Multi-criteria Decision Analysis Approach Research Articles

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.

Development of a decision support tool to compare diagnostic strategies for establishing the herd status for infectious diseases: an example with Salmonella Dublin infection in dairies

The diagnosis of infectious diseases at herd level can be challenging as different stakeholders can have conflicting priorities. The current study proposes a “proof of concept” of an approach that considers a reasonable number of criteria to rank plausible diagnostic strategies using multi-criteria decision analysis (MCDA) methods. The example of Salmonella Dublin diagnostic in Québec dairy herds is presented according to two epidemiological contexts: (i) in herds with no history of S. Dublin infection and absence of clinical signs, (ii) in herds with a previous history of infection, but absence of clinical signs at the moment of testing.Multiple multiparty exchanges were conducted to determine: 1) stakeholders’ groups; 2) the decision problem; 3) solutions to the problem (options) or diagnostic strategies to be ordered; 4) criteria and indicators; 5) criteria weights; 6) the construction of a performance matrix for each option; 7) the multi-criteria analyses using the visual preference ranking organization method for enrichment of evaluations approach; 8) the sensitivity analyses, and 9) the final decision.A total of nine people from four Québec’s organizations (the dairy producers provincial association along with the DHI company, the ministry of agriculture, the association of veterinary practitioners, and experts in epidemiology) composed the MCDA team. The decision problem was “What is the optimal diagnostic strategy for establishing the status of a dairy herd for S. Dublin infection when there are no clinical signs of infection?”. Fourteen diagnostic strategies composed of the three following parameters were considered: 1) biological samples (bulk tank milk or blood from 10 heifers aged over three months); 2) sampling frequencies (one to three samples collection visits); 3) case definitions to conclude to a positive status using imperfect milk- or blood-ELISA tests. The top-ranking diagnostic strategy was the same in the two contexts: testing the bulk tank milk and the blood samples, all samples collected during one visit and the herd being assigned a S. Dublin positive status if one sample is ELISA-positive. The final decision favored the top-ranking option for both contexts.This MCDA approach and its application to S. Dublin infection in dairy herds allowed a consensual, rational, and transparent ranking of feasible diagnostic strategies while taking into account the diagnostic tests accuracy, socio-economic, logistic, and perception considerations of the key actors in the dairy industry. This promising tool can be applied to other infectious diseases that lack a well-established diagnostic procedure to define a herd status.

Multicriteria Model to Support the Hiring of Road Freight Transport Services in Brazil

The transportation of goods plays a fundamental role in the global economy. In Brazil, specifically, a significant portion of what is transported goes through highways, and the provision of this service is carried out through the hiring of specialized companies or independent drivers, who end up serving companies from various sectors. This study successfully constructed a decision model to support the hiring of road freight transport services in Brazil. To achieve this, the multicriteria decision analysis (MCDA) approach was used, employing the Measuring Attractiveness By a Categorical-Based Evaluation Technique (MACBETH) method. The development of this model was informed by a comprehensive literature review, interviews with three transportation professionals, and a simulation involving eight anonymous Brazilian companies. This initiative aims to create a practical framework for effectively selecting logistics operators in the road transport sector to meet the needs of companies dependent on these services.

Ten Years of Mediterranean Monk Seal Stranding Records in Greece under the Microscope: What Do the Data Suggest?

This paper presents the results of an analysis of stranding events of the Mediterranean monk seal Monachus monachus over a decade. The analysis involved categorization according to the cause of stranding and seasonality, the identification of hotspot stranding areas and an assessment of possible correlations between stranding events and environmental/climatic patterns using time series analysis. Moreover, Generalized Additive Models (GAMs) were applied to explore the effects of the size of small-scale fishing grounds, the number of species sightings, and the occurrence of reproduction sites on "human-related" strandings. Finally, special focus was put on the central part of the eastern Ionian Sea for the assessment of stranding hotspot areas by means of the Multi-Criteria Decision Analysis (MCDA) approach, based on different kinds of spatial information such as anthropogenic pressures and the location of breeding sites and feeding grounds. Time series analysis results revealed that oscillation indices, during the first half of the year, and sea surface temperature (SST) in the Mediterranean from October to December were positively correlated with monk seal stranding events. GAMs underlined that areas combining extended small-scale fishery grounds and a higher number of sightings were more likely to cause more strandings. Regarding spatial analyses, the central Aegean Sea was highlighted as a hotspot for "human-related strandings", while the MCDA approach emphasized that the southern coasts of Cephalonia and the gulf between Lefkada and mainland Greece were susceptible to subadult strandings.

