Search Results (207)

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

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

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

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

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. 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

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability. 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

Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evaluation system based on the triad of laborers, labor objects, and means of production. Employing spatial difference-in-differences and double machine learning methodologies within a quasi-natural experimental design, the research investigates the causal mechanisms through which digital empowerment and novel productive forces influence regional green innovation efficiency. The findings reveal that both digital empowerment and novel productive forces significantly enhance regional green innovation efficiency, exhibiting pronounced positive spatial spillover effects on neighboring regions. Heterogeneity analyses demonstrate that the promotive impacts are more pronounced in eastern provinces compared to central and western counterparts, in provinces participating in carbon trading relative to those that do not, and in innovation-driven provinces versus non-innovative ones. Mediation analysis indicates that digital empowerment operates by fostering the aggregation of innovative talent and elevating governmental ecological attentiveness, whereas new-type productivity exerts its influence primarily through intellectual property protection and the clustering of high-technology industries. The results offer empirical foundations for policymakers to devise coordinated regional green development strategies, refine digital transformation policies, and promote industrial structural optimization. Furthermore, this research provides valuable data-driven insights and theoretical guidance for local governments and enterprises in cultivating green innovation and new-type productivity. Full article

Improving forest carbon sink efficiency (FCSE) is the key to mitigating climate change and achieving sustainable forest resource management in China. However, current research on FCSE remains predominantly focused on static perspectives and singular linear effects. Based on panel data from 30 provinces (autonomous regions and municipalities) in China from 2008 to 2022, this study integrated the super-efficiency Slack-Based Measure (SBM)-Malmquist–Luenberger (ML) model, spatial autocorrelation analysis, and dynamic fuzzy set qualitative comparative analysis (fsQCA) to reveal the spatiotemporal differentiation characteristics of FCSE and the multi-factor synergistic driving mechanism. The results showed that (1) the average value of the FCSE in China was 1.1. Technological progress (with an average technological change of 1.21) is the core growth driver, but the imbalance of technological efficiency change (EC) among regions restricts long-term sustainability. (2) The spatial distribution exhibited a U-shaped gradient pattern of “eastern—southwestern”, and the synergy effect between nature and economy is significant. (3) The dynamic fsQCA identified three sustainable improvement paths: the “precipitation–economy” collaborative type, the multi-factor co-creation type, and “precipitation–industry-driven” type; precipitation was the universal core condition. (4) Regional differences exist in path application; the eastern part depends on economic coordination, the central part is suitable for industry driving, and the western part requires multi-factor linkage. By introducing a dynamic configuration perspective, analyzing FCSE’s spatiotemporal drivers. We propose a sustainable ‘Nature–Society–Management’ interaction framework and region-specific policy strategies, offering both theoretical and practical tools for sustainable forestry policy design. Full article

Urban agglomerations serve as essential platforms for regional innovation, while agricultural technology innovation and diffusion play pivotal roles in enhancing agricultural eco-efficiency (AEE). Based on panel data from the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) (2001–2023), this study employs a super-efficiency slacks-based measure model incorporating undesirable outputs to evaluate agricultural eco-efficiency. A modified gravity model is utilized to construct agricultural technology innovation networks (ATINs) in urban agglomerations, and a spatial Durbin model is applied to examine the spillover effects of network structure on eco-efficiency. The results indicate that: (1) Higher-degree centrality within the innovation network significantly improves local agricultural eco-efficiency and produces positive spillover effects on neighboring cities; (2) both direct and spillover effects are significant in central cities, whereas sub-central cities exhibit only a significant direct effect, and peripheral cities display an insignificant direct effect but a significant spillover effect; and (3) enhanced urban informatization, agricultural financial development, and industrial scale substantially strengthen the spatial spillover effects of the innovation network, thereby further advancing agricultural eco-efficiency within the agglomeration. These findings offer theoretical and empirical support for optimizing agricultural technology pathways and enhancing eco-efficiency in urban agglomerations. Full article

As China steadily advances its “dual carbon” strategy, understanding the factors influencing carbon emission efficiency (CEE) is crucial for promoting high-quality urban development. This study examines Western Valley cities (WVCs), which play a key role in regional development and exhibit a distinct spatial structure. Using a super-efficiency slacks-based measure (SBM) model and economic and social panel data, we measured CEE and analyzed its spatiotemporal evolution. A geographically and temporally weighted regression (GTWR) was then applied to assess the spatiotemporal heterogeneity of influencing factors. Our findings revealed that the overall CEE of these cities remains relatively low, with a complex pattern of change. While efficiency levels in northern, southern, and central cities have gradually increased, there are notable differences in the quantity and spatial distribution of cities with high, relatively high, relatively low, and low efficiency over time. Additionally, the positive effects of technological investment, road density, population density, and per capita gross domestic product on CEE follow an increasing trend, whereas the negative impacts of energy intensity, green space ratio, secondary industry proportion, land use scale, and gas consumption gradually weaken. Additionally, the magnitude and direction of these effects vary significantly across northern, central, and southern cities. These findings provide important theoretical and practical insights for region-specific strategies aimed at reducing emissions and improving efficiency in WVCs. Full article

