Spatial Heterogeneity of the Carbon Emission Effect Resulting from Urban Expansion among Three Coastal Agglomerations in China

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007-2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

Refined Carbon Emission Measurement Based on NPP-VIIRS Nighttime Light Data: A Case Study of the Pearl River Delta Region, China

The accurate measurement of CO₂ emissions is helpful for realizing the goals of "carbon neutralization" and "carbon peak". However, most current research on CO₂ emission measurements utilizes the traditional energy balance coefficient and top-down methods. The data granularity is large, and most studies are concentrated at the national, provincial, municipal, or district/county administrative unit scale. As an important part of the Guangdong-Hong Kong-Macao Greater Bay Area of China, the Pearl River Delta region has good nighttime light vitality and faces huge carbon emission pressure. Using the Pearl River Delta as the research area, this study constructed an optimized pixel-scale regression model based on NPP-VIIRS (The Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-Orbiting Partnership spacecraft) nighttime light data and CO₂ emissions data at the district and county levels for 2017. In addition, the spatial pattern of CO₂ emissions in the Pearl River Delta was analyzed based on the predicted CO₂ emission status. The results showed that the spatial pattern of CO₂ emissions in the Pearl River Delta had the distinct characteristics of the "center-edge" effect, the spatial spillover effect, and high-value aggregation, which should be considered when making related social or public decisions.

Examining the Density and Diversity of Human Activity in the Built Environment: The Case of the Pearl River Delta, China

Rapid urbanization in China has been accompanied by spatial inefficiency in patterns of human activity, of which ‘ghost towns’ are the most visible result. In this study, we measure the density and diversity of human activity in the built environment and relate this to various explanatory factors. Using the Pearl River Delta (PRD) as an empirical case, our research demonstrates the distribution of human activity by multi-source data and then explores its dynamics within these areas. This empirical study is comprised of two parts. The first part explores location information regarding human activity in urbanized areas and shows density and diversity. Regression models are applied to explore how density and diversity are affected by urban scale, morphology and by a city’s administrative level. Results indicate that: 1) cities with smaller populations are more likely to be faced with lower density and diversity, but they derive greater marginal benefits from improving land use efficiency; 2) the compactness of the layout of urban land, an index reflecting the plane shapes of the built environment, is highly correlated with density and diversity in built-up areas; and 3) the administrative importance of a city has a significant and positive impact on the density of human activity, but no obvious influence on its diversity.