Multi-Criteria Decision Analysis and its Role in Sustainable Urban Planning
Urban areas are home to a great number of people, all living within close proximity to one another. In 2022, “the world’s population reached 8 billion… with over half (55 percent) living in urban areas, a figure projected to rise to 70 percent by 2050” (United Nations). With that said, it is crucial that individuals have access to clean air, water, public transportation, green spaces, etc., to ensure happy and healthy lives. Sustainable urban planning aims to accomplish this by balancing social, economic, and environmental needs. How? With Multi-Criteria Decision Analysis (MCDA). In this sense, it is a method for evaluating several options or objectives, where each objective's importance is weighted against others. This article will discuss how this technique works along with other algorithms used for sustainable urban planning.
Multi-Criteria Decision Analysis is a structured and systematic decision-making framework used to evaluate options with multiple conflicting criteria. Like any project, you must start with a structured problem. Because of the nature of suburban-urban planning, proper MDCA execution involves identifying stakeholders such as local government authorities, residents, developers, environmental groups, etc. The next step consists of setting objectives and criteria for the project along with a scoring technique. This scoring technique converts objectives, such as the number of green spaces, emissions reductions, and public transportation quality, into standardized numerical values. This is important because the objective’s relative importance will be directly compared to others and integrated into the overall decision-making process.
The two types of techniques used to convert the performance of each option are measured either quantitatively or qualitatively. The “qualitative element refers to working with stakeholders to explore their perspectives” (The Government Analysis Function). Using a quantitative scoring technique means relying on measurable, numerical data. This would use mathematical functions to map objectives such as energy consumption or carbon emissions to a scale of 0 to 100. This enables the ability to compare options based on defined metrics.
A use case for this technique would look something like this: Option A reduces emissions by 500 tons/year, option B by 200, and option C by 350. Option A may score close to 100 because it achieves the maximum reduction, with B being around 60 and C around 80. Now, pulling in other objectives, maybe economic criterion (cost savings) and social criterion (public transit accessibility), you must weigh their importance. Let’s say stakeholders have determined that the relative importance of reduced emissions is 40%, with economic and social criteria both being 30%. With these scores given, the performance of each option is calculated by multiplying each criterion’s score by its weight and then summing the results.
Overall Score=(w1×Score1)+(w2×Score2)+⋯+(wn×Scoren)
This “overall score” represents the combined measurement of the performance of an option across all objectives. This allows decision-makers to see how an option performs in one area relative to others and how individual scores or weights changes affect the overall score.
When discussing the objectives for sustainable urban planning, the ones that seem to come into focus the most are reducing carbon emissions, minimizing environmental pollution, promoting sustainable transportation options, and ensuring access to green public spaces. These objectives, combined with others, lead to the goal for this type of development, which is to provide long-term resilience and quality of life for its citizens while ensuring sustainability. While MCDA is one technique, others follow a similar yet different framework, each with its nuances and benefits. The Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) are two other methods used to make decisions, with ANP looking to be better for sustainable urban planning.
The Analytic Hierarchy Process breaks down a complex, multi-objective problem into a hierarchy of levels. The overall goal is placed at the top, followed by layers of criteria and sub-criteria, and a set of alternatives at the bottom. Unlike MCDA, where decision-makers assign absolute weights directly to each criterion, AHP asks to compare two criteria simultaneously. This is considered a pairwise comparison. Generally, it’s more intuitive because it focuses on relative importance rather than absolute numbers. The only thing decision-makers need to state is which of the two criteria is more important and by how much. For example, this relative importance is recorded if reducing CO2 emissions is considered three times more critical than lowering development costs. The math related to this involves the eigenvector method, which is a longer explanation than this article has time for.
While the AHP method is valid and is less prone to biases, the Analytic Network Process is used more in sustainable urban planning projects. This is because it “Allows both interaction and feedback within clusters of elements (inner dependence) and between clusters (outer dependence). Such feedback can capture the complex interplay effects in human society, which is especially important when risk and uncertainty are involved” (Creative Decisions Foundation). While AHP organizes decision factors into a strict hierarchical structure, assuming independence among criteria, ANP allows for a networked structure where criteria can influence one another. This is beneficial because it reflects the true interconnectedness of urban factors and helps reveal potential ripple effects between different planning objectives. Overall, it leads to a more balanced and integrated decision-making outcome.
Marta Bottero et al. discuss this method in their article, “The use of the Analytic Network Process for the sustainability assessment of an urban transformation project.” Bottero et al. discuss that the model can be divided into four main stages, the first being the “development of the structure of the decision-making process.” It involves defining the issues through recognition of the main objective. “Such objectives must be later divided into groups (clusters), constituted by various elements (nodes), and alternatives or options where to choose.” Once the nodes have been chosen, relationships between them must be identified. Bottero et al. stated that “each element can be a source, that is an origin of path influence, or a sink, that is a destination of paths influences”.
