Green Total Factor Productivity Growth Research Articles

Neighbor's profit or Neighbor's beggar? Evidence from China's low carbon cities pilot scheme on green development

China has implemented three sets of low-carbon city pilot policies (LCCP), making it the world's most extensive low-carbon and green development program. Many studies have examined the impact of this policy on green development. However, studies rarely discuss spillover effects. This deficiency can lead to biased policy evaluations. This study employs a quasi-experimental approach to investigate the spillover effects of LCCP on the green total factor productivity (GTFP) of neighboring non-pilot cities and identify the underlying mechanisms. Using panel data from 283 cities in China spanning from 2004 to 2020, this study employs the time-varying difference-in-difference method. The empirical evidence suggests that LCCP can significantly enhance the GTFP growth of non-pilot cities located within 100 km, with an average annual increase of approximately 1.43%. Mechanism analysis indicates that increasing technological innovation and learning from the pacesetter play crucial intermediary roles in promoting GTFP improvements in neighboring cities. Furthermore, the spillover effects exhibit noticeable heterogeneity, particularly among cities in the eastern region, middle region, and large cities. These findings provide empirical evidence regarding the spillover effects of China's largest carbon pilot policies, contributing to a comprehensive assessment of policy impacts and offering fresh insights for climate policy tools.

RESEARCH ON DIGITAL ECONOMY AND GREEN TOTAL FACTOR PRODUCTIVITY GROWTH OF CHINA’S INDUSTRY AND SPATIAL EFFECTS

This paper evaluates the impact and role of digital economy on industrial green total factor productivity based on the perspective of China’s industrial green transformation and provides a realistic basis and decision-making reference for industrial green transformation. This paper constructs the industrial green total factor productivity and digital economy development index of 30 provinces from 2013 to 2019, and empirically tests the spatial-temporal evolution relationship between them and the impact of digital economy on industrial green development by using time-fixed effect and spatial Durbin model. The results show that the digital economy and industrial green total factor productivity digital economy are unevenly developed, basically showing a distribution trend of high in the east and low in the west, and the two have a significant positive spatial correlation in the years observed. The results of local spatial autocorrelation analysis show that there is a high clustering of digital economy and total factor productivity in the eastern coastal region and low clustering in the western region. The regional heterogeneity analysis shows that the development of digital economy is beneficial to optimize the distortion of capital and labor allocation and thus improve industrial green total factor productivity, but the indirect effect of digital economy on industrial green total factor productivity is not significant and the direct effect plays a dominant role.

Impact of Foreign Direct Investment on Green Total Factor Productivity: New Evidence from Yangtze River Delta in China

China has entered a period of high-quality development. As an important feature of high-quality development, green total factor productivity (GTFP) has attracted much attention. With the opening-up and economic globalization, the Yangtze River Delta, one of the strongest and most technological regions in China, has been attracting an increasing amount of foreign direct investment (FDI). This study investigates if FDI is conducive to GTFP under the constraints of specific resources and a specific environment, which has important practical significance for the utilization of FDI in the Yangtze River Delta and China. Through a literature review and sorting the current FDI in the Yangtze River Delta, the GTFP and its decomposition indicators of 27 cities from 2004 to 2019 are calculated based on their energy consumption and pollution. Using the fixed-effects model and threshold model of panel data, this study tests whether FDI promotes GTFP and whether a nonlinear impact of FDI on GTFP exists. It finds that (1) the GTFP of most cities in the Yangtze River Delta improved during the sample period, but their annual growth declined. Technology is the dominant factor affecting the growth of GTFP. (2) FDI in the Yangtze River Delta has increased, and the investment structure has improved, but the distribution among cities is uneven. (3) The scale and quality of FDI have a positive impact on GTFP, which supports the “Pollution Halo” hypothesis. Economics, education, networks, and trade openness can promote the growth of GTFP, while environmental regulation, government intervention, and industrialization have a negative impact. (4) The quality of FDI, economics, the industrial structure, the environmental regulation, and the internet are each a significant single threshold characteristic for the impact of FDI on GTFP. When one of these factors is lower than a certain threshold, FDI has less impact on GTFP. When one exceeds a certain threshold, FDI’s positive promotion effect on GTFP significantly improves. Based on the analysis, this study offers some suggestions. The government should improve the FDI selection mechanism based on realities, make appropriate environmental regulatory policies, strengthen the construction of networks, and improve the “Internet+” effect on productivity.

