Low-carbon transformation path in energy-intensive industries in China: a multilevel perspective and configuration effects

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

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.

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

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

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

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.

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

The existing literature on China’s industrial green and low-carbon transformation primarily concerns the government’s top-down formal environmental regulation. A few studies have systematically investigated the role of informal environmental regulation represented by public environmental supervision and environmental non-governmental organizations The impact of public environmental supervision and ENGOs on industrial green and low-carbon transformation and its mechanism is empirically examined in this paper using a system GMM model and provincial panel data from 2005 to 2018. According to the findings, both Public environmental supervision and ENGOs can achieve green and low-carbon industrial transformation by promoting green technology progress; Public environmental supervision cannot collaborate with ENGOs to promote green and low-carbon industrial transformation based on national data. Further analysis indicates that the effect of public environmental supervision and ENGOs on industrial green and low-carbon transformation has significant regional heterogeneity. Public environmental supervision has a positive impact on industrial green and low-carbon transformation in the eastern and central regions. It has a negative impact on industrial green and low-carbon transformation in the western region. ENGOs promote green and low-carbon industrial transformation in the eastern and western regions. Only the eastern region has a synergistic effect on industrial greening and low-carbon transformation.

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

This paper explores China’s strategies for addressing climate change on the industrial level. Focusing on six energy-intensive industries, this paper applies gray relational analysis theory to the affecting factors to CO2 emissions of each industry after calculating each industry’s CO2 emissions during 2001–2010. Further research based on GM(1, 1) model is conducted to forecast the trend of the factors, the energy consumption and each industry’s CO2 emissions during the 12th Five-Year Plan period. As a breakthrough in previous conclusions, energy consumption structure was divided into the respective proportion of coal, oil, natural gas and electricity in the primary energy consumption, with which industrial output and energy intensity are combined to analyze each of their impacts on the energy-intensive industries. It turns out that all the factors’ impacts on emissions of the six major energy-intensive industries are significant, despite their differentiated extents. It is worth noting that, contrary to previous findings, industrial output is not the leading affecting factor to CO2 emissions of the energy-intensive industries compared with the proportion of coal and electricity in the primary energy consumption. The GM(1, 1) forecast results of energy consumption and CO2 emissions by the end of 2015 show that coal and electricity will remain a large proportion in primary energy consumption. This research may shed some light on China’s adjustment of energy structure under the pressure of addressing climate change and hence provide decision support for the acceleration of renewable energy utilization in the industrial departments.

Policies for eliminating low-efficiency production capacities and improving energy efficiency of energy-intensive industries in China

Assessment of a green credit policy aimed at energy-intensive industries in China based on a financial CGE model

As energy-intensive industries significantly impact the ecological environment, they serve as both foundational sectors for national economic development and cornerstones for ensuring the security and stability of industrial and supply chains. These industries are critical for promoting regional sustainable development. Considering the multiple challenges posed by the development of energy-intensive industries in China, such as environmental governance and energy supply issues, this study aims to explore how local governments can use environmental regulation to address these challenges. Using evolutionary game theory, this paper constructs an evolutionary game model between local governments and energy-intensive enterprises in China. The model examines the equilibrium points and their stability in their strategic interactions. MATLAB simulations are employed to illustrate how non-ideal equilibrium states can evolve into ideal equilibrium states. The findings reveal four equilibrium states in the game between local governments and energy-intensive enterprises: undesirable, worst, suboptimal, and ideal. The initial intentions of participants do not affect the equilibrium state of the system. However, selectively adjusting other cost-benefit variables in the model can guide the system from undesirable, worst, or suboptimal equilibrium states toward the ideal equilibrium state. This study not only deepens the understanding of industrial transfer research but also provides novel insights for local governments to leverage environmental regulation in designing policies that promote regional sustainable development.

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.

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