Are Smart Cities like mangoes?

Published on 19 June 2019

Jaideep Gupte

Research Fellow

Kunal Kumar

‘Show me the evidence!’ is the hackneyed phrase on the lips of each and every policymaker and urban local authority tasked with making their city ‘smart’. Smart City initiatives have begun playing a prominent role in the international development agenda. There are a variety of local expressions, but the primary rationale is that smartening up urban development can trigger economic growth and other opportunities, and therefore be a route out of poverty. However, there continues to be a fundamental gap between the types of technological solutions being proposed to enable data driven urban governance, and whether these solutions, and the manner in which they are being implemented, are necessarily promoting inclusivity, resilience and sustainability.

One perceived drawback has been the lack of reflexivity across experiences in the Global South, particularly those of economically and socially disadvantaged urban residents. The rapidly changing Smart City landscape has tended to be driven largely by ideas and experiences rooted in the Global North, while evidence from developing country contexts, including instances of South-South learning, have not been brought into focus.

Is this really the case? We used a machine-based search algorithm to identify every single piece of evidence published and peer-reviewed on implementing data-based Smart City initiatives over the past ten years. Our algorithm searched multiple databases including The Web of Science (90million+ records), Scopus (69 million records), as well as the World Bank and IMF joint libraries (JOLIS). To note, we take it as a given that data is required for urban governance. What we searched for is evidence on how data should be governed.

There are literally thousands of pieces of evidence, on issues as diverse as urban green infrastructure to blood donation drives, where data plays a critical role in urban policy making! Our teams whittled these down to 1157 studies, and sorted through to 107 studies that provide evidence from (or on) low- or middle-income countries, and on policies, practices and actions. We actively stayed clear of unimplemented technologies to focus on the policy implications of what works in the real world.

A big caveat of our strategy is that we are restricted to evidence published in English. This of course excludes the vast literatures from China or much of Latin America, for example, but our review aims to be comprehensive in other ways – we do not limit our evidence gathering to any one discipline, area of policy, or urban practice.

Here is what we found:

1. The evidence is not evenly spread

The studies identified through our search are heavily concentrated in three countries (China, India and Brazil). We found sparse evidence on implementation of Smart City practices from only a handful of countries in Africa and Latin America (see caveat above). While this does not in any way equate to the magnitude of on-ground initiatives, it is entirely plausible to ask whether the higher prevalence of rigorous academic scrutiny of implementation of data practices in some contexts implies a more iterative system of implementation and learning in those contexts? And might this eventually lead to better, more robust innovation?

Evidence of implementation of smart city practices
‘Evidence of implementation of smart city practices’. © Gupte and Kumar

2. The neighbourhood is almost completely absent as a unit of analysis

83 per cent of the evidence we found is at city-, multi-city- or national-level. Only 17 per cent is at the sub-municipal or neighbourhood-level. Neighbourhoods are the beating heart of any city, and small-scale innovation can be deeply impactful. Are we missing the wood for the trees by focussing on the macro-scales?

3. There is almost no evidence on how institutions are changing

This to us is the most meaningful (if distressing) finding. The literature is almost entirely comprised of evidence on regulatory compliance, data quality, and policies relating to data. While this is a function of our objective to identify academic work interrogating the governance of data, we had expected to find an equal amount on organisational and institutional changes in response to a shift towards data-based governance. However, less than three per cent of the studies we found spoke to this aspect. Does this mean that institutional change is not happening? Hardly. (See for example, Simon Joss’s recent research on smart cities which illustrates a pronounced transformative governance agenda.) Does that mean that we, as a community of academics and practitioners, have not been looking for (or have not been able to identify) the type of institutional transformations that are at play? Or have we been looking for the wrong kind of evidence focusing on use cases but forgetting to look at the system level and link back to daily urban practices?

If true, this is worrying because no matter how large or small the innovation, it is institutional transformation that eventually sustains and scales innovation.

Focus of evidence on Smart City practice. © Gupte and Kumar

Smart cities are like mangoes

So much of what we do in Smart Cities depends on how we define ‘smart’. So, what is ‘smart’? To find out, we ran a ‘proximity algorithm’ to capture the words that appear often and close to the word ‘smart’. In this snapshot the size of the word is a function not only of the number of times it occurs, but also how proximal it is to ‘smart’:

‘Wordcloud illustrating the different meanings of smart’. Credit: Gupte and Kumar / Wordcloud

Some of the usual suspects pop up in the wordcloud, and ‘data’ is a big one. But the main message is that there are as many ways to answer the ‘what is smart?’ question as there are types of mangoes. Because even if you don’t know or recognise all of them, there are thousands of varieties of mangoes worldwide (and nearly 300 in India alone)! From everyday varieties that are mind-blowingly flavourful, to maddeningly expensive varieties that don’t really taste of much. But the analogy only goes so far, because the multiplicity of definitions also implies a gruelling level of complexity, and the need for gutsy decision making on the part of the policymaker to find ways forward.

So, stay tuned. In our next blog post, we delve into country specific findings. Starting with the evidence we gathered from India – watch this space!

Kunal Kumar is Joint Secretary and Mission Director, Smart Cities, Ministry of Housing and Urban Affairs, Government of India.
Jaideep Gutpe is IDS Fellow and leads the Cities and Sustainable Infrastructure portfolio of the Global Challenges Research Fund, UKRI.

This blog is based on a Systematic Review undertaken by teams at the National Institute of Urban Affairs, India and IDS, UK. The teams include Debjani Ghosh, Yogita Lokhande, Priyanka Mehra and Asif Raza (NIUA), and Eric Kasper, Rajith Lakshman, Vedika Inamdar, Pippa Page, and Matthias Rueda (IDS). To find out more on the European Commission funded collaboration see Smart Data for Inclusive Cities.

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