AI expected to improve carbon reporting as enterprises struggle with data issues

Artificial intelligence (AI) is expected to play a leading role in reporting carbon emissions in the coming months as firms scramble to meet their lofty corporate climate goals, but the journey remains an uphill climb.

Several AI-based solutions are currently in development, focusing on data analysis to address the traditional challenges associated with reporting carbon emissions by enterprises.

It can be recalled that United States-based pension fund Calstrs was forced to push back the release of its 2023 carbon footprint report, citing jarring data issues that could skewer the final results. Before Calstrs’ delay, wealth fund New Zealand Super issued a disclaimer warning of several risks linked to “lack of completeness of data” for its climate management approaches.

“The reality is we don’t yet have satisfactory datasets,” said climate scientist Ben McNeil. “It’s hard for corporations to invest the time and money to understand their carbon footprint. Even today, 80% of listed companies around the world – that’s 50,000 companies – don’t report anything.”

Technology firm Emmi Solutions has noted considerable success through AI integrations designed to allow finance clients to process large swathes of datasets. After processing, financial institutions can use the data to craft strategies to manage portfolio climate risks.

“There are ways to assess each aspect of emissions data, and using the different techniques of machine learning, we can optimise which carbon footprint models work best,” said McNeil. “We analyze how that translates into portfolio risk, taking the financial data from their investments and propagating future climate scenarios.”

Emmi Solutions is not the only firm burrowing into the rabbit hole of climate reporting; a similar trend is being observed across Southeast Asia. Singapore-based technology firm STACS has rolled out a platform to enable firms to meet their ESG goals in the face of tightening regulations.

Rather than follow the status quo, STACS collects data from the source to stifle errors by keeping the reliance on proxies to the barest minimum. To improve service delivery, partnerships with AI firms will enable firms to obtain more data, while integrating blockchain technology will encourage transparency.

AI’s rising number of use cases

In recent months, AI has been making a case for itself outside traditional office spaces to more nuanced utilities. Medicine appears to be the biggest recipient of AI innovation, with one report predicting the rise of autonomous robotic surgeons powered by AI before the decade’s end.

Outside of climate reporting, AI has recorded cameos in environmental protection efforts by conservative groups across Southeast Asia. Use cases have since expanded to finance, education, media, supply chain, and manufacturing verticals, with CEOs bracing for impact stemming from increased adoption rates.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

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