Total Factor Energy Efficiency In China Research Articles

Measurement and influencing factors of TFEE, and potentials of energy conservation and emission reduction in china based on GW-SBM and bootstrap model

With the aggravation of energy shortage and ecological deterioration, it becomes more and more essential for China to improve energy efficiency and realize the targets of energy conservation and emission reduction. This paper focuses on the measurement of the total factor energy efficiency (TFEE), the potentials of energy conservation and emission reduction, and the influencing factors of TFEE in China. Firstly, the model of Gray Weight Slack-Based Measure (GW-SBM) is established, with the weights of the indicators obtained via the grey relational analysis method, to calculate the TFEE of thirty provinces and six regions in China from 2009 to 2019. Then, the potentials of energy conservation and emission reduction are analyzed on the basis of TFEE measurements. Finally, the influencing factors of TFEE are identified via the truncated bootstrap model. The findings show that the TFEE values of 30 provinces fluctuate in the range of 0.20 and 1.0, varying significantly among provinces. Energy conservation potential reached 8.7490 million tons of standard coal (potential 39.06%), which means that more than one-third of energy consumption is inefficient. The top two factors in the TFEE measurement identified by the truncated bootstrap model, are energy consumption structure and environmental protection expenditure. Based on the research conclusions, this paper puts forward some countermeasures and suggestions.

Can China's regional carbon market pilots improve power plants' energy efficiency?

Power plants are facing huge climate change and policy risks. This paper identifies the effect of China's carbon emissions trading system (ETS) on plants' energy efficiency. For this purpose, we use a two-step approach. In the first stage, we apply a Meta-frontier Stochastic Frontier Analysis (MSFA) method to estimate the total-factor energy efficiency of China's large coal power plants. In the second stage, we use a bootstrapped truncated difference-in-differences (BT-DID) estimator to investigate the ETS' impact on power plants. Results show that the ETS trading policy significantly improves participating plants' energy efficiency by 0.043, compared to non-ETS plants, while the announcement policy doesn't. Besides, several other methods are introduced to solve the potential autocorrelation and heteroskedasticity problems in the second-stage regression. Further, we unveil that the trading policy improves plants' energy efficiency by reducing coal consumption without affecting power generation. Finally, the heterogeneity analysis proves that China Southern Power Grid, local plants, and plants with high carbon prices benefit more from ETS.

Total-Factor Energy Efficiency and Its Driving Factors in China’s Agricultural Sector: An Empirical Analysis of the Regional Differences

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc.

Exploring the Role of Educational Human Capital and Green Finance in Total-Factor Energy Efficiency in the Context of Sustainable Development

The problem of lower total-factor energy efficiency (TFEE) has become a bottleneck for economic growth, and how to break this bottleneck and achieve high-quality development is one of the urgent issues to be solved nowadays. The study selects 30 provincial units in mainland China during 13 years, from 2008 to 2020; then adopts slack-based measure (SBM) method to measure the TFEE values of each province; and on this basis, finally explores the impact of educational human capital and green finance on regional TFEE in China; It concludes as follows: (1) The average value of TFEE in China is 0.776, which is at a lower level, and TFEE shows a gradual increase during the study period; the mean value decreases from east to west in descending order. (2) Educational human capital’s impact on the TFEE of the whole country and all regions is negative, and it does not show a significant U-shaped relationship; the effect of eastern region is the smallest; green finance’s impact on TFEE shows a U-shaped relationship, except in eastern regions, where it is not significant; and the coefficient of the central region is stronger. (3) Environmental regulation’s impact on TFEE show a U-shaped relationship in all regions; science and technology investment can improve TFEE all regions; and in the eastern region, it is most significant. Industrial structure is positively correlated with TFEE in all regions, and it has the most obvious effect on the improvement of TFEE in the central region; economic development can promote TFEE in all regions. This research has important theoretical implications for achieving regional TFEE improvement.

Can new urbanization improve urban total-factor energy efficiency in China?

