Energy Intensity In China Research Articles

Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors.

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types.

Opportunity or trap - the policy effect of China’s energy quota trading

ABSTRACT This paper assesses the policy effect of the energy quota trading (EQT) policy from the perspectives of energy consumption, economic growth, and energy intensity, based on the panel data of 30 provinces in China from 2006 to 2019. The energy intensity has been decomposed into technical effect and structural effect using the LMDI model. The triple difference method, the corresponding parallel dynamic trend test and the placebo test are employed in this study. The result suggests that the EQT policy is capable of suppressing the growth of energy consumption, and will not dampen economic growth. Moreover, the policy facilitates the decrease of energy intensity in China. The EQT policy has spurred energy technology innovation, improved energy efficiency, and exerted about innovative compensation effects. However, the initial allocation of energy quota is free, thus leading to the structural effect of enterprises converging to high-energy-consumption industries. Compared with the white certificate policy, the EQT policy has some defects in the regulatory mechanism and operating rules. It is imperative for the policymakers to formulate guiding policies when considering the policy shocks of the above low industrialization area on the rapidly developing country.

The Role of Technological Innovation and Foreign Direct Investment in Influencing Energy Intensity in China

This article examines the role of technological innovation and foreign direct investment on energy intensity in China by employing yearly data from 1990 to 2020. For this purpose, ADF test and ARDL model are utilized for data estimation. The results exhibit that technological innovation and foreign direct investment are negative and significant factors of energy intensity in China. Likewise, the interaction term of FDI and technological innovation also significantly reduces China's intensity of energy. In contrast, the variables urbanization and gross fixed capital formation are encouraging factors of energy intensity. Considering the study outcomes, it is concluded that technological innovation and FDI play an imperative role in declining intensity of energy in China. Therefore, technological innovation and FDI inflows that encourage energy-saving technology to decline the intensity of energy in China should be encouraged.

Impact of Energy Productivity and Industrial Structural Change on Energy Intensity in China: Analysis Based on Provincial Panel Data

Since 2000, China’s energy intensity has shown a fluctuating downward trend. Most existing studies have attributed the decline to technological progress, and only a few studies have recognized the role of industrial structure change. In this paper, a multi-region and multi-sector CGE (computable general equilibrium) model and a numerical simulation method are used to study how technological progress and structural transformation affected the energy intensity of 30 provincial-level regions in China from 2000 to 2019. The results show the following points. (1) The contribution of technological progress to the decline in energy intensity was the highest in the central region, followed by the western region, and was the lowest in the eastern region. (2) The progress of energy technology in the agriculture and industry sectors promoted the transition of energy consumption to the service sector, thereby reducing the overall energy intensity. This effect was the strongest in the eastern region, followed by the central region, and was the weakest in the western region. (3) The progress of energy technology in the service industry promoted the transition of energy consumption to industry and agriculture, thereby enhancing the energy intensity, and this effect was the strongest in the eastern region, followed by the western region, and the weakest in the central region. The conclusion of this paper provides a theoretical basis for realizing regional carbon peaking in sequence in China.

How does industry-university-research collaborative innovation affect energy intensity in China: a novel explanation based on political turnover

As a form of integrating knowledge resources and promoting technological innovation, industry-university-research (IUR) collaborative innovation is thought to influence energy intensity reduction. However, the boundary conditions of IUR collaborative innovation affecting energy intensity have yet to be discussed. To fill this gap, we explore the impact of IUR collaborative innovation on energy intensity and the role of political turnover in its influencing mechanism, using a panel dataset of 30 Chinese provinces from 2010 to 2018. IUR collaborative innovation inhibits energy intensity, but this effect is only significant in the eastern region. Interestingly, political turnover positively moderates the inhibitory effect of IUR collaborative innovation on energy intensity. However, this moderating effect is only significant in the central and western regions. Moreover, the robustness tests provide abundant evidence to support the above findings. Finally, some policy implications are suggested.

