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.

With the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales.

Low-carbon management plays an important role in mitigating climate change and adapting to it. Localities should adopt differentiated low-carbon management policies according to the state of their environment. To help formulate specific and realistic low-carbon management policies, this paper took into account specific low-carbon management sectors. Likewise, it carefully considered the differences in various resource endowments and proposed a method for evaluating low-carbon management efficiency and potential. The method was applied to an empirical study from 2015 conducted on 1771 Chinese counties. Significant spatial heterogeneity was found during the research. The counties bordering central and Western China and the ones in the southeast coastal areas showed higher efficiency in the industrial sector. Southern and Northern China had higher efficiency in the housing and transportation sector, respectively. Moreover, counties in remote areas showed more potential in the industrial sector. Central China had higher potential in the housing sector, while counties bordering provinces had more potential in the transportation sector. Therefore, Chinese counties were divided into eight management zones where differentiated management strategies were identified to shape low-carbon management policies.