Global Malmquist-Luenberger Research Articles

Navigating Asia's sustainable growth trajectory: a cross-regional comparative analysis.

This study examined the green total factor productivity (GTFP), technical progress change (GTC), and technical efficiency change (GEC) trajectories across 39 Asian countries from 2006 to 2021. GTFP was assessed across five Asian regions using the global Malmquist-Luenberger (GML) index with a slacks-based measure model and directional distance function. The results of the study show that West Asia, with a mean GTFP value of 1.0011, is ahead of Southeast Asia and South Asia, while Central and East Asia show room for improvement. Gini index analysis further shows that GTFP varies significantly within and outside the region, with large fluctuations especially in Southeast Asia and South Asia. GTC stagnated significantly in 2009 and 2015, indicating a bottleneck in technological progress, while GEC showed a positive upward trend, reflecting the improvement in resource efficiency. The 'decoupling' of GTC and GEC highlights the inconsistency between efficiency growth and the pace of technological progress, particularly in East and Central Asia. In 2021, Asia's average GTFP reaches 1.0048, signaling the region's progress towards sustainable productivity. This study provides important theoretical and practical implications by quantifying the regional differences in GTFP, GTC, and GEC, which provide a quantitative basis and decision-making reference for the formulation of green development policies in the Asian region.

Two-stage efficiency evaluation of industrial water resources and the role of digital inclusive finance: insight from Yangtze River Delta

The Yangtze River Delta (YRD) occupies 2.1% of China's territory but accounts for 1/4 of its GDP. Boosting efficiency of industrial water resources in YRD will drastically decrease water pollution and resource constraints. The current paper employed Network Super-SBM and the Global Malmquist-Luenberger index to evaluate industrial water use efficiency and wastewater treatment efficiency of YRD’ cities. Furthermore, digital inclusive finance and green innovation are introduced to investigate the mechanisms underlying their influence on the overall industrial water resources efficiency. The empirical findings posts that: (1) there is still room to improve the overall efficiency, especially the water treatment efficiency; (2) the MI value of each stages in most samples is greater than 1, indicating that the water treatment efficiency and water use efficiency are improving. It is mainly driven by the improvement of the efficiency of the management; (3) there are substantial differences in water resource efficiency among metropolitan areas, with Nanjing-metropolitan areas and Hefei-metropolitan areas having higher water use efficiency and water treatment efficiency for both stages, while Hangzhou-metropolitan areas and Shanghai-metropolitan areas have lower water treatment efficiency. As shown by the Kernel density Estimation, the degree of efficiency dispersion within same metropolitan area is reducing; and (4) digital inclusive finance will boost overall efficiency and water treatment efficiency, with green innovation as a mediating factor. The findings herein offer compelling evidence that financial development increases the effectiveness of water resources, introduce fresh perspectives for the government to put forward water-related policies, and provide ideas for high-quality economy.

Assessing the provincial renewable energy generation efficiency in China considering the energy justice and sustainable development

The rapid increase in renewable energy (RE) installed capacity and the resulting issues of energy justice and RE generation efficiency (REGE) require urgent attention. Therefore, this study calculated the China’s RE justice indexes (REJIs) and the provincial RE justice contribution degree from 2012 to 2021 via the Gini coefficient and modified coupling cooperation degree model. Hydropower, wind, and solar power generation efficiencies (HPGE, WPGE, and SPGE) were evaluated using the super-efficiency slack-based measure model. The results show that the REJIs of wind and solar power demonstrated an increasing trend, whereas that of hydropower slowly declined in the following order: wind power > solar power > hydropower. The HPGE fluctuated without remarkable change. The WPGE and SPGE gradually increased, but the SPGE increased rapidly. Furthermore, the Global Malmquist-Luenberger productivity index results revealed that the HPGE and SPGE were driven by efficiency change (catch-up effect), whereas the WPGE was driven by technical change (innovation effect). The REGE exhibited considerable regional heterogeneity. It was the highest in Western China, followed by that in Central China, and the lowest in Eastern China. Our findings can guide policymakers in improving policies to enhance REGE and promote just and equitable energy transition development.

