Carbon Emissions in China’s Construction Industry: Calculations, Factors and Regions

Abstract

The production of construction projects is carbon-intensive and interrelated to multiple other industries that provide related materials and services. Thus, the calculations of carbon emissions are relatively complex, and the consideration of other factors becomes necessary, especially in China, which has a massive land area and regions with greatly uneven development. To improve the accuracy of the calculations and illustrate the impacts of the various factors at the provincial level in the construction industry, this study separated carbon emissions into two categories, the direct category and the indirect category. The features of carbon emissions in this industry across 30 provinces in China were analysed, and the logarithmic mean Divisia index (LMDI) model was employed to decompose the major factors, including direct energy proportion, unit value energy consumption, value creation effect, indirect carbon intensity, and scale effect of output. It was concluded that carbon emissions increased, whereas carbon intensity decreased dramatically, and indirect emissions accounted for 90% to 95% of the total emissions from the majority of the provinces between 2005 and 2014. The carbon intensities were high in the underdeveloped western and central regions, especially in Shanxi, Inner-Mongolia and Qinghai, whereas they were low in the well-developed eastern and southern regions, represented by Beijing, Shanghai, Zhejiang and Guangdong. The value creation effect and indirect carbon intensity had significant negative effects on carbon emissions, whereas the scale effect of output was the primary factor creating emissions. The factors of direct energy proportion and unit value energy consumption had relatively limited, albeit varying, effects. Accordingly, this study reveals that the evolving trends of these factors vary in different provinces; therefore, overall, our research results and insights support government policy and decision maker’s decisions to minimize the carbon emissions in the construction industry.

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

“…The major methods to identify the determinants of carbon emissions in the construction industry are the index decomposition analysis (IDA) and the structural decomposition analysis (SDA). The IDA has a wider application than the SDA for its flexibility and lower data requirement [10,[16][17][18]. However, the IDA only focuses on the direct effect and ignores the effect of the indirect energy demand and final demand [1,19,20].…”

“…Previous studies paid little attention to the spatial spillover effects when examining the determinants of carbon emissions in the construction industry [1,[16][17][18]. They assumed that the studied regions were spatially independent from each other, and the characteristics of one region would not affect the carbon emissions of its neighboring regions.…”

Multi-Agents-Based Modeling and Simulation for Carbon Permits Trading in China: A Regional Development Perspective

“…It has some characteristics: (1) Heterogeneity is when the individual agent is depicted with characteristics and attributes [22]. (2) Adaptivity is when the external environment changes, different agents conduct different adjustment behaviors to better adapt themselves to these changes of environment [23]. (3) Learnability is when each agent is rational, but the learning ability is different.…”

“…It became the world's largest carbon emitter in 2006, and was responsible for 64.8% of the increase in global carbon emissions between 2007 and 2012. Moreover, in 2013, carbon emissions of China approximately made up 28.6% of the world's total carbon emissions [2,3]. As China is facing great pressure from the international community on the issue of carbon emissions, the government is making an effort to control the carbon emissions.…”

Calculation of Joint Return Period for Connected Edge Data

“…Currently, the theory of multivariate distribution has been applied in many fields [10][11][12][13][14][15][16]. Many application examples have also shown that the design parameters derived from a joint probability distribution can better meet the design need of ocean engineering [17][18][19][20], reduce the building cost of ocean engineering more reasonably, explore return periods of multivariable waves, and calculate joint design level values more accurately. Additionally, all of these are of great significance for the design and risk management of related projects [21].…”