Slacks-based Measure Research Articles

Impact of Digital Villages on Agricultural Green Growth Based on Empirical Analysis of Chinese Provincial Data

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

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

Synergistic Impacts of Carbon Emission Trading Policy and Innovative City Pilot Policy on Urban Land Green Use Efficiency in China

Reducing the conflict between economic expansion and environmental conservation requires green-based innovation policies. The synergistic impacts of these policies on promoting urban land green use efficiency (ULGUE) remain underexplored. Utilizing panel data from 285 prefecture-level cities in China (2006-2020), we apply the super-efficiency slacks-based measure (SBM) to calculate ULGUE. Employing the staggered difference-in-difference (SDID) model and mediation model, we investigate the synergistic effects of the Carbon Emissions Trading Policy (CETP) and the Innovative City Pilot Policy (ICPP), along with their influencing mechanisms on ULGUE. The results demonstrate that the average ULGUE keeps increasing from 2006 to 2020 and the simultaneous implementation of CETP and ICPP enhances ULGUE, with a more pronounced effect when CETP precedes ICPP. The concurrent implementation of dual policies boosts ULGUE by leveraging technological innovation and industrial structure as mediators. The magnitude of the mediating effect is as follows: green innovation > general innovation > rationalization of industrial structure > advancement of industrial structure. We also observe heterogeneous synergistic impacts of CETP and ICPP on ULGUE, with greater effectiveness in non-resource-based and large-scale cities. These results offer valuable insights for crafting effective policy combinations to advance sustainable development trajectories for China and other developing countries.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Mutual Causality Between Urban Transport Superiority Degree and Urban Land Use Efficiency: Insights from County Cities in Gansu Province Under the Belt and Road Initiative

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions.

HOW DOES THE DIGITAL ECONOMY AFFECT ENERGY EFFICIENCY?

As a new way of promoting high-quality economic development, whether the digital economy can enhance energy use efficiency by improving resource allocation is of great practical significance to global energy conservation and emission reduction efforts. This research employs principal component analysis and slacks-based measure data envelopment analysis in a case study of 30 provinces in China between 2014 and 2020 to evaluate the digital economy and energy efficiency. The analysis explores how the digital economy impacts energy efficiency, considering direct impacts, mediating effects, and their nonlinear relationship. The findings indicate a declining trend in China’s energy efficiency before it starts to moderate. The digital economy can boost energy efficiency by encouraging industry upgrades and reducing labor mismatches. The threshold regression model reveals that when the capital mismatch level exceeds 0.8871, the digital economy adds considerably to energy efficiency. The study’s findings confirm the linear and nonlinear energy efficiency impact trajectories of the digital economy, offering useful references for sustainable development policymaking.

Quantitative evidence of the community of shared future for mankind as a driver of sustainable development in human society

In an era marked by global economic challenges, an increasing array of non-traditional security threats, and a proliferation of conflicts and issues, China has put forth the building of a community of shared future for mankind as a way to address this question of the times. The theory of the community of shared future for mankind offers a framework to address contemporary issues by promoting equality, mutual benefit, security, justice, openness, innovation, multicultural exchange, and green development. This paper innovatively quantifies the five core principles of the theory of the community of shared future for mankind using the entropy method. It calculates the proxy variable for sustainable development, green total factor productivity, using the super-efficiency Slack-Based Measure model in Data Envelopment Analysis. Baseline regression is conducted on this basis. The Principal Component Analysis method and another Data Envelopment Analysis method, the Malmquist index, are utilized to recalculate the indicators, followed by another round of regression analysis. The results are consistent with the baseline regression. Additionally, Ordinary Least Squares, Partial Least Squares, Maximum Likelihood Estimation, Ridge regression, and Lasso regression are employed to ensure the robustness of the results. The findings indicate a significant positive relationship between the community of a shared future for mankind and the sustainable development of human society, with consistent conclusions drawn from different calculation methods and regression techniques. Through value recognition, international cooperation based on benefit-sharing and mutual reciprocity can guide humanity towards a harmonious coexistence with nature, irrespective of regional and economic disparities. This study provides quantitative evidence of the impact of the community of a shared future for mankind on the sustainable development of human society, filling a gap in existing research. Based on these findings, feasible policy recommendations are proposed to promote sustainable development in human society.

