Energy Intensity In China Research Articles

Analysis of the spillover effect of energy intensity among provinces in China based on space-time lag model.

Based on inter-provincial energy intensity data in China from 1996 to 2016, using the model combining STIRPAT and dynamic SDM analyzes energy intensity and its influencing factors under the conditions of spatial lag, time lag, and space-time lag. Considering endogenous issues, it then explores the basic characteristics of energy intensity in space and its path dependence. The results show that spatial distribution of energy intensity in China is uneven and generally shows a pattern of decreasing from northwest to southeast. Energy intensity itself has a significant spillover effect, which can affect neighboring regions through pollution heaven effect and pollution halo effect. It can also be reduced as a result of the joint effect of driving factors. Economic development level, foreign direct investment, and technological progress have significant effects on reducing energy intensity, while industrial structure and urbanization rate increase it. The difference among driving factors lies in spatial spillover effect, and the short-term indirect effect is greater than the long-term one. Therefore, the key to realize China mode of green development is to promote factors of reducing energy intensity brought into full play and the inhibitory factors effectively controlled.

Convergence analysis of city-level energy intensity in China

Understanding the convergence patterns of energy intensity and the drivers leading to the club convergence are of great significance for local governments to implement targeted policies to improve energy efficiency. With this in mind, we begin with the collection of energy consumption data of 193 Chinese cities at prefecture level or above, then we adopt the log t-test and clustering algorithm to investigate convergence characteristics of energy intensity. Besides, the Ordered Probit model is adopted to investigate the drivers that affect the formulation of convergent club. We identify four convergent clubs among total 193 cities, and these clubs show great differences in energy intensity. Marketization degree, population density, foreign direct investment, resource endowment, and industrial structure are recognized as the drivers of the formation of convergence clubs. This paper adds more evidence to understand the energy intensity gap, we propose that upgrading the industrial structure, exerting economic assemble advantage, enhancing the level of opening up, and improving the marketization level are favorable measures to reduce energy intensity.

Domestic R&D activities, technology absorption ability, and energy intensity in China

Domestic research and development (R&D) activities are critical for the development of China's low-carbon economy. The higher level of R&D inputs, the more advanced technology will generate, therefore contributing to the energy intensity reduction. In empirical studies, the effect of R&D activities on energy intensity has been investigated extensively, however, most studies consider R&D activities as a whole. Consequently, we lack detailed information on the role of each R&D player and stage for appropriate policy consideration. Furthermore, the effects of R&D activities may be determined by factors affecting technology absorptive ability. To understand the influence of R&D activities on energy intensity, we employ linear and nonlinear analyses using a Chinese provincial dataset covering 2000–2016. The linear analysis suggests that domestic R&D is powerful in reducing energy intensity. However, this positive effect is mainly from experimental and developmental R&D activities rather than basic R&D and application activities. Further, R&D activities by industrial enterprises have a stronger effect on energy intensity reduction than those of higher education and independent R&D institutions. Additional study using panel threshold models suggests that the effects are not linear but experience structural breaks when human capital stock and full-time equivalent (FEP) R&D personnel are at different levels.

What cause the decline of energy intensity in China's cities? A comprehensive panel-data analysis

China's progress in energy efficiency has contributed significantly to the reduction in global energy intensity (EI), but the descending speed of per unit GDP energy consumption in China has been decreasing during recent years. The present study aims to investigate the core driving forces of EI from 2005 to 2016 in 289 Chinese cities based on Granger Casualty Test and regression analysis. For this purpose, the study employs panel data analysis with both fixed coefficient and time-varying coefficient which are suitable for examining the effects of influence factors on EI and their change trend. The Granger Casualty Test investigates that there supposed to be a bidirectional causality between air-pollutant emission and energy consumption in China. However, due to China's weak environmental regulations, the negative effect of emission on EI is not significant. Further, regression analysis shows that a percentage increase in electricity price decreases EI by 0.722% in long-term, but this adjustment takes time. Furthermore, analysis of short-term relationship reveals that optimizing energy structure may be the best way for China to reduce EI in short run. Finally, the time-varying coefficients of panel data model indicate that inhibitory effect of promoting electricity consumption on EI will keep improving with expansion of electricity use.

