Search Results (107)

This study examines whether and through what channels ecological compensation efficiency affects urban economic resilience from a watershed-scale perspective. Using panel data for 108 prefecture-level cities in China’s Yangtze River Economic Belt from 2011 to 2023, ecological compensation efficiency is first measured with a super-efficiency SBM model incorporating undesirable outputs. A two-way fixed effects model, a mechanism-testing framework, robustness checks, and a spatial Durbin model are then employed to investigate its direct effect, transmission mechanisms, and spatial spillovers. The results show that (1) ecological compensation efficiency significantly enhances urban economic resilience, and this finding remains robust under alternative indicator measurements and model specifications; (2) mechanism analysis indicates that ecological compensation efficiency strengthens urban economic resilience by promoting green technological innovation and facilitating digital–real economy integration; and (3) spatial analysis further reveals significant positive spillover effects on neighboring cities. These findings suggest that improving ecological compensation efficiency can enhance both local and regional economic resilience. This study enriches the literature on ecological compensation and resilient urban development and provides policy implications for efficiency improvement, green and digital transformation, and cross-regional collaborative governance. Full article

Freshwater aquaculture in China has expanded rapidly in recent decades, raising growing concerns about its environmental sustainability. However, the relationship between industrial agglomeration and environmental efficiency in freshwater aquaculture remains insufficiently understood. Using panel data from 18 major freshwater aquaculture provinces in China from 2009 to 2023, this study investigates the nonlinear effects of industrial agglomeration on environmental efficiency. Environmental efficiency is evaluated using a Global Super-SBM model incorporating undesirable outputs, while industrial agglomeration is measured by the location quotient index. A two-way fixed-effects model is employed for empirical estimation. The results reveal a significant inverted U-shaped relationship between industrial agglomeration and environmental efficiency, with a turning point at an agglomeration level of 2.519. Moderate agglomeration improves environmental efficiency through economies of scale and technology diffusion, whereas excessive agglomeration generates crowding effects that reduce efficiency. Further mechanism analysis shows that technology diffusion, proxied by the number of trained fishermen, plays a significant mediating role in this relationship. This study provides new empirical evidence on the nonlinear environmental effects of industrial agglomeration in freshwater aquaculture and offers policy implications for optimizing industrial spatial layout and developing differentiated environmental regulations to support the green and sustainable development of the sector. Full article

Urban rail transit (URT) has developed rapidly in parallel with the rapid development of the Chinese economy. Some cities suffer from under-investment leading to traffic congestion and environmental pollution, while others suffer from over-investment, leading to operating losses. This paper constructs a three-part evaluation index system consisting of input, intermediate variables, and output, and uses a two-stage super slacks-based measure (super-SBM) model to analyze the development efficiency of URT. The development of URT is divided into two sub-processes of investment and operation, and the investment efficiency and operation efficiency are calculated separately. In addition, a coupling coordination model is used to assess the degree of coupling coordination between the two stages. The results show that the cities with the best overall performance were those with fast-growing economies, such as Beijing, Shanghai, and Guangzhou. By contrast, the entire development process of URT in cities such as Zhuhai and Jinan were ineffective. The problems were more severe in cities where URT was just starting. The degree of coupling and coordination between the two phases of URT investment and operation varied significantly among cities and, in general, could be substantially improved. These findings provide important references for future policy design and implementation of URT, which will help alleviate urban traffic congestion and promote sustainable urban development. Full article

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article

Enhancing green development efficiency (GDE) is crucial for promoting ecological protection and high-quality growth in the Yellow River Basin (YRB). Using panel data from 48 prefecture-level cities in the YRB from 2010 to 2022, this study applies a Super-SBM model that accounts for undesirable outputs to measure GDE. Then, a modified gravity model and social network analysis (SNA) are used to identify the evolution of its spatial correlation. Additionally, a spatial Durbin model (SDM) is employed to examine the driving mechanisms from the dual perspectives of the innovation rebound effect and external micro-ESG (Environmental, Social, and Governance) reverse-forcing pressure. The results reveal the following: First, the spatial pattern of GDE in the YRB has changed significantly, showing an overall spatial imbalance, with efficiency improvements in the middle reaches and declines in the lower reaches. Notably, resource-based cities have improved GDE due to environmental regulations. Second, the spatial correlation network has evolved from a point-axis layout to a more complex network structure. However, spatial links among cities are mainly driven by geographic proximity, while collaborative ties between cities with similar economic features remain weak. Third, technological innovation has a significant negative effect on local GDE, likely due to the energy rebound effect. Meanwhile, the cross-regional transmission of the external supply chain ESG reverse-forcing mechanism remains weak, constrained by the carbon lock-in effect in the middle and upper reaches. These findings suggest that internal technological structures and external market constraints both influence GDE in the YRB. This research offers an empirical foundation for developing targeted, cross-regional collaborative governance policies. Full article

