scholarly journals Does the Construction of National Eco-Industrial Demonstration Parks Improve Green Total Factor Productivity? Evidence from Prefecture-Level Cities in China

2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Lu Liu ◽  
Xiaodong Yang ◽  
Yuxin Meng ◽  
Qiying Ran ◽  
Zilian Liu
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Mediation Effect ◽  
Eastern Region ◽  
Mechanism Analysis ◽  
Natural Experiments ◽  
Factor Productivity ◽  
The Difference ◽  
The Impact ◽  
Industrial Demonstration

This study conducted quasi-natural experiments based on the panel data of 239 prefecture-level cities in China from 2005 to 2017. The difference-in-difference (DID) and mediation effect model are used to test the impact and mechanism of the construction of national eco-industrial demonstration parks (NEDP) on green total factor productivity (GTFP). The results show that: (1) The construction of NEDP has significantly improved the urban GTFP, and the conclusion is still valid after running the robustness test. (2) Mechanism analysis shows that the construction of NEDP has improved GTFP through technological innovation and industrial structure upgrading. (3) The heterogeneity results reveal that NEDP has a significant positive effect on GTFP in the central and western regions, while the effect was insignificant in the eastern region. Moreover, NEDP significantly contributes to GTFP in resource-based and non-resource-based cities, while the contribution of resource-based cities is greater than that of non-resource-based cities. This study provides a reference for China to further promote the construction quality of NEDP and green development.

scholarly journals Have China’s Pilot Free Trade Zones Improved Green Total Factor Productivity?

2021 ◽  
Vol 18 (21) ◽  
pp. 11681
Author(s):  
Qingshan Ma ◽  
Yuanmeng Zhang ◽  
Kexin Yang ◽  
Lingyun He
Keyword(s):  
Total Factor Productivity ◽  
Free Trade ◽  
Industrial Structure ◽  
Positive Impact ◽  
Trade Openness ◽  
Factor Productivity ◽  
Free Trade Zones ◽  
Green Areas ◽  
The Difference ◽  
The Impact

Free trade zones (FTZ) are designated areas for promoting trade openness and investment facilitation. In China, FTZs are also regarded as “green areas” in which planning actions and institutional innovations are implemented, and there is a commitment to promoting urban green and healthy development. Given that green total factor productivity (GTFP) is an important measure of a city’s health and green performance, this study exploits the difference-in-differences method to explore the impact of pilot FTZs on urban GTFP in 280 cities in China for the period between 2005 and 2017. The results show that the green areas positively contributed to the growth of GTFP. Moreover, the outcome holds with robustness tests. Statistically, the positive effect emerged in cities during the first three years after introducing the initiative, with the effect disappearing afterward. It also had a strong positive impact in the central and western regions and in large and medium-sized cities, while the influence remained insignificant in the remaining areas in China. Furthermore, the paper also reveals that the promotion of foreign direct investment and industrial structure upgrading are the primary channels through which the positive relationship between pilot FTZs and GTFP is established.

scholarly journals The Impact of Participation in PPP Projects on Total Factor Productivity of Listed Companies in China

2021 ◽  
Vol 13 (14) ◽  
pp. 7603
Author(s):  
Xiangdong Liu ◽  
Guangxi Cao
Keyword(s):  
Total Factor Productivity ◽  
High Speed ◽  
Operation Mode ◽  
Listed Companies ◽  
Financing Constraints ◽  
Eastern Region ◽  
Time Varying ◽  
Factor Productivity ◽  
The Impact ◽  
China’S Economy

The key to transforming China’s economy from high-speed growth to high-quality development is to improve total factor productivity (TFP). Based on the panel data of China’s listed companies participating in PPP (Public–Private Partnerships) projects from 2010 to 2019, this paper constructs the time-varying DID method to test the impact of participation in PPP projects on the company’s TFP empirically, explore the mechanism of the effect of participation in PPP projects on the company’s TFP, and then conduct heterogeneous analysis from four perspectives: region, industry, ownership form, and operation mode. The empirical results show that participation in PPP projects can significantly promote the growth of the company’s TFP, which mainly comes from the promotion of the innovation level of listed companies and the alleviation of financing constraints by participating in PPP projects. In addition, participation in PPP projects has a significant impact on TFP of listed companies in the eastern region, listed companies in the secondary and tertiary industries, state-owned listed companies, and listed companies participating in PPP projects under the BOT mode.

