An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale
Abstract
:1. Introduction
2. Research Background
3. Methodological Framework
- Structuring the decision-making problem: this is the preliminary phase in which the problem is defined. In this case, the model focuses on the prioritization of different retrofit actions for the residential building stock to allocate resources through fiscal measures.
- Description of the building stock under examination, in order to collect information on the type of buildings (geometric and heating system characteristics) and proposed retrofit actions. It is possible to identify information on costs (investment and maintenance), environmental costs, and qualitative characteristics of the solutions.
- Structuring of the multi-criteria model: the building stock is grouped into elementary asset families according to building type and age of construction. Once the evaluation criteria were defined, the performance of each stock was measured for each criterion. This step allows the performance matrix to be outlined.
- Application of ELECTRE TRI-B: the sorting model is the MCDA method chosen to group the actions into priority groups, ranking them by the level of importance. In this phase, criteria are weighted, reference profiles are outlined, and priority categories are defined according to the opinions of a group of experts. SRF (Simos-Roy-Figueira) method was used for the weighting step.
- Definition of guidelines for the development of the master plan: A critical reading of the results was carried out to provide useful guidelines for the DMs involved in the project.
3.1. ELECTRE TRI-B
3.2. SRF Method
4. Application
4.1. Vanchiglietta District in Turin
4.2. District Characterization
4.3. Multi-Criteria Model Structuring
- g1: Investment costs (€) for the implementation of the different efficiency measures. For the definition of this criterion, the costs for the installation of the external envelope and the replacement of external windows and doors, the connection to DH, the installation of PV, and measures to reduce drinking water consumption were taken into account [40]. The initial assumption in implementing retrofit actions is that all assumed actions should be implemented to achieve a satisfactory level of sustainability. In this sense, according to this assumption, for each type of building, all measures will be implemented at the same time. Therefore, the investment costs consider the total investment costs of implementing all assumed retrofit actions. The criterion should be minimized, meaning that interventions that cost the least and maximize the other aspects considered should be favored.
- g2: Pre-intervention operating costs (€) criterion gives priority for intervention to the most energy-consuming building stock, allowing it to reach its target for energy and drinking water reduction more quickly. In fact, it is proposed to maximize the criterion to favor interventions on the most energy-intensive buildings.
- g3: Avoided external costs (€) translates into economic terms the prevented health costs due to the presence of pollutants in the air thanks to PV installation [38]. A criterion that maximizes the positive effects of PV is proposed because it is intended to consider the promotion of RES and emphasize its crucial role in the energy transition. A criterion that takes into account the reduction of pollutant emissions for all measures could be indirectly proportional to g2 criterion and produce an evaluation bias. The criterion is to be maximized.
- g4: Water reduction (m3) measures the water that is saved by installing the proposed solutions (i.e., installing aerators at the taps and installing dual-flow drainage trays). For the definition of the criterion referring to the saving of drinking water, reference was made to the various sustainability certifications of the built environment, such as BREEAM, Green Mark, ITACA, and LEED [41,42,43,44], which define different thresholds to reward the most virtuous buildings. The criterion is to be maximized.
- Social criteria maximize the acceptance of measures by the community.
- g5: Green jobs (No.) measures the number of new jobs generated based on the investment costs incurred [45,46]. The promotion of new green jobs especially in times of economic stagnation is crucial and also allows for increased investment acceptance by promoting local know-how. The criterion is to be maximized.
- g6: Visual impact [1,2,3,4,5] measures on a qualitative scale the visual impact of the measures implemented for the different types [47]. Historic buildings are often subject to protection restrictions, and the options in terms of measures to be taken are greatly reduced especially those related to insulation and installation of RES, and not so much those related to drinking water consumption. Favoring interventions on buildings without any artistic merit maximizes the benefits in terms of reduced energy demand [21]. The criterion is to be minimized.