The impact of fintech on the efficiency of urban commercial banks in China-based on super slacks-based measures with undesirable output

This study endeavours to elucidate the influence of financial technology (fintech) on the operational efficiency of urban commercial banks in China. By scrutinizing the integration of Internet-based fintech solutions, it seeks to illuminate their potential for streamlining banking processes, elevating customer experiences and augmenting the precision of credit assessments and lending decisions through sophisticated data analytics and risk management tools. This research evaluates the efficiency of Chinese urban commercial banks employing the Super Slacks-Based Measure (Super-SBM) which incorporates undesirable output. Furthermore, it leverages the System Generalized Method of Moments (System GMM) to investigate fintech’s specific impact on urban commercial bank efficiency across various Chinese regions. The findings underscore fintech’s pivotal role in catalyzing digital transformation within banks, thereby bolstering operational efficiency and customer satisfaction. Moreover, this study highlights the critical importance of fintech in fortifying risk management capabilities within the banking sector. The insights gleaned from this research contribute to the banking industry’s sustainable growth and competitive resilience. Additionally, the findings furnish policymakers with empirical evidence to support the strategic integration of fintech and the optimization of banking service processes. Urban commercial banks can streamline operations, enhance customer experiences, refine credit assessments and ultimately attain higher efficiency and long-term sustainability levels in the rapidly evolving financial landscape by harnessing the transformative potential of fintech.

On the efficiency of US community banks around the COVID-19 outbreak

ABSTRACT This paper examines how the COVID-19 pandemic affected the efficiency of 569 publicly traded community banks in the United States. Using a slacks-based measure (SBM) DEA modelling framework, we analyze quarterly data around the pandemic outbreak to estimate overall technical efficiency, pure technical efficiency, and scale efficiency scores. We use four assessment perspectives: general, loan-volume focused, loan-income focused and income-focused models. Additionally, we incorporate credit risk adjustment in loan and income-focused models. Our findings indicate that U.S. community banks enhanced their efficiency during and after COVID-19, surpassing pre-pandemic levels. Managerial efficiency drove this improvement. Adjusting for default risk weakened efficiency improvements, revealing a more accurate picture of efficiency changes during and after the pandemic.

Influence mechanism of digital economy development on the supply efficiency of ecological products

Improving the supply efficiency of ecological products (EPSE) is of great significance to protect the ecological environment, promote the development of green industry and the sustainable growth of the economy. This paper focuses on constructing an evaluation index system for EPSE and exploring the relationship between the development level of the digital economy (DE) and EPSE in China. To measure the EPSE, the study employs the Undesirable Slacks-Based Measurement (SBM) Model across 30 provinces, cities and districts in China from 2011 to 2022. Furthermore, the research utilizes spatial econometric models, panel threshold effect models, and other methodologies to investigate the impact mechanism and non-linear relationship between DE and EPSE. The research shows that: (1) The overall level of EPSE in the study area is relatively low, with significant development differences observed; (2) The DE has a nota-ble spatial spillover effect on EPSE, with a significantly negative impact in neighboring areas; (3) The development of the DE promotes EPSE through the upgrading of human capital structure, industrial structure, and increases in local government fiscal revenue and corporate operating profits; (4) Heterogeneity analysis shows that the impact of DE on EPSE varies significantly across eastern, central, and western China, with positive effects pronounced in the eastern and western regions; (5) Regression results of the threshold effect indicate a significant single threshold effect on the impact of DE development level on EPSE. Specifically, when the threshold value is less than 0.1232, DE significantly contributes to the improvement of EPSE. This paper contributes new literature evidence and factual references to the understanding of the causal relationship between DE and EPSE. The findings highlight the importance of considering spatial spillover effects, impact mechanisms, and regional heterogeneity in analyzing the relationship between DE and EPSE. The research also suggests that promoting the development of the digital economy could be a viable strategy to enhance EPSE, especially in regions where the threshold value is below 0.1232

Economic and Environmental Effects of Farmers’ Green Production Behaviors: Evidence from Major Rice-Producing Areas in Jiangxi Province, China

