The cooling degree days (CDDs) can indicate the hot climatic impacts on energy consumption and thermal environment comfort effectively. Nevertheless, seldom studies focused on the spatiotemporal characteristics, influence factors, and simulation of global CDDs. This study analyzed the spatial-temporal characteristics of global CDDs from 1970 to 2018 and in the future, explored five determinants, and simulated CDDs and their interannual changing rates. The results showed that the global CDDs were generally higher at lower latitudes and altitudes. Many places experienced significant positive changes of CDDs (p < 0.05), and the rates became larger at lower latitudes and attitudes. In the future, most CDDs had the sustainability trends. Besides, significant negative partial correlations existed between not only CDDs but also their variation rates with latitude, altitude, and average enhanced vegetation index in the summer, while positive with the annual PM2.5, distance to large waterbodies (p = 0.000). Moreover, the values and variation rates of CDDs can be deduced using the generalized regression neural network method. The root-mean-square errors were 231.73 °C * days and 1.71 °C * days * year-1, respectively. These conclusions were helpful for the energy-saving for cooling with the climate change and optimization of thermal environment.
Keywords: Climate change; Cooling degree days; Energy consumption; GIS; General regression neural network; PM2.5; Relative importance analysis; Thermal environment.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.