Factor Productivity In China Research Articles

The intermediate role of farmland transfer in the impact of digital financial inclusion on agricultural total factor productivity in China

IntroductionEnhancing agricultural productivity and promoting sustainable development in China are critical priorities, given their significant contribution to food security and rural development. However, achieving consistent agricultural productivity growth across regions and sectors has been challenging, leading to inefficiencies and environmental pressures. To address this, the potential of digital inclusive finance (DIF) as a catalyst for progress has been explored.MethodsThis study investigates the impact of DIF on agricultural total factor productivity (ATFP) through farmland transfer, with implications for rural revitalization and high-quality agricultural development. Using the HMB index method, we calculate and decompose China’s inter-provincial ATFP from 2011 to 2020. Employing the dynamic panel fixed effect model and SYS-GMM method, we empirically test the influence of DIF on agricultural productivity in relation to farmland transfer.Results and discussionOur findings reveal a slow upward trend in China’s ATFP, driven primarily by scale efficiency. Notably, a 1% increase in the general index of DIF correlates with a 0.136% increase in ATFP. We analyze the sub-classification indexes of digital inclusive finance, namely coverage breadth, depth of use, and digitalization degree. Their contributions to agricultural productivity growth are significant, with coverage breadth having the greatest impact, followed by depth of use and digitalization degree. Farmland transfer acts as a crucial intermediary, enhancing agricultural allocation efficiency and driving the relationship between digital finance and total factor productivity growth. Interestingly, the western region exhibits the highest growth rates, followed by the eastern region, while the central region shows no significant effects.ConclusionTo comprehensively enhance ATFP, aligning supply and demand for digital financial services, leveraging service depth, and guiding farmland transfer according to local conditions are essential. These findings offer valuable insights into effective strategies for maximizing the potential of DIF, promoting rural development, and achieving sustainable agricultural growth.

Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China

Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China

The Influence Mechanism of Bidirectional Foreign Direct Investment on Green Total Factor Productivity in China’s Manufacturing Industry

The Influence Mechanism of Bidirectional Foreign Direct Investment on Green Total Factor Productivity in China’s Manufacturing Industry

Impact of Forest City Selection on Green Total Factor Productivity in China under the Background of Sustainable Development

Impact of Forest City Selection on Green Total Factor Productivity in China under the Background of Sustainable Development

THE CONTRIBUTION OF NATURAL CAPITAL ON CARBON TOTAL FACTOR PRODUCTIVITY IN CHINA

This study focuses on the contribution of natural capital to achieving carbon neutrality in China based on the provincial data from 1998 to 2019. This paper first measures the natural capital of each province by ecological footprint (EF), then explores the contribution of natural capital on carbon total factor productivity (CTFP) through a global non-radial Luenberger productivity indicator (GLPI), and decomposes the new CTFP into efficiency change (EC) and technical change (TC). We find that the contribution of natural capital to CTFP is relatively small, mainly due to the negative impact of infrastructure land and energy footprint and the decline of the marginal effect of natural capital and environmental carrying capacity. Second, technology innovation is still a main driver of promoting the CTFP of EF. Third, the contribution of natural capital to CTFP varies greatly with different types of natural capital and regions. We provide corresponding policy implications based on the main conclusions.

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.

Urban green total factor productivity in China: A generalized Luenberger productivity indicator and its parametric decomposition

In this study, by taking the distance elasticity shares as the input weights in the production function, the generalized Luenberger productivity indicator (GLPI) was proposed to analyze the urban GTFP in China, and the stochastic frontier analysis (SFA) was used for parametric decomposition to obtain the technological change (TC), technical efficiency change (TEC), and scale efficiency change (SEC). The results show that the entire model and specific parameters passed the test, and the production function equation has good economic and statistical significance, which shows that it is feasible to use the parameter approach to measure and decompose the GLPI. Second, the value of the GLPI showed a rapid growth trend between 2000 and 2019, with a cumulative increase of 140.59%, indicating that various inputs were saved by 3.43% in a specific proportion, and that remarkable achievements have been made in the sustainable development of Chinese cities. Third, TC, TEC, and SEC all showed significant spatiotemporal heterogeneity, in which SEC was the main numerical component and TC was the driving force of the GLPI. Through mathematical proof in theory and data matching in reality, it is confirmed that the GLPI proposed in this study is scientific and applicable.