Considering the growing inconsistencies between urban green development and energy consumption, studying the influence of new urbanization (NU) on urban total-factor energy efficiency (TFEE) is essential for new urbanization and sustainable urban development from both a theoretical and practical standpoint. Selecting the 285 Chinese cities panel data from 2005 to 2020, DID model is adopted to examine the effect of the NU on urban TFEE and its transmission mechanism in this article. Results reveal the NU can greatly enhance urban TFEE. Following a battery of robustness tests, this conclusion is valid. In addition, the heterogeneity results indicate that, in the later stages of urbanization, non-resource cities and large cities, the promotion is particularly pronounced. Further examination of its conduction pathway demonstrates that NU increases urban TFEE through an innovation effect, an industry effect, a structural effect, and a regulation effect.

Urban broadband infrastructure and green total-factor energy efficiency in China

Using a quasi-natural experiment and city-level panel data from 2009 to 2018 in China, this paper investigates the impact of broadband infrastructure on green total-factor energy efficiency. Empirical results show that Broadband China Strategy has improved the green total-factor energy efficiency by an average of 2.61%, and the improvement effect is only found in central and western as well as small and medium-sized cities. Broadband infrastructure can promote trade openness and improve green total-factor energy efficiency in cities with higher initial trade costs and those with lower initial trade openness. Besides, broadband infrastructure promotes green total-factor energy efficiency through technology diffusion rather than innovation.

Non-linear effects of outward foreign direct investment on total factor energy efficiency in China

China is experiencing rapid economic development where outward foreign direct investment (OFDI) ranked third globally in 2017. OFDI could affect energy efficiency in China through reverse technology spillover. However, few studies have studied the impact of OFDI on total factor energy efficiency (TFEE), notably on the non-linear relationships and potential threshold level of the variables that would impact non-linearity. A panel smooth transition regression (PSTR) model was developed to analyse the non-linear impact of OFDI (explaining variable) on TFEE (explained variable). The data from 30 provinces, municipalities and autonomous regions (except Tibet) in China from 2006 to 2017 were considered. Industrial structure, opening-up level and human capital were selected as the transition variables to analyse potential threshold levels on how OFDI would enhance TFEE. The PSTR modelling results show that increasing OFDI in China can improve TFEE where the relationship is non-linear. The thresholds for opening-up level, industrial structure and human capital are 36.11%, 37.59% and 8.33%. With the change of the industrial structure, opening-up level and the human capital, the effect of OFDI on TFEE transited slowly and smoothly between high and low regimes. The opening-up level in Fujian, Zhejiang, Tianjin, Jiangsu, Guangdong, Beijing, and Shanghai is above the threshold level. Most regions show the industrial structure and human capital levels above the threshold. This study shows the importance of developing a non-linear model to accurately evaluating the impact of OFDI in enhancing TFEE in China. The model enables policymakers to assess the thresholds of the transition variables to observe the positive effect of OFDI on TFEE.

Effects of diversified openness channels on the total-factor energy efficiency in China's manufacturing sub-sectors: Evidence from trade and FDI spillovers

Abstract Unparalleled, wide-spread, innovative, even intrusive are all words regularly used to describe the tremendous growth that has seen China has a well-established manufacturing system. It happened so quickly that it is often easy to lose sight of the factors that led to this success and, moreover, the costs that may have been paid by the environment. The burgeoning “Made in China 2025” strategy is firmly based on the premise of modernizing manufacturing and becoming a mode for environmentally-friendly practice. But these are goals that cannot reasonably be separated from the need to improve total factor energy efficiency (TFEE) across the manufacturing. Therefore, we investigated the impact of foreign direct investment (FDI) and trade on the TFEE of China's manufacturing at the sub-sector level. Specifically, we assessed capital stocks with a heterogeneous non-fixed depreciation method and used multi stochastic frontier analysis (SFA) models with linear-form inefficient variables to measure credited TFEE in 26 sectors of manufacturing from 2000 to 2016. To deepen our analysis, we also conducted an overall and period-by-period analysis with a modified STRIPAT model and feasible generalized least squares (FGLS) estimation. Our analysis shows an average TFEE of 0.7471 for China's manufacturing, but with several significant differences between the 26 sectors. Trade and FDI spillovers showed an overall increase across the period, with the highest export elasticity at 0.0607%. High-tech industries were generally quite efficient, while the elasticity coefficient of 0.0276% shows that increasing imports is an effective method of improving TFEE for energy-intensive sub-sectors. The regression analysis by periods reveals that the positive effect of imports gradually improved over time but, of the three FDI spillover effects modeled, only backward spillovers had a positive impact.

