Effects of technological innovation on energy efficiency in China: Evidence from dynamic panel of 284 cities

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.

This article considers energy, labor force and capital stock as the input variables that are commonly adopted by most of scholars. In order to demonstrate how the low-carbon economy affects energy efficiency in China, we use the so called low-carbon GDP as the output variable, which has never been discussed before and is different from the Green-GDP appearing in references. Based on the low-carbon GDP, we build a completely new input-output framework by using the data envelopment analysis (DEA) approach to explore total-factor energy efficiency (TFEE) in 30 provinces and cities of China during 1998–2016. The Malmquist index is applied to analyze the variation tendency of TFEE. Selecting the cross section data in 2016 for empirical comparative analysis, the result shows that TFEE varies greatly from region to region. The TFEE is highest in the eastern region, followed by the central region and lowest in the western region. Especially, TFEEs in Shanghai, Fujian and Beijing obtain the optimal efficiencies. The Malmquist index of most regions is larger than 1, indicating that the productivity has increased but there may be the rebound effect. Besides, most of the provinces and cities in China show a downward tendency in technological progress.

It is assumed that technological progress plays a vital role in energy efficiency improvements when the effects of industrial restructuring, infrastructure, environmental challenges, and economic shocks seem more dubious. However, a limited number of studies have been conducted to examine the impact of technological innovation on countries’ energy efficiency levels. This study aims to explore the relationship between energy efficiency, technological innovation, and economic growth in 30 European countries by utilizing data from 2012 to 2020. To this end, a two-stage analysis is carried out. The first step involves estimating the total factor energy efficiency (TFEE) by the countries to illustrate the effects of energy parameters on economic growth and the environment, and technological innovation (TI) to estimate the innovation capability of each country by using data envelopment analysis (DEA) methodology. The second step includes a panel regression model to explore how technological innovation affects energy efficiency, considering the degree of government intervention, industrial structure, infrastructure, and economic openness.The results indicate that the bottom-15 countries, whose TFEE scores were the lowest, are mainly countries of Central and Eastern Europe. Regarding the countries’ technological capability, the results were similar, but the score was lower than the TFEE. Moreover, the regression analysis shows that a one percent increase in innovation activity contributes to an increase in energy efficiency by 0.27 percent. Hence, it confirms the notion of a positive impact of new technology on energy efficiency. AcknowledgmentsThe study is supported by the grant from the Research Based Innovation “SFI Marine Operation in Virtual Environment (SFI-MOVE)” (Project No. 237929) in Norway.

Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces

How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China

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.

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.

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.

Under the assumption of “technology will not be forgotten,” this study estimates and decomposes the total-factor energy efficiency (TFEE) using the sequential data envelopment analysis-Malmquist productivity index and directional distance functions that consider undesirable output based on the provincial panel data of China from 2001 to 2013. On this basis, we make an empirical study of the relationship between foreign direct investment and energy efficiency with the dynamic panel model. The result shows that over the sample period, on the national level, the trend of the TFEE was upward, but the growth rate showed a downward trend. On the regional level, the TFEE in the eastern region was higher than that in the central and western regions. In addition, foreign direct investment enhanced the energy efficiency significantly, which demonstrated that the “pollution halo” effect was greater than the “pollution haven” effect. It is indicated that technical progress was the main cause of the increase in the TFEE, but technical efficiency played the opposite role. This conclusion remains valid even if the TFEE indicator is changed into the single-factor energy efficiency indicator.

Can digital technology innovation promote total factor energy efficiency? Firm-level evidence from China

Enhancing Total Factor Energy Efficiency (TFEE) is pivotal for achieving China’s “dual carbon” goals and navigating the global challenge of sustainable development. The Digital Economy (DE) serves as a significant driver of TFEE improvement. However, China’s rapid industrialization has exacerbated energy insecurity and environmental degradation, highlighting the need to explore how the DE can address these challenges through biased technological progress. Building on panel data from 282 prefecture-level cities in China (2011–2022), this study employs the theory of biased technological progress to empirically examine the impact of the DE on TFEE from dual perspectives: skill-biased versus task-biased technological change. The findings reveal that the DE significantly enhances TFEE, a conclusion robust to rigorous testing and endogeneity controls; the DE primarily promotes TFEE through facilitating human capital and industrial transformation; the positive effect of the DE on TFEE is particularly pronounced in eastern and western regions, as well as in areas exhibiting moderate energy dependence; and the DE not only elevates local TFEE but also generates positive spatial spillover effects that significantly improve TFEE in neighboring regions. This study provides a framework for leveraging digitalization to enhance TFEE, with implications for policy design in developing countries pursuing sustainable transitions.

The digital economy (DE) is characterized by invention, low energy consumption, cross-sector integration, and open sharing. It can effectively enhance social production methods, influence consumer behavior, and provide new pathways to enhance total factor energy efficiency (TFEE). This paper studies 280 Chinese cities, employing the entropy method and data envelopment analysis (DEA) model to evaluate and analyze urban DE and TFEE. It also constructs a system generalized method of moments model (SGMM model) and a threshold regression model (TR model) to examine the impact of the DE on TFEE in China. The main study findings include the following: (1) The regression results of the SGMM model indicate that the effect of DE on TFEE in Chinese cities shows a U-shaped trend. (2) The regression results of the TR model further confirm a U-shaped association connecting DE and TFEE, with the threshold estimated at 0.304. (3) The economic factors and industrial structure have a major impact on inhibiting the improvement of TFEE, whereas technological advancements and environmental regulations significantly facilitate its improvement.

A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions

Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.

Global agreements have emerged in order to achieve carbon neutrality as the needs for renewable energy sources and carbon reduction continue to grow. To achieve the global carbon neutrality goals, China, one of the largest carbon emitters, must improve urban energy efficiency. Using a three-stage slacks-based measure (SBM) technique, this study analyses the total factor energy efficiency of 270 prefecture-level cities from 2011 through 2020. It tries to monitor and evaluate energy efficiency without taking into account variations in the outside environment. China's total factor energy efficiency shows an overall rising trend, despite significant regional variations. Improved energy efficiency levels can be attributed to technical advancements, while limitations in technology contribute to lower energy efficiency in certain areas. The study highlights the importance of considering external environmental factors in evaluating energy efficiency, able to avoid an overestimation of China's overall energy efficiency. It is noteworthy that the eastern region consistently outperforms the national average in terms of energy use efficiency. Even after taking environmental factors out of the equation, the central, west, and northeast regions still have worse energy efficiency and slower rates of growth. On this basis, from strengthening energy management and planning; facilitating cross-regional sharing of expertise; take targeted policy measures to adapt to the characteristics of the western and northeastern regions and put forward policy suggestions. These guidelines contribute to international energy cooperation and carbon reduction initiatives, while promoting sustainable energy development in China.