Measurement and evaluation of Chinese regional energy efficiency based on provincial panel data

Zou, Gaofeng; Chen, Longmei; Liu, Wei; Hong, Xiaoxin; Zhang, Guijun; Zhang, Ziyi

doi:10.1016/j.mcm.2012.09.020

Cited by 35 publications

(10 citation statements)

References 25 publications

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“…The non-negativity of z j t implies that the production technology exhibits constant returns to scale (Färe and Grosskopf, 1996). It is worth noting that Model (2) eliminates province k to construct the production frontier when evaluating it, meaning the super-efficiency DEA model is used to calculate the directional distance functions (DDF), so the results of Model (2) will be fully comparable across observations (Zou et al, 2013).…”

Section: Technology Progress: An Improved Malmquist-luenberger Index Approachmentioning

confidence: 99%

Heterogeneity analysis of the effects of technology progress on carbon intensity in China

Lin

2016

International Journal of Climate Change Strategies and Management

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Purpose – The purpose of this paper is to investigate the effects of technology progress on carbon intensity in China. Abatement of carbon emission has become one of the most important targets for the Chinese government. Numerous studies confirm that technology progress is the main factor responsible for reduction in CI. However, very few studies analyze the impacts of technology progress on CI for various regions. There is also inadequate knowledge on the transmission mechanisms of the impacts. These are the motivations for this research. Design/methodology/approach – Given energy consumption and CO2 emissions, an improved MLPI, which stands for the generalized technology progress related to energy and environment, is introduced and decomposed into technical and efficiency changes. Using a panel data of 30 provinces from 1997 to 2012, the authors construct different panel data models to investigate the effects of technology progress (and its decomposition elements) on CI. Findings – Results show that technology progress is conducive for reducing CI, with the main factor being technical change. It also finds that the two components of technology progress have completely different effects in the three regions of China. Dynamic panel data models with threshold effects indicate that capital deepening enforces and weakens the negative effects of technical change and efficiency change on CI. Originality/value – The above conclusions provide new evidences for policy-makers with respect to capital deepening, technical innovation and allocative efficiency enhancement with a view to achieving CI reduction.

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Section: Technology Progress: An Improved Malmquist-luenberger Index Approachmentioning

confidence: 99%

Heterogeneity analysis of the effects of technology progress on carbon intensity in China

Lin

2016

International Journal of Climate Change Strategies and Management

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“…Data-driven methods, such as principle component analysis (PCA), statistical frontier models were used to establish an energy efficiency evaluation model. An undesirable output case can be processed by the energy efficiency evaluation models [10], [24]- [26]. In [27], machine learning methods and extracted information from big raw data are used for modeling a system to improve the energy efficiency management.…”

Section: Introductionmentioning

confidence: 99%

Provincial Energy Efficiency Prediction in China Based on Classification Method

et al. 2019

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Improving the development and utilization of renewable energy is one of the critical goals in the green industrial revolution of China. The energy efficiency measurement and the detection of influence factors are the very important issues to accomplish this task. In this paper, feature recognition is applied to determine the factors that affect the energy efficiency. Based on the feature recognition algorithm, data mining methods are used to derive knowledge from a dataset of 24 provinces or cities in China. In the process of data mining, the following three problems are addressed: 1) the optimal feature subset is selected from the original feature set that affects the energy efficiency; 2) the energy efficiency based on the optimal feature subset is evaluated; and 3) the energy efficiency in China is predicted by seven good-fitted classification models, whose accuracy rates are higher than 90%. Combining the results of feature selection and energy efficiency prediction, the strategy and policy to improve the energy efficiency in China is suggested. Green energy policies and prediction institutions of energy efficiency are really necessary in each province to improve their energy consumption structures, especially in coordinating the development between the traditional energy and renewable energy.

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“…For example, Fan and Zhou (2012) referring to Hu and Wang's (2006) research ideas, calculated total-factor energy efficiency in various regions of China based on DEA method while considering environmental pollution as undesirable output. Zou et al (2013) measured and evaluated regional energy efficiency in China based on provincial panel data using DEA method. Some of these studies are basically only consider the expected output as a single output indicator such as GDP, some consider the external environment, such as environmental pollution will be considered undesirable output to study of Chinese provincial energy efficiency, but no papers studies the relationship of between natural disasters total-factor energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Total-Factor Energy Efficiency of Regions in China Considering Natural Disaster Effects

Liu¹

2017

Appl Ecol Env Res

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Abstract. This paper analyzed total-factor energy efficiency (TFEE) of 30 regions in China during the period of 2004-2010, considering natural disasters effects. Based on the input-oriented data envelopment analysis (DEA), the paper computes the total-factor energy efficiency of 30 regions in China and the relationship among three main regional area of China, the east area, the middies and the west. The paper also further explored the total-factor energy efficiency by using Malmqusit index to find out the main factor of it and proposed improvement advices based on the analysis. The empirical results of the paper indicate that the total-factor energy efficiency of China in the period is decreasing and that of different regions has varies changes during the period; the total-factor energy efficiency of three main areas is also different with each other but has the trend of convergence. The paper also came to the conclusion that technical change is a double-edged sword, which makes less contribution to total factor energy efficiency compared to ignoring the effects. Thus, the government needs to make reasonable use of technology to minimise the negative effects and increase investment in technology science and encourage technological innovation, thus really improve the energy efficiency in China.

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Measurement and evaluation of Chinese regional energy efficiency based on provincial panel data

Cited by 35 publications

References 25 publications

Heterogeneity analysis of the effects of technology progress on carbon intensity in China

Heterogeneity analysis of the effects of technology progress on carbon intensity in China

Provincial Energy Efficiency Prediction in China Based on Classification Method

Total-Factor Energy Efficiency of Regions in China Considering Natural Disaster Effects

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