Industrial structure, technological innovation, and total-factor energy efficiency in China.

The promotion of industrial restructuring and technological innovation is the most important and realistic way of improving energy efficiency. This thesis uses the modified Super-SBM method to measure China's total-factor energy efficiency and then uses the dynamic spatial panel model (DSPM) to verify the effect of industrial structure and technological innovation on total-factor energy efficiency. The study found that from 2003 to 2016, China's total-factor energy efficiency showed a fluctuating trend of "falling first and then rising." The inflection point appeared in 2012; total-factor energy efficiency in the Eastern region was significantly higher than the national average, while in the Central and Western regions, it was significantly lower. The results of the analysis show that both the service adjustment of the inter-industry structure and the productivity growth of the intra-industry structure significantly promote improvements in total-factor energy efficiency. However, due to the low conversion rate of scientific and technological achievements in China, the impact of technological innovation input on total-factor energy efficiency is not significant. This is in contradistinction to technological innovation output which does significantly improve total-factor energy efficiency. The above research conclusion is still robust and reliable after changing the measurement method and spatial weight matrix.

Effectiveness-based innovation or efficiency-based innovation? Trade-off and antecedents under the goal of ecological total-factor energy efficiency in China.

Pursuing innovation effect or efficiency is an important trade-off that Chinese local governments need to face in the process of developing economy and protecting the environment. From the perspective of the policy portfolio, we employ the industrial panel data of 30 provinces in China during 2000-2015 to analyze the impacts of effectiveness-based innovation and efficiency-based innovation on ecological total-factor energy efficiency (ETFEE), and further analyze the effects of command-and-control, market-based and voluntary environmental regulations on innovation. The findings reveal that (1) both effectiveness-based innovation and efficiency-based innovation have significant promoting effects on ETFEE. (2) Three types of environmental regulations have significantly inhibitory effects on effectiveness-based innovation and efficiency-based innovation. (3) The interaction term of command-and-control and market-based regulations plays a significant role in promoting effectiveness-based innovation and efficiency-based innovation, whereas the interaction term of market-based and voluntary regulations merely promotes efficiency-based innovation. The interaction term of three types of regulation only has a synergetic and positive effect on the efficiency-based innovation. Finally, this paper gives specific policy recommendations.

Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials

This paper investigates total-factor energy efficiency and analyses the trends of the efficiency changes in China’s agricultural production across 30 provinces and three regions from 2001 to 2011, based on data envelopment analysis (DEA) approach. The potential amount of energy savings and five potential factors for energy efficiency improvement are also empirically studied by Tobit regression model. The findings show that (1) total-factor energy efficiency in China’s agricultural sector is increasing over years but performs heterogeneously across regions; (2) agriculture intensive regions and energy abundant provinces tend to be relatively energy inefficient in agricultural production; and (3) economic structure, agricultural production structure, technological progress and income effect are major potentials for improving energy efficiency, whereas energy price is not a significant factor. This phenomenon results from the divergence of economic development, endowment effects as well as the scale of agricultural production. Policy implications drawn from this research are to upgrade industrial structure and promote agricultural transformation to enhance farmers’ income as well as to establish a land market with entitling land property rights to farmers. This conclusion can assist to form more scientific rural energy policy decision-making in China and also can be extended to other developing economies for sustainable agriculture.

