The Need To Define Smart Cities

The Need To Define Smart Cities
The Need To Define Smart Cities

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By citiesabc resources - Nov 03, 2022

As we progress toward the newer definitions of modernism, we understand that the indicators that shape the blueprint of a smart city index are very dynamic in their implementation. Feasibility of initiatives and operations follows the ‘Principle of Relativism’. The need to define smart cities, therefore, is imperative more than ever. 

The Need To Define Smart Cities

This is an extract of the “Blueprint for Smart Cities: A Social Contract”, a research report by Prof Kiran Fernandes with Atunu Chaudari and Ashish Kakar produced by the University of Durhan and citiesabc.com / citiesabc indexdna part of ztudium group / techabc.org in collaboration with openbusinesscouncil.org

As of October 2022, the population of the world is about 7.98 billion. According to The World Bank data, about 4.4 billion inhabitants out of this demographic live in urban areas around the world, i.e. over 50% of the global population. At this pace, the world's urban population will increase by 1.5 times to 6 billion by 2045. This means that nearly 7 out of 10 people will live in cities. The accelerating urban culture also needs a robust infrastructure including better mobility, affordable housing, and secured employment.

On the other hand, 80% of global GDP is generated in cities. Thus, urbanisation can contribute to sustainable growth through increased productivity and innovation, if managed well. While governments and various organisations are channelising all their resources and efforts towards making these urban dwellings more efficient and smarter, citiesabc is advancing with its exhaustive research to define the smart city frameworks, being guided by global think tanks and policy makers.

Cities are self-organising networks that form holistic patterns of human interaction, accentuated with feedback systems. This means that a dynamic technology platform like citiesabc is to be designed that 'understands' this complex adaptive system, and carve out a comprehensive smart city index.

The existing models of Smart City framework: An analysis

Each city customises its own system of metrics that caters to its variety of needs. However, most of these metrics fail in terms of scalability or application. For instance, the metrics adopted by the China Urban Sustainability Index (2016) exemplify a typical lack of capaciousness: “Society” and “Environment” each account for 33% of the overall weighting, while “Economy” and “Resources” each account for 17%.

Streamlining individual criteria under such broad headings runs the risk of being reductive; a high GDP cancels out the shortcomings of significant income inequalities when these are replaced within the same category (Economy).

More troubling still, the subsections within these metrics are incomplete: it is conspicuous that the “Society” metric is so heavily weighed when the only subsection within it is “social welfare”; a highly important rubric, but by no means the only criterion to assess the performance of “Society” in Chinese cities.

Moreover, the distinct absence of a Governance metric (one that is otherwise fairly common) is symptomatic of the country’s politics, where corruption runs high and the government controls censuses, so they do not reflect this.

Of course, more complete and reliable indexes exist, such as the IMD Smart City Index. It evaluates cities according to five metrics (Health & Safety; Mobility; Activities; Opportunities (Work/ School); Governance) in two separate categories: Structures and Technologies.

Yet the report remains static and rooted in metrics which are inflexible and leave no room for relativism. For example, Mexico City ranks in 88th place worldwide according to IMD. The city performs badly in the Structure rubric, and though it may fare slightly better on the technologies front, Mexico City’s score remains low overall. There is little data that seems to predict any upward trend in the city’s smartening.

This is because the IMD Index fails to consider context and feasibility. Mexico City’s low mobility ratings, for instance, are in great part explained by the difficulty to secure funds and financing for roadworks and public transport, as well as the immense population and voracious demand for (and use of) present road networks.

A static index will not consider the contextual differences between cities which leads to incongruous results nearly always favouring developed cities. It seems flawed to compare the results of green initiatives undertaken in Paris with those undertaken in Mexico City, as the demand, infrastructure and financing options are simply not the same.

Crafting a dynamic 'understanding' of smart cities

In addition to the common challenges in the world, developing nations carry the burden of additional challenges like the digital divide, lack of infrastructure, insufficiency of funds, and political instability.

A more essential aspect of the smart city index is the human core that only citiesabc offers. They are the pioneers of creating assessment tools to engage local and regional stakeholders in the development and implementation of the initiatives, while also maintaining the capacity to blend to the locally specific conditions.

This is in compliance with what The World Bank suggests: building green, inclusive, and resilient cities for a "working" model of smart cities.

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