Sustainability is hard to come by. Whether it’s a sustainable organization, a sustainable way of life, or a sustainable relationship, building something that lasts is tricky.
Take a sustainable city, for instance. The modern outlook is that a city is sustainable when it’s smart, and that it’s smart when it uses technology to address problems like traffic, security, pollution, and waste management.
Building a smart city takes work, funding, infrastructure, and technical expertise. Distressingly for us Filipinos, it also takes political will and progressive thinking to get the ball rolling to the end; two things we aren’t exactly swimming in at the moment.
Many people think that all it takes to achieve widespread sustainability are good politics, advanced technology, and big capital. It makes sense to say that, at a glance, sustainability is all about resource abundance.
But that would only be telling half the story.
On the road to sustainability, your starting conditions are a major factor. However, what you have at the onset isn’t nearly as impactful as what you do with it.
There’s a method behind sustainability, and that method depends on data.
The Problem: Really Shitty Foresight
In the 1880s, the world’s most advanced cities were covered in horseshit.
Horses were essential to the urban world, once upon a time. They were widely used to transport goods, and they played a central role in early versions of public mass transit. In fact, towards the end of the 19th century, New York was home to over 150,000 horses: horses that answered the call of nature frequently, and in great volume.
As you’d expect, the amount of urine and manure produced on a daily basis brought cities to the verge of a crisis. The air was rank, vermin were thriving, and public health was at risk; even food security was threatened by the agricultural strain of feeding enough horses to support the craze.
Luckily, our cities managed to avoid the equine apocalypse thanks to urgent administrative action and the rise of the automobile. City leadership addressed the symptoms of the ill, and the problem was eliminated by the eventual shift in demand from one kind of horsepower to another.
For a miraculous moment, the world shifted away from an unsustainable and environmentally disastrous method of transporting goods and people.
The world did this by embracing the combustion engine.
Nobody at the time could have guessed that their four-wheeled saviors would end up contributing to another catastrophe at a wider scale. Likewise, the scientific community at the time had no reason to assess the risk of global environmental degradation.
The moral of the story is that we often find ourselves worse off than when we started because none of us are capable of peering into the future, and therefore committing to solutions that are inherently flawed.
The struggle to solve problems with finality is universal, but some of today’s cities have managed to do better than replacing broken things and systems —they’ve come pretty damn close to sorting certain things out for good.
Bright Lights, Smart City
Like Taleb’s black swan, a handful modern cities stand defiant of the fear that our answers are only ever bound to create more problems.
A few examples follow:
- Smart grids allow cities like San Francisco to track energy consumption across time and geography, and make calculated adjustments for the sake of greater efficiency. SF residents can monitor their energy consumption in real time.
- Traffic lights in cities like Pittsburgh independently calculate the optimal green time based on real-time traffic data, and coordinate with the rest of the city’s lights to keep commutes efficient.
- A number of cities in Europe have outfitted their public parking spaces with sensors that send data on slot availability straight to motorists. The deployment of smart parking systems reduce traffic, save time, and preserve the environment all at once.
There are countless other examples of processes, systems, and devices that make urban living more bearable and more efficient. Upgrades to city life abound, and they range from the novel to the life-saving.
Needless to say, this is all a step up from drowning in excrement.
Smart city innovations present no visible drawbacks—but then again, neither did automobiles at the turn of the 20th century. Unlike their predecessors, however, the minds behind today’s cities have a distinct advantage: access to sprawling datasets and figures.
Data Matters (A Lot)
Any entity or organization with access to data and the expertise to use it is in a good position to keep their solutions from turning sour. There are various reasons for this, but for the sake of this article, we’ll focus on how data helps us see the future.
Data allows for the creation of predictive models and simulations. The world would be better with a little more foresight, and data can help us make smarter projections. This isn’t to say that predictive modeling is 100% accurate (you won’t find that kind of certainty anywhere in big data), but at the very least, it can indicate sharper decision-making than a shrug and a guess.
This takes into consideration the fact that many of tomorrow’s problems would be unthinkable today.
Smart cities provide an example of what being data-driven looks like in practice; in the world’s most advanced urban centers, nearly every data point that could be logged is being logged. This means that there’s a good chance an analyst could piece together the causes of a negative externality and arrive at a sound, statistically-backed recommendation about where to go next.
Let’s play with the example of smart parking. Say that one day, we discover that a component of the sensors used to gather parking slot availability data reacts with rainwater to produce a toxic gas.
(It’s a wild idea, sure, but it would have been just as hard to convince someone in 1890 that cars would one day push us closer to the end of the world. Just bear with me for a bit.)
If it turns out that our parking sensors could end the world, then we’re in luck. Since collecting data points factors heavily in their use, we can tell where these deadly parking sensors are, and which parts of our cities are at most risk of a toxic fiasco (think: occupancy rates).
Correlate that data with rainfall predictions, wind forecasts, and other numbers that state agencies are monitoring by default, and you end up with a very manageable disaster situation.
Not a perfect example, but you get the principle: obsessive data collection and analysis can make headaches significantly easier to deal with.
At this point it also bears mentioning that even the most banal data project can be used for nefarious purposes, causing far worse problems than they were meant to solve.
An article published by The Guardian in 2014 argued that smart cities would be the death of democracy, and though I hesitate to agree outright, they raise valid concerns about privacy in an age where personal data is caught in a political and economic free-for-all.
Approach data with caution, and stay ethical.
Conclusion: Necessary But Not Sufficient
Today’s smart cities are far from perfect, but they follow a framework that brings us closer to real sustainability. They weave data into the very core of their solutions, allowing for more accurate prediction and faster responses to sudden complications.
Data in and of itself is not a cure, nor is it ever the only piece of a solution. On top of an obvious need for ethical guidance, you still need the effort, the funding, the expertise, the infrastructure, and the collective determination to correct society’s problems.
However, throwing data into the mix can drastically increase a solution’s chances to succeed, and keeps them from blowing up in people’s faces. A culture of data reliance lets us predict, assess, and improve faster than ever before in our history.
With data on our side, we stand a better chance of getting stuff right for the long-term.
Embrace it, apply it, and demand it. To settle for anything less nowadays is a load of horseshit.