Uncategorized

By Lieto Cuccurullo, Eros Uber and Prof. Kristjan Jespersen

Every crash is an audit. When prices climb, almost any story about why a company is sound can pass for insight, because almost everything climbs together and nobody bothers to check the reasoning. Then the market falls, and the checking starts. The tide goes out, to borrow the old line, and you find out which beliefs were carrying weight. The pandemic crash tested one belief in particular. A great deal of capital still rests on it, so it is worth getting right.

The belief is simple enough. Companies with strong ESG ratings, especially the ones the major agencies all agree about, should hold up better when the world turns dark. It is an attractive idea. It is not even a foolish one. Five independent providers landing on the same view of a firm feels like proof that something real sits underneath, a business that is well run and not merely well marketed. And if that is true, these are exactly the companies you would want to own when the trouble starts.

Here is where it breaks. Look at how stocks moved through the crisis, set aside the ordinary market risk that drags every share around, and the ESG score explains nothing. The agreement between raters explains nothing either. This is not a matter of an unkind test. The resilience was never as solid as the consensus assumed; it only looked solid while the weather was calm. That ought to bother anyone who has been paying for it. A belief that survives only in good times is not really a belief. It is a habit.

Something did carry firms through the shock. It just was not the rating. It was the depth of a company’s intangible assets, the know how, the brand, the trust built up with the people the business depends on. And here is the part worth sitting with: those are the very things a serious ESG programme tends to build. So ESG was not wrong. It was redundant. It kept claiming credit for a strength that belonged to plain company quality, while that strength quietly did the work. Label and substance had ridden together so long that the market stopped telling them apart.

Figure 1. Rating vs. Substance. ESG took the credit; intangible assets did the work.

None of this means you throw ESG out, and the evidence is careful to say so. Two things held up. Where the signal did anything at all, it surfaced in the recovery and not the crash, once markets had the room to price the quieter details. Consensus also carried more weight in emerging markets, where investors have fewer other places to look. Hold on to that second one. It states the whole principle in a line. A signal is worth most where information is thin and least where it is everywhere. The same rating does not carry the same weight in Jakarta as it does in Frankfurt.

Figure 2. Developed Markets vs. Emerging Markets. Left: information everywhere, the signal is one of many. Right: information scarce, the same signal stands out.

So, no, ESG is not empty. But its usefulness is conditional, and most of the resilience people credit to it really belongs to the capability sitting underneath. For anyone putting money to work, that distinction is the entire game. Pay for the substance, the operational strength a company has truly built, and treat the rating as a clue to it rather than a substitute for it. The crash did not prove that sustainability fails to matter. It proved something narrower and more useful. It told us to stop mistaking the signal for the thing it is supposed to point at, and to ask, before leaning on it, which of the two we are actually holding: the signal, or the noise.

The Authors

Lieto Cuccurullo studied at Copenhagen Business School a MSc in Finance and Strategic Management and completed a minor in ESG and Sustainable Investments. Together with Eros he wrote a thesis on ESG ratings, provider agreement and stock price behaviour during the Covid-19 crisis. He currently works in the Commercial Strategy department at Trackman.

Eros Uber studied at Copenhagen Business School an MSc in Finance and Strategic Management, with an exchange semester at the University of St. Gallen. Together with Lieto he wrote a thesis on ESG ratings, provider agreement and stock price behaviour during the Covid-19 crisis. He currently works in the Global Strategy department at Circle K.

Prof. Kristjan Jespersen is an Associate Professor in Sustainable Innovation and Entrepreneurship at Copenhagen Business School (CBS) and Director of the Nordic ESG Lab. His research focuses on ESG indicators, sustainable finance, and the integration of environmental and climate-related risks into investment decision-making.

