Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Zhao, Wei; Niu, Dongxiao

doi:10.3390/su9122377

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…Drivers influencing CO 2 emissions are classified under four headings. The first is population, represented by total population or urbanization level in earlier literature, which are found to be the contributory drivers of CO 2 emissions [27][28][29][30][41][42][43][44]. The second is affluence, usually reflected by gross domestic product (GDP) per capita.…”

Section: Stirpat Modelmentioning

confidence: 99%

“…Wen et al [29] explored drivers of CO 2 emissions in China's power sector based on ridge regression and proposed related policy measures. Zhao et al [30] applied ridge regression analysis and a neural network optimized by a Gauss prediction model to predict the CO 2 emissions of the power sector. Lindner et al [31] estimated direct CO 2 emissions deriving from electrical exports and imports among China's provinces.…”

Section: Introductionmentioning

confidence: 99%

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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

Cui

Zhao

2018

Energies

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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.

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Section: Stirpat Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cui

Zhao

2018

Energies

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“…(3) Research on the influencing factors of carbon emissions at the industrial level: ref. [31] found that the key influencing factors of CO 2 emission of power generation industry in China are population, per capita GDP, standard coal consumption and thermal power specific gravity by using the STIRPAT model. Ref.…”

Section: Analysis On the Influencing Factors Of Carbon Emissionsmentioning

confidence: 99%

Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015

Chen

Yang

2018

Sustainability

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Based on provincial panel data from 2005 to 2016, this paper analyzes evolving temporal-spatial trends, spatial correlation and influencing factors of carbon emissions in China. The results show that there is a great heterogeneity in the evolving temporal-spatial trends of carbon emissions among provinces and regions in China, with the heterogeneity in eastern provinces most obvious. At the same time, there exists significant spatial correlation and agglomeration of carbon emissions in 30 provinces. It is found that the distribution characteristics of carbon emissions are affected by various economic and social factors based on the extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Population pressure, affluence, energy intensity, industrial structure, urbanization level and investment in fixed assets can significantly promote the increase of carbon emissions. The technological level and government environmental supervision have significant inhibitory effects on carbon emissions, but foreign direct investment (FDI) has no significant impact. Therefore, it is necessary to strengthen environmental supervision and upgrade technology level to promote carbon emission reduction.Keywords: carbon emissions; evolving temporal-spatial trends; spatial correlation; influencing factors Highlights(1) Analysis of the temporal-spatial trends and spatial correlation of CO 2 in China's 30 provinces.(2) Research on the influencing factors based on the extended STIRPAT model. (3) There exists great heterogeneity and significant spatial correlation of CO 2 . (4) It is necessary to strengthen environmental regulation and improve technological level to promote carbon emission reduction.

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“…These models are often combined with scenario analyses to forecast and plan future energy needs, estimate the corresponding environmental impacts, and perform cost-benefit analyses. In these models, the emissions of atmospheric pollutants and greenhouse gases during energy production, transport, and consumption were calculated, and the scenarios for reducing energy consumption and the impact factors were discussed [5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

Luo

Chen

et al. 2019

Atmosphere

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In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOx, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter.

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Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Cited by 18 publications

References 38 publications

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

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

Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

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