Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area

Abstract

Understanding the spatio-temporal pattern of carbon emission (CE) is prerequisite for formulating carbon reduction policies. Previous studies emphasized quantitative analysis of CE inventory while ignoring sectoral spatial distribution. This study fills this gap by developing a framework for coupling the CE quantitative prediction model with the sectoral CE spatial model based on the Long-range Energy Alternatives Planning (LEAP) model, spatial proxy data and local climate zone (LCZ). The framework's sectoral CE results reveal a great varied landscape within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the leading bay areas in the world with rapid urbanization and emphasis on low-carbon development, under four carbon reduction scenarios. By 2060, the CN3 scenario that considers both energy-supply and consumption sides, predicts a drastic emission cut to 35.76 million tons, just 10 % of the business as usual (BAU) scenario's forecast, mainly from transportation (29.45 million tons) and industry (9.34 million tons) sectors. Besides, compared with the common CE spatial products, the spatial simulation results of sectoral CE in our framework present detailed spatial differences at the jurisdictional level. The findings are conducive for governments to formulate accurate CE reduction and optimization strategies of the cities towards to the 2060 carbon neutrality.