The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

Yong Wang, Guangchun Yang, Ying Dong, Yu Cheng and Peipei Shang
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Yong Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Guangchun Yang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Ying Dong: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Yu Cheng: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Peipei Shang: Editorial Department, Dongbei University of Finance and Economics, Dalian 116025, China

Energies, 2018, vol. 11, issue 5, 1-25

Abstract: Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

Keywords: household consumption; total carbon emissions; CLA Model; influence factor; STIRPAT model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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