Search Results (255)

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

Following the transfer of farmland, new agricultural entities exhibit clearer profit-oriented goals and heightened sensitivity to changes in profitability. Changes in farmland transfer prices significantly affect producers’ crop selection, input choices, technology adoption, farming methods, and intensity. This study establishes a motivation–behavior–outcome analytical framework by integrating producer behavior theory with the mechanism of farmland health formation, suggesting that rising transfer prices may prompt producers to exhibit five types of positive or negative behaviors. The SBM-DEA model is employed to measure the grain green total factor productivity of farmland across 102 counties in China’s Henan Province from 2017 to 2022, reflecting the healthy utilization of farmland. Results from the two-way fixed-effects and threshold effect models reveal an inverted U-shaped relationship, indicating initially positive and later negative impacts of increasing transfer prices on farmland health utilization. GTWR model findings highlight that economic disparities and the pace of price increases dictate the intensity of producers’ positive and negative motivations, while the economic capacity for absorbing shocks and the natural endowment capacity for absorbing shocks influence the likelihood and magnitude of these effects. Government regulation should, therefore, focus on regulating producer interests. Full article

Classical DEA models typically assume a linear valuation approach in performance assessment. However, in practical applications, many DMU inputs and outputs exhibit nonlinear valuation. A linear valuation may fail to accurately capture the variations in value across different DMUs. One critical challenge in efficiency evaluation is the presence of undesirable outputs, which negatively affects DMU performance. To address this, decision-makers aim to incorporate the impact of undesirable factors into efficiency measurements, enabling them to identify high-performing DMUs under comparable conditions and use them as benchmarks for inefficient ones. In response to this issue, this study introduces a novel approach based on the SBM Network DEA model to enhance airport efficiency within a two-stage framework while accounting for undesirable outputs. By applying piecewise linear theory, the model assigns lower weight to excessive quantities of undesirable outputs, effectively distinguishing DMUs that generate fewer undesirable outputs from those producing higher amounts. Furthermore, this research offers a practical benchmarking strategy for inefficient airports, aiming to improve their efficiency while considering the priority of each stage. Full article

This paper proposes a two-stage framework integrating Data Envelopment Analysis (DEA) and fuzzy multi-criteria decision-making methods to evaluate the performance of logistics firms in Vietnam. In the first stage, DEA models (CCR, BCC, and SBM) are employed to measure relative efficiency and identify benchmark firms among 15 leading logistics companies. In the second stage, FAHP–FTOPSIS is used to incorporate qualitative and sustainability-oriented criteria and to provide a comprehensive ranking of the efficient firms. The results indicate that a considerable proportion of firms operate below the efficiency frontier, implying substantial opportunities for resource optimization. Environmental and technological dimensions are found to be the most influential factors, while companies implementing green distribution strategies and strong data security practices consistently achieve higher rankings. Sensitivity analysis confirms the robustness and stability of the proposed framework. This study contributes by bridging operational efficiency assessment with broader strategic and sustainability considerations, overcoming the limitations of single-method evaluations used in prior research. The integrated DEA–FAHP–FTOPSIS approach offers managers a practical tool to diagnose weaknesses, prioritize improvement actions, and benchmark against top performers. In addition, it offers policymakers valuable insights to support digital transformation and green logistics initiatives in developing economy contexts. Full article

China’s cross-border e-commerce (CBEC) has expanded rapidly, yet province-level evidence remains limited on how AI development conditions the contribution of logistics fulfilment efficiency (LEF) to cross-border e-commerce development (CBED), especially across regions with uneven digital maturity. This study tests whether AI capability amplifies the marginal effect of logistics fulfilment efficiency (LEF) for CBED and whether this complementarity varies across eastern, central, and western China. Using a balanced panel of thirty-one provinces over 2017–2023 (N = 217), we combine a Super-SBM DEA logistics fulfilment efficiency measure (LEF), a four-pillar AI Development Index (AIDI), and customs-based CBED indicators. Two-step System GMM models are estimated for the full sample and regional subsamples to account for dynamic persistence and endogeneity concerns. Results indicate that higher LEF is associated with higher CBED and that AIDI strengthens this relationship via the interaction term; the complementarity is the largest in eastern provinces and remains positive but smaller in central and western regions. Overall, the evidence suggests that logistics fulfilment efficiency and AI capability act as complementary enablers of cross-border e-commerce development, supporting provincial competitiveness as CBEC scales. Sustainability implications are therefore discussed via operational-efficiency channels rather than direct environmental outcomes. Full article

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

With increasing emphasis on ecological conservation and food security, cultivated land issues have become more prominent. This study focuses on Jilin Province and uses nine prefecture-level administrative units and prefectures as the basic analytical units. Using continuous data for 2000–2023, this study analyzes the spatiotemporal evolution of cultivated land use efficiency (CLUE). By 2023, most regions had achieved ecological safety (ES), examined through their coupling and coordination. The Super-Efficiency SBM-DEA model and the Malmquist–Luenberger (ML) index were used to evaluate the static and dynamic changes in CLUE. A DPSIR–PLS-SEM integrated framework was applied to identify causal mechanisms influencing ES, while the TOPSIS method was employed to assess overall evolutionary trends. In addition, the coupling coordination degree (CCD) model combined with kernel density estimation (KDE) was used to characterize the interaction between CLUE and ES and their spatial evolution. Results indicated the following: (1) From 2000 to 2023, overall CLUE in Jilin Province showed an upward trend with fluctuations, while regional disparities narrowed and spatial distribution became more balanced. (2) The composite ES index increased from 0.3009 to 0.7900, accompanied by a marked expansion of areas classified as secure. (3) The CCD improved from a basic level to a high-quality coordination level, indicating enhanced synergistic development. Higher coordination was observed in central and eastern regions, whereas western and peripheral areas lagged. This study integrates multi-dimensional modeling approaches to systematically assess the coupled dynamics on cultivated land use efficiency and ecological safety, providing insights for land management and policy formulation. Full article

