Search Results (141)

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

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

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

The core hallmark of new quality productive forces (NQPFs) is a substantial increase in total factor productivity. Developing NQPFs tailored to local conditions significantly promote green, low-carbon, and environmentally sustainable development. This paper selects 30 provinces and municipalities in China (excluding Hong Kong, Macao, Taiwan, and Tibet) as research samples. It employs the super-efficiency Slacks-Based Measure (SBM) model, coupling coordination degree analysis, kernel density estimation, Dagum Gini coefficient, and β-convergence analysis to measure and analyze the coupling coordination degree between NQPFs and carbon total factor productivity (CTFP). The results indicate that CTFP exhibits an upward trend overall. At the same time, the NQPFs show an initial increase, followed by a decline, with significant regional variations observed in both. There is notable regional heterogeneity in the coupling coordination degree between NQPFs and CTFP. The eastern region demonstrates the highest coupling coordination degree, followed by the central, western, and northeastern regions. The primary cause of this differential distribution is inter-regional disparities, particularly widening the gap between the eastern region and others. Further analysis reveals that, except for the eastern region, the dynamic evolution trend of coupling coordination nationwide and in other regions tends to converge. Regarding absolute β-convergence, the northeastern region converges the fastest, while the western region converges the slowest. Regarding conditional β-convergence, the convergence speeds in the central, western, and northeastern regions are consistent, but the convergence results remain unchanged. This study provides important theoretical support for achieving a balanced development of NQPFs and comprehensively enhancing CTFP, ensuring significant contributions to the sustainable development of a low-carbon economy. Full article

Accurately evaluating territorial space use efficiency is a prerequisite for promoting the realization of high-quality development. Existing efficiency evaluation models all treat decision making units (DMUs) as independent individuals, ignoring geospatial effects between geographical spaces, which leads to unreliable results. This study proposes a geographic data envelopment analysis (GeoDEA) model, integrating a spatially constrained multivariate clustering model with generalized data envelopment analysis (DEA). The GeoDEA model reconstructs evaluation and reference sets considering spatial adjacency, cluster numbers, and socio-economic indicators and then applies a slack-based measure (SBM) super-efficient formula. It is verified that the efficiency value evaluated using the GeoDEA model is higher than that of the traditional DEA model, but it is also more consistent with cognition and more reliable. This is mainly explained by the fact that the GeoDEA model takes into account the geospatial effect and selects DMUs with relatively close geographic distance and higher levels of development as the reference frontier for efficiency evaluation. The GeoDEA model optimizes the traditional DEA model and avoids the problem that the efficiency of DMU is underestimated when the geographical background and development mode of DMU are very different from the reference frontier. It enhances the reliability of the evaluation of territorial space use efficiency. Full article

The increasing frequency of extreme climate events has posed severe challenges to China’s socio-economic development and ecological environment due to geological disasters. Therefore, there is an urgent need for effective adaptive strategies to enhance geo-disaster resilience. Environmental governance, as an effective measure to reduce risks from extreme climates and disasters while promoting high-quality social development, remains underexplored in terms of its impact on geo-disaster resilience. This study innovatively constructs a resilience assessment framework that considers extreme climate and geo-disaster intensity, integrating various statistical methods, including the Super-Efficiency Slacks-Based Measure Data Envelopment Analysis (SBM-DEA) model, spatial Markov chains, and methods such as Geodetector and the Geographically and Temporally Weighted Regression (GTWR), to reveal the spatiotemporal evolution of geo-disaster resilience in China from 2007 to 2022, while also analyzing the mechanisms through which environmental governance influences resilience and its spatiotemporal variations. The findings indicate that China’s geo-disaster resilience exhibits unstable growth with significant regional disparities. Spatially, resilience shows notable spillover effects and a tendency toward convergence within similar regions. Environmental governance unevenly enhances resilience over time and space: soil and water conservation and afforestation are generally effective measures, while the contributions of ecological water replenishment, environmental facility management personnel, fiscal expenditure, and nature reserve protection vary by region. This research offers key insights into improving geo-disaster resilience and optimizing environmental governance strategies to enhance China’s disaster response capacity and regional sustainable development. Full article

Currently, urban land use in China faces many challenges, such as irrational land use structure and inefficiency, which is especially obvious in resource-based cities. In order to improve this situation, this paper uses the super-efficient Slack-Based Measure (SBM) model to measure the green land use efficiency (GLUE) of 113 resource-based cities in China, analyzes its spatial-temporal evolution law, and identifies the formation law of heterogeneous GLUE in resource-based cities using the Tobit model. The research results show that: (1) GLUE in resource-based cities shows year-on-year growth and has certain stage characteristics, in which the eastern region is the best, followed by the western and central regions, and the northeastern region is the worst; regenerative cities are significantly better than mature, growth, and declining cities; oil and gas cities are better than non-metal, forest, metal, and coal cities in turn; (2) High-value resource-based cities are concentrated in the eastern and western regions, while low-value ones are concentrated in the central and northeastern regions. Moreover, the number of high-value resource-based cities is continuously increasing, while the number of low-value ones is significantly decreasing; (3) The level of economic development, industrial structure, level of technological input, number of green patents granted, government financial support, sewage treatment rate, and policy constraints all exhibit significant positive effects on the GLUE of resource-based cities. Furthermore, there is notable heterogeneity among resource-based cities in different regions, development stages, and resource types. In the future, policies should be implemented on a city-by-city basis, and a sound long-term mechanism for policy implementation should be established to enhance the long-term awareness of managers and land users so as to improve the GLUE in resource-based cities. Full article