By Sebastian Manfred Streyffert and Prof. Kristjan Jespersen

Consider a composite example drawn from my thesis interviews. A smaller firm receives sustainability requests from several directions within a short period. A large customer wants emissions figures in its own template. The firm’s bank asks a different set of questions as part of a credit review. A reporting platform requires the same information in a third format. An adviser, brought in to help, recommends a fourth approach. Each request is reasonable on its own. Together, they produce confusion, duplicated effort and a quietly defensive response: do the minimum needed to keep each counterparty satisfied, then move on.

This is not primarily a compliance story. The firm is outside direct CSRD scope and has no statutory obligation to report. The pressure is real, but it arrives sideways, through commercial and financial relationships rather than through law. Once the immediate requests are answered, however, little remains. The firm is often no better equipped to handle the next request than it was before.

That gap – between pressure arriving and capacity forming – is the problem my thesis set out to explain. Across 28 interviews with actors across the Danish ESG reporting ecosystem – SMEs, financial institutions, investors, private equity firms, advisers, industry organisations, reporting platforms and larger companies – the same pattern kept surfacing. ESG-related reporting pressure reaches exposed smaller firms reliably. It just does not reliably stick.

A Simple Way to See Where it Breaks

To make sense of this, I use a three-stage diagnostic framework: transmission, filtering and conversion. The point is not the three labels. It is the two hand-offs between them, because that is where pressure tends to leak.

Figure 1. The transmission-filtering-conversion framework explains how external ESG pressure reaches SMEs, is interpreted and is converted – or fails to be converted – into internal capacity.

Transmission is how the pressure travels. For many exposed SMEs, it does not come from regulation directly. It comes through four channels: commercial relationships, where a larger customer needs supplier data to complete its own value-chain reporting; financial actors, where a bank or investor incorporates ESG questions into credit and ownership decisions; ownership structures, where a parent or private equity owner sets expectations from above; and reporting infrastructure, where platforms, templates and questionnaires carry requests that originated elsewhere. A firm can sit outside direct reporting mandates and still be asked for climate data, policies, baselines and reduction plans because someone upstream needs them.

Filtering is how the signal is translated en route and prioritised when it reaches the firm. Advisers, platforms and industry bodies can simplify, standardise, intensify or fragment a request. Management must then decide which requests matter and which are likely to persist. A firm with that clarity can sort the noise. A firm without it treats every request as equally urgent or equally ignorable. When several channels ask differently worded versions of the same question, filtering does not produce one coherent task. It produces several small ones. Transmission was strong; the signal that survived the first hand-off was fragmented.

Figure 2. Pressure can move efficiently through the value chain yet fragment at two hand-offs inside the firm: when external requests must be prioritised, and when priorities must become lasting capacity.

Conversion is whether the filtered pressure becomes durable internal capacity – a person, a process or a dataset that can be reused next time. This is the second hand-off, and it is where the deeper failure sits. Responding to a request and building the capacity to respond are not the same thing, and firms routinely do the first without the second.

The Sticking Point

A recurring outcome in my material was neither refusal nor genuine capability. It was what I came to call the minimum-response sticking point. The firm answers enough to satisfy the immediate counterparty, often by assembling figures by hand, then puts the work down until the next request forces it up again. Nothing accumulates. The spreadsheet is rebuilt from scratch each time. ESG sits with whoever has spare capacity that month – finance, operations, sometimes marketing – without a clear mandate or routine behind it.

This is why two firms under broadly similar external pressure can end up in completely different places. Responses ranged from reactive, where the firm does only what the immediate request demands, to strategic, where the firm treats the data as something worth owning. The difference was rarely the strength of the pressure. It was whether the second hand-off – from a filtered request to internal capacity – actually happened. Where it did not, the firm remained reportable on paper while staying unprepared in practice, and the cost of every future request remained high.

Seen this way, the recurring complaint that ‘ESG reporting is a burden for small firms’ becomes more precise. The burden is not simply the existence of requests. It is the repeated, uncoordinated conversion of the same information without the infrastructure or ownership that would let a firm collect it once and reuse it. That is a coordination problem dressed up as a compliance problem. It does not disappear when a firm is taken out of formal scope. If anything, it becomes harder to see because there is no longer a regulation to point at.

