Is it necessary to calibrate existing decision-making results based on real option analysis during the process of climate change? A case study from Xiamen, China

Abstract

Previous studies have rarely examined how well Representative Concentration Pathways (RCP) scenarios fit with real weather data making climate change related decision-makings. This paper proposes a real option analysis (ROA) decision-making calibration method based on RCP (learning) scenarios uncertainty caused by the gap between the simulated and real weather conditions. Firstly, EnergyPlus is used to predict the energy consumption of a case building based on future climatic conditions, and the learning scenarios uncertainty is quantified. Secondly, a ROA decision-making calibration model (ROACM) is established through optimization to minimize the learning scenarios uncertainty. Finally, the ROACM is applied to a case study of an office building located in Xiamen, China, aiming to calibrate individual and sequential investment decisions for three retrofitting measures ($R_1$: horizontal shading, $R_2$: low-e windows, $R_3$: improving COP of chiller). The accuracy of ROACM is assessed through a sensitivity analysis. The results indicate that the learning scenarios uncertainty leads to annual energy consumption differences of approximately 8,451 kW $\bullet$h to 17,545 kW$\bullet$h in the case study. After calibration, the optimal timing for both individual and sequential investment has advanced by at least 4 years, significantly boosting returns. The optimal individual investment $R_3$ generated an additional return of up to $52,744, while the sequential investment $R_1+R_{13}$ (shading first, and improving COP of chiller later), increased by $175,017. The proposed ROACM demonstrated high performance during model validation in a case study, and could also aid governments or investors in ROA-based mitigation strategies analysis and increasing the reliability of the results.