High-quality marine economic development in China from the perspective of green total factor productivity growth: dynamic changes and improvement strategies

High-quality marine economic development (HMED) is regarded as a new development pattern of the marine economy in China. This paper aims to examine the dynamic changes and improvement strategies of HMED from the perspective of the green total factor productivity (GTFP) growth. First, the GTFP growth of the marine economy in China’s coastal regions for the period 2007–2020 is calculated using the bootstrapped Malmquist index. Second, the dynamic changes and spatial impacts of the GTFP growth are characterized using kernel density estimation (KDE). Moreover, a novel analytical framework to study the improvement strategies of the GTFP is developed. Within this framework, the fuzzy set qualitative comparative analysis (fsQCA) method is used to explore the paths to achieve HMED. The findings show that: (i) the GTFP growth for coastal regions shows significant fluctuations, suggesting that a stable pattern of marine economic development has yet to be established; (ii) the regional distribution of GTFP growth varies significantly, with provinces with fast GTFP growth gathering resources from neighboring provinces, resulting in a siphon effect; (iii) for coastal provinces that lack certain development conditions, the combined effect of other advantageous factors can be used to achieve HMED. Finally, this study presents policy recommendations for achieving HMED, which can provide insights into the design of China’s future marine economic policies.

Heterogeneous Porter Effect or Crowded-Out Effect: Nonlinear Impact of Environmental Regulation on County-Level Green Total Factor Productivity of Pigs in the Yangtze River Basin of China

Green total factor productivity (GTFP) is critical to both the economic and ecological objectives of pig breeding. This research utilizes the SBM-ML model to calculate the GTFP of pig breeding in 381 counties within the Yangtze River Basin from 2014 to 2021. Then the GTFP is further decomposed into technical efficiency (MLEC) and technical progress (MLTC) to conduct in-depth exploration. The regression results reveal that: (1) Environmental regulation (ER) has significant double-threshold effects on GTFP, MLEC, and MLTC. (2) MLTC is the main force of GTFP growth, and stronger ER does not always lead to better GTFP growth. (3) GTFP is boosted by mechanization enhancement and industrial agglomeration limitation. (4) Counties in non-provincial capital cities and those closer to the river exhibit greater ER threshold effects. (5) Both pig price and transportation efficiency play a moderating role. (6) Further analysis demonstrates that ER simultaneously reduces pig production capacity and carbon emissions, as well as improves the water quality. And the reduction of ER, although beneficial for capacity, has a significant negative impact on GTFP. Finally, this study concludes with policy recommendations to boost the new quality productivity in the pig industry.

China’s foreign direct investment: driving green growth or seeking pollution havens? Based on the perspective of green total factor productivity

China has significantly increased its foreign direct investment (FDI). Although these investments help to boost global development, their impacts on the environment are still controversial and deserve careful investigation. This paper uses the super-efficiency SBM model to measure the green total factor productivity (GTFP) of 123 host countries of China’s FDI. On this basis, we adopt the two-step system GMM and dynamic panel threshold effect model to analyze the impact of China’s FDI on the GTFP of host countries. The research results show that: 1) The impact of China’s FDI on the GTFP of host countries is mainly through green technology efficiency. Its impact is inhibitory, followed by a promotional one, with the latter being significantly larger than the former. 2) There is no evidence of the “pollution haven effect” associated with China’s FDI. In fact, for countries with lower environmental regulations, China’s FDI promotes the growth of GTFP more effectively. 3) The innovation level of host countries moderates the relationship between China’s FDI and GTFP growth. For host countries with stronger innovation level, their GTFP growth can be enhanced to a greater extent by China’s FDI.

Does government environmental attention drive green total factor productivity? Evidence from China

Based on city-level panel data spanning 2008 to 2021, this study investigates the impact of government environmental attention (GEA) on green total factor productivity (GTFP). The findings suggest that increased GEA substantially enhances the growth of GTFP. After conducting robustness and endogeneity tests, the study's results consistently show reliability and robustness. Further analysis elucidates several mechanisms through which GEA influences GTFP, including fostering green technology innovation, optimizing resource allocation, and promoting upgrades in industrial structure. Heterogeneity analyses reveal that the impact of GEA on GTFP is notably pronounced in eastern cities, as well as in cities characterized by low resource dependency, mature industrial development, and high market competition. Conversely, the influence of GEA on GTFP is less discernible in cities prioritizing economic development goals, possibly due to differing policy orientations and resource allocation strategies. This study offers a novel perspective on understanding how GEA shape green development and provides empirical support for policy formulation.