Why This Matters Now

The current Omnibus simplification story is that the load on smaller firms has been eased. At the level of direct statutory obligation, that is true. But the transmission channels remain live. Customers still need value-chain data, banks still incorporate ESG into risk dialogue, owners still set expectations and platforms still circulate templates. For exposed SMEs, the pressure has not been switched off. It has been shifted further into the value chain, where firms with limited capacity must filter and convert it.

The useful question, then, is not simply how to add or remove obligations. It is how to make conversion work: how an exposed SME can turn the pressure it receives into capacity it keeps. That depends on conditions outside the firm as much as inside it, and it is the subject I will turn to in a follow-up piece. For now, the diagnosis is enough. The real question is not whether ESG pressure can reach exposed Danish SMEs. It can. The question is whether that pressure becomes lasting reporting capacity – and in many firms, it still does not.

About the Authors

Sebastian Manfred Streyffert recently completed an MSc in Finance and Strategic Management at Copenhagen Business School. His thesis examines how ESG-related reporting pressure reaches exposed Danish SMEs through customers, financial institutions, ownership structures and reporting intermediaries. The study draws on 28 interviews across the Danish ESG reporting ecosystem.

Prof. Kristjan Jespersen is an Associate Professor in Sustainable Innovation and Entrepreneurship at Copenhagen Business School (CBS) and Director of the Nordic ESG Lab. His research focuses on ESG indicators, sustainable finance, and the integration of environmental and climate-related risks into investment decision-making.

By Camilla Solem, Elga Hysa, and Prof. Kristjan Jespersen

Climate change is one of the most significant risks for today’s organizations (TCFD, 2017). Actuaries at the Institute and Faculty of Actuaries (IFoA) now warn that unchecked climate change could wipe out half of global GDP between 2070 and 2090 (Trust et al., 2025; Laville, 2025). For pension funds, whose liabilities stretch decades into the future, those numbers are not distant hypotheticals – they fall squarely within the investment horizon of the savings being set aside today. That makes scientifically grounded climate-risk assessment not merely a technical exercise, but a fiduciary one.

Financial institutions use climate-risk models to assess how their investment portfolios are exposed to climate-related risks. However, recent research has raised concerns that widely used climate-risk models may underestimate the long-term economic consequences of climate change, particularly physical climate risks and the systemic consequences of climate tipping points.

In this post, we examine the climate-risk models used by Danish pension funds and the methodological assumptions behind them. By comparing current modelling practices across providers, we highlight key limitations in existing models and discuss how pension funds can strengthen their climate-risk assessments.

How Climate-Risk Models Work

Climate-risk models aim to estimate the financial impact of climate change on economic activity, asset values, and investment portfolios. These models can be built around different assumptions and rely on two foundational components: climate scenarios and damage functions.

Climate scenarios enable organizations to better understand and test the resilience of their strategies and portfolio under different potential climate futures. Scenario frameworks developed by institutions such as the Intergovernmental Panel on Climate Change (IPCC) and the Network for Greening the Financial System (NGFS) are widely used in the financial sector. However, it’s important to note that they represent hypothetical pathways and are not designed to give precise predictions of the future or quantify the financial implications of these futures.

Climate-risk models fill this gap by translating scenario outcomes into economic impacts. This is possible through the presence of a damage function, which quantifies the relationship between global temperature change and economic loss. Two of the most commonly used damage functions in climate-risk models are quadratic and logistic. The quadratic damage function assumes a smooth and gradual increase in GDP losses as temperature rises. As it excludes tipping points and abrupt system changes, it produces relatively low estimates of GDP losses even at high levels of warming (like 5°C). By contrast, the logistic damage function assumes that economies can adapt only up to a certain point, after which losses accelerate rapidly and may approach total collapse.

