Dual carbon economy modeling in advanced economies

Abstract Utilizing the cross-sectional fully modified OLS (FMOLS) method, this paper tackles the pressing need to address climate change disasters, particularly in the context of recent COVID-19 restrictions. By developing an econometric model specifically for advanced economies, the study aims to enhance understanding and reduce carbon emissions. Examining ten advanced economies from 1995 to 2019, it identifies a significant gap in the literature concerning the integration of demographic, social, economic, and political (DSEP) factors in dual carbon economy models. The results show that a 1% improvement in good governance is associated with a 0.23% reduction in carbon footprint, due to stricter environmental regulations. In contrast, a 1% increase in economic growth results in a 0.17% rise in carbon footprint, driven by industrial activities. Similarly, a 1% rise in electricity consumption leads to a 0.32% increase in carbon footprint, reflecting dependence on fossil fuels. Additionally, a 1% increase in population size corresponds to a 0.21% rise in carbon footprint, linked to higher urban energy consumption. Finally, a 1% increase in the poverty ratio results in a 0.17% rise in carbon footprint, underscoring the influence of socio-economic factors on sustainability. The paper advocates for a comprehensive approach to fostering a dual carbon economy, emphasizing green economic growth, smart, sustainable cities, social sustainability literacy, and sustainable governance.

Authors

Institutions

Publication Details

Journal
Humanities and Social Sciences Communications
Published
2024-09-27
DOI
https://doi.org/10.1057/s41599-024-03794-9
Citations
3
Primary Topic
Climate Change Policy and Economics
Type
article
Field-Weighted Citation Impact
3.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dual carbon economy modeling in advanced economies

Weiyu Gu, Sheng Xu
3 citations
Humanities and Social Sciences Communications
Climate Change Policy and Economics
3.39
article

Dual carbon economy modeling in advanced economies

Weiyu Gu, Sheng Xu
article en
3 citations

Abstract

Abstract Utilizing the cross-sectional fully modified OLS (FMOLS) method, this paper tackles the pressing need to address climate change disasters, particularly in the context of recent COVID-19 restrictions. By developing an econometric model specifically for advanced economies, the study aims to enhance understanding and reduce carbon emissions. Examining ten advanced economies from 1995 to 2019, it identifies a significant gap in the literature concerning the integration of demographic, social, economic, and political (DSEP) factors in dual carbon economy models. The results show that a 1% improvement in good governance is associated with a 0.23% reduction in carbon footprint, due to stricter environmental regulations. In contrast, a 1% increase in economic growth results in a 0.17% rise in carbon footprint, driven by industrial activities. Similarly, a 1% rise in electricity consumption leads to a 0.32% increase in carbon footprint, reflecting dependence on fossil fuels. Additionally, a 1% increase in population size corresponds to a 0.21% rise in carbon footprint, linked to higher urban energy consumption. Finally, a 1% increase in the poverty ratio results in a 0.17% rise in carbon footprint, underscoring the influence of socio-economic factors on sustainability. The paper advocates for a comprehensive approach to fostering a dual carbon economy, emphasizing green economic growth, smart, sustainable cities, social sustainability literacy, and sustainable governance.

Humanities and Social Sciences CommunicationsVol. 11(1)
Central University of Finance and Economics (CN), Washington University in St. Louis (US)
Openalex Percentile: Top 6%
Climate Change Policy and Economics
3.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.