Is this the golden age of Climate AI?

As generative AI - especially large language models such as ChatGPT - rise to prominence, diverse tech start-ups are looking to deploy the emerging technology to solve problem areas in the transition to net zero.

AI-augmented smart grids, ESG data and analytics platforms that institutional investors can chat with, and energy efficiency tools are among the many innovations gaining traction.

Still in the early investment stage, these start-ups receive their funding mostly from angel investors, venture capitalists, and grants, though institutional investors are likely to become more directly involved once the market matures.

Pension funds invest in venture capital funds, but typically wait for emerging technologies to mature before making equity investments either directly or via private equity funds.

“Some companies are starting to enter that critical growth phase but we need to see more,” said Sebastian Michaud, investment manager at venture capital firm Foundamental. “Success stories will boost the whole ecosystem and encourage institutional investors.”

Despite a restrictive macroeconomic environment characterised by inflation and higher interest rates, Michaud said climate tech fundraising in Europe, although affected by the downturn, remained buoyant. 

And it’s not just private funding fuelling the rapid development of AI solutions for climate.

The recent launch of the UN-led AI Advisory Body advances a growing global trend to harness machine learning to find solutions to common challenges, including climate change and biodiversity loss.

Krista Tukiainen, co-founder of ClimateAligned, an AI-driven climate analytics platform for institutional investors, attributed the rapid rise of climate AI to the fact that innovators “don’t need to build a model from scratch” but can start with existing models.

“That delivers a certain level of, if not maturity – this technology still has a long way to go – then at least commercial grade investability as companies don't need to spend all their runway on fundamental model development,” she explained. “Lots of money is being poured into this space to build industry-specific applications. We are entering a kind of golden age.”

ESG data and analytics solutions

Institutional investors frequently cite data quality, consistency and availability issues as major roadblocks in their climate and nature transition plans.

AI can help in two ways: by providing “synthetic” data solutions – using model data to fill the gaps – or by leveraging existing data to make it easier to embed in financial decisions

ClimateAligned does the latter, using machine learning technology to gather, process and analyse data from multiple sources and render the climate and sustainability credentials of bonds and issuers transparent and comparable, through a single access point.

Its aim is to enable institutional investors to find new opportunities that accelerate finance aligned with climate and emissions targets and avoid greenwashing risks.

Its natural language function enables turning each investor's in-house preferences and definitions of green investing into practice. This includes both conversation-like interactions with the algorithm, as well as automated workflows and structured data sets for building and managing green financial products.

“Fixed income and credit markets tend to have far more ESG data problems than the public equity markets,” Tukiainen said. “That’s why we decided to put the focus there.”

The ClimateAligned platform goes live in March. It has received £1.5 million of funding from various venture capital investors, including Pale blue dot and Frontline Ventures, as well as strategic angel investors.

Real estate: focus on energy efficiency

The built environment is responsible for about 40% of annual global CO2 emissions, according to the UN.

This means improving energy efficiency of buildings is essential for net zero.

Investing in cost-effective energy efficiency measures is an essential part of Canada Pension Plan's decarbonisation strategy, for example.

“Upgrading and electrifying buildings is a big topic for us,” said Michaud. “It’s a no-brainer. The technology is ready to deploy. It’s more of a supply chain problem. Focused-AI application can ease the bottlenecks in specific problem areas.”

Preoptima uses AI to integrate real-time whole life carbon assessments (WLCAs) into conceptual building design, helping developers to make critical carbon-informed decisions.

AI-for-materials company CarbonRE believes that "zero carbon cities" need "zero carbon materials at their foundation". However, the production of such materials involves "complex physical and chemical processes that interact together over time", a problem it claims to have solved.

Smart grids

Tripling renewables by 2030 – a target agreed at the latest COP – sounds good on paper but is impossible to deliver without major upgrades to grids. The International Energy Agency (IEA) has warned that electricity grids are not keeping pace with the rapid growth of key clean energy technologies such as solar, wind, electric cars and heat pumps.

A key problem is the intermittency issues associated with renewables. Unlike their fossil fuel counterparts, renewables don’t provide a constant source of energy, as the wind doesn’t always blow nor the sun shine.

A recent Global Data report highlights that AI can be used to monitor and diagnose system problems, avert blackouts and match supply and demand from renewable energy sources like solar and wind.

Tech companies like NVIDIA have already developed AI-powered smart grid chips, which can be integrated into smart meters to develop grid resilience.

Smart grids are electrical grids that use digital technology to monitor and transmit real-time information about demand and supply. The development and proliferation of these networks are significant for the generation and provision of renewable power.

Sustainable and regenerative agriculture

While most institutional investors understand the negative impact of the global food system on climate and biodiversity, their arguments for not investing more in sustainable and regenerative agriculture usually come down to a simple: it’s just too risky.

The regenerative agriculture platform Agreena enables farmers to transition from traditional to regenerative agriculture by monitoring, reporting, verifying and issuing soil carbon credits and by providing fintech products that help bridge the transitional risk of switching to regenerative agriculture.

Tasked with solving Africa's environmental data scarcity, Amini utilises AI and space technologies at scale "to drive systemic change and promote economic inclusivity for farmers and supply chain resilience across Africa and beyond".

"The scarcity of high quality environmental data of Africa is a concern as it prevents others from building important climate solutions such as improving farmer insurance, monitoring climate risk or supply chains," said Heidi Lindvall, general partner at Pale Blue Dot, a venture capital firm that invests in seed-stage climate tech startups such as Amini.

Monitoring physical risk and impact

AI-driven technologies offer previously unheard-of capabilities to process enormous volumes of data, extract insightful knowledge and improve predictive models, according to the UN’s World Meteorological Organization (WMO).

That means improved modelling and predicting climate change patterns that can help communities and authorities to draft effective adaptation and mitigation strategies.

It can also better prepare countries for climate-related disasters.

For areas susceptible to landslides, for example, mapping can help local authorities plan and implement sustainable development measures, reduce risks and ensure the safety of residents in vulnerable communities.

High demand potential

A new study from Capgemini suggests that businesses are keen to integrate AI into their workflows.

The study showed how the convergence and maturation of a host of technologies, alongside the pressing need to reduce emissions and energy consumption, is driving over three quarters (77%) of organizations to transition to a dual digital and sustainable business model.

“Automating business processes and workflows” is a top investment priority for the firms assessed in the report.