Source: Trust et al. (2023), p. 25

Finally, models can also differ based on their level of analysis. Bottom-up models assess climate risks at the company or asset level and aggregate these impacts across a portfolio, while top-down models estimate macroeconomic effects (GDP, productivity) first and then translate these shocks to asset classes or portfolios.

All these methodological choices strongly influence model outcomes and can lead to significantly different estimates of climate-related financial risks.

Climate-Risk Models Used by Danish Pension Funds

The findings from the analysis show that Danish pension funds rely heavily on externally developed climate-risk models, with MSCI’s Climate Value-at-Risk (CVaR) model being the most widely used framework for assessing climate risk. Most pension funds that use this model apply climate scenarios developed by the NGFS, typically assessing portfolio exposure under scenarios corresponding to approximately 1.5°C, 2°C and 3°C of global warming. This model captures both transition risks, such as policy changes and technological shifts, and physical risks, referring to direct or physical damage to assets caused by events such as floods or rising temperatures.

Despite the growing use of these models, the findings reveal considerable uncertainty among pension funds regarding the reliability of current modelling approaches. There were concerns that existing models may underestimate physical climate risks, particularly over long-term time horizons. This was evident as certain model outputs suggested that higher temperature scenarios could result in relatively limited negative impacts on portfolios – an intuitively surprising result that several funds themselves flagged.

Limitations in Current Climate-Risk Models

When using climate-risk models it is important to be aware of their potential limitations. First, there are concerns that existing models may underestimate climate-related risks. Earlier versions of MSCI’s CVaR model relied primarily on a bottom-up approach focused on company-level exposure. This approach made it difficult to capture broader systemic impacts of climate change on the global economy. For instance, Norges Bank Investment Management (NBIM) estimated significantly larger economic losses in its internal top-down model than those produced by the earlier CVaR framework. Recent updates to the MSCI CVaR methodology have attempted to address this limitation by using updated NGFS Phase V scenarios and introducing a macroeconomic physical risk component and adding an additional layer to the damage functions to capture the nonlinear, accelerating economic losses related to physical impacts.

Another limitation of current models is the exclusion of explicit modelling of climate tipping points. Climate science increasingly highlights the risk that relatively small increases in global temperature may trigger abrupt and potentially irreversible changes in the Earth system. Because most climate-risk models do not explicitly incorporate these tipping points, they may underestimate the potential magnitude of long-term physical and systemic risks.

Source: Lenton et al. (2025)

Another important methodological element to be aware of with the MSCI CVaR model is that it discounts future climate-related costs and damages to present value to estimate their impact on current company valuations. While this approach is common in financial modelling, it means that climate risks occurring further in the future appear less significant in today’s valuation calculations. For long-term investors such as pension funds, this discounting mechanism may downplay risks that are highly relevant over their investment horizon.

Alternative Modelling Approaches

There are alternative modelling approaches that attempt to address some of these limitations. One example is Ortec Finance’s ClimateMAPS framework, a climate-scenario tool designed specifically for long-term institutional investors. ClimateMAPS uses top-down economic modelling combined with nonlinear damage functions and explicitly incorporates selected climate tipping points. These modelling choices lead to more pessimistic projections of long-term economic impacts compared to models based on NGFS scenarios alone.

Source: Ortec Finance (n.d.)

Another key difference from the MSCI CVaR model is that ClimateMAPS can be integrated into Ortec’s GLASS framework, which provides year-by-year economic evolution without collapsing everything into a single discounted number.

Conclusion

Climate-risk modelling is becoming an essential tool for institutional investors seeking to understand the financial implications of climate change. Danish pension funds have begun integrating such models into their investment processes, but the analysis shows that important methodological limitations remain, and confidence in current model outputs is still constrained.