Universities are central to regional high-quality development, yet existing studies often rely on output-based indicators and neglect efficiency as well as the contributions of architecture and engineering disciplines. This study addresses this limitation by constructing an evaluation–identification framework that links technological innovation efficiency to regional development. Regional progress is measured with a composite index derived from multi-criteria decision analysis; innovation efficiency is evaluated using a non-oriented DEA–SBM model under constant returns to scale; and causal effects are tested with a two-way fixed-effects panel approach. Results reveal steady growth in regional development, marked spatial disparities in efficiency, with frontiers concentrated in certain provinces, and a consistently positive effect of efficiency on development, with stronger marginal impacts in central and western regions. By adopting an efficiency–mechanism perspective, the study highlights architecture-related disciplines as key drivers of sustainable growth and provides guidance for innovation alliances, evaluation reform, and managerial enhancement. Full article

As global climate change intensifies, achieving the dual goals of economic efficiency and low-carbon development has become a pressing challenge. Using panel data for 269 Chinese cities from 2010 to 2021, based on their carbon emission efficiencies (CEEs) measured using the DEA-SBM model, a staggered difference-in-differences (SDID) model is employed to identify the policy impacts, which is further extended into a triple-difference (DDD) framework to examine the causal impact of the dual-pilot policy. The results show that (1) China’s CEE has improved gradually but remains relatively low, with significant regional disparities. (2) Empirical results indicate that the dual-pilot policy leads to a significant improvement in CEE, raising it by approximately 4.06%. The positive impact is particularly pronounced in cities characterized by more advanced industrial structures and stricter environmental regulatory frameworks. (3) Industrial upgrading and green technological innovation serve as key mediating channels, contributing 2%, 7%, and 10.7% to the total mediation effect. (4) The positive impacts are particularly evident in eastern, large-scale cities. These results underscore that the integration of digitalization and low-carbon initiatives serves as an effective pathway to improving CEE. Therefore, policymakers are encouraged to further advance the dual-pilot programs, foster green technological innovation, and accelerate industrial upgrading toward a digitally empowered and low-carbon development model. Full article

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm². (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale. Full article

Amid intensifying geopolitical tensions and global uncertainties, regional economies face mounting pressures that threaten both stability and sustainability. Against this backdrop, building resilient regional systems has become a central issue in sustainable urban development. As a key driver of resilience, innovation has been central to China’s development agenda. Continuous and large-scale R&D investment has redirected focus from input expansion to efficiency improvement, positioning R&D efficiency at the heart of resilience-building. Under external shocks and uncertainty, can improvements in R&D efficiency enhance regional economic resilience? If so, which additional factors embedded in sustainable urban development planning can further amplify this effect? To address these questions, this study employs provincial panel data from 2000 to 2021 and integrates the SBM-DEA approach with an entropy-weighted resilience index for regression analysis. The results indicate that (1) R&D efficiency exerts a positive but limited impact on resilience, with an average increase of only 0.188 units, indicating that efficiency alone cannot generate resilient economies without institutional coordination; (2) human capital agglomeration and financial density strengthen this relationship, highlighting the need to integrate talent and financial strategies; (3) the positive effect is observed in eastern provinces but remains insignificant in central and western regions, revealing pronounced structural disparities that risk widening the resilience gap across regions rather than fostering balanced development; and (4) targeted government intervention effectively converts innovation efficiency into resilience gains, fostering coordinated and sustainable development. This study empirically demonstrates that improving R&D efficiency significantly enhances regional resilience in China and based on this evidence introduces the ICT Synergy Framework as a novel analytical lens for understanding how innovation, capital, and talent jointly drive resilience and sustainable development. The findings further suggest that targeted government intervention in R&D resource allocation can reinforce resilience, offering broader lessons for other developing economies. By integrating innovation outcomes with spatial and institutional planning, the study provides actionable insights for advancing sustainable urban development and coordinated regional growth. 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

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

The North China Plain faces severe water scarcity, and the efficient use of brackish water has become a crucial pathway for sustaining agricultural development. In this study, we combine scenario analysis with Data Envelopment Analysis to establish a multi-scenario efficiency evaluation framework. Focusing on six counties in Handan, Hebei Province, we employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model to systematically evaluate brackish water irrigation efficiency (BWIE) across a baseline year (2020) and eight projected scenarios for 2030. The results show that the mean efficiency values across scenarios range from 0.646 to 0.909. Scenarios combining universal adoption of water-saving irrigation with normal hydrological conditions achieve the highest mean efficiency (>0.9), with minimal regional disparities and optimal system stability. The promotion of water-saving irrigation technologies is the primary driver of improved BWIE, whereas simply increasing brackish water application yields only limited marginal benefits. Redundancy analysis further indicates that water resource inputs are the main source of efficiency loss, with brackish water redundancy (42.3%) far exceeding that of land inputs (10.5%). These findings provide quantitative evidence and methodological support for optimizing regional water allocation and advancing sustainable agricultural development. Full article