Search Results (118)

The digital economy empowers the development efficiency of the accessible industry, which is crucial for its sustainable development. Previous studies have focused on a single part of the accessible industry, lacking an overall grasp of the industry. Furthermore, they have not yet elaborated on the driving role of the digital economy in the accessible industry. This paper constructs an index system for evaluating the development efficiency of the accessible industry empowered by the digital economy, and uses sample data from 31 provinces (cities) in China. By comprehensively employing the three-stage DEA model method, this paper explores the reasons for the differences in development efficiency among accessible industries, empirically analyzes their influencing factors and the mechanism of efficiency improvement, and fills the gap in research on the digital economy’s impact on the accessible industry. The purpose is to deeply understand the development model of the accessible industry empowered by the digital economy through systematic evaluation and analysis, to accurately identify efficiency bottlenecks and clarify paths for improvement. Full article

Green financial inputs (GFI) play an important role in promoting green innovation in the manufacturing industry, and accurately evaluating GFI utilization efficiency and its industry heterogeneity is crucial for optimizing green resource allocation. To address this, this study applies a three-stage Data Envelopment Analysis (DEA) model, using panel data of 29 Chinese manufacturing sectors from 2011 to 2024. This model eliminates the interference of environmental factors and statistical noise via the Stochastic Frontier Analysis (SFA) in the second stage, thus obtaining more reliable efficiency evaluation results. The empirical results show that: (1) GFI can effectively improve manufacturing green innovation efficiency (GIE), but the overall utilization efficiency remains at a low level; (2) there exists significant industry heterogeneity, with technology-intensive industries performing best in GFI utilization efficiency, followed by capital-intensive industries, and labor-intensive industries the worst; (3) environmental regulation and green financial market environment significantly improve GFI utilization efficiency, while government green finance support and market structure have no significant effects on it; (4) after eliminating external disturbances, the real GFI utilization efficiency tends to be stable, and the efficiency decline in 2023–2024 is mainly caused by external shocks. Corresponding targeted implications are put forward to optimize GFI allocation and promote balanced green development of China’s manufacturing industry. Full article

Sustainable low-carbon logistics serves as a key driver for economic development in China’s eastern coastal regions. This study evaluates the efficiency of low-carbon logistics across 12 provinces from 2013 to 2022, incorporating both environmental and economic dimensions. The analysis begins with Pearson’s correlation tests to examine relationships between input and output variables, followed by a three-stage Data Envelopment Analysis (DEA) model to compute efficiency scores. After adjustment, overall comprehensive technical efficiency slightly declined from 0.811 to 0.799, while pure technical efficiency improved from 0.919 to 0.931 and scale efficiency decreased from 0.885 to 0.859. Provinces such as Hebei and Liaoning demonstrate high and stable development, whereas Beijing and Hainan are constrained by declining scale efficiency. Expanding the research scope from individual provinces to the entire eastern coastal region, this study combines a three-stage DEA model with the Malmquist index to provide both static and dynamic analysis. A scientifically constructed indicator system incorporates carbon emissions, highlighting the synergy between economic and environmental performance. A key finding is the identification of scale diseconomies as a significant constraint on regional low-carbon logistics efficiency. The results suggest that policymakers should adopt tailored strategies, prioritize targeted environmental investments, and enhance cross-regional collaboration. For corporate managers, we emphasize shifting from scale-driven expansion to technology-enabled refinement, with a focus on advancing precision in operations. These insights offer a valuable reference for promoting sustainable, high-quality, and low-carbon logistics development in other regions. Full article

The high brand premium of geographical indication (GI) tea has not been efficiently converted into widespread economic benefits through its supply chain. The current performance evaluation system is confronted with a dual predicament: first, the strong external environment (such as policy support and industrial agglomeration) interference is hard to isolate, making it impossible to distinguish between “environmental advantages” and “true management levels”; second, the general agricultural indicators fail to capture the output essence of GIs centered on “brand value”. Therefore, this study constructs an evaluation framework integrating methodological and indicator innovations. Methodologically, a three-stage DEA model is adopted to eliminate the influence of exogenous environments and random noises, precisely measuring the “pure management efficiency” of the supply chain. Indicatively, common variables are abandoned, and a customized system is established with logistics facilities, production area, and regional digital investment as inputs, and brand reputation, value, and income as outputs. Based on the panel data of twelve representative tea GIs from 2021 to 2024, the study finds that the following: (1) The “pure management efficiency” of the supply chain is the key factor influencing performance evaluation. (2) “Diseconomies of scale” are the main structural bottleneck restricting performance improvement rather than technological backwardness. (3) Solving the above-mentioned management efficiency problems, especially resolving “diseconomies of scale”, is the micro foundation for achieving sustainable industrial development. This research not only provides methodological support and empirical evidence for the refined management and sustainable development of the geographical indication agricultural product supply chain, but also has significant practical significance for promoting the quality and efficiency improvement of the tea industry and facilitating the sustainable development of related agriculture. Full article

