It will be a huge challenge for China to achieve carbon neutrality by 2060. At present, China needs to understand its own carbon neutrality status and then scientifically plan a path to achieve carbon neutrality. In order to evaluate the carbon neutrality capacity of China’s provinces, this paper firstly constructs an evaluation indicator system, which includes 20 indicators at six levels. Then, a combination of subjective and objective weighting methods, as well as an improved technique for order preference by similarity to an ideal solution (TOPSIS) model, are used to calculate evaluation results. On this basis, the reasons for their different carbon neutrality capacities are analyzed. The results show that the use of renewable energy, maintaining ecological environmental quality, and low-carbon technology are important factors affecting China’s carbon neutrality capacity, and according to the evaluation results, China’s provinces are divided into three categories. Finally, corresponding suggestions for speeding up the pace of carbon neutrality are put forward.

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

In order to accurately predict China’s future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model to forecast China’s future energy consumption in 2022–2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions.