Total-factor Energy Efficiency Research Articles

Study on Total Factor Energy Efficiency and Energy Rebound Effect in BTH Urban Agglomeration

Study on Total Factor Energy Efficiency and Energy Rebound Effect in BTH Urban Agglomeration

Energy Efficiency Measurement: A VO TFEE Approach and Its Application

Energy efficiency is crucial to the 2030 UN Sustainable Development Goals (SDGs), but its widely measured indicator, energy intensity, is still insufficient. For this reason, in 2006, total factor energy efficiency (TFEE) was proposed with capital, labor, and energy as inputs and GDP as the desirable output. The later TFEE approach further incorporated pollution as the undesirable output. However, it is problematic to regard GDP (the total value of final products) as the desirable output, because GDP does not include the intermediate consumption, which accounts for a large part of the production activities and may even be larger than the value of GDP. GDP is more suitable for measuring distribution, while VO (value of output) is more appropriate for sustainable production analysis. Therefore, we propose a VO TFEE approach that takes VO as the desirable output instead and correspondingly incorporates the other intermediate materials and services except energy into inputs. Finally, the empirical analysis of the textile industry of EU member states during 2011–2017 indicates that the VO TFEE approach is more stable and convergent in measuring energy efficiency, and is more suitable for helping policymakers achieve the SDGs of energy saving, emissions reduction, and sustainable economic development.

European Industries’ Energy Efficiency under Different Technological Regimes: The Role of CO2 Emissions, Climate, Path Dependence and Energy Mix

The assessment of industrial-level energy efficiency’s (EE) development is a critical research topic that has entrenched in the global battle against climate change. Under the Energy Efficiency Directives 2012/27/EU and 2018/2002/EU, European Commission sets specific industrial energy efficiency targets, rules and obligations for the 2020-2030 period aiming, among others, at specific energy intensity reduction and energy efficiency improvements. In this paper we use a balanced panel of fourteen European industries from twenty-seven countries for the period 1995-2011 under a metatechnology framework. The aim of this study is to evaluate, at a first stage, the industrial total factor energy efficiency (TFEE) at a national and European level by incorporating technological heterogeneity through a nonparametric approach. Reflecting the divergent views on the importance of desirable and undesirable outcomes in the pursuit of TFEE, we additionally estimate industrial performance by prioritizing either economic or environmental aspects. At the second stage of our analysis, econometric models are applied to investigate the main factors of industrial TFEE using sector specific and country characteristics while we further proceed with a ft and ^-convergence analysis for our TFEE measures. The results of this study reveal that small-scale economies exhibit persistent high TFEE scores. At the same time, TFEE determinants suggest that path dependence phenomena have a strong presence, climatic characteristics occur while energy mix displays both linear and non-linear relationship. Either considering one desirable output or consolidating the undesirable output in the production function our results indicate a strong evidence of conditional and unconditional convergence in TFEE scores.

Measurement and Analysis of Urban Total Factor Energy Efficiency in China

The improvement of urban energy efficiency is of great significance to reduce carbon emissions and achieve China’s goal of carbon peak in 2030. Taking 266 cities as the research objects, the energy efficiency of each city is measured by Super-SBM model. Then the energy efficiency is decomposed by Malmquist index. The results show that: In 2006, the number of cities in high-efficiency areas is small and high efficiency areas are mostly distributed in the northeast and southeast coastal areas. There was a phenomenon of low efficiency areas agglomeration. But in 2017, the number of high-efficiency cities increased significantly. The number of high-efficiency cities in Northeast China increased, the number of high-efficiency cities in Southeast coastal areas decreased, and the number of high-efficiency cities in Central China increased and showed a phenomenon of agglomeration of high-efficiency areas or relatively high efficiency areas. According to the decomposition of urban energy efficiency, technological progress is the main driving force for the improvement of energy efficiency, and the decline of technical efficiency inhibits the improvement of energy efficiency in various cities.

How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China

Green development is critical for China's economic transformation. Enhancing green total factor energy efficiency (hereafterGTFEE) is vital to emission reduction and to win-win industrial development. This study uses the panel data of 30 Chinese provinces for the period 2005–2016 to investigate the relationship between environmental regulation and China's GTFEE. The spatial Durbin model is employed to control for the possible spatial spillover effect. A dynamic threshold panel model that can effectively address the endogeneity problem and regional heterogeneity is utilized to examine the potential non-linear relationship between environmental regulation and GTFEE under different conditions of environmental decentralization. The estimation results indicate that there is a significant U-shaped relationship between environmental regulation and China's GTFEE. With the further expansion of environmental decentralization, the local government's autonomous choice of pollution control is improved. The improvement of environmental decentralization can lead to negative moderating effect of environmental regulation on GTFEE. Additionally, the regression results of dynamic threshold model show that environmental decentralization can increase the negative influences of environmental regulation on GTFEE. Interestingly, the non-linear impact of environmental regulation on GTFEE is dependent on the specific type of environmental decentralization. Higher degree of environmental decentralization can lead to an increase in the restraining effect of environmental regulation on GTFEE. However, an improvement in the decentralization of environmental supervision and environmental monitoring can increase the negative influences of environmental regulation on GTFEE.

