At the recent COP 26 climate summit in Scotland an intergovernmental roadmap on Artificial Intelligence’s (AI) role in fighting global warming was launched, as the technology is being used to send natural disaster alerts in Japan and monitor deforestation in the Amazon.
While machine learning (ML) and AI have revolutionised software technologies because of its ability to analyse and draw insights from large amounts of data, it is now used to innovate across a variety of sectors including energy, food and agriculture, transportation and logistics, and infrastructure.
Greenplan, a DHL-financed startup, is driving sustainable logistics with its route optimisation algorithm that lowers operational costs up to 20 per cent and the environmental impact of deliveries by reducing kilometres driven.
Companies are making climate change a strategic priority because adverse weather events will disrupt business’ core operations. Responding to climate-driven disruptions, need actionable intelligence about the risks that organisations face.
Simply looking at past weather patterns is not a reliable means of assessing future risk.
In June, the Rise Fund invested $100 million in a data and AI-driven “nowcasting” system devised by startup Climavision to predict weather patterns with granular accuracy.
Tech giants like Google and Microsoft are applying AI research to help address climate change. DeepMind through applying ML programs reduced Google’s data centre cooling energy usage by 40 per cent, and helped improve operational efficiency. DeepMind has also applied deep learning to improve renewable energy generation by forecasting wind energy generation, which helped boost the value of the wind energy generation by 20 per cent.
Microsoft has applied ML research to its services and calculated, in a study, that its cloud services are up to 93 per cent more energy-efficient and up to 98 per cent more carbon efficient than traditional enterprise data centres. Microsoft has pledged $50 million to tackle environmental challenges through their AI for Earth program.
Startups gaining attention
Producing zero-carbon electricity is at the very heart of the fight against climate change.
In the energy sector, several startups have gained attention and investments from venture funds like Shell Ventures and National Grid Partners, even the billionaire-backed Breakthrough Energy Ventures (BEV).
Autogrid, a ML and analytics company that helps utilities, electricity retailers, and renewable energy developers flexibly extract capacity from distributed energy resources, is backed by Shell Ventures and National Grid Partners.
Traverse Technologies, an AI-driven prospecting for optimal hydropower and wind generation sites is backed by Y Combinator, and KoBold Metals, a computer vision and ML-enabled digital prospecting to search for likely sources of cobalt, is backed by BEV and Andreessen Horowitz.
Gridware is using edge AI to predict and detect faults in the grid’s physical infrastructure in real-time, reducing the risk of fires and other systemic failures.
Applying AI to help decarbonise the grid is Raptor Maps, a computer vision-based platform, which facilitates the deployment and management of solar energy assets using data from drones, satellites and on-the-ground sensors.
Predictive analytics platforms
If there is one thing at which AI and ML excels, it is making predictions about complex systems based on data.
In the last few years, climate intelligence startups have emerged offering predictive analytics platforms, combining ML with more traditional weather modeling techniques to train their models to enable organisations to better anticipate and prepare for extreme weather events.
One Concern is developing a “digital twin” of the world’s natural and built environments to model the effects of climate change, offering its customers Resilience-as-a-Service. Jupiter Intelligence is another company in this category with an impressive set of blue-chip customers that includes NASA and BP. With a freemium business model, climate intelligence platform Cervest hopes its strategy will lead to network effects. In a few years, more startups in this space will leverage ML to build a scalable, category-defining technology company in climate intelligence.
Meanwhile, legacy insurance companies are struggling to assess the financial risks posed by climate change. According to Aon, there is a whopping $171 billion gap in climate insurance globally. New startups have started leveraging real-time analytics and AI to price risk more accurately and create novel insurance products for climate change.
Descartes Underwriting offers parametric insurance for a wide range of climate-related risks including floods, droughts, supply chain disruptions, renewables yield, construction interruptions and more. It uses ML to underwrite and monitor its parametric policies, ingesting data in real-time from satellite imagery, stationary sensors, Internet-of-Things devices, radar and sonar.
Another parametric insurance for climate risk is Arbol. It uses smart contracts on the Ethereum blockchain to codify its insurance policies, enabling it to automatically pay out claims fast.
Unlike traditional insurance, parametric insurance eliminates the need for insurance agents to assess and verify individual policyholder losses, makes insurance more automated, data-driven and transparent, with faster and more certain payouts.
While still relatively small, the carbon offsets market has been expanding rapidly — from $34 million of offsets issued in 2016 to $181 million in 2020 — as companies around the world prepare to go on an offsets buying spree. Influential climate financier and policymaker Mark Carney has stated that carbon offsets could be a $100 billion market by 2030.
But verifying the legitimacy of a carbon offset project — for example, that a tree has actually been planted, that it would not otherwise have been planted, that it stays alive and continues to grow over time — is operationally daunting. These challenges have limited the size of the offsets market to date.
To tackle these challenges, a new group of startups is applying ML to streamline, digitise and automate the carbon offsets market, and serve the new multi-billion-dollar industry.
Pachama and NCX are building AI-powered carbon offset marketplaces with a focus on forestation. Both companies apply computer vision to aerial imagery and other sensor data to automatically estimate the carbon stored in forest trees and to continuously monitor the integrity of carbon offset projects on their platforms.
Patch, which is backed by Andreessen Horowitz, abstracts away the complexity of managing carbon offsets, making offset projects accessible via an API and a few lines of code.
But to offset its carbon footprint, an organisation must first understand what its carbon footprint is. This is a challenging data-intensive process.
Over the past year, few startups have emerged to provide tools to help organisations measure and track their carbon emissions. These startups’ product visions go beyond quantifying emissions.
Once a company has a comprehensive view of its carbon footprint, it can switch to renewable electricity sources, adapting its real estate footprint, pushing its suppliers to adopt more low-carbon practices, or buying carbon offsets. The most high-profile company building an enterprise carbon accounting platform is Watershed.
Agriculture is a major driver of climate change as well, accounting for between 10 per cent and 15 per cent of the world’s greenhouse gas emissions. Fertiliser alone is responsible for over 2 per cent of all greenhouse gas emissions.
A massive opportunity exists to apply digital technologies to reduce its carbon footprint while increasing food yields. Precision agriculture optimises crop inputs on a targeted, localised basis, sometimes even plant by plant, and AI startups are playing a key role in making precision agriculture a reality on farms around the world.
Semios and Arable are precision agriculture startups that use on-the-ground hardware sensors to enable more precise crop management. Semios says it has installed over 2 million sensors on farms to date, from which it collects over 500 million data points every day. We expect to see multiple large companies built in precision agriculture in the years ahead.
Given the magnitude of climate change, it requires a variety of technologies and solutions, and ML and AI are powerful tools that can help navigate complex data to help improve decision making. A combination of better technology in improved algorithms and better data will allow businesses to not only reduce their impact on the environment but also operate more efficiently.
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