An Automatic Decision Support System for Low-Carbon Real Estate Investments

Laura Gabrielli, Aurora Ruggeri and Massimiliano Scarpa

ERES from European Real Estate Society (ERES)

Abstract: In order to plan and manage low-carbon investments in wide real estate assets, thereby meeting the European energy efficiency requirements recently presented in EU Directive 2018/844, a methodological change in both research and practice is now necessary. Since a sharp increase in retrofit rates of large building stocks is going to be promoted, new strategic approaches should take into consideration building portfolios as a whole, overcoming the single-building perspective, so that to identify the level of energy retrofit leading to the overall maximum benefit. In this contribution, a decision support system is developed for the automatic assessment of both the monetary and non-monetary benefits produced by a retrofit investment, and determine the optimal efficiency program over a large building stock. The core idea is to consider the energy enhancement as an optimization issue and identify the configuration of retrofit design that brings to the greatest possible benefit, by balancing conflicting objectives, and within several constraints. As far as the monetary benefit is concerned, we estimate the savings produced by the investment over a life-cycle perspective. Among the non-monetary values, we first consider the environmental benefit in terms of avoided CO2 emissions. We also assess the value of the improved indoor comfort and the value of the safeguard of the building, when the energy efficiency is also intended as a measure to protect the heritage. To this end, a set of different and interdisciplinary techniques has been employed, such as parametric energy modelling, neural network analysis, economic and financial feasibility assessment, calculation of thermal comfort indexes (Fanger), multi-criteria approaches (Analytic Hierarchy Process), and multi-objective constrained optimization analysis. Among the results of this research, the extreme flexibility in comparing countless design scenarios and the simplicity of application of the model developed are the most important contributions obtained. The effectiveness of the decision-making tool was then verified through the implementation on a case study of an interesting and heterogeneous portfolio of buildings located in Northern Italy.

Keywords: automatic assessment; building stock; low carbon investment; Neural network analysis (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-cmp, nep-ene, nep-env and nep-isf
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