The effects of industrial intelligence on China's energy intensity: The role of technology absorptive capacity

This study examines the impact of industrial intelligence on energy intensity in China, with a special perspective on the role of technology absorptive capacity in the relationship. Based on panel data from 30 Chinese provinces from 2006 to 2020, the direct and moderating effect model results show that industrial intelligence reduces energy intensity, and the interaction of industrial intelligence with technology absorptive capacity (i.e., assessed by the variables of information infrastructure, human capital, and technological innovation) tends to enhance the energy intensity reduction effect of industrial intelligence. Further, the threshold effect model shows that the reduction effect is heterogenous across regions: industrial intelligence tends to reduce energy intensity more in regions with higher information infrastructure, human capital, and technological innovation levels than in those with lower levels. Comprehensive governmental strategies to improve energy efficiency should concomitantly promote the sustainable development of industrial intelligence and technology absorption capacity.

Does Off-Farm Employment Promote the Low-Carbon Energy Intensity in China’s Rural Households?

A study linking the two economic and social phenomena of rural labor force migration and energy transition can help analyze the underlying causes of rural “Energy Poverty”. However, how off-farm employment affects household low-carbon energy consumption and its potential mechanisms requires further research. Using 1351 sampled rural households from the “Rural Energy, Population Transfer and Well-being” survey in 2018 and 2021 to explore response mechanisms through which off-farm employment can influence low-carbon energy intensity. Utilizing the multivariate regression, Sobel test, and moderating effect test, the results demonstrate that off-farm employment, including short-term and long-term off-farm employment, significantly increases the intensity of low-carbon energy use among rural households. Specifically, long-term off-farm employment tends to have a greater positive contribution to the low-carbon energy intensity than short-term off-farm employment. Furthermore, off-farm employment can affect household low-carbon energy intensity through the total income, and effect of the surrounding people in the off-farm employment process also increases their consumption intensity. The research reveals that the rural energy revolution under the constraints of “Carbon Neutral” and “Carbon Peak” should relate to the off-farm development of rural households to achieve “Precise Energy Poverty Alleviation”.

Research on the impact of green finance development on energy intensity in China

China has accelerated its energy transition to help achieve carbon peaking and neutrality. This resulted in significant investment and financial demand, and green finance has developed into a potent tool to support energy transition. This study used Chinese provincial panel data from 2007 to 2019 and investigated the effects of green finance development on energy intensity based on the mediating effect model, threshold model, and spatial Durbin model. The findings indicate that green finance development significantly lowers energy intensity, in which industrial structure upgrading plays a mediating role. Based on the threshold effect, when the green finance index rises above 0.199, the effect of reducing energy intensity becomes significant and exhibits a nonlinear feature of diminishing marginal effect. By examining the spatial spillover effect, the analysis shows that the development of green finance not only reduces the energy intensity of the region but also reduces the energy intensity of neighboring regions. Based on these findings, we proposed regionally targeted and differentiated green finance development policies to reduce energy intensity as soon as possible and assist in reaching carbon neutrality.

The spatial impact of digital economy on energy intensity in China in the context of double carbon to achieve the sustainable development goals.

Using the provincial panel data of China during 2012-2019, the present study employed spatial Durbin error model to explore the spatial effect of the digital economy on energy intensity. The results show that both digital economy and energy intensity have spatial autocorrelation, showing the distribution characteristics of spatial aggregation. The digital economy has a significant negative influence on energy intensity. The result shows a significant spatial spillover effect of digital economy on energy intensity, and the development of the digital economy in neighboring regions reduces energy intensity in the central region. Additionally, industrial structure, urbanization, energy price, and foreign direct investment have a heterogenous impact on energy intensity. Thus, it is crucial to give importance to the development of the energy intensity, plan the spatial layout of the digital industry as a whole, drive the coordinated growth of the regional digital economy, quicken the upgrading of industrial structure, promote urbanization, perfect the energy price formation mechanism, raise the entry threshold for foreign direct investment, to effectively reduce the energy intensity, and facilitate the smooth realization of the "double carbon" goal.