Does Internet Development Improve Green Total Factor Energy Efficiency? Evidence from China

Information and communication technology supported by the internet has become an important driving force that promotes the intelligent development of environmental governance in China. Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China’s green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. The empirical results indicate that the GTFEE has a significant positive spatial correlation. Internet development can not only directly improve local GTFEE but also improve GTFEE in neighboring regions. After accounting for potential endogeneity, this conclusion is still valid. Meanwhile, internet development can indirectly improve regional GTFEE by reducing the degree of resource mismatch while enhancing GTFEE by improving regional innovation capabilities and promoting industrial structure upgrades. In addition, the regression results of the dynamic threshold model show that there is a nonlinear relationship between the influence of the internet development and GTFEE. Specifically, due to an increase in the degree of labor resource mismatch and capital resource mismatch, the impact of the internet on GTFEE has gradually decreased, and this effect has gradually increased with the improvement of regional innovation capabilities and the industrial structure.

Can energy efficiency progress reduce PM2.5 concentration in China’s cities? Evidence from 105 key environmental protection cities in China, 2004–2015

At present, PM2.5 is still one of the most serious air pollution issues in China. By empirically testing the actual inhibitory effect of China’s total factor energy efficiency progress on PM2.5, this paper can provide a new evaluation index for energy conservation in China and help Chinese cities understand the beneficial environmental effect of total factor productivity (TFP) progress under the constraint of energy consumption to point out an effective path of energy conservation and emission reduction that is highly in line with China’s current high-quality development strategy. Based on the panel data of 105 key environmental protection cities in China, this paper evaluates total factor energy efficiency with range directional model (RDM) and global Malmquist (GM) productivity index based on data envelopment analysis (DEA); it then uses spatial econometric models to test the impact of energy efficiency progress on PM2.5 concentration. The efficiency evaluation results show that the total factor energy efficiency of 105 key environmental protection cities has increased by an average of 35.9% from 2004 to 2015, with an average annual progress rate of 2.2%. Compared with Eastern and Central China, the progress of energy efficiency in the western region during 2004–2015 is relatively lagged. The average energy efficiency progress rate of higher administrative hierarchy cities and super cities with more than 10 million permanent residents is much higher than that of other cities. The results of GM index decomposition show that technological progress is the main driving force of energy efficiency progress. The spatial econometric analysis results show that there is a significant positive spatial autocorrelation in the concentration of PM2.5 in sample cities, and the energy efficiency progress can significantly inhibit the PM2.5 pollution in local and adjacent cities. The influence of each explanatory variable on PM2.5 concentration is mainly composed of the spatial spillover effect.

Total-factor energy efficiency change of the road transportation industry in China: A stochastic frontier approach

The mismatch between the supply and consumption of energy indicates that it is important to promote energy efficiency in the road transportation industry which is high in energy consumption. This paper employs a stochastic frontier approach to measure the total-factor energy efficiency (TFEE) change of the road transportation industry in China. Then, a novel two-dimensional matrix is used to illustrate the energy efficiency of the road transportation industry in China’s eight economic areas. The combination of these approaches leads to the following conclusions: (a) The annual TFEE growth rate of Chinese road transportation systems has slowed down in recent years, but the average annual TFEE growth has reached 17.34%. (b) Both technological change and scale efficiency change boost the TFEE growth, and technical efficiency change hinders the TFEE growth. (c) Almost all regional road transportation industry TFEE scores increased during the study period, but the growth rates were significantly higher in the Northern Coastal and Eastern Coastal areas than in other areas. Finally, this paper suggests increasing investment in highways to expand the operation scale, developing advanced technology for high-quality development of the industry, and carefully designing measures to improve transportation technical efficiency according to regional economic development orientations.

Energy efficiency of the mining sector in China, what are the main influence factors?

The mining industry is the basic industrial sector in China. Perfecting the energy efficiency of the mining sector is extremely important for ensuring China's energy security, emission reduction, and promoting China's economic growth. Various factors influence the mining sector's energy efficiency. Based on the provincial-level panel data in China, this paper adopts a two-way fixed-effect model to explore the effect of industrial agglomeration on the total factor energy efficiency (TFEE) of the mining sector in China. Also, the threshold panel model is used to calculate the impact of industrial agglomeration in regions with different economic development levels on the energy efficiency of the mining industry. The results indicate that increasing industrial agglomeration improves the energy efficiency of the mining sector. Furthermore, with the increase in regional economic development, the impact of industrial agglomeration on energy efficiency is gradually decreased. The government could reduce the capacity, which eliminates redundant mining capacity in eastern China, and develop high-quality mining clusters in central and western China.