A community-scale study on nature-based solutions (NBS) for stormwater management under tropical climate: The case of the Asian Institute of Technology (AIT), Thailand

ABSTRACT Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, Personnel Computer Storm Water Management Model (PCSWMM) software was used to develop a numerical model. It was found that the MCDA approach is appropriate to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate.

Assessing the relative contribution of various anthropogenic sources to atmospheric methane in Rivers State, Nigeria: A multi-criteria decision analysis approach

Methane is a significant greenhouse gas, with a global warming potential many times greater than carbon dioxide over a 20-year period. Its release from sources like landfills, agriculture, and the energy sector exacerbates climate change, making it crucial to monitor and reduce methane emissions to mitigate global warming and achieve climate goals. This research utilized the Multi-Criteria Decision Analysis (MCDA) method, specifically the Analytic Hierarchy Process (AHP), to analyze the sources of atmospheric methane in Rivers State, Nigeria. It addressed the challenge of assessing the contributions of various anthropogenic sources such as fossil fuels, landfills, agriculture, wetlands, and oceans to atmospheric methane emissions. By incorporating expert opinions, literature reviews, and surveys, the study constructed a hierarchical model to prioritize these sources based on their impact. Findings identified fossil fuels and landfills as the main contributors. The study demonstrated MCDA's effectiveness in environmental analysis and provided a replicable framework for similar assessments in other regions, contributing to targeted emission mitigation and policy formulation efforts.

Optimizing Mini Dam Placement for Sustainable Water Management in FUTA: A GIS-MCDA Approach

Addressing the increasing demand for water resources at the Federal University of Technology, Akure (FUTA) due to the growing population requires a data-driven solution. This study determined the suitable locations for the siting of mini-dams as an alternative solution to the problem of water scarcity in FUTA using a Geographical Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) approach by integrating five thematic factors: drainage density, slope, elevation, land cover and soil texture. Preference values were assigned to the criteria based on their importance to a dam. Analytical Hierarchy Process (AHP) was used to assign weights to these criteria, and they were combined using a weighted sum to produce a mini dam suitability map within the study area. The suitable areas were divided into five classes: highly suitable (9%), suitable (18%), marginally suitable (30%), least suitable (26%) and not suitable (17%). The suitability map was validated against the locations of existing dams in the study area. This study contributes to the efforts to manage water resources for a growing population in FUTA and to evaluate the GIS-AHP approach for dam siting for a small study area.

Development of a Value Assessment Framework for Pediatric Health Technologies Using Multicriteria Decision Analysis: Expanding the Value Lens for Funding Decision Making

ObjectivesA health technology assessment (HTA) does not systematically account for the circumstances and needs of children and youth. To supplement HTA processes, we aimed to develop a child-tailored value assessment framework using a multicriteria decision analysis approach. MethodsWe constructed a multicriteria-decision-analysis-based model in multiple phases to create the Comprehensive Assessment of Technologies for Child Health (CATCH) framework. Using a modified Delphi process with stakeholders having broad disciplinary and geographic variation (N = 23), we refined previously generated criteria and developed rank-based weights. We established a criterion-pertinent scoring rubric for assessing incremental benefits of new drugs. Three clinicians independently assessed comprehension by pilotscoring 9 drugs. We then validated CATCH for 2 childhood cancer therapies through structured deliberation with an expert panel (N = 10), obtaining individual scores, consensus scores, and verbal feedback. Analyses included descriptive statistics, thematic analysis, exploratory disagreement indices, and sensitivity analysis. ResultsThe modified Delphi process yielded 10 criteria, based on absolute importance/relevance and agreed importance (median disagreement indices = 0.34): Effectiveness, Child-specific Health-related Quality of Life, Disease Severity, Unmet Need, Therapeutic Safety, Equity, Family Impacts, Life-course Development, Rarity, and Fair Share of Life. Pilot scoring resulted in adjusted criteria definitions and more precise score-scaling guidelines. Validation panelists endorsed the framework’s key modifiers of value. Modes of their individual prescores aligned closely with deliberative consensus scores. ConclusionsWe iteratively developed a value assessment framework that captures dimensions of child-specific health and nonhealth gains. CATCH could improve the richness and relevance of HTA decision making for children in Canada and comparable health systems.

