scholarly journals Has the Economic Structure Optimization in China’s Supply-Side Structural Reform Improved the Inclusive Green Total Factor Productivity?

2021 ◽  
Vol 13 (22) ◽  
pp. 12911
Author(s):  
Feng Wang ◽  
Jianxiong Wu ◽  
Min Wu ◽  
Wen Zheng ◽  
Danwen Huang
Keyword(s):  
Environmental Pollution ◽  
Total Factor Productivity ◽  
Structure Optimization ◽  
Economic Structure ◽  
Structural Reform ◽  
Energy Structure ◽  
Supply Side ◽  
Annual Growth Rate ◽  
Income Disparity ◽  
Factor Productivity

One of the goals of China’s supply-side structural reform is to improve total factor productivity (TFP). Considering the problems of environmental deterioration and income disparity faced by China, this paper first incorporates environmental pollution and income disparity as undesirable outputs into the TFP analysis framework, and extends the concept of inclusive green TFP (henceforth IGTFP).We measure and analyze the IGTFP in China’s provinces from 1995 to 2017 using the Malmquist–Luenberger index, and then examine, for the first time, the impacts of economic structural optimization in the supply-side structural reform on the IGTFP. The results are shown as follows. First, China’s national IGTFP index is significantly smaller than the traditional TFP index. That is, the traditional TFP without the constraints of environmental pollution and income disparity overestimates China’s real TFP. Second, there are significant regional differences in China’s IGTFP, the average annual growth rate of IGTFP shows a gradual downward trend from east to west. This would further exacerbate the regional imbalance in China’s economic development. Third, among the structural factors in economic structure optimization, industrial structure and energy structure are negatively correlated with the IGTFP, while factor structure, labor structure and urban–rural structure are all positively correlated with the IGTFP. These results imply that the economic structure optimization driven by supply-side structural reform will improve China’s IGTFP.

scholarly journals Modeling the Impact of Innovation on Economic Quality and Environmental Pollution Change under Consideration of Environmental Regulation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinfei Li ◽  
Yuan Tian ◽  
Yueming Li ◽  
Chang Xu ◽  
Xiaobing Liu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Environmental Pollution ◽  
Total Factor Productivity ◽  
Environmental Regulations ◽  
Pollutant Emissions ◽  
Factor Productivity ◽  
So2 Emissions ◽  
The Impact ◽  
China’S Economy

Under the constraints of resources and the environment, exploring the channels to improve the quality of China’s economy is very important for China’s current sustainable development. Therefore, this paper studies whether innovation can improve the quality of China’s economy and explore the path of sustainable development from the perspective of the city. Based on the Malmquist–Luenberger index and DEA-Malmquist index, this paper, respectively, measures the green total factor productivity (GTFP) and total factor productivity (TFP) of 193 cities in China. On the basis of obtaining the GTFP, TFP, and various pollutant emissions of 193 cities, this paper selects environmental regulations as the threshold variable and the number of urban patents as the explanatory variable to measure the level of urban innovation. On this basis, we examine the impact of innovation quality on economic quality and environmental pollution under different environmental regulatory intensities. The research results show that the impact of innovation on GTFP and TFP under different environmental regulations is always positive, but the impact coefficient and significance level vary. In addition, the impact of innovation on SO2 emissions under different environmental regulations has also changed. With the increase of environmental regulations, the effect of reducing emissions is gradually significant. The conclusion of this paper better interprets the development of TFP and GTFP under the innovation-driven strategy, provides a decision-making basis for departments at all levels to formulate innovation support policies, and explores the path of sustainable development.

scholarly journals Analysis of Factors Influencing Coal Prices Under the New Normal of China's Economy

2020 ◽  
Author(s):  
Yang LI ◽  
Haozhou CHENG ◽  
Yuqi REN ◽  
Kunpeng YANG
Keyword(s):  
Coal Industry ◽  
Structural Reform ◽  
Energy Structure ◽  
Supply Side ◽  
New Normal ◽  
Fossil Energy ◽  
Coal Price ◽  
Empirical Results ◽  
Factors Influencing ◽  
China’S Economy

Abstract Background: After China's economy entered a new normal, the economic growth slowed down, energy market demand shrank, and the coal industry suffered from serious overcapacity. In this context, the Chinese government carried out supply-side structural reform and implemented a series of policies to cut overcapacity and destocking.Methods: By using time series data, this paper establishes vector error correction model, combined with Granger causality test and variance decomposition, to study the factors influencing coal prices under the new normal of economy. Results: The empirical results show that under the new normal of the economy, coal social inventory, oil production and natural gas production have a greater impact on the coal price, while coal consumption and raw coal production have a smaller impact on the coal price. According to the analysis, at the present stage, fossil energy, especially coal, occupies too high proportion in China's energy structure, which can no longer meet the requirements of energy use under the new normal of economy. Conclusion: According to the empirical results and analysis, the Chinese energy structure must be adjusted. In the short term, supply-side structural reform policies must be deepened to change the status quo of China's coal industry as soon as possible. In the long run, non-fossil energy technologies should be vigorously developed to provide stable and cheap non-fossil energy, thus reducing the use of fossil energy and increasing the use of non-fossil energy.

scholarly journals Does FDI Promote or Inhibit the High-Quality Development of Agriculture in China? An Agricultural GTFP Perspective

2019 ◽  
Vol 11 (17) ◽  
pp. 4620 ◽  
Author(s):  
Yafei Wang ◽  
Li Xie ◽  
Yi Zhang ◽  
Chunyun Wang ◽  
Ke Yu
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Annual Growth ◽  
Eastern Region ◽  
Annual Growth Rate ◽  
Factor Productivity ◽  
Promoting Effect ◽  
Index Method ◽  
Agricultural Growth