Digital economy and green total factor productivity in China.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

Economic Policy Uncertainty, Political Connections, and Total Factor Productivity in China

Purpose – This paper aims to elucidate the complex relationship between Economic Policy Uncertainty (EPU) and Total Factor Productivity (TFP) in the context of Chinese A-share listed companies, exploring how various factors influenced this relationship from 2008 to 2020. Design/Methodology/Approach – Our research design utilizes a comprehensive dataset from Chinese A-share listed companies across various sectors to explore the impact of Economic Policy Uncertainty (EPU) on Total Factor Productivity (TFP). We analyze a range of factors, including pollution intensity, manufacturing status, market indices, institutional attention, analyst coverage, internal control mechanisms, and bank shareholding. Findings – The findings provide a nuanced understanding of how Economic Policy Uncertainty (EPU) impacts Total Factor Productivity (TFP) across Chinese A-share listed companies. Our analysis reveals that the influence of EPU on TFP is not uniform but varies significantly across different contexts, underscored by a variety of moderating factors. Among these, institutional attention and analyst coverage emerge as critical elements that shape the dynamics between EPU and TFP. Institutional attention, indicating the extent to which firms are monitored by regulatory bodies and investment institutions, plays a pivotal role in mitigating the negative effects of EPU. Research Implications – The findings provide valuable insights for policymakers and industry stakeholders, emphasizing the importance of strategic alignment in managing economic uncertainties. The study contributes to understanding the dynamics of economic policy uncertainties in a complex market like China, helping inform more effective policy and management strategies.

The role of highway construction in influencing agricultural green total factor productivity in China: agricultural industry structure transformation perspective

Highway construction is vital in agricultural production, directly influencing food security and public health. This study utilized the Epsilon-based measurement-global-Malmquist-model (EBM-GML) and the spatial econometrics model with 31 Chinese provinces’ panel data from 2002 to 2020 to investigate the impacts and the mechanisms of low-grade and high-grade highways on the agricultural green total factor productivity (AGTFP). Research findings indicated that (1) low-grade and high-grade highway construction significantly promote the AGTFP through technological progress, and the high-grade highway exerts a significant positive spillover effect on the adjacent areas’ AGTFP. (2) The mechanisms of low-grade and high-grade roads on the AGTFP are heterogenous. Low-grade roads could improve the AGTFP by encouraging the rationalization of the agricultural industry structure. High-grade highways promote the AGTFP by upgrading the industry structure. And each-grade road could promote the AGTFP by stimulating the integration of planting and breeding. (3) Both low-grade and high-grade highways improved the AGTFP in non-main-grain-producing areas but inhibited it in main-grain-producing areas. Therefore, this study provided practical policy recommendations for green development in China’s agricultural sector and valuable insights for other developing countries.

From resource curse to green renaissance: Analyzing the dynamics of natural resource abundance on China's green total factor productivity during business cycles

The current body of research fails to thoroughly examine how the abundance of natural resources affects China's green total factor productivity during different phases of economic cycles. The interconnection between the natural resource abundance and the green total factor productivity in China, the second-biggest economy worldwide, offers valuable insights into the sustainable management of resources amidst fast economic expansion. This research aims to analyze how the abundance of natural resources influences China's total green factor productivity during different phases of business cycles. A fully modified and dynamic ordinary least squares methodology uses cyclical data from 1990Q1 to 2019Q4. The findings indicate that during boom periods, a surge in natural resource abundance, gross domestic product, financial development, foreign direct investment and renewable energy consumption are correlated with heightened levels of green total factor productivity, suggesting that resource-dependent industries experience positive effects due to improved availability and quality of resources. Conversely, the findings infer that a decline in these variables is observed during the recession period, leading to a decrease in green total factor productivity. The results indicate a pro-cyclical relationship between natural resource abundance, gross domestic product, financial development, foreign direct investment and renewable energy consumption during periods of expansion and contraction. Based on the research findings, the study suggests that legislators should emphasize measures that foster investments in clean energy, ethical resource management practices, and financial development during economic booms. In contrast, during economic contractions, attempts should be concentrated on sustaining or boosting investments in green technology, encouraging feasible resource utilization, and enacting policies that foster foreign direct investment.

Does the digital economy improve comprehensive total factor productivity in China?

Total factor productivity is an important symbol of high-quality economic development. At present, the question of whether the digital economy can infuse fresh impetus into enhancing total factor productivity has emerged as a prominent concern in China. This paper constructs a new undesirable output to measure comprehensive total factor productivity (CTFP) with the slack-based measure (SBM) undesirable Malmquist-Luenberger index by using 2011-2020 Chinese provincial panel data. Then, this paper explores the impact of the digital economy (DIG) on CTFP with a fixed effects (FE) panel model and a mediating effect model. The results show that CTFP increases by an average of 3.9%, technical efficiency contributes -1.1%, and the contribution rate of technological progress is 5.0%. Technological progress is the main source of CTFP growth. The empirical findings show that the DIG has a positive and significant impact on CTFP. This paper conducts various robustness tests, and the results remain consistent with the previous conclusion. Moreover, mechanism tests suggest that the promoting effect of the DIG on CTFP can be attributed to three main effects: technological innovation, the factor endowment structure and the educational level. Furthermore, the results of heterogeneity analysis demonstrate that the promoting effect of the DIG on CTFP exists in China's eastern, central and western regions. The findings of this research can serve as a valuable reference for informing decision-making processes related to environmental governance and high-quality economic development in China.