A pathway to sustainable development in China: The impact of local higher education expenditure on green total factor productivity

With the global warming crisis looming, the question of how to advance green total factor productivity (GTFP) has become an important concern confronting many developing countries. Although existing studies have demonstrated that total human capital can improve GTFP, the research has neglected to consider the influence of local higher education expenditure (LHEE), and no research has examined how LHEE spatially affect GTFP. Therefore, based on spatial economics theory, this study explores the spatial autocorrelation of LHEE and GTFP in China's 30 provinces from 2004 to 2021, employing a spatial Durbin model to analyze the spillover effect and influence mechanism of LHEE on GTFP. The results reveal that LHEE and GTFP exhibit positive global spatial autocorrelation. LHEE primarily improves GTFP and its subcomponents through spillover effects. The positive spillover effects in the three regions of China are significantly higher than the direct effects, whereas the direct effects in the eastern and central regions are positive but insignificant. Furthermore, LHEE promotes GTFP by advancing green technological innovation. The findings provide valuable insights to help policymakers address sustainable development goal 4 and develop synergistic regional GTFP growth policies to establish sustainable societies worldwide.

How can green finance promote the transformation of China's economic growth momentum? A perspective from internal structures of green total-factor productivity

Green financing is a crucial tool for promoting China's economic growth and helping to achieve carbon neutrality, but its implications on the internal dynamics of green economy remain uncertain. This study examines the impacts of green finance on China’s Green Total Factor Productivity (GTFP) and its internal factors, including technical innovation, industrial structure, balanced regional growth, scale, and resource allocation efficiency, by using a three-hierarchy meta-frontier Data Envelopment Analysis (DEA) and provincial panel data from 2008 to 2020. The findings show that: (1) regional green finance development may greatly boost the growth of GTFP, and the findings remain reliable after applying a series of robustness tests; (2) further research finds that green finance enhances GTFP by promoting technological innovation, facilitating the optimization of industrial structure, and improving resource allocation efficiency; and (3) there are significant spatial heterogeneities in this effect. The effect of green finance on GTFP is more pronounced in developed and market-oriented regions. This study helps to understand the pathways of green finance affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

The Impacts of Water Ecological Carrying Capacity on Green Total Factor Productivity: The Case of Chemical Industry in Jiangsu, China

The chemical industry is not only a crucial sector of national economy, but also a significant consumer of water resources and a major initiator of water pollution. The sustainable development of this sector is intricately linked to the regional water ecological carrying capacity (WECC). Based on SBM-DEA and Global Moran’s I, the green total factor productivity (GTFP) and spatial correlation characteristics of the chemical industry in 13 cities within China’s chemical agglomeration region in Jiangsu Province were estimated from 2015 to 2019. By combining the WECC results, the Tobit model was employed to reveal the driving factors of WECC in optimizing GTFP. The results indicated that the regional WECC in southern Jiangsu was increasing compared with that in northern Jiangsu, which promoted the growth of GTFP. WECC has been a positive radiation-driven effect since 2017, and the optimization of the various subsystems of WECC has had a different impact on GTFP. For the sustainable development of Jiangsu’s chemical industry, effective water resource policies should be formulated by the government, while enterprises need to pursue sustained structural adjustments.

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.

Analysis of the impact factors of industrial structure upgrading on green total factor productivity from the perspective of spatial spillover effects

Green development has already been a vital part of China's high-quality economic progress in the future, and accelerating the restructuring and improvement of the industrial structure plays a crucial roll in promoting a regional green economy. In this article, based on the data of 278 prefecture-level cities in China from 2011 to 2020, the SBM directional distance function and Malmquist-Luenberger (ML) index are used to gauge regional green total factor productivity (GTFP). The spatial Durbin model is also introduced to investigate the spatial spillover mechanism of GTFP and its influencing factors under the optimized and upgraded industrial structure. The study indicates that the optimization of the industrial structure have an important effect in promoting the growth of GTFP. Specifically, industrial structure integration has a greater bearing on the increase in GTFP, while there is a difference in the effectiveness of industrial structural advancement and rationalization of GTFP. Economic growth level inhibits the enhancement of GTFP, and urbanization level and industrial agglomeration have a significant negative impact on the enhancement of GTFP in the spatial dimension. On a different hand, foreign investment degree and government intervention level have a significant positive affect on regional GTFP. In addition, this study fills the research gap of the regional industrial structure upgrading influence on GTFP, which has great theory and practice value for promoting China's high quality growth of green economy.

