Mapping of the Greenhouse Gas Emission Potential for the Offshore Wind Power Sector in Guangdong, China

Abstract

This study aims to assess the potential greenhouse gas (GHG) emissions of delivering 1 kWh from planned offshore wind farm sites to the grid in the Guangdong Province, China. In contrast to most previous studies, we avoided underestimating GHG emissions per kWh by approximately 49% by adopting a spatialized life-cycle inventory (LCI)-improved stock-driven model under the medium scenario combination. We also developed a callable spatialized LCI to model the spatial differences in the GHG emissions per kWh by cells in planned offshore wind farm sites in Guangdong. The modeling results indicate that, under the medium scenario combination, the GHG emissions per kWh will range from 4.6 to 19 gCO2eq/kWh and the cells with higher emissions are concentrated in the deep-water wind farms in the eastern ocean of the Guangdong Province. According to the mechanism by which the different scenarios affect the modeling results, increasing the unit capacity of turbines is the most effective approach for reducing the GHG emissions per kWh and decreasing the impact of natural conditions. Air density can be used as an empirical spatial variable to predict the GHG emission potential of planned wind farm sites in Guangdong. The modeling framework in this study provides a more reliable quantitative tool for decision-makers in the offshore wind sector that can be used directly for any offshore wind system with a monopile foundation and be extended to wind power systems with other foundation types, or even to the entire renewable energy and other infrastructure systems after certain modifications.