Factor substitution, diversified sources on biased technological progress and decomposition of energy intensity in China's high-tech industry

On the path to industrialization, the energy and environmental problems caused by the rapid growth of the manufacturing industry have attracted widespread attention. At the top scale of technological innovation, high-tech manufacturing is considered to have achieved a balance between the “Made in China 2025” strategy and energy conservation. Therefore, in this paper, we explore inter-factor substitution and the influence of different sources of technological progress on China's high-tech industries using an estimation model that incorporates a translog cost function and the ‘panel seemingly unrelated regression’ method. Changes in energy intensity are further decomposed into different causes including budget, output, factor substitution, and technological progress at various points throughout the period under study. The empirical findings show Morishima substitutions among three different input factors. The substitution elasticity between labor and energy is the highest, and further most sources of technological progress are biased toward saving energy. Technological progress and budget are the critical driving forces throughout the period. However, the effect of factor substitution has gradually increased in recent years. From a regional perspective, energy intensity in the Beijing-Tianjin-Hebei region shows the most apparent decline, while the smallest decline occurs in China's western region.

Carbon intensity and emission reduction potential in China: spatial measuring method

This paper analyzes the spatial and temporal distribution of CO2 emissions intensity and energy intensity in China by using spatial measuring method from 2000 to 2013 and estimates the potential for CO2 emissions reduction. The results obtained in this study include: (1) Both CO2 emissions intensity and energy intensity are declining; (2) the spatial distribution of carbon emission intensity and energy intensity in China shows the characteristics of lower from north to south; (3) China’s total growth of energy consumption and carbon emissions is clearly slowing, which will peak before 2030; the carbon emission reduction potential in China is great with 167,316.91 million tons, and Shanxi, Inner Mongolia and Hebei have the greatest potential to reduce CO2 emissions with 29,885.8 Mt, 32,704.49 Mt and 34,222.1 Mt, respectively; (4) the differences of CO2 emissions intensity and energy intensity among provinces are distinctive. This study can provide a reference for the sustainable development of China’s energy and environment.

R&D Efforts, Total Factor Productivity, and the Energy Intensity in China

ABSTRACT This paper first measures the levels and growths of regional TFP in China using a panel dataset of China’s 30 provinces from 1978–2014. It then estimates the effects of R&D on regional TFP and its growth further explores the impact of TFP on China’s energy intensity. We find no evidence that R&D has innovation and spillover effects on TFP and its growth in China’s provinces. Nevertheless, R&D can promote growth in regional TFP by helping to absorb new technologies embodied in FDI and foreign trade. The results indicate that TFP plays a significant role in the decline of China’s energy intensity.

Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach

Public infrastructure not only promotes economic growth, but also influences energy intensity, which plays an important role in the strategies related to energy. Therefore, infrastructure policy can be used as an important instrument to reconcile the dilemma of energy, economy, and environment in China. However, few studies have been made to assess the effect of public infrastructure on energy intensity in China. This paper presents an analysis of how three typical types of public infrastructure (i.e., transportation, energy, and information infrastructure) affect energy intensity for 30 Chinese provinces, from 2001 to 2016. To account for nonlinearities, we adopt the panel smooth transition regression (PSTR) approach. The results show that transportation infrastructure has a significantly negative effect on energy intensity, and this negative effect gradually strengthens when the transportation infrastructure stock exceeds the threshold value. Adversely, energy infrastructure has a significantly positive effect on energy intensity, and this positive effect gradually strengthens with the development of energy infrastructure. Our results also suggest that the development of information infrastructure could not only strengthen its own significantly negative effect on energy intensity, but also could promote the negative effect of transportation infrastructure on energy intensity. Moreover, the positive impact of energy infrastructure on energy intensity gradually decreases when the stock of information infrastructure surpasses the larger threshold value. Our findings suggest that policy makers could reduce energy intensity by accelerating the development of transportation and information infrastructure. Furthermore, they could strengthen the negative effects of transportation and information infrastructure on energy intensity and weaken energy infrastructure’s positive effect on energy intensity by increasing their information infrastructure investment.