In particular, the exclusion of climate tipping points and the discounting of long-term impacts may lead to an underestimation of physical climate risks. These individual model limitations risk turning into systemic blind spots when there is an overreliance on a single vendor. As climate-risk modelling evolves, actors must develop stronger internal expertise and critically assess the models they use to ensure climate risks are reflected in long-term investment decisions.

For Danish pension funds, the question is no longer whether to use climate-risk models, and whether they already do, but whether those models can carry the weight being placed on them. Building internal expertise, diversifying modelling approaches, and interrogating the assumptions embedded in external vendors are no longer optional; they are prerequisites for managing a risk that current frameworks may be systematically under-pricing.

References

Laville, S. (2025, January 16). Global economy could face 50% loss in GDP between 2070 and 2090 from climate shocks, say actuaries. The Guardian. https://www.theguardian.com/environment/2025/jan/16/economic-growth-could-fall-50-over-20-years-from-climate-shocks-say-actuaries

Lenton, T. M., Milkoreit, M., Willcock, S., Abrams, J. F., Armstrong McKay, D. I., Buxton, J. E., Donges, J. F., Loriani, S., Wunderling, N., Alkemade, F., Barrett, M., Constantino, S., Powell, T., Smith, S. R., Boulton, C. A., Pinho, P., Dijkstra, H. A., Pearce-Kelly, P., Roman-Cuesta, R. M., Dennis, D. (eds). (2025). The Global Tipping Points Report 2025. University of Exeter, Exeter, UK. https://global-tipping-points.org/

Ortec Finance. (n.d.). The role of damage functions in assessing physical climate risks. https://www.ortecfinance.com/en/insights/blog/the-role-of-damage-functions-in-assessing-physical-climate-risks

Task Force on Climate-Related Financial Disclosures (TCFD). (2017, June). Final Report: Recommendations of the Task Force on Climate-Related Financial Disclosures. https://assets.bbhub.io/company/sites/60/2021/10/FINAL-2017-TCFD-Report.pdf

Trust, S., Saye, L., Bettis, O., Bedenham, G., Hampshire, O., Lenton, T. M., & Abrams, J. F. (2025, January 16). Planetary Solvency — Finding our balance with nature. Global Tipping Points. https://global-tipping-points.org/wp-content/uploads/2025/09/planetary-solvency-finding-our-balance-with-nature.pdf

Trust, S., Joshi, S., Lenton, T., & Oliver, J. (2023, July). The Emperor’s New Climate Scenarios. British Institute and Faculty of Actuaries. https://actuaries.org.uk/media/qeydewmk/the-emperor-s-new-climate-scenarios.pdf

About the Authors

Camilla Kjellevold Solem is completing her MSc in Strategy, Organization and Leadership at Copenhagen Business School, with a minor in ESG: Metrics, Reporting and Sustainable Investments. Her master’s thesis investigates how Norwegian companies make sense of the EU Omnibus I simplification package and what this means for the future of corporate sustainability work. Alongside her studies, she has worked as a Customer Experience Specialist at Penneo. What drives her is a fundamental curiosity about how organizations truly embed sustainability, and how to effectively lead this change.

Elga Hysa is completing her MSc in Strategy, Organization and Leadership at Copenhagen Business School, with a minor in ESG: Metrics, Reporting and Sustainable Investments. Alongside her studies, she works at DSV in Group Operational Sustainability, where she contributes to the cross-divisional and cross-regional alignment of the company’s sustainability strategy and supports its ongoing improvement. What drives her is understanding the intersection of corporate and sustainability strategy, particularly how long-term environmental and organizational goals translate into short-term decision-making within complex global organizations.

Prof. Kristjan Jespersen is an Associate Professor in Sustainable Innovation and Entrepreneurship at Copenhagen Business School (CBS) and Director of the Nordic ESG Lab. His research focuses on ESG indicators, sustainable finance, and the integration of environmental and climate-related risks into investment decision-making.

