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The line between climate action and financial performance is blurring quickly. Corporations are increasingly relying on new technologies to navigate, filter and create data to better execute ESG obligations and ambitions.
Before AI, human analysts drove ESG reporting, which gave way to biases and reporting pitfalls, especially for analysts using spreadsheets and other relatively primitive tech tools. Robust analyses of a framework as dynamic as ESG demands a rigorous technological underpinning.
Robust analyses of a framework as dynamic as ESG demands a rigorous technological underpinning.
The shift toward tech arrives right on time. More shareholders than ever are viewing ESG metrics as a proxy for reduced risk and improved financial performance, and nearly every company, it seems, has set out on a path to provide more data to investors and other stakeholders.
All of this is becoming table stakes. A report by Natural Capital Partners found that 23 percent of companies have made a public commitment that by 2030 they will be carbon neutral or meet an internal emission-reduction target, and a G&A study found 90 percent of S&P 500 companies published sustainability reports in 2019. Earlier this year, the SEC stated that maximizing a company’s bottom line aligns with the pursuit of public interest. Both can be complimentary, although achieving this is no small task.
Companies that hesitate to fully disclose their stake in ESG goals are falling behind, according to Jared Westheim, VP in sustainable finance at Goldman Sachs.
"An increasingly important area for investors is that companies actually make disclosures that demonstrate there is a solid strategy in place for decarbonization, that their lobbying activity is aligned [with ESG goals]," Westheim said at the recent VERGE Net Zero conference. He spearheads Goldman Sachs’ climate commitments, the most prominent being the firm’s goal to deploy or advise upon $750 billion in capital related to sustainability.
"We wanted to think about our commitment as one that is oriented toward building the new economy we’re all facing, one that’s thematically oriented," Westheim said. Goldman Sachs’ new goals involve better guiding investors and asset owners toward decarbonization within the financial sector.
But when a company makes a commitment to investors and the public, it can be held accountable. That leaves room for liability if it can’t achieve what they’ve openly committed to, which can then lead to reticence in disclosures.
Or worse: failure to make the commitments in the first place.
Qualitative measure, quantitative implications
A lack of transparent disclosures makes it difficult to determine just how sustainable a company really is. Data can help bridge the gap but when companies omit findings or opacity clouds their reports, aligning intent with action becomes difficult.
Making a public commitment doesn’t always translate onto a balance sheet or earnings report. Accruing social clout through marketing verbiage is one thing, but turning words into action takes a multi-pronged effort that combines a broad range of solutions.
"ESG investing is a qualitative measure of risk that has quantitative implications," said Amberjae Freeman, board chairman and chief executive officer for Etho Capital, a financial technology company that aims to align investor profits with sustainable outcomes. She helps manage capital for companies to avoid or reduce emissions.
Algorithmic processes can comb through massive datasets faster than a human analyst, yet AI can’t convey the human sentiment needed for the advising process.
At Goldman Sachs, Westheim has put a varied and broad analysis of ESG into practice. Targets are measured by production data, stranded assets, management disclosures, lobbying activity and more, depending on the sector. The more granular the analysis, however, the greater the need for more powerful technology.
"Setting and achieving carbon targets in the rate of change for those carbon targets is really driven by climate scenarios," Westheim said. "There’s a lot of judgments associated with them about the appropriate rate of decarbonization by sector, how quickly you might capture decarbonization opportunities."
That’s where companies such as Entelligent come in. Pooja Khosla is VP of client development at Entelligent, where she works with Smart Climate Technology, a company-branded risk-assessment tool, to build companies’ roadmaps that align business and climate goals. The company employs AI to help investors reduce emissions and climate-based risk in their portfolios, all while optimizing financial performance.
"Matching [ESG] goals with profit and planet gets tricky," Khosla told the Net Zero audience. "I look at resiliency, how balance sheet indicators and gauges will look in 10, 20, 30 years to see what investments would be financially stable and would drive economies toward mitigation [of climate risks]."
Khosla and Entelligent are part of the new wave of executives and investors that rely on big data to measure progress to close in on net zero. More executives than ever have relied on technology to drive sustainability measures, and that makes a difference for shareholders. Gathering both structured and unstructured data can help better analysts assess companies’ progress, or lack thereof, in an increasingly dynamic world. This can directly influence whether and how investors deploy capital to companies committed to reducing emissions.
Looping in machine learning for ESG analyses does run the risk of minimizing human input, according to Freeman. Algorithmic processes can comb through massive datasets faster than a human analyst, yet AI can’t convey the human sentiment needed for the advising process.
"The key piece really is that it’s still so important to keep humans in the loop," Freeman said. "Sustainable investing uses objective information. But it needs to have a perspective to it when it's practiced appropriately. This whole notion that it’s purely quantitative and removed from humanity is a false start."