This study examines the economic and environmental impacts of green production practices among farmers. It aims to contribute to sustainable agricultural development, mitigate agricultural non-point source (NPS) pollution, and align environmental protection with economic growth. This paper utilizes survey data from 1345 farm households in the main rice production areas of Jiangxi Province, China, using the example of reduced fertilizer application (RFA) among rice farmers. This study constructs a slack-based measure data envelopment analysis (DEA—SBM) model with undesirable outputs to measure environmental effects and applies an endogenous switching regression model (ESRM) to test the economic and environmental effects of farmers’ adoption of green production technologies. We found the following: (1) The RFA behavior of farmers has a significant positive impact on their net profit per hectare (NPH), helping farmers increase their income, with the increase ranging from 2.05% to 6.54%. (2) Farmers’ RFA behavior has a significant positive impact on agricultural green productivity (AGP), contributing to the improvement of the environment, ranging from 44.09% to 45.35%. (3) A heterogeneity analysis found inconsistencies in the income-enhancing and environmental-enhancing effects at different quantiles of NPH and AGP. Therefore, attention should be placed on improving the agricultural product quality supervision system under the market circulation mechanism, creating land scale conditions conducive to the promotion and application of fertilizer reduction technologies and promoting the implementation of externality internalization compensation systems.

Using super-efficiency slack-based measure data envelopment analysis with enhanced neural network frontier to evaluate the operating efficiency of Chinese-listed real estate companies

China's real estate industry has faced significant changes and challenges in the aftermath of the COVID-19 pandemic and recent years, and has been unable to maintain its former prosperity. Facing a rapidly changing market environment and new loan restriction policies, real estate companies are encountering unprecedented challenges. Efficiency evaluation and analysis of real estate companies are essential to guide them in improving operational efficiency and adapting to new development models. This paper examines 87 real estate development companies listed on the Shenzhen and Shanghai markets, first evaluating and analyzing their operational efficiency in 2023 using the output-oriented super-efficiency slack-based measure data envelopment analysis (SBM-DEA) method. To address the issue of overestimated operational efficiency in the original model, this paper integrates it with the backpropagation neural network (BPNN) algorithm to obtain more robust re-evaluated efficiency scores. The results align with expectations, showing an overall decline in efficiency scores, and more significant decreases for companies with smaller output shortfalls in the original evaluation. The re-evaluated efficiency scores do not excessively underestimate efficiency and can serve as a reliable proxy for the original scores. The enhanced model combining BPNN with super-efficiency SBM DEA provides reliable and robust efficiency evaluations for these 87 listed real estate companies.

An assessment of economy- and transport-oriented health performance

BackgroundGood health can prolong one’s lifespan and is a fundamental human right. Thus, human health is being influenced by prejudiced from sociological, environmental, economic, and geographic aspects. The economy and transportation system pose a serious challenge to the assessment of the health performance of economies.ObjectiveThis study aims to assess the health performance of Organization for Economic Cooperation and Development (OECD) economies by using economic and transport-related indicators and examining the role of health expenditure and governance in improving efficiency.MethodsThis study measures the economy- and transport-oriented health efficiency of 35 OECD economies for the period of 2000–2022. In the first stage, this study employs a slacks-based measure and the data envelopment analysis–window analysis approach to conduct individual (economy and transportation) and joint assessments to measure health efficiency. In the second stage, this study uses the tobit regression method to investigate the effects of influencing factors, namely, government general health and pharmaceutical expenditures, the medical infrastructure, and governance, on health efficiency.ResultsEmpirical results reveal that a 1-unit change in the health expenditure during the research period improves economy-oriented health efficiency by 71% and transport-oriented health efficiency by 58%. The econometric analysis demonstrates that all the coefficients of economy- and transport-oriented health efficiency are significant and positive. Notably, a 1-unit change in the medical infrastructure increases economy- and transport-oriented health efficiency by 50.8%, and a 1% increase in pharmaceutical expenditure increases the health, economy, and transport efficiency scores by 16.3%, 33%, and 58.6%, respectively.ConclusionsThe findings suggest that some of the economies were efficient with regard to their health-oriented outputs, that is, quality of life and mortality and morbidity rates, and most of the economies demonstrated excellent economic performance. The findings of the transport-oriented health efficiency assessment reveal that the economies were unable to perform well in the last year of the research period owing to the nationwide lockdowns. Nonetheless, they demonstrated efficiency in the first half of the research period. The joint assessment of economy- and transport-oriented health efficiency indicates that economic and transport input resources can adversely affect the GDP and life expectancy simultaneously, and the medical infrastructure, pharmaceutical expenditure, and number of medical graduates serve as constructive stimuli for health efficiency improvement.