Citrus Industry Agglomeration and Citrus Green Total Factor Productivity in China: An Empirical Analysis Utilizing a Dynamic Spatial Durbin Model

In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China’s agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China’s major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth.

Stimulating innovation or enhancing productivity? The impact of environmental regulations on agricultural green growth

A comprehensive understanding of the impact of environmental regulations on agricultural green growth is crucial for formulating appropriate policy designs and achieving effective climate change mitigation. This study, based on the Porter Hypothesis framework, aims to propose our hypotheses in the influence of environmental regulations on agricultural green growth. This study first utilizes the Super-SBM model combined with the global Malmquist-Luenberger index to measure agricultural green growth in G20 countries. Subsequently, the study analyzes the impact of environmental regulations on agricultural technological innovation and green growth, highlighting variations among countries with different development levels. The results indicate: (1) Significant differences exist in agricultural green growth among G20 countries, generally exhibiting an increasing trend. (2) Environmental regulations have a significant impact on both agricultural green growth and agricultural technological innovation, but the effects of different types of environmental regulations are various. Furthermore, different conclusions are observed for countries with heterogeneous development levels. (3) There is a lag effect in the impact of environmental regulations on agricultural green growth and technological innovation, highlighting the importance of pre-establishing an environmental policy framework. (4) An inverted U-shape relationship exists between environmental regulations and agricultural green growth, while no such relationship is found in agricultural technological innovation.

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.

Environmental–economic total factor productivity of vegetable production in China from the life cycle perspective

Enhancing agricultural green total factor productivity (GTFP) is crucial for promoting sustainable economic development. However, the absence of systematic approaches to assess the environmental-economic productivity from a full life cycle perspective hinders sustainable agricultural development and constrains the transition to a green economy. This study introduced an innovative framework of life cycle GTFP (LCGTFP) through interdisciplinary collaboration. This framework integrated regionalized life cycle impact assessment (LCIA), multimedia LCIA-based life cycle costing analysis, and the epsilon-based measure combined with the global Malmquist–Luenberger approach. This integration allows for the inclusion of comprehensive environmental burdens into GTFP analysis. Empirical results from the production of 10 types of vegetables in China reveal a “win–win” trend in vegetable industry from an environmental–economic perspective. The LCGTFP reached a value of 1.036 (CV: 0.021) over the past decade, indicating a 3.6% increase in environmental–economic productivity. However, challenges (e.g., fertilizer overuse and regression on the technical frontier) persist in sustainable vegetable production. This study highlights the necessity of technical dissemination, agricultural education, high-quality crop varieties, and e-commerce. Furthermore, the development of the vegetable industry must incorporate environmental and economic conditions, because environmental–economic misallocation occurs in areas that are recognized as having geoclimatic–locational advantages.

Does Government Digital Transformation Drive High-Quality Urban Economic Development? Evidence from E-Government Platform Construction

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the pilot policy of “construction and application of e-government public platforms based on cloud computing” as an example to assess the impact of government digital transformation on the qualitative development of the economy by using a difference-in-differences model to explore the path of its role and driving mechanism. The results reveal that government digital transformation promotes the qualitative improvement of the city’s economic development, and its driving effect shows a marginal incremental law. Moreover, government digital transformation can contribute to the formation of a “latecomer advantage” in the lagging regions, which creates a “catch-up effect” on the regions with favorable development foundations, excellent geographical conditions, high urban ranking, and high education quality. Additionally, government digital transformation boosts economic and social development quality through both innovation spillover and structural optimization.

