Energy-intensive Industries In China Research Articles

Decoupling effect and spatial-temporal characteristics of carbon emissions from construction industry in China

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of “double carbon target”. The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is “high in the east and low in the west, high in the south and low in the north,” showing obviously regional differences.

Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries

Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a ‘digital twin’ is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the ‘cradle-to-gate’ lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.

Analysis on the dynamic evolution of the equilibrium point of "carbon emission penetration" for energy-intensive industries in China: based on a factor-driven perspective.

In order to achieve the carbon peaking and carbon neutrality goals, energy-intensive industries in China, as the main sectors of energy consumption and carbon emissions, had huge pressure to reduce emissions. In addition, the reduction of vegetation area led to a decline in carbon sink capacity, which further exacerbated the imbalance of mutual penetration between carbon source and carbon sink. Therefore, this article considered the role of carbon source and carbon sink and defined and calculated the “carbon emission penetration” (CEP) of the six energy-intensive industries from 2001 to 2020. The KAYA formula and the LMDI method were used to decompose the driving factors of CEP in the three aspects of scale, intensity, and structure. The combined model of STIRPAT and the environmental Kuznets curve (EKC) was used to simulate and analyze the equilibrium points of energy-intensive industries in China from the perspective of factor driving. The analysis results indicated that there were differences in the fluctuation trend of CEP in the six energy-intensive industries, which can be divided into three types: “two-stage growth,” “steady growth,” and “single peak.” Secondly, the driving factors from the three aspects of scale, intensity, and structure—emission intensity (CE), energy consumption intensity (EI), industrial structure (IS), economic scale (GP), and carbon sequestration scale (PCA)—had differences in industry and time dimensions. And the realization time of the CEP equilibrium points of six industries showed a three-level gradient feature significantly. This can provide some reference for the low-carbon transformation of six energy-intensive industries and optimization of China’s environmental management under the carbon peaking and carbon neutrality goals.Graphical abstract

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000-2019.

China has been reported as the world’s largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China’s EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of “high in the west and low in the east” and “high in the north and low in the south” spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

Air pollutant emission and reduction potentials from the sintering process of the iron and steel industry in China in 2017

The iron and steel industry (ISI) is one of the most energy-intensive industries in China, which makes a substantial contribution to the emissions of air pollutants. Among the various manufacturing processes, sintering is the major emitting process, which shares over half of the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx) and particulate matter (PM) for the entire industry. In this study we made a comprehensive evaluation of the air pollutant emissions from the sintering process of China's ISI in 2017 based on the Continuous Emission Monitoring System (CEMS) database and estimated the future reduction potentials. We found that there was a general decreasing trend of emission concentrations in the sintering flue gas in response to the strengthened emission control policies, but the mild increase of the oxygen content in the second half of the year flattened the decreasing trend, indicating the necessity for simultaneous control of the oxygen content in the flue gas. Despite the relative high standard-reaching rates of 90% to the emission concentration limits in GB 28662-2012, the standard-reaching rates to the ultra-low emission standards were only 12%, 40% and 27% for NOx, SO2 and PM respectively, with the lowest value mostly occurred in the western provinces. In 2017, the NOx, SO2 and PM emissions from the sintering process were 378.6 kt, 169.0 kt and 51.9 kt, respectively. If the ultra-low emission standards were met, the corresponding NOx, SO2 and PM emissions would decrease by 69.9%, 52.9%, and 56.4% respectively, illustrating large emission reducing potentials by achieving the ultra-low emission standards.