Analysis of knowledge services efficiency and influencing factors of public libraries in China: a mixed study based on the SBM model and dynamic QCA

PurposeThe core objective of this study is to provide an in-depth quantitative assessment of the efficiency of public library knowledge services (PLKS) in China and to scrutinize the factors that have a significant impact on the efficiency of PLKS. Furthermore, this study also aims to examine the characteristics and evolving patterns of PLKS in China and to propose strategies for enhancing the quality of services provided by public libraries. The findings of this paper are expected to provide valuable references for current academic research and practice areas, guiding and promoting exploration and development in related fields.Design/methodology/approachTo analyse and evaluate the operation mechanism and efficiency of PLKS, this study creatively constructs a multi-stage PLKS efficiency evaluation model and provides related indicators. Based on this process, the super-efficiency network slacks-based measure model was used to analyse the efficiency, and dynamic qualitative comparative analysis was adopted to analyse the recipes about the influencing factors of PLKS. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.FindingsIn the first stage, PLKS in China meets the basic requirements, but in the second stage, there is evident resource wastage. In addition, there are five recipes in PLKS of China. These five recipes reveal the configuration relationship between the factors that affect PLKS. The results show that PLKS in China are different in different regions, and the effects of the influencing factors are also different.Originality/valueIn this study, the authors provide an exhaustive deconstruction and interpretation of PLKS, thereby proposing a three-stage PLKS efficiency conversion process. Furthermore, the authors have identified a set of readily accessible and quantifiable indicators. It is worth emphasizing that the authors have taken a unique approach to analyse the elements affecting PLKS from the perspective of configuration, which has significantly broadened the boundaries and depth of PLKS research.

Airline performance measurement using bias-corrected network data envelopment analysis estimates: Evidence from a turbulent era

This study's objective is to estimate airline performance and identify the most resilient business model during periods affected by global crises, including financial crises, natural disasters, pandemics, terrorist attacks, and post-crisis periods. A balanced panel of 80 airlines, classified into full-service carriers (FSC), low-cost carriers (LCC), high perceived quality, and low perceived quality, during 2008–2021 is used in this work. The data is sourced from the ICAO Data+ and Skytrax databases. Following the recent literature on airline efficiency, we consider airlines' internal structure, which unfolds into three stages: (a) operations, (b) services, and (c) sales. Also, we employ a novel three-stage multi-parametric bias correction (MPBC) slacks-based measure (SBM) network data envelopment analysis (NDEA) approach to estimate system- and stage-specific efficiencies. It has been proven that the MPBC estimates are consistent, while the MPBC-SBM-NDEA approach addresses the limitations of conventional NDEA and SBM-NDEA models. This work highlights that airline passengers have become more price-sensitive, selecting value-for-money airline products and services after the Global Financial Crisis. Given this trend and the remainder of the study's findings, LCCs offering value-for-money products and services and operating smaller-scale networks with smaller plane capacities are less vulnerable to external shocks and are easier to rebound during post-crisis periods.

Research on the dynamic characteristics and improvement path of urban sustainable development level: Based on gravitational model and scenario simulation method

The rapid expansion of cities has brought new challenges to sustainable development (SD). Therefore, how to balance between SD and the economic growth has become the key to regional development. With the aim of enhancing the research system of SD level, founded on the deep research of SD level from a new perspective of “input-output” and the dynamic evolution law of SD level, this paper has explored the path for cities to improve their level of SD to achieve regional coordinated development. By virtue of the Super-efficiency Slack-Based Measure model based on unexpected output (unexpected super efficiency SBM model), it has measured the SD level of 21 cities in Sichuan from 2010 to 2019. Using methods such as equilibrium coefficient, obstacle analysis and gravitational model, this paper has studied the spatiotemporal evolution law of SD level and constructed six scenarios to simulate the future trend of SD level. The results have indicated that the level of SD in Sichuan is showing a gradual decline with significant constraints. The focus of SD in Sichuan is gradually shifting from Chengdu to a city cluster centered around Chengdu, Ziyang, and Meishan. In an ideal development scenario, the level of SD has significantly improved. This study has revealed the current status and potential of SD in Sichuan. In the future, with more concentration on the role of core urban agglomerations, Sichuan should adjust policies to address input–output barriers to promote SD throughout the province.

