Energy Intensity Of China Research Articles

Impact of technological progress on factor bias and energy intensity of China’s thermal power generation sector

Impact of technological progress on factor bias and energy intensity of China’s thermal power generation sector

The impact of technological progress on energy intensity in China (2005–2016): Evidence from a geographically and temporally weighted regression model

This paper selected the relevant data of China’s thirty administrative regions from 2005 to 2016, and constructed a geographically and temporally weighted regression model of technological progress and energy intensity, to fully analyze the heterogeneous impact of technological progress on energy intensity. The results showed that: (1) The energy intensity of China’s different regions, in the long run, had an obvious spatial agglomeration effect and a significant positive correlation in spatial. (2) The spatial heterogeneity of the impact of technological progress on energy intensity changed smoothly and steadily in the temporal dimension, and the dominant factors affecting the energy intensity gradually shifted from foreign direct investment, opening-up degree, human capital to research and development funds from 2005 to 2016. (3) The temporal heterogeneity of the effect of technological progress on energy intensity varied greatly from different spatial dimensions, specifically, the influence of research and development funds on energy intensity was high in the western regions and low in the eastern regions, but that of the foreign direct investment was the opposite, the spatial distribution of the effects of opening-up degree, human capital on energy intensity was dispersed and had no obvious aggregation phenomenon.

Policy incentives, R&D investment, and the energy intensity of China’s manufacturing sector

The Chinese strategic plan “Made in China (2025)” aims at improving the independent innovation capacity of the manufacturing industry. It also commits to constructing green manufacturing systems through cleaner production and green technologies. The Chinese government provides research and development (R&D) preferential policies to promote the realization of these aims. Using the two-stage least square (2SLS) method, this paper tests the impact of R&D capital and its spillover on the energy intensity of the Chinese manufacturing industry. It also evaluates the effectiveness of R&D policy in promoting R&D investment at the industry level. The findings show that R&D capital is positively linked to the reduction of energy intensity, and heterogeneity exists in the relationship between energy intensity and R&D capital across industries. This study finds that in general, R&D subsidies and tax relief do not significantly promote R&D investment, but R&D subsidies generate a significantly positive effect on the R&D intensity in the high-tech industries.

Spatial distribution characteristics and convergence of China's regional energy intensity: An industrial transfer perspective

With the economic transformation and regional economic restructuring of China, there is a significant inter-regional transfer trend of industries among the eastern, central, western and northeastern regions. The industrial transfer is able to change the spatial distribution of regional energy intensity by affecting the regional industrial structure, thus impacting the convergence of energy intensity. This paper established a spatial Durbin panel model, and used the relevant data of thirty administrative regions of China from 2004 to 2017 to empirically study the impacts of industrial transfer on regional energy intensity. The results showed that: (1) As a whole, the energy intensity of China's regions was low in the southeast region and high in the northwest region, and there was a significant positive spatial autocorrelation among the energy intensity of different regions. (2) There was β-convergence in the regional energy intensity. It indicated that, on the one hand, the differences in the energy intensity of China's different regions were being gradually narrowed; on the other hand, the energy intensity of each region would converge to its own steady state. (3) The industrial transfer and economic development could promote the convergence of the regional energy intensity.

The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries

China’s industrial sectors have an approximate consumption amounting to 70% of the aggregate power of the entire country. Investigating the driving forces of the decline in the energy intensity is essential for accelerating China’s conversion into a low-carbon economy. Nowadays, there has been no agreement as yet when it comes to the impacts of China’s industrial sectors on energy intensity. The current research work studies the impacts of key driving forces, in particular foreign as well as indigenous innovations, on China’s industrial energy intensity in 34 industrial sectors between 2000 and 2010. Linear and nonlinear analysis methodologies are put to use. The linear empirical findings show that indigenous innovation primarily contributes to driving down the industrial energy intensity across the sampling duration. The foreign innovations, which take the shape of FDI as well as imports, are seen as benefiting the decline in industrial energy intensity; on the other hand, exports ramp up the industrial energy intensity. An additional investigation, on the basis of the panel threshold framework, indicates that the impact of foreign innovations by means of openness as well as industrial energy intensity has an association with the technological absorptive potential. The empirical evidence puts forward some pivotal inferences for policymakers with regard to China’s declining industrial energy intensity—for instance, exploitation of the maximum benefit associated with the technology spillovers; in addition, it is important to take into consideration the attributes and scenarios that impact industrial energy intensity.

The impact of factor price changes and technological progress on the energy intensity of China's industries: Kalman filter-based econometric method

This paper uses the 1981–2012 input-output tables for adopting the translog cost function to construct a Kalman filter-based econometric model to estimate the share equation of energy, non-energy, capital and labor in the 33 China’s sectors and to conduct empirical research on the impact on China’s energy intensity caused by input price changes and biased technological progress. The findings are that between 1981 and 2012, the level of the sectors’ energy intensity was subject to the combined impact of factor prices and biased technology progress. For some sectors, the average Allen-Uzawa elasticity of substitution (AES) for energy to capital or labor rental price was negative. For the 18 sectors, the biased technological progress was from energy savings, while one for the rest from energy consumption. In the 21 sectors, input price changes decreased the sectors’ energy intensity, while 17 of them had the sectors’ energy intensity decreased by technological progress.

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.

