Green electricity (GE) is of great importance for effectively combating climate change and global Sustainable Development Goals (SDGs). The willingness-to-pay (WTP) for GE by end users has great influence on its widespread application, especially at the household level. Researchers have analyzed the mechanisms of residents’ WTP and predicted how much they could afford for GE. However, most of these studies have focused on developed (affluent) countries and regions, and residents’ WTP as well as the corresponding monetary amount of their WTP were not considered together in previous studies. In this study, we try to fill the gap of the inadequate research of residents’ WTP in underdeveloped areas through an analysis of the WTP of urban residents in a small Chinese city (Anyang). A total of 274 household-scale samples were collected by face-to-face interviews in December 2019. We found that approximately 60% of respondents gave a positive response to WTP, with an average value of 8.39 yuan per month. Educational attainment, per capita disposable income in the household, the length of residents’ time living in urban areas and the rate of neighbors’ approval of WTP are all positively correlated with the tendency of the residents’ positive response toward WTP. The three factors of per capita disposable income, the rate of neighbors’ approval of WTP and the degree of residents’ environmental concerns are positively correlated with the amount of residents’ WTP. More importantly, we demonstrated the existence of a weak “Herd Effect” in residents’ views of WTP. Finally, some suggestions for policymakers are given, such as raising household income through retraining and raising residents’ environmental awareness through education and community advertisements. This study also highlights that the widespread application of GE at the household level will be of great assistance to the GE industry overall. Therefore, the rising economic cost of GE should not be shared by the households in the long term.

In this paper, the quadratic polynomial and cubic polynomial functions were applied to analyze the environmental Kuznets curve (EKC) of carbon emissions in Hebei Province. The improved STIRPAT model was also applied to assess the driving factors and reduction paths for carbon emissions in Hebei Province. The results lead to three main conclusions. Firstly, carbon emissions and economic growth in Hebei Province are in a positive cor-relation stage which has not formed the EKC curve, and the “decoupling” stage between carbon emissions and economic growth has not arrived yet. Secondly, the industrial structure, per capita GDP, fixed assets investment, population size and urbanization rate account for the highest proportion of carbon emissions. Carbon emissions can be reduced greatly by changing the energy structure, in which the proportion of coal is decreased year by year. Environmental regulation also has an obvious effect on the reduction of carbon emissions. Thirdly, it is suggested that the reduction of carbon emissions in Hebei Province should focus on four tasks: controlling the development of heavy industry, avoiding overcapacity, optimizing the industrial structure and accelerating the development of clean energy.