This paper innovatively brings the undesirable output of agricultural carbon emission into the agricultural Total Factor Productivity (TFP) accounting framework as a measure of Green Total Factor Productivity (GTFP) and uses the Slack-based Measure and Malmquist-Luenberger (SBM-ML) index method to measure the agricultural GTFP of 24 provinces in China from 2004 to 2016. Further, the two-step system generalized moment method (GMM) is adopted to reveal the effect of agricultural (Foreign Direct Investment) FDI on the growth of agricultural GTFP and various subitems. We find that the average annual growth rate of agricultural GTFP is 3.1%, and its contribution rate to agricultural growth is 52%; the growth of agricultural GTFP shows that the progress of agricultural technology is accompanied by the deterioration of agricultural technical efficiency; the agricultural GTFP in the Eastern region, the Central region and the Western region increases in a stepped-up form, with an annual growth rate of 3.1%, 3.3% and 3.4%, respectively. Agricultural FDI has a significant promoting effect on agricultural GTFP and subitems, however, it has an inverted U-shaped feature in the long term.

scholarly journals What Is the Focus of Structural Reform in China?—Comparison of the Factor Misallocation Degree within the Manufacturing Industry with a Unified Model

2018 ◽  
Vol 10 (11) ◽  
pp. 4051 ◽  
Author(s):  
Laiqun Jin ◽  
Changwei Mo ◽  
Bochao Zhang ◽  
Bing Yu
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Severity Index ◽  
Unified Model ◽  
Structural Reform ◽  
Industrial Enterprise ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Production Factors

The misallocation of production factors, with structural misallocation as an important aspect, is a key instigator of low total factor productivity (TFP) growth rate in China, but one important question is which structural misallocation of what factor is more serious in China. Using China’s manufacturing industrial enterprise data from 1998 to 2013, we calculated and compared the factors misallocation degree among industries, ownerships and regions. The results indicated that, the misallocation among industries was most serious, which led to a TFP loss of 8.12% annually. The misallocation among ownerships ranked second, which led to a TFP loss of 5.49%. The least degree of the misallocation recorded among provinces led to TFP loss of 3.05%. By using the relative severity index, the rank is the same. As to the capital, the misallocation among ownerships was most serious, which led to TFP loss of 4.62%. But as to the labor, the misallocation among industries was most serious, which led to TFP loss of 4.58%. Moreover, the misallocation among ownerships alleviated rapidly from 1998 to 2007, while alleviated slower among industries and regions. However, from 2008 to 2013, all three types of structural misallocation have become worse, especially in labor. These conclusions are important to identify the focus of structural reform in China.

Evaluation analysis on industrial green total factor productivity and energy transition policy in resource-based region

2021 ◽  
pp. 0958305X2110054
Author(s):  
Yaozu Xue
Keyword(s):  
Environmental Pollution ◽  
Total Factor Productivity ◽  
Pollution Control ◽  
Energy Transition ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Shanxi Province ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Positive Effect

Based on the input-output panel data of industrial sectors of Shanxi Province, which is the only province-wide resource-based region in China, this paper uses the non-parametric DEA model and the Malmquist productivity index to construct the DEA-Malmquist model for evaluation analysis of the green total factor productivity (GTFP) and its decomposition value of the sub-sector, and then through fixed effect panel regression model studies the ways of energy transition of the SDG’s. The results show that the technological progress index has the greatest contribution to the growth of GTFP, while the scale efficiency index has the lowest contribution. And the amount of investment in environmental pollution control has a significant positive relationship with the GTFP of the three major polluting industries in Shanxi. Among them, investment in environmental pollution control has the greatest positive effect on the GTFP of light polluting industries characterized by high technology, high added value and low emissions; investment in environmental pollution control has the largest positive effect on the GTFP of heavy polluting industries with heavy chemical industry and pollution-intensive industries; investment in environmental pollution control has the weakest positive effect on the GTFP of the medium polluting industries that manufacture life service products and some heavy industrial products. Based on these results, the paper puts forward effective policy for the energy transition of resource-based regions.

scholarly journals The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

2021 ◽  
Vol 13 (15) ◽  
pp. 8581
Author(s):  
Chaoxun Ding ◽  
Ruidan Zhang
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Data Model ◽  
Industrial Structure ◽  
Positive Impact ◽  
Malmquist Index ◽  
Annual Growth Rate ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Urbanization Rate

Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.

scholarly journals Evaluation of Green Total Factor Productivity of China’s Tobacco Manufacturing Industry Based on DEA-Malmquist Index Method

2021 ◽  
Vol 261 ◽  
pp. 03027
Author(s):  
Xiaoyu Qu ◽  
Ziyue Wang
Keyword(s):  
Energy Consumption ◽  
Panel Data ◽  
Environmental Pollution ◽  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Malmquist Index ◽  
Technical Level ◽  
Factor Productivity ◽  
Upward Trend ◽  
Index Method

Using panel data from China’s tobacco manufacturing industry from 2014 to 2018, the DEA-Malmquist method is used to measure the green total factor productivity of China’s tobacco manufacturing industry on the basis of comprehensive consideration of environmental pollution and energy consumption. The study found that from 2014 to 2018, the Malmquist index of green total factor productivity of China’s tobacco manufacturing industry basically showed a fluctuating upward trend; the technical level of 2014-2017 needs to be improved, and the technical level of 2017-2018 has begun to improve.

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