There are three ways of understanding a city: at the level of physical infrastructure and civic facilities, as a collection of soft infrastructure and socio-cultural ecosystems and as a set of narratives created by people and their interaction with the city. Urbanists, town planners, architects, developers and city planners generally adopt the first understanding. Social scientists, political economists, anthropologists, behavioral psychologists and cultural theorists usually prefer the second. Poets, philosophers, folklorists and creative practitioners often gravitate towards the third. However, there is a fourth way of understanding the city; it is through digitalisation, artificial intelligence, connected sensors, and big data. Though the understanding of this system is nascent, it is already proving to be potently disruptive in redefining the way we have traditionally seen, analysed, understood, planned, managed and run cities. In short, it could change urbanisation. Its disruptive potency comes from highlighting the ways — different digital technologies have started connecting with each other in unexpected ways, collapsing the boundaries of various disciplines. In many ways, it is also making the traditional ways of understanding and analysing the city look extremely narrow and redundant.
There is a fourth way of understanding the city; it is through digitalisation, artificial intelligence, connected sensors, and big data.
This fourth way is often called Data Science, Big Data Analytics or the Internet of Things (IoT). At this point in time, these terms function more as placeholders, rather than as a solidified definition befitting an integrated discipline. It is also true that the potential of this fourth way is best understood when the concept of bringing efficiency in linear and non-linear structures is discussed; issues of urbanisation, social engineering, city planning and development are still not as developed. Further, understanding how this increasing connectedness can unlock new models of urban sustainability and resilience needs to be looked at. For this, we must unpack the following questions: What is urban sustainability and resilience and what exactly is the fourth way?
In the urban context, and in its most practical form, sustainability and resilience means two things. First: the system of the city should be designed, managed and operated in such a way that the resource input into the systems becomes part of a regenerative cycle that transforms the current ‘take, make, dispose’ model to a one that is based on ‘reduce, reuse, refurbish, repair and remanufacture’. What this means is that every big or small business model associated with a city’s system is circular rather than linear. The ideal scenario for urban sustainability would be a series of small and big circular economies that are linked to where the resource inputs lead to minimal amount of carbon emission and waste. In short, a carbon neutral or carbon negative city system that use biodegradable materials would be extremely energy efficient.
City systems should be designed, managed and operated in such a way that they have inbuilt redundancies, and can be scaled up or scaled down quickly without any major disruption, investment, or intervention. This would also give it the capacity to recover from an unforeseen natural or man-made disaster quickly. Urban sustainability and resilience are two sides of the same coin, simply because they both cater to the urban citizens. Two examples make the twin concepts real and practical: The Deonar landfill in Mumbai is an excellent symbol of the ‘take, make, dispose’ model of waste management. The annual flooding and disruption of basic transportation services caused by the monsoon in Mumbai shows how the civic system lacks resilience.
In the urban context, and in its most practical form, sustainability and resilience means two things. First: the system of the city should be designed, managed and operated in such a way that the resource input into the systems becomes part of a regenerative cycle that transforms the current ‘take, make, dispose’ model to a one that is based on ‘reduce, reuse, refurbish, repair and remanufacture’.
Now to the second question: The fourth way is composed of five layers. The first layer is the foundation. It consists of undersea and underground fibre optic cables, mobile tower networks, Internet protocols and standards upon which all digital transactions runs. The second layer consists of source codes, software infrastructure, and systems in the applications ecosystem. The third layer is the domain of business; it involves transactions between institutions, companies and urban citizens. The fourth layer is the smart sensors and virtual and augmented reality tools that are increasingly deployed in built and non-built urban spaces. The fifth layer speaks to how discrete data points from the other four layers are mashed together to generate insights, analyses and patterns about urban people. It is this fifth layer that is referred to as Data Science, Big Data Analytics or the Internet of Things. It is again, this fifth layer that has come to narrowly mean the fourth way. But as we see now, the fourth way is a combination of all these five aspects. It is this combination, infinite in the way it can combine and yield models and insights, that provides the foundation for immense urban sustainability and resilience possibilities. So what can it actually do? There are at least three clear possibilities that can emerge from the fourth way if it is leveraged by urban planners, policy makers and community groups without any fear or favour; its potential also of course, must be examined within the still emerging norms of data protection, privacy and fair usage. The three possibilities are also dependent on emerging global and national regulatory frameworks around data that needs to start treating at least some data points emerging from the networked set of digital transactions as a public good, within the norms of a fair usage policy.