Is information and communications technology effective for industrial energy conservation and emission reduction? Evidence from three energy-intensive industries in China

Information and communications technology (ICT) can significantly contribute to industrial energy conservation and emission reduction by improving production and management efficiency. However, few studies have analyzed the technical mechanisms nor quantified the effects of ICT in the industrial sector, hence, existing studies cannot fully support the technology promotion strategy. To overcome this research gap, a nationwide industrial ICT system is built for the first time. Taking China's iron and steel, cement, and coal-fired power industry as case study, this paper has adopted bottom-up modeling approaches to simulate the material and energy metabolism of industrial production process, divided ICTs into six types based on their application units, and then quantified their energy conservation and emission reduction potential. In addition, Conservation Supply Curves, Latin Hypercube Sampling, and HSY algorithm methods are used to analyze their technical performance. Results show: (1) The application of ICT has considerable effects, as it can enlarge the industrial energy conservation and emission reduction potential by 10.7–21.7% in the next 15 years; (2) The differences between the technical costs of ICTs are significant, meanwhile more than 70% of this potential is cost-effective; (3) Uncertainty factors will affect technical potential and cost by 4–15%, amongst which technical popularity is the most sensitive factor. These findings reveal the significant effects of ICT and put forward policy suggestions on their promotion strategy in industrial energy conservation and emission reduction.

Changes in carbon emission performance of energy-intensive industries in China.

As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. The paper builds a super slack-based model (SBM) considering this undesirable output and calculates the carbon emission efficiency. Then, the meta-frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyze the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed, and the conclusions are as follows: there is a big difference in the carbon emission technology gap ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering, while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing.

Complex causalities between the carbon market and the stock markets for energy intensive industries in China

The low-carbon development of energy intensive industries is of vital importance to achieve China's energy and climate targets in 2030 while carbon market is an important mechanism to promote carbon reduction. This paper investigates three types of causality, including positive, negative and dark causality, between China carbon prices and four energy intensive stock indexes using Pattern Causality method from a nonlinear symbolic dynamic perspective. Our findings show that there exists weak bidirectional causality between these two markets, which is manifested in that the fluctuation of 1% in one market approximately cause the fluctuation of 0.15%–0.3% in the other market. Moreover, we further analysis the impact of policies on the causalities between these two markets by dividing the whole timescale into seven stages. The results indicated that the document announcing the formal launch of China's carbon trading system prompted the dominant market of their causality shifting from carbon market to stock markets. Carbon markets gradually show stronger causal influence on the stock markets before December 2017, and the opposite after April 2018. And the Covid-19 has further exacerbated the weakening role of the carbon finance. Finally, the delay effect of carbon market on power industry stock market can be identified when unveiling the dark causality type.

Examining Effect of Green Transformational Leadership and Environmental Regulation through Emission Reduction Policy on Energy-Intensive Industry’s Employee Turnover Intention in China

This study explored a theoretical model of factors affecting employee turnover intentions in the energy-intensive industry from the perspective of environmental regulation through emission reduction policy. Moreover, we examined whether green transformational leadership has a negative effect on the perception of turnover risk of energy-intensive corporate employees and their turnover intentions; we collected data on 531 employees in the energy-intensive industries in China. Data analysis was conducted using exploratory factor analysis, reliability and validity analysis, stepwise regression model analysis, and a structural equation model to test the research hypothesis. The results revealed that environmental regulation through emission reduction policy has a significant impact on employee perception of turnover risk and turnover intention in energy-intensive industries in China. The perception of turnover risk has a greater effect on the turnover intention among employees than the emission reduction policy. Moreover, we found that the perceived risk of turnover has a mediating effect in the relationship between environmental regulation through carbon emission reduction policy and turnover intention. However, green transformational leadership has an inhibiting effect on the perception of turnover risk and turnover intention among employees. This research has crucial theoretical significance for the transformation of energy-intensive enterprises and promoting the sustainable development of energy-intensive enterprises in China.

Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path

Energy-intensive industries are high-energy-consumption and high-pollution industries, and their green technology innovation efficiency deserves in-depth investigation. This paper explores the efficiency of green technology innovation and its combinatorial improvement path in energy-intensive industries from 2005-2015 with a two-stage data envelopment analysis model based on shared and additional input resources and fuzzy-set qualitative comparative analysis. The results indicate that (1) the overall efficiency of green technology innovation in energy-intensive industries as a whole showed a fluctuating upward trend from 2005 to 2015, benefiting from the improvement of technology R&D efficiency and achievement conversion efficiency; (2) there is high industry heterogeneity in the green technology innovation capacity of energy-intensive industries, and the difference in the average efficiency in green technology innovation between the industries with the strongest innovation strength and that with the weakest is as high as 0.615; (3) small-scale enterprises’ strategies should be based on foreign scientific research support or environmental regulations, supplemented by a small amount of industry-university-research cooperation. Large-scale enterprises’ strategies should be based on foreign scientific research support and industry-university-research cooperation, supplemented by appropriate environmental regulations and government investment. This study provides a reference for the formulation of green technology innovation development strategies for energy-intensive industries.

