ESG Due Diligence in Private Equity: The Questionnaire Is the Easy Part

    Most guidance explains how to send an ESG due diligence questionnaire. For the person inside the firm who has to collect and reconcile the answers every year, the harder problem starts after the responses come back.

    ESG Due Diligence in Private Equity: The Questionnaire Is the Easy Part

    Search for ESG due diligence and almost every result is written for the same reader: an advisory team running a one-off review before a deal closes. The checklists are thorough and the questionnaires are long. What they rarely mention is what happens next: the same data has to be collected again the following year, from a portfolio company that has never measured any of it before.

    Inside a private equity firm, ESG due diligence is rarely a single event. It is the front end of a reporting cycle that repeats every year, often run by one person covering an entire portfolio. That person has read the guidance. Their problem sits somewhere the guidance does not go.

    Why does ESG due diligence in private equity feel harder than the questionnaire suggests?

    Because the questionnaire is the easy part. Designing good questions takes a week. Getting reliable answers from twenty portfolio companies, some with no sustainability function, then reconciling those answers into a format an LP will accept, takes most of the year.

    The public standards for ESG due diligence in private equity are already strong. ILPA publishes a widely used due diligence questionnaire with a dedicated ESG section (Source: ILPA, 2021), and Invest Europe maintains a standard ESG questionnaire built for general partners and their portfolio companies (Source: Invest Europe ESG Due Diligence Guide, 2024). A firm does not need to invent its own from scratch, and a good pre-deal questionnaire earns its place. It should pin down current policies and any live incidents, and set a baseline carbon position with the method written next to each number so it can be reproduced later. Once the responses arrive, though, the real work starts, and no template covers it.

    What actually goes wrong with the data?

    The answers come back inconsistent, and often wrong, because the portfolio company frequently does not understand what was asked. The most common failure sits in carbon.

    Take electricity. The GHG Protocol requires two figures for purchased energy, a location-based number using the average grid intensity and a market-based number reflecting what the company actually contracted for (Source: GHG Protocol Scope 2 Guidance, 2015). A portfolio company will often report one, call it the other, and never notice the gap. Scope 3 is worse. The Scope 3 Standard sets out fifteen categories of value-chain emissions, and the Calculation Guidance shows how a spend-based estimate and an activity-based estimate for the same category can produce very different results (Source: GHG Protocol Corporate Value Chain Standard, 2011; Scope 3 Calculation Guidance, 2013). When a company reports a scope 3 figure that looks suspiciously low, the reason is usually a category quietly left out. The same gap runs through the social and governance answers, where a policy can exist on paper but have been ignored in practice, and a questionnaire cannot tell the two apart.

    This is the part the advisory-led model handles badly. A consultant handholds the company through data collection during diligence, produces a clean number, then leaves. The following year the ESG lead inherits a figure they cannot fully explain and cannot easily reproduce. The headline looks precise. Underneath, it does not match last year, or the company down the corridor in the same fund. This is the difference between software that stores ESG data and an agent that acts on it.

    Frameworks that sit above the portfolio make the gap visible. LP-facing standards such as the EDCI, and disclosure regimes such as the EU's SFDR and the ISSB's IFRS S2, all ask how each metric was produced (Source: EU SFDR, 2021; IFRS S2, 2023). Answering honestly means knowing the method every portfolio company used, which means the method has to be captured at the point of collection, not guessed afterwards.

    Should each deal get its own bespoke questionnaire?

    Not from a blank page. The stronger approach is one base questionnaire that every portfolio company answers, plus a small set of add-on modules for sector-specific questions. A services business gets asked more about how it treats its workforce. An asset-heavy business gets asked more about energy and waste. The core stays identical, so the data stays comparable across the fund.

    Duplicating a full questionnaire for every deal, then editing it by hand, feels faster on the first deal and costs more on every one after. Comparability quietly erodes. Each manual edit is a chance for a definition to drift, and drift is what breaks a portfolio-wide report. A base-plus-module structure keeps diligence tailored where it needs to be, while protecting the one thing an LP report depends on: that a metric means the same thing everywhere it appears.

    How do ESG agents change portfolio data collection?

    They move the effort from chasing to checking. An agent can run the collection loop itself. It sends each portfolio company its questionnaire and chases the ones who go quiet. Then it reads the returned answers against the framework the firm reports into, flagging a scope 3 category that looks missing or an energy figure reported on the wrong basis before it reaches a report. The category has moved quickly over the past year, and the strongest agents now handle that loop end to end.

    This is where an in-house lead gets time back. Ella, built for exactly this pattern, sits inside the collection flow and pushes verified data into the frameworks a firm uses, so the same figure does not get re-keyed by hand into an EDCI template and again into the annual report. It works with more than 150 businesses, many carrying reporting across a whole group from a team of one. Ella is an AI ESG agent that completes sustainability, carbon and compliance work rather than only tracking it.

    That is the job Ella, our AI ESG agent, is built for: the collection and reconciliation that sits underneath ESG due diligence, and repeats long after the deal has closed.

    None of this removes the investor's judgement. A machine can gather the data and flag the obvious errors. The point of getting the data layer right is that the human judgement gets made on numbers that hold up.

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