Search Results (169)

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. 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

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

The dynamic relationship between foreign direct investment (FDI) and sustainable development has become a central topic of inquiry for academics and policymakers with rapid global economic growth. This study aims to clarify the impact mechanism and regional heterogeneity of FDI on total factor productivity (TFP) of Chinese high-tech enterprises, providing empirical evidence for optimizing foreign investment policies and promoting sustainable growth of enterprises. We utilized panel data from 30 provinces in China from 2009 to 2022. The DEA-Malmquist index method is firstly employed to dynamically measure the TFP of high-tech enterprises, while a static panel model is utilized to empirically test the impact of FDI on TFP. A particular emphasis is then placed on analyzing the regional heterogeneity of technology spillovers. The findings reveal that FDI significantly enhances both the production efficiency and the technological innovation capacity of high-tech enterprises overall, thereby facilitating the sustainable growth of enterprises. Furthermore, technological innovation emerges as the core driving force behind TFP growth, whereas the expansion of labor input significantly decreases efficiency improvements. Notably, the technology spillover effects of FDI illustrate significant heterogeneity across different regions and types of enterprises. To promote the sustainable development of high-tech enterprises, this study provides evidence-based insights for foreign direct investment technologies to better enhancing the overall sustainable competitiveness of the economy in China. Full article

This study explores the intellectual landscape and methodological evolution of Data Envelopment Analysis (DEA) in the context of renewable energy and carbon emission efficiency. Using bibliometric techniques and data extracted from the Web of Science Core Collection (2389 publications from 2000 to 2024), the research identifies influential authors, institutions, and thematic clusters shaping the field. The results reveal that DEA has evolved from a traditional efficiency assessment tool into a comprehensive analytical framework supporting sustainable energy transition and carbon mitigation policies. Six major research clusters were identified, encompassing carbon emission measurement, efficiency benchmarking, methodological innovations, industrial applications, circular economy perspectives, and international productivity comparisons. Notably, Asian scholars, particularly from China and Taiwan, dominate the research landscape, reflecting strong regional leadership in empirical and methodological advancements. The findings demonstrate that recent studies increasingly adopt advanced models such as network DEA, dynamic DEA, DEA–Malmquist, and hybrid DEA–machine learning approaches to address complex energy systems. Comparative insights highlight DEA’s advantages over Stochastic Frontier Analysis (SFA) in handling multi-dimensional, non-parametric data, while emphasizing the need for hybrid frameworks to improve robustness. This study contributes to the ongoing discourse on energy sustainability by mapping knowledge structures, revealing methodological trajectories, and providing guidance for future research on efficiency and carbon reduction strategies. 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

Corporate failure prediction remains a major topic in the literature. Numerous methodologies have been established for its assessment, while data envelopment analysis (DEA) has received particular attention. This study contributes to the literature, establishing a new approach in the construction process of prediction models based on the combination of logistic LASSO and an advanced version of data envelopment analysis (DEA). We adopt the modified slacks-based super-efficiency measure (modified super-SBM-DEA), following the “Worst practice frontier” approach, and focus on the selection process of predictive variables, implementing the logistic LASSO regression. A balanced sample with one-to-one matching between forty-five firms that filed for reorganization under U.S. bankruptcy law during the period 2014–2020 and forty-five non-failed firms of a similar size from the U.S. energy economic sector has been used for the empirical analysis. The proposed methodology offers superior results in terms of corporate failure prediction accuracy. For the dynamic assessment of failure, Malmquist DEA has been implemented during the five fiscal years prior to the event of failure, offering insights into financial distress before the event of a default. The model outperforms alternatives by achieving higher overall prediction accuracy (85.6%), the better identification of failed firms (91.1%), and the improved classification of non-failed firms (80%). Compared to prior DEA-based models, it demonstrates superior predictive performance with lower Type I and Type II errors and higher sensitivity as well as specificity. These results highlight the model’s effectiveness as a reliable early warning tool for bankruptcy prediction. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity (TFFP) in China’s 31 provinces between 2001 and 2021. Using the data envelopment analysis (DEA) model through the slack-based measure (SBM framework) and Malmquist–Luenberger index (MLI), we examine the efficiency and productivity growth of forestry, both with and without accounting for carbon emissions. The study reveals that when carbon emissions are not taken into account, traditional measures of productivity tend to overstate both efficiency and total factor forestry productivity (TFFP) growth, resulting in an average of 7.7 percent higher efficiency and 1.6 percent of additional TFFP growth per year. If we compare the regions, coast provinces with stricter technical regulations have improved efficiency in usage, but places like Tibet and Qinghai, with more vulnerable ecosystems, endure harsher consequences. Regardless of incorporating bad output into the TFFP estimation, China’s growth in forestry productivity primarily depends on efficiency change (EC) rather than technological change (TC). Full article