This longitudinal study applies decade-spanning socioeconomic indicators (2013–2022) from the Beijing–Tianjin–Hebei urban agglomeration. An integrated analytical framework was developed, merging the super-efficiency slack-based measurement (SBM) methodology with entropic weighting techniques to quantify tourism efficiency and economic development. Subsequent phases employed a multi-method analytical cascade: coupling coordination assessment modeling for system interaction analysis, standard deviation ellipses for spatial dispersion characterization, and Markovian transition matrices for temporal pattern identification. The investigation concludes with evolutionary trajectory projections using gray system forecasting GM(1,1) modeling. The analytical findings reveal the following patterns: (1) Within the Beijing–Tianjin–Hebei metropolitan cluster, tourism efficiency demonstrates a consistent upward trajectory, manifesting spatial differentiation characteristics characterized by a dual-core structure centered on Tianjin and Baoding, with higher values observed in northwestern areas compared to southeastern regions. Concurrently, regional disparities exhibit progressive convergence over temporal progression. (2) The level of economic development in the Beijing–Tianjin–Hebei city cluster has been rising steadily, demonstrating a geospatial distribution of ‘central concentration with peripheral attenuation, with the north-east being better than the southwest’, and the gap between the regional differences has become broader over time. (3) The coupling between tourism efficiency and the level of economic development in the Beijing–Tianjin–Hebei city cluster has generally improved, with Beijing and Tianjin predominantly in a coordinated regime, and some cities in Hebei Province about to shift from dysfunctional to coordinated, and, spatially, the coupling and coordination in northern sectors demonstrate superior performance compared to southern counterparts nationally. (4) The coupling coordination degree of the Beijing–Tianjin–Hebei city cluster in the next eight years is predicted by the gray GM(1,1) prediction model and the overall continuation of the growth trend of the Beijing–Tianjin–Hebei city cluster over the past ten years, thus verifying the importance of the regional integrated policy frameworks in the system integration of the Beijing–Tianjin–Hebei metropolitan system. Full article

China has already joined the ranks of innovative nations. Accelerating technological innovation to lead a green transformation in land use is an urgent requirement for promoting ecological civilization and, in turn, driving high-quality economic development. This study examines urban data spanning from 2006 to 2021, focusing on cities classified at the prefecture level or above. Employing the Chinese Innovative City Pilot Policy (ICPP) as a quasi-natural experiment, this study utilizes a super-efficiency Slack-Based Measure (SBM) model that incorporates undesirable outputs to assess Green Land Use Efficiency (GLUE). Additionally, a multi-period Difference-in-Differences (DID) model, combined with a mediation effect model, is employed to evaluate the influence of innovation-driven policies on GLUE. The findings are as follows: (1) Although GLUE showed variability throughout the study period, it generally trended upwards, with significant improvements noted in the eastern regions and coastal city clusters. (2) Innovation-driven policies have effectively enhanced urban GLUE, a conclusion supported by extensive robustness tests. (3) The heterogeneity investigation indicates that the ICPP’s impact on GLUE is more significant in cities with advantageous geographic locations, increased environmental awareness, and strong market potential. (4) A mechanism analysis demonstrates that the ICPP positively influences GLUE by reducing urban sprawl and promoting the concentration of digital service industries. Based on these results, this study proposes policy recommendations aimed at refining innovation-driven approaches to improve urban GLUE. These recommendations are pivotal in promoting a green, low-carbon transformation in China’s economic and social development. Full article

Existing studies on eco-efficiency evaluation often neglect the social and ecological benefits of land use, as well as the spatial heterogeneity in the effects of natural factors. This study aims to establish a systematic eco-efficiency evaluation framework and examine the dynamic impacts of various factors on land use eco-efficiency at the county scale. An evaluation framework integrating economic, ecological, and social outputs was constructed using multi-source remote sensing data. The super-efficiency Slack-Based Measure model and exploratory spatial data analysis method were employed to analyze the land use eco-efficiency and its spatiotemporal characteristics in the Huaihai Economic Zone from 2000 to 2022. Furthermore, the enhanced Geographically and Temporally Weighted Regression model was applied to examine the spatiotemporal heterogeneity of industrial structure, ecological background, land use, and other factors influencing land use eco-efficiency. The results indicated the following: (1) The average land use eco-efficiency in the Huaihai Economic Zone followed a pronounced “U-shaped” pattern, initially decreasing before increasing. At the county level, efficiency values generally showed an upward trend, with municipal districts or county-level cities accounting for more than half of the areas. (2) A weak positive spatial agglomeration of land use eco-efficiency was observed at the county level. High-efficiency areas shifted from a dispersed pattern to a “T-shaped” spatial distribution, while low-efficiency areas gradually converged toward the southern regions, evolving from an “H-shaped” cluster. (3) The influence of industrial structure, urbanization, environmental regulation, ecological factors, and land use on ecological efficiency exhibited clear spatial and temporal heterogeneity. The multi-dimensional land use eco-efficiency evaluation offers an effective tool for comprehensively assessing ecological conservation, environmental sustainability, and socio-economic development. It also provides valuable insights for improving land use eco-efficiency at the county level. Full article