Predicting combined carbon emissions in urban regions considering micro-level enterprise electricity consumption data and macro-level regional data

Abstract

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In the context of “dual carbon” goals, governments need accurate carbon accounting results as a basis for formulating corresponding emission reduction policies. Therefore, this study proposes a combined carbon emission prediction method for urban regions, considering micro-level enterprise electricity consumption data and macro-level regional data. Considering the different applicability of prediction methods and the requirements for the data volume, a region-level carbon emission prediction method based on the long short-term memory neural network is proposed, which takes into account the micro-level electricity–carbon coupling relationship. Additionally, a region-level carbon emission prediction method based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is proposed, considering the macro-level economic–carbon coupling relationship. The generalized induced ordered weighted averaging method is employed to assign differential weights to micro- and macro-prediction values, yielding regional carbon emission predictions. An empirical analysis is conducted using a key city in the eastern region as an example, analyzing the main influencing factors and predicting carbon emissions based on relevant data from 2017 to 2021, and the accuracy of the models is analyzed and validated.

Keywords

• LSTM
• STIRPAT
• GIOWA
• regional carbon emissions
• combined prediction