Search Results (380)

Efficient urban land use is a cornerstone of sustainable city development, yet the drivers of such efficiency are increasingly complex in an era of spatial transformation. As industrial specialization and collaboration deepen, cities are becoming interconnected through complex networks. These “invisible threads” are redefining the dynamics of land use and spatial efficiency. This study examines the influence of intercity industrial networks on urban land use efficiency by constructing urban networks from multi-regional input–output data and evaluating city performance using a super-SBM model. We employed Tobit regression and mediation analysis to identify the mechanisms. Results indicate that both the quantity and quality of urban network connections significantly enhance land use efficiency, with notable differences across city types. The positive effect of industrial network centrality is most pronounced in large cities. In growing cities, both the number and quality of industrial linkages promote efficiency, whereas in shrinking cities, connection quality is more critical than quantity. Mechanism analysis reveals that industrial networks improve land use efficiency primarily by expanding intermediate goods markets and fostering technological innovation. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Corporate failure prediction remains a major topic in the literature. Numerous methodologies have been established for its assessment, while data envelopment analysis (DEA) has received particular attention. This study contributes to the literature, establishing a new approach in the construction process of prediction models based on the combination of logistic LASSO and an advanced version of data envelopment analysis (DEA). We adopt the modified slacks-based super-efficiency measure (modified super-SBM-DEA), following the “Worst practice frontier” approach, and focus on the selection process of predictive variables, implementing the logistic LASSO regression. A balanced sample with one-to-one matching between forty-five firms that filed for reorganization under U.S. bankruptcy law during the period 2014–2020 and forty-five non-failed firms of a similar size from the U.S. energy economic sector has been used for the empirical analysis. The proposed methodology offers superior results in terms of corporate failure prediction accuracy. For the dynamic assessment of failure, Malmquist DEA has been implemented during the five fiscal years prior to the event of failure, offering insights into financial distress before the event of a default. The model outperforms alternatives by achieving higher overall prediction accuracy (85.6%), the better identification of failed firms (91.1%), and the improved classification of non-failed firms (80%). Compared to prior DEA-based models, it demonstrates superior predictive performance with lower Type I and Type II errors and higher sensitivity as well as specificity. These results highlight the model’s effectiveness as a reliable early warning tool for bankruptcy prediction. Full article

To advance global climate governance, this study investigates the carbon emission efficiency (CEE) of 110 Chinese resource-based cities (RBCs) using a super-efficiency SBM-GML model combined with kernel density estimation and spatial analysis (2006–2022). Spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) further elucidate the driving mechanisms. The results show that (1) RBCs achieved modest CEE growth (3.8% annual average), driven primarily by regenerative cities (4.8% growth). Regional disparities persisted due to decoupling between technological efficiency and technological progress, causing fluctuating growth rates; (2) CEE exhibited high-value clustering in the northeastern and eastern regions, contrasting with low-value continuity in the central and western areas. Regional convergence emerged through technology diffusion, narrowing spatial disparities; (3) energy intensity and government intervention directly hinder CEE improvement, while rigid industrial structures and expanded production cause negative spatial spillovers, increasing regional carbon lock-in risks. Conversely, trade openness and innovation level promote cross-regional emission reductions; (4) the influencing factors exhibit strong spatiotemporal heterogeneity, with varying magnitudes and directions across regions and development stages. The findings provide a spatial governance framework to facilitate improvements in CEE in RBCs, emphasizing industrial structure optimization, inter-regional technological alliances, and policy coordination to accelerate low-carbon transitions. Full article

Tourism ecological efficiency (TEE) is a significant indicator of the development level of green and intensive tourism. However, conventional directional and radial TEE measurement approaches overlook critical factors such as intermediate process influences and input–output slack variables, potentially leading to biased estimates. Urban areas are key to coordinating tourism across provinces, so accurately assessing the TEE is vital for sustainable regional tourism. This study uses an improved TEE measurement model to measure the TEE of the Guangdong–Fujian–Zhejiang (GFZ) coastal city clusters from 2010 to 2021. The improved TEE measurement model is a three-stage super-efficiency SBM approach. It then uses standard deviation ellipses and geographic detectors to analyze the TEE’s spatiotemporal characteristics and influencing factors. The findings indicate the following: (1) The three-stage super-efficiency SBM approach improves the accuracy and validity of measurement results by removing external environmental variables. (2) During the study period, the TEE values of the GFZ coastal city clusters were above average (except for Meizhou, where the efficiency improved). Temporally, the TEE values of 75% of the cities showed an increasing trend; spatially, the high-value areas increased significantly, the middle- and low-value areas decreased, and the center of gravity shifted to the north and south. (3) The years 2016–2021 saw an increase in external development factors and the use of external resources. The study’s findings can serve as scientific benchmarks for TEE measurement, as well as the low-carbon and environmentally friendly growth of tourism in urban agglomerations. Full article