The purpose of this study is to propose a novel approach for measuring productivity changes in decision-making units (DMUs) over time and evaluating the performance of each DMU under uncertainty in terms of progress, regression, and stagnation. To achieve this, the Malmquist productivity index (MPI) and the data envelopment analysis (DEA) models are extended, and a new productivity index capable of handling uncertain data are introduced through a robust optimization approach. Robust optimization is recognized as one of the most applicable and effective methods in uncertain programming. The implementation and calculation of the proposed index are demonstrated using data from 15 actively traded stocks in the petroleum products industry on the Tehran stock exchange over two consecutive years. The results reveal that a significant number of stocks exhibit an unfavorable trend, marked by a decline in productivity. The findings highlight the efficacy and effectiveness of the proposed robust Malmquist productivity index (RMPI) in measuring and identifying productivity trends for each stock under data uncertainty. Full article

Enhancing production efficiency in major sugarcane-producing regions is of strategic significance for ensuring the security of China’s sugar industry and promoting its industrial upgrading. Using the DEA–Malmquist–Tobit modeling framework, this study dynamically evaluates production efficiency from 2011 to 2023, spanning China’s 12th to 14th Five-Year Plan periods, with a focus on the primary sugarcane-producing regions: Guangdong, Guangxi, Yunnan, and Hainan. Results indicate a U-shaped fluctuation in national comprehensive technical efficiency, with a historical low in 2022 due to a collapse in scale efficiency, pinpointing scale management as the central constraint. Regionally, Guangdong consistently maintained optimal dual efficiency. Yunnan stabilized its efficiency through rigid policy mechanisms. Guangxi experienced setbacks due to competition between eucalyptus and sugarcane cultivation, while Hainan faced a precipitous decline in scale efficiency following industry exits. Total factor productivity (TFP) analysis revealed that stagnation in technological advancement was the primary cause of productivity decline, leading to asynchronous regional technology diffusion and subsequent reliance on scale adjustments. During the 12th Five-Year Plan, Hainan led in TFP growth but experienced a sharp downturn in the 13th period due to policy tightening. In contrast, Guangdong achieved notable TFP growth in the 14th period through technological breakthroughs, whereas Yunnan lagged behind Guangxi due to technological inertia. Analysis of the driving mechanisms showed that urbanization rates significantly boosted efficiency through intensified land use. Full article

Theoretically, regions with rich natural resources often tend to develop resource-based industries more intensively, resulting in resource-dependent land development. China’s resource-dependent cities exhibit certain exceptions. Natural resource dependence (NRD) plays a relatively positive role in the total factor productivity change (TFPCH) in these cities, primarily attributable to their relatively mature technological efficiency. However, while such positive impacts exist, their overall effect remains limited. Many resource-based cities in China still face challenges in achieving sustainable growth. This raises a key question: why have some resource-based cities failed to achieve sustainable development? In order to explore the root cause of this problem, this paper systematically analyses the impact of resource dependence on TFPCH, and its governance mechanism based on the balanced panel data of 112 resource cities in China from 2003 to 2021, using the Super-SBM-DEA-Malmquist index method in the first stage, and the OLS model in the second stage. The main findings of this paper are as follows: First, NRD has a significantly positive impact on TFPCH, especially in growing and regenerating cities. The empirical results further validate the applicability of the resource blessing theory in China. Second, government regulation has a dampening effect on TFPCH in resource cities, which suggests that in the future development of resource cities, government intervention should be moderately reduced, and more emphasis should be placed on stimulating the city’s own autonomous mobility and endogenous development drive. Third, heterogeneity analyses show that this promotional effect is mainly realized through the improvement of technical efficiency. Fourth, the analysis of the moderation effect shows that research and development (R&D) intensity plays a positively moderating role in the sustainable development of resource-based cities. Through a stepwise approach, this paper reveals why resource-based cities cannot achieve sustainable development. The level of R&D in some resource-based cities remains relatively low, while it is the key factor for the applicability of the resource blessing (RB) hypothesis in China’s resource city. The findings not only provide new perspectives for theoretical research, but also important policy recommendations for the sustainable governance of land use in resource-based cities worldwide. Full article

