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

• Main Authors: Shijie Yang, Yunjia Wang, Rongqing Han, Yong Chang, Xihua Sun
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Sustainability
• Subjects: spatial spillover; linear regression model; carbon emissions; energy consumption; global warming
• Online Access: https://www.mdpi.com/2071-1050/13/15/8304

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.