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Understanding the coupling mechanism and coordinated development between digital transformation and operational efficiency in logistics enterprises is vital for optimizing resource allocation and promoting high-quality, sustainable growth in the logistics industry. This study analyzes panel data from 52 listed logistics enterprises in China from 2014 to 2023. It constructs evaluation index systems for digital transformation and operational efficiency and applies an integrated methodology comprising the super-efficiency SBM model, coupling coordination degree model, and random forest regression model to evaluate efficiency, assess coupling dynamics, and forecast future trends. The main findings are as follows: (1) Overall operational efficiency has shown a pattern of fluctuating growth, increasing from 0.520 to 0.585. Road transport consistently outperformed other sectors, water transport maintained steady growth, and air transport exhibited significant volatility, particularly during the COVID-19 pandemic. (2) The coupling coordination degree remains in the initial coordination stage (0.642–0.677), with road transport achieving intermediate-level coordination (0.718) by 2021. Water transport showed gradual but stable improvement, and air transport remained unstable due to external shocks. (3) Road transport leads in overall industry performance, while water transport exhibits stable progress, and air transport is hindered by international supply chain disruptions and technological adoption challenges. (4) Projections for 2024–2026 suggest an average annual growth rate of 0.31% in coupling coordination across all subsectors, although inter-sectoral synergistic mechanisms require further enhancement. Based on these findings, this study proposes targeted recommendations: increasing comprehensive investments in digital technologies across the entire supply chain, cultivating interdisciplinary talent, optimizing risk management frameworks, and refining policy support. These measures aim to strengthen the integration of digital transformation and operational efficiency, contributing to the sustainable development of the logistics industry. Full article

China has adopted extensive restoration practices to improve ecosystem function. The efficiency of these restoration efforts remains unclear, which may hinder the supply of ecosystem services (ESs). In this context, this study first employed InVEST models to clarify spatio-temporal changes in five key ESs. The static and dynamic efficiencies of ecosystem service production in 130 cities from 2015 to 2021 in the Yangtze River Economic Belt (YREB) were then measured using the Super-SBM-Malmquist model, with ESs considered as outputs. The results indicated that water conservation (WC), water purification (WP), and soil retention (SR) exhibited overall declining trends, decreasing by 28.32%, 3.22%, and 10.00%, respectively, while carbon storage (CS) and habitat quality (HQ) remained steady. More than 70% of studied cities exhibited static efficiency levels below 50%, which were attributed to inefficient utilization of labor, capital, and technology. Significant spatial heterogeneity was observed, with high-efficiency cities mainly located in mountainous areas and low-efficiency cities concentrated in flat regions. The downward trend in dynamic efficiency has been reversed from a 39.02% decline in 2015–2018 to a 38.31% increase in 2018–2021, despite being adversely affected by technological regression. Finally, several policy implications are proposed, including optimizing resource allocation, introducing advanced technology and setting the intercity cooperation and complementarity mechanisms. Full article

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect. Full article

Accurate assessment of green productivity is essential for advancing sustainable agriculture in ecologically fragile regions. This study examined the evolution of agricultural green total factor productivity (AGTFP) in Tibet over the period 2002–2021 by applying a super-efficiency SBM-GML model that accounts for undesirable outputs. We decompose AGTFP into technical change and efficiency change, conduct redundancy analysis to identify sources of inefficiency and explore its spatiotemporal dynamics through kernel density estimation and convergence analysis. Results show that (1) AGTFP in Tibet grew at an average annual rate of 0.78%, slower than the national average of 1.6%; (2) labor input, livestock scale, and agricultural carbon emissions are major sources of redundancy, especially in pastoral regions; (3) technological progress is the main driver of AGTFP growth, while efficiency gains have a limited impact, reflecting a technology-led growth pattern; (4) AGTFP follows a “convergence-divergence-reconvergence” trend, with signs of conditional β convergence after controlling for regional heterogeneity. These findings highlight the need for region-specific green agricultural policies. Priority should be given to improving green technology diffusion and input allocation in high-altitude pastoral areas, alongside strengthening ecological compensation and interregional coordination to enhance green efficiency and promote high-quality development across Tibet. Full article

