Key points are not available for this paper at this time.
As global temperatures rise, there is a significant increase in the demand for cooling energy in buildings, leading to heightened attention on urban energy demands. The direct impact of cooling demand on urban energy burdens, coupled with a growing body of literature on energy, poverty, and other social factors, has underscored the importance of this issue. This study focused on exploring the relationship between Taiwan's regional climate conditions in high-temperature environments, social factors in geographical context, and energy burdens. The objective was to analyze the distribution of energy burdens affected by thermal shocks and social vulnerability, identifying vulnerable areas to facilitate climate change adaptation strategies.Covering 20 counties and cities in Taiwan, this study employed administrative divisions as analytical units to discuss the distribution of energy burdens in Taiwan in 2018 and 2023. Regarding climatic factors, this study adopted the dynamical downscaling data produced by National Science and Technology Center for Disaster Reduction (NCDR). The data included the changes in energy demand under the current and IPCC-defined RCP8.5 warming scenarios. Additionally, the study examined the influence of social factors such as population structure, socioeconomics, and social vulnerability.The results reveal variations in the distribution of energy burdens in 2023, ranging from a minimum of 0.2% to a maximum of 30%. Over half of the counties and cities have energy burdens concentrated between 5.4% and 7.9%. Cooling demand in Taiwan is highest in July, followed by August. Therefore, the high energy burden is reflected in the months of August and September. In the climate change scenario, the demand for cooling increases by up to 26.3% in August. The spatial distribution shows that energy burdens are higher in mountainous and suburban areas, despite lower cooling demand there. Conversely, high cooling demand is concentrated in major urban centers, where energy burdens are lower. Finally, the study integrates distribution maps of cooling demand and other social factors to explore the relationship between energy burdens and climate, population, and society.Keywords: Energy Burden; Cooling Degree Hours; Spatial Analysis; Climate Change
Chang et al. (Fri,) studied this question.