By Silje Thon Skaanes, Freja Hejlskov, Kristine Aass Rud and Prof. Kristjan Jespersen

Key Takeaways: Current ESG ratings reward disclosure maturity over genuine inclusion outcomes. A new DEI Performance Model (DPM) tested on 9,000+ companies across 95 countries quantifies both representation and belonging – and penalises imbalance between the two. For investors, the DPM offers a more credible tool for portfolio screening and targeted engagement

For years, the debate around diversity, equity, and inclusion (DEI) in corporate settings has run into the same problem: what gets measured gets managed, yet what is being measured is often the wrong thing. DEI has moved firmly into the mainstream of corporate governance, sustainability reporting, and investor due diligence. And yet a credible, holistic, and investment-grade measure of DEI performance remains elusive. This blog post introduces new research from Copenhagen Business School that proposes a structured framework – the DEI Performance Model (DPM) – to address this gap.

The dominant assumption in many organisations is that workplaces operate as pure meritocracies, where talent alone determines advancement. The evidence consistently contradicts this view. Modern discrimination rarely manifests at the hiring gate alone; it operates through the cumulative accumulation of everyday micro-processes which subjective managerial judgements, opaque promotion criteria, and unequal perceptions of comparable contributions (Pager & Shepherd, 2008). These invisible disadvantages compound over time, systematically disadvantaging minority employees, whose performance ratings are demonstrably more susceptible to stereotype-based evaluation bias (Pulakos et al., 2019; Elvira & Town, 2001). If capital markets are expected to reward talent allocation efficiently, then the capacity to measure whether organisations actually create equal opportunities is not merely an ethical imperative but also financial one.

The DEI Data Problem Is Worse Than You Think

The core challenge is not simply data scarcity. The data that do exist are fragmented, methodologically inconsistent, and susceptible to misinterpretation (Kotsantonis & Serafeim, 2019). A structural bias towards input-based indicators compounds the problem: OECD analysis suggests that output-based metrics account for only around one-third of standard ESG measures, with the remainder comprising policy proxies and management disclosures that infer performance rather than directly measuring it (OECD, 2025).

Input-based metrics cannot capture the systemic conditions that produce, reproduce, or mitigate discrimination within organisations. More critically, they measure representation indicators in isolation, detached from the organisational dynamics that determine whether diverse employees actually experience inclusion or equitable outcomes (Luthra & Muhr, 2023). A company can achieve a high diversity score by hiring proportionally across demographic groups, while its culture quietly excludes those same employees from informal networks, sponsorship, and promotion. Current ESG ratings, by rewarding disclosure maturity over outcome quality, risk encoding this gap into investor decision-making (Bowe et al., 2023; Huber et al., 2023).

“ESG scores increasingly reward disclosure maturity rather than capturing whether diverse employees truly experience inclusion or equitable outcomes.” – a structural limitation that distorts market signals and undermines the business case for genuine inclusion.

Introducing the DEI Performance Model: No Diversity without Inclusion

To address this structural gap, the research proposes the DEI Performance Model (DPM): a two-pillar framework that translates fragmented and often incomparable ESG data into an interpretable, actionable assessment of DEI performance. The model is grounded in Optimal Distinctiveness Theory (ODT), which posits that individuals hold two simultaneous, and potentially conflicting, needs: to feel validated as part of a group, and to retain a sense of unique identity (Brewer, 1991; Shore et al., 2011; Luthra & Muhr, 2023).

Genuine inclusion, in this framework, is not achieved by maximising either assimilation or differentiation. It is achieved when both needs are met in balance. Too much enforced similarity generates resentment and internal competition; too much isolation reinforces exclusion and stereotype threat (Shore et al., 2011). The DPM operationalises this insight into a measurable structure.

Figure 1: The Balance between Representation & Belonging based on the ODT (inspired by Luthra & Muhr, 2023).

