scholarly journals Industrial Structure, R&D Staff, and Green Total Factor Productivity of China: Evidence from the Low-Carbon Pilot Cities

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shengqian Guo ◽  
Xue Tang ◽  
Ting Meng ◽  
Jincan Chu ◽  
Han Tang
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Structure Optimization ◽  
Mediating Effect ◽  
Green Economy ◽  
Low Carbon ◽  
Factor Productivity ◽  
Local Conditions ◽  
Carbon Policy ◽  
Using Data

Using data of 26 cities in China from 2004 to 2017, the green total factor productivity is investigated by the SMM-GML method. The corresponding empirical analysis is conducted with the DID model. This paper investigates the relation between low-carbon pilot policy (LCC) and green total factor productivity and discusses the mediating effect of industrial structure and the number of R&D staff (RDS). First, we find that LCC has a significant effect on pilot cities’ GTFP. And, it also promotes GTFP via industrial structure. Second, LCC can improve industrial structure optimization and realization, and industrial structure realization affects GTFP significantly, while optimization cannot. Third, LCC cannot attract more RDS, and RDS harms local GDFP because of talent misallocation. At last, the rate of GTFP presented different upward trends in the order of non-eastern cities and eastern cities. The effect of LCC on GTFP is significant in non-eastern cities, but not eastern ones, which clearly demonstrates the imbalanced development of the green economy. Therefore, the governments of eastern and non-eastern regions should adopt different measures based on local conditions in industrial structure transformation and recruitment and strengthen environmental regulations to make the effect of the low-carbon policy lasting and promote GTFP growth balance in all regions.

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 Corporate Carbon Disclosure, Financing Structure and Total Factor Productivity: Evidence from Chinese Heavy Polluting Listed Corporates

2021 ◽  
Author(s):  
Sai Yuan ◽  
Xiongfeng Pan
Keyword(s):  
Total Factor Productivity ◽  
Mediating Effect ◽  
External Financing ◽  
Low Carbon ◽  
Factor Productivity ◽  
Disclosure Quality ◽  
Financing Structure ◽  
Financing Costs ◽  
Internal Financing

Abstract Low-carbon economy has become the current global economic development trend, and Corporate carbon disclosure has attracted more and more attention from scholars and investors. This paper creatively explores the mechanism of corporate carbon disclosure quality on total factor productivity with financing structure as a mediating variable. The content analysis method is used to construct a carbon disclosure evaluation index system that is suitable for Chinese companies. Through the mediating effect model and Sobel test, the internal mechanism of carbon disclosure quality affecting total factor productivity is analyzed, with Chinese heavy polluting listed corporates from 2015 to 2018 as research samples. The empirical results show that, Firstly, the Quality of carbon disclosure has a positive effect on the improvement of total factor productivity. The effect of monetary carbon disclosure quality on the improvement of total factor productivity is higher than that of non-monetary carbon disclosure quality. Secondly, the financing structure has a mediating effect on the quality of carbon disclosure and total factor productivity, and the mediating effect of internal financing capabilities is better than those of external financing costs. Finally, external financing costs and internal financing capabilities have mediating effects in both heterogeneous carbon disclosure quality and total factor productivity. The mediating effect of internal financing capabilities is significantly higher than the mediating effect of external financing costs. Meanwhile, the effect of monetary carbon disclosure quality on total factor productivity indirectly through internal financing capabilities is higher than that of non-monetary carbon disclosure quality.

scholarly journals Study on the Effect of Environmental Regulation on the Green Total Factor Productivity of Logistics Industry from the Perspective of Low Carbon

2019 ◽  
Vol 12 (1) ◽  
pp. 175 ◽  
Author(s):  
Zijing Liang ◽  
Yung-ho Chiu ◽  
Xinchun Li ◽  
Quan Guo ◽  
Yue Yun
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Jiangsu Province ◽  
Spatial Effects ◽  
Low Carbon ◽  
Method Of Moment ◽  
Factor Productivity ◽  
Logistics Industry ◽  
Forward Logistics ◽  
The Government

Under the low-carbon background, with the aid of the Malmquist–Luenberger SBM (Slack-based Measure) model of unexpected output, the green total factor productivity (GTFP) of the logistics industry in Jiangsu Province, China, was measured and decomposed in this study based on the reality and experience of logistics industry development in 13 cities in three regions of Jiangsu Province in the years 2006–2018 by taking resource consumption into the input system and discharged pollutants into the output system. It is concluded that the environmental regulation (ER) has a significant positive effect on the growth of the GTFP of the logistics industry, and technological progress has become an important endogenous force that promotes the GTFP of the logistics industry in Jiangsu Province. On this basis, a dynamic GMM (Generalized method of moment) model and a Tobit model were constructed to further study the possible temporal and spatial effects of ER on the GTFP of the logistics industry. The research results reveal that the ER can exert both promoting and inhibitory effects on the GTFP of the logistics industry, and there is a temporal turning point for the effects. Besides, the effects notably differ spatially and temporally. Finally, some policies and advice for the green sustainable development of the logistics industry were proposed. For example, the government and enterprises should pay attention to the green and efficient development of the logistics industry and dynamically adjust the ER methods. They should consider the greening of both forward logistics links and reverse logistics system in the supply chain.

