Efficiency analysis of Chinese industry: A directional distance function approach

Watanabe, Michio; Tanaka, Katsuya

doi:10.1016/j.enpol.2007.07.013

Cited by 234 publications

(104 citation statements)

References 21 publications

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“…The differenced generalized method of moments (DIF-GMM), proposed by Arellano and Bond [59], and the system generalized method of moments (SYS-GMM), proposed by Arellano and Bover [60] and Blundell and Bond [61], can be used to solve the endogenous problem. Based on that, DPD models based on GMM estimation are established to test the determinants of GTFP, as follows: (20) where i denotes different industrial sector; t denotes the year; GTFP, INS, TEC, and STR denote the variables of green total factor productivity, institution, technology, and structure, respectively; µ i denotes the individual effect and ε it denotes the random error term. The aim of setting I NS 2 is to test whether there is a turning point between GTFP and institution, and the aim of setting I NS·TEC is to test the indirect effect of institution on GTFP by the route of technology.…”

Section: Model and Variablesmentioning

confidence: 99%

“…The TFP growth measured by computing the change of the ML index could be called green total factor productivity (GTFP) growth (the concepts of green total factor productivity, GTFP, green productivity, and green TFP, are the same in this paper). The approach has been wide applied in the studies of efficiency and productivity [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

et al. 2018

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This paper employs directional distance function (DDF) and the global Malmquist-Luenberger (GML) productivity index to measure the green total factor productivity (GTFP) growth of China's 36 industrial sectors from 2000 to 2014. Based on this, this paper ascertains the determinants of GTFP from the perspectives of institution, technology, and structure, and the determinant factors that affect GTFP are empirically tested by a dynamic panel data (DPD) model. The research shows that, considering energy consumption and environmental undesirable outputs, the industrial GTFP goes backwards by 0.02% per year on average, and the contributions of GTFP to output growth are far from the target value of 50% in all industrial sectors, which indicates that the growth of industrial economy sacrifices resources and environment to a certain degree. In terms of the determinant factors of GTFP, environmental regulation does improve the GTFP, while environmental regulation is difficult to promote GTFP by the route of technological innovation. Compared with technology importation, the driving effect of independent research and development on GTFP is obvious, especially promoting the GTFP of moderately and lightly polluting industries, while the driving effect in heavily polluting industries is poor. Endowment structure and property right structure play a positive role in improving GTFP, but the impacts of capital structure and energy structure on GTFP are poor.

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Section: Model and Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

et al. 2018

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“…(2) Negative industrial environmental output reflects the environmental performance of industrial development. Industrial pollution is the basic index indicating the impact of industry on the environment, and industrial pollution is mainly reflected in three aspects: industrial waste gas output, industrial wastewater output and industrial solid waste output, i.e., undesirable output in a typical sense [28]. On this basis, seven indices, such as CO 2 emissions per unit of industrial added value, are established to specify the industrial pollution discharge.…”

Section: Index Selection and Explanation Of Evaluation Systemmentioning

confidence: 99%

Measuring the Level of Industrial Green Development and Exploring Its Influencing Factors: Empirical Evidence from China’s 30 Provinces

2016

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Nowadays, with the green economy becoming mainstream in the world, an industrial revolution as the core of green development has emerged. Based on the empirical evidence from China's 30 provinces, this study establishes an evaluation index system of China's industrial green development and applies the analytic hierarchy process to determine the indices' weights and properties to measure the level of industrial green development in China's 30 provinces. Then, an empirical study is conducted to explore the relevant factors influencing China's industrial green development by using the dynamic panel data model and a panel threshold test. The results show that China's level of industrial green development has the characteristic of typical regional differentiation with a ladder-like distribution from the east and middle to the west, and the eastern region has the highest level on industrial green output, industrial green efficiency and industrial green innovation. Technological progress and innovation can stimulate industrial green development. The impact of environmental regulation and foreign investment on industrial green development presents a nonlinear "N"-type trend, and the positive effects are mainly observed in eastern China. Capital deepening, heavy chemical industries and an unreasonable energy structure are not effective in industrial green development. There is no direct relation between the enterprise scale and industrial green development.

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“…Most of them concentrated in the regions of China (Watanabe and Tanaka, 2007;Bian and Yang, 2010;Guo et al, 2011;Shi et al, 2010;Wang et al, 2012). In addition Mandal and Madheswaran (2010) measured regional environmental efficiency for 20 Indian states in terms of cement production, whereas Macpherson et al (2010) In our analysis we are using regional data collected from two different regional databases (EUROSTAT 9 and OECD 10 ) for the year 2007.…”

Section: Data and Variablesmentioning

confidence: 99%

A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions

Halkos

Tzeremes

2013

European Journal of Operational Research

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Efficiency analysis of Chinese industry: A directional distance function approach

Cited by 234 publications

References 21 publications

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

Measuring the Level of Industrial Green Development and Exploring Its Influencing Factors: Empirical Evidence from China’s 30 Provinces

A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions

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