4.4. Weights of Criteria, Intra-Criterial Preference Information, and Reference Actions
5. Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Good, N.; Martínez Ceseña, E.A.; Mancarella, P. Ten questions concerning smart districts. Build. Environ. 2017, 118, 362–376. [Google Scholar] [CrossRef]
- Copiello, S. Economic viability of building energy efficiency measures: A review on the discount rate. AIMS Energy 2021, 9, 257–285. [Google Scholar] [CrossRef]
- Bottero, M.; Dell’Anna, F.; Morgese, V. Evaluating the Transition Towards Post-Carbon Cities: A Literature Review. Sustainability 2021, 13, 567. [Google Scholar] [CrossRef]
- Barfod, M.B.; Salling, K.B.; Leleur, S. Composite decision support by combining cost-benefit and multi-criteria decision analysis. Decis. Support Syst. 2011, 51, 167–175. [Google Scholar] [CrossRef]
- Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew. Sustain. Energy Rev. 2009, 13, 2263–2278. [Google Scholar] [CrossRef]
- Bertoldi, P.; Economidou, M.; Palermo, V.; Boza-Kiss, B.; Todeschi, V. How to finance energy renovation of residential buildings: Review of current and emerging financing instruments in the EU. WIREs Energy Environ. 2021, 10, e384. [Google Scholar] [CrossRef]
- Dell’Anna, F.; Marmolejo-Duarte, C.; Bravi, M.; Bottero, M. A choice experiment for testing the energy-efficiency mortgage as a tool for promoting sustainable finance. Energy Effic. 2022, 15, 27. [Google Scholar] [CrossRef]
- Almeida-Dias, J.; Figueira, J.R.; Roy, B. A multiple criteria sorting method where each category is characterized by several reference actions: The Electre Tri-nC method. Eur. J. Oper. Res. 2012, 217, 567–579. [Google Scholar] [CrossRef] [Green Version]
- Figueira, J.R.; Greco, S.; Roy, B.; Słowiński, R. ELECTRE Methods: Main Features and Recent Developments. In Handbook of Multicriteria Analysis. Applied Optimization; Zopounidis, C., Pardalos, P., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 51–89. [Google Scholar]
- Conforto, E.C.; Amaral, D.C. Evaluating an Agile Method for Planning and Controlling Innovative Projects. Proj. Manag. J. 2010, 41, 73–80. [Google Scholar] [CrossRef]
- Sanchez, O.P.; Terlizzi, M.A.; de Moraes, H.R.d.O.C. Cost and time project management success factors for information systems development projects. Int. J. Proj. Manag. 2017, 35, 1608–1626. [Google Scholar] [CrossRef]
- Behnam, A.; Harfield, T.; Kenley, R. Construction management scheduling and control: The familiar historical overview. MATEC Web Conf. 2016, 66, 00101. [Google Scholar] [CrossRef] [Green Version]
- Rashed, M.S.; Alnassar, W.I. Evaluating the performance of project management using network diagrams methods: A case study in the Ramadi Municipality. Rev. Int. Geogr. Educ. Online 2021, 11, 3971–3984. [Google Scholar] [CrossRef]
- Gelbard, R.; Pliskin, N.; Spiegler, I. Integrating system analysis and project management tools. Int. J. Proj. Manag. 2002, 20, 461–468. [Google Scholar] [CrossRef]
- Pontrandolfo, P. Project duration in stochastic networks by the PERT-path technique. Int. J. Proj. Manag. 2000, 18, 215–222. [Google Scholar] [CrossRef]
- Pinto, M.C.; Crespi, G.; Dell’Anna, F.; Becchio, C. Combining energy dynamic simulation and multi-criteria analysis for supporting investment decisions on smart shading devices in office buildings. Appl. Energy 2023, 332, 120470. [Google Scholar] [CrossRef]
- Costa, A.S.; Govindan, K.; Figueira, J.R. Supplier classification in emerging economies using the ELECTRE TRI-nC method: A case study considering sustainability aspects. J. Clean. Prod. 2018, 201, 925–947. [Google Scholar] [CrossRef]