This study examines whether and under what conditions the digital economy (DGE) improves the green development efficiency of China’s tourism industry. Drawing on panel data for 30 Chinese provinces from 2012 to 2023, we develop a multidimensional index of the DGE that captures digital infrastructure, digital industrialization, and industrial digitalization. To evaluate tourism green development efficiency, we employ a non-radial, non-angular super-efficiency slacks-based measure (SBM) model that incorporates both desirable outputs and undesirable environmental externalities. From a theoretical perspective, we extend the Cobb–Douglas production framework by embedding DGE-induced technological progress, showing that digitalization can improve green efficiency through two complementary pathways: it expands expected output while reducing carbon intensity. Empirically, the baseline two-way fixed-effects results show that DGE significantly promotes tourism green development efficiency ($\beta$ = 0.0153, p < 0.05), and this result remains robust in instrumental-variable (IV) estimation ($\beta$ = 0.0383, p < 0.05). We further show that this relationship is conditioned by three important external conditions. First, environmental regulation strengthens the enabling effect of digitalization, consistent with a compliance-induced Porter effect. Second, tourism industry agglomeration enhances the benefits of digital transformation by deepening knowledge spillovers and network complementarities. Third, green finance relaxes financing constraints and creates more favorable conditions for digital investment. By integrating a formal theoretical model with panel-data evidence, this study provides a unified explanation of both the mechanism and the boundary conditions through which the DGE promotes tourism green development efficiency. Overall, the findings suggest that the DGE is an important driver of sustainable tourism development and offer useful policy implications for coordinated digital and green transformation. Full article

Against the backdrop of increasing resource and environmental constraints, improving the land use efficiency of state-owned agricultural land is of great significance for promoting sustainable agricultural development. This study measures the land use efficiency of state-owned agricultural land across 29 provinces in China based on data from the China State Farms Statistical Yearbook (2019–2023). The super-efficiency slack-based measure model (Super-SBM), incorporating both desirable and undesirable outputs, is employed, and global and local spatial autocorrelation methods are further applied to analyze the spatiotemporal evolution of land use efficiency. The results indicate the following: (1) from 2019 to 2023, the overall land use efficiency of state-owned agricultural land in China remained below or slightly above the efficiency frontier, exhibiting a fluctuating trend characterized by an initial increase followed by a decline; (2) significant regional disparities exist, with high-efficiency areas mainly concentrated in Northeast China and the eastern coastal regions, while low-efficiency areas are primarily distributed in western regions and parts of central China; (3) spatial autocorrelation analysis reveals that land use efficiency shows an increasingly pronounced spatial clustering pattern at the provincial scale. After 2022, high–high and low–low clustering became more evident, although a certain degree of spatial heterogeneity still persists overall. These findings provide empirical evidence for understanding the spatial differentiation and evolutionary patterns of the land use efficiency of state-owned agricultural land and offer useful insights for optimizing land resource allocation and management. Full article

This study assesses regional science and technology (S&T) innovation efficiency across 30 Chinese provinces from 2011 to 2022, incorporating a sustainable development perspective. Employing a non-oriented global frontier super-slack-based measure (SBM) model that accounts for undesirable outputs, along with kernel density estimation, cluster analysis, and Moran’s I, the research investigates the spatiotemporal evolution of innovation dynamics. The findings demonstrate a marked upward trend, with the national average efficiency score rising from 0.260 to 0.703. Temporally, efficiency advanced through three stages: an initial period of universally low efficiency, a phase of widening disparities, and a final stage of overall improvement and stabilization. Spatial analysis reveals a persistent “strong in the east, weak in the west” disequilibrium; however, absolute β-convergence tests indicate a significant reduction in regional disparities (p < 0.05). Kernel density estimation reveals a shift from a polarized “pyramid” shape to a more balanced “spindle-shaped” distribution. This is evidenced by a decrease in kurtosis and a rightward shift in the median. Spatial autocorrelation, as measured by the Global Moran’s I, evolved from a statistically insignificant distribution in 2011 to a strong positive correlation (0.223, p < 0.05) by 2022. This progression reflects a transition from isolated “unipolar” hubs to integrated “multi-center block linkages.” The results suggest that, although polarization is diminishing and the national innovation baseline is improving, policy efforts should prioritize the development of emerging innovation corridors to address the remaining east–west divide. Full article

