As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.


  • As the world’s largest developing country, China is the world’s largest carbon emitter [1]

  • We found that various scholars use more than one optimization approach to optimize the whale optimization algorithm (WOA) algorithm

  • We introduce a backward learning mechanism into the whale algorithm to achieve the goal of maintaining population diversity while avoiding becoming trapped in local optimization

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As the world’s largest developing country, China is the world’s largest carbon emitter [1]. Xi Jinping, President of the People’s Republic of China, stressed that the “14th Five-Year Plan” is a key period and a window period for a peak in carbon emissions, and an important plan needs to be made for the five years to reduce carbon emissions, laying out a clear “construction plan” for the carbon emission peak in the 14th. In order to smoothly reduce carbon emissions, the accurate prediction of carbon emissions has become an important issue. The most widely used prediction methods are various algorithms in the field of artificial intelligence (AI), which are widely used because of their high computing speed and high computing precision


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