To meet the ambitious targets set by the European Union to reduce CO2 emissions, action in cities is essential. In fact, cities are responsible for 67% of the world's primary energy consumption and about 70% of energy-related CO2 emissions. To support the urban energy transition, widespread implementation of net-zero districts, or even better, positive energy districts (PEDs), is expected. PEDs could be defined as energy efficient and energy flexible urban areas that aim to provide a surplus of clean energy to the city through renewable energy. However, the development of the PED concept needs to take into account not only the technical issue of energy systems, but also the environmental, social, and economic aspects. To be effective, it is important to provide decision makers with tools based on a Multi-Criteria Decision Analysis (MCDA) approach that can effectively assess the complexity of impacts from a multi-stakeholder perspective. The MCDA approach can be supported by a Geographic Information System (GIS) that helps to analyze the data and make it communicable to everyone. The purpose of this research, through a scientific literature review, is to investigate different MCDA supported by GIS in the framework of economic evaluation methods, aiming to contribute to the definition of an effective multi-criteria spatial economic decision making method to support and sustain the design and development of PEDs.
1 Introduction
The European Union has placed great emphasis on reducing CO2 emissions in cities and related systems. Cities account for more than 50% of the global population, 80% of the global GDP, two-thirds of global energy consumption and more than 70% of annual global carbon emissions (IEA 2020). These factors are expected to increase significantly in the coming decades: it is anticipated that by 2050 more than 70% of the world’s population will live in cities, resulting in massive growth in demand for urban energy infrastructure (European Commission 2023). Climate action in cities is essential to achieve the ambitious net-zero emissions goals. From this perspective, it is known that urban development in the coming years will have to shift from simple building solutions to positive-energy neighbourhoods and districts (Becchio et al. 2020). All of this, along with other innovative concepts developed in the past for cities of the future, will be key to achieving Europe's energy and climate change goals (Suppa et al. 2022). With the new Horizon Europe research and innovation plan (which will cover the period 2021–2027), Europe is aiming to vigorously address a number of global challenges that affect our cities and society: health and safety, digitization, energy and climate change in the first place (Guarino et al. 2022). With this in mind, PEDs fall under this heading. The area of Smart Cities and Communities was already defined as a priority and strategic by both the previous European Horizon 2020 program and the 17 Sustainable Development Goals established by the UN and the 2030 Agenda (Kroll et al. 2019). Over time, however, it became apparent that financing large smart city projects at the urban level was a complex task, with a huge demand for resources and investment. For this reason, the authors decided to focus efforts on smaller urban areas, such as city blocks, pilot districts and neighbourhoods, towards a concept of a diffused smart land focusing initially on energy efficiency in buildings and on-site local renewable energy production. In recent years, to sustain the urban energy transition, the concept became even more ambitious, from highly efficient buildings to net-zero ones. Later on, by including energy sharing, waste heat recovery, e-mobility and energy storage, the scope was broadened to include the implementation of net-zero districts or even better PEDs (Guarino et al. 2022). PEDs represent a new approach towards a sustainable and efficient city and urbanization model. An urban Positive Energy District combines the built environment, mobility, sustainable production and consumption to increase energy efficiency and decrease greenhouse gas emissions and to create added value for citizens. Positive Energy Districts also require integration between buildings, users and various energy networks, mobility services and IT systems.
2 Context of the Research
2.1 Features of PEDs
Research all around the world is still struggling to find a unique definition for PEDs. From an energy-focused perspective, a PED is seen as an energy self-sufficient and carbon–neutral urban district. Indeed, positive energy means that energy districts also play an important role in producing excess energy using renewable energy sources and feeding it back into the grid (Bossi et al. 2020). However, widening the perspective, it is expected that PEDs will increase the quality of life in European cities, help achieve the COP21 goals and improve European capabilities and knowledge to become a global model (Derkenbaeva et al. 2022).