Cultivated land is fundamental to agricultural production, and the eco-efficiency of cultivated land utilization is widely acknowledged as a crucial indicator for assessing rational land use. Accordingly, this study applies a Super-SBM model with undesirable outputs to evaluate the eco-efficiency of cultivated land utilization (ECLU) across 31 provinces in China utilizing provincial panel data from 2005 to 2023 and further employs dynamic fuzzy-set qualitative comparative analysis to investigate, across spatial and temporal dimensions, how government policy, agricultural technology, socioeconomic conditions, and natural conditions interact to achieve a high ECLU and to elucidate the diverse configurational pathways through which these factors converge to deliver a high ECLU. Our findings demonstrate that the ECLU originates from the joint influence of several factors, and no single factor alone can provide a high level of eco-efficiency. In particular, a high GDP per capita and strong government agricultural expenditure intensity are pivotal for achieving a high ECLU, whereas a low GDP per capita and weak government agricultural expenditure intensity are the core conditions associated with poor eco-efficiency outcomes. We identify three distinct driving pathways that foster a high ECLU: the Economy–Technology–Government Synergistic Pathway, Nature–Economy Dual-Driver Pathway, and Government-Supported Land–Economy Pathway. Between-configuration consistency (BECONS) exhibits no significant temporal effect; however, a constellation of external factors triggered a pronounced, collective reduction in configurational consistency from 2008 to 2014. Regional analysis reveals pronounced heterogeneity: Spatially, the Economy–Technology–Government Synergistic Pathway is concentrated in China’s central and eastern provinces, the Nature–Economy Dual-Driver Pathway clusters mainly in the central belt, and the Government-Supported Land–Economy Pathway predominates in the west. Full article

Understanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-producing regions in China from 2001 to 2022. Kernel density estimation and the Dagum Gini coefficient are used to analyze spatiotemporal disparities, while a geographically and temporally weighted regression (GTWR) model explores the underlying drivers. Results indicate that the national average maize CEE was 0.86, exhibiting a “W-shaped” fluctuation with turning points in 2009 and 2016. From 2001 to 2015, the Southwestern Mountainous Region led with an average efficiency of 0.76. Post-2015, the Northern Spring Maize Region emerged as the most efficient area, reaching 0.90. Efficiency levels have generally become more concentrated across regions, though the Southern Hilly and Northwest Irrigated Regions showed higher volatility. Inter-regional differences were the primary source of overall CEE disparity, with an average annual contribution of 46.66%, largely driven by the efficiency gap between the Northwest Irrigated Region and other areas. Spatial heterogeneity was evident in the impact of key factors. Agricultural mechanization, cropping structure, and environmental regulation exhibited region-specific effects. Rural economic development and agricultural fiscal support were positively associated with CEE, while urbanization had a negative correlation. These findings provide a theoretical foundation and policy reference for region-specific emission reduction strategies and the green transition of maize production in China. Full article

To explore the integration efficiency of ecology, culture and tourism in China, this study uses a Super-Efficiency SBM model with undesirable outputs to measure integration efficiency, employs kernel density estimation (KDE) to analyze dynamic spatial distribution characteristics, applies the standard deviational ellipse (SDE) to examine the migration trend of the spatial agglomeration center of gravity, and uses Tobit regression to identify spatiotemporal influencing factors. The findings show that: the national integration efficiency presents a trend that first decreases and then increases, with North and South China having relatively high integration efficiency. The national integration efficiency has gone through three stages: narrowing differences, coexistence of slow efficiency, and gradient effects, and increasing efficiency with weakened multipolarization. The degree of spatial agglomeration has gradually increased, and the center of gravity has shifted eastward as a whole. The internal gaps in East and South China have expanded, while the internal balance in North China has improved; the internal differences in other regions have narrowed. The influencing factors of integration efficiency have shifted from traditional economy-led to innovation and institutional collaboration. Economic development level and market openness have a positive impact on the overall integration efficiency, while transportation conditions show a restraining effect. Full article