of digital infrastructure, and the spatial consequences of local gains. This research defines digital–intelligent integration as the coupling coordination between digitalization and intelligentization subsystems. Using panel data for 39 prefecture-level cities in the Middle Reaches of the Yellow River Urban Agglomeration from 2013 to 2022, it applies Global Moran’s I, a spatial Durbin model, partial-derivative effect decomposition, alternative spatial weight matrices, alternative dependent variable tests, and multidimensional heterogeneity analysis. The own-city coefficient of digital–intelligent integration in the carbon-efficiency model is positive (0.0282, p < 0.05), whereas the spatial-equilibrium direct effect is statistically insignificant. These quantities are not short- and long-run estimates: the former is a conditional model coefficient, while the latter incorporates spatial feedback. The indirect effect on neighboring carbon efficiency is negative and remains negative under contiguity, economic-distance, and geo-economic nested matrices. Under an otherwise identical fixed-effects specification, digital–intelligent integration lowers local construction-land carbon intensity but raises neighboring intensity. The structural estimates further show that local conversion is weaker in industrially and energy-intensive cities. Digital–intelligent integration should therefore be interpreted as a governance capacity rather than a net-carbon technology; its regional effect depends on industrial lock-in, infrastructure-energy demand, and cross-city responsibility sharing. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is measured from the dimensions of industrial diversification and urban innovation capacity. Double machine learning is employed for baseline estimation, supplemented by mediation analysis, threshold regression, spatial econometric analysis, and a series of robustness tests. The results show that green infrastructure investment significantly enhances industrial chain resilience, and the finding remains robust to alternative model specifications, cross-fitting settings, generalized propensity score weighting, continuous-treatment entropy balancing, winsorization, and the exclusion of pandemic-period observations. Resource allocation efficiency plays a partial mediating role in this relationship. The threshold analysis identifies a significant nonlinear effect associated with energy consumption intensity, with the positive effect of green infrastructure investment being stronger below the estimated threshold and weakening above it. Spatial analysis further shows significant spatial dependence in both green infrastructure investment and industrial chain resilience, together with positive spillover effects on neighboring cities. These findings highlight the importance of improving green infrastructure investment efficiency, strengthening factor allocation, and promoting regional coordination in enhancing urban industrial chain resilience. Full article

The logistics industry remains a critical bottleneck for decarbonization due to its extensive physical networks and high-carbon path dependencies. To address the spatial heterogeneity and transition constraints in the logistics industry of China, this study proposes a Performance–Topology–Mechanism (PTM) framework. Analyzing panel data from 30 Chinese provinces, we integrate a Super-SBM model with the Global Malmquist–Luenberger (GML) index, Dagum Gini decomposition, and a panel Tobit model to decode the spatiotemporal dynamics of green innovation performance (GIP) and its underlying spatial lock-in mechanisms. The results reveal a steady but spatially uneven increase in the national GIP (from 0.4526 to 0.5724), characterized by a leading East and a lagging West/Northeast. GML decomposition indicates this growth is predominantly driven by outward shifts in the technological frontier rather than efficiency improvements, highlighting the weak spatial conversion of green technologies into transport optimization. Topological tracing demonstrates that inter-regional disparities have become the dominant source of spatial inequality, with their contribution rising from 60% to 75%. Spatial autocorrelation further exposes a deepening core–periphery polarization and persistent low-performance spatial lock-in. Crucially, the mechanism analysis identifies a pronounced infrastructure-based carbon lock-in. While economic capacity and green patents stimulate GIP, road network density exerts a significant negative effect, reflecting a systemic path dependence on high-carbon road freight. The findings suggest that decarbonizing transport logistics requires shifting from singular technological investments toward multimodal transport restructuring, overcoming physical network dependencies, and promoting the regional diffusion of green innovations. These findings provide an empirical basis for differentiated green-logistics policies, coordinated low-carbon transport infrastructure planning, and cross-regional diffusion of green technologies. Full article

