Driving factors and emission reduction scenarios analysis of CO2 emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics

Xichun Luo; Chengkun Liu; Honghao Zhao

doi:10.1016/j.scitotenv.2023.161966

Driving factors and emission reduction scenarios analysis of CO₂ emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics

Sci Total Environ. 2023 Apr 20:870:161966. doi: 10.1016/j.scitotenv.2023.161966. Epub 2023 Feb 1.

Authors

Xichun Luo¹, Chengkun Liu², Honghao Zhao³

Affiliations

¹ The Institute for Sustainable Development, Macau University of Science and Technology, Taipa 999078, Macao.
² The Institute for Sustainable Development, Macau University of Science and Technology, Taipa 999078, Macao; School of Business, Macau University of Science and Technology, Taipa 999078, Macao.
³ The Institute for Sustainable Development, Macau University of Science and Technology, Taipa 999078, Macao; School of Business, Macau University of Science and Technology, Taipa 999078, Macao. Electronic address: hhzhao@must.edu.mo.

PMID: 36737020
DOI: 10.1016/j.scitotenv.2023.161966

Abstract

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO₂ emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO₂ emissions in the GBA and surrounding cities. The results show that (a) CO₂ emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO₂ emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO₂ emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO₂ emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

Keywords: CO(2) emissions; Guangdong-Hong Kong-Macao Greater Bay Area; Logarithmic Mean Divisia Index; System dynamics.

MeSH terms

Carbon Dioxide* / analysis
Carbon* / analysis
China
Cities
Hong Kong
Macau

Substances

Carbon Dioxide
Carbon