Abstract
| Original language | English |
|---|---|
| Article number | 116502 |
| Journal | Journal of Environmental Management |
| Volume | 325 |
| DOIs | |
| Publication status | Published - 1 Jan 2023 |
Keywords
- Driving factors
- Household carbon dioxide emissions
- Ridge regression analysis
- STIRPAT
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In: Journal of Environmental Management, Vol. 325, 116502, 01.01.2023.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China
AU - Yu, Shiwang
AU - Zhang, Qi
AU - Hao, Jian Li
AU - Ma, Wenting
AU - Sun, Yao
AU - Wang, Xuechao
AU - Song, Yu
N1 - Funding Information: The authors gratefully acknowledge the support of the PGRS (20190621) project from Xi'an Jiaotong-Liverpool University and Key Special Fund Exploring Program ( KSF-E-29 ) from Xi'an Jiaotong-Liverpool University and Suzhou Industrial Park, China. Publisher Copyright: © 2022 Elsevier Ltd
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.
AB - Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.
KW - Driving factors
KW - Household carbon dioxide emissions
KW - Ridge regression analysis
KW - STIRPAT
UR - http://www.scopus.com/inward/record.url?scp=85140092390&partnerID=8YFLogxK
U2 - 10.1016/j.jenvman.2022.116502
DO - 10.1016/j.jenvman.2022.116502
M3 - Article
C2 - 36274310
SN - 0301-4797
VL - 325
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 116502
ER -