Development of An Integrated Method (MGCMs-SCA-FER) for Assessing the Impacts of Climate Change: A Case Study of Jing-Jin-Ji Region

Abstract

In this study, an integrated method (abbreviated as MGCMs-SCA-FER) is developed for assessing the impacts of climate change, which incorporates multiple global climate models (MGCMs), stepwise cluster analysis (SCA), and fixed-effects regression (FER) within a general framework. MGCMs-SCA-FER is capable of (i) dealing with the uncertainty in climate change projection caused by heterogeneity of structures and parameters of GCM; (ii) capturing nonlinear relationship between input variables and outputs without assumption of their functions; (iii) identifying interaction of different units and quantifying the effects of climate change on electricity demand. MGCMs-SCA-FER is then applied to Jing-Jin-Ji for assessing the impacts of climate change on single-city and entire-region electricity demands. Results demonstrated that climate change projections and electricity demand predictions varied significantly across different GCMs and RCPs. Results disclose that (i) Jing-Jin-Ji region would experience a warmer climate in the next 80 years of 2021 ~ 2100 (For every decade, temperatures would increase by [0.17, 0.23] °C under RCP4.5 and [0.35, 0.54] °C under RCP8.5); (ii) For 1 °C increase in temperature, annual electricity demand would rise by 4.5%; (iii) electricity intensity has the most significant impact on electricity demand for Jing-Jin-Ji region; (iv) electricity demand would increase under all scenarios, and the electricity demand under RCP8.5 would be higher than that under RCP4.5. From a long-term perspective, analyzing the climate change impacts on electricity demand and making adaptive management strategy are important for the regional sustainability

Keywords: climate change, electricity demand, fixed-effects regression, impact assessment, multi-GCMs, stepwise cluster analysis