scholarly journals DINAMIKA TOTAL FACTOR PRODUCTIVITY INDUSTRI BESAR DAN SEDANG INDONESIA

2014 ◽  
Vol 16 (3) ◽  
pp. 277-308
Author(s):  
Ndari Surjaningsih ◽  
Bayu Panji Permono
Keyword(s):  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Manufacturing Sector ◽  
Jel Classification ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Catching Up ◽  
Using Data

This paper calculates and decomposes the Total Factor Productivity (TFP) for large and medium scale industry in Indonesia covering the period of 2000-2009. By using Data Envelopment Analysis (DEA)  method, the result shows there is a shift of the supporting factors on the growth of TFP on manufacturing sector within the 2 (two) sample period. In the period of 2000-2004, efficiency change becomes the main contributor on the growth of TFP. Whereas in the period of 2005-2009, technical change becomes the main supporting factor of TFP,however it goes along with the growth of negative efficiency change or the decline of the company’s catching-up effect ability to adapt with the more advance technology. The grouping of the sample across subsectors, technical change and also efficiency change shows the declining amount of manufacture industry with superior productivity. Furthermore, the number of low and weakening catching-up industry is increasing.  Keywords: Indonesian manufacturing, total factor productivity, technical change, efficiency change, economic scale change, Data Envelopment Analysis JEL Classification: L6, M11

scholarly journals Does Low-Carbon City Construction Improve Total Factor Productivity? Evidence from a Quasi-Natural Experiment in China

2021 ◽  
Vol 18 (22) ◽  
pp. 11974
Author(s):  
Hongfeng Zhang ◽  
Lu Huang ◽  
Yan Zhu ◽  
Hongyun Si ◽  
Xu He
Keyword(s):  
Economic Development ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Progress ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Low Carbon ◽  
Factor Productivity ◽  
City Construction ◽  
Low Carbon City

Low-carbon city construction (LCC) is an important strategy for countries desiring to improve environmental quality, realize cleaner production, and achieve sustainable development. Low-carbon cities have attracted widespread attention for their attempts to coordinate the relationship between environmental protection and economic development. Using the panel data from 2006 to 2017 of prefecture-level cities in China, this study applied the difference-in-differences (DID) method to analyze the effects of LCC on the total factor productivity (TFP) of the cities and its possible transmission mechanism. The results show significantly positive effects on TFP, but the effects on each component of TFP are different. Although the LCC has promoted technical progress and scale efficiency, it has inhibited technical efficiency. The accuracy of the results has been confirmed by several robustness tests. Mechanism analysis showed that the pilot policy of low-carbon cities has promoted technical progress and scale efficiency by technological innovation and the upgrading of industrial structure, but resource mismatches among enterprises have been the main reason for reduced technical efficiency. Regional heterogeneity analysis showed that the effects on TFP in the eastern region have been more significant than in the central and western regions. In the eastern region, they have promoted technical progress, while in the central and western regions, they have promoted technical progress and scale efficiency but hindered technical efficiency. This paper presents our findings for the effects of LCC on economic development and provides insightful policy implications for the improvement of technical efficiency in low-carbon cities.

scholarly journals Total factor productivity and its components for sheep and goat farms in 37 Southern European regions (2004-2012)

2018 ◽  
Vol 17 (2) ◽  
pp. 05
Author(s):  
Cristina Hidalgo González ◽  
Mª Pilar Rodríguez Fernández
Keyword(s):  
Total Factor Productivity ◽  
Stochastic Frontier ◽  
Farming Systems ◽  
Positive Development ◽  
European Regions ◽  
Factor Productivity ◽  
Economic Determinants ◽  
Production Processes ◽  
Using Data ◽  
Goat Farming

<p>The decline of Southern Europe’s traditional sheep- and goat-farming systems creates a need for studies on the economic determinants that underlie their production processes. Using data from the FADN, we built a panel of 37 regions from 5 countries over an 8-year period (2004–2012). A Cobb-Douglas was specified and a stochastic frontier production was estimated. Total Factor Productivity (TFP) and its components were calculated. The farms have had sustained, positive development of TFP since 2008, with a significant correlation with the labour factor of production. We detected moderate technical progress change, which was accompanied by decreasing efficiency.</p>

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.

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