Outward foreign direct investment and energy intensity: evidence from the listed companies in China.

Since 2005, China's outward foreign direct investment (OFDI) has increased year by year, which corresponds to the continuous decline of energy intensity. But there is limited literature concerning their relationship nowadays. To answer whether or not OFDI can reduce energy intensity, this paper selects data from 29 provinces in China from 2006 to 2015 and establishes a fixed-effects model to analyze the relationship. Further, this paper divides OFDI into technology-intensive ones and non-technology-intensive ones in order to distinguish the impact of outward foreign direct investment from different types of enterprises on the energy intensity. Combined with the micro-data of A-share listed companies in Shanghai and Shenzhen stock exchanges, we find that OFDI plays a significant role in reducing the energy intensity in China, and OFDI of high-technology-intensive enterprises has a greater effect on the decrease of energy intensity than that of low-technology-intensive enterprises. This paper classifies OFDI from the perspective of enterprise technology intensity, which enriches the existing research results in the field of international cooperation and energy intensity. It also overcomes the limitations of previous literature data and provides new evidence for encouraging high-tech enterprises to strengthen international cooperation from a micro level.

Characteristics and driving factors of carbon emissions in China

Many regions or countries seek to transition to a low-carbon economy, but there is ambiguity about how carbon emissions change with economic growth. This paper captures characteristics of carbon emissions in China using the Tapio Method. To detect driving factors of carbon emissions in economic growth, we adopt the Log-Mean Divisia Index decomposition method. The results of the paper are as follows: (1) Economic growth in most regions of China has outpaced the growth rate of carbon emissions, showing the characteristics of decoupling. (2) Metropolitan cities have a greater impact on the level of decoupling in neighboring provinces and municipalities. (3) The decoupling of carbon emissions from economic growth is caused by declining energy intensity in China. (4) The energy mix and industrial structure have little impact on the growth rate of carbon emissions. In addition, some policy implications are summarized according to the findings of this paper.

Trade-offs between poverty alleviation and household energy intensity in China

The link between energy use and poverty alleviation is the root of the right balance between energy consumption reduction and ending poverty. However, only a few studies evaluated such issues with the decoupling analysis. We estimate the poverty alleviation level using the Foster-Greer-Thorbecke index and investigate the decoupling relationship between poverty reduction and household energy intensity (HEI) during 2010–2017. Based on the change in the decoupling state, we develop two scenarios to project the impacts of poverty alleviation on household energy footprints (HEFs) by 2035. A pessimistic scenario assumes that the decoupling state by consumption category in each province stays unchanged, while an optimistic scenario assumes that all provinces achieve strong decoupling across consumption categories. The decoupling analysis shows an overall strong decoupling state in China during 2010–2012, 2012–2015, and 2015–2017. During 2010–2017, basic consumption (i.e., food and clothing) shifted from weak to strong decoupling, while enjoyable consumption (i.e., transport) reflected the opposite direction. The scenario analysis indicates that the total HEFs by 2035 in the optimistic scenario will be 3.64 * 107 TJ, which is smaller than that in the pessimistic scenario by 58.7%. Our study implies that fast economic development provides a pathway for the synchronous achievement of energy use reduction and poverty alleviation goals in China.

Dynamic Effects and Regional Differences of Industrialization and Urbanization on China’s Energy Intensity under the Background of “Dual Carbon”