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Moreover, considering the keen interest of the European Commission to deliver at least 100 PEDs by 2050 and the current situation of European cities (IEA 2020), it is necessary to address this concept not only for new areas of urban development and the construction of new buildings and neighbourhoods, but especially for redevelopment of the existing building stock (Derkenbaeva et al. 2022).
The discussion on how and where to define the boundaries of these entities is still open and conclusions may differ depending on whether one considers physical limits and management aspects or those related to the overall energy balance and energy carriers, ranging therefore from local to regional scale (Zhang et al. 2021; Bossi et al. 2020). The discussion also often starts from the local dimension of city blocks, up to the urban dimension. To this regard, some interesting research on existing tools to support decision-making toward climate neutrality in cities and districts has been already carried out by Suppa et al. (2022).
In an attempt for extreme simplification, it can be said that PEDs have to strike an optimal balance between energy efficiency, energy flexibility and local energy production (European Commission 2023) in turn also achieving integrated sustainability based on environmental, economic and social features (see Fig. 1) (Muñoz et al. 2020).
A diagram of 4 areas for evaluating P E Ds. The areas are environmental sustainability, economic sustainability, social sustainability, and energy efficiency, production and flexibility.
Fig. 1
The four main areas for evaluating PEDs.
Source Own elaboration
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Consequently, in the evaluation of a PED using the model proposed by Binda et al. (2022), these four dimensions include intrinsic and extrinsic features of a PED that are intertwined without precise separation but rather highlight areas of overlap and coexistence in fuzzy logic. Economic evaluation approach tools used in the evaluation process enable decision-makers to have the effects of their decisions on the basis of selected KPIs under control. Even more, evaluation of urban projects is inspired by a circularity approach, which corresponds to the relationship between the Life Cycle Assessment (LCA) of the same projects and the overall Whole-Life Cost (WLC) (Grazieschi et al. 2020). LCA is a process to evaluate the effects a product has on the environment over the entire period of its life thereby increasing resource-use efficiency and decreasing liabilities (Grazieschi et al. 2020).
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WLC is basically rooted in a monetary perspective and thus related to the economic sustainability of investments by accounting for the total expense of owning an asset over its entire life from purchase to disposal, as determined by financial analysis (Fregonara 2020). The Life Cycle Cost (LCC) related to urban projects includes land acquisition and site preparation, design and building costs, operating costs, maintenance, associated financing costs, depreciation and disposal/demolition costs (Becchio et al. 2020).
As shown in Fig. 2, WLC also considers certain costs that are usually overlooked, such as factors related to environmental and social impact. In addition, we have an “extended” version of costs, which includes costs/benefits related to externalities (Becchio et al. 2020), cost savings and other effects (Fregonara 2020). This extended version includes the co-benefits commonly adopted to define the additional positive impact of smart energy renovation projects alongside the desired primary goal (Bisello 2020).
A chart of whole life cost. It has non-construction cost, income, externalities, and life cycle cost. Externalities have co-benefits such as better resource management. Life cycle cost has land acquisition, planning and finance, design, construction, management, and demolition and redevelopment.
Fig. 2
Whole-life cost with externalities.
Source Own elaboration based on Becchio et al. (2020), Bisello (2020), Fregonara (2020)
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2.2 Towards an Integrated Sustainability Evaluation
To move from a mere economic evaluation of PEDs towards an integrated sustainability assessment (Binda et al. 2022), including the spatial dimension, in this research, two additional elements were considered: Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS).
As shown in Fig. 3, there are different characteristics in each circle that by complementing one another contribute to defining methods and tools. MCDA is a powerful tool that enables the concurrent evaluation of qualitative, quantitative and monetary elements (Binda et al. 2022). It is a simplified way to consider environmental social and governance (ESG) criteria (European Commission 2023). This method also increases the transparency of the decision-making processes making the disadvantages and vantages in the alternatives clear (Bottero et al. 2021). Under this category, several different applications can be identified such as Fuzzy Multi-Criteria Decision Analysis (FMCDA), Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Multi-Criteria Decision Making (MCDM), Spatial-Multi-Criteria Analysis (SMCA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).