scholarly journals Research on the Impact of Green Technology Innovation on Energy Total Factor Productivity, Based on Provincial Data of China

2021 ◽  
Vol 9 ◽  
Author(s):  
Mengxin Wang ◽  
Yanling Li ◽  
Gaoke Liao
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Technology Innovation ◽  
Threshold Model ◽  
Threshold Effect ◽  
Green Technology ◽  
Mediating Effect ◽  
Factor Productivity ◽  
Influence Mechanism ◽  
The Impact

Against the background of carbon peaking and carbon neutralization, green technology innovation plays an important role in promoting the energy total factor productivity (TFP). This study verifies the impact of green technology innovation on energy TFP in a complete sample and the subsamples by region, by constructing a panel threshold model, and analyzes its influence mechanism on the basis of the mediating effect test based on annual provincial data of mainland China from 2005 to 2018. The empirical results reveal the following: first, with the level of economic development as the threshold variable, there is a threshold effect in the impact of green technology innovation on the energy TFP; second, green technology innovation has an impact on the energy TFP through industrial structure upgrading; that is, industrial structure has a mediating effect in the influence mechanism; and third, there is heterogeneity in the impact of green technology innovation on the energy TFP among different regions in China, and the threshold effect only exists in the western region, since the central and eastern regions have crossed a certain developmental stage.

scholarly journals Energy Efficiency, Policy and GTFP

2018 ◽  
Vol 53 ◽  
pp. 01033
Author(s):  
Fangqing Yi ◽  
Zenglian Zhang
Keyword(s):  
Economic Growth ◽  
Energy Efficiency ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Industrial Structure ◽  
Provincial Government ◽  
Factor Productivity ◽  
Linkage Mechanism ◽  
The Government ◽  
The Impact

The environmental and resource constraints on economic growth are increasingly evident. China urgently needs to reshape its economic growth momentum. The increase in green total factor productivity is particularly necessary for the growth of the quantity and quality of the economy. This paper selects the provincial panel data of 30 provinces in China from 2001 to 2015, and establishes a panel exchangeable errors model to analyze the impact of eight indicators on green total factor productivity (GTFP) and verifies its effectiveness. Empirical analysis shows that inter-provincial government competition, environmental regulation, energy consumption, and capital stock have a significant impact on green total factor productivity. The influence of foreign direct investment, industrial structure, and industrialization level on the total factor productivity of green is not significant. Therefore, the government should adopt suitable, flexible and diverse environmental regulation policies, promote energy-saving emission reduction and technology innovations through policies such as taxes and subsidies, strengthen the linkage mechanism between industrial structure upgrading and energy efficiency, to increase green total factor productivity.

scholarly journals Influence of the Evolution of Marine Industry Structure on the Green Total Factor Productivity of Marine Economy

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1108
Author(s):  
Xinyi Wei ◽  
Qiuguang Hu ◽  
Weiteng Shen ◽  
Jintao Ma
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Government Intervention ◽  
Malmquist Index ◽  
Marine Ecology ◽  
Advanced Technology ◽  
Factor Productivity ◽  
Marine Economy ◽  
Low Efficiency ◽  
The Impact