The influence of the guidance on building a green financial system on environmentally friendly firms’ total factor productivity in China

With China's proposed green development strategy, environmental protection investment needs are growing exponentially, and government funding alone is inadequate to meet the massive demand. To encourage social investment in green industries and achieve green development goals, the authorities implemented the major green finance policy entitled the Guidance on Building a Green Financial System (GBGFS) in late 2016. Whether this policy effectively promotes the development of environmentally friendly (EF) firms is an essential under investigated question. This study employs a difference-in-differences approach to explore whether and how the GBGFS affects EF firms' total factor productivity (TFP) with a sample from 2012 to 2021. Our benchmark regression and robustness tests demonstrate that the GBGFS effectively promotes EF firms' TFP. We also obtain three notable research findings with a number of additional analyses. (a) Although there is a certain lag, the policy effect was obviously enhanced with the passage of time. (b) Our threshold effect analyses reveal that the enhancing effect of the GBGFS on EF firms' TFP only becomes significant when research and development (R&D) investment, profitability (ROA), and government subsidies (GSF) exceed certain values. Further analysis demonstrates that firms with higher ROA and more GSF can effectively increase R&D. (c) Finally, GBGFS implementation has a greater impact on firm TFP in China's more developed region than its less developed regions. The findings suggest that regulatory authorities should persist in enforcing the GBGFS policy and encourage corporate R&D investment. In addition, the central government should design targeted policies based on the characteristics of different regions to improve the policy effect.

Empirical Analysis of the Impact of Green Finance and Technological Progress on Total Factor Productivity in China’s Industrial Sector

The enhancement of industrial green total factor productivity is pivotal for achieving high-quality and sustainable economic development. This study assesses China’s performance using the SBM-GML model, employing province-level panel data spanning from 2004 to 2020. Furthermore, we examine the influence of green finance and technological progress on industrial green total factor productivity using a spatial econometric model. The findings uncover that the relationship between the level of green financial development and industrial green total factor productivity follows a U-shaped curve. Initially, low levels of green financial development exert a suppressive effect on industrial green total factor productivity, proving ineffective in the short term. However, with the progression of green finance development, a positive and significant long-term impact on industrial green total factor productivity emerges. Moreover, technological progress demonstrates a noteworthy promotional effect on industrial green total factor productivity. The analysis delves deeper into revealing that industrial structure and environmental regulation intensity exhibit a significant negative relationship with industrial green total factor productivity. In contrast, both energy structure and education level showcase a substantial positive relationship with industrial green total factor productivity.

Does the digital economy enhance green total factor productivity in China? The evidence from a national big data comprehensive pilot zone

Digital economy has become a major driver of the green transformation of the economy. The “National Big Data Comprehensive Pilot Zone” policy is taken as a quasi-natural experiment. The Difference-in-Differences model is employed to explore the impact of digital economy on green total factor productivity, and the heterogeneity of this impact is further explored. The mechanism test model is applied to investigate its indirect transmission mechanism. It is found that digital economy significantly enhances green total factor productivity. Further, the impact is larger and more significant in the mid-western, non-resource-based, and large cities, whereas it is smaller or insignificant in the eastern, resource-based, and small and medium-sized cities. Results of mechanism analysis show that digital economy enhances green total factor productivity through the transmission channels of technological innovation, industrial structure optimization, and resource misallocation. Policy recommendations are provided on the basis of these results to establish the environment and digital economy that are mutually beneficial.

Beyond the modern productivity paradox: The effect of robotics technology on firm-level total factor productivity in China

This study aims to provide firm-level evidence on the burgeoning debate about the productivity effect of robotics technology. Taking China as an example, we find that city-level robot exposure plays a significant role in augmenting total factor productivity for firms in industries with high degrees of task replaceability. The results of the mechanism analysis further reveal that firms with substantial exposure to robotics technology are incentivized to improve their innovation performance and cultivate human capital. Conversely, there is evidence that firms characterized by low task replaceability experience a reduction in productivity as a consequence of robot exposure, potentially attributable to the effect of inter-industry resource reallocation. These findings offer empirical support for theoretical arguments regarding the productivity enhancements stemming from the development of robotics technology, while also shedding light on strategies to elevate productivity through the adoption of new technologies.