The first is to lay the sensors, and digital and data infrastructure that is necessary for creating a series of small and inter-connected circular economies; this will reorient our urban systems so that they are as close to being carbon neutral as possible. This would mean that our large civic systems will have to be re-engineered and redesigned so that data points about resource inputs, system usage, utilisation capacity – both peak and non-peak – redundancies and efficiencies (particularly energy efficiency), are captured, calculated and meshed together for an integrated set of insights for real time decision making. Many large city systems are already doing and implementing this, but not yet in terms of creating a business model based on the principles of a circular economy. One notable is the
Swedish waste to energy system that has started reorienting itself to the principle of circular economy. Each neighborhood has access to sensors, data systems, software, and algorithms which reveal how much waste is generated and segregated, and how much is recycled or gets pumped back into the reuse economy. This data also shows how much waste is incinerated to produce energy, including energy for electricity and heating. This data can either be analysed at a national level or it can be drillable up to the household level. Such simultaneous universality and granularity allows for unique business models of waste collection and segregation to take root and exist as viable community models that pays for itself in the long run.
The second is to reorient our retail economy, specifically to trace the source and origin of raw materials and the people involved in producing and sourcing it. It is then important to link that production to other parts of the value chain, including the associated people and physical infrastructure, and to show the methods that went into bringing the final product or a service to the end customer. Such a reorienting, quite clearly, requires all the five layers of the fourth way to integrate and communicate with each other in a clear, transparent and a direct manner. This would mean sensors, digital infrastructure, internet connections, devices, software systems and technologies like Block-chain integrating with each other. This reorientation is critical to ensure that the basic building blocks of a circular economy, especially the aspects related to reuse and recycle, is set up, managed and integrated in existing value chains that are currently linear and oriented towards a ‘take, make and dispose’ model of production, distribution and consumption.
The third is to create different sets of aggregation models that are specifically oriented towards leveraging excess capacity at local and hyperlocal levels. These models will be matched with the needs of communities and groups on a real time basis.
There are two good examples of this: the first is the way in which IKEA has been creating stories around the origin of its products. Many IKEA stores in Stockholm have a QR code or a barcode associated with a product. When you scan it, you can either download or stream a video that shows you exactly where the raw material was sourced from, who sourced it, and how it was converted into a product in the store. The eventual plan is to create a framework, which will tell the end customer how carbon intensive the entire process is, so that customers have all the relevant information they need to make a sustainable choice. The second example is the brand, ReTuna. Its store, located on the outskirts of Stockholm, is based on the twin principles of recycling and upcycling. The store has created a business model around the idea that nothing is waste, and everything has a value. People can drop off anything that they want to dispose of. The store takes it, refurbishes it, or adds more value to it. They then, re-sell it to customers who are looking at a way to get something that is inexpensive and sustainable at the same time. In doing so, ReTuna is deploying state-of-art sensors and software systems to track the entire life of products.
The third is to create different sets of aggregation models that are specifically oriented towards leveraging excess capacity at local and hyperlocal levels. These models will be matched with the needs of communities and groups on a real time basis. This is still a niche area and the business models around it are not yet stable. But the fundamental principle is quite robust, and as more software systems connected to big data engines, sentiment algorithms and artificial intelligence systems emerge, the business model will stabilise. A good example -- if not yet a robust business model -- is the way some local and hyperlocal businesses are sought by several start-ups around the world in order to deliver the actual needs of communities at the right price-points. Many aggregation models are also able to do this because they are integrated with the analog KYC requirements in a nationwide digital identity system. A good example would be the way in which the Swedish Tax System is able to reach out to micro and small enterprises (usually single entrepreneurs) and connect them directly to local tax lawyers – in both individuals and large firms -- so that tax compliance is achieved. As a result, new revenue streams are created and local and hyperlocal community business models are seeded.
It would be naïve to assume that these are the only possibilities, considering the history of technology as an unpredictable disruptor. But these three possibilities are also real simply because they have already taken root, and seem here to stay. However, a key question remains: Can our urban planners wake up to these possibilities and lay out a robust, flexible and appropriate regulatory and governance framework for urban sustainability and resilience? We must ponder and answer with equal measure.
Swaminathan Ramanathan is Senior Research Fellow at Uppsala University. He heads Future Urbanisms, a long term multidisciplinary research program focused on issues of sustainability and resilience in cities in India and Asia.
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