Green technological innovation integrates the two major strategies of innovation-driven development and green development and serves as a crucial pathway to achieving the goal of high-quality and sustainable development in the Yangtze River Economic Belt (YREB). Against the backdrop of regional integration, it is of great significance to study the coordinated development trend of green technological innovation, with urban agglomerations as the unit of study. This study takes 108 cities in the YREB as research objects, constructs a Green Technological Innovation Efficiency (GTIE) measurement framework based on a two-stage DEA model, and decomposes GTIE into Technological Innovation Efficiency (TIE) and Green Production Capacity (GCP). On this basis, using the System GMM model, this study examines the mechanism by which the economic connection structure affects GTIE, TIE, and GCP from the perspective of urban agglomeration spatial networks. The empirical results show that from 2006 to 2020, the overall GTIE of the YREB showed a steady upward trend, and its spatial pattern evolved from “high in the east and low in the west” to “coordinated development of the three major urban agglomerations.” The three urban agglomerations played a core leading role in the diffusion of regional green innovation. Specifically, the economic integration development of urban agglomeration spatial networks significantly promoted the improvement of GTIE; the spatial network structure of TIE within the urban agglomerations exerted a significant positive spillover effect on GCP, while the GCP network structure also showed a significant feedback effect on TIE. Overall, through strengthening the inter-city flow of innovative factors and collaboration, regional integration has effectively promoted the coordinated growth and diffusion of green technological innovation, providing important support for the high-quality improvement of regional productivity and contributing to the sustainable development of the region. Full article

Achieving carbon peaking and neutrality is critical for global sustainability efforts and addressing climate change, yet improving land use carbon emission efficiency (LUCE) remains a challenge, especially in resource-dependent regions like Shanxi Province. Existing studies often overlook the spatial heterogeneity of LUCE and the mechanisms behind its driving factors. This study assesses LUCE disparities and explores low-carbon land use pathways in Shanxi to support its sustainable transition. Based on county-level land use data from 1990 to 2022, carbon emissions were estimated, and LUCE was measured using a three-stage super-efficient SBM-DEA model, with stochastic frontier analysis (SFA) to control for external noise. eXtreme Gradient Boosting (XGBoost) with SHAP values was used to identify key socio-economic and environmental drivers. The results show the following: (1) emissions rose 2.46-fold, mainly due to expanding construction land and shrinking cultivated land, with hotspots in Taiyuan, Jinzhong, and Linfen; (2) LUCE improved due to gains in technical and scale efficiency, while pure technical efficiency stayed stable; (3) urbanization and government intervention promoted LUCE, whereas higher per capita GDP constrained it; and (4) population density, economic growth, urbanization, and green technology were the dominant, interacting drivers of land use carbon emissions. This study integrates LUCE assessment with interpretable machine learning, demonstrating a framework that links efficiency evaluation with driver analysis. The findings provide critical insights for formulating regionally adaptive low-carbon land use policies, which are essential for achieving ecological sustainability and supporting the sustainable development of resource-based regions. Full article