Can development of large scale agricultural business entities improve agricultural total factor productivity in China?: an empirical analysis

Large scale agricultural business entities (hereinafter referred to as “the large scale entities”) are newly sprouted in the process of China’s agricultural transformation from traditional production to modern mode, while the improvement of agricultural total factor productivity is an important driving force to realize agricultural transformation in China. Therefore, it is of great significance to explore whether the former has a promoting effect on the latter. First of all, the effective improvement of agricultural total factor productivity by the large scale entity development is analyzed from the two aspects of scale effect and technology utilization effect, and the effect mechanism is discussed in depth in this paper. Secondly, empirical test was carried out by using the panel data of prefecture-level cities from 2015 to 2020. The benchmark regression results show that the development of large scale entities can significantly improve agricultural total factor productivity. The mechanism test results indicate that the development of large scale entities improves the agricultural total factor productivity by raising the agricultural mechanization level. The results of heterogeneity analysis show that the difference between large scale entities would affect the benchmarking results. Therefore, it is believed through the study as discussed in this paper that devoting greater effort to support the development of large scale entities is an important measure to improve agricultural total factor productivity and promote China’s agricultural transformation, but it is necessary to act according to local conditions based on the difference of entity types in the process.

Impact of technological innovation on agricultural green total factor productivity in China: considering the threshold effect of environmental regulation

Based on the panel data for 30 provinces in China from 2004 to 2020, this study explores the impact of technological innovation and environmental regulation on agricultural green total factor productivity by using the dynamic spatial Durbin panel model and the spatial panel threshold regression model. The results show that technological innovation significantly contributes to the improvement of AGTFP. Such positive impact is derived from the direct effect of technological innovation within the local province and the spatial spillover effect of technological innovation in neighboring provinces. Additionally, environmental regulation has a significant double-threshold effect on the technological innovation - AGTFP relationship. Within medium-level environmental regulation, the positive impact of technological innovation on AGTFP achieves a leap forward. Therefore, technological innovation serves as a motivator of agricultural green development, and appropriate intensity of environmental regulation is recommended in the endeavor of promoting green total factor productivity by technological innovation.

Effect on high-quality economic development of foreign direct investment in China from the triple perspectives of financial development

High-quality economic development focuses on achieving effective qualitative improvement and reasonable quantitative growth in the economy, which is the essential way for China to achieve sustainable development. This paper aims to investigate the role of financial development in the impact of foreign direct investment on high-quality economic development. GDP and green total factor productivity are used to measure the quantity and quality of economic development, respectively. We construct benchmark panel regression models, nonlinear interaction term models, and panel threshold models to examine the threshold effect of FDI on high-quality economic development based on the scale, structure, and efficiency of financial development. A series of tests are conducted to check the robustness of the estimates. Our results show that green total factor productivity in China is primarily driven by green technology progress. Additionally, foreign direct investment has a positive impact on China's high-quality economic development, with a threshold effect and nonlinear characteristics. Specifically, the scale of financial development affects the influence of foreign direct investment on the quantity of economic development, while the efficiency of financial development affects its influence on the quality of economic development. Furthermore, the structure of financial development affects both the quantity and quality of economic development. This study presents novel findings that contribute to understanding the role of financial development in the impact of foreign direct investment on high-quality economic development. Likewise, our research has implications for achieving high-quality development. The introduction of foreign direct investment under the high-quality development of the economy should comprehensively consider financial development.

Non-Linear Nexus of Technological Innovation and Carbon Total Factor Productivity in China

Scientific and technological innovation is the main driving force of the growth in the 14th Five-Year Plan with the aim of “carbon peaking and neutralization.” This research analyzes the carbon total factor productivity (CTFP) improvement mechanism induced by micro-subject technological innovation and macro-technological progress (TP). This research constructed the Malmquist index based on a relaxed nonparametric DEA model, measured the TP level and CTFP in China, and considered the non-strict externalization of technological progress. The endogenous dynamic threshold model was used to test the nonlinear dynamic effect of TP driving the increase in CTFP. Through the intertemporal distance DEA model, undesired output model, and dynamic threshold regression model, we found that science and technology innovation of the TP drive the function of the carbon total factor productivity; there was a threshold effect (−0.556) on the driving impact of TP caused by technological innovation on CTFP, and the lag period of TP and CTFP had a positive driving role for CTFP. The driving effect on the left side of the threshold value was better than that on the right side. Considering the reality of slowing down the growth of capital and labor factor input in the 14th Five-Year Plan, it is essential to take active policy measures to promote the growth rate of TP by promoting the speed of micro-scientific and technological innovation. It is crucial to promote green TP in micro renewable energy enterprises, which, in turn, drive the growth of CTFP, improve the performance of low-carbon development, and reduce the negative impact of the “two-carbon” target on economic growth while realizing low-carbon transition.