- de Miranda Mota, C.M.; de Almeida, A.T.; Alencar, L.H. A multiple criteria decision model for assigning priorities to activities in project management. Int. J. Proj. Manag. 2009, 27, 175–181. [Google Scholar] [CrossRef]
- Gagnon, M.; d’Avignon, G.; Aouni, B. Resource-constrained project scheduling through the goal programming model: Integration of the manager’s preferences. Int. Trans. Oper. Res. 2012, 19, 547–565. [Google Scholar] [CrossRef]
- Heravi, G.; Gerami Seresht, N. A Multi Criteria Decision Making Model for Prioritizing the Non-Critical Activities in Construction Projects. KSCE J. Civ. Eng. 2018, 22, 3753–3763. [Google Scholar] [CrossRef]
- Napoli, G.; Bottero, M.; Ciulla, G.; Dell’Anna, F.; Figueira, J.R.; Greco, S. Supporting public decision process in buildings energy retrofitting operations: The application of a Multiple Criteria Decision Aiding model to a case study in Southern Italy. Sustain. Cities Soc. 2020, 60, 102214. [Google Scholar] [CrossRef]
- Hiebert, J.; Allen, K. Valuing environmental amenities across space: A geographicallyweighted regression of housing preferences in Greenville County, SC. Land 2019, 8, 147. [Google Scholar] [CrossRef] [Green Version]
- Kannimuthu, M.; Raphael, B.; Ekambaram, P.; Kuppuswamy, A. Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem. Eng. Constr. Archit. Manag. 2019, 27, 893–916. [Google Scholar] [CrossRef]
- Yang, P.P.J.; Chang, S.; Saha, N.; Chen, H.W. Data-driven planning support system for a campus design. Environ. Plan. B Urban Anal. City Sci. 2020, 47, 1474–1489. [Google Scholar] [CrossRef]
- Apollo, M.; Miszewska-Urbańska, E. Influence of passive house technology on time and cost of construction investment. E3S Web Conf. 2018, 44, 00004. [Google Scholar] [CrossRef]
- Dell’Ovo, M.; Oppio, A.; Capolongo, S. Policy Implications. How to Support Decision-Makers in Setting and Solving Complex Problems. In SpringerBriefs in Applied Sciences and Technology; Dell’Ovo, M., Oppio, A., Capolongo, S., Eds.; Springer: Cham, Switzerland, 2020; pp. 113–121. [Google Scholar]
- Bertoncini, M.; Boggio, A.; Dell’Anna, F.; Becchio, C.; Bottero, M. An application of the PROMETHEE II method for the comparison of energy requalification strategies to design Post-Carbon Cities. AIMS Energy 2022, 10, 553–581. [Google Scholar] [CrossRef]
- Mousseau, V.; Slowinski, R.; Zielniewicz, P. ELECTRE TRI 2.0 a Methodological Guide and user’s Manual. In Document du LAMSADE; Université Paris-Dauphine: Paris, France, 1999; Volume 111, pp. 263–275. [Google Scholar]
- Emamat, M.S.M.M.; de Miranda Mota, C.M.; Mehregan, M.R.; Sadeghi Moghadam, M.R.; Nemery, P. Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context. Financ. Innov. 2022, 8, 11. [Google Scholar] [CrossRef]
- Fernández, E.; Figueira, J.R.; Navarro, J.; Roy, B. ELECTRE TRI-nB: A new multiple criteria ordinal classification method. Eur. J. Oper. Res. 2017, 263, 214–224. [Google Scholar] [CrossRef]
- Figueira, J.; Roy, B. Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur. J. Oper. Res. 2002, 139, 317–326. [Google Scholar] [CrossRef] [Green Version]
- Abastante, F.; Lami, I.M.; Lombardi, P.; Toniolo, J. District energy choices: More than a monetary problem. a SDSS approach to define urban energy scenarios. Valori e Valutazioni 2019, 22, 109–120. [Google Scholar]
- Dell’Anna, F.; Pederiva, G.; Vergerio, G.; Becchio, C.; Bottero, M. Supporting sustainability projects at neighbourhood scale: Green visions for the San Salvario district in Turin guided by a combined assessment framework. J. Clean. Prod. 2023, 384, 135460. [Google Scholar] [CrossRef]