Based on China’s provincial panel data during 2012–2019, this paper performs an empirical analysis of the dynamic effect and regional difference of industrialization and urbanization on the energy intensity in China by separating the energy intensity into three levels including low, middle and high and using the dynamic panel data with system GMM estimation. The results show that the energy intensity will increase by 0.4298% for every 1% increase in the industrialization level on the premise of keeping other variables unchanged. For every 1% increase in the urbanization level, the energy intensity will increase by 0.5674% on average. For every 1% increase in energy intensity in the previous period, the energy intensity in that year will increase by 0.7968% on average. Moreover, there are regional differences in the effects of industrialization and urbanization on the energy intensity in areas with different energy intensities. In addition, all of the factors including the development level of the regional economy, energy price, and technological innovation have different effects on the energy intensity in China. Meanwhile, there exist the rebound effects of the technological innovation in China, and the energy price has an induced effect on the technological innovation. Undoubtedly, industrialization and urbanization jointly promote the increase in energy intensity. At the same time, the level of economic development, energy prices and technological innovation are also reasons for the differences in the energy intensity among regions. Therefore, in order to effectively reduce energy intensity while carrying out technological innovation, promoting high-quality development and increasing income, it is necessary to improve the internal quality of industrialization and urbanization, and to promote new resource-saving and environmentally friendly methods of industrialization and urbanization.

Vertical fiscal imbalance and energy intensity in China

Vertical fiscal imbalance and energy intensity in China

The Impact of Industrial Intelligence on Energy Intensity: Evidence from China

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve the productivity and energy efficiency of factories and governments. Meanwhile, the social ecological environment change has also caused widespread social concern in recent years, and energy efficiency, which is related to climate change, has forced almost every country to reduce their carbon emissions for bettering environmental quality. However, there is little research that has studied this problem from the perspective of industrial robots, even though they are an indispensable part in modern industrial systems. In order to promote the development of artificial intelligence and its application in industrial fields effectively and raise the energy consumption efficiency of production, this paper investigates the impact of industrial intelligence on energy intensity in China, as it is the largest manufacturing and energy consumption country in the world, and we also hope that the experimental results in this study can guide relevant departments and governments to formulate reasonable policies to enhance the utilization efficiency of energy and improve the environmental quality synchronously. For the sake of the rigor of this research and the accuracy of the experimental results, this study explores the corresponding effect mechanisms of industrial intelligence on China’s energy intensity from 2008 to 2019 by using the classical linear regression model OLS (Ordinary Least Squares) and WLS (Weighted Least Squares) separately, which were applied in the previous studies. The results of this study reveal three major findings. The first is that it further proves that the application of artificial intelligence can indeed reduce energy intensity, and the wide applications of artificial intelligence can reduce energy intensity significantly by reducing energy consumption. Besides, the ownership structure of state-owned enterprises will have a positive impact on energy efficiency. The environmental performance of state-owned enterprises is better than that of foreign-funded and private enterprises. Finally, the models further verify the significant impact of the enterprise scale effect on energy intensity. It will bring about the improvement of economic efficiency, and the larger the enterprise, the more obvious the economies of scale effect and the lower the energy intensity.

Trade openness, economic growth, and energy intensity in China

The growing demand for energy and the associated pressure of environmental pollution has drawn considerable attention from the Chinese government. Reducing energy intensity in maintaining economic growth has positive implications for China's future green transformation. We systematically examine energy-intensive factors in the context of provincial and regional development in China, at both theoretical and empirical levels. We estimate the energy intensity of 30 provinces and regions in the Chinesemainland from 2005 to 2018 and provide a normative interpretation of the impact of trade openness and economic growth on China's energy intensity using a dynamic panel model. Economic growth and trade openness reduce energy intensity when control variables are included; however, the effect of economic growth on energy intensity is more obvious. Foreign trade affects energy intensity mainly through the export route, while the effect of the import route is not significant. The regional variability of the effect of trade openness and economic growth on energy intensity is insignificant between the east and west of China. Accordingly, to reduce energy intensity and coordinate their development, the government should play an active role in opening trade and economic growth. Additionally, a synergistic mechanism of energy control between provinces and regions is necessary.

Asymmetric macroeconomic determinants of energy intensity in China: does economic development matter?

Previously, several studies have tried to analyze the various determinants of energy intensity for different countries and regions. However, none of the studies have focused on the asymmetric determinants of energy intensity in the context of China. Therefore, the primary aim of this study is to investigate the asymmetric determinants of energy intensity in China. We have gathered data for China over the period 1985-2019 and applied linear and non-linear ARDL models. In the linear model, only energy prices reduce the energy intensity in the long run. Moreover, the long-run effects of both energy prices and economic development are asymmetric; however, positive changes in both variables help to reduce energy intensity. Clearly, the results are asymmetric in nature; hence, the policymakers should consider both positive and negative shocks in energy prices and economic development while making any policy with regard to lowering energy intensity.

