2023
DOI: 10.3390/ijerph20020921
|View full text |Cite
|
Sign up to set email alerts
|

Drivers and Decoupling Effects of PM2.5 Emissions in China: An Application of the Generalized Divisia Index

Abstract: Although economic growth brings abundant material wealth, it is also associated with serious PM2.5 pollution. Decoupling PM2.5 emissions from economic development is important for China’s long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 66 publications
0
1
0
Order By: Relevance
“…Research has been conducted at the national level [4][5][6][7], the local level [8,9], and the industry sector level [10,11]. The research method perspective is mainly attributed to the decomposition analysis method, which includes the index decomposition analysis method (IDA) and the structural decomposition analysis method (SDA) [12,13]. For instance, Yan et al (2019) used the IDA method to identify the main influencing factors of the overall change in CO 2 emissions from provincial thermal power generation in China [14].…”
Section: Introductionmentioning
confidence: 99%
“…Research has been conducted at the national level [4][5][6][7], the local level [8,9], and the industry sector level [10,11]. The research method perspective is mainly attributed to the decomposition analysis method, which includes the index decomposition analysis method (IDA) and the structural decomposition analysis method (SDA) [12,13]. For instance, Yan et al (2019) used the IDA method to identify the main influencing factors of the overall change in CO 2 emissions from provincial thermal power generation in China [14].…”
Section: Introductionmentioning
confidence: 99%