scholarly journals Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data

2021 ◽  
Vol 13 (2) ◽  
pp. 472
Author(s):  
Shiying Hou ◽  
Liangrong Song
Keyword(s):  
Total Factor Productivity ◽  
Market Integration ◽  
Economic Transformation ◽  
Green Economy ◽  
Factor Productivity ◽  
Spatial Econometric ◽  
Positive Effects ◽  
Spatial Econometric Models ◽  
Level Data ◽  
Positive Effect

The development of market integration has an important effect on regional green total factor productivity (GTFP). Based on the panel data of 30 provinces in China from 2008 to 2017, this paper studies the spatial effect and transmission mechanism of market integration on regional green total factor productivity by calculating the Malmquist–Luenburger index and using spatial econometric models. It was found that market integration can promote the improvement of regional green total factor productivity. This positive effect is not only directly reflected in the region, it also indirectly promotes the growth of GTFP in nearby regions. In addition, market integration has shown significant positive effects on efficiency improvement and technological progress, and market integration has affected regional green total factor productivity through them. The above conclusions are of great significance for China to develop a green economy and promote high-quality economic transformation.

scholarly journals Does Corporate Financialization Have a Non-Linear Impact on Sustainable Total Factor Productivity? Perspectives of Cash Holdings and Technical Innovation

2021 ◽  
Vol 13 (5) ◽  
pp. 2533
Author(s):  
Hui Wang ◽  
Qing Wang ◽  
Xia Sheng
Keyword(s):  
Total Factor Productivity ◽  
Threshold Model ◽  
Threshold Effect ◽  
Sustainable Growth ◽  
Technical Innovation ◽  
Financial Assets ◽  
Factor Productivity ◽  
Positive Effects ◽  
Optimal Thresholds ◽  
Positive Effect

This study explores the conditions under which financialization may foster sustainable total factor productivity (TFP). We examine the inverted U-shaped relationship between corporate financialization and TFP by employing a panel threshold model using microeconomic non-financial panel data from Chinese firms in the 2007 to 2018 period. Our results suggest that the turning point is more significant in holding short-term financial assets and state-owned enterprises. The threshold effect suggests that technical innovation determines the optimal threshold at which TFP is affected by financialization. Further, financialization is considered an alternative to cash in order to increase the value of capital, leading to a positive effect on TFP. Contrary to their positive effects below the optimal thresholds, financialization exceeds a certain level, displaces technical innovation, and becomes detrimental to TFP. Our analysis thus establishes the importance of sustainable growth of TFP and minimize the adverse effect of financialization.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries

2003 ◽  
Vol 33 (9) ◽  
pp. 1653-1660 ◽  
Author(s):  
Atakelty Hailu ◽  
Terrence S Veeman
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Productive Efficiency ◽  
Set Covering ◽  
Data Envelopment Analysis Model ◽  
Factor Productivity ◽  
Data Set ◽  
Term Survival ◽  
Positive Effects ◽  
Logging Industry

The Canadian boreal logging industry has attracted little or no attention from economic researchers in spite of its importance for the competitiveness and long-term survival of other forest-based industries. This article uses a panel data set covering the period from 1977 to 1995 to analyze technical efficiency, technical change, and total factor productivity growth in the logging industries for six boreal provinces. The production technology is represented using a data envelopment analysis model. A transitive measure of productivity change that combines technical progress and changes in the degree of productive efficiency is computed. The empirical investigation reveals that logging activities in the boreal region are characterized by substantial efficiency differentials among the regions. Results from a Tobit analysis of efficiency differentials indicate that forest resource characteristics such as forest density and proportion of hardwood production were found to have positive effects. There was also evidence of significant positive scale effects. Engineering construction per area seems to be negatively related to efficiency. Total factor productivity in the boreal logging industry progressed at an average annual rate of 1.56%.

scholarly journals Research on Total Factor Productivity and Influential Factors of the Regional Water–Energy–Food Nexus: A Case Study on Inner Mongolia, China

2019 ◽  
Vol 16 (17) ◽  
pp. 3051 ◽  
Author(s):  
Chen ◽  
Ding ◽  
Wang ◽  
Yu
Keyword(s):  
Total Factor Productivity ◽  
Inner Mongolia ◽  
Influential Factors ◽  
Government Support ◽  
Factor Productivity ◽  
Tobit Regression ◽  
Negative Effects ◽  
Lower Efficiency ◽  
Positive Effects ◽  
Regional Water

With the supply of water, energy and food facing severe challenges, there has been an increased recognition of the importance of studying the regional water–energy–food nexus. In this paper, Inner Mongolia, including 12 cities in China, was selected as a research case. A super-efficiency slack based measure (SBM) model that considered the undesirable outputs was adopted to calculate the regional total factor productivity (TFP) and the Malmquist–Luenberger index was used to investigate the change trend of the TFP from 2007 to 2016 based on understanding the water–energy–food nexus. Finally, influential factors of the TFP were explored by Tobit regression. The results show that the 12 Inner Mongolia cities are divided into higher, moderate and lower efficiency zones. The higher efficiency zone includes Ordos, Hohhot, Xing’an, and Tongliao, and the lower efficiency zone includes Chifeng, Xilin Gol, Baynnur, Wuhai and Alxa. There is a serious difference in TFP between Inner Mongolia cities. During the study period, the TFP of the water–energy–food nexus in Inner Mongolia cities shows a rising trend, which is mainly driven by the growth of technical progress change. However, the average ML values of the lower and moderate efficiency zones were inferior to the higher efficiency zone in six of the ten years, so the difference between Inner Mongolia cities is growing. According to the Tobit regression, the mechanization level and degree of opening up have positive effects on the TFP, while enterprise scale and the output of the third industry have negative effects on the TFP. Government support does not have any significant impact on the TFP. Finally, suggestions were put forward to improve the TFP of the water–energy–food nexus in Inner Mongolia cities.

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 Determinants Of Productivity Of Ukrainian Firms

2017 ◽  
Vol 20 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Andrzej Cieślik ◽  
Iryna Gaugner ◽  
Jan Jakub Michałek
Keyword(s):  
Total Factor Productivity ◽  
Firm Size ◽  
Capital Intensity ◽  
Factor Productivity ◽  
Empirical Results ◽  
Ownership Status ◽  
Level Data ◽  
Firm Internationalization ◽  
Service Sectors ◽  
Micro Level

This paper examines the determinants of total factor productivity (TFP) of Ukrainian firms in manufacturing and services, using micro-level data for the years 2005 and 2013. We first estimate regressions for the pooled dataset for the manufacturing and service sectors jointly, and then separately for each sector. Our empirical results show a positive link between the total factor productivity, intangible assets, capital intensity, firm size, competition in the industry, ownership status, and firm internationalization (exports and imports). In addition, we find that the determinants of productivity differ among the sectors and years of our sample.

Sign in / Sign up

Export Citation Format

Share Document