In the context of increasingly frequent and severe natural disasters, scientifically measuring and analyzing the efficiency of natural disaster emergency management in China is of great practical significance for enhancing the performance of the emergency management system and promoting its systematic and high-quality development. This study first applies a super-efficiency SBM-DEA model with undesirable outputs to systematically measure the efficiency of China’s natural disaster emergency management system during the period 2019–2023. Subsequently, the Dagum Gini coefficient and Kernel Density estimation are employed to examine regional disparities and dynamic evolution across eastern, central, western, and northeastern China. Finally, the coefficient of variation and spatial econometric models are applied to test the spatial convergence characteristics of emergency management efficiency. The results indicate that: (1) China’s overall disaster emergency management efficiency remains at a relatively low level and exhibits a fluctuating trend characterized by an initial increase followed by a decline. The regional distribution pattern of emergency efficiency is ranked as “Northeast > Central > West > East”. (2) The average annual contributions of intra-regional disparities, inter-regional disparities, and transvariation density to the overall variation in national emergency management efficiency are 27.58%, 39.90%, and 32.53%, respectively, indicating that inter-regional disparities and transvariation density are the dominant sources of systemic differences among regional subsystems. (3) The national distribution of emergency management efficiency displays a bimodal pattern, indicating polarization; however, the secondary peak is relatively flat, suggesting a weakening trend of provincial-level polarization and a gradual narrowing gap with high-efficiency regions. (4) σ-divergence is observed at the national level and in the central region, while both absolute and conditional β-convergence exist to varying degrees at the national level and across all four regions. Nevertheless, the enhancement of natural disaster emergency management efficiency has not yet realized a system-level transition from convergence in growth rates to convergence in efficiency gaps. In addition, economic development, technological progress, urbanization, and industrial structure exert significantly heterogeneous effects on disaster emergency management efficiency across different regions. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

The Yellow River Basin (YRB), a typical river system facing the challenge of balancing ecological conservation and economic development, offers valuable insights for global sustainable watershed governance through its forestry green transformation. Based on panel data from nine provinces in the basin from 2005 to 2022, this study constructs an efficiency evaluation indicator system for forestry green development. This system incorporates four inputs (labor, land, capital, and energy), two desirable outputs (economic and ecological benefits), and three undesirable outputs (wastewater, waste gas, and solid waste). By systematically integrating the undesirable outputs-based super-SBM model and the global Malmquist–Luenberger (GML) index, this study provides an assessment from both static and dynamic perspectives. The findings are as follows. (1) Forestry green development efficiency showed fluctuations over the study period, with the basin-wide average remaining below the production frontier. Spatially, it exhibits a pattern of “downstream > upstream > midstream”. (2) The average GML index is 0.984 during the study period, representing an average annual decline in forestry green total factor productivity of 1.6%. The growth dynamics transitioned from a stage dominated solely by technological progress to a dual-driver model involving both technological progress and technical efficiency. (3) The drivers of forestry green total factor productivity growth in the basin show profound regional heterogeneity. The downstream region demonstrates a synergistic dual-driver model of technical efficiency and technological progress, the midstream region is trapped in “dual stagnation” of both technical efficiency and technological progress, and the upstream region differentiates into four distinct pathways: technology-driven yet foundationally weak, efficiency-improving yet technology-lagged, endowment-advantaged yet transformation-constrained, and condition-constrained with efficiency limitations. The assessment framework and empirical findings established in this study can provide empirical evidence and policy insights for basins worldwide to resolve the ecological-development dilemma and promote forestry green transformation. Full article

Promoting cultivated land green utilization efficiency (CLGUE) through agricultural scale operation is critical for reconciling the conflict between food security and sustainable land use. Based on panel data from 30 provinces in China (2007–2022), this paper calculates CLGUE using the Super-efficiency SBM-Undesirable model and empirically examines the impact mechanisms and nonlinear characteristics of scale operation using Tobit and threshold models. The findings reveal that: (1) Agricultural scale operation has a significant positive impact on CLGUE, but this effect is non-linear and characterized by diminishing marginal returns, validating the “moderate scale” operation theory. (2) Substantial heterogeneity exists across different functional grain production zones and geographic regions. (3) Mechanism analysis identifies technological innovation as a key transmission channel through which scale operation boosts CLGUE. (4) A significant double-threshold effect is observed in fiscal support for agriculture; specifically, the positive enabling effect of scale operation is maximized only when fiscal support intensity is maintained within a specific rational range. Consequently, this study suggests that policymakers should prioritize “moderate scale” strategies, tailor policies to regional conditions, and optimize the allocation of fiscal funds to foster a transition toward green and sustainable agriculture. Full article

Against the backdrop of accelerating global spatial restructuring, metropolitan areas have become crucial spatial units for enhancing regional competitiveness and securing industrial chains. Although China has continuously advanced metropolitan area development, low land use efficiency remains a key constraint on sustainable progress. Metropolitan integration presents a new approach to addressing this challenge. This study constructs an analytical framework of “direct effects–indirect effects–dynamic evolution” and measures metropolitan integration and land use efficiency using a multidimensional indicator system and a super-efficiency slacks-based measure (SBM) model incorporating undesirable outputs. Employing the system generalized method of moments (System GMM) estimator, this study conducts both baseline and mediation analyses using balanced panel data for 32 Chinese metropolitan areas from 2016 to 2022. The results show that both metropolitan integration and land use efficiency improved steadily during the study period. The coefficient on metropolitan integration is positive and statistically significant, and the lagged dependent variable is also positive and statistically significant, indicating substantial persistence over time. Heterogeneity analyses further indicate that the estimated association is more pronounced in eastern metropolitan areas and nationally designated metropolitan areas. In addition, industrial agglomeration and industrial specialization operate as important mediating channels in this relationship. Based on these findings, the study proposes policy recommendations to strengthen metropolitan integration and industrial collaboration, thereby improving land use efficiency. Full article