Evaluating the method of total factor productivity growth and analysis of its influencing factors during the economic transitional period in China

Zhang, Shuiping

doi:10.1016/j.jclepro.2014.09.097

Cited by 37 publications

(21 citation statements)

References 13 publications

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“…Economic development is an important cause of carbon dioxide emissions and promotes total factor productivity growth accounting for undesirable outputs [8]. Therefore, this study selects the per capita GDP, defined as the ratio of GDP to total population.…”

Section: Three Input Variablesmentioning

confidence: 99%

“…ML it = λ 0 + λ 1 coal it + λ 2 regulation it + λ 3 industry it + λ 4 fdi it + λ 5 per it + ε it (8) where t represents the year, i refers to the province, ML is the total factor productivity index, λ i (i = 0, 1, . .…”

Section: Three Input Variablesmentioning

confidence: 99%

“…In the second stage, this study used influence factor data associated with total factor productivity growth from Equation (8) and Stata software (Stata, 13.0; Stata Corp. LP: College Station, TX, USA, 2013) to perform a stochastic Tobit panel regression analysis. For a detailed introduction to the stochastic Tobit regression model, please refer to Pérez-Reyes and Tovar [37].…”

Section: Influence Factor Analysis Of Coal Utilization Efficiencymentioning

confidence: 99%

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Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China

et al. 2016

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Abstract:Presently, China's social development is facing the dilemma of supporting economic growth and reducing emissions. Therefore, it is crucial to analyse productivity growth and examine its relationship with influencing factors in China. This study evaluated the total factor productivity (TFP) growth of 30 provinces in China by adopting the Malmquist-Luenberger (ML) productivity index and incorporating undesirable outputs from 2011-2014. Then, a Tobit regression model was employed to explore the factors that influence China's TFP growth. The results show that the average annual growth of the Malmquist-Luenberger productivity index was lower than that of the traditional Malmquist (M) productivity index growth during the research period. The findings reveal several key conclusions: First, the true TFP growth in China will be overestimated if undesirable outputs are ignored. Second, technical changes are the main contributor to TFP growth. Third, there are huge regional disparities of productivity growth in China. Fourth, coal intensity, environmental regulations, and industrial structure have significantly negative effects on productivity growth, while real per capita gross domestic product (GDP) and foreign direct investment (FDI) have strongly positive effects on productivity growth.

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Section: Three Input Variablesmentioning

confidence: 99%

Section: Three Input Variablesmentioning

confidence: 99%

Section: Influence Factor Analysis Of Coal Utilization Efficiencymentioning

confidence: 99%

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Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China

et al. 2016

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“…Chung et al, 1997;Macpherson et al, 2010;Picazo-Tadeo et al, 2005) we apply the DDF approach in order to measure states' environmental efficiency levels. More 6 For some recent innovative applications of DEA measuring environmental efficiency and productivity see the studies by Fujii et al (2011), Long et al (2013), Chung and Heshmati (2014), Rashidi et al (2014), Zhang (2014) and Bian et al (2015). specifically, we apply a direction vector ( )…”

Section: The Null Jointness Assumption Of Good and Bad Outputs)mentioning

confidence: 99%

Population, economic growth and regional environmental inefficiency: evidence from U.S. states

Halkos

Stern

Tzeremes

2016

Journal of Cleaner Production

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We apply a conditional directional distance function allowing multiple exogenous factors to measure environmental performance. We evaluate the air pollution performance levels of U.S. states for the years 1998 and 2008. States' environmental inefficiency is determined by population size and GDP per capita (GDPPC). The overall results reveal that there is much variation in environmental inefficiencies among the U.S. states. A second stage nonparametric analysis indicates a nonlinear relationship between states' population size, GDPPC levels and states' environmental inefficiency levels. Our results indicate that environmental inefficiency on the whole decreases with increased population and income per capita but there are limits to this improvement and at high income and population levels the tendency may reverse.

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“…Although there are many models and methods for assessing the sustainable development of the urban economy, the Malmquist-Luenberger index analysis method is also one of the important methods to measure the economic efficiency of the city. Most M-L index methods measure labor and capital investment as the input of the urban economy, and GDP as the output of the urban economy [33][34][35][36]. For example, Zhang [33] proposed a method for the evaluation of total factor productivity (TFP) based on the super-efficiency Data Envelopment Analysis (DEA) model and the Malmquist-Luenberger index, to analysis of the low-carbon TFP index of the Wan Jiang region and the northern Anhui region in Anhui Province in central China.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sustainable Urban Development under Environmental Constraints: A Case Study of Jiangsu Province, China

et al. 2020

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Sustainable development is a long-term solution for urban development and a guideline for urban development. Only by better coordination around the population, resources, and environment can the city achieve sustained and steady development. In order to evaluate the efficiency of sustainable urban development under environmental constraints, this paper takes 13 prefecture-level cities in Jiangsu Province as an example. To address the infeasibility problem in the Malmquist-Luenberger (M-L) index, a modified M-L index analysis method based on the new directional distance function (DDF) is adopted. Combined with the consideration of desirable and undesirable outputs, a linear programming model for sustainable urban development evaluation is constructed to provide a scientific decision-making basis for sustainable development of the city. The results show that the growth of sustainable urban development efficiency is not only related to desirable outputs but also affected by undesirable outputs. Technical change is the main driver of most sustainable urban development efficiency growth. However, efficiency changes also affect the sustainable development potential of cities to a certain extent. Through the modified M-L index analysis, we analyze the characteristics of different regions of Jiangsu Province, the changes in efficiency and the impact of technological innovation on sustainable urban development, and give policy recommendations to promote sustainable urban development.

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Evaluating the method of total factor productivity growth and analysis of its influencing factors during the economic transitional period in China

Cited by 37 publications

References 13 publications

Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China

Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China

Population, economic growth and regional environmental inefficiency: evidence from U.S. states

Evaluation of Sustainable Urban Development under Environmental Constraints: A Case Study of Jiangsu Province, China

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