A Study on the Changes of Green Total Factor Productivity in Chinese Cities under Resource and Environmental Constraints

Confronting the dual challenges of excessive resource consumption and environmental pollution, the traditional extensive economic development pattern significantly impeded the high-quality development of the Chinese economy. Examining variations in green total factor productivity across different types of cities holds substantial practical significance for promoting coordinated regional development and facilitating the green transformation of urban economies. Panel data from 283 cities in China spanning the years 2006 to 2020 were selected for analysis. The window-Malmquist–Luenberger index model, incorporating a mixed distance function, was employed to assess changes in green total factor productivity among the sample cities. The results were then categorized and analyzed based on different city attributes. The findings indicate that (1) the variation in green total factor productivity across China’s four major regions from 2006 to 2020 is generally characterized by an initial decline followed by an increase; (2) the proportion of cities with significantly improved green total factor productivity decreases from the east to the central, western, and northeastern regions; (3) the increase in green total factor productivity is positively correlated with city size, suggesting that larger cities experience higher growth in green total factor productivity; (4) first- and second-tier cities exhibit a relatively high mean value of green total factor productivity growth, while third-, fourth-, and fifth-tier cities demonstrate relatively lower growth.

Effects of environmental information disclosure on the green development of mining industry: Evidence from Chinese provincial mining sub-sectors

How environmental information disclosure (EID) affects green transformation of mining industry (MI) is still unknown. Based on the panel data of China's provincial mining sub-sectors, this study explores the effects of EID on the green total factor productivity (GTFP) of China's mining sub-sectors and its influence mechanism. The research shows that EID exerts an inverted U-shaped influence on the GTFP growth of MI, implying that reasonable EID facilitates the green transformation of China's MI, while excessively EID requirements may be counterproductive. The above conclusion still holds after considering the endogeneity and conducting a series of robustness tests. Further analysis shows that the impact of EID on the GTFP of MI has a lag and long-term, and the current EID affects its future GTFP. Meanwhile, EID has a more remarkable effect on the GTFP of the MI in the eastern and central regions and the mining and washing of coal (S3) and extraction of petroleum and natural gas (S4). Mechanism analysis reveals that EID affects the GTFP of the MI through technological innovation, but due to the ‘black expected effect’, EID is still unable to increase the GTFP of the MI through energy structure. This study provides important insights for the government to fully leverage the positive role of social supervision in promoting the green development of heavy polluting industries such as MI.

Going green while getting lean: Decomposing carbon and green total factor productivity

While research on carbon productivity is growing rapidly, the role of structural change in green transition remains unexplored due to the scarcity of firm-level emission data. This study addresses this gap by utilizing unique register-based greenhouse gas emission data from Finland’s energy-intensive manufacturing firms for 2000–2019. Applying a structural change productivity decomposition, we break down the sector’s carbon productivity and green total factor productivity into four components: contributions from non-switching continuing firms, industry-switching continuing firms, the effects of entry and exit, and resource allocation. The findings highlight the important role of structural change in the sector’s productivity. Non-switching continuing firms emerged as the key drivers of both carbon and green total factor productivity growth. The contribution of entry and exit was negative during the financial crisis, while inefficient resource allocation significantly hindered productivity growth throughout the study period. These findings emphasize the importance of public subsidies targeted at environmentally efficient firms to enhance their competitiveness under challenging market conditions. Furthermore, the establishment of a stable yet positive carbon price would incentivize less-productive firms to adopt environmentally friendly technologies.