A Venn diagram of framework for Multicriteria-Spatial Economic analysis. There are 3 overlapping circles for M C D A, economic analysis, and G I S. They are essential to sustainability assessment and positive energy districts.
Fig. 3
Conceptual framework for Multicriteria-Spatial-Economic analysis of PED diagram (Source Own elaboration)
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GIS is important to represent and analyse geo-referred data that are used to analyse the spatial development and temporal dynamics of transformation processes and is therefore the graphical representation of the results for a suitability map (Coutinho-Rodrigues et al. 2011). These two additional elements complement the economic analysis, which focuses on setting an evaluation as the balance between overall advantages/disadvantages (Binda et al. 2022), identifying economic efficiency measures and meaning a cost-effective situation as a socially appropriate condition. Finally, a Cost Benefit Analysis (CBA) makes it possible to synthesize the performance of different evaluation aspects, transfer all impact into monetary terms (Bottero et al. 2021) and elaborate specific KPIs such as Net Present Value (NPV) or Return of investment (ROI) (Bottero et al. 2016). In this framework of economic and financial analysis, the Social Return on investment (SROI) is also becoming popular to evaluate urban projects (Hunter et al. 2022).
3 Research Methodology
Structure of the analysis (Fig. 4) is composed of three steps. In the first, we searched the literature from a scientific bibliographic database, in the second, we have the review based on the combination of three concepts shown in Fig. 3, and finally, the analysis phase. Literature bibliography analysis was conducted using the SCOPUS database with the following keywords in different combinations: Multicriteria, GIS, Economic. For a more informative literature review, we believe it is best to push the district boundary so as not to exclude valid methodologies for narrowings to which we have no explanation. Analysis was conducted to see how many documents are present for different combinations, specifically four combinations were found. Group A-B-C is research that shows historical production, country productivity affiliation and research topic, while Group D was conducted using more specific analysis as it is the heart of the analysis. Below are the strings used for the different groups:
A chart of the research method. It starts with review of the aim statement and goes to reference database, and keyword search. Review structure has 4 groups, A to D. Analysis occurs for historical production, country productivity, research topic, and economic territorial scale.
Fig. 4
Research method (Source Own elaboration)
1.
GROUP A: “Multicriteria | GIS”, limited research “title, abstract, the keyword”, using the words: (“Multicriteria” OR “Multicriteria Analysis” OR “MCDA” OR “MCA” OR “Multi-Criteria” OR “Multiple Criteria Decision Analysis”) AND (“GIS” OR “geographic information system” OR “Spatial Decision Support System”) = 4,440 documents
2.
GROUP B: “Multicriteria | Economic”, limited research “title, abstract, the keyword”, using the words: (“Multicriteria” OR “Multicriteria Analysis” OR “MCDA” OR “MCA” OR “Multi-Criteria” OR “Multiple Criteria Decision Analysis”) AND (“economic evaluation” OR “economic valuation” OR “economic assessment”) = 356 documents
3.
GROUP C: “Economic | GIS”, limited research “title, abstract, the keyword”, using the words: (“economic evaluation” OR “economic valuation” OR “economic assessment”) AND (“GIS” OR “geographic information system” OR “Spatial Decision Support System”) = 370 documents
4.
GROUP D “Multicriteria | Economic | GIS”, limited research “title, abstract, the keyword”, using the words: (“Multicriteria” OR “Multicriteria Analysis” OR “MCDA” OR “MCA” OR “Multi-Criteria” OR “Multiple Criteria Decision Analysis”) AND (“GIS” OR “geographic information system” OR “Spatial Decision Support System”) AND (“economic evaluation” OR “economic valuation” OR “economic assessment”) = 29 documents
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As shown in Fig. 4, to identify the research articles in group A, B, C, analysis was conducted both by year of publication of the papers, chronological order to assess the increase or decrease in techniques as the years passed, an analysis regarding publication status, and finally according to the research fields belonging to the macro fields. For group D specific analysis was conducted related to the Economic, GIS-Multicriteria and Territorial Scale.
