Decomposition and Forecasting of CO2 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,Tian Zhao

doi:10.3390/en11112985

Abstract

China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 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 CO2 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 CO2 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 CO2 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.

Highlights

The impacts of climate change have become increasingly severe, arousing extensive attention worldwide [1,2,3]
CO2 emissions were continually increasing, with emissions growth changing at various stages
This paper explores drivers of CO2 emissions of the power industry in China and obtains high-precision estimates of CO2 emissions based on partial least square (PLS) and Grey-Markov models

Summary

Introduction

The impacts of climate change have become increasingly severe, arousing extensive attention worldwide [1,2,3]. Global warming is mostly attributed to greenhouse gas (GHG) emissions, especially. CO2 originating from enhanced industrialization [4,5]. Substantial CO2 emissions can bring about adverse effects on the natural environment. The use of fossil fuel is the main origin of CO2 emissions, which poses significant challenges to environmental protection in the context of securing worldwide energy supplies [6]. The power industry converts primary energy sources, such as coal, into electrical energy, which plays a natural monopoly role within the energy sector [7]. The electricity industry is the crucial energy sector in the process of economic growth, and an industry fundamental to people’s livelihoods [8]. The electricity industry is a substantial energy-related CO2 emitter, and its global

Methods

Results

Conclusion