Spatiotemporal evolution and influencing factors of land-use emissions in the Guangdong-Hong Kong-Macao Greater Bay Area using integrated nighttime light datasets

Abstract

Carbon emissions from land-use and land-cover change (together referred to as ‘land-use emissions’) are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.