Analysis of Agriculture Total-Factor Energy Efficiency in China Based on DEA and Malmquist indices

With the panel data of 30 administrative regions in China, this paper first used mechanical power, thermal energy, chemistry, biodynamic energy as input index, gross output value of agriculture, forestry, animal husbandry and fishery value as output index, built the DEA model to analyze the agricultural total factor energy efficiency(TFEE) of each province from 1997 to 2014. By using the Malmquist index, the total factor productivity(TFP) is decomposed into the technology progress change(TPCH) and the technology efficiency change(TECH) from 1997 to 2014. The results show that the average agricultural total factor energy efficiency level in the eastern region is the highest, followed by the central region, the western region is the lowest. China’s agricultural TFP has the slow growth trend in the fluctuations, which was powered by TECH.

Ecological total-factor energy efficiency of China’s energy intensive industries

This paper uses a meta-frontier slack-based DEA model to measure the ecological total-factor energy efficiency as well as the energy conservation potential of China’s four energy intensive subsectors. We incorporate both desirable and undesirable output together in the period, 2000–2013. The conclusions are: firstly, under the meta-frontier, the four subsectors of energy intensive industries have low average level of ecological total-factor energy efficiencies. They are 0.137, 0.212, 0.238, and 0.307 in the non-metallic mineral products manufacturing industry, raw chemical materials and chemical products manufacturing industry, smelting and pressing of ferrous metals industry, and smelting and pressing of non-ferrous metals industry, respectively. Secondly, the ecological energy efficiency in East China is the highest among three regions. Central China and West China are behind, but they are extremely close to each other. Thirdly, East China almost has no technology gap pertaining to energy efficiency, while Central China and West China almost have the same gap. Finally, Sichuan is considered to be the best province in West China under group frontier due to its perform in the three energy intensive subsectors. For Central and East China, no province has higher ecological energy efficiency in more than two energy intensive subsectors.

Total-factor energy efficiency in China’s sugar manufacturing industry

Purpose – The purpose of this paper is to evaluate the total-factor energy efficiency (TFEE) in China’s sugar manufacturing industry using firm-level data from 2002/2003 to 2012/2013 crushing seasons, and further explore the determinants of TFEE. Design/methodology/approach – Modified data envelopment analysis is used to measure the TFEE of each sugar mill during the crushing seasons. Then heteroskedastic fractional probit model is applied to estimate the determinants of TFEE because of the bounded nature of TFEE and heteroskedasticity of unbalanced panel. Findings – The results show that throughout the crushing seasons, the average TFEE is 0.57; there are spatial differences of TFEE in Guangxi sugar industry, highest in southern area; the TFEE of foreign-owned sugar mills is larger than that of private-owned and state-owned sugar mills; the larger the enterprise size, the higher the TFEE; private ownership, large size, raw material, safe productivity, total recovery rate as well as technical progress can improve TFEE significantly. Originality/value – This paper analyzes TFEE using a rich data set at firm level, allowing the existence of firm heterogeneity, as well as being complementary to the study of energy efficiency in China’s sugar industry. Moreover, ownership structure is involved in the determinants of TFEE, which is rarely done in literature. Lastly, heteroskedastic fractional probit model is employed to recognize the bounded nature of TFEE as well as selection bias of unbalanced panel to study the determinants of TFEE.

Total-factor energy efficiency of regions in China

This paper analyzes energy efficiencies of 29 administrative regions in China for the period 1995–2002 with a newly introduced index. Most existing studies of regional productivity and efficiency neglect energy inputs. We use the data envelopment analysis (DEA) to find the target energy input of each region in China at each particular year. The index of total-factor energy efficiency (TFEE) then divides the target energy input by the actual energy input. In our DEA model, labor, capital stock, energy consumption, and total sown area of farm crops used as a proxy of biomass energy are the four inputs and real GDP is the single output. The conventional energy productivity ratio regarded as a partial-factor energy efficiency index is computed for comparison in contrast to TFEE; our index is found fitting better to the real case. According to the TFEE index rankings, the central area of China has the worst energy efficiency and its total adjustmentof energy consumption amount is over half of China's total. Regional TFEE in China generally improved during the research period except for the western area. A U-shape relation between the area's TFEE and per capita income in the areas of China is found, confirming the scenario that energy efficiency eventually improves with economic growth.