Analysis of regional difference decomposition of changes in energy consumption in China during 1995–2015

With the increase in energy consumption in China, the influential factors and contributions to such increase have received more attention. In this paper, the decomposition of energy consumption and energy intensity in China from 1995 to 2015 was conducted by using a complete decomposition model. This study focuses on regional difference, which is not in line with the existing studies. The decomposition results indicate that the economic growth has a significant driving effect on the energy consumption, while the energy intensity effect reduces the growth of the total energy consumption. The structural effect plays a negative role but has less effect on the growth of the energy consumption. The contribution rate on the growth of the energy consumption in the eastern region gradually decreases. However, in the western region it grows fast and even exceeds that of the eastern region to become the highest energy consumption area in China in 2010–2015. The energy intensity declines substantially in China. It is mainly due to the improvement of regional energy efficiency and negligible contribution from the structural effect. In conclusion, the energy consumption and energy intensity in China follow different forms in different regions. Therefore, the application of differentiated regional energy policies is preferable over a single national energy policy.

Foreign direct investment and energy intensity in China: Firm-level evidence

Given the significance of energy use, which emits greenhouse gases and generates air pollution, decreasing energy intensity is considerably important for both China and the global environment. We employ a unique dataset of firm-level data on 13 cities in Jiangsu Province of China and investigate the relationship between FDI and energy intensity. Taking into account the heterogeneity characteristics of firms, we confirm a significant and negative coefficient of the FDI variable, which implies that FDI firms have lower energy intensity than their local counterparts. We introduce the interaction term of FDI and regional absorptive capacity, and the empirical results show that regions endowed with more absorptive capacity usually have lower energy intensity. Specifically, more spending on technology tends to narrow the technology gap between foreign and local firms, since local firms absorb international technology transfer more effectively and efficiently. Moreover, examining the cases of the textile and chemical industries, we find that FDI firms in the chemical industry have lower energy intensity than their local counterparts, while we observe no difference between FDI firms and non-FDI ones in the textile industry.

Estimating urban residential building-related energy consumption and energy intensity in China based on improved building stock turnover model

Accurate estimation of urban residential building-related energy consumption (URBEC) and energy intensity per unit floor area at the national level has significant implications for the analysis of carbon emission peaks. However, reliable data on China's building floor space (BFS) are lacking, resulting in unclear energy intensity levels. This study proposes a China BFS estimation method (CBFSEM) based on improved building stock turnover model. Using CBFSEM, it estimates the BFS of historic urban dwelling stock, the demolished and newly built dwelling from 2000 to 2015. It then estimates the corresponding energy consumption and intensity based on the obtained urban residential BFS data. Results showed that total URBEC in China increased dramatically from 217.1 Mtce in 2000 to 417.2 Mtce in 2015 with an average annual growth rate of 4.45%. China's total dwelling stock almost doubled, from 10.6 billion m2 in 2000 to 27.4 billion m2 in 2015 with an annual growth rate of 6.56%. The operational energy consumption accounted for approximately 70% of total URBEC and the building material production energy intensity was the highest in total URBEC, >60 kgce/m2. A comparison with the China Population Census showed that the deviations were well below 8%, which indicated the reliability of the CBFSEM and the estimated results. In general, this study fills the gap in available data and addresses the shortage of estimation methods for BFS and energy intensity. It also provides the government with technical support and scientific evidence to promote building energy efficiency.

What new technology means for the energy demand in China? A sustainable development perspective.

This paper explores the direct impact of new technology on the energy intensity in China. The autoregressive distributed lag (ARDL) bounds test approach to cointegration is utilised over the extended period of 1985-2013. The variables found cointegrated and confirm the long-run association among all the underlying vectors. Furthermore, the results of long- and short-run analysis reveal that new technology spurs energy intensity in China. A 1% increase in technological innovation boosts energy intensity by 0.4% and 0.03% in the long and short run, respectively. The findings suggest that the establishment of smart grids and solar energy parks followed by the reforms in energy sector is yet to achieve plausible efficiency in China. The existing investment and innovation policy reforms are insufficient to assist the energy sector to cope up with the country's exceptional economic growth trend. Unlike other studies, this paper accommodates structural break in the series. During sensitivity analysis, the model is found stable. Hence, the findings possess important policy implications for China and open up new discussion in the field.