Energy conservation and CO2 emission reduction roadmap in China's energy-intensive industries based on a bottom-up approach

In the process of energy conservation and emission reduction (ECER) in energy-intensive industries, ECER technologies play a vital role. To formulate efficient and cost-effective technology promotion roadmap, a unified framework is needed to systematically compare the ECER effects and costs in different energy-intensive industries. Since this requires adequate understanding of the technical system in each industry and a large quantity of technical and economic parameters, studies so far have not worked on it. By adopting conservation supply curve method, this article systematically assessed the performance of 141 technologies from six energy-intensive industries in China during 2015–2030. The results revealed that: (1) on the level of all energy-intensive industries as a whole, promoting ECER technologies would reduce 68 Mtce of energy consumption from 2015 to 2020, 123.1 Mtce by 2025, and 201.5 Mtce by 2030. More than 60% of the 141 technologies were cost-effective. The thermal power industry and the petrochemical and chemical industry should be the focus of China's future ECER work while reducing the cost of conserved energy in the non-ferrous metal industry would be a critical task; (2) On the level of each energy-intensive industry, the contribution rates of main production processes or sub-industries during different periods were evaluated, and key ECER technologies that should be emphasized on in each industry were identified; and (3) the 141 technologies were categorized into five groups according to their ECER approaches, and key ECER approaches were energy efficiency technologies, management improvement technologies, and equipment modification technologies in the long term.

Temporospatial pattern of carbon emission efficiency of China’s energy-intensive industries and its policy implications

Energy-intensive industries are the primary sectors of energy and resources consumption and carbon emissions. Exploring the temporospatial pattern and influencing factors of carbon emission efficiency (CEE) of energy-intensive industries helps discover the contribution of energy-intensive industries to regional carbon emissions and formulate different regional low-carbon industry development strategies. Through three-stage data envelopment analysis (DEA) model, the CEE of energy-intensive industries of China was estimated from provincial level, and the temporospatial distribution and influencing factors of CEE were analyzed by spatial autocorrelation analysis and Tobit model. The results showed that CEE of energy-intensive industries decreased from 0.649 to 0.587 during 2005–2017 with a decreasing rate of 9.60% annually, while the carbon emissions increased from 0.80 to 1.31 billion tonnes during 2005–2017. CEE of energy-intensive industries in China has obvious spatial heterogeneity and spatial agglomeration. CEE of the eastern provinces is relatively higher, while that of the western regions is lower. It showed that the scale efficiency (SE) of energy-intensive industries is close to the optimal frontier, while the pure technical efficiency (PTE) of energy-intensive industries should be improved in the future. Change of CEE of China’s provinces may fall into five categories: high-high-high, low-high-high, low-low-high, low-low-low, and low-high-low. Industry scale (IS) and research and development (R&D) are promoting factors for the CEE of energy-intensive industries, while energy intensity (EI), capital formation rate (CF) and water intensity (WI) are negative factors. To improve CEE and promote cleaner production of energy-intensive industries, the technological innovation capacity should be further improved, energy structure should be adjusted, energy and water consumption should be restrained and the open economic system should be further promoted in central and western China in the future, which help to realize the dislocation, coordination, and high-quality development of energy-intensive industries among China’s provinces.

