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Carbon emission prediction models: A review.

Yukai Jin1, Ayyoob Sharifi2, Zhisheng Li3

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Effective Carbon Emission Prediction Models (CEPMs) are crucial for climate change mitigation. This review highlights statistical and neural network models, emphasizing optimization techniques and improved accuracy post-optimization for CO2 emission trends.

Keywords:
Artificial intelligenceCarbon emissionClimate change mitigationMachine learningNeural networkPrediction model

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Area of Science:

  • Environmental Science
  • Climate Modeling
  • Data Science

Background:

  • Growing concerns over the greenhouse effect necessitate robust Carbon Emission Prediction Models (CEPMs).
  • Understanding and predicting CO2 emission trends is vital for effective climate change mitigation strategies.

Purpose of the Study:

  • To review and categorize existing CEPMs based on their primary functions: prediction, optimization, and factor selection.
  • To analyze the prevalence and evolution of different modeling approaches, particularly statistical and neural network models.
  • To evaluate the impact of optimization techniques on CEPM accuracy and identify key factors influencing carbon emissions.

Main Methods:

  • A comprehensive literature review of 147 CEPM studies.
  • Classification of models based on function (prediction, optimization, factor selection) and methodology (statistical, neural networks, metaheuristics).
  • Analysis of model performance metrics, focusing on Root Mean Square Error (RMSE) before and after optimization.

Main Results:

  • Statistical models were most prevalent (75%), followed by neural network models (21.8%), with a notable increase in neural network usage from 2019-2022.
  • Metaheuristic models were widely used for optimization (94.4%), primarily focusing on parameter and structure optimization.
  • Optimization significantly improved CEPM accuracy, reducing RMSE values substantially.

Conclusions:

  • CEPMs are essential tools for climate change mitigation, with ongoing advancements in modeling techniques.
  • Optimization strategies, particularly metaheuristic approaches, play a critical role in enhancing prediction accuracy.
  • Further research should consider the identified factors, evaluation methods, and spatial-temporal scales for more comprehensive carbon emission analysis.