Analyzing the spatiotemporal carbon change mechanism: a land-based carbon flow network (CFN) for cities.

High carbon emissions played a significant role in global climate change, which made cities with rapid urbanization responsible for local carbon mitigation. In this study, a land-based CFN framework was established by taking 15 land use types as different network nodes. The framework was intended to be a dynamic structure containing the carbon emissions/sequestration tracking, land-based carbon network and utility analysis, and carbon change mechanism identification.

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development.

Working towards sustainable population development is an important part of carbon mitigation efforts, and decoupling carbon emissions from population development has great significance for carbon mitigation. Based on the construction of a comprehensive population development index (PDI), this study adopts a decoupling model to explore the dependence between carbon emissions and PDI across 30 Chinese provinces from 2001 to 2017. Then, the stochastic impacts by regression on population, affluence and technology (STIRPAT) model is used to investigate the impact of population factors on carbon emissions.

Impact of uncertainty on regional carbon peak paths: an analysis based on carbon emissions accounting, modeling, and driving factors.

Regional carbon emission paths have an important impact on the realization of China's carbon emission peak target. Due to the uncertainty of future development model, the change of carbon emissions will also face uncertainty, which will make achieving the peak target challenging. Taking Shandong, Henan, and Guangdong, three of China's most populous provinces, as examples, this study analyzed the impacts of uncertainties in carbon accounting principles, driving factors, and simulation mechanism on achieving the peak target.