Using GIS-supported MCDA method for appropriate site selection of parking lots: The case study of the city of Tetovo, North Macedonia

The provision of adequate parking spaces for vehicles has emerged as a prominent and challenging issue confronted by towns, cities, and municipal authorities in recent years. Addressing this problem necessitates a thorough examination of the prevailing physical conditions in existing parking areas, while simultaneously undertaking analyses to identify suitable locations for new parking areas or parking lots. This study focuses on the city of Tetovo, North Macedonia, investigating and assessing the available parking areas while analyzing potential sites in accordance with the city's needs and requirements. To facilitate decision-making, a Multi-Criteria Decision Analysis (MCDA) approach is employed to address the parking site selection analysis problem. The weightage of criteria utilized in the analysis is estimated, and potential parking solutions or site selections for new parking areas are identified through the combined application of Geographical Information System (GIS) and Analytic Hierarchy Process (AHP) techniques, identifying primary and sub-criteria, with a focus on Land Use and Transportation as the main criteria for selecting parking lots. The integration of GIS and AHP offers an effective and optimal methodology for site selection and identifying suitable parking locations. AHP method, applied to criteria, determined relative weights through expert opinions, while GIS facilitated spatial analysis for identifying suitable parking locations. The study identifies accessibility to main roads as the criterion carrying the greatest weight (0.517), while accessibility to cultural facilities holds the lowest weight (0.117). The study serves as a pivotal resource for sustainable urban management and decision-making, providing insights into future urban planning and the identification of suitable parking lot sites to foster sustainable development within the city.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Evaluation of the Performance of Drilling Machines in the Aegean Lignite Corporation (Türkiye) by Analytical Hierarchy Process (AHS) and TOPSIS Method

Multi-criteria decision making techniques are designed to enable better selection when it comes to complexdecisions involving various parameters. When a complex problem is mentioned, statements with immeasurable andcontradictory criteria such as efficiency, performance, safety, reliability, cost, and economy are understood. One of theknown purposes of the multi-criteria decision analysis approach is to assist the people who are responsible for organizingand synthesizing such information that makes them feel confident in taking a decision, and to minimize potential decisionafter regret by ensuring satisfaction when all criteria and factors are considered. Analytical Hierarchy Process (AHS) iswidely used in solving complex problems involving more than one criterion. The AHP method is based on a hierarchicalsystem consisting of many goals, criteria and alternatives. TOPSIS method can be applied directly on the data withoutmaking a qualitative conversion.

Assessing Entrepreneurial Performance of G7 Countries Under Context of Social Sustainable Development Goals: A Multicriteria Decision Analysis Approach

Entrepreneurship is a multilevel concept that connects social, environmental, and economic aspects of sustainability for societal developments, entrepreneurial processes, and market transformations. Social sustainability covers various social aspects and concerns that modern societies should achieve to improve their social infrastructures. Entrepreneurship is one of the main pivots that can strongly affect achieving social sustainability goals. Therefore, considering the current efforts of countries to reach their optimum level of social sustainability and strategic decision-making for long-term goals requires evaluating the current entrepreneurial performance in the first step. In this article, we aim to develop a multicriteria evaluation model using Shannon's entropy and measurement of alternatives and ranking according to the compromise solution to evaluate countries with the most influential entrepreneurial activities which contribute to socially sustainable development goals. As a case study, member countries of the Group of Seven (G7) are selected to assess their entrepreneurial performance using seven key indicators. These indicators are employees by business size, enterprises by business size, starting a business, women inventors, running a business, self-employed with employees, and young self-employed and are chosen for the analysis. The results show that Italy and the United States of America have been the pioneers in utilizing entrepreneurial activities to improve their social sustainability.

