Women and Household carbon Emissions: An empirical study based on input-output method and Machine Learning in China (2005-2019)

Abstract

The international community is increasingly recognizing the importance and urgency of gender issues in the field of climate change and paying particular attention to the important role of women in this field. This study calculates household carbon emissions (HCE) in China from 2005 to 2019 using the input-output method and explores the relationship between women and HCE from the perspective of statistics and machine learning based on the SHAP algorithm. The study found that in the consumption categories such as housing, transportation, and clothing, every 1% increase in the proportion of women reduced indirect carbon emissions by 0.441–10.001%, but this inhibitory effect weakened with the increase in income. Stochastic forest nonlinear regression shows that there is an inverted U-shaped relationship between women, R&D investment, energy structure, and HCE. In addition, a small number of single women showed a role in promoting high carbon emissions, while women with lower education had a more significant inhibiting effect on HCE. The study also analyzed regional differences between 2005 and 2019. This study enriches women's research in HCE and provides a novel research method, which has important reference value for promoting gender equality and climate change research.