Impacts of China's differential electricity pricing on the productivity of energy-intensive industries

Using China's Industrial Enterprise Database (CIED) from 1998 to 2007, this paper investigates how the differential electricity pricing (DEP) policy affects the total factor productivity (TFP) of energy-intensive industries in China. Based on the TFP of energy-intensive industries estimated through the Olley-Pakes (OP) and Levinston-Petrin (LP) approaches, this paper estimates the treatment effects of the DEP policy through the method of propensity score matching and difference in differences (PSM-DID). The results indicate a significant negative effect of the DEP on the TFP of energy-intensive industries shortly after the DEP implementation. This finding could be explained by the rapidly increasing costs for energy-saving technologies that occurred within firms over a short time. Moreover, the estimated dynamic marginal effects demonstrate that the policy effect became positive in the following years, implying that the role the DEP policy plays shifted from increasing costs to stimulating technology improvement for energy-intensive industries. Heterogeneity analysis suggests that the treatment effects remain large and statistically significant for regions less dependent on energy-intensive industries, regions with better policy enforcement, and small-sized firms. The results remain robust in the counterfactual test, in the robustness check that takes account of the lagging effect of policy and in the robustness checks that considers more control variables or extends the time period of analysis. Based on these findings, this paper provides important policy implications for better design of the mechanisms to improve the productivity in China.

Toward green development? Impact of the carbon emissions trading system on local governments' land supply in energy-intensive industries in China

Whether carbon emissions trading system promotes green development is important, especially from the land supply perspective. This paper investigates the effects of the carbon emissions trading pilot program on the land supply of the energy-intensive industries using the difference-in-difference approach, based on a big land-transaction dataset for the Chinese land market from 2007 to 2017. This paper finds that the carbon emissions trading program decreases the supply of energy-intensive industries' land by 25%, suggesting that it promotes the green development. Local governments' behavior affects the supply of energy-intensive industrial land. Industry structure dependence reinforce the effects of the carbon emissions trading program. The impacts of the carbon emissions trading program on the supply of energy-intensive industrial land would be less in cities with stronger regional competition, higher fiscal pressure or during political cycles. This study implies that the supply of energy-intensive industrial land can be used as an indicator for evaluating the effects of the carbon emissions trading system from the factor input perspective. The incentives of local government should be considered for understanding and assessing the effects of the carbon emissions trading system on land supply for future study.

China’s carbon emissions abatement under industrial restructuring by investment restriction

This study applied CIECIA, a climate-economy integrated assessment model, to study the carbon reductions and the economic impacts under industrial restructuring realized by capital investment restriction policies in China. The carbon reductions and economic effects of the policy mix combining investment restrictions with low carbon technology progress measures were also assessed. The results show that investment restriction policies for energy-intensive industries in China effectively accelerate industrial restructuring and have significant carbon reduction effects. However, those restrictions hurt China’s economy, causing investment outflow that benefits other countries. Low carbon technology progress realized by investing revenues from investment restrictions in R&D and the sharing of advanced technologies between countries improves the carbon reduction potential of China, but it is difficult to obtain significant reduction in a short time period. Despite this finding, technology progress measures compensate for economic losses and therefore have great significance to carbon governance.

Economic evaluation of the trilateral FTA among China, Japan, and South Korea with big data analytics

This study quantitatively analyzes the impact of a free trade agreement (FTA) among China, Japan, and South Korea using big data analytics. Using game theory and the computable general equilibrium approach, it proposes a compromise between the two countries for agricultural protection, to reduce possible divergences and confrontations. The findings show that game results differ among the three countries as per interest indexes. Compared to full tariff exemption, an FTA with agricultural protection not only stimulates economic growth in the three countries but also reduces Japan and South Korea’s agricultural concerns and impact on employment. We also evaluate the impacts of the trilateral FTA on manufacturing and services industries. The results show that China will increase imports of energy-intensive products from Japan and South Korea, which may reduce domestic output and generate environmental benefits. Implementing the trilateral FTA with agricultural protection may reduce carbon emission in Northeast Asia by 6.53 million tons. This study can promote economic integration in Northeast Asia and coping with climate change. The analysis also highlights the importance of lifecycle management of energy-intensive industries in China, Japan, and South Korea.

