In order to examine the key determinants of carbon dioxide emissions and judge whether China’s carbon dioxide emissions can reach their peak value before 2030, this study first uses the extended STIRPAT model to analyze the determinants of China’s carbon dioxide emissions from 1995 to 2019 and then uses the model regression result to forecast the carbon dioxide emissions from 2020 to 2040 under six scenarios to investigate their prospect. It is found that population size, GDP per capita, energy intensity, the share of coal consumption, urbanization level, the share of secondary industry, and investment have significant positive effects on carbon dioxide emissions. Among them, the influence of population size is the biggest and energy intensity is the weakest. China’s carbon dioxide emissions can reach their peak in 2029 under the baseline scenario. Increasing the rate of population growth, energy intensity, and share of coal consumption will push back the peak year. A lower rate of economic growth and share of the secondary industry will bring the peak year forward. Therefore, it is necessary to optimize the industrial structure and energy consumption structure, reduce the energy intensity, and control the population size in order to achieve the goal of peaking carbon dioxide emissions as soon as possible.

In order to investigate the impact of green energy technology on the environmental sustainability of China, take the Beijing-Tianjin-Hebei region as an example, this paper first calculates the per capita ecological footprint (ef), ecological carrying capacity (ec) and ecological deficit (ed) of China and Beijing-Tianjin-Hebei region from 1990 to 2019 by using the ecological footprint (EF) model, and then uses an expanded STIRPAT model and Partial Least Squares (PLS) regression to explore the impact and importance of green energy technology on EF in China and Beijing-Tianjin-Hebei region. It is found that the ec of China and Beijing-Tianjin-Hebei region is much lower than that of the ef from 1990 to 2019. It is always in the state of ecological deficit, and the sustainable development is faced with severe challenges. Progress in green energy technology can significantly reduce the EF of China and Beijing-Tianjin-Hebei region. The importance of each factor on the EF of China and Beijing-Tianjin-Hebei region is different. The degree of dependence on foreign trade and urbanization rate are important influencing factors of Beijing’s EF. Urbanization rate, per capita GDP, population size, energy consumption per unit GDP and built-up area are the important influencing factors of EF in Tianjin and Hebei. Therefore, to reduce the EF of Beijing, Tianjin and Hebei, it is necessary to accelerate the progress of green energy technology, develop compact ecological city and change people’s consumption patterns.

To investigate the impact of solar energy on the carbon footprint, to find effective measures to reduce the carbon footprint and slow global warming as soon as possible, this paper takes 30 provinces in China as an example. First, the inter‐regional input–output model is used to calculate the carbon footprint of each province. Then, the panel quantile regression model is used to investigate the impact of solar energy generation on different quantiles of carbon footprint. The results show that from 2012 to 2020, China's carbon footprint is at a high level, which is not conducive to the achievement of the goal of peaking carbon dioxide emissions. The eastern region has the highest carbon footprint, followed by the western region, the central region has the lowest carbon footprint, and its carbon footprint proportion continues to decline. The increase of solar energy generation can significantly reduce the carbon footprint of high quantile location, but has no significant impact on the carbon footprint of the middle and low quantile locations. Except for the negative impact of afforestation area on carbon footprint, the others control variables are all positive. Therefore, to reduce the carbon footprint of China, it is necessary to upgrade solar technology, increase solar energy generation, reduce energy consumption per unit of GDP (Gross Domestic Product) and the share of coal consumption, strengthen forestry management and reduce reliance on private vehicles.