Search Results (322)

As a pivotal driver of high-quality development, new quality productivity (NQP) forms an indispensable synergistic relationship with land use efficiency (LUE) for achieving regional sustainability. Based on panel data from 29 prefecture-level cities in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, this study integrates the entropy-weighted TOPSIS method, super-efficiency Slack-Based Measure (SBM) model, Malmquist index, and fixed-effects models to systematically explore the spatiotemporal evolution of NQP and its underlying impact mechanism on LUE. Key findings reveal: (1) The comprehensive NQP index of the CPUA increased from 0.280 to 0.828, exhibiting a “stepwise rise” trend, with a spatial pattern characterized by a “core–secondary–periphery” three-tier gradient distribution. Zhengzhou, as the core growth pole, played an innovative leading role, while peripheral cities (e.g., Handan, Hebi) remained constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. (2) The average LUE in the study area increased from 0.917 to 1.031. Cities within Henan Province generally performed better than those in Hebei, Shanxi, and Anhui provinces. Total factor productivity grew at an average annual rate of 16.4%, with technological progress serving as the primary driver. (3) NQP exerts a significantly positive impact on LUE, yet with notable heterogeneity: large-scale cities enhanced intensive land use substantially through technological agglomeration and industrial upgrading; cities with scarce arable land and high economic development levels effectively leveraged NQP to boost LUE; in contrast, small cities, regions rich in arable land, and areas with low economic development have not established effective synergistic mechanisms, hindered by limited technological absorption capacity, path dependence, and factor bottlenecks. This study provides empirical support and actionable insights for optimizing land resource allocation and advancing coordinated development between NQP and LUE in similar urban agglomerations. Full article

Green finance is increasingly expected to support decarbonization while enhancing productivity, yet evidence on whether green credit bonds raise green total factor productivity (GTFP) remains limited. Using panel data for 29 provincial-level regions in China from 2016 to 2023, we compute GTFP using a slacks-based measure Malmquist–Luenberger (SBM–ML) index and estimate two-way fixed-effects models. To address endogeneity, we employ a Bartik shift–share instrumental-variable strategy. We found that green credit bonds significantly increase GTFP, with gains driven mainly by technological change (TC) rather than efficiency change (EC). The effect is stronger in eastern and western regions, in provinces that are not low-carbon pilot areas, and in regions with stronger low-carbon governance orientation. Public environmental attention directly improves GTFP but dampens the marginal effect of green credit bonds. Mechanism analyses further indicate that the low-carbon transition of the energy mix (LCEM) is an important transmission channel. Overall, these findings suggest that scaling up and better targeting green credit bonds, alongside complementary governance and public scrutiny, can accelerate China’s transition toward higher green productivity. This provides sustainability-relevant evidence that market-based green finance can support decarbonization while sustaining productivity growth, contributing to long-term sustainable development. Full article

Against the backdrop of accelerating global spatial restructuring, metropolitan areas have become crucial spatial units for enhancing regional competitiveness and securing industrial chains. Although China has continuously advanced metropolitan area development, low land use efficiency remains a key constraint on sustainable progress. Metropolitan integration presents a new approach to addressing this challenge. This study constructs an analytical framework of “direct effects–indirect effects–dynamic evolution” and measures metropolitan integration and land use efficiency using a multidimensional indicator system and a super-efficiency slacks-based measure (SBM) model incorporating undesirable outputs. Employing the system generalized method of moments (System GMM) estimator, this study conducts both baseline and mediation analyses using balanced panel data for 32 Chinese metropolitan areas from 2016 to 2022. The results show that both metropolitan integration and land use efficiency improved steadily during the study period. The coefficient on metropolitan integration is positive and statistically significant, and the lagged dependent variable is also positive and statistically significant, indicating substantial persistence over time. Heterogeneity analyses further indicate that the estimated association is more pronounced in eastern metropolitan areas and nationally designated metropolitan areas. In addition, industrial agglomeration and industrial specialization operate as important mediating channels in this relationship. Based on these findings, the study proposes policy recommendations to strengthen metropolitan integration and industrial collaboration, thereby improving land use efficiency. Full article