As a major energy producer and consumer, China has witnessed rapid growth in carbon emissions, which are closely linked to changes in regional climate and the environment. Water, energy, and food (W-E-F) are the three most critical components of human production and daily life, and achieving the coordinated development of these three resources and connecting them with climate change through the carbon emissions generated during their utilization processes has become a key issue for realizing regional ecological sustainable development. This study constructs a dynamic two-stage network slack-based measure-data envelopment analysis (SBM-DEA) model, which integrates the water–energy–food (W-E-F) system with the climate change process to evaluate China’s comprehensive ecological efficiency from 2011 to 2022, and adopts the Dagum Gini coefficient decomposition, kernel density estimation, hierarchical clustering, and geographical detector model to analyze provincial panel data, thereby assessing efficiency patterns, regional differences, and driving mechanisms. The novelty and contributions of this study can be summarized in three aspects. First, it establishes a unified framework that incorporates the W-E-F nexus and climate change into a dynamic network SBM-DEA model, enabling a more systematic assessment of ecological efficiency. Second, it uncovers that interregional overlap effects and policy-driven factors are the dominant sources of spatial and temporal disparities in ecological efficiency. Third, it further quantifies the interactive effects among key driving factors using Geodetector, thus offering practical insights for regional coordination and policy design. The results show that China’s national ecological efficiency is at a medium level. Southern China has consistently maintained a leading position, while provinces in northwest and southwest China have remained relatively backward; the efficiency of the water–energy–food integration stage is relatively high, whereas the efficiency of the climate change stage is medium and exhibits significant temporal fluctuations. Interregional differences are the main source of efficiency gaps; ecological quality, environmental protection efforts, and population size are identified as the primary driving factors, and their interaction effects have intensified spatial heterogeneity. In addition, sub-indicator analysis reveals that the efficiency related to total wastewater, air pollutant emissions, and agricultural pollution shows good synergy, while the efficiency associated with sudden environmental change events is highly volatile and has weak correlations with other undesirable outputs. These findings deepen the understanding of the water–energy–food-climate system and provide policy implications for strengthening ecological governance and regional coordination. Full article

This paper innovatively constructs a comprehensive material cycle network framework for the circular economy system of the coal industry and evaluates the circular economy efficiency of China’s provincial coal industry from 2011 to 2021 using a comprehensive evaluation model that integrates emergy analysis and dynamic network data envelopment analysis (DEA). The research delves into the evolutionary characteristics of the coal industry’s circular economy and identifies the underlying causes of inefficiency. The results reveal that the circular economy in China’s coal industry has gone through three stages: the transformation period, the reinforcement period, and the growth period, with the inefficiency of waste reutilization being the key factor restricting the overall improvement in efficiency. The circular economy model in the production phase is gradually shifting from an extensive linear model to a clean, closed-loop model, while a significant gap remains between the high-emission linear model and the low-pollution closed-loop model in the utilization phase. Furthermore, regional heterogeneity mainly arises from imbalances in the operational efficiency of the circular economy system. This study not only reveals the deep-seated reasons for the low efficiency of circular economy in China’s coal industry but also provides strategies and directions for achieving a more efficient circular economy and carbon mitigation goals. Full article

Under the background of global green sustainable development and the urgent need to understand complex regional innovation systems, it is crucial to scientifically assess China’s Tourism Green Innovation Efficiency (TGIE) as a dynamic networked system and reveal its system-level evolution driving mechanism. This article presents the construction of the TGIE evaluation indicator system, measures the inter-provincial TGIE in China in 2011–2023 based on the three-stage super-efficiency SBM-DEA model, analyzes the spatial correlation network characteristics of TGIE by using the motif analysis method and the social network analysis method, and explores the evolutionary driving mechanism by using the time-exponential random graph model (TERGM). The study shows the following: (1) The TGIE of China exhibits a regional distribution pattern characterized by “high in the east and low in the west.” The efficiency of the eastern coastal region is significantly higher than that of the central and western regions, and the overall efficiency shows a fluctuating upward trend. (2) The local structure of China’s TGIE network is dominated by the chain structure, and the partially closed structure is gradually enhanced. It indicates that the bridge role of intermediary nodes in the cross-regional flow of innovation resources is becoming more and more significant. (3) The overall network evolves from a single center to a polycentric collaboration model. High-efficiency regions attract low-efficiency regions to collaborate through high connectivity, and intermediary nodes play a key role in connecting high- and low-efficiency regions. (4) The evolution of China’s TGIE network is driven by both exogenous and endogenous dynamics, showing significant path dependence and path creation characteristics. This study enhances the theoretical framework of complex systems in tourism innovation and offers theoretical support and policy insights for optimizing the network structure of China’s TGIE as a complex adaptive system and maximizing regional cooperation networks. 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 construction of ecological civilization is intrinsically connected to green development. The green development of forestry enterprises serves as a key approach to achieving this goal. The research purpose of this paper is to explore the realization path of green development of forestry enterprises. First, an improved CRITIC (Criteria Importance Through Intercriteria Correlation)–entropy weight method was used to construct a reasonable input-output indicator system. Next, a three-stage data envelopment analysis (DEA) model was employed to evaluate the comprehensive technical efficiency of green development across 33 forestry enterprises in China, using panel data from 2017 to 2022. Finally, the study explored various configurational pathways for achieving green development by integrating the Technology–Organization–Environment (TOE) framework with dynamic qualitative comparative analysis (QCA). The findings reveal that green development in forestry enterprises is shaped by the interplay of multiple factors. Four distinct configurations were identified as instrumental in driving high green development. These configurations could be classified into two categories: the environment–organization synergistic development model and the technology–organization dual-driven model. This study provides empirical insights into the complex configurational relationships underlying green development in forestry enterprises, offering valuable guidance for optimizing development strategies. Full article

