Energy Efficiency and Its Driving Factors in China’s Three Economic Regions

Shi, Sheng-An; Long, Xin; Meng, Ming

doi:10.3390/su9112059

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…However, temperature change showed a negative relationship with profit efficiency (Tables 2–4). The estimation outcomes are consistent with the studies of Fleiter et al (2012), Filippini et al (2014); and Shi et al (2017). Major economic activities in the EU are powered by fossil fuels and other non-RESs such as coal and nuclear.…”

Section: Discussionsupporting

confidence: 84%

“…It suggested that the difference between x * and x o and y * and y o pointed at the need for managerial improvement (Cooper et al , 2000). In this section, the authors evaluate how well economic variables are enhancing the profit efficiency of the bioenergy industry in the EU28 region following the models from prior studies (Martínez, 2013; Filippini et al , 2014; Filipović et al , 2015; Flues et al , 2015; Shi et al , 2017). The following equation (3) is based on a balanced panel data for observed countries EU28 ( i = 1,…, 28) during the period 1990–2013 ( t = 1,…,24): …”

Section: Methodsmentioning

confidence: 99%

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Evaluating the profit efficiency of bioenergy industry and its determinants in EU28 region

Zubair

Alsaleh

Abdul‐Rahim

2021

IJESM

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Purpose The purpose of this study is to evaluate the profit efficiency of bioenergy industry and its determinants in EU28 region roadmaps for the transition towards energy efficiency which is increasingly perceived by stakeholders, researchers and the public as a pathway to bring dependency on fossil resources to a significant reduction. Many studies overlooked the importance of profit efficiency as a factor for bioenergy industry business improvement. More so, external environmental variables can play a key role in achieving profit efficiency in the industry. Design/methodology/approach This paper seeks to answer the questions on the following: (1) the profit efficiency level using the data envelopment analysis (DEA) approach in the EU28 region during the period between 1990 and 2018; and (2) to explore the impacts of external environmental variables on the profit efficiency level using panel regression model in the EU28 region during the period between 1990 and 2018. Findings Results revealed that gross domestic product, size of biomass and investment are essential for the development of the bioenergy industry and positively influence on profit efficiency level. The increase in temperature change decreased the profit efficiency level during 1990–2018. Research limitations/implications For those profit-inefficient bioenergy industries in countries such as Cyprus and Ireland, participation in innovative programs, expanding a knowledge-based economic system and implementation of support policy for bioenergy technologies, by investing in biomass sources that are suitable for their respective renewable energy development will enhance specialization, resource efficiency and improved profitability can be expected in future. Originality/value Unlike other previous studies, this study investigated the profit efficiency by applying the DEA statistical method. Moreover, the authors have applied a second regression analysis to estimate the impacts of macroeconomic and microeconomic variables on the profit efficiency level. This study has focused on the EU28 region, including both developed and developing countries, to compare the level of profit efficiency levels in the selected sample. The authors have applied data panel analysis for the period from 1990 to 2018. No previous study has applied the methods, samples and periods as those used in this study. Therefore, this study contributes significantly to the bioenergy industry specifically and the renewable energy industry in general and to the associated extant research.

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Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Evaluating the profit efficiency of bioenergy industry and its determinants in EU28 region

Zubair

Alsaleh

Abdul‐Rahim

2021

IJESM

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“…They stated that influencing factors have various impacts on the carbon emission efficiency in the Chinese regions [53]. Some other scholars also investigated the carbon emission efficiency by using Tobit studies recently [54][55][56].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Zeng

Liu

et al. 2019

Energies

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With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

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“…When multiplying the energy input by (1-θ), we can obtain the radial adjustment, which is a measure of energy saving. Recently, a number of extended studies have been based on the DEA method (such as [24][25][26][27][28][29][30][31]).…”

Section: Energy-economic-efficiency-estimatementioning

confidence: 99%

Energy Efficiency: Indicator, Estimation, and a New Idea

et al. 2020

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Energy efficiency has long been an important issue to the global economic and political theaters; however, searching for an effective and concise measure for efficiency remains a contentious and intriguing topic. There are two obvious flaws with the commonly used metrics in existing literature. First, there is a sense of confusion and misunderstanding between the definitions of energy efficiency and efficacy. As a result, the formulae and methods for measuring efficiency are often the subject of criticism. Second, even if the definition of efficiency is clear, the method of estimation can be quite cumbersome, making it difficult to comprehend or implement. This study attempts to address these two issues. With an OECD comparative dataset, it first presents the contradiction between efficiency and efficacy, explains the loss of effectiveness with the existing measurements, and then proposes a new and easy-to-use method for gauging energy efficiency, so that the succinctness and robustness of the measurement can be re-established. The paper serves as a guide to those who are interested in the controversial issues related to measuring energy efficiency. Both practitioners and policy makers will find an easy and reliable tool from this paper for measuring energy efficiency.

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Energy Efficiency and Its Driving Factors in China’s Three Economic Regions

Cited by 11 publications

References 10 publications

Evaluating the profit efficiency of bioenergy industry and its determinants in EU28 region

Evaluating the profit efficiency of bioenergy industry and its determinants in EU28 region

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Energy Efficiency: Indicator, Estimation, and a New Idea

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