多重不确定性下基于LMDI的城市工业碳排放量影响因素分析及预测：以苏州市为例

doi:10.3969/j.issn.2097-0706.2022.02.001

摘要/Abstract

摘要：

在“双碳”背景下,准确计算碳排放量、分析碳排放结构并综合预测未来产业能源碳排放的走势,是科学制定城市产业发展与规划政策的基础。在经典Kaya恒等式的基础上,考虑产业结构、能源消费结构及外来电比例的影响,重构了Kaya恒等式,基于对数平均迪氏指数法（LMDI）对碳排放量影响因素进行分解。基于箱型不确定集,对各影响因素的不确定性进行建模,建立了改进的可拓展的随机性环境影响评估（STIRPAT）模型,通过求解基于不确定集的优化模型方式构建了碳排放量预测方法。最后以苏州市规模以上工业为例,对该方法进行了验证并提出了相应的政策建议。

关键词: 碳中和, 碳排放量影响因素, 对数平均迪氏指数法, 箱型不确定集, 改进STIRPAT模型, 碳排放量预测

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

中图分类号:

X321：TK01：F407.2

王盛, 谈健, 马亚辉, 邹风华. 多重不确定性下基于LMDI的城市工业碳排放量影响因素分析及预测：以苏州市为例[J]. 综合智慧能源, 2022, 44(2): 1-7.

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.

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