The impact of industrial structure efficiency on provincial industrial energy efficiency in China

Xiong, Siqin; Ma, Xiaoming; Ji, Junping

doi:10.1016/j.jclepro.2019.01.095

Cited by 149 publications

(50 citation statements)

References 65 publications

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“…Most of these studies, however, either showed a correlation between policy and energy efficiency or derived the results based on models that are dependent on their assumptions. For example, the Tobit regression conducted by Xiong et al [16] shows a correlation and does not reveal the causation between policy and energy efficiency. In fact, relatively few studies have used appropriate empirical strategies to determine policy impact on energy efficiency.…”

Section: Introductionmentioning

confidence: 96%

“…Cox et al [15] provided a review of non-energy-related policies. Among these studies, Xiong et al [16] claim that a policy to restructure the industrial organization would have a large positive impact on provincial industrial energy efficiency in China. They used a slacks-based measure (SBM) that is a sophisticated variation of data-envelopment analysis (DEA), or a linear programing approach, where they allowed the existence of undesirable outputs to address the environmental burden due to inefficiency.…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of a Power Factor Correction Policy in Improving the Energy Efficiency of Large-Scale Electricity Users in Ghana

Lotsu¹,

Yoshida

Fukuda

et al. 2019

Energies

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Confronting an energy crisis, the government of Ghana enacted a power factor correction policy in 1995. The policy imposes a penalty on large-scale electricity users, namely, special load tariff (SLT) customers of the Electricity Company of Ghana (ECG), whose power factor is below 90%. This paper investigates the impact of this policy on these firms’ power factor improvement by using panel data from 183 SLT customers from 1994 to 1997 and from 2012. To avoid potential endogeneity, this paper adopts a regression discontinuity design (RDD) with the power factor of the firms in the previous year as a running variable, with its cutoff set at the penalty threshold. The result shows that these large-scale electricity users who face the penalty because their power factor falls just short of the threshold are more likely to improve their power factor in the subsequent year, implying that the power factor correction policy implemented by Ghana’s government is effective.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Effectiveness of a Power Factor Correction Policy in Improving the Energy Efficiency of Large-Scale Electricity Users in Ghana

Lotsu¹,

Yoshida

Fukuda

et al. 2019

Energies

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“…This is because the central region has historically been a gathering area for heavy industry. The accelerated development of industry makes it difficult to reduce the energy consumption of these high-energy-consuming industries, while the impact of the tertiary industry on energy efficiency is less important [31].…”

Section: Tobit Regression Results and Analysismentioning

confidence: 99%

Research on China’s Regional Energy Efficiency Evaluation and Influencing Factors Based on the DEA-Tobit Model

Liang¹,

Liu²,

Wang³

et al. 2020

Pol. J. Environ. Stud.

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Our paper uses the ultra-efficient data envelopment analysis and Tobit model to evaluate the energy efficiency and regional differences of 30 provinces in China during 2006-2018. Based on this, the factors affecting energy efficiency in various regions of China are analyzed. The conclusions are as follows: From 2006 to 2018, China's energy efficiency has generally improved, indicating that energy efficiency is getting more and more attention, but the efficiency level is still relatively low. There is still much room for improvement, and there is a gap between regions. Increase the difference. From the regional perspective, the areas with high energy efficiency from high to low are the eastern, central and western regions. Analysis of the factors affecting energy efficiency shows that industrial structure, marketization level, economic development level, foreign direct investment, technological progress and energy prices all have an impact on energy efficiency, but for different regions the degree of impact is different, and the energy in the eastern region Efficiency is most affected by technological progress and marketization. The main factor affecting energy efficiency in the central region is the energy price level, while the factors affecting energy efficiency in the western region are mainly the level of industrial structure.

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“…The industrial production consumes a lot of fossil energy and emits pollutants for the eastern area. The eastern region transferred some industrial enterprises with high pollution and energy consumption to neighboring areas [72]. In this case, the increased industrialization level in the surrounding areas has a positive effect on local EE in eastern area.…”

Section: Analysis Of Condition β-Convergencementioning

confidence: 93%

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

Tao

et al. 2020

Sustainability

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This study uses the undesirable output and super-efficiency slacks-based measure combined with window (WIN-US-SBM) data envelopment analysis (DEA) to evaluate the environmental efficiency (EE) in 30 Chinese provinces, from 2005 to 2016, explores regional differences in the EE, and uses the dynamic spatial Durbin model (DSDM) to analyze regional differences in effects of important factors on the convergence of EE. It reveals that EE in the eastern area is higher than EE in the central and western areas, and a positive spatial autocorrelation exists in the interregional EE. The difference in provincial EE gradually narrows over time and tends to converge to its own steady-state level. Economic growth reduces EE for the central and western areas and improves efficiency for the eastern area; economic growth from surrounding areas indirectly promotes local EE for the eastern area. Foreign direct investment (FDI) promotes EE in the eastern and central areas, and FDI in the adjacent areas has a positive effect on local EE for the eastern area. Export reduces EE for all areas, and export in surrounding areas indirectly promotes local EE for the central area. Industrialization reduces EE in the western area, and industrialization in the surrounding areas increases local EE for the eastern area. Energy efficiency promotes EE for the central area, urbanization increases EE for the central area, and urbanization of the surrounding areas reduces local EE for the eastern area.

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The impact of industrial structure efficiency on provincial industrial energy efficiency in China

Cited by 149 publications

References 65 publications

Effectiveness of a Power Factor Correction Policy in Improving the Energy Efficiency of Large-Scale Electricity Users in Ghana

Effectiveness of a Power Factor Correction Policy in Improving the Energy Efficiency of Large-Scale Electricity Users in Ghana

Research on China’s Regional Energy Efficiency Evaluation and Influencing Factors Based on the DEA-Tobit Model

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

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