Abstract
The aim of this paper is to propose a methodology for supporting decision making processes in urban regeneration projects. Focus is posed on economic–environmental sustainability evaluation of building retrofit projects, at district scale, in presence of risk and uncertainty. An application of a conjoint Probability Analysis with Life-Cycle Cost Analysis (LCCA) is proposed for selecting the preferable solution between technological alternative scenarios with different energy production systems. The model input (cost drivers) and model output (Global Cost) are expressed in stochastic terms. A complex project is proposed as a case-study: a social-housing district in a town in Northern Italy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boussabaine A, Kirkham R (2004) Whole life-cycle costing: risk and risk responses. Blackwell Publishing, pp 56–83, 142–162
Davis Langdon Management Consulting (2007) Life cycle costing (LCC) as a contribution to sustainable construction: a common methodology—final methodology. Available online: http://ec.europa.eu/enterprise/sectors/construction/studies/life-cycle-costing_en.htm. Accessed on January 2019
Department of Energy (DOE) (2014) Life cycle cost handbook guidance for life cycle cost estimate and life cycle cost analysis. Washington DC, USA
European Commission (2010) Directive 2010/31/EU, energy performance of building directive recast (EPBD recast), 2010, Bruxelles
European Commission (2018) Directive 2018/844/EU of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency, 2018, Bruxelles
European Committee for Standardization (CEN). Standard EN ISO 15459:2007. Energy performance of buildings—economic evaluation procedure for energy systems in buildings; European Committee for Standardization, 2007, Bruxelles
European Parliament, Guidelines Accompanying Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 Supplementing Directive 2010/31/EU, European Parliament, 2012, Brussels
Fente J, Knutson K, Schexnayder C (1999) Defining a beta distribution function for construction simulation. In: Proceedings of the 1999 winter simulation conference, Phoenix, AZ, USA, 5–8 Dec 1999
Fregonara E, Ferrando DG (2018) How to model uncertain service life and durability of components in life cycle cost analysis applications? The stochastic approach to the factor method. Sustainability 10(10):3642
Fregonara E, Giordano R, Ferrando DG, Pattono S (2017) Economic-environmental indicators to support investment decisions: a focus on the buildings’ end-of-life stage. Buildings 7(3):1–20
Fregonara E, Ferrando DG, Pattono S (2018a) Economic-environmental sustainability in building projects: introducing risk and uncertainty in LCCE and LCCA. Sustainability 10(6):1901
Fregonara E, Carbonaro C, Pasquarella O (2018b) LCC Analysis to evaluate the economic sustainability of technological scenarios on the district scale. J Valori Valut 21:59–74
International Organization for Standardization. ISO 15686:2008 (2008) Buildings and constructed assets—service-life planning—Part 5: Life Cycle Costing; ISO/TC 59/CS 14; International organization for standardization: Geneva, Switzerland
Jafari A, Valentin V, Russell M (2014) Probabilistic life cycle cost model for sustainable housing retrofit decision-making. In: International conference on computing in civil and building engineering, Orlando, Fl, USA, pp 1925–1933
Johnson D (1997) The triangular distribution as a proxy for the beta distribution in risk analysis. The Statistician 46:387–398
Maio C, Schexnayder C, Knutson K, Weber S (2000) Probability distribution function for construction simulation. J Constr Eng Manage 126–4:285–292
Pagani R, Savio L, Carbonaro C, Boonstra C, De Oliveira Fernandes E (2016) Concerto AL Piano. Sustainable urban transformations. Franco Angeli, Milano, pp 1–171
Pereira EJDS, Pinho JT, Galhardo MAB, Macêdo WN (2014) Methodology of risk analysis by Monte Carlo Method applied to power generation with renewable energy. Renew Energ 69:347–355
Rosato P, Valcovich E, Stival CA, Berto R, Cechet G (2016) Le coperture piane con inverdimento estensivo su edifici esistenti. Parte Seconda—Aspetti economici. Valori Valut Teorie Esper 16:5–27
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Fregonara, E., Ferrando, D.G., Carbonaro, C. (2020). Cost-Risk Analysis for Supporting Urban Regeneration Technological Projects. In: Mondini, G., Oppio, A., Stanghellini, S., Bottero, M., Abastante, F. (eds) Values and Functions for Future Cities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-23786-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-23786-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23784-4
Online ISBN: 978-3-030-23786-8
eBook Packages: EnergyEnergy (R0)