Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China

Abstract

A large population size and rapid economic growth have resulted in a huge amount of housing consumption in China. Therefore, it is critical to identify the determinants of housing carbon footprint (CF) and prepare appropriate carbon mitigation measures. By employing the IPCC accounting method, input-output analysis and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, this study aims to study the spatio-temporal patterns and identify the driving factors of housing CF. The results show that regional disparities and urban-rural differences existed during the period 2012–2017. The results of the extended STIRPAT model show that population scale and energy consumption per unit building area are the two dominant contributors to the housing CF increments in all areas. While, family size only shows significant negative impact in eastern and western regions, the per capita disposable income only induces higher housing CF in rural areas, and energy structure had a remarkable positive impact in urban area of western region and all rural areas. Policy recommendations are proposed to mitigate the overall housing CF, including; controlling population growth and promoting urbanization benefits; encouraging green consumption; optimizing household energy consumption structure, and; enhancing residential building energy management.

Research on energy consumption in household sector: a comprehensive review based on bibliometric analysis

“…Quantifying household indirect and direct energy consumption-demand relationships using IOA is a particularly widely studied topic (Supasa et al, 2017;Singh et al, 2018;Long et al, 2019). In addition, input-output models have established an important role in measuring household carbon emissions, especially in China (Xu L et al, 2021;Tian et al, 2017;Zhang and Lei, 2017). Many studies have confirmed the contribution of IOA models in promoting energy efficiency and in developing energy efficiency policies of household (Stagnitta et al, 2020).…”

“…Large population sizes and rapidly growing economies lead to large energy consumption and carbon emissions in the residential sector (Xu L et al, 2021), which are threatening environmental sustainability. Identifying and analyzing the key drivers affecting household energy consumption (HEC) has proven to be an important step in improving energy efficiency and pursuing sustainable energy strategies .…”

“…Faced with the continual growth in energy consumption pressures, enhancing residential energy efficiency is regarded as a pivotal means to mitigate energy costs and reduce emissions (Kaufmann et al, 2023;Zheng et al, 2024). Xu L et al (2021) expressed the pioneering nature of future research in the study. Studies identified behavioral interventions, energy efficiency and energy poverty were identified as possible research frontiers.…”

Dynamic Threshold Effect of Directed Technical Change Suppress on Urban Carbon Footprint in China

“…The capital stock of urban-oriented technological progress is estimated by the perpetual inventory method, with 2000 as the base period, of which the total employment and real GDP are derived from the 2010-2020 China Urban Statistical Yearbook. This paper uses 2009-2019 DMSP/OLS data to obtain nighttime light data in 283 cities in China, and uses the method of Xu et al (2020) to calibrate the problem of inconsistent data sources around 2013 [59]. This paper obtains the basic data for calculating the DEA-malmquist index from the China Urban Statistical Yearbook.…”