Against the backdrop of accelerating global energy transition, China, as the world’s largest energy producer and consumer, has a crucial impact on achieving carbon neutrality goals through the green development of its power industry. Green total factor productivity is an important indicator for measuring the green development of the power industry. Utilizing provincial panel data from 30 regions in China covering the period 2012–2023, using MATLAB R2021a software, this study firstly measures the static GTFP of China’s power industry using a Super-Efficiency Slack-Based Measure (SBM) model incorporating undesirable outputs. Subsequently, the dynamic GTFP is measured and analyzed using the Global Malmquist–Luenberger (GML) index model. The model decomposes GTFP change to examine the contributions of technical efficiency change and technological progress. The findings reveal that (1) the static GTFP of China’s provincial power industry is generally low, with significant regional disparities, with Jiangsu, Yunnan, Beijing, Zhejiang and Sichuan ranking among the top five nationally; (2) the average GTFPs in eastern and western China are higher than in the central region. Overall, the GTFP of China’s power industry exhibits an upward trend, which is primarily driven by technological progress. Based on these conclusions, the study proposes policy recommendations to enhance the power industry’s GTFP, which can offer theoretical insights for facilitating its green transition and sustainable development. Full article

As an integrated framework linking resource use and environmental sustainability, the WEF (Water–Energy–Food) system plays a vital role in achieving sustainable agricultural development. Focusing on the irrigation districts in the lower reaches of the Yellow River, this study constructed and applied a Super-Undesirable-SBM (super-efficiency undesirable slacks-based measure) model and a GTWR (geographically and temporally weighted regression) model from a WEF perspective to systematically analyze the spatiotemporal evolution and driving mechanisms of WEFSE (Water–Energy–Food Synergistic Efficiency) from 2000 to 2020. The overall WEFSE exhibited a continuous upward trend, with the spatial pattern gradually shifting from the southwest to the northeast and regional disparities becoming more pronounced. The efficiency demonstrated a significant positive spatial autocorrelation, indicating a stable clustering pattern of “high–high” and “low–low” efficiency areas. In terms of driving mechanisms, WEFSE evolved from being dominated by socio-economic drivers to a composite system jointly influenced by ecological and structural factors. Among these, PD (population density) and WP (proportion of water area) had increasingly positive effects, whereas PRE (precipitation) and NDVI (normalized difference vegetation index) imposed notable constraints. Meanwhile, PCL (proportion of cultivated land), GP (proportion of grassland), and AT (average temperature) exhibited significant spatial differentiation. This study highlights that the assessment of WEFSE and identification of its driving mechanisms using the Super-Undesirable-SBM and GTWR models can help to uncover the spatiotemporal dynamics of agricultural resource utilization, providing methodological support and decision-making insights for optimizing resource allocation and promoting sustainable development in the Yellow River irrigation districts and other complex agricultural systems. Full article

To solve the spatial water resources shortage, lots of water diversion projects have been constructed for sustaining development. As the water resource utilization efficiency (WRUE) is assumed not to decrease after the operation of water diversion projects, it is necessary to analyze the WRUE and its driving factors in a water-receiving area. Taking the Tao River Diversion Project as a case study, a Super-SBM (Super Slack-Based Measure) model and the Malmquist–Luenberger index are applied in estimating the WRUE values in the seven counties or districts in the water-receiving area of the Tao River Diversion Project. Spatial autocorrelation and a geographical detector are applied to explore the patterns and influencing factors. The results show that there is significant spatial variation in WRUE across the water-receiving areas from 2010 to 2019. High-efficiency areas maintain or improve their efficiencies, while low-efficiency areas show a stagnant or declining trend. The nondecreasing premise of WRUE is not fully satisfied in any area and at any time. The water diversion project is found to be a key driver for the shifting spatial patterns of WRUE from a cold spot dominance to a stronger hot spot agglomeration. The influencing factors on WRUE’s spatial differentiation are also dynamic with the operation of the water diversion project. Therefore, our study will not only help to assess the benefits of the Tao River Diversion Project, but can also provide many valuable insights for water resource planning. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

To evaluate the effectiveness of green finance, this study treats China’s green finance reform and innovation pilot zones as a quasi-natural experiment to assess their impact on urban energy efficiency. This research utilizes a panel dataset of 282 Chinese prefecture-level cities from 2010 to 2023 and employs a multi-period difference-in-differences (DID) model. The core dependent variable, urban green total factor energy efficiency (UGTFEE), is quantified using a non-radial Slack-Based Measure (SBM) efficiency model combined with the Malmquist-Luenberger index. The empirical findings reveal four key points. First, the green finance pilot zones significantly enhance UGTFEE, with policy-affected cities demonstrating an average improvement of approximately 2.0% relative to non-pilot cities. Second, this positive impact is transmitted through two primary mechanisms: the advancement of green technology research and development and the deepening of financial market development. Third, the policy’s effectiveness is heterogeneous, varying according to regional characteristics such as geographical location, environmental regulation stringency, and resource endowments. Finally, a negative spatial spillover is identified, wherein the policy creates a siphoning effect that competitively suppresses the UGTFEE of neighboring cities. These findings provide critical theoretical insights and empirical evidence for optimizing green finance initiatives, thereby facilitating urban industrial transformation toward greater green energy efficiency. Full article

