Decomposition and Forecasting of CO 2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Herui Cui, Ruirui Wu and Tian Zhao
Additional contact information
Herui Cui: Department of Economics and Management, North China Electric Power University, Huadian Road No.689, Baoding 071003, China
Ruirui Wu: Department of Economics and Management, North China Electric Power University, Huadian Road No.689, Baoding 071003, China
Tian Zhao: Department of Economics and Management, North China Electric Power University, Huadian Road No.689, Baoding 071003, China

Energies, 2018, vol. 11, issue 11, 1-19

Abstract: China faces significant challenges related to global warming caused by CO 2 emissions, and the power industry is a large CO 2 emitter. The decomposition and accurate forecasting of CO 2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO 2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO 2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO 2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy.

Keywords: power industry; CO 2 emissions forecasting; driving factors; partial least square (PLS) model; stochastic impacts by regression on population; affluence and technology (STIRPAT) model; PLS-Grey-Markov optimized approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/11/2985/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/11/2985/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:11:p:2985-:d:179818

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().