CO₂ is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO₂ and climate change. The main questions studied in this paper are as follows: What are the drivers of CO₂ emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO₂ emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO₂ emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO₂ emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO₂ emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages.

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO₂/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO₂. The carbon emissions will peak at 5275.5 tCO₂. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO₂. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents’ awareness of energy conservation.

Low-carbon governance at the county level has been an important issue for sustainable development due to the large contributions to carbon emission. However, the experiences of carbon emission governance at the county level are lacking. This paper discusses 5 carbon emission governance zones for 1753 counties. The zoning is formed according to a differentiated zoning method based on a multi-indicator evaluation to judge if the governance had better focus and had formulated a differentiated carbon emission governance system. According to zoning results, there is 1 high-carbon governance zone, 2 medium-carbon governance zones, and 2 low-carbon zones. The extensive high-carbon governance zone and medium-carbon zones are key governance areas, in which the counties are mainly located in the northern plain areas and southeast coastal areas and have contributed 51.88% of total carbon emissions. This paper proposes differentiated governance standards for each indicator of the 5 zones. The differentiated zoning method mentioned in this paper can be applied to other governance issues of small-scale regions.

Due to the limited availability of medical facilities and the urgency and irreplaceability of medical-seeking behaviors, the transportation processes used to access these resources inherently result in high carbon emissions. Unfortunately, pediatric medical facilities are among the least substitutable destinations, making it challenging to reduce travel-related CO₂ emissions by traditional means such as decreasing travel frequency or optimizing transportation means. This study proposes enhancing the spatial allocation of pediatric medical facilities to effectively reduce travel-related CO₂ emissions. This study selects 27 hospitals with pediatric departments in Tianjin as the research subject. It introduces a model for measuring travel-related CO₂ emissions for pediatric medical-seeking, STIRPAT, and ridge regression models as well as conducts simulations under various scenarios to test the hypotheses. Therefore, methods for enhancing the spatial allocation of pediatric medical facilities are proposed. The results show that (1) travel-related CO₂ emissions for pediatric medical-seeking are the highest in the city center, outpatient-related CO₂ emissions surpass inpatient ones, and children’s hospital-related CO₂ emissions are higher than those related to comprehensive hospitals, from which potential carbon reduction points can be explored; (2) children’s hospitals with multibranch and composite functional allocations can significantly reduce CO₂ emissions; (3) comprehensive hospitals can further alleviate CO₂ emissions from children’s hospitals by enhancing the medical level, transportation infrastructure, population distribution, and other spatial environmental factors; (4) from the perspective of low-carbon travel and equity, a spatial allocation strategy should be adopted for children’s hospitals that includes multiple branches and composite functions, while comprehensive hospitals should focus on service capacity, parity, supply–demand ratio, and the population density of children.