Exploring regional differences in the impact of high energy-intensive industries on CO2 emissions: Evidence from a panel analysis in China

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.

Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China

The rising concentration of carbon in the atmosphere leads to increasing climate change, and it has become a worldwide consensus to reduce emissions. Considering the degree of economic development and industrial structure of different regions and the vast differences in the spatial distribution of clean energy reserves, it is essential to develop localized emission reduction programs. This study investigates the changes in city GDP after implementing carbon pricing policies. The results show that the carbon pricing policy could effectively reduce inequalities between "rich" and "poor" regions. The Moran index before and after the implementation of the policy decreases from 0.416 to 0.401. We also found spatial clustering patterns of carbon emissions, with the main drivers of carbon emissions differing significantly between developed and developing cities, resource-based and industrial cities, and southern and northern cities in China. The most crucial driver of carbon emissions is still the demand for economic development, which can explain more than 30% of carbon emissions. This study focuses on the impact of carbon market & carbon pricing on poverty alleviation and carbon reduction, makes up for the lack of "spatial justice" in the existing studies and provides a feasible carbon reduction plan for different cities.

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.

Nexus Between Financial Development, Renewable Energy Investment, and Sustainable Development: Role of Technical Innovations and Industrial Structure

Significant challenges confronting China include reducing carbon emissions, dealing with the resulting problems, and meeting various requirements for long-term economic growth. As a result, the shift in industrial structure best reflects how human society utilizes resources and impacts the environment. To meet China's 2050 net-zero emissions target, we look at how technological innovations, financial development, renewable energy investment, population age, and the economic complexity index all play a role in environmental sustainability in China. Analyzing short- and long-term relationships using ARDL bounds testing, we used historical data spanning 1990–2018. According to the study's findings, the cointegration between CO₂ emissions and their underlying factors was found. The deterioration of the environment directly results from financial development, increasing economic complexity, and population aging. Technical advancements, investments in renewable energy sources, and changes to the industrial structure all contribute to lower CO₂ emissions. Granger causality results were also reliably obtained in this study. According to our findings in the fight against environmental problems, a key tool for meeting long-term sustainability goals is policy prescriptions that use technological innovations, renewable energy investment, and industrial structure.

Impact of climate change on cereal production: evidence from lower-middle-income countries

This study empirically examines the impact of climate change on cereal production in selected lower-middle-income countries with a balanced panel dataset spanning 1971-2016. The study uses average annual temperature and rainfall to measure climate change. Besides this, CO2 emissions, cultivated land under cereal production, and rural population are used as the control variables. Second-generation unit root tests, i.e., CIPS and CADF, are used to test the stationarity of the variables. Feasible generalized least square (FGLS) and fully modified ordinary least square (FMOLS) models are used to achieve the objective. Pedroni cointegration test confirms the presence of cointegration between cereal production and climate change variables. The findings show that a rise in the temperature reduces cereal production in lower-middle-income countries. In contrast, rainfall and CO2 emissions have a positive effect on cereal production. For robustness purpose, the Driscoll-Kraay standard regression and dynamic ordinary least square (DOLS) models have also found similar results. Dumitrescu-Hurlin test has found the bidirectional causality of cereal production with temperature and CO2 emissions. Also, unidirectional causality is running from rainfall and rural population to cereal production. The adverse effects of temperature on cereal production are likely to pose severe implications for food security. The paper recommends that governments of the sample countries should research and develop heat-resistant varieties of cereal crops to cope with the adverse effects of temperature on cereal production and ensure food security.

Spatial Heterogeneity of Factors Influencing CO2 Emissions in China’s High-Energy-Intensive Industries

In recent years, China has overtaken the United States as the world’s largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions; by contrast, the growth of the services sector’s share in China’s gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions.

Global characteristics and trends of research on industrial structure and carbon emissions: a bibliometric analysis

The relationship between industrial structure and carbon emissions has been widely identified as a critical research topic by international organizations and academics. Using bibliometrics analysis, this study aimed at dissecting the global characteristics and trends of research on industrial structure and carbon emissions. Based on the 806 documents from 2004 to 2019 in Web of Science, this work was implemented from four aspects, including basic characteristics analysis, country/territory and institution analysis, category and journal analysis, and reference and keyword analysis. The results of this study showed rapid growth trends of research on industrial structure and carbon emissions from 2015 to 2019. The collaborations among countries and institutions were extensive worldwide with China, the USA, and the UK as the main participants. Furthermore, the corresponding research topics, research priorities, and research paths were summarized according to the references co-citation analysis and keywords cluster analysis, which from the perspective of the correlation between different types of industry with carbon emissions. Finally, the timezone view of the top 100 keywords indicated that the emerging trends in the research on industrial structure and carbon emissions were regional analysis, industrialization, and environmental efficiency, and prediction of carbon emissions peak and the spatial distribution in different types of industries were the hotspots in recent years. The findings provide a better understanding of global characteristics and trends that have emerged in this field, which can also offer reference for future research.

