Responding to the climate crisis requires untapped innovation

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The climate crisis is painfully evident around the world. From historic droughts and wildfires to hurricanes and global warming, it’s clear that climate change isn’t just a future danger. It is a present reality problem.

As a recent New York Times headline succinctly put it, climate change has made extreme weather ever more normal.

Fortunately, some help is on the way. In August, the Inflation Reduction Act of 2022 was signed into law. This provides millions of dollars in tax incentives, investment finance and other resources to accelerate renewable energy and climate technology innovation. Despite its finance-focused name, it is the most significant investment in US history in renewable energy and climate change preparedness.

At the same time, the cost of renewable energy solutions such as solar PV has decreased by almost 90% over the past decade, while the price of wind turbines has decreased by more than 50% during this period.

This is great news as renewable energy is fast becoming a top priority for energy companies, regulators and ordinary citizens. According to a Pew Research Center poll, 69 percent of Americans believe we should prioritize the development of alternative energy sources, such as wind and solar.

We need more innovation now

But obstacles to broad implementation remain. For example, a recent report by Net-Zero America, a research group at Princeton University, concluded that a zero-carbon economy might require a land area equivalent to West Virginia to house the number of solar panels and wind turbines needed to power the US economy. Meanwhile, global energy consumption is projected to increase by 50% by 2050, requiring more and better technologies to respond to the climate crisis.

In other words, we need more innovation, and we need it now. Breakthrough Energy, the umbrella organization founded by Bill Gates that is committed to accelerating innovation in sustainable energy, observes: “We must find new ways to generate, store and use low-carbon electricity, while increasing existing technologies such as wind and solar energy, advanced nuclear energy, geothermal energy and thermal generation with carbon capture.

Advances in artificial intelligence (AI) and machine learning (ML) can accelerate R&D outcomes, enabling new products and services to get to market faster. Here are three best practices for climate tech startups leveraging these powerful technologies to accelerate their R&D processes.

Establish good data management practices

Research and development settings are often oriented towards experimentation, combining the ethics of an art studio with the processes of a scientific laboratory. This can make it difficult to develop practical AI and ML models that rely on careful curation of high-quality data to be successful.

Even small mistakes like typos, mislabeled data, or formula misconfiguration can render these templates ineffective. These errors are often due to human errors, which can be eliminated with automated data processing and data capture.

In addition, it is important to invest time in developing processes that ensure the best data is available, accessible and used. Notably, AI doesn’t just rely on big data. It can also be applied to small datasets to accelerate development, which requires both good data management practices and intentional cultivation by data specialists.

Data scientists are estimated to spend 39% of their time preparing data, underscoring the critical impact of AI on the R&D process.

Artificial intelligence is particularly effective in identifying the hidden relationships in complex data and processes, accelerating the research and development process with faster and more effective developments. This allows teams to innovate quickly. However, its impact relies on clean data, making it critical for R&D teams to establish good data management practices.

Invest in human expertise

When developing new technological solutions for the climate, AI and ML technologies are no substitute for human ingenuity. They improve people’s efforts by aggregating data, identifying relationships between information or ideas, and accelerating computational results.

While there are many different models of ML and AI, one that is common and valuable in R&D settings is supervised learning.

AI based on supervised learning still requires a significant amount of human input. That is why engineering and domain expert knowledge is essential to the R&D process.

Simply put, climate tech innovators, entrepreneurs, and startups cannot expect to plug in and play with AI technologies. They must invest in human expertise to inform computational models, evaluate outcomes, and build better products or services.

For example, supervised AI models require close relationships between experiential data and computational technologies. This allows AI and ML models to evaluate data and determine the most relevant information.

These models are based on the human input of scientists, engineers and other skilled professionals. Otherwise, they operate blindly, diminishing output and eroding R&D results. For best AI outcomes, break down barriers to communication and collaboration across teams to ensure the best data and insights are always available.

Together, teams can leverage AI in a step-by-step instrumental process working toward a world-changing climate technology solution.

Use clean energy as part of your workflow

While AI and ML models can accelerate research and development for new climate technology solutions, they can also contribute to the problem.

Training AI and ML models requires significant amounts of energy. Training a single AI model can contribute the carbon equivalent of five vehicles’ lifetime emissions, according to an analysis by researchers at the University of Massachusetts.

Researchers and startups can mitigate this impact by relying on renewable energy to fuel their efforts.

Relying on renewable energy to power data centers and computing resources dedicated to AI innovation can help businesses make an impact as they develop the climate technology solutions that will change the world.

AI can help identify the climate solutions we need now

In November 2020, a report by Ernst and Young identified the importance of AI and ML technologies in climate technology research and development, explaining that AI has the ability to unlock the vast potential of renewable energy. Not embracing it means risking being left behind the curve.

This is even more true today as the implications of climate change are being felt more than ever. To bring the next big breakthrough to market, early-stage startups need to demonstrate proof of concept to investors and stakeholders.

Right now, we have a unique opportunity to sustain innovation and accelerate development. Climate tech is experiencing an investment boom as venture capital funds pour money into the sector.

In other words, as a recent headline in The Atlantic proclaimed, The climate economy is about to explode.

To face the moment, climate technology companies need R&D results now. Rather than relying on intuition and previous experience, innovators and entrepreneurs can turn to AI and ML technologies to accelerate and improve their results.

Matthew Sykes is director of materials development for Ubiquitous Energy.

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