Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

Abstract

As the world’s largest carbon emitter, China is under enormous pressure to decrease carbon emissions. With the economic development in recent years, China has increased its investment in infrastructure, and the construction industry has become an essential source of carbon emissions. Using the social network analysis (SNA) methodology, this article analyzes the evolutionary characteristics of the spatial correlation network for carbon emissions in the construction industry from 2003–2017 and its affecting factors. The results of the empirical analysis in this paper are: (1) the spatial association of carbon emissions in Chinese inter-provincial construction industry shows an intuitive network layout and the spatial network has gradually stabilized since 2014; (2) according to the results of degree centrality, betweenness centrality and closeness centrality, it can be concluded that the regions with higher level of association with other provinces are the central and the eastern regions (Henan, Hubei, Hunan, Guangdong, Jiangsu, etc.) and Xinjiang; the linkage of construction-related carbon emissions was mainly achieved through the regions of Henan, Anhui, Shanxi, Hebei, Guangdong, and Inner Mongolia; the regions with higher level of construction industry development (Jiangsu, Henan, Hunan, Guangdong, etc.) are more closely associated with other provinces; (3) geographical proximity and reduction of difference in energy intensity and in industrial structure have substantial positive effects on the carbon emission association of the construction industry. Finally, based on the research results, this article proposes corresponding policy recommendations.

The Construction and Application of E-Learning Curricula Evaluation Metrics for Competency-Based Teacher Professional Development

“…Centrality represents the position and function of each network node and its central location, including weighted degree centrality (C WD ), degree centrality (C D ), betweenness centrality (C B ), and closeness centrality (C C ). Based on previous studies, the centrality metrics of SNA can validly measure the position and function of nodes in a complex network, which can assist researchers in understanding the importance of the roles played by individual nodes in the associated network [24]. Thus, the current study adopted a mixedmethod approach that included bibliometric analysis and SNA to construct a keyword co-occurrence network (KCN) [25,26].…”

Analysis of the spatial and temporal heterogeneity of factors influencing CO2 emissions in China’s construction industry based on the geographically and temporally weighted regression model: Evidence from 30 provinces in China

“…Global warming caused by greenhouse gases is a global environmental problem that has become a significant threat to the survival of humans and other species (Lu et al, 2020). Among the many greenhouse gases, CO 2 accounts for about 70% (Li and Chen, 2020)and is the most important anthropogenic greenhouse gas in the environment (Li et al, 2021). To fight global warming, it is necessary to ensure that countries worldwide effectively reduce CO 2 emissions (Lu et al, 2020).…”

“…Previous studies have shown that the construction industry accounts for more than 40% of the world's energy consumption and 36% of the world's CO 2 emissions (Li et al, 2021). It is an important pillar industry of China's national economy and a resource-intensive industry (Wang and Feng, 2018).…”

Preparation of artificial aggregate using waste concrete powder and CO2 fixed by microorganisms

“…Countries and organizations all over the world are contributing to carbon emission reduction (Ma et al, 2020). As the world's largest carbon emitter, China is under enormous pressure to decrease CO 2 emissions (Li et al, 2021). China has committed to achieving carbon dioxide emissions peaking by 2030 and carbon neutralization by 2060 (Zheng et al, 2019).…”