Elasticity of factor substitution and driving factors of energy intensity in China’s industry

Based on the translog cost function and factor substitution theory, the input and output data from China’s industrial sector and the three sub-sectors during the period 1984–2011 are firstly used to calculate substitution elasticity among capital, labor, energy, and intermediate input. And then from the perspective of factor substitution, the driving factors of energy intensity in China’s industry are explored. The main findings introduced in this paper are listed as follows: firstly, the production of China’s industrial sector is sensitive to changes of energy and labor prices; secondly, except the complementary relationships between energy and labor in the manufacturing industry, and between energy and intermediate input in electricity, gas, and water industry, substitution relationships exist among all other factors; thirdly, the budget constraint of energy consumption is the most effective impetus to the reduction of energy intensity in industrial sector, and factor substitution especially the substitution of capital and labor for energy has an important role in the reduction of energy intensity; fourthly, the rapid expansion of economic scale causes output effect to become the biggest factor of impeding the reduction of energy intensity; fifthly, technical progress has different effects on energy intensities in different industrial sub-sectors, but generally speaking, technical progress does not promote the reduction of energy intensity.

The evolution and driving forces of industrial aggregate energy intensity in China: An extended decomposition analysis

This study adopts the log-mean Divisial index (LMDI) method to decompose the changes in the industrial aggregate energy intensity (IAEI) of China into both macro and technological factors: sectoral energy intensity, industrial structure, research and development (R&D) efficiency, R&D intensity and investment intensity. Afterwards we determine the contributions of 36 industrial sub-sectors to IAEI through different factors using attribution analysis. The results show that the IAEI decreased by 38.26% from 2003 to 2015. This drop is predominantly caused by R&D efficiency (−76.01%). The sub-sectors of ferrous metals (−14.94%) and non-metallic mineral products (−13.36%) are the main contributors to the R&D efficiency effect. The sectoral energy intensity effect contributes −27.19%, mainly due to the sub-sectors of ferrous metals (−15.97%) and non-ferrous metals (−5.68%). The industrial structure effect also contributes to a decline of IAEI (−15.06%), of which, petroleum, coking and nuclear fuel (−5.57%) and ferrous metals (−4.73%) are the sub-sectors that contribute the most. Conversely, investment intensity (174.09%) and R&D intensity (52.06%) contribute to increase the IAEI, largely owing to sub-sectors of petroleum, coking and nuclear fuel processing, chemical materials and non-metallic mineral products. Our findings suggest that the combined effects of the policies implemented during the time frame of 2003 to 2015 led to a reduction in IAEI, with investment intensity being the focus of improvement. Nevertheless, different policies and measures should be put forward in different sub-sectors due to their varying degrees of adaptability and policy sensitivity.

Revisiting cross-province energy intensity convergence in China: A spatial panel analysis

Understanding the convergence of energy intensity helps in assessing whether policies targeted at reducing energy intensity are effective and should thus be continued or reinforced. This paper analyzes the convergence of cross-province energy intensity in China based on panel data of 29 provinces for the period 2003–2015 by means of sigma convergence, kernel density analysis, and unconditional and conditional beta convergence. The empirical results show that the omission of spatial spillovers underestimates conditional beta convergence. We thus argue that the implementation of effective policy for reducing energy intensity depends not only on the driving forces in the own province but also on the spatial spillover effects in neighboring provinces, particularly technology transfer, knowledge spillovers, and the input-output relationship. From the analysis, it follows that adopting low-energy capital, attracting more FDI inflows and developing indigenous innovation capabilities are major policy handles.

The Effect of Financial Development on Energy Intensity in China

In this study, we analyse the relationship between financial development and energy intensity in 28 Chinese provinces over the period 1999 to 2014. Using a wide variety of financial development measures, as well as specific indicators capturing the level of state intervention in the financial system and the degree of market-driven financing in the economy, we examine whether limited access to finance acts as a barrier to reducing energy intensity. Our estimations control for variables such as state investment, stock market capitalization and the composition effect. Further, a GMM estimator is used to control for endogeneity in our models. Our results provide evidence that a poorly functioning financial system constrains the reduction of energy intensity across regions. However, the strength of these effects has been gradually declining over time, especially following the implementation of the Green Credit Policy. Limitations in domestic access to finance as well as the misallocation of funds and the efficient use of capital have policy implications, as they can reduce the incentives for investment in the energy sector. These findings are a source of considerable interest in light of the new policy based on green credit, and they highlight new opportunities as well as challenges to sustainable economic growth.