4 Results
4.1 GROUP A (Multicriteria I GIS)
From the perspective of historical production analysis, this theme first appeared in 1976. Between 1976 and 1999, this theme only appeared in 18 articles. While, since 1999 production has increased with an exponential growth every year reaching 605 papers in 2021. As shown in Fig. 5, in Country Productivity Affiliation, the country that has produced the most regarding this topic is India with 455 papers, followed by Iran with 391 papers and then the United States with 373 papers.
A bar graph of analysis of nations of research publications that use multicriteria methods compiled by the authors. It plots numbers versus countries. India has the highest bar and Tunisia has the lowest bar.
Fig. 5
Analysis by nations of publications that use multicriteria/GIS methods
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As shown in Fig. 6 in research topic analysis, the main areas of development of this topic are Environmental Sciences, Earth, Planetary Sciences and Social Sciences with respectively, 2,121, 1,226, 1,219 and 879 papers. The energy field has only 514 papers.
A bar graph of analysis for field of research that uses G I S method compiled by the authors. It plots numbers versus field. Environmental Science has the highest bar and Chemistry has the lowest bar.
Fig. 6
Analysis by field of research of publications that uses multicriteria/GIS method
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4.2 GROUP B (Multicriteria I Economic)
As far as historical analysis of production is considered group B began publishing in 1981, with exponential growth, so much so that a substantial increase in publication was noted from 2005, gradually increasing to 46 items in 2021. As shown in Fig. 7, in Country of Productivity, the largest producing country in this area is Italy, producing 60 papers, followed by the United Kingdom with 41 papers and the United States with 40 papers.
A bar graph of analysis of nations of research publications that use economic methods compiled by the authors. It plots numbers versus countries. Italy has the highest bar and Brazil has the lowest bar.
Fig. 7
Analysis by nation of publications that use multicriteria/economic
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As shown in Fig. 8, in the Research topic, the main research Topic of analysis regarding Group B (MCA | Economic) is Environmental Sciences with 144 documents, followed by Engineering with 88 documents and Energy with 85 documents.
A bar graph of analysis for field of research that uses economic method compiled by the authors. It plots numbers versus field. Environmental Science has the highest bar and Psychology has the lowest bar.
Fig. 8
Analysis by field of research of publications that uses multicriteria/economic methods
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4.3 GROUP C (Economic I GIS)
This topic started to appear in 1988, and the period with the most production was 2019 with 29 documents, followed by 2017 and 2013 with 23 documents. As shown in Fig. 9, in Country Analysis, the analysis showed that the United States is the country, which has produced the most documents, specifically 69 documents, followed by the United Kingdom with 40 documents and Italy with 39 documents.
A bar graph of analysis of nations of research publications that uses economic method compiled by the authors. It plots numbers versus countries. United States has the highest bar and Israel has the lowest bar.
Fig. 9
Analysis by nations of publications that use economic/GIS
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As shown in Fig. 10, in Research Topic Analysis, the areas regarding this topic are Environmental Sciences with 163 documents followed by Agricultural and Biological Sciences with 71 documents and Social Sciences with 69 documents.
A bar graph of analysis for field of research that uses economic method compiled by the authors. It plots numbers versus field. Environmental Science has the highest bar and Neuroscience has the lowest bar.
Fig. 10
Analysis by field of research of publications that use economic/GIS method
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4.4 GROUP D (Multicriteria I GIS I Economic)
Group D as the main topic of research-specific analysis was conducted regarding the 28 documents related to the Economic, GIS-Multicriteria and Territorial Scale (see Table 1).