Dynamic evolution and influencing factors of industrial green total factor energy efficiency in China

This paper sets up an evaluation index system (EIS) for industrial green total factor energy efficiency (IGTFEE), which contains undesired outputs. Next, the directional distance function (DDF) model was adopted to evaluate the IGTFEEs in 2003–2017 of 30 provincial administrative regions (provinces) in China. The results show that: The 30 provinces differed greatly in mean IGTFEE through the sample period. Most eastern coastal provinces had relatively high IGTFEE, while most inland provinces had undesirable IGTFEE. Eastern region had far better IGTFEE than central and western regions, while central and western regions had similar IGTFEEs. Through kernel density estimation (KDE), it is learned that the kernel density curves of IGTFEE in most years have only one peak, and moved to the left with the elapse of time, revealing the declining trend of China’s IGTFEE. The results of the Tobit model indicate that IGTFEE is greatly promoted by technological progress, opening-up, and environmental regulation, obviously suppressed by energy structure, and insignificantly promoted by human capital.

Chinese construction industry energy efficiency analysis with undesirable carbon emissions and construction waste outputs

As the construction industry generates more than 30% of global greenhouse gases and more than 40% of global urban waste every year, energy conservation and emission reduction has become extremely important. This study proposes an innovative output system that includes undesirable carbon dioxide and construction waste outputs. A three-stage DEA-Malmquist model is used to measure the energy efficiency of the construction industry in 30 Chinese provinces from 2008 to 2017, and a stochastic frontier method is used in the second stage to analyze and remove the energy efficiency influences of environmental factors and random errors. It was found that the total factor energy efficiency change (TFEECH) and technology change (TECH) in China's construction industry was underestimated because of the environmental factors and random errors. GRP per capita, energy consumption structures, industrial development degrees, and industrial concentrations were all found to play a positive role in improving energy efficiency; however, urbanization levels, technical equipment, policy support, and marketization were found to have a negative effect. Policy suggestions are given based on the empirical results.

Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities

At present, there are serious problems of excessive energy consumption and carbon emissions in Chinese manufacturing industry. To solve these problems, it is of great importance to measure and improve total factor energy efficiency reasonably. At the same time, Chinese manufacturing industry differs significantly in various sub-sectors and regions, which means the prevalence of dual heterogeneity. Such strong heterogeneities can have a significant impact on the accurate measurement of total factor energy efficiency. However, it is unfortunately neglected in existing studies. In this paper, we empirically analyze the total factor energy efficiency of 19 Chinese manufacturing sub-sectors in 9 provinces from 2001 to 2011 and further analyzes its influencing factors from the perspective of the dual heterogeneity. To model industry and regional heterogeneities, we propose a three-level meta-frontier slacks-based measure (SBM) approach based on data envelopment analysis (DEA). The results show that: (1) The energy efficiency of Chinese manufacturing industry is generally low, and varies significantly across different industries and regions. Moreover, the energy inefficiency mainly comes from management inefficiency; (2) The dual heterogeneity affects the technology gap rate (TGR) deeply. The technology gap rate caused by industry heterogeneity (ITGR) is larger than that caused by regional heterogeneity (RTGR); (3) Enterprise scale, foreign direct investment, economy development level and industrial structure affect energy efficiency positively, while energy consumption structure and energy price affect energy efficiency negatively. In response to the above research findings, some policy recommendations are put forward in the end.

Link between Energy Efficiency and Sustainable Economic and Financial Development in OECD Countries

The growing risk of climate change caused by the emission of greenhouse gases poses new challenges to contemporary countries. The development of economies is usually related to increasing levels of greenhouse gas emissions. Therefore, the question arises whether it is possible to achieve sustainable economic and financial development and simultaneously reduce greenhouse gas emissions. This paper assumes it is possible if energy efficiency is increased. The aim of the paper is to show the link between energy efficiency and sustainable economic and financial development in Organisation for Economic Co-operation and Development (OECD) countries for the period 2000–2018 by using data envelopment analysis (DEA) and regression analysis. The results show a slight upward trend of total factor energy efficiency (TFEE) in OECD countries for the analysed period; however, there is a difference in TFEE levels. Developed OECD countries have higher TFEE levels than developing OECD countries. The links between total factor energy efficiency and sustainable economic and financial development reveal different impacts depending on the variables taken into consideration.