Transportation infrastructure development and China’s energy intensive industries - A road development perspective

In this paper, we investigate how road infrastructure affects the energy consumption and development of energy intensive industries in China. From the perspective of profit function, the endogeneity problem which may be caused by reverse causality can be avoided. China’s provincial data for the period 2000–2013 and seemingly uncorrelated regression method are used to estimate the parameters. The results show that increase in road density will increase energy consumption and promote the development of energy intensive industries. The short term elasticity of output with respect to road infrastructure is smaller than the long run elasticity. However, this is not the case for energy consumption. Additionally, increase in road density can reduce energy intensity only in the long run and the decrease is largest in western China. Therefore, more road construction in the western regions will be more helpful for energy intensive industries in their bid to reduce energy intensity. Furthermore, we find evidence of energy price distortion in China’s energy intensive industries. Lastly, we calculate the changes in output and energy consumption of China’s energy intensive industries both in the short and long run caused by the increase in road density from 2000 to 2013.

A short-term based analysis on the critical low carbon technologies for the main energy-intensive industries in China

The thermal power industry, the cement industry and the iron & steel industry are among the top energy consumers and air-polluters in China, which have significant impacts on climate change. The annual productions of these three industry sectors are all ranked as the top one in the world. In order to assist the short-term policy making for the development of critical LCTs in China, an evaluation of the performance of low carbon technologies (LCTs) for the top three energy-intensive industries in China is conducted in this paper through multi-criteria decision making (MCDM) method. There are 31 LCTs identified for these three industries based on the criteria including environmental performance, technological performance, and economic performance. Based on the experts’ evaluation, top 10 LCTs are selected as the critical LCTs which should be given priority in the short-term technology diffusion in China. Suggestions are provided for policy-makers on the carbon mitigation and cleaner production in the main energy-intensive industries in China.

Changes in carbon intensity of China’s energy-intensive industries: a combined decomposition and attribution analysis

There has been growing interest among researchers in factors influencing carbon emissions of energy-intensive industries in China due to the important roles they play. Such studies mainly focused on evaluating carbon emissions and identifying the contributing factors separately for each energy-intensive industry. Regarding energy-intensive industries as a whole and investigating the contribution of each industry to changes in carbon intensity have not yet been sufficiently addressed and quantified. In order to deeply understand this issue, this study employed the LMDI decomposition analysis to study driving forces (e.g., emission coefficient, energy intensity, and industrial structure) of carbon intensity of energy-intensive industries. Then, attribution analysis was further used to study the contribution of each energy-intensive industry to the percent change in carbon intensity through each impact factor. The results showed that the carbon intensity of energy-intensive industries dropped by 31.83% from 1996 to 2014. The energy intensity effect was largely responsible for this decrease, of which, five industries were the contributors except for the fuel-processing industry. The industrial structure effect also contributed to the decrease, and non-metallic industry and fuel-processing industry played important roles. However, the emission coefficient effect showed a slight impact on increasing carbon intensity, which principally due to chemical industry and power generation industry. The findings suggested that the adaptability and sensitivity of different energy-intensive industries to the implemented policies were various. Based on the results, differentiated and feasible policies related to energy intensity, industrial structure, and energy structure for energy-intensive industries were provided to further mitigate carbon intensity.

The effects of climate policy on corporate technological upgrading in energy intensive industries: Evidence from China

The innovation effect is an important component when measuring the performance of environmental policy instruments. Based on a questionnaire survey, this research has examined corporate energy conservation and emission reduction efforts in energy intensive industries in China under the pressure of different climate policies, and in particular looked into their adoption of those technological innovation and diffusion activities. The results show a large variety of corporate adoption of energy-saving practical activities. In general, climate policies have played a greater role in promoting the adoption of managerial energy-saving activities in respondent companies, while comparatively their influences on the adoption of technology upgrading activities are relatively weak. Regulatory measures have exerted greater pressure and influence on corporate short-term behavioural change, as stated by the respondent companies. However, market based instruments show greater incentive effect in promoting adoption of energy conservation and emission reduction activities that refer to corporate long-term oriented strategic planning or adjustment. For instance they exert a significant incentive effect on increasing long-term research and development investment for technological innovation, and also play an important role in optimising corporate organisational structure. The econometric analysis further proves the influences of market-based instruments in promoting corporate adoption of technological innovation and diffusion activities.