Green technology innovation (GTI) is the core force driving the sustainable and advanced progression of the economy. Accurately assessing the green technology innovation efficiency (GTIE) of Chinese industrial firms will help promote the green transformation and development of industrial firms. Based on the three-stage DEA(3S-DEA) model, the article measures the GTIE of Chinese industrial firms from 2013 to 2022. Then, its spatial distribution characteristics and spatial spillover effects were analyzed using the spatial Durbin model (SDM). The study shows that after excluding environmental elements and stochastic interference, the value of GTIE of industrial firms shows the trend of east > central > northeast > west; the GTIE of industrial firms has significant spatial correlation, and there are differences in direct and spillover effects under different spatial weight matrices. The level of economic development, industrialization, openness, and information infrastructure has a remarkable positive influence on the GTIE of industrial firms, while government support does not show a significant effect. Full article

Hydrogen energy, characterized by its abundant resources, green and low-carbon attributes, and wide-ranging applications, is a critical energy source for achieving carbon peaking and carbon neutrality goals. The operational efficiency of the hydrogen energy industrial chain is pivotal in determining the security of its supply chain and its contribution to China’s energy transition. This study investigates the efficiency of China’s hydrogen energy industrial chain by selecting 30 listed companies primarily engaged in hydrogen energy as the research sample. A three-stage data envelopment analysis (DEA) model is applied to assess the industry’s comprehensive technical efficiency, pure technical efficiency, and scale efficiency. Additionally, kernel density estimation is utilized to analyze efficiency trends over time. Key factors influencing efficiency are identified, and targeted recommendations are provided to enhance the performance and sustainability of the hydrogen energy industrial chain. These findings offer valuable insights to support the development and resilience of China’s hydrogen energy industry. Full article

As new-energy vehicles (NEVs) gradually gain public attention, their carbon-reduction issues have become a focal point in academia. This study evaluates the carbon-reduction efficiency of NEVs in 21 Chinese provinces using an improved three-stage DEA model, analyzes spatial disparities with the Dagum Gini coefficient, and decomposes carbon-emission factors using the LMDI method. Results show that the overall carbon-reduction efficiency is low, with an average value of only 0.266. Significant differences exist in production- and consumption-stage efficiencies across regions. Shanxi Province performed the best, with efficiency scores of 1 in both stages, while the carbon-reduction stage showed the lowest efficiency, ranging between 0.2 and 0.3 in most regions. The central region exhibited the highest carbon-reduction efficiency, followed by the western and eastern regions, primarily influenced by intra-regional disparities. Energy intensity significantly suppresses carbon emissions, followed by energy structure, while economic development and population size positively contribute to carbon emissions. This study provides theoretical support for regional governments to formulate policies related to the NEV industry and offers practical guidance for its further development. Full article

The marine aquaculture industry holds a significant position in the development of Weihai City’s fishing industry, with its ecological efficiency having a direct impact on the sustainable progress of the regional fishing sector. Utilizing a three-stage DEA model as an unexpected output model, this study systematically assessed the ecological efficiency of Weihai City’s marine aquaculture industry. By employing kernel density estimation, we analyzed the temporal dynamic evolution of ecological efficiency within the marine aquaculture industry. The results indicate that the overall ecological efficiency of marine aquaculture in Weihai City has improved to some extent, influenced by environmental factors such as government support, urbanization level, and regional economic development level. After removing environmental and random factors, it was found that the overall ecological efficiency of the marine aquaculture industry in Weihai City shows a more stable upward trend. Furthermore, using a gray dynamic model, GM (1, 1), we predicted the trend of ecological efficiency in the marine aquaculture industry. The findings indicate that, with the progressive adoption of advanced aquaculture technologies, the ecological efficiency of Weihai City’s marine aquaculture is anticipated to continue growing in the future. However, the pace of growth has decelerated. To maximize ecological efficiency, it is imperative to optimize resource allocation, foster technological innovation, and elevate awareness regarding ecological and environmental preservation. By assessing the ecological efficiency of Weihai City’s marine aquaculture industry, this article aims to shed light on the industry’s progress, thereby promoting its high-quality and sustainable development. Full article