The Architecture of the Diversity Performance Model

The Representation Pillar reflects “who gets in” and “who progresses” in an organization, which can highlight possible biases and glass ceilings that might exist (Taparia & Lenka, 2022). The Belonging Pillar captures whether under-represented groups feel included:

Table 1: Included metrics in the DPM

How the Model Calculates

The model calculates as:

DEI Performance = 0.5 × Representation + 0.5 × Belonging − Penalty

In order to ensure comparability across metrics and companies, each metric is normalised. For metrics where 100% is optimal, we use standard min-max normalization: (X−min)/(max−min). For metrics where 100% is not the optimal value, a parity is calculated: Parity = |X − OV|, where X refers to the company score and OV refers to the optimal score. This ensures that higher raw values translate into lower normalised scores when they indicate worse performance. For example, gender distribution has an optimal value of 50%, where the best score is closest to 50% (regardless of whether the reported number is above or below 50%), and the worst score is furthest from 50%.

The Representation pillar aggregates scores across Workforce, Leadership, and Board levels, with each level averaging its four sub-metrics. The Belonging pillar averages its five metrics, with flexible working hours weighted at 0.75 to avoid overweighting a binary indicator. These two pillars together refer to the DEI base.

The penalty mechanism is critical: Penalty = DEI base × Imbalance, where imbalance is the absolute difference between Representation and Belonging scores. In line with the ODT and the DPM, high representation scores have no true value if not accompanied by equally high inclusion rates and vice versa. This calculation ensures a penalty that increases proportionally with the size of the imbalance, preventing organizations from achieving high scores through representation alone without fostering genuine inclusion.

What This Means for Investors

The DPM offers investors several practical applications. As a portfolio screening tool, it enables more credible differentiation among companies on the social dimension of ESG than existing ratings which, as noted, often conflate disclosure quality with performance quality. As an engagement tool, it provides a structured basis for targeted dialogue with portfolio companies: identifying whether underperformance is driven by weak representation, inadequate belonging infrastructure, or the imbalance between the two has direct implications for the nature of engagement.

From a fiduciary perspective, the model’s capacity to surface hidden risks is material. A company with strong diversity optics but poor belonging scores may face elevated exposure to retention risk, legal liability, and reputational damage/risks that do not appear in conventional DEI metrics but are captured in the DPM’s penalty logic.

For investors, the DPM reframes DEI from a compliance exercise into a risk and opportunity lens – one that is rigorous enough to support allocation decisions and engagement strategies alike.

Several limitations and development priorities are worth flagging. The model currently applies a universal scoring logic that does not fully account for structural and cultural heterogeneity across regions and industries. Future enhancements – including industry-specific and country-specific weightings, and a more granular penalty calibration – would materially improve the model’s contextual sensitivity. Data availability also remains a constraint; the model’s scope is bounded by what companies disclose, which itself reflects the current state of the reporting ecosystem rather than the full range of relevant DEI dynamics.

Conclusion: Measuring the Unmeasured

The growing institutionalisation of DEI within investment frameworks is a welcome development. But it will only deliver its promised impact if the metrics underpinning those frameworks actually measure what they purport to measure. The current generation of DEI indicators largely does not. The DEI Performance Model presented here represents a step towards closing that gap by measuring representation and belonging jointly, penalising their imbalance, and grounding the methodology in a theoretically coherent framework.

True meritocracy cannot exist when systemic barriers remain invisible and unmeasured. The DPM is an attempt to make them visible and to give investors, companies, and researchers the tools to act on what they find.

About the Authors

Silje Thon Skaanes is currently completing her MSc in Business and Development Studies at Copenhagen Business School, with a minor in ESG and a specialisation in the Spanish language, complemented by a year studying Spanish language and politics at the University of Barcelona. With professional and academic experience spanning the RoPax, cruise, LNG, and port sector, she has developed a strong interest at the intersection of the maritime industry and climate-related risk management. Her bachelor’s thesis explored shipping decarbonisation, and her master’s thesis investigates the mechanisms through which a green value proposition can be achieved in the container port industry, using a fully electrified and partly automated APM Terminal in Croatia as a case study. Alongside her studies, Silje works as a Digital Marketing Student Assistant in the MarCom department at Go Nordic Cruiseline. The role of context and culture in sustainable development is of particular interest to her, especially within emerging economies and developing countries.

