Quantifying the economic impact of changes in energy demand for space heating and cooling systems under varying climatic scenarios

Abstract

The building sector is highly sensitive to climate change, where energy is used for numerous purposes such as heating, cooling, cooking and lighting. Space heating and cooling account for a large proportion of overall energy use, and the associated energy demand is also affected by climate change. Here, we project the economic impact of changes in energy demand for space heating and cooling under multiple climatic conditions. We use an economic model coupled with an end-use technology model to explicitly represent the investment costs for air-conditioning technologies, which influence the macroeconomy. We conclude that the negative effects on the economy from increases in the use of space cooling are sufficiently large to neutralize the positive impacts from reductions in space heating usage under climate change, which results in significant economic loss. The economic loss under the highest emissions scenario (RCP8.5) would correspond to a −0.34% (−0.39% to −0.18%) change in global gross domestic product (GDP) in 2100 compared with GDP without any climate change, while the impact under the lowest emissions scenario (RCP2.6) would result in a −0.03% (−0.07% to −0.01%) change in global GDP in 2100. The economic losses are mainly generated by incremental technological costs and not by changes in energy demand itself. The amount of economic loss can vary substantially based on assumptions of technological costs, population and income. To reduce the negative impacts of climate change measures for reducing the costs of air conditioning will be an important consideration for the building sector in the future.

Avoided economic impacts of energy demand changes by 1.5 and 2 °C climate stabilization

“…The GDP's negative impacts in 2100 are highest (median: −0.94%) in the 4 • C scenario, whereas the 1.5 • C scenario maintains a low GDP change (median: −0.05%). These range and median values are slightly different from previous estimates (Hasegawa et al 2016) mainly due to different HDD and CDD threshold assumptions. The gap between the 1.5 • C and 4 • C scenarios is 0.31% in 2050, and grows larger in the latter half of the century, reaching 0.79% in 2100.…”

“…As heating and cooling changes tend to offset each other, most studies agree that the effect of climate-induced changes in heating and cooling demand on the global economy is minuscule , Eom et al 2012, Isaac and van Vuuren 2009, Mima and Criqui 2009, Bosello et al 2006. Meanwhile, considering technology costs, earlier studies have pointed out that meeting the high cooling demand caused by climate change could require incremental investments for air conditioning (Waite et al 2017b, Hasegawa et al 2016, Davis and Gertler 2015, Jenner and Lamadrid 2013, Labriet et al 2013, Tol 2013, Roson and Mensbrugghe 2012, Eboli et al 2010, Isaac and van Vuuren 2009, McNeil et al 2008. However, the limited impact of climate change on global energy may have a much greater effect on the economy because the economic impact of fluctuations in energy use depends on energy systems and/or industrial structures (Hasegawa et al 2016).…”

“…Moreover, newly developed SSPs developed by Riahi et al (2017) can be used to carry out the most up-to-date assessment. Hasegawa et al (2016) is a pioneering study in this context, although they only considered GDP and population variations for the SSPs. In principle, SSPs should include autonomous adaptation that can reflect future social practices against climate change in the assessment.…”

Limited Role of Working Time Shift in Offsetting the Increasing Occupational‐Health Cost of Heat Exposure

“…The parameters in the model were calibrated based on the social accounting matrix (Dimaranan, 2006) and energy balance table (International Energy Agency, 2013a, 2013b) in the base year and were updated every year recursively. The AIM/CGE model has been widely applied in global-scale mitigation and impact studies Hasegawa, Park, et al, 2016;Masui et al, 2011;Takakura et al, 2017). The production of each industrial sector is represented by a multinested production function, with labor as one of its inputs multiplied by a labor productivity coefficient.…”

“…The penetration rate of air conditioning devices is estimated based on climatic and socioeconomic conditions (Hasegawa, Park, et al, 2016). The maximum potential demand (P) of air conditioning devices is determined by the number of degree-days.…”