The Impact of Digital Economy on TFP of Industries: Empirical Analysis Based on the Extension of Schumpeterian Model to Complex Economic Systems

The digital economy (DE) is a new driver for enhancing total factor productivity (TFP). Using panel data from 30 provinces in China between 2011 and 2022, this study measures DE and TFP using the entropy-weighted TOPSIS method and the Global Malmquist–Luenberger method and further examines the impact of DE on the TFP of industries. The main findings are as follows: (1) DE can significantly improve TFP, though the extent of improvement varies. DE has the greatest impact on the TFP of the service industry, followed by the manufacturing industry, with the weakest effect on the agricultural industry. (2) The enhancement effect of DE on agriculture and the service industry is more pronounced in the central and western regions, while the improvement effect on manufacturing is more evident in the eastern region. (3) DE has facilitated the improvement of TFP in manufacturing industries such as textiles and special equipment manufacturing, as well as in service industries like wholesale and retail. However, it has not had a significant impact on the TFP of industries such as pharmaceutical manufacturing and real estate. This study has significant theoretical value and policy implications for China and other developing countries in exploring DE and achieving high-quality industrial development.

Regional Differences, Dynamic Evolution, and Convergence of Global Agricultural Energy Efficiency

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation.

Smart-City Policy in China: Opportunities for Innovation and Challenges to Sustainable Development

Urban development relies on the promotion of innovation, while sustainable development is an inevitable requirement for green urban development. As the primary carrier of innovation and sustainable development, cities have seen the construction of smart cities become a hotspot topic of public concern against the backdrop of rapid advancements in information technology. Based on the Chinese smart-city pilot policies, this paper selects data from 278 prefecture-level cities between 2007 and 2020, employing difference-in-difference (DID), epsilon-based measures and global Malmquist–Luenberger index (EBM-GLM), and the Spatial Durbin Model (SDM) to analyze the direct impact, spatial effects, and regional differences of smart-city construction on urban innovation capacity and sustainable development. The research results indicate the following: (1) the implementation of smart-city policies significantly enhances the urban innovation capacity (UCI), but its impact on green total-factor productivity (GTFP) is unstable and even insignificant; (2) the UCI and GTFP of smart cities have spillover effects, and the implementation of policies may inhibit the improvement of UCI and GTFP in neighboring cities; (3) the impact of smart-city construction varies across different regions, with a more significant promotion effect on the innovation capacity of economically developed cities.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist–Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Efficiency evaluation and promoter identification of primary health care system in China: an enhanced DEA-Tobit approach

BackgroundWith Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement.MethodsUtilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China’s healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation.ResultsThe study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion.ConclusionsHorizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

Countries’ green brands within the context of sustainable development goals

This study aims to fill the gap in estimating the Green Country Brand Strength Index and its convergence among EU countries and Ukraine (as a potential EU candidate) from 2006 to 2020. Drawing upon data from the World Data Bank, Eurostat, and The Food and Agriculture Organization, the study employs various analytical tools including principal component analysis, the global Malmquist‒Luenberger productivity index, the entropy method, and σ- and β-convergence theories. The findings suggest a notable increase in the green brand values of the analyzed countries from 2006 to 2020, with a significant expansion of the measurement scale. Notably, France, Germany, Spain, the Netherlands, and Sweden emerged as leaders in the Green Country Brand Strength Index by 2020. Furthermore, the study uncovers convergent policies among EU countries aimed at fostering green brands aligned with the Sustainable Development Goals, as evidenced by a decline in σ- and β-convergences. This research provides valuable insights into the dynamics of green branding and policy convergence within the EU and prospective candidate countries. It introduces a distinctive approach to assessing and analyzing green brand strength, emphasizing its critical importance to the formulation of sustainable development policies and strategies.

Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis

The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

Carbon productivity growth in China’s transportation industry: empirical analysis using a global Malmquist-Luenberger productivity index based on an endogenous directional distance function

In China, transportation is one of the fastest-growing industries for CO2 emissions, so scientifically measuring the carbon total factor productivity growth of this industry could provide accurate guidance to achieve sustainable development. To this end, this study proposes an endogenous directional distance function (DDF) approach using a common directional vector based on satisfaction degree, and then integrates it with the global Malmquist-Luenberger productivity index (GMLPI) to measure the carbon total factor productivity growth. We employ this novel endogenous DDF-based GMLPI approach to evaluate and decompose the carbon total factor productivity growth of China’s 30 provincial-level transportation industries from 2010 to 2020. The empirical results show that the carbon total factor productivity of 14 provinces improved during 2010–2020, and 28 provinces from 2015 to 2020. Efficiency improvement and technological progress have become the joint drivers for the carbon total factor productivity growth. Targeted policy implications are provided.