The impact of clean development mechanism on energy-water-carbon nexus optimization in Hebei, China: A hierarchical model based discussion

Clean development mechanism (CDM) is an important principle in CO₂ emissions mitigation since it was proposed. However, there will be a long way for the large-scale application of CDM in China, for thermal power is still in the dominant position, while renewable power is far from being competitive. In this study, a mix integer bi-level hierarchical programming model (MIBLHP) is developed to investigate the potential impact of the CDM application on the regional energy-water-carbon nexus optimization in electric power system in China. The model integrates the advantages of bi-level programming and mix integer liner programming in dealing with conflicting objectives with hierarchical and sequential decision making process and help to achieve the optimized strategies with the highest overall system satisfaction degree. Results show that CDM will play an important role in the regional energy-water-carbon nexus optimization, the MIBLHP is able to stabilize the increasing trend of system cost and make better tradeoffs between the economic and environmental goals. Optimal strategies including power generation and capacity expansion path, system cost control, CO₂ emissions abatement strategies, water consumption and waste water discharge are presented. The system would reach the supreme satisfaction degrees (λ = 0.934) when the CO₂ reduction level is 40%, which means that every part of the system has achieved the best condition. The proposed model has great potential in dealing with similar regional planning problems in energy-water-carbon nexus optimization.

Biomonitoring of aromatic hydrocarbons in clam Meretrix meretrix from an emerging urbanization area, and implications for human health

Pollution with total petroleum hydrocarbons (TPHs) is a global concern and particularly in coastal environments. Polycyclic aromatic hydrocarbons (PAHs) are regarded as the most toxic components of TPHs and they can also be derived from other sources. Fangcheng Port is considered as a representative emerging coastal city in China, but the status, sources, and hazards to organisms and humans with respect to contamination with PAHs and TPHs are unknown in the coastal regions of this area. Therefore, in this study, we cloned cytochrome P450 family genes (CYP1A1, CYP3A, and CYP4) and heat shock protein 70 gene (HSP70) in the clam Meretrix meretrix as well as optimizing the method for measuring the 7-ethoxyresorufin O-deethylase activity. These molecular indicators and four specific physiological indexes were found to be appropriate biomarkers for indicating the harmful effects of PAHs and TPHs on clams after exposure to the crude oil water-soluble fraction. In field monitoring surveys, we found that the 2- and 3-ring PAHs were dominant in the clams whereas the 4- to 6-ring PAHs were dominant in the sediments at each site. The PAH levels (3.63-12.77 ng/g wet weight) in wild clams were lower, whereas the TPH levels (13.25-70.50 μg/g wet weight) were higher compared with those determined previous in China and elsewhere. The concentrations of PAHs and TPHs in the sediments (19.20-4215.76 ng/g and 3.65-866.40 μg/g dry weight) were moderate compared with those in other global regions. Diagnostic ratio analysis demonstrated that the PAHs were derived mainly from pyrogenic sources. The TPHs may have come primarily from industrial effluents, land and maritime transportation, or fishing activities. The Integrated Biomarker Response version 2 indexes indicated that the clams collected from site S5 exhibited the most harmful effects due to contamination by PAHs and TPHs. Human health risk assessments demonstrated that the risks due to PAHs and TPHs following the consumption of clams can be considered acceptable. Our results suggest that continuous monitoring of contamination by PAHs and TPHs is recommended in this emerging coastal city as well as assessing their human health risks.

A study on carbon transfer and carbon emission critical paths in China: I-O analysis with multidimensional analytical framework

As environmental issues aggravated heavily, China faces increasing pressure and challenges on carbon emission reduction and distribution. we used non-competitive input-output table (I-O table) combined with the methods of Structural Path Analysis (SPA) and Multidimensional Analytical Framework (MAF), based on the data of China in 2012, to analyze the current situation of inter-sector carbon emission transfer and identify the key sectors and the critical paths from multiple perspectives. Our results show that total fixed capital formation is the main final demand. The electricity, petroleum, and metal smelting are the largest carbon outflow sectors, which emit carbon at the upstream of the path. Construction and other services are the most obvious carbon inflow sectors, which belong to the middle and downstream of the path and lead to indirect carbon emissions through their demands for other sectors. "Metal smelting → Construction → Total fixed capital formation," "Nonmetallic products → Construction → Total fixed capital formation," and "Petroleum → Urban consumption," "Electricity → Urban consumption" are the top four paths with large carbon emission, which deserve attention. Finally, this paper puts forward some policy implications on emission reduction based on the results.