From cost-optimal to nearly Zero Energy Buildings' renovation: Life Cycle Cost comparisons under alternative macroeconomic scenarios. (15th March 2021) - Baldoni, Edoardo; Coderoni, Silvia; D'Orazio, Marco; Di Giuseppe, Elisa; Esposti, Roberto
Abstract: Policies and financial framework aiming to encourage energy efficient building renovations should contribute to "fill" the existing investment gap between Cost-Optimal (CO) solutions, that are more economically convenient, and nearly Zero Energy (nZE) solutions, which have the lower energy consumption, in order to make more convenient to the investors to choose the more energy efficient options. This investment gap depends on the long-term expected value and volatility of several interdependent macroeconomic variables. However, standardized LCC methods used for CO assessments disregard the long-term uncertainty and interdependence affecting these variables and, consequently, misrepresent the impact of the associated risk on the economic convenience. The present work aims to model alternative macro-economic scenarios where to carry out a "stochastic" LCC of predetermined building renovation solutions, in order to provide a useful and effective decision tool for building LCC, and especially to evaluate whether and how much the future macro-economic scenario could influence the investment gap between a CO and a nZE refurbishment solution. At this aim, we estimated the Vector AutoRegressive (VAR) models of four alternative macro-economic scenarios, ranging from a "regular growth" case to more extreme conditions as experienced by major western economies in the last decades, based on real data, i.e. observed time series. The scenarios modelling and its relation to the stochastic LCC is the main result and novelty of the work compared to the conventional approach adopted in most of the literature and suggested by international regulations and standards. The method is illustrated through a case study, which demonstrates the potential of the developed methodology in providing interesting and informative results on the nature of the investment gap between CO and nZE solutions, that the policy should contribute to fill in order to address the environmental challenge in the building sector, and on how much this gap may vary depending on the volatility of the macro-economic context. The novelty of the work mainly lies on the possibility to highlight in which specific macroeconomic conditions the convenience of taking an investment decision under risk-aversion may be jeopardised (augmented), thus requiring a stronger (weaker) compensating public support. Highlights: Macroeconomic scenarios are included in a stochastic LCC of building retrofit. These scenarios are modelled with Vector AutoRegression based on real time series. This novel stochastic LCC provides decision support in the building sector. It investigates the investment gap across solutions under alternative scenarios. It can be used to assess the investment gap of nZEB and develop proper policies.