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Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China currently is undergoing rapid urbanization and motorization, which will result in far-reaching impacts on energy and the environment. According to estimates, energy use and carbon emissions in the transportation sector will comprise roughly 30% of total emissions by 2030. Since the late 1990s, transportation-related issues such as energy, consumption, and carbon emissions have become a policy focus in China. To date, most research and policies have centered on vehicle technologies that promote vehicle efficiency and reduced emissions. Limited research exists on the control of greenhouse gases through mode shifts in urban transportation-in particular, through the promotion of public transit. The purpose of this study is to establish a methodology to analyze carbon emissions from the urban transportation sector at the Chinese city level. By using Jinan, the capital of China's Shandong Province, as an example, we have developed an analytical model to simulate energy consumption and carbon emissions based on the number of trips, the transportation mode split, and the trip distance. This model has enabled us to assess the impacts of the transportation mode split on energy consumption and carbon emissions. Furthermore, this paper reviews a set of methods for data collection, estimation, and processing for situations where statistical data are scarce in China. This paper also describes the simulation of three transportation system development scenarios. The results of this study illustrate that if no policy intervention is implemented for the transportation mode split (the business-as-usual (BAU) case), then emissions from Chinese urban transportation systems will quadruple by 2030. However, a dense, mixed land-use pattern, as well as transportation policies that encourage public transportation, would result in the elimination of 1.93 million tons of carbon emissions-approximately 50% of the BAU scenario emissions. [ABSTRACT FROM AUTHOR]

This study examines the relationship between urbanization, economic growth, industrial transformation, technological change, public services, demographical change, urban and natural environmental changes, and carbon emissions using a dataset of 182 prefecture-level cities in China between 2001 and 2010. Specifically, this paper differs from previous studies in two aspects. First, the extant literature has focused on how economic processes accompanying rapid urbanization affect carbon emissions in urban areas but gives little attention to the other dimensions of urbanization, including social and environmental changes, which may have important effects on carbon emissions. We assessed the effects of 17 key processes accompanying urbanization in a full range of economic, social, and environmental dimensions on carbon dioxide emissions in urban areas. The results showed that social processes accompanied with rapid urbanization had distinct effects on carbon emissions, compared to economic and environmental processes. Specifically, improvement in public services, indicated by education and cultural developments, reduces the increase in carbon emissions during urbanization, while economic growth and urban construction reinforces the growth in carbon emissions. Second, we examined the impact of various urbanization processes on carbon dioxide emissions using a unique dataset of 182 prefecture-level cities that covers a wide span of regions in China. The results of our analyses on the city level have important implications for the formulation of comprehensive policies aimed at reducing carbon dioxide emission in urban areas, focusing on different urbanization processes in economic, social, and environmental phases. [ABSTRACT FROM AUTHOR]