The impact of port green competitiveness on the hinterland economy: A case study of China.

In the global trade landscape, ports are not only critical nodes for the transportation of goods but also increasingly recognized as key factors driving sustainable development through their green competitiveness. This paper takes a green competitiveness perspective to delve into the interactive relationship between port development and the hinterland economy, aiming to provide a theoretical foundation and policy recommendations for the green development of ports. An evaluation index system for green competitiveness of ports was constructed, and a combination of the Slacks-Based Measure (SBM) and the Entropy Weighted-Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) method was employed to quantitatively measure and assess the 25 major coastal ports and their hinterland economic systems in China. The study reveals that the overall green competitiveness of these major coastal ports is on the rise, yet notable disparities exist among individual ports. Regionally, the green competitiveness of the Yangtze River Delta port group stands out, while that of the Bohai Rim, southeastern, Pearl River Delta, and southwestern coastal port groups is relatively low. Empirical analysis conducted in this paper demonstrates the positive impact of port green competitiveness on the hinterland economic system, particularly its significant role in enhancing the total and quality aspects of the hinterland economy. Furthermore, it was found that the green competitiveness of ports exerts a more pronounced promotional effect on the hinterland economies of the Yangtze River Delta and Pearl River Delta port groups compared to that on the Bohai Rim, southeastern, and southwestern coastal port groups. The results of this paper also hold important guiding value for optimizing the allocation of port resources, promoting port technological innovation, and improving port environmental management, contributing to the harmonious coexistence and long-term development of ports and their hinterlands.

How free trade pilot zones enhance agricultural green total factor productivity? Theory and evidence from China

IntroductionGiven the dual constraints of limited resources and environmental concerns, achieving green development in agriculture helps to enhance national food security, resource security, and ecological security.MethodsUtilizing panel data sourced from 30 provinces within China, spanning the years from 2001 to 2021, this paper employs the super-efficiency Slack-Based Measure (SBM) model and the Global Malmquist-Luenberger (GML) index to measure agricultural green total factor productivity (AGTFP). It utilizes methods such as the stepwise Differences in Differences (DID), mediation effect models, and moderating effect models to systematically explore the impact of the implementation of Free Trade Pilot Zones (FTPZs) on AGTFP and its mechanisms of action.ResultsThe study finds: (1) Establishing FTPZs significantly boosts AGTFP, indicating a profound positive influence; (2) The promotional effect of FTPZs on AGTFP is stronger in the eastern and central regions, as well as in major grain-producing areas; (3) FTPZs may enhance AGTFP by promoting the effect of agricultural structure upgrading, i.e., a transmission mechanism exists from FTPZs to the agricultural structure upgrading effect to AGTFP; (4) The agricultural scale effect plays a positive moderating role in the impact of FTPZs on AGTFP.DiscussionIt is advisable to optimize agricultural measures related to FTPZs, promote agricultural structural upgrading and scale enhancement, adapt strategies to local conditions to advance green agricultural development, and thereby promote the coordinated development of FTPZs and AGTFP.

Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation

With the rapid development of the digital economy, China's digital technology and industry continue to integrate deeply into all areas of society. Digital transformation(DT) has become an important engine and key pedestal for economic and social transformations and upgrades. This paper takes 3525 listed companies from 2013 to 2021 as a sample to measure the green total factor productivity(GTFP) of listed enterprises based on the super-efficiency slacks-based measure with the Global Malmquist-Luenberger Index (SBM-GML) and the super-efficiency epsilon-based measure with the Global Malmquist-Luenberger Index (EBM-GML). It draws on the two existing DT indicators to explore the impact and mechanism of enterprise DT on GTFP. The results show that DT can significantly enhance GTFP, and this conclusion still holds after double/debiased machine learning and other robustness tests; the heterogeneity analysis shows that DT of high-tech enterprises, manufacturing industries and low-financialisation enterprises has a more obvious effect on the enhancement of GTFP; and the four-stage mediated impact mechanism suggests that the effect of DT on GTFP can be achieved by improving internal control ability and technological innovation ability. This paper will provide relevant policy insights on how to better drive enterprise DT and green low-carbon development under the “dual-carbon” goal.

Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing

Purpose This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.