- Liposcak, M.; Afgan, N.; Duic, N.; Dagracacarvalho, M. Sustainability assessment of cogeneration sector development in Croatia. Energy 2006, 31, 2276–2284. [Google Scholar] [CrossRef]
- Grujić, M.; Ivezić, D.; Živković, M. Application of multi-criteria decision-making model for choice of the optimal solution for meeting heat demand in the centralized supply system in Belgrade. Energy 2014, 67, 341–350. [Google Scholar] [CrossRef]
- Ballarini, I.; Corgnati, S.P.; Corrado, V. Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project. Energy Policy 2014, 68, 273–284. [Google Scholar] [CrossRef]
- Brunet, C.; Savadogo, O.; Baptiste, P.; Bouchard, M.A.; Rakotoary, J.C.; Ravoninjatovo, A.; Cholez, C.; Gendron, C.; Merveille, N. Impacts generated by a large-scale solar photovoltaic power plant can lead to conflicts between sustainable development goals: A review of key lessons learned in Madagascar. Sustainability 2020, 12, 7471. [Google Scholar] [CrossRef]
- Bickel, P.; Friendrich, R. ExternE-Externalities of Energy-Methodology 2005 Update; European Communities: Brussels, Belgium, 2004. [Google Scholar]
- Roy, B. Multicriteria Methodology for Decision Aiding; Nonconvex Optimization and Its Applications; Springer: Boston, MA, USA, 1996; Volume 12, ISBN 978-1-4419-4761-1. [Google Scholar]
- Dell’Anna, F.; Vergerio, G.; Corgnati, S.P.; Mondini, G. A new price list for retrofit intervention evaluation on some archetypical buildings. Valori e Valutazioni 2019, 22, 3–17. [Google Scholar]
- Kaur, H.; Garg, P. Urban sustainability assessment tools: A review. J. Clean. Prod. 2019, 210, 146–158. [Google Scholar] [CrossRef]
- Dell’Anna, F.; Bottero, M. Green premium in buildings: Evidence from the real estate market of Singapore. J. Clean. Prod. 2021, 286, 125327. [Google Scholar] [CrossRef]
- USGBC U.S. Green Building Council—Green Building Rating System. Available online: https://www.usgbc.org/leed (accessed on 24 November 2020).
- Asdrubali, F.; Baldinelli, G.; Bianchi, F.; Sambuco, S. A comparison between environmental sustainability rating systems LEED and ITACA for residential buildings. Build. Environ. 2015, 86, 98–108. [Google Scholar] [CrossRef]
- Dell’Anna, F. Green jobs and energy efficiency as strategies for economic growth and the reduction of environmental impacts. Energy Policy 2021, 149, 112031. [Google Scholar] [CrossRef]
- Mirasgedis, S.; Tourkolias, C.; Pavlakis, E.; Diakoulaki, D. A methodological framework for assessing the employment effects associated with energy efficiency interventions in buildings. Energy Build. 2014, 82, 275–286. [Google Scholar] [CrossRef]
- Haurant, P.; Oberti, P.; Muselli, M. Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework. Energy Policy 2011, 39, 676–688. [Google Scholar] [CrossRef]
- Figueira, J.R.; Mousseau, V.; Roy, B. ELECTRE Methods. In International Series in Operations Research and Management Science; Springer: New York, NY, USA, 2016; pp. 155–185. [Google Scholar]
- Figueira, J.; Mousseau, V.; Roy, B. Electre Methods. In Multiple Criteria Decision Analysis: State of the Art Surveys; Springer: New York, NY, USA, 2005; Volume 78, pp. 133–153. [Google Scholar]
- Roy, B. The outranking approach and the foundations of electre methods. Theory Decis. 1991, 31, 49–73. [Google Scholar] [CrossRef]
- Subhashini, S.; Kesavaperumal, T.; Noguchi, M. An adaptive thermal comfort model for naturally ventilated classrooms of technical institutions in Madurai. Open House Int. 2021, 46, 682–696. [Google Scholar] [CrossRef]
