A two-stage analytical approach to assess sustainable energy efficiency

Ervural, Beyzanur Çayır; Zaim, Selim; Delen, Dursun

doi:10.1016/j.energy.2018.08.213

Cited by 54 publications

(20 citation statements)

References 76 publications

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“…Based on the existing research [ 30 , 31 , 55 , 67 , 68 ], considering the reality of China’s energy economy and the availability of data, the economic development level ( EDL ), technological progress ( TP ), energy price ( EP ), urbanization level ( UL ), industrial structure ( IS ), energy consumption structure ( EC ), government intervention ( GI ) and openness degree ( OD ) are selected as explanatory variables, as shown in Table 6 . The energy efficiency value ( EE ) calculated by BCC model is chosen as the explained variable.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

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Revisiting China’s provincial energy efficiency and its influencing factors

Liu

Zhang

Tao

et al. 2020

Energy

122

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Improving the energy efficiency is a fundamental way to ensure energy security and sustainable development, and is also the requirement of supply-side structural reform of China’s energy. This paper uses the DEA-BCC model to estimate China’s energy efficiency at the provincial level, analyzes its regional differences from 2006 to 2016, and applies a panel data model to analyze the influencing factors of energy efficiency. It selects labor, capital stock and total energy consumption as inputs and takes real GDP and comprehensive index of environmental pollution as desirable and undesirable outputs, respectively. The results show that (1) energy efficiency when undesirable output is included is generally lower than when undesirable output is excluded; (2) There is a considerable difference in energy efficiency among provinces, and China’s energy efficiency, by and large, shows a trend of declining. The energy efficiency of four major regions demonstrates obvious regional differences: coastal region>northeastern region> middle region >western region; (3) The economic development level, technological progress, energy price and urbanization level are positively associated with energy efficiency, while the proportion of secondary industry and the energy consumption structure dominated by coal and oil are negatively correlated with energy efficiency.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

“…Ervural et al. [ 30 ] used traditional CCR-DEA model to analyze the energy efficiency of Turkey. Ouyang and Yang [ 8 ] analyzed the network energy and environment efficiency of 27 OECD countries by a multiplicative network DEA model.…”

Section: Literature Reviewmentioning

confidence: 99%

Revisiting China’s provincial energy efficiency and its influencing factors

Liu

Zhang

Tao

et al. 2020

Energy

122

View full text Add to dashboard Cite

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“…Borozan 15 used DEA and Tobit models to analyze EU technology and energy efficiency from 2005 to 2013, finding that the technology and energy efficiencies in most countries varied widely and that human capital and innovative energy efficiencies had a significant impact. Cayir et al 16 used CCR DEA and Tobit regression models to analyze energy efficiency in China's provinces and found that per capita GDP, population size, and renewable energy production all affected energy efficiencies, and some provinces needed to actively invest to improve their overall technology efficiency. He et al 17 used rough set theory (RS), a fuzzy artificial neural network, and DEA models to explore the impact of energy efficiency in China's provincial industrial sectors, finding that as most provincial industrial sectors had low energy efficiencies, significant structural adjustments were needed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Energy, economic growth, air pollution, and environmental pollution have been widely studied using impact factor analysis, [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] with some studies having also examined the effects of air pollution on health, and some studies exploring the impact of energy and environmental efficiency on air pollution emissions. 9,40,[44][45][46] Therefore, the main past research directions on the effect of environmental pollution on health have been primarily from energy, economic, carbon dioxide emissions reduction, and environmental pollution perspectives, and rarely from health, economic, energy and environmental pollution perspectives.…”

Section: Introductionmentioning

confidence: 99%

The Impact of the Media and Environmental Pollution on the Economy and Health Using a Modified Meta 2-Stage EBM Malmquist Model

Chiu

Liu

et al. 2020

INQUIRY

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China’s pursuit of economic growth, rapid industrialization, and urbanization over the past few decades has resulted in high energy consumption, which in turn has caused serious environmental pollution problems, such as CO2 and PM2.5 emissions, the long-term exposure to which can seriously affect resident health. To resolve these air pollution problems, the Chinese government has put in place several policies to reduce air and environmental pollution. Past studies on energy and environmental efficiency have been mostly static, have ignored the dynamic changes over time and regional differences, and have rarely considered human health factors. Therefore, this study employed a modified meta 2-stage Epsilon-Based Measure (EBM) Malmquist model to explore the relationships between the economy, energy, the environment, health and media, and the regional differences in 31 Chinese cities from 2014 to 2016. It was found that (1) Haikou and Lhasa’s efficiencies were 1 and were the best in all 3 years, and Shijiazhuang, Jinan and Shenyang’s were the most improved; (2) there was a gap between the eastern, central and western technological frontiers, with Chengdu, Hohhot, Chongqing, and Nanchang having technological gap ratios below 0.70 in the western and central Chinese regions, and Haikou, Guangzhou, and Shanghai in eastern China having technological gap ratios above 0.90 in all 3 years; and (3) the variations in the health treatment stage were greater than in the production stage, indicating that technological changes and efficiency improvements in the health treatment stages in each city were not stable.

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“…Among them, two-stage stochastic programming (TSP) has been widely used to tackle uncertainties expressed as a probability distribution [3,[16][17][18]. For instance, Gong et al [16] proposed a two-stage programming method to optimize electric power systems considering air pollutant emissions and CO 2 mitigation.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties

et al. 2019

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Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization - energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.

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A two-stage analytical approach to assess sustainable energy efficiency

Cited by 54 publications

References 76 publications

Revisiting China’s provincial energy efficiency and its influencing factors

Revisiting China’s provincial energy efficiency and its influencing factors

The Impact of the Media and Environmental Pollution on the Economy and Health Using a Modified Meta 2-Stage EBM Malmquist Model

Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties

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