How does artificial intelligence affect the transformation of China's green economic growth? An analysis from internal-structure perspective

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

Digitalization and sustainable development: Evidence from OECD countries

To examine the intricate relationship between digitalization development and sustainable development, this study commences its analysis by employing the entropy method to establish an integrated indicator of digitalization development among advanced nations. Subsequently, we utilize a panel dataset spanning a decade from 2010 to 2020, encompassing 36 developed countries, to estimate Green Total Factor Productivity (GTFP) growth. We establish a fixed effects model, a mediation effect model and a threshold regression model to explore the relationship between the two variables. The empirical findings of this research reveal a significant positive association between the progress and enhancement of digitalization and the advancement of sustainable development (GTFP). Furthermore, this research elucidates the underlying mechanism by which digitalization development indirectly fosters the advancement of green productivity. This facilitation occurs through the promotion of technological innovation, the enhancement of human capital, and tupgrades in the industrial framework. Notably, our study finds the non-linear relation between digitalization development and GTFP growth, suggesting that the magnitude of impact on environmental performance is contingent on the scale of technological innovation, the growth of human capital, and the level of financial development.

The impact of digital economy on green total factor productivity considering the labor-technology-pollution factors

The digital economy provides new impetus for the high-quality development of manufacturing industry. Through the DEA-Malmquist model and panel regression model, this paper confirmed that there is a positive and significant relationship between the development of digital economy and the green total factor productivity (GTFP) of manufacturing industry. The research result is as follows: (1) the development of digital economy can enhance the overall GTFP of manufacturing industry. (2) The green technology progress brought by the development of digital economy is the main path to promote the GTFP of manufacturing industry. (3) The heterogeneity analysis shows that the impact of digital economy on GTFP of high pollution manufacturing industry is significantly positive, the impact of labor-intensive manufacturing industry is significantly negative, and the impact of technology intensive manufacturing industry is not obvious. The contributions of this study are as follow. In terms of theory, this study theoretically continues Solow’s classical theory, demonstrating the scientific nature of digital technology progress in promoting GTFP growth. In empirical analysis, this study build a new digital economy development level evaluation index system based on the perspective of manufacturing industry. In addition, this study also add a labor-technology-pollution perspective for the development of relevant policies.

Green total factor productivity growth and its driving forces in China’s fisheries sector

The rapid development of China’s fisheries sector has brought about significant environmental problems, which are detrimental to the sustainable development of the sector. Balancing environmental protection while promoting fisheries development has become an urgent issue in China. Based on data from 30 provincial-level administrative regions in China from 2004 to 2019, this study utilizes the Epsilon-based Measure (EBM) model considering undesirable outputs and the global Malmquist-Luenberger (GML) index to measure the green total factor productivity (GTFP) growth in China’s fisheries sector. Furthermore, it explores the spatiotemporal evolution and driving forces of fisheries GTFP growth using spatial Durbin model (SDM). The results indicate that ignoring the resource and environmental costs in fisheries production would overestimate the growth of total factor productivity (TFP) by 1.3%. The growth of fishery output primarily comes from the increase in input factors, exhibiting extensive characteristics that have been gradually diminishing over time. During the sample period, the provinces with the fastest growth in GTFP shifted from being mainly concentrated in the central and western regions to the eastern region. The local driving forces behind the improvement of fisheries GTFP include internet penetration rate, transportation convenience, education level of rural residents. The driving forces from economically similar provinces include the positive spatial interplay between provinces, fishery disaster rate, fisherman training, fishery trade openness, and urbanization rate. Overall, these finds offer a novel approach to reexamine the growth of China’s fisheries and provide valuable insights for the future fisheries development.

Challenges or drivers? Threshold effects of environmental regulation on China’s agricultural green productivity

This study investigates the effect of environmental regulation (ER) on the growth of Chinese agricultural Green Total Factor Productivity (GTFP). Threshold model results suggest an inverted-U shaped interconnection between command-control ER and growth of agricultural GTFP, a decreasing positive marginal effect of voluntary ER, and an absence of influence of incentive-based ER. Regional heterogeneity test indicates that command-control ER affects agricultural GTFP growth in a far more favorable way in less developed provinces than in more developed provinces, and only the former can benefit from the synergistic effect between command-control and voluntary ER. Mediating effect test results reveal that agricultural green innovation and grain crop proportion are channels through which ER affect agricultural GTFP growth, highlighting the pressing need to stimulate green innovation and realize green transformation of grain production.