In 2021, in the U.S. alone, there were already 18 extreme climate-related disasters with losses exceeding $1 billion each, according to the National Oceanic and Atmospheric Administration.
When looking at the world’s natural calamities on a consequence and frequency scale, floods and earthquakes have a more devastating effect on people and property, but they occur less frequently than heat waves, which generally take the form of urban heat islands (UHIs). These are also known as heat pockets, which are found across cities’ downtown areas, where temperatures are higher than the peripheries.
With urbanized areas warming up fast, many more populations globally are bound to face the deadly consequences of the heat-island effect, highlighting urban public health disparities. Between 2000 and 2016, according to the World Health Organization, the number of people exposed to heat waves jumped by 125 million, claiming more than 166,000 lives between 1998 and 2017.
City officials in the U.S. now worry that intense heat could lower comfort levels and conditions for residents, especially the most vulnerable populations — but cities are not equipped with the right data to mitigate effects.
Working at a design-led data science company, I know that building sustainable solutions for organizations or solving complex business, societal and socioeconomic problems can be solved using advanced analytics, artificial intelligence (AI) techniques and interactive data visualizations.
Despite this, these emerging technologies can only be rolled out through collaborations among public health professionals, enterprises, local governments, communities, nonprofits and technology partners. This cross-sector intervention is the only way to democratize technology and rectify the urban heat-island devastation. So, how are these players working together to reduce urban heat islands?
Understanding which countries make significant contributions
A handful of companies, governments and NGOs across the world are working to solve the problem with heat waves.
However, since Canada warmed by an average of 1.6°C between 1948 and 2012, roughly double the global average rate of warming, it’s way ahead of the game when using AI to predict heat waves. By nature, Canadian cities are technology-driven and tech-savvy, so cities across the world can look to the country for in-depth analysis and innovative ideas. For example, MyHeat tracks the solar potential of buildings, taking the heat wave and using it to create sustainable energy.
European cities, including Helsinki and Amsterdam, are also trying to tackle this challenge. AI4Cities is an EU-funded project bringing together leading European cities looking for AI solutions to accelerate carbon neutrality. The total funding amount of 4.6 million euros will be divided among selected suppliers.
Despite these projects leveraging AI to solve climate change issues, they are still concentrating on other niches, such as reducing carbon footprint. They focus on the mitigation of the cause of climate change instead of the effect.
Therefore, the impact of heat waves remains a largely unsolved problem. This is also because other natural disasters, such as floods that cause huge immediate effects, get more attention. Heat waves are silent killers with their undercurrents of thermal discomfort, more energy usage and power outages. And perhaps the biggest challenge is that the kind of technology to face heat waves is not openly available to municipalities or nonprofits.
Leveraging AI-powered solutions
Through working with Evergreen, a nonprofit building resilient cities and mitigating climate risks, we were introduced to a network of cities in Canada. And after research and surveys, we realized that there’s a lot of digital infrastructure and data-driven decision-making for floods and earthquakes, but none or very few solutions for heat waves.
Heat waves remain largely an unsolved problem, and there’s a huge opportunity for AI, as a scalable tool, to inform cities to make evidence-based decisions.
Evergreen uses geospatial analytics, AI and big data, alongside a data visualization tool created through the Microsoft AI for Earth grant, to integrate and analyze different datasets that examine urban heat islands across cities. This helps municipalities pinpoint problem areas with low vegetation or impermeable surfaces and mitigate the effects of heat islands by installing cool roofs, water fountains and green roofs.
The AI-powered analysis and visualization tool, built on the Microsoft Azure Stack, offers several capabilities. A map, or a topographic view, allows climate teams in municipalities to get the land surface temperature of every 30-meter block on the ground. Additionally, there’s a Scenario Modeling View that enables them to generate scenarios of the future urban sprawl of cities by modifying features like building counts and height, albedo levels and other urban sprawl parameters.