The 14th five-year plan emphasizes the importance of marine ecology and environmental protection, and the green concept is incorporated into the high-quality development system of the marine economy. This research used the data of 11 coastal provinces and cities in China from 2006 to 2016, based on the super-efficiency slack-based measure model and global Malmquist index model. The objective was to calculate the green total factor productivity (GTFP) of the marine economy, to study the impact of the evolution of the marine industrial structure on marine economic GTFP. The study found the following: (1) in general, the upgrade of marine industrial structure promoted the growth of marine economic GTFP and presented an inverted “U” trend of initially promoting and then suppressing. Spatially, only the advancement and rationalization of industrial structure in the Yellow and Bohai Sea regions inhibited the growth of marine economic GTFP. In terms of time, the advanced marine industrial structure promoted the growth of GTFP from 2006 to 2010, whereas that of industrial structure inhibited the growth of GTFP from 2011 to 2016. (2) The GTFP of the marine economy showed an increasing trend, but the conversion rate of production technology is low. Falling into the “efficiency trap” of highly advanced technology input and low-efficiency technology output should be avoided. (3) Affected by the mismatch of regional resources or industrial structure, government intervention showed an “opposite” mechanism in areas with different marine economic strengths. Government intervention in areas with higher marine economic strength was conducive to GTFP growth, whereas government intervention in areas with weaker marine economic strength would hinder GTFP growth.

scholarly journals Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 4989
Author(s):  
Yining Zhang ◽  
Zhong Wu
Keyword(s):  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Positive Impact ◽  
Mechanism Analysis ◽  
Factor Productivity ◽  
Green Development ◽  
Central Regions ◽  
Intelligent Technology ◽  
Deep Integration ◽  
The Impact

The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

scholarly journals Analyzing the Effect of Local Government Competition on Green Total Factor Productivity From the Market Segmentation Perspective in China—Evidence From a Three-Stage DEA Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Juan Tang ◽  
Fangming Qin
Keyword(s):  
Local Government ◽  
Total Factor Productivity ◽  
Market Segmentation ◽  
Production Efficiency ◽  
Fiscal Decentralization ◽  
Negative Impact ◽  
Mediation Effect ◽  
Eastern Region ◽  
Factor Productivity ◽  
The Government

Under both Chinese-style fiscal decentralization (vertical competition) and promotion tournament systems (horizontal competition), the economic development system used by the government determines whether local government competition significantly influences green total factor productivity (GTFP). Moreover, market segmentation, an important strategic tool for local government competition, will significantly impact GTFP because of the implied changes in production efficiency and blocked factor flows. This study applies GMM and the mediation effect model to explore the relationship between local government competition and GTFP from the market segmentation perspective using statistical data from 30 provinces from 2006 to 2017 in China. Overall, our results demonstrate that local government competition significantly inhibits GTFP promotion. Local government competition also has a negative impact on GTFP by promoting market segmentation. As a mediating variable, the market segmentation coefficient was statistically significant. Considering regional heterogeneity, in the eastern region, local government competition has no significant inhibitory effect on GTFP. Moreover, market segmentation has no intermediary effect. In the central and western regions, GTFP remains significantly inhibited by local government competition, and the mediation effect of market segmentation is significant. Finally, our empirical results are robust.

scholarly journals Analysis of the Impact of Local Government Competition on Green Total Factor Productivity From the Perspective of Factor Market Distortion--Based on the Three-Stage DEA model

2021 ◽  
Author(s):  
Juan Tang ◽  
Fangming Qin
Keyword(s):  
Local Government ◽  
Total Factor Productivity ◽  
Mediation Effect ◽  
Factor Productivity ◽  
Dea Model ◽  
Effect Model ◽  
Central Regions ◽  
Internal Mechanism ◽  
Market Distortion ◽  
The Impact

Abstract From the perspective of factor market distortion, this paper explores the effect and internal mechanism of local government competition on green total factor productivity (GTFP). A three-stage DEA model was applied to measure the GTFP of 30 provinces from 2008 to 2017. Furthermore, the article analyses local government competition and factor market distortions influence on GTFP using the Spatial SDM model and mediation effect model. The statistical results reveal that the spatial correlation of GTFP is significantly present across Chinese different provinces. The growth of GTFP will be significantly inhibited by local government competition. Local government competition can indirectly restrict the improvement of GTFP through factor market distortion. Regional heterogeneity indicates that, in the eastern and central regions, local government competition does not significantly inhibit the growth of GTFP. Moreover, local government competition failed to restrain the improvement of GTFP through factor market distortion. However, in the western region, local government competition not only inhibited the growth of GTFP, but also inhibited the growth of GTFP by causing factor market distortion.