A multi-criteria system for performance assessment and support decision-making based on the example of Premier League top football strikers

The effective management of sports teams plays a pivotal role in achieving substantial success. In this pursuit, an increasing number of teams are resorting to information systems to enhance decision-making processes. These systems ensure the implementation of well-informed decisions, thereby contributing additional insights into training management. Through their assistance, it becomes feasible to optimize both player potential and overall team performance. In this paper, we propose a decision system rooted in the Multi-Criteria Decision Analysis (MCDA) approach, complemented by sensitivity analysis, to assess the performance of soccer players. The evaluation focused on football players in the forward position in the Premier League during the seasons 2015-2021. Subsequently, a sensitivity analysis of the results was conducted to delineate key elements in the game that significantly influence performance quality. Based on these findings, adjustments to a player's training plan can be made, targeting specific aspects of the game to enhance the player's potential. The study's results underscore that, with appropriate enhancements to players' performance, they can achieve markedly superior ratings compared to their counterparts.

Evaluating country performance in preventing industrial accidents: A multi-criteria decision analysis approach

This study presents a methodology that adopts a multi-criteria decision analysis (MCDA) approach to evaluate countries' performance in preventing industrial accidents, using accident records and combining three MCDA techniques (TOPSIS, Electre II, and CoCoSo) to create a robust ranking. Applied to OECD countries, the methodology provides valuable insights into countries' commitment to industrial accident prevention. It highlights the impact of European Union legislation, variations in the ranks of G7 economies, and identifies role models like Spain and Italy while shedding light on the neglect of regulations in developing countries like Hungary and Turkey. In a world where industrial accidents have far-reaching consequences, this study emphasizes the importance of sincerity and dedication in accident prevention efforts.

A System Thinkingbased Framework for Sustainable Governance in Healthcare

This study emphasises the importance of thinking beyond the constrictive perspective of shareholder profit maximisation and putting stakeholder value maximisation front and centre in healthcare. This study uses system thinking approaches to understand the pitfall of the narrow approach of shareholder profit maximisation in healthcare organisations. The study further performs stakeholder analysis to identify each stakeholder’s interests, needs, expectations, and level of influence and power over the health systems and policy. The study used multi-criteria decision analysis approach to identify the priority of the stakeholder organisations. The criteria used for the prioritisation are levels of interest, influence, and impact. Contrary to belief pharmaceutical companies were found to be the most significant player in driving the outcomes of the healthcare system. The study also emphasises the role of government and regulators in the achievement of the same. Stakeholder analysis done in the study also concurs with these findings. Finally, the study uses the grounded theory approach to suggest a framework for the achievement of objectives and providing sustainable governance. This study concludes that by thinking beyond shareholder profit maximisation, healthcare organisations can prioritise patient-centred care, social responsibility, and innovation, ultimately leading to better outcomes for patients and society.

Landslide susceptibility mapping for the Red Sea Mountains: A multi-criteria decision analysis approach

The Earth's climate system is clearly warming. Unquestionably, climate change has an impact on landslides by affecting the stability of both naturally occurring and artificially constructed slopes. Less certain are the type, extent, degree, and direction of the changes in stability conditions as well as the location, abundance, activity, and frequency of landslides in response to the expected climate changes. Climate change threatens global ecosystems, causing severe human and economic losses through sea level rise, hurricanes, flooding, landslides, and heat waves. Landslides in high mountains could become more common as a result of these changes. The high-speed electric train project in Egypt seeks to provide sustainable transportation, lessen climate change, and enhance public health. The study area was conducted along a 70-km track in the Eastern Desert, Egypt, across steep and rugged basement rocks of the Red Sea mountain chain (RSM). The geographic pinpointing of landslide susceptibility zones plays a significant role in promoting hazards mitigation in mountainous areas, such as RSM. The aim of this research project is to create a landslide susceptibility map (LSM) utilizing multi-criteria decision analysis (MCDA) in the region using information value (IV) and analytical hierarchy process (AHP) approaches that encompass the third proposed track of the high-speed electric railroad Qena-Hurghada between the RSM. The landslide inventory map, which has a total of 12 single landslide locations, was created based on fieldwork using hand-held GPS and Landsat 8 images. The two decision-making techniques were used to contrast the LSM factors based on the knowledge of experts using the qualitative method of AHP with the statistical approach that depends on the field data IV. Ten landslide-influencing parameters taken into consideration are slope, lithology, rainfall distribution, distance to fault, distance to track, seismicity of area, distance to epicenter, land cover, distance to stream, and aspect. The parameters were obtained using LANDSAT8 and SRTM DEM of a 30 m resolution, vectorized lithological structure units from a 1:2000, 000 map, and data between (1913–2000) for yearly precipitation and (1997–2018) for seismic activity of the research area. A GIS-based method produces a themed layer map for each geoenvironmental element, weighing each parameter after examination. A LSM was produced using a combination of thematic layers that were then separated into four grades: low, moderate, high, and very high, based on the intensity of the landslide vulnerability. According to AUC data, the IV model has a better success rate 0.85 than the AHP model 0.82. The study's models, especially when applied to the IV method, showed good accuracy in forecasting landslide vulnerability. The method shows rising landslide percentages with susceptibility and delivers accurate results that are compatible with the landslide inventory map. Environmental decisions often lead to unintended consequences due to lost information, incorrect factor selection, limited expert expertise, and ignoring uncertainty. This investigation emphasizes the importance of LSM as a tool for decision-makers to minimize landslide losses and damage.

