Machine learning based prediction for China's household cooking energy transition under the Shared Socioeconomic Pathways

Promoting clean cooking fuels is one target of sustainable development goal 7 (SDG7). China is a populous developing country striving for carbon neutrality, it is therefore critical for China to achieve household cooking energy transition (HCET). This study characterizes the spatio-temporal evolution features of China's HCET, identify the key HCET drivers for the period of 2010–2022, and forecasts future HCET progress by 2030 (SDG target year) and 2060 (China's carbon neutrality target year) under Shared Socioeconomic Pathways (SSPs) scenarios by applying machine learning models. The results show that 31.43 % of Chinese households transitioned from solid fuels to clean energy as their primary cooking energy source. However, such HCET progress presents regional disparity, with some provinces within China's southwestern, northwestern, and central regions lagging behind. Among different machine learning algorithms, the XGBoost model achieves superior predictive performance than others. Household per capita income and gas accessibility rate are key drivers of HCET. The probability of China's HCET would reach up to 89.20 % in 2030 and 93.28 % in 2060. Among all the scenarios, the SSP1 (Sustainability) scenario is most suitable for China's HCET, while the SSP2 scenario is close to the SSP1. The forecast results indicate that China's southwestern region will continue to be backward in their HCET progress. Finally, targeted policy recommendations (including enhance energy infrastructure, ensure clean energy access for low-income households, and encourage HCET behaviors) are proposed to further promote the HCET progress in China.