Impact of investing
Private markets pre-investment ESG analysis is broken. Disclosure-based analysis is why.
The biggest sustainability risks to exit valuation are not in company reports – they sit in the value chain, and current analysis doesn't reach them. That is now changing.
Published Mar 6, 2026
Executive summary
- The problem: Deal timelines in private markets are accelerating, but manual sustainability due diligence still takes days per deal.
- The reality: The biggest sustainability risks to exit valuation are not in company reports. They sit outside target companies, in the value chain, and disclosure-based analysis does not cover them.
- What's changing: A growing number of GPs are replacing manual, disclosure-led analysis with automated, outside-in modelling that surfaces company-specific, financially quantified risks in minutes – often before formal DD even begins.
Why traditional ESG due diligence is failing deal teams
To protect exit valuations, deal teams need hard data to identify material sustainability risks upfront—not just generic ESG narratives. The answer for GPs has been manual ESG analysis. That rarely delivers, for three reasons:
- Too generic for deal decisions. Industry-level materiality matrices apply the same risk categories across industries and geographies. That level of generalisation does not support company-specific decisions.
- Too slow for early DD. Manual research takes days per deal. Sustainability screening either delays the process or gets skipped entirely. Neither is acceptable when you need to proceed with the deal swiftly but confidently.
- Coverage gaps hurt comparisons. In private markets, most targets don't report. Available data is patchy and incomparable, covering a fraction of the portfolio and leaving the rest unassessed.
Urs Bitterling, Chief Sustainability Officer at Cubera Private Equity, described the overall challenge sustainability teams face, in our recent webinar:
"In order to help us as sustainability professionals build the piping into the investment teams, we need to be spot on and we need to be quick. We need to come in with: these three things matter for that company, with these clients, in this region – that's what you need to focus on."
The risks that affect exit valuation sit beyond the holding period
There is a second timing problem that even faster or better disclosure-based screening cannot fix. Bitterling describes the situation:
"We have this tragedy of the horizons. A lot of times, private equity is considered to be a long term owner of assets. In reality, that’s a holding period somewhere between three to ten years at the most."
Most sustainability risks materialise over a horizon that extends well beyond a typical holding period, yet GPs often only focus on material risks for their holding period.
But when it comes to exit, the next buyer's due diligence team will be asking the same sustainability questions – and if they find financially material issues the original DD missed, it lands directly on valuation.
The relevant analysis window is therefore not the holding period. It is the holding period plus the next buyer's – 15 to 20 years of risk exposure that traditional ESG screening, built around what a company reports about its past, was never designed to reach.
How outside-in analysis reaches what disclosures miss
GPs are moving away from the disclosure-first model as it is structurally incapable of reaching the value chain risks that matter most at exit.
As Bitterling put it:
"It's always a question of trade-offs. We will rather live with consistent and systematic data that might not be spot on, than patchy, incomparable, but fairly exact reported data. Directionally you will be right. In individual cases you might be exactly wrong, but that we usually can handle – rather than having a set of reported data with a coverage of 17% of the portfolio and the rest we don't know."
AI-enabled outside-in modelling starts from what a company actually does – its products, services, and the value chains those sit within.
The practical benefits of outside-in modeling include:
- Surface red flags early. Red flags identified before allocating further resources to a deal that shouldn't have made the shortlist, as supply chain risks, geographic exposures, and product dependencies surface without requiring anything from the target.
- Understand financial materiality. Outputs map directly to revenue exposure, cost of goods, and CapEx risk. Deal teams and ESG teams looking at the same numbers in the same language, rather than translating between two separate documents
- Improved efficiency. Less time collecting and reconciling asset-level data means more capacity for the judgement calls that data alone cannot make
8 years of outside-in modeling
Upright has been developing proprietary outside-in analysis for investors since 2017, trusted by EQT, APG, LGT, Churchill, Coller Capital, Altor, and the European Investment Bank. The data engine assesses impacts, risks, and opportunities of any asset – listed or unlisted – in minutes, with fully traceable outputs that users can inspect, challenge, and adjust.
Try it yourself
The best way to understand what this looks like in practice is to run it on a deal you are currently evaluating.
Book a discovery call with us to start a free trial.
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