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Rumble in the bundle: data specialist knocks ESG scores

Shame-based investment strategies yoked to simplistic environmental, social and governance (ESG) scoring systems have come under fire in a new report from UK data analytics firm, Util.
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Util chief Patrick Wood Uribe says in the paper that in a “complex global economy, there are few obvious, absolute ‘good’ or ‘bad’ investments”.

“Inevitable tradeoffs exist in every value chain, the implications of which change depending on the social, environmental, or economic goal in question,” Uribe says. “Each objective must be approached uniquely, yet their outcomes overlap considerably. Bundled ESG scores aren’t the answer.”

Much of the current ESG discourse and product development centres on, often-divergent, scoring systems developed by the likes of Sustainalytics or MSCI despite long-acknowledged data, consistency and subjectivity issues.

The Util ‘Impact Investment: Leaders and Laggards’ study, says the consolidated ESG score is the “Schrödinger’s cat of sustainability” – referencing the famous feline-based dead-or-alive scenario floated by influential 20th century physicist, Erwin Schrödinger, to illustrate the gnarly questions posed by quantum mechanics.

“There’s no such thing as a ‘sustainable investment.’ Almost every company, industry, and fund impacts some goals positively, others negatively,” the report says.“… Time to unbundle E, S, G; planet, people, prosperity; et al. Each represents a suite of different, even conflicting, objectives. An acronym or catchall concept obscures valuable information and misdirects flows.

“Without looking at the data inside Schrödinger’s box, it’s impossible to know and optimise your investment impact.”

Util argues for a more data-heavy, artificial intelligence (AI) assault on ESG, saying “Only with comprehensive company, industry, and fund data can tradeoffs be understood and managed, and positive impact optimised.”

Specifically, the Util study used mass data-gathering and AI analytical tools to rank the best and worst US-based equity funds as per contributions to the 17 United Nations Sustainable Development Goals (SDGs). The analysis covered all US-domiciled equity funds, applying natural-language processing to a massive dataset.

As well as debunking score-bundlers, the Util report also highlights financial inequality and green energy “sustainability tradeoffs” as important challenges for the ESG movement. For example, the paper says any transition to a clean energy future depends on the continuing operations of ‘dirty’ industries such as mining, which muddies the ESG investment waters.

“The inconvenient truth? Solving climate change is, at once, the lynchpin of global sustainable development and its major conundrum,” the Util report says. “Building renewables at necessary scale is a mine-digging, energy-burning, acid-leaching, waste-dumping business, the effects of which are — like climate change — distributed unevenly. But it is necessary.”

Launched in 2017, Util casts its natural-language processing system across 50,000 or so global listed companies, scanning “120 million peer-reviewed texts to extract relationships between any product and the SDGs, their sub-targets, and an underlying 2,000 concepts”.

The group counts PGIM Quantitative Solutions and Danske Bank Asset Management as clients, among others. Uribe joined Util in 2020 as the Washington-based chief executive after five years at Kensho Technologies, the data technology firm now owned by index provider S&P Global.




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