Impact of economic growth and labor productivity dispersion on energy intensity in China

This paper quantitatively measures the labor productivity dispersion index of 30 provincial-level political regions (excluding Tibet) in mainland China from 2006 to 2018 based on industrial employment and output share data. Based on clarifying each region's economic variability, a spatial panel model is used to examine regional economic growth and labor productivity dispersions' effects on energy intensity. It was found that energy intensity in China is significantly spatially correlated. There is a clear gap between the share of output and the share of employment in China's three industries where labor productivity has deviated. The highest index of labor productivity dispersion is found in the western region, followed by the central region, with the lowest in the eastern region. Although, China's economic growth has had a significant negative impact on energy intensity, the current stage of China's economic development model is able to gradually achieve efficient energy use. However, the dispersion of labor productivity is not conducive to the improvement of energy factor allocation efficiency, and rationalization of industrial structure and employment structure transformation are urgently needed. To control energy intensity, China should actively adjust the current labor productivity dispersion; promote the transfer of primary industry labor to secondary and tertiary industries; strengthen control mechanisms for energy prices; optimize the impact of investment activities on energy intensity; and improve energy efficiency.

Energy technology of conservation versus substitution and energy intensity in China

A major challenge for sustainable development is providing adequate energy services while mitigating adverse environmental effects, which unquestionably requires a reduction in the energy consumed per unit of real GDP (i.e. energy intensity) and novel energy technologies. However, our knowledge of how energy technologies influence energy intensity is incomplete. This paper thus empirically investigates the effects of energy technologies on energy intensity by dividing them into energy-conservation and energy-substitution technologies. Additionally, we classify energy technologies according to their executors, as having two purposes. Using a China's provincial dataset over 2000–2018, we find that energy-conservation technology plays a more important role in decreasing energy intensity compared to energy-substitution technology. In addition, from the purposes of these energy technologies, the energy technologies coming from utility-type enterprises exert the most significant effect on energy intensity reduction. Since the effect of energy technologies on energy intensity may be related to technology absorptive ability, an additional analysis based on the dynamic panel threshold model indicates that technology absorptive ability shows an important effect on energy intensity.

The effect of technological factors on industrial energy intensity in China: New evidence from the technological diversification

Based on China's energy consumption structure, the reduction of energy intensity is conducive to the realization of China's carbon neutrality goal, and technological innovation is the core driving force for reducing energy intensity. This paper utilizes the statistical data of industrial enterprises in China during the period of 2011–2018 to explore the technological innovation value chain efficiency under the background of the new development stage. This study divides the innovation value chain into technological innovation stage and the economic conversion stage. The network slacks-based measure (NSBM) approach is applied to calculate the two different stages’ efficiency. LSDV is utilized to analyze the influence mechanism of technological innovation factors on industrial energy intensity. The main results are as follows: first, the development trend of technological innovation efficiency (TIE) and the economic conversion efficiency (ECE) has heterogeneous effect across economic regions. The comprehensive development trend of innovation value chain in central China is the best among the four economic regions. Second, the effect of TIE on industrial energy intensity is negative, while the ECE is positive related to industrial energy intensity. In terms of technology spillover effect, the impact of import and FDI on industrial energy intensity is negative, respectively. The relationship between export and energy intensity is positive. The energy price has the negative relationship with industrial energy intensity, and the impact of energy price on industrial energy intensity is the largest. Through analyzing the efficiency of innovation value chain and the influence mechanism of technological innovation factors on energy intensity, this paper puts forwards relevant countermeasures and suggestions for effectively reducing industrial energy intensity and promoting high-quality industrial sustainable development. In addition, relevant theoretical research is provided for the sustainable green development.