Impact factor analysis and forecasting of the carbon emissions from industries based on LMDI method under multiple uncertainties： The case of Suzhou City

doi:10.3969/j.issn.2097-0706.2022.02.001

Abstract

Abstract:

To achieve carbon peak and carbon neutrality, accurately measuring the carbon emissions, analyzing the composition and comprehensively studying the trend of carbon emissions from industries are the foundation of scientifically formulating policies on city development planning. The Kaya equation is reformulated by taking industrial structure, energy consumption composition and the influence of power from outside into account. And the Logarithmic Mean Divisia Index （LMDI） decomposition method is taken to analyze the impact factors on carbon emissions. The models for multiple uncertainties of carbon emission impact factors are set up based on the box-type uncertainty set. Then, a Stochastic Impacts by Regression on Population, Affluence and Technology （STIRPAT） model is construct after optimization, and a forecasting method for carbon emissions is made by solving the optimized model based on the uncertain set. Finally, the proposed method is validated by using large-scale industries in Suzhou as examples, and political suggestions are proposed.

Key words: carbon neutrality, impact factor of carbon emission, LMDI method, box-type uncertainty set, improved STIRPAT model, carbon emission forecasting

CLC Number:

X321：TK01：F407.2

WANG Sheng, TAN Jian, MA Yahui, ZOU Fenghua. Impact factor analysis and forecasting of the carbon emissions from industries based on LMDI method under multiple uncertainties： The case of Suzhou City[J]. Integrated Intelligent Energy, 2022, 44(2): 1-7.

Figures/Tables 6

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