Evaluation of China’s Marine Aquaculture Sector’s Green Development Level Using the Super-Efficiency Slacks-Based Measure and Global Malmquist–Luenberger Index Models

Given China’s rapidly expanding marine aquaculture industry, the associated ecological issues have garnered widespread attention. Therefore, it is crucial to speed up the green growth of marine aquaculture in order to save the environment and use resources sustainably. In order to statically assess and dynamically analyze the green development efficiency levels of marine aquaculture in nine coastal provinces of China from 2012 to 2021, this study uses the non-expected output super-efficiency Slacks-Based Measure model and the Global Malmquist–Luenberger index method. Additionally, it integrates input–output redundancy rates to analyze the causes of efficiency loss. Static efficiency primarily reflects whether a region’s inputs and outputs at a given point in time reach an effective efficiency level, while the level of dynamic efficiency mainly gauges the dynamic changes in the efficiency of green production. The results show that, from 2012 to 2021, China’s marine aquaculture industry’s average static efficiency of green output was 0.705. The southern marine economic zone exhibited the highest static efficiency value in the green development of marine aquaculture, displaying a stepped distribution pattern of “south–north–east” in decreasing order. The input–output redundancy analysis reveals that the primary causes of static efficiency loss in China’s marine aquaculture industry are attributed to varying degrees of redundant inputs and carbon emission outputs. Looking through the lens of the GML index, the annual average growth rate of the green total factor productivity in China’s marine aquaculture stands at 11.1%, with an annual average change in technical efficiency of 1.8%, while the annual average change in technological progress amounts to 9.1%, suggesting that technological advancement is the primary driver of the rise in green total factor productivity in China’s marine aquaculture sector. According to the study, in order to encourage China’s marine aquaculture industry to grow sustainably, efforts should be made not only to accelerate technological advancements but also to enhance technical efficiency. Policies that are specifically designed for the local environment should be developed to support the sustainable development of the marine aquaculture sector and to make resource allocation easier.

How green finance boosts carbon efficiency in agriculture: a quasi-experiment from China

PurposeThe research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.Design/methodology/approachIn this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.FindingsAfter the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.Originality/valueWithin the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.

Clean air policy and green total factor productivity: Evidence from Chinese prefecture-level cities

Clean air policy is an important mechanism for improving the urban environment and promoting green economic development. Green total factor productivity (GTFP) is an important way to measure these impacts. We apply the super-efficiency slack-based measure model, considering undesirable output, and the Global Malmquist-Luenberger index to measure the GTFP of 278 prefecture-level cities in China from 2005 to 2020. We find that GTFP growth is driven by both green technology progress (GTC) and green technology efficiency change (GEC). The traditional total factor productivity is overestimated, because energy and environmental factors are not considered. GTFP growth is higher in the cities in the eastern coastal regions compared to the western regions. We analyze the effect of clean air policy on GTFP using a difference-in-differences model and spatial-difference-in-differences model, and further conduct a heterogeneity analysis and mechanism analysis. The results show that clean air policy promotes GTFP growth. In addition, there is a significant negative spatial spillover effect with respect to the impact of clean air policy on GTFP. The heterogeneity analysis results show that the impact of clean air policy on GTFP growth is lower in resource-based cities and cities in the northern region, compared with non-resource-based cities and cities in the southern region. Further, the impact of clean air policy on GTFP growth is mainly achieved through GTC, GEC, innovation, and financial development. In resource-based cities and cities in the northern region, clean air policy decreases GTFP by inhibiting GTC, innovation, and financial development.