Financial resources play a crucial role in rural revitalization. Understanding the efficiency of financial support is essential for the scientific and rational allocation of these resources. Therefore, we conducted an assessment over the period 2011–2020 utilizing the three-stage DEA model and the Malmquist index model to measure the efficiency of financial support for rural revitalization across 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from both static and dynamic perspectives. The results indicate the following: (1) Despite an overall downward trend, efficiency increased during specific intervals, namely 2012–2013, 2015–2016, and 2018–2019. (2) Regionally, the decline in the efficiency of financial support for rural revitalization is particularly notable in the northeast region. The eastern and central regions also experienced this trend to a lesser extent, whereas the western region experienced a more moderate decrease. However, a detailed analysis revealed that 10 provinces experienced efficiency gains. (3) Stochastic Frontier Analysis (SFA) regression results suggest that environmental variables have a measurable impact on the efficiency of financial support for rural revitalization. Full article

With the growth of the new energy sector, China’s energy industry is experiencing significant transformations. This research aims to evaluate the technological progress and changes in scale efficiency of listed companies in China’s energy industry, with a particular focus on the comparison between new and traditional energy sectors. This research investigates various efficiency values, types of returns to scale, the role of patents in fostering technological progress, and the influence of financial leverage on scale efficiency changes, a comprehensive evaluation of the industry that offers a critical foundation for formulating targeted strategies. This research uses data from A-share listed energy companies spanning from 2017 to 2023, constructs input–output indicators centered on research and development (R&D) and profitability, and applies the DEA model to examine the operating efficiency of energy listed companies. A Malmquist indices is developed to analyze the dynamic evolution of technological change and scale efficiency change. In contrast to the conventional approach of using DEA efficiency scores as the dependent variable in Tobit regressions, this research uses the Malmquist indices, which more effectively captures the dynamic evolution of technological progress and scale efficiency. The study empirically assesses the impact of patent accumulation on technological progress through a Tobit panel model with random effects and the effect of financial leverage on scale efficiency changes using a Tobit four-stage incremental regression. Finally, the study draws the following conclusions: 1. In terms of industry static correlation, listed new energy companies exhibit polarization in returns to scale types; in contrast, traditional energy listed companies have a more stable and mature returns to scale structure. 2. In terms of dynamic correlation, technological progress in the new energy sector is substantial, while the traditional energy sector faces bottlenecks; efficiency changes in both industries are dependent on scale efficiency changes, rather than pure efficiency changes. 3. Regarding influencing factors for new energy listed companies, patent accumulation has a limited impact on technological progress, while financial leverage and scale efficiency change exhibit a non-linear relationship, with an inflection point effect observed in companies with high financial leverage. Finally, this study offers targeted policy recommendations for new energy and traditional energy listed companies based on the findings. Full article

The rural population aging poses a great challenge to China’s agricultural production, which is dominated by small farmers. Based on the panel data of 30 provinces or cities (except Tibet) in China from 2005 to 2020, the DEA-Malmquist index is employed to measure the agricultural total factor productivity (ATFP) in each province (city), and then the mediation effect model is used to reveal the mechanism by which the rural population aging affects the ATFP through farmland transfer, agricultural social services, and agricultural machinery. The results show that the rural population aging has made a significant contribution to the ATFP, and farmland transfer, agricultural socialized services and agricultural machinery have a intermediary effect on the increase of the ATFP. Further decomposition of ATFP reveals that the rural population aging can significantly contribute to the scale efficiency and technical progress rate through farmland transfer, agricultural socialization services and agricultural machinery, but does not have a significant effect on pure technical efficiency. In order to promote the high-quality and high-efficiency development of agriculture in the context of population aging, it is necessary to optimize the market environment for farmland transfer, improve the agricultural socialized service system, and continue to strengthen agricultural science and technology innovation. Full article