Effects of Climate Change on Residential Energy Structure

Climate change may influence energy demand, with shifts in energy needs not only altering the energy structure but also posing challenges to the sustainability and resilience of energy systems. These impacts could further complicate the feasibility of achieving decarbonization goals. Residential energy sector is a critical component of global energy consumption. As temperature fluctuates and weather variability intensifies, households will adapt energy use to maintain comfortable living conditions. Energy consumption may increase due to climate change, but the magnitude remains uncertain. Considering various income groups around the world, residents may react to climate change heterogeneously.

Traditionally, some models use Heating Degree Days (HDD) and Cooling Degree Days (CDD) to serve as index of temperature change, which are often calculated by formulas below, where i means gridded cell, j means region,  means daily temperature, and represents comfortable temperature,  means population. First, calculate gridded HDD/CDDs as the difference between daily temperature and comfortable temperature. Then aggregate the gridded daily HDD/CDDs to region.

  (1)

   (2)

                                      (3)

However, calculation for HDD/CDDs still have several aspects that could be further improved. First, most temperature data used are predicted on SRES, and HDD/CDDs are assumed to be constant, so HDD/CDDs need to be updated to better reflect future climate change. Second, previous calculation always neglects the impact of crucial factors such as GDP when aggregating gridded temperature difference to regional level, only considering population distributional effects. Third, the difference resulted from income and climate also should be considered, for rich residents can afford more energy consumption, and long-term climate also impact response of people when faced with climate change.

Considering potential shortcomings mentioned above, we update the global HDD/CDDs of 32 regions in Global Change Analysis Model (GCAM). First, we use daily temperature data predicted by four climate models under different Shared Socioeconomic Pathways (SSPs) combining Representative Concentration Pathways (RCPs) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6), thus bridging the gap between climate model and GCAM. For in GCAM climate input module, HDD/CDDs are calculated based on historical climate data and are lack of fine-scale calculation. Second, our calculation adopts two weighting methods considering influence of population and GDP distribution on residential energy demand respectively. Third, beyond global-scale calculation, we refine calculation for China to the provincial level.

Fig. 1 Research Framework

Based on the updated HDD/CDDs, we use GCAM to analyze how climate change impact residential energy demand, aiming to provide scientific support for formulating policies that address the challenges posed by climate change to energy system. Our analysis offers comprehensive insights into residential energy demand change under SSPs and RCPs scenarios, accounting for income heterogeneity. These findings are informative to design effective mitigation policies in the context of climate change.