The role of government in industrial energy conservation in China: Lessons from the iron and steel industry

This paper tracks the energy use and changes to China's energy-efficiency regulatory framework governing the iron and steel industry for the last thirty years. The detailed institutional analysis identifies both abating and augmenting effects of the regulatory framework changes on the energy intensity changes. The paper then assesses the impacts of various other factors on the energy intensity of China's iron and steel industry by using time-series data on the sector level. The quantitative analysis shows that technology progress, specifically the production process transition from high-energy intensive open-hearth furnace (OHF) to more energy efficient basic-oxygen furnace (BOF), is the biggest contributor to energy intensity reductions in China's iron and steel industry in the last thirty years, even after controlling for regulatory change and policy incentives. The ownership reform resulting from changes in the regulatory framework change contributed to energy intensity reductions in China's iron and steel industry, while fast market expansion resulting from market liberalization and regulation decentralization served as a principal barrier for energy efficiency improvements. Government policies, represented by financial subsidies from governments, correspond with energy intensity reductions in key large and medium sized enterprises, but interestingly, seem to be ineffective at producing energy intensity reductions for the industry as a whole. As other research indicated, rising coal prices also contributed to energy intensity reductions in China's iron and steel industry. Finally, the paper concludes that, to further incentivize energy intensity reductions, the Chinese government should consider correcting the negative impacts of the regulatory framework change, as well as transferring production processes to the highly energy efficient electric-arc furnace (EAF) and extending policy regulations to wider enterprise groups.

Regional disparity in energy intensity of China and the role of industrial and export structure

In this paper we decompose the China’s regional disparity of energy intensity into the contributions of pure energy intensity gap and the difference in both industrial structure and export structure by trade regimes. Our decompositions suggest that the high energy intensities in western and central regions are mainly attributable to the high sectoral energy intensity, which accounts for more than half of the overall energy intensity gap. In contrast, the low energy intensities in coastal regions are mainly attributable to low sectoral energy intensity and to high proportions of output in relatively “clean” rather than energy intensive industries. A high proportion of processing exports also contribute positively to the low energy intensities of coastal regions, but the magnitude is very small, less than 6%. Our decomposition results provide policy implications for energy saving across Chinese regions.

Club Convergence in the Energy Intensity of China

We contribute to energy policy discourse in China by demonstrating the existence of multiple energy-intensity equilibria across its provinces. Using recently developed club convergence methods, we identify three unique clubs in China, each with markedly different energy intensity profiles. Unlike in previous studies, our club groupings do not strictly adhere to common geographic separations e.g. east, west and central divisions. To better understand what commonalities/disparities lay behind their groupings, we undertake a regression of the determinants of energy intensity, in a similar vein to a number of recent studies. Doing so, we demonstrate a number of significant differences in the determinants for each of the identified clubs, given which we are able to offer a rich set of policy implications. Not all determinants are common across the three clubs, and where they are common, they can differ both in magnitude and sign, reflecting the fundamental differences across the groups.

Promoting energy conservation in China's iron & steel sector

The iron & steel industry is one of the major energy-intensive sectors in China. In this paper, we define the variable of energy intensity to analyze the energy conservation potential in China's iron & steel sector using the co-integration method and scenario analysis. We find that there is a long-term relationship between energy intensity and factors such as R&D intensity, labor productivity, enterprise scale, and energy price. Monte Carlo simulation technique is further used to address uncertainty problem. The results show that under baseline scenario, the energy intensity of China's iron & steel sector will reach 17.09 tons of coal equivalents per 10,000 Yuan (Tce/10,000Yuan) in 2020. The energy saving potential in 2020 will be 344.05 Mtce (million tons of coal equivalents) and 579.43 Mtce under moderate energy-saving scenario and advanced energy-saving scenario respectively. Finally, based on the results of the elasticity coefficients of the long-term equation, we propose future policy for promoting energy conservation in China's iron & steel industry.

Mathematical Decomposition for Influence Factors of Unit GDP Energy Consumption and Demonstration in China

Analysed various influence factors of unit GDP energy consumption, discussed the mathematical decomposition and calculation method and did empirical analysis of the influence and role on unit GDP energy consumption changes since “The Fifth Five-Year Plan”.The main conclusions are:(1) the drop of the energy efficiency is caused both by technical factors and structural factors , in which structural factors being the fundamental reason during the Eleventh Five-Year period. (2) technological energy saving is the main direct driving force of the dramatic decrease in energy intensity of China , and the alternation of structural energy saving from negative to positive is the crucial factor determining the dramatic decrease of the energy intensity of China ,and energy-saving in livelihood is a stable impetus for improving China energy efficiency improvement in the Eleventh Five-Year period .The research provided the methodological foundation for exploring the specific ways to achieve the energy efficiency goal of “The Twelfth Five-Year Plan” and also to provide a reference for the policy making of “The Twelfth Five-Year Plan” energy efficiency.

Dynamic mechanism for the decline of China’s energy intensity during the 11th FYP (Five-Year Plan)

From 2006 to 2009, the aggregate energy intensity of China fell by 14.38%,1) which means that China has made substantial progress towards its goal of achieving approximately a 20% reduction in energy intensity during the period of the 11th FYP (Five-Year Plan). This paper describes some new properties of the mechanism for the decrease in China’s energy intensity since the beginning of the 11th FYP. First, compared with the period of the 10th FYP, technical, structural, and residential energy-savings have been improved to a certain extent. Secondly, unlike the dynamic mechanism before 2000, technical energy savings have been the main driver of momentum since the beginning of the 11th FYP. Next, although structural energy savings are not the main factor, they are still a key factor for the decrease in energy intensity. Finally, residential energy savings are still a stable driver of momentum for the decline in China’s energy intensity.