The effect of telecommunication services on energy intensity in China

Little attention has been paid to the influence of telecommunications on energy consumption, especially from an empirical perspective in the emerging economies. This paper firstly employs an autoregressive distribution lag bound test to examine the co-integration relationship among our model variables. Then, it mainly follows a partial least squares regression to investigate the long-run effect and an error correction model to explore the short-run impact of telecommunication services on energy intensity in China. Our main conclusions reveal a co-integration relationship among all the variables in our expanded STIRPAT model. Besides, it is also indicated that the energy intensity reduction effect of telecommunication services is statistically significant both in the short and long term. Additionally, the impacts of population, income, industrial structure, energy consumption structure, and energy price on energy intensity are also examined.

What factors lead to the decline of energy intensity in China's energy intensive industries?

This paper seeks to investigate the main factors causing the decline in energy intensity of China's energy intensive industries. Index Decomposition Analysis and Production Decomposition Analysis methods are combined to complete the decomposition analysis. Overall, seven factors are related to the decline in the energy intensity and technology improvement effect is the most significant factor. Technical efficiency effect is positively related to the decline in twelve provinces but negatively related in seventeen provinces. Capital-energy substitution effect is beneficial to the decline in twenty provinces. Labor-energy substitution effect undermines the decline and substitution effect among different categories of energy can be ignored. Considering provincial contribution, only Xinjiang Province has a negative contribution. Liaoning, Hebei and Shanghai provinces make the largest contributions to the decline in energy intensity. The main policy implications include enhancing investments in research and development in China's energy intensive industries; transforming the intensive development model of the energy intensive industries; gradually reforming energy price; and improving the layout of energy intensive industries.

How to reduce energy intensity in China's heavy industry—Evidence from a seemingly uncorrelated regression

Abstract With rapid development and large scale urbanization, China's environmental and resource constraints are becoming increasingly severe. Compared to other industries, China's heavy industry is energy-intensive and emission-intensive, which exerts pressure on energy conservation policies. In this paper, by establishing a theoretical model of the impact factors of energy intensity, we investigate the effects of energy prices, ownership structure, and industrial concentration and R&D investment on the energy intensity of China's heavy industry, and seemingly unrelated regression model was used in the estimation of corresponding coefficients. The results show that increase in energy prices, decrease in state-owned enterprises and increase in industrial concentration may reduce the energy intensity of the industry. Also, the study points to evidence that an increase in R&D investment may reduce the oil intensity of heavy industry.

Analysis of Influencing Factors of Change of Manufacturing Energy Intensity in China Based on WSR System Methodology and VAR Model

Since China’s reform and opening up, although energy intensity of manufacturing industry has showed a trend of decline overly, compared with developed countries, it remains high. The research object of this paper is energy intensity of manufacturing industry, based on Wuli–Shili-Renli system methodology, it builds factors system of energy intensity of manufacturing industry. Based on time series data from 1980 to 2014, it uses VAR model to explore influence law of various factors on manufacturing energy intensity. Impulse response analysis shows that in the short term, the whole society fixed asset investment structure, technological progress, economic development level and structure of property rights have a great influence on energy intensity. With the increase of technological progress rate, per capita GDP, proportion of state-owned enterprises, manufacturing energy intensity starts to reduce; with the increase of proportion of investment in fixed asset of energy intensive industries, manufacturing energy intensity starts to rise. In the long term, energy price, energy consumption structure, FDI and industry structure have a great influence on manufacturing energy intensity. With the increase of proportions of coal consumption and heavy industry, manufacturing energy intensity starts to rise; with the increase of energy price, manufacturing energy intensity starts to reduce. Variance decomposition shows that energy consumption structure, energy price and technological progress have a greater contribution to manufacturing energy intensity, optimizing energy consumption structure, controlling energy price and promoting technological progress are important approaches to reducing manufacturing energy intensity.