Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

Sun, Han; Huang, Chao; Ni, Shan

doi:10.1007/s10668-021-01862-7

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…The generalized Divisia index method (GDIM) is an effective decomposition approach that links the changes in pollutant emissions with socio-economic factors through the deformation of Kaya identity [28,42,43]. In contrast to classical econometric models, the GDIM approach mainly decomposes PM 2.5 emissions based on time-series data into different influencing factors without residual terms.…”

Section: Decomposition Of Pm 25 Emission Factorsmentioning

confidence: 99%

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

Wang

Zhang

Cheng

2023

IJERPH

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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 states between PM2.5 emissions and economic growth and to identify the main factors leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM2.5 emissions, while innovation input PM2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.

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Section: Decomposition Of Pm 25 Emission Factorsmentioning

confidence: 99%

“…Thus, to address these drawbacks, the generalized Divisia index method (GDIM) was presented by Vaninsky [37]. Subsequently, the application of this approach is gradually growing [37][38][39][40][41], with only a few cases focusing on PM 2.5 pollution [28,42,43].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhang

Cheng

2023

IJERPH

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Do subjective well-being influence people’s willingness to pay for improved air quality: evidence from China

Wang

2023

Environ Dev Sustain

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How to improve the resilience of power generation from energy intensity perspective? Evidence from the generalized Divisia index approach

Wang

Baležentis

2023

Environmental Impact Assessment Review

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Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

Cited by 7 publications

References 43 publications

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

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

Do subjective well-being influence people’s willingness to pay for improved air quality: evidence from China

How to improve the resilience of power generation from energy intensity perspective? Evidence from the generalized Divisia index approach

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