Method | ||||
---|---|---|---|---|
Features | Gantt chart | Program evaluation and review technique | Critical path method | Multiple criteria decision aiding |
Aim | Determining how long each task will take. | Determining the minimum time required to complete the project. | Determine the minimum time required depending on cost to complete the project. | Determining an order of priority for intervention considering different evaluation criteria. |
Project scope | Great for smaller project. | Ideal for complex project. | Ideal for complex project. | Ideal for complex project. |
Flexibility | Easy to modify as possible contingencies change. | Not easy to modify as possible contingencies change. | Not easy to modify as possible contingencies change. | Easy to modify as possible contingencies change. |
Stakeholders’ involvement | There is no involvement. | There is no involvement. | There is no involvement. | There is involvement. |
Results | Easy to understand. | Not easy to understand because of the representation structure. | Not easy to understand because of the representation structure. | Easy to understand. |
Building Typology | Construction Period | Surface (m2) | Percentage (%) | Partial Percentage (%) |
---|---|---|---|---|
SFH | before 1919 | 81 | 0.05 | |
SFH | 1919–1945 | 1559 | 1.02 | |
SFH | 1946–1960 | 68 | 0.04 | |
SFH | 1961–1970 | 3332 | 2.18 | |
SFH | 1971–1990 | 2059 | 1.35 | |
SFH | 1991–2000 | 284 | 0.19 | |
SFH | after 2005 | 709 | 0.46 | 5.3 |
TH | before 1919 | 559 | 0.37 | |
TH | 1919–1945 | 6177 | 4.05 | |
TH | 1946–1960 | 3872 | 2.54 | |
TH | 1961–1970 | 4578 | 3.00 | |
TH | 1981–1990 | 481 | 0.31 | |
TH | after 2005 | 583 | 0.38 | 10.6 |
MFH | before 1919 | 444 | 0.29 | |
MFH | 1919–1945 | 25,001 | 16.38 | |
MFH | 1946–1960 | 21,874 | 14.33 | |
MFH | 1961–1970 | 5633 | 3.69 | |
MFH | 1971–1980 | 1192 | 0.78 | |
MFH | 1981–1990 | 607 | 0.40 | |
MFH | 1991–2000 | 1600 | 1.05 | |
MFH | 2001–2005 | 2479 | 1.62 | |
MFH | after 2005 | 2128 | 1.39 | 39.9 |
AB | before 1919 | 275 | 0.18 | |
AB | 1919–1945 | 10,534 | 6.90 | |
AB | 1946–1960 | 26,384 | 17.29 | |
AB | 1961–1970 | 18,605 | 12.19 | |
AB | 1971–1980 | 2542 | 1.67 | |
AB | 1981–1990 | 3061 | 2.01 | |
AB | 1991–2000 | 3276 | 2.15 | |
AB | 2001–2005 | 2065 | 1.35 | |
AB | after 2005 | 577 | 0.38 | 44.1 |
Total surface (m2) | 152,618 |
Building Typology | Criteria | ||||||
---|---|---|---|---|---|---|---|
Building Typology | Construction Period | g1 | g2 | g3 | g4 | g5 | g6 |
Investment Costs | Pre-Intervention Operating Costs | Avoided External Costs | Water Savings | Green Jobs | Visual Impact | ||
€ | € | € | m3 | No. | 1–5 | ||
Min | Max | Max | Max | Max | Min | ||
SFH | before 1919 | 14,108 | 573 | 12,071 | 30 | 0 | 5 |
SFH | 1919–1945 | 271,913 | 31,052 | 235,746 | 435 | 6 | 5 |
SFH | 1946–1960 | 46,627 | 2209 | 8542 | 30 | 1 | 5 |
SFH | 1961–1970 | 2,972,579 | 75,725 | 431,541 | 195 | 61 | 3 |
SFH | 1971–1990 | 1,245,846 | 20,584 | 262,598 | 30 | 26 | 3 |
SFH | 1991–2000 | 171,745 | 322 | 33,204 | 60 | 4 | 1 |
SFH | after 2005 | 77,607 | 3476 | 82,937 | 30 | 2 | 1 |
TH | before 1919 | 106,276 | 19,303 | 35,010 | 165 | 2 | 5 |
TH | 1919–1945 | 903,704 | 103,324 | 794,226 | 1515 | 19 | 5 |
TH | 1946–1960 | 2,334,393 | 54,020 | 490,613 | 600 | 48 | 5 |
TH | 1961–1970 | 3,212,195 | 78,466 | 574,472 | 165 | 66 | 3 |
TH | 1981–1990 | 320,838 | 5309 | 60,283 | 30 | 7 | 3 |
TH | after 2005 | 82,902 | 4681 | 72,652 | 30 | 2 | 1 |