Sustainability assessment of the energy generation systems

Purpose: This paper aims to assess the sustainability of energy generation systems in various European Union countries. It seeks to understand the diverse challenges and opportunities these countries face in transitioning to sustainable energy. Methodology: The study employs a multi-criteria decision analysis (MCDA) approach, considering economic, environmental, and social aspects. Data from various databases, including Eurostat, the International Energy Agency, and the World Bank, were analyzed. Results: The results reveal significant variations in sustainability performance across different countries and regions. With strong policy support and high public awareness, Scandinavian countries generally performed well across all sustainability indicators. In contrast, Eastern European countries faced more challenges due to their heavy reliance on fossil fuels. Theoretical Contribution: This study contributes to the ongoing discourse on sustainable energy by providing a comprehensive assessment of the sustainability of energy generation systems. It underscores the complex and multifaceted nature of sustainability and the various factors that influence it. Practical Implications: The findings provide valuable insights for policymakers and energy providers. They highlight the importance of policy support, public awareness, and acceptance in promoting sustainable energy. They also underscore the need for continued efforts and interventions to address the challenges faced by countries heavily reliant on fossil fuels.

Site suitability assessment for solar power plants in Bangladesh: A GIS-based analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) approach

Solar power plants are important alternatives to fossil fuel-based power plants because they reduce greenhouse gas emissions and mitigate the effects of climate change. The potential of harnessing solar energy is highly dependent on selecting the optimal locations for plant installation. This study primarily aims to select optimal sites for solar energy plant installation using the Analytic Hierarchy Process (AHP) of Multi-Criteria Decision Analysis (MCDA) in a Geographic Information System (GIS) environment. In the underlying processing phase, the AHP method was used to calculate the relative weights of the study area's physiographic, utility, and climate data. These weights were then used in the MCDA to identify the optimal locations for solar plants. The results of the MCDA were presented in the form of a solar plant suitability map, which showed that 44.59 % (66506.49 km2) of the study area in the south and southwest of Bangladesh is highly suitable for the development of utility-scale solar power plants. In contrast, there are substantial areas of about 31.18 % (46507.37 km2) of the country where the installation of any type of power plant is completely prohibited. This research can help authorities and stakeholders decide where to invest in solar energy.

A critical review of multicriteria decision analysis practices in planning of urban green spaces and nature-based solutions

Abstract Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database. We selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS MCDA tools often integrating a direct ranking method or the AHP method.

A Review and Analysis of Energy Systems Planning Models and Tools for Renewable Energy Integration in Cities

This review study evaluates a range of urban energy system planning models/tools (UEPMs) with respect to their capabilities to consider, identify, integrate and scale up renewable energy technology (RET) deployment in cities. Working with local stakeholders, we define twenty weighted evaluation criteria in order to assess UEPM capabilities using a multi-criteria decision analysis approach. We then discuss the top-ranking UEPMs in the context of our aims. We also survey and interview UEPM practitioners and experts in order to gain a better understanding of their needs, challenges and perceived barriers in the field. The outcomes of this work are useful for both UEPM users and researchers in order to improve the uptake and application of UEPMs by decision-makers to develop sustainable energy plans.