The central challenge of the energy transition lies in whether an economy possesses the institutional capacity to systematically regulate its own energy inputs and carbon emissions. Drawing upon social metabolism theory, this study constructs an indicator of carbon–energy metabolic control capacity (CMCC). Building on business ecosystem theory, it conceptualizes the intelligent economic ecosystem (IEE) and incorporates industrial chain intellectual property empowerment (IP) into a causal framework of institutional provision → ecosystem development → enhancement of metabolic control capacity. Using panel data from 30 provincial-level administrative regions in China covering the period 2010–2022, this study employs a spatial Durbin difference-in-differences (SDID) model and a double machine learning (DML) framework for empirical analysis. The results indicate that industrial chain intellectual property empowerment significantly enhances carbon–energy metabolic control capacity and generates positive spatial spillover effects on neighboring regions through the public diffusion of patent information. Furthermore, intelligent economic ecological development serves as a significant partial mediator between intellectual property empowerment and carbon–energy metabolic control capacity, with the indirect effect accounting for more than one-third of the total effect. This mediating mechanism remains robust after replacing machine learning algorithms, altering sample-splitting ratios, controlling for concurrent innovation policies, and excluding the impact of the COVID-19 pandemic. Path-specific mediation analysis further reveals that computing power acquisition and value transformation together with digital substrate robustness constitute the dominant transmission channels, whereas innovation metabolic flux contributes a relatively smaller mediating effect due to the long gestation period required for translating fundamental research into practical applications. Heterogeneity analysis further demonstrates that the transmission mechanism exhibits full mediation in the dimension of metabolic structure, indicating that the contribution of industrial chain intellectual property empowerment to the clean substitution of energy structures depends almost entirely on the mediating role of the intelligent economic ecosystem. These findings provide clear actionable guidelines for three specific policy-making domains to advance low-carbon transitions. First, intellectual property authorities should transition from quantity-driven patent creation to establishing cross-regional patent navigation and industrial chain IP pooling. Second, digital economy and industry regulators need to prioritize computing power value conversion (CCV) over raw infrastructure expansion to mitigate energy rebound effects. Third, energy and environmental agencies ought to integrate real-time algorithmic dispatching with green finance incentives. Ultimately, this study demonstrates that achieving deep low-carbon transformation requires leveraging institutional public goods to catalyze digital ecosystems, which in turn enable precise, dynamic carbon–energy metabolic control. Full article

Fan-Out Panel-Level Packaging (FO-PLP) enables high area utilization and manufacturing efficiency, but process-induced warpage caused by the coefficient of thermal expansion (CTE) mismatch and polymer shrinkage remains a major challenge. This study presents a classifier-gated hybrid machine-learning framework for the rapid and accurate FO-PLP warpage prediction using a database generated from a validated three-dimensional finite element process model. A Random Forest classifier first estimates the probability of each global warpage mode, while cluster analysis reduces the spatial training dataset. Two mode-specific artificial neural networks are then combined through probability-weighted fusion to predict the full warpage field and enable warpage prediction for previously unseen geometry layouts. The framework was evaluated on 16 independent finite element designs spanning both warpage modes. Compared with an equivalent single-network model, the proposed approach consistently achieved lower mean and maximum prediction errors across all designs, with the greatest improvements at the panel edges and corners where the prediction is most challenging. In addition, the clustering strategy reduced the training-set size and computational cost. These results demonstrate that integrating warpage-mode classification with mode-specific learning improves both the prediction accuracy and training efficiency, providing a practical tool for the fast warpage assessment of new FO-PLP layout designs. Full article

Enhancing urban ecological resilience is key to addressing resource exhaustion and urban decline in resource-exhausted cities, as it facilitates the re-coordination of the human-land relationship territorial system. Using panel data from 281 prefecture-level cities in China from 2005 to 2023, we apply the multi-period Difference-in-Differences (DID) model to estimate the impact of supportive policies on urban ecological resilience. The results show that supportive policies significantly improve ecological resilience, with pronounced effects on resistance and adaptability, while the effect on recoverability remains limited. Mechanism analysis indicates that the policy works through promoting green innovation, reducing energy consumption, and strengthening environmental governance. Heterogeneity analysis shows that the effects vary across geographic locations and resource types. Supportive policies generate significant spatial spillover effects on enhancing urban ecological resilience and exhibit an evolutionary sequence of radiation, siphoning, equilibrium, and decline as geographic distance increases. These findings inform policy design for ecological restoration and regional coordination in resource-dependent regions. 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

Whether economic growth decouples from municipal solid waste (MSW) generation in upper–middle-income economies remains contested. We test the Waste Kuznets Curve and a disposal-to-recovery substitution effect using a 13-year panel of 1101 Colombian municipalities, combining step-wise fixed-effects models with a non-parametric generalized additive model (GAM), a family of spatial specifications, and a selection-aware recovery model. We find no evidence of income-driven decoupling in landfilling. Once the urban density and demographic structure are controlled, the income terms lose significance, the non-parametric estimate is predominantly monotonic, and density emerges as the main structural driver. Material recovery grows faster than disposal with income (relative substitution), but this signal is concentrated where recovery is measured—only 27% of municipalities report it, and coverage falls from every metropolitan municipality to one in five in the rural periphery, and the income terms are stable across every spatial representation—so that once selection is corrected the recovery elasticity falls from about 5.9 to a non-significant 1.3. Rather than spontaneous decoupling, Colombia exhibits persistent coupling alongside an institutionally engineered, spatially unequal recovery capacity. Achieving SDG 12 therefore requires stratified policies that mandate consumption reduction in mature urban economies while subsidizing shared circular infrastructure for historically neglected rural jurisdictions. Full article