MFH | before 1919 | 65,548 | 8654 | 52,815 | 60 | 1 | 5 |
MFH | 1919–1945 | 1,850,787 | 359,081 | 3,921,868 | 4230 | 38 | 5 |
MFH | 1946–1960 | 7,638,072 | 531,021 | 3,202,932 | 3195 | 157 | 5 |
MFH | 1961–1970 | 2,118,888 | 57,825 | 711,759 | 435 | 44 | 3 |
MFH | 1971–1980 | 386,467 | 8613 | 139,001 | 135 | 8 | 3 |
MFH | 1981–1990 | 196,788 | 4088 | 76,469 | 105 | 4 | 1 |
MFH | 1991–2000 | 531,404 | 10,781 | 201,659 | 135 | 11 | 1 |
MFH | 2001–2005 | 823,031 | 7193 | 309,603 | 165 | 17 | 1 |
MFH | after 2005 | 131,798 | 6175 | 265,817 | 165 | 3 | 1 |
AB | before 1919 | 20,351 | 6672 | 40,264 | 30 | 0 | 5 |
AB | 1919–1945 | 622,977 | 148,016 | 1,490,956 | 990 | 13 | 5 |
AB | 1946–1960 | 8,275,853 | 361,718 | 3,495,130 | 2010 | 170 | 5 |
AB | 1961–1970 | 5,610,217 | 210,877 | 1,892,519 | 1215 | 115 | 3 |
AB | 1971–1980 | 557,605 | 14,401 | 282,602 | 225 | 11 | 3 |
AB | 1981–1990 | 671,558 | 14,086 | 358,084 | 165 | 14 | 1 |
AB | 1991–2000 | 841,229 | 15,071 | 383,133 | 270 | 17 | 1 |
AB | 2001–2005 | 530,437 | 6480 | 221,559 | 105 | 11 | 1 |
AB | after 2005 | 25,089 | 1811 | 61,911 | 30 | 1 | 1 |
Criterion | Position | White Card | Normalized Weight |
---|---|---|---|
Investment costs | 1 | 24.6 | |
Pre-intervention operating costs | 1 | 24.7 | |
Avoided external costs | 2 | 21 | |
1 | |||
Green jobs | 3 | 13.6 | |
Water reduction | 4 | 9.9 | |
Visual impact | 5 | 6.2 |
Investment Costs | Pre-Intervention Operating Costs | Avoided External Costs | Water Savings | Green Jobs | Visual Impact | |
---|---|---|---|---|---|---|
qβ | 10,000 | 1000 | / | 50 | / | 2 |
pβ | 500,000 | 10,000 | 5000 | 500 | 20 | 4 |
b0 | 15,000 | 2000 | 50,000 | 100 | 50 | 3 |
b1 | 100,000 | 300 | 1000 | 20 | 15 | 5 |
Building Typology | Construction Period | Priority | Building Typology | Construction Period | Priority |
---|---|---|---|---|---|
SFH | before 1919 | Low priority | MFH | 1961–1970 | Medium priority |
SFH | 1919–1945 | Medium priority | MFH | 1971–1980 | Medium priority |
SFH | 1946–1960 | Low priority | MFH | 1981–1990 | Low priority |
SFH | 1961–1970 | Medium priority | MFH | 1991–2000 | Medium priority |
SFH | 1971–1990 | Medium priority | MFH | 2001–2005 | Medium priority |
SFH | 1991–2000 | Low priority | MFH | after 2005 | Medium priority |
SFH | after 2005 | Low priority | AB | before 1919 | Low priority |
TH | before 1919 | Medium priority | AB | 1919–1945 | Medium priority |
TH | 1919–1945 | Medium priority | AB | 1946–1960 | High priority |
TH | 1946–1960 | Medium priority | AB | 1961–1970 | High priority |
TH | 1961–1970 | Medium priority | AB | 1971–1980 | Medium priority |
TH | 1981–1990 | Low priority | AB | 1981–1990 | Medium priority |
TH | after 2005 | Low priority | AB | 1991–2000 | Medium priority |
MFH | before 1919 | Low priority | AB | 2001–2005 | Medium priority |
MFH | 1919–1945 | High priority | AB | after 2005 | Low priority |
MFH | 1946–1960 | High priority |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dell’Anna, F. An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale. Sustainability 2023, 15, 1250. https://doi.org/10.3390/su15021250
Dell’Anna F. An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale. Sustainability. 2023; 15(2):1250. https://doi.org/10.3390/su15021250
Chicago/Turabian StyleDell’Anna, Federico. 2023. "An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale" Sustainability 15, no. 2: 1250. https://doi.org/10.3390/su15021250
APA StyleDell’Anna, F. (2023). An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale. Sustainability, 15(2), 1250. https://doi.org/10.3390/su15021250