The second step involves pairwise comparisons to “establish the relative importance of the different elements with respect to a certain network component. In the case of interdependencies, components with the same level are viewed as controlling components for each other”. A ratio scale of 1-9 is used with the definition going from equally important to extremely more important. Bottero et al. also state that “the numerical judgments established at each level of the network make up pair matrixes. Through pairwise comparisons between the applicable elements, the weighted priority vector is calculated”, which “corresponds to the main eigenvector of the comparison matrix.”
Step three is to create a supermatrix formation. Bottero et al. describe this formation as allowing “for a resolution of interdependencies that exist among the elements of the system. It is a portioned matrix where each sub-matrix is composed of a set of relationships between and within the levels as represented by the decision maker’s model (step 1)”. The supermatrix is divided into smaller sections or blocks, with each block labeled Wij, representing the influence that the factors (elements) in the i-th cluster have on the factors in the j-th cluster. The numbers in these blocks are priority weights (values typically range between 0 and 1). 0 means no influence, and a value closer to 1 indicates a powerful influence. This super matrix gives a complete picture of all the interrelationships among the groups and factors in the decision model.
The fourth and final step is to raise the weighted supermatrix to a high power to see the long-term effects of all the interrelationships between factors. When the matrix converges (stabilizes), it gives the global priority vector. This set of numbers shows the overall importance of each factor in the decision-making process.
A real-world application of the Analytical Network Process is its use to evaluate and assist in the decision-making process for an urban-margin area transformation project in the city of Nichelino. The study evaluates three options representing different planning phases: the current state (0 option), an initial project proposal, and a final project proposal. Option 0 maintains the existing agricultural land with its environmental significance, while the initial project reimagines the area’s historical role to enhance Nichelino’s identity. The final project refines this vision by focusing on environmental sustainability and integrating new residences, a multi-functional facility, and a shopping centre.
Three relevant objectives for the area include environmental sustainability, correct relation with the landscape, and socio-economic sustainability. Correct relation with the landscape means the historical values of the area must “maintain an equilibrium between beauty and utility…, without forgetting the innovation in the social and environmental sustainable sense” (Bottero et al.). These objectives are grouped into three interconnected clusters with inner and outer dependences. These elements with the clusters are connected to the alternatives, as shown below.
From this, an unweighted and weighted supermatrix is created, which gives us a “final vector with global priority deriving from the limiting matrix” (Bottero et al.). The final results show significant importance placed on the final project compared to Option 0 and the initial project. Bottero et al. also state, "it is interesting to point out that the initial project is in the middle of the priority list: this means that the decision work made in the process led to useful improvements”.
It can also be seen that the ANP “is a suitable tool for the analysis and the evaluation of complex systems, because it allows to clarify the relations among the various components of the problem” (Bottero et al.).
As the population of urban areas increases, planning the development of these spaces is crucial. If we are to create spaces that lead to happy, healthy, and enjoyable lives, sustainable urban planning is the best option. Leveraging technology methods such as the analytic network process for decision-making will significantly increase the effectiveness and reduce the time it takes for these projects to be planned.
Works Cited
- SDG indicators. (n.d.). Retrieved from https://unstats.un.org/sdgs/report/2023/goal-11/#:~:text=The%20world’s%20population%20reached%208,70%20per%20cent%20by%202050.
About the Analytic Network Process (ANP). (n.d.). Retrieved from https://www.isahp.org/about/?About-the-Analytic-Network-Process-ANP-2#:~:text=The%20ANP%20allows%20both%20interaction,risk%20and%20uncertainty%20are%20involved.
Bottero, M., Mondini, G., & Valle, M. (2007). The use of the Analytic Network Process for the sustainability assessment of an urban transformation project. Retrieved from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://d1wqtxts1xzle7.cloudfront.net/34626882/Bottero-libre.pdf?1409828312=&response-content-disposition=inline%3B+filename%3DThe_use_of_the_Analytic_Network_Process.pdf&Expires=1739239091&Signature=A8eHvfQxhr0xQ1fQSYYTnSmbE8H-meT6o6Xtuh7ixzK-8SQJfvhmlP9quFnhRTXlEx8iqGHgi~M8wD9j4Oyqs04wCJnwRC8hUApuuCssb8GapGfmkaGDZlnRDVtGPik7ZFJbpNycm0MvToc90roNho~weM0LmeKgsM6czWgTwvbaM4Nm43tdq9YLR3Sv3PMuplyaII3afIJ2NLwO87Etany6-Q2lllDnbLQMCU33i2WFaaSFDGZEND9tybp4WNZRGND20LTYNdk7C8PCyFBJlncFXRdHLvBkgdrvmFnL-zDmxFOz9wlKSmhruOXK9YRWGUoe3aHTx3GA-1emAjcwSQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Carli, R., Dotoli, M., & Pellegrino, R. (2018). Multi-criteria decision-making for Sustainable Metropolitan Cities Assessment. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0301479718308375
An Introduction Guide to Multi-Criteria Decision Analysis. (2024). Retrieved from https://analysisfunction.civilservice.gov.uk/policy-store/an-introductory-guide-to-mcda/#:~:text=MCDA%20combines%20both%20qualitative%20and,the%20performance%20of%20different%20options.