Achieving environmental sustainability has become a global priority, with energy efficiency (EE) emerging as a critical pathway. This study examines the influence of information and communication technology service exports (ICT) on EE by integrating the moderating role of regulatory quality. We employ a super-slack-based measure (Super-SBM) and generalized least squares models in G20 economies throughout 2001–2023. The findings show that the average EE is 0.855, which indicates a potential for further improvement of 14.50%. The findings further show that ICT is positively related to EE, and regulatory quality delivers a conducive environment for the adoption of technologies to optimize energy usage. The findings also indicate a synergistic effect between ICT and regulatory quality, which can lead to substantial improvements in EE, emphasizing the importance of governance in facilitating technological advancements. The findings highlight the role of renewable energy and economic openness in shaping EE. Furthermore, Argentina and South Africa achieved the highest EE, reflecting their proximity to the efficient frontier. In robust tests, this study verifies its results using the generalized method of moments, panel-corrected standard error, and feasible generalized least squares models. The findings suggest that ICT and governance perspectives can provide valuable insights for policymakers aiming to enhance energy sustainability through digital transformation and institutional reforms. Full article

To protect the environment, promote sustainable tourism, and enhance the quality and efficiency of the tourism industry, accounting for and identifying the ecological efficiency of tourism is crucial. In this study, we constructed a scientific measurement system for assessing the ecological efficiency of tourism in Huangshan City from 2011 to 2022 using a Super-SBM (slack-based measure) model and a Tobit regression model. Our findings revealed the following: (1) The overall trend in the total efficiency in Huangshan City fluctuated considerably during the period under study but demonstrated an overall positive trend of development. Scale efficiency mostly showed consistent annual improvement, while pure technical efficiency remained relatively stable. (2) Economic, social, environmental, and human-resource- and transportation-related factors all exerted significant positive influences on the ecological efficiency of tourism in Huangshan City. Based on these results, in aiming to enhance the ecological efficiency of tourism in Huangshan City, comprehensively considering multiple factors, such as the economy, society, the environment, human resources, and transportation, is recommended. Attention should be paid to balancing industrial expansion with technological innovation while simultaneously investing in the development of education and human resources. Emphasis should also be placed on protecting and improving the environment alongside efforts to improve capabilities in technological innovation and the level of management. The research findings presented in this article offer a valuable theoretical underpinning, as well as practical guidance for Huangshan City and other representative tourist destinations on how they can enhance the efficiency of their tourism ecosystems, thereby facilitating high-quality and sustainable development within the tourism industry. Full article

Taiwan is currently an aging society and will be a super-aging society in the near future. The purpose of this research is to use two models of data envelopment analysis (DEA)—the slacks-based measurement (SBM) model and the dynamic slacks-based measurement (DSBM) model—to analyze the efficiency of long-term care (LTC) in Taiwan. This analysis aims to explore the current situation of LTC in Taiwan and provide policy recommendations for LTC. The computation empirical result on the LTC efficiency score presents that the DSBM model exhibits higher efficiency than the SBM model after considering the carry-over variable in the former model. The result from the SBM model indicates that Taiwan’s outlying islands display the worst LTC efficiency, but this result does not appear in the DSBM model. Lastly, these two models both indicate that the number of elderly people being serviced in institutions exhibits higher efficiency and lower slack than those serviced in homes in 2017 and 2018. This paper concludes that the DEA approach is a viable method for examining the performance of the LTC services system as Taiwan approaches a super-aged society. Full article

The construction of digital villages has progressed in tandem with the transformation of traditional production methods, offering new perspectives for agricultural green growth and sustainable development. This study employs the entropy value method alongside the super-efficient global SBM (Slacks-Based Measure) mixed function model, which assesses efficiency by accounting for both inputs and outputs, thereby facilitating a comprehensive evaluation of agricultural green growth. This methodology facilitates the examination of the correlation between digital villages and agricultural green growth, as well as the influence of digital villages on this growth. Furthermore, the utilization of financial resources is employed as a mediating variable to elucidate the mechanism of action. The utilization of green finance and agricultural insurance can be facilitated by the establishment of digital villages, and that has been shown to promote agricultural green growth. Additionally, the promotion of agricultural green growth by digital village construction is stronger in middle-altitude regions, non-grain-producing regions, and regions where the digital literacy of the rural labor force is higher than average, as well as areas where the use of agricultural film is higher than average. Accelerating the construction of digital villages and promoting the utilization of rural financial resources while adapting the digital village development to local conditions are crucial for effectively fostering agricultural green growth and sustainable agricultural development. Full article