Green Space Compactness and Configuration to Reduce Carbon Emissions from Energy Use in Buildings

Abstract

Building sector consists of a major part of global energy consumption and carbon emission. Reducing energy consumption in buildings can make a substantial contribution towards the strategic goal of carbon neutrality. Building energy consumption carbon emission (BECCE) is highly correlated with microclimate. Green space has long been recognized as the natural way to improve the microclimate and reduce BECCE. However, the effective distance and optimized configuration of green space for the reduction in BECCE are hardly known. To this purpose, we developed a green space compactness (GSC) index as an indicator of microclimate around the People’s Bank, located in 59 cities across China, and used statistical, deep learning, and spatial analysis methods to obtain the most effective distance with respect to the effect of GSC on BECCE. We used hot and cold spot spatial analysis methods to detect the spatial heterogeneity of BECCE and analyzed the corresponding GCS to discover the optimal way for BECCE reduction. The results clearly showed that BECCE was highly correlated with the GSC, and the influence of GSC on BECCE was the highest at the distance of 250 m from the building. The hot and cold spots analysis suggested that BECCE has a significant spatial heterogeneity, which was much higher in the north part of China. Improving the configuration of green space for certain cities could lead to considerable emission reductions. If the BEECE is reduced from 4675 tons to 486 tons, the GSC needs to be increased from 0.39 to 0.56. The study suggests that 250 m is the most effective distance to reduce BECCE, and optimal green space configuration can provide a feasible way to mitigate carbon emissions and valuable information for the development of low-carbon cities.