Table 1
Articles combining economic analysis, GIS and MCDA for PEDs evaluation
Legend (LCOE) Levelized Cost of Electricity, (CBA) Cost Benefit Analysis, (CRA) Cost Revenue Analysis, (PBP) Pay Back Period, (EI) Environmental Impact, (DCF) Discounted Cash Flow, (LCC) Life Cycle Cost, (GIS) Geographic Information Modelling, (MCDA) Multicriteria Decision Analysis, (FMCDA) Multi-Criteria Decision Analysis, (AHP) Analytic Hierarchy Process, (ANP) Analytic Network Process, (MCDM) Multi-Criteria Decision Making, (SMCA) Spatial-Multi-Criteria Analysis, (TOPSIS) Technique for Order of Preference by Similarity to Ideal Solution. (MCDM + WC) Multi-Criteria Decision-Making Weighted Combination. (WLC) Whole-life Cost
Starting with “Economic Analysis”, the main economic method used is the “Techno-Economic Assessment”, with 11 documents, followed by Cost Benefit Analysis (CBA) with 6 articles and Cost and Revenue Analysis with 5 documents.
There are different uses of Multicriteria Analysis and GIS. In particular, the methods found in the analysis are those which are most used in the general Weighted Combination (WC) followed by Analytic Hierarchy Process (AHP).
MCDA-WC aims to include normalized criteria that are weighted to determine the relative importance of each criterion, prioritizing some criteria over others. This is necessary to achieve a flexible decision-making method that can balance choices based on set objectives (Martín-Hernándaez et al. 2021).
MCDA AHP makes it possible to compare multiple alternatives with a plurality of criteria, either quantitative or qualitative, and derive an overall evaluation for each. This makes it possible to sort the alternatives in order of preference, select the best alternative, and ultimately be able to assign the alternatives to predefined subsets (Muñoz et al. 2020).
It became clear that all articles were divided into these three categories, namely, regional scale, city and parts of buildings. Interestingly, most of these analyses focus extensively on regional spatial contexts, which produced the majority of the documents with 21 papers, followed by “Parts of the building” with 4 documents and in the end with the “city” (Fig. 11).
A horizontal bar graph of examination of the scale of application of the papers. On the scale, the region is the highest at 20, followed by part of building, and city, both below 5.
Fig. 11
Spatial reference scale of the publications considered, distinguishing between building (part of building), city-district and regional scales
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5 Conclusion and Future Perspectives
The literature review of the above-mentioned elements was twofold.
Through further analysis of the scientific literature, this research can help to define an effective multi-criteria spatial economic decision-making methodology to support and sustain the design and development of PEDs. In this sense, the authors would like to investigate evaluation tools in the context of PEDs in order to understand the potential and critical elements of the available approaches to support decision-making processes in this field of application. To develop the PED concept, proper consideration must be given not only to the technical issue of energy systems but also to the environmental, social and economic spheres. To be effective, it is important to provide decision-makers with tools based on a multi-criteria decision analysis (MCDA) approach that can effectively evaluate the complexity of the impact from a multi-stakeholder perspective. The MCDA approach can be supported by a geographic information system (GIS), that helps to analyse data and make it accessible to all.
As a future outlook, however, it would be interesting to try to combine these three tools to support decision-making to identify the best area to apply PEDs to evaluate the full range of benefits of their implementation. In this regard on multiple benefit analysis to evaluate PEDs, it would be interesting to link the process of building PEDs with Building Information Modelling (BIM) implementations and District Information Modelling (DIM), to be able to support, a robust analysis as a support to the decision-making process even in the post-feasibility stages.
Consent to Publish
The authors do not declare any conflicts of interest, and all approve final consent to publish.
This work was developed within the context of the International Energy Agency (IEA) Energy in Buildings and Construction (EBC) Annex 83 working group on “Positive Energy Districts”. The research leading to these results has been done in the framework of the European project ProLight. This project has received funding from the European Union’s Horizon 2020 program under grant agreement no. 101079902. The sole responsibility for the content of this publication lies with the authors. It does not necessarily represent the opinion of the European Union. Neither the EASME nor the European Commission are responsible for any use that may be made of the information contained herein.
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