scholarly journals The effect of total factor productivity of forestry industry on CO2 emissions: a spatial econometric analysis of China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shen Zhong ◽  
Hongli Wang
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Spillover Effect ◽  
Factor Productivity ◽  
Spatial Econometric ◽  
Spatial Spillover ◽  
Negative Case ◽  
Forestry Industry ◽  
The Impact ◽  
Spatial Spillover Effect

AbstractForestry plays an essential role in reducing CO2 emissions and promoting green and sustainable development. This paper estimates the CO2 emissions of 30 provinces in China from 2008 to 2017, and uses Global DEA-Malmquist to measure the total factor productivity of the forestry industry and its decomposition index. On this basis, by constructing a spatial econometric model, this paper aims to empirically study the impact of forestry industry's total factor productivity and its decomposition index on CO2 emissions, and further analyze its direct, indirect and total effects. The study finds that the impact of forestry industry's total factor productivity on CO2 emissions shows an "inverted U-shaped" curve and the inflection point is 0.9395. The spatial spillover effect of CO2 emissions is significantly negative. The increase of CO2 emissions in adjacent areas will provide a "negative case" for the region, so that the region can better address its own energy conservation and emission reduction goals. TFP of forestry industry also has positive spatial spillover effect. However, considering the particularity of forestry industry, this effect is not very significant. For other factors, such as foreign direct investment, urbanization level, industrial structure and technology market turnover will also significantly affect regional CO2 emissions.

Examining the Impact and Influencing Channels of Emission Trading Pilot Markets in China

2020 ◽  
Vol 11 (1) ◽  
pp. 136-136
Author(s):  
Qiong Wu ◽  
Kanittha Tambunlertchai ◽  
Pongsa Pornchaiwiseskul
Keyword(s):  
Total Factor Productivity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Emission Trading ◽  
Chinese Government ◽  
Synergy Effect ◽  
Factor Productivity ◽  
Plan Period ◽  
Carbon Emission Trading ◽  
The Impact

The global warming has become a serious issue in the world since the 1980s. The targets for the first commitment period of the Kyoto Protocol cover emissions of the six main greenhouse gasses (GHGs). China is the world's largest CO2 emitter and coal consumer and was responsible for 27.3 percent of the global total CO2 emission and 50.6 percent of the global total coal consumption in 2016 (BP, 2017). As China plays an important role in the global climate change, China has set goals to improve its environmental efficiency and performance. In 2011, the Chinese government for the first time announced an intent to establish carbon emission trading market in China. Eight regional emission trading schemes have been operating since 2013 (seven pilot markets during the 12th Five Year Plan period and one pilot market during the 13th Five Year Plan period) including provinces of Guangdong, Hubei, and Fujian, and cities of Beijing, Tianjin, Shanghai, Shenzhen, and Chongqing. The goal of these regional emission trading pilot markets is to help the government establish an efficient carbon emission trading scheme at national level. Some researchers have been focused on examining the impact of emission trading schemes in China using CGE model by constructing different scenarios and ex-ante analysis using data prior to emission trading pilot markets implementation. While this paper tries to conduct an ex-post analysis with data of 2005-2017 to evaluate the impact of emission trading pilot markets in China at provincial level using difference-in-difference (DID) model. By including both CO2 and SO2 as undesirable outputs to calculate Malmquist-Luenberger (ML) Index to measure green total factor productivity, this paper plans to evaluate the impact of carbon emission trading pilot markets in China via emission reduction, regional green development, synergy effect and influencing channels. This paper tries to answer the following research questions: (1) Do emission trading pilot markets reduce CO2 emission and increase regional green total factor productivity? (2) Is there any synergy effect from emission trading pilot markets? (3) What are the influencing channels of emission trading pilot markets? Keywords: Emission trading, CO2 emissions, Different-in-difference

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