This multipurpose tool is already impacting climate resilience in municipalities across Canada by tracking greenhouse gases. It could also positively impact policy shifts around greenhouse and carbon dioxide emissions worldwide in years to come.
Sustainable Environment and Ecological Development Society (SEEDS), with Microsoft India, also announced its second phase of an AI model for predicting heat wave risks in India and offering cost-effective interventions. If a heat wave occurs, governments can work out which areas of the city need help and attention. SEEDS uses ground-truth data, and the AI model generates results that are validated on the ground with thermal sensors, among other devices.
City officials should welcome AI as an economical way to face heat wave problems as it is scalable and quickly applicable worldwide — it is agnostic to locality or ground presence. AI can also be packaged into a tool to extract data sources, which makes the knowledge easily shareable across departments and key stakeholders, and digestible for decision-makers.
With Evergreen, the idea is to create a public-facing app, which informs communities about the kind of impact AI has, offering real-life solutions and bringing them alive in a storytelling mode. For example, the app could show how temperatures decreased due to a green roof installation. It would allow users to see data insights as easily consumable stories and help them understand the different complexities that shape the issue they are tackling.
Democratizing and scaling AI at the speed of trust
Working with multiple data sources for AI or machine learning (ML) projects calls for cross-sector solutions. The involvement of nonprofits and community builders is crucial — they act as conduits between technology players, enterprises, other nonprofits, governments, communities, city planners, real estate developers and mayors’ offices.
Technology partners cannot just arrive in a city with an AI solution and expect officials to subscribe to it. You have to make a business case and enable all players to be part of the conversation; it is a multisectoral endeavor.
Equally, the stakeholders who would use this innovative technology won’t just automatically adopt this tool if they are told: “You have a heat pocket. I can install a green roof to help you.”
Let’s take a geospatial example, developed in partnership with Microsoft AI for Earth’s initiative. The entire population of a city was mapped — with release points within blocks of 100 square meters in a 40-meter grid — to release genetically modified mosquitoes to kill dangerous, disease-carrying mosquitoes.
This scalable solution with AI can bring resolutions to communities suffering from dengue and yellow fever. But if someone came to your house and said that you would be inundated with genetically modified mosquitoes, you would most likely say no, mainly due to the idea of being overrun with mosquitoes, but also because of the global resistance against AI as it evolves. There are worries around it magnifying the ability to use personal information that intrudes on privacy interests.
This is why the projects that succeed are often executed by educating communities. Community partnerships are key to spreading positive messaging about bringing down temperatures, using less energy and adopting climate-friendly AI technology.
In Canada, for example, every city has its own climate team, weather model and sensors in crucial places across urban zones. It is challenging for large data or technology companies to get hold of this weather data; cities must be willing to share. It is the same with high-resolution, high-quality satellite imagery working out cloud coverage; you need data providers to inform you about population data and socioeconomic considerations.
Therefore, projects have to be done at the speed of trust. Cities will be more inclined to share data points with technology companies that can offer real, scalable solutions when they have established credibility. Without this, these companies will have to rely on publicly available and open source data from NASA and Copernicus.
So, what does this all mean for enterprise players and their CEOs? AI solutions for cities are targeted toward climate teams and communities in municipalities. But what about oil and gas companies? They are under huge pressure to report their carbon footprints as they contribute to many of the emissions in cities.
An AI solution for them would involve a tracking command center to follow on a real-time basis how much carbon emissions their refineries or freight is leaving in their wake. CEOs have a mandate to decrease their per-product, per-employee carbon footprint. Adopting an AI solution would hold them accountable for environmental effects while showing that they are also aware of being part of the problem with heat waves.
COVID-19 brought attention to heat waves as more people were living at home than working in offices. This means populations experienced higher temperatures and discomfort in more pronounced ways, removed from the general facilities and comforts of offices.
Leaders across the social change community can reverse these dire effects of climate change and heat waves through facilitating collaborations among enterprises, NGOs, governments, technology partners and community leaders. This will mean the potential solutions that have arisen from AI and ML can be rolled out sooner rather than simply too late.