I sat down with George LeeFounder and CEO of Hydrus.ai in San Francisco to talk about environmental, social and corporate governance (ESG) reporting, how it is leveraged and how companies can leverage data, analytics and fraud to improve their visibility with investors. What started as a technocratic liability turned into political gas. Considered a leading environmental company for its electric vehicles, Tesla was removed from the S&P 500 ESG Exchange Traded Fund (ETF) due to “lack of carbon strategy”. Lee explains his experience working with companies on their ESG, the risk of non-financial data misreporting and why a software-driven, transparent and auditable approach is essential to balancing an increasingly polarized field.

Q: ESG seems like a great idea at first glance, but what went wrong? Previously, we appreciate a company’s reports on what it is doing for the environment. Now, many do not trust him.

A: “Greenwashing” in ESG, the misleading marketing that a product or service is less environmentally friendly than claimed, has always been a problem and dates back decades. Previous terminology was CSR or corporate social responsibility. Over time, “environment, social and governance” have emerged as the top three sustainability issues facing businesses.

One of the proposed value propositions for ESG/CSR is to improve a company’s sales, marketing and brand. However, ESG can backfire on a company and reduce those assets. Various stakeholders (e.g. lenders, rating agencies) are demanding more ESG information, creating reporting headaches and incomprehensible standards applied widely to companies with little visibility – especially around environmental factors and social. In turn, activists and the media often exploit the non-financial indicators reported by companies to make negative accusations and statements.

Indeed, the ESG reporting process has always been messy; organizations hire ESG exports to cobble together supposed ESG data. These Excel sheets and reports generally lack auditability. Software has historically served this sector poorly and is made up of many tools that are not intended for both quantitative and qualitative data management.

Fortunately, Hydrus.ai solves this problem and links the data to relevant auditing, accounting and other parameters. The software controls and audits the data collection and reporting process.

Q: It’s almost like the data isn’t “real” anymore. How do you know what data to trust?

Wall Street firms have the power to require management to comply with ESG standards to continue to receive investment. Blackrock and other major financial institutions have learned that they can make ESG investing a profitable business by charging fees for “green” investment products. These products are constructed with vague and often negative sector screening, so investments in categories such as guns, oil/gas, tobacco, etc. are automatically excluded and considered non-ESG.

If the company does not respect these subjective ESG standards, it is divested and excluded from investments not only in listed index funds (ETFs), but also in pension funds and sovereign institutional investments (notably in Europe with Sustainable Finance Disclosures Regulation Where SFDR).

This shift in financial investment has single-handedly increased the risk to U.S. national security from infrastructure, as industries, energy, manufacturing, transportation, and many traditional sectors are underinvested. As such, they are arbitrarily considered non-ESG when other metrics might determine that they legitimately address these factors.

Only audited and transparent data with disclosed methodology can be trusted, e.g. how greenhouse gas calculations were made, what methodology was used (AR4 vs AR5, etc.).

Q: What about Diversity, Equity and Inclusion (DEI)? Is their number cumulative and trustworthy? Can you give me examples of advantages and disadvantages?

DEI data has been available for some time but it is not perfect. There can be large and potentially damaging discrepancies between a company’s intent and the conclusions drawn from DEI data. It is essential to know exactly how DEI data was obtained and reported. Some HR professionals have made major errors in judgment.

For example, I am ethnically Asian, but Accenture, a Fortune 500 global consulting firm with over 500,000 employees, categorized me as Caucasian/White in their Workday HR system. I never specified my ethnicity when I joined the company, so how did they “assume” my ethnicity?

This begs the question, what does self-identification mean? What categories should exist for gender, ethnicity and other areas of DEI? There is no commonly accepted standard for DEI reporting.

Companies like BlackRock say they will “boost the number of black employees by 30% by 2024”, but that means little without context. If there are 100 employees and 10 of them are black, a 30% increase is equivalent to three people. Many DEI reports, statistics and communications are inherently misleading.

Similarly, BlackRock said it would “donate $5 million to organizations focused on improving racial equity.” But this assertion of “equity” is not linked to a tangible and recognized measure. This amounts to pointing out a selfish virtue. In any event, $5 million is a drop in the bucket for BlackRock.

Q. Are there biases in the measurement tools themselves? How can this be resolved?

Many software entrepreneurs may be ideologically aligned with ESG and DEI goals and therefore design software to overestimate or underestimate relevant data. The market is trying to correct this, as investments make companies focus on business risks, costs and opportunities. It sounds a lot like “Business 101”. The bottom line is that ESG measurement is nuanced.

Most ESG and DEI software platforms have been designed for purposes unrelated to these goals, for example, tracking social impact data, health and safety, measuring greenhouse gases, etc. . Copying and pasting these metrics for business communication can be risky.

It is important that the ESG software accurately reflects the assumed measurement rather than the simple translation of thought of the software developer or contractor. To circumvent this problem, companies must first audit, then define the relevant data, and beware of ideological biases that could confuse company information.

Q. Companies’ ESG data is used in regulatory proceedings. How can policy makers be more critical of data?

ESG ratings, standards and reports are highly subjective. Rating agencies like MSCI, Refinitiv, S&P, Morningstar and others hire legions of offshore workers in emerging countries to rate organizations on ESG and DEI metrics. There are no common standards for these measurements. And yet, institutional investors and regulators are increasingly requiring companies to disclose this information. Policy makers should consider how to reduce the misuse of ESG information.

The same PhD scholars who publish academic studies also work for rating agencies, creating conflicts of interest and “principal-agent” issues like the 2008 financial crisis. Roberto Rigobon of MIT”The Global Confusion Project“Observed bond ratings are uniformly correlated at 0.9, while ESG scores are only correlated at 0.6.

ESG standards and reporting cannot be standardized globally due to local differences in culture, values ​​and industry, making it impossible to assess the same ESG principles across companies . Indeed, the indiscriminate demand for data could diminish innovation and growth could diminish and, in fact, create an incentive to overestimate.

Hydrus solves the problem with an integrated and highly differentiated approach. We reduce the noise by automating the collection of raw data from a variety of sources ranging from finance to energy to HR. As all data is aggregated into a single system and cryptographically linked to the relevant actor or transaction, the ability to audit and analyze the data is greatly enhanced. Customers have greater confidence that the resulting information is accurate, meaningful and actionable.