Cultivated land (CL) is essential for human survival, as its coordinated utilization plays a crucial role in both food production and ecological protection. In this study, we focus on Aksu, a typical oasis in arid areas of Xinjiang, to explore how to improve the eco-efficiency of cultivated land utilization (ECLU) from the perspective of carbon emissions under different ownership structures. The goal is to provide policy support for the sustainable intensification of CL in Aksu. The super-efficiency slack-based measure (Super-SBM) model was used to calculate the ECLU, while the carbon emissions coefficient method was employed to estimate cultivated land carbon emissions (CLCE). Additionally, the random forest regression (RFR) model was utilized to analyze differences in CLCE between collective and state-owned cultivated lands. Finally, a Geo-detector analysis was conducted to identify driving factors of CLCE. The findings indicate that the overall ECLU values in Aksu initially increased and subsequently decreased over time. During the study period, Kalpin showed the highest ECLU, followed by Wensu and Wushi. The total CLCE in Aksu demonstrated an initial increase followed by a decrease, but the overall trend was growth, from 3.7 t in 2008 to 5.63 t in 2019, on average. It was observed that carbon emissions from state-owned cultivated land were greater than those from collective cultivated land, and carbon emissions from non-food crops were higher than those from food crops. Furthermore, spatial heterogeneity was evident in the CLCE. The single factor detection results showed that the Local_GDP (q = 0.763, representing the explanatory power of the Local_GDP on cultivated land carbon emissions) was identified as the main driver of CLCE in Aksu. The interactive detection results indicated that the Local_GDP and Farmer income (0.839) had stronger effects on CLCE in Aksu than any other two factors. It was also found that ownership of CL directly affects CLCE and indirectly affects the ECLU. In conclusion, it is necessary to formulate corresponding countermeasures for improving the ECLU involving government intervention, as well as cooperation with farmers and other stakeholders, to address these issues effectively within Aksu’s agricultural sector. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article

The adoption of robust fiscal policies is a critical pathway for China to control pollution, promote green development, and advance ecological civilization. This study examines the “Comprehensive Demonstration City of Energy Conservation and Emission Reduction Fiscal Policy” pilot project as a quasi-natural experiment. Employing the Slack-Based Measure Directional Distance Function (SBM-DDF) model and a multi-period Difference-in-Difference (DID) approach, we assess the impact of the energy conservation and emission reduction fiscal policy (ECERFP) on urban green total factor productivity (UGTFP). The results indicate that ECERFP significantly enhances UGTFP. This finding remains robust across multiple tests, including parallel trends, placebo tests, and the Goodman–Bacon decomposition. Mechanism analysis indicates that ECERFP enhances UGTFP mainly through technological innovation and improved energy efficiency. However, its effectiveness varies by geographical location, resource endowment, and city size. While ECERFP can promote urban energy conservation and end-of-pipe pollution control, it also carries the potential risk of inducing a “pollution haven” effect. To maximize the fiscal policy’s leverage and the resource allocation effects, a comprehensive strategy is required—one that advances energy efficiency, stimulates technological innovation, tailors energy conservation measures to local conditions, and nurtures the development of new productive forces to support sustainable urban growth. 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

The sustainable development of society requires the incorporation of environmental, social, and governance (ESG) principles. While ESG assessments are widely used in corporate settings, their application in healthcare settings, such as hospitals, remains underexplored. This study aimed to develop a comprehensive evaluation framework integrating ESG and digital transformation (DX) with traditional hospital efficiency and effectiveness assessments. Using open data, financial reports, and hospital website scraping, we applied a slack-based model (SBM) of data envelopment analysis (DEA) and super-efficiency SBM-DEA to calculate sustainability scores across four dimensions: overall sustainability, efficiency, effectiveness, and ESG/DX performance. Results showed that all three components—efficiency, effectiveness, and ESG/DX—were positively associated with overall sustainability. However, ESG/DX performance negatively impacted profitability in smaller hospitals, and improved effectiveness in rehabilitation hospitals was linked to higher operational costs. These findings suggest that while ESG and DX contribute to long-term sustainability, their short-term financial burden may challenge certain hospital types. The proposed index provides valuable insights for hospital management and policy development, aiming to advance ESG and DX initiatives in healthcare. Full article