Drivers of civil aviation carbon emission change: A two-stage efficiency-oriented decomposition approach

This study divided the whole process of airline activities into production stage and operation stage to explore the two-stage efficiencies of 15 global airlines. Then a new decomposition approach was proposed to decompose carbon emission changes into seven driving factors, including several efficiency-related factors. The empirical study resulted in three key findings. First, the overall efficiency had clear heterogeneity among different airlines and the values experienced an “up-down-up” trend, which was mainly affected by the total factor energy efficiency change. Second, the transportation load capacity (TLC) and the transportation revenue operation performance (ROP) were the dominant factors driving the increase and decrease of carbon emissions, respectively. Third, the contribution coefficient structures showed different features with different airlines. Four fast-growing airlines were identified as being the main contributors to the TLC effect, while four other well-operated airlines were the main contributors to the ROP effect.

Spatial and Temporal Evolution of Energy Efficiency in Coastal Areas of China Based on Super Efficiency DEA Model

Cai, L. and Wang, C., 2020. Spatial and temporal evolution of energy efficiency in coastal areas of China based on super efficiency DEA model. In: Wan, Z. (ed.), Coordinated Sustainable Development in Coastal Areas: Environment and Economy. Journal of Coastal Research, Special Issue No. 109, pp. 216–222. Coconut Creek (Florida), ISSN 0749-0208.This paper takes the unexpected output into the input-output index system, applies super efficiency DEA to calculate the total factor energy efficiency of 11 coastal provinces in China from 2008 to 2018. Based on the measurement and analysis of industrial structure advanced degree and industrial structure rationality degree of each province, this paper analyzes coupling coordination relationship between energy efficiency and spatial characteristics of industrial structure of the 11 provinces in China's coastal areas in 2018 with the coupling coordination degree model, and then relevant suggestions are put forward according to the calculation results. The results show that: (1) in terms of time series, the overall energy efficiency of the provinces and cities in China's coastal areas shows a slow downward trend, but the overall gap is widening. Among them, Shanghai and Guangdong have always been at a higher level of energy efficiency. (2) as for spatial differentiation, the energy efficiency of China's coastal areas is basically north-south symmetrical pattern, which to a certain extent shows that the energy utilization efficiency of each province or city is consistent with regional economic strength.

Study on ETFEE in the BTH Region Based on the Window-SBM-Undesirable Model

Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans.

The impact of China’s trade with economies participating in the Belt and Road Initiative on the ecological total factor energy efficiency of China’s logistics industry

The environmental impact of trade liberalisation is uncertain. Investigating the impact of China’s trade with economies participating in the Belt and Road Initiative (BRI-participating economies) on the green development of China’s logistics industry is of great significance. This research not only may help the dividends from BRI construction activities reach their full potential but also promote global ecological improvement. This study is the first attempt to analyse the impact of China’s trade with BRI-participating economies on China’s logistics industry’s ecological total factor energy efficiency (ETFEE) from the perspective of ecological economics, and identify its relative role in China’s trade with other regions. The research indicates that China’s logistics industry’s ETFEE exhibits high growth momentum. China’s trade with BRI-participating economies positively promotes its logistics industry’s ETFEE through ecological technological progress. This effect becomes more distinct with the advancement of BRI construction. Compared to China’s trade with the whole world and non-BRI-participating economies, the promotion effect of China’s exports to BRI-participating economies on ETFEE remains to be further released. Based on the findings, corresponding policy implications are proposed.

Measurement and Convergence Analysis of Total Factor Energy Efficiency in Yangtze River Economic Belt

Measurement and Convergence Analysis of Total Factor Energy Efficiency in Yangtze River Economic Belt

Green Total Factor Energy Efficiency in China and Its Influencing Factors

Green Total Factor Energy Efficiency in China and Its Influencing Factors

Different types of environmental regulations and heterogeneous influence on energy efficiency in the industrial sector: Evidence from Chinese provincial data

Using China's provincial panel data during 2000–2017, this study attempts to research the impacts of different environmental regulatory instruments and relative stringency on total factor energy efficiency. A Super-SBM model with undesirable outputs is used to evaluate the energy efficiency of industrial sector for thirty provinces in China. The result indicates a relatively low energy efficiency of industrial sector and significant difference between provinces in China. The dynamic panel regression was employed to examine the heterogeneous effects and mechanisms on energy efficiency by different types of environmental regulations. We have two main findings: (1) At a certain stringency level, both command-and-control and market-based environmental regulations have positive effects on total factor energy efficiency. (2) There exist nonlinear relationships between environmental regulations and total factor energy efficiency. The current regulation level of command-and-control exceeds the optimal level, while the current regulation level of market-based is reasonable. Moreover, market-based environmental regulation is more effective based on China's reality. These findings provide new evidence in confirming the Porter effect and some policy implications for China to further improve energy efficiency.