Freja Hejlskov is finalising her MSc in Diversity & Change Management at Copenhagen Business School, where she has also pursued a minor in ESG. Her master’s thesis explores how Spanish mining companies navigate the trade-offs between CRMA requirements, economic objectives, and minimizing environmental and social impact, addressing the broader paradox of enabling a green transition through resource extraction. Next to her studies, she works as a People & Culture Assistant at PSV, a venture house, where she has focused on DEI within startups and investment processes, with a particular emphasis on advancing gender diversity in the tech-investments. Her passion for people, social sustainability and DEI has been a consistent thread throughout her academic and professional journey, including her bachelor’s thesis on DEI in SMEs and her experience working at FLA Leadership.

Kristine Aass Rud is currently completing her MSc in Business and Development Studies at Copenhagen Business School, with a minor in ESG. She has developed a strong regional focus on Spanish-speaking markets, building on her academic and international experience, including an exchange semester at Tecnológico de Monterrey in Mexico City during her bachelor’s degree. Her master’s thesis explores energy justice in Peru, a topic she has engaged with firsthand through recent fieldwork in Cusco. Alongside her studies, Kristine is actively involved in her program’s student union and works at a global logistics company, gaining practical experience in an international business environment. She is particularly driven by issues related to ESG, the green energy transition, and the development of emerging markets.

Prof. Kristjan Jespersen is an Associate Professor in Sustainable Innovation and Entrepreneurship at the Copenhagen Business School (CBS). Kristjan is an Associate Professor at the Copenhagen Business School (CBS). As a primary area of focus, he studies the growing development and management of Ecosystem Services in developing countries. Within the field, Kristjan focuses his attention on the institutional legitimacy of such initiatives and the overall compensation tools used to ensure compliance. He has a background in International Relations and Economics.   

By Daniel Schou, Nelly Chi, Sebastian Manfred Streyffert, and Christian Munch Jørgensen and Prof. Kristjan Jespersen

The Translation Gap

There is a persistent and costly blind spot at the heart of private market investing: climate and nature risks rarely get translated into the financial variables that drive investment decisions. On the public markets side, a portfolio manager can pull data like Climate Value at Risk from MSCI and, quite easily, size up exposure at the asset and portfolio level. In private markets, the gap is wider than most assume. The usual suspect is data scarcity, but that is only half the story. The deeper problem is the absence of a structured bridge from risk signals to valuation. In practice, climate and nature risks are too often buried under broad ‘ESG risk’ labels, assessed through qualitative heatmaps, inconsistent terminology, and checkbox exercises. Treating these as “soft topics” erodes risk comparability across assets, funds, and managers, and strips information of its utility for pricing, insurance, portfolio steering, and reporting. That is a costly mistake, because what is not understood cannot be managed, and what cannot be managed is unpriced exposure sitting quietly on the balance sheet.

The stakes are not abstract. Climate and nature risks span both physical and transition categorizations established by TCFD (2017) and TNFD (2023), and the urgency is accelerating alongside our understanding of the underlying science. Global insured losses from natural catastrophes hit $137 billion in 2024, growing 5 to 7% annually in real terms (Swiss Re, 2025), while Bloomberg (2026) finds that 10pp higher physical asset damage risk corresponds to 22bp higher cost of capital. Nature risk is no less material, with losses already estimated at $430 billion annually across eight major sectors (Ceres, 2025). Yet the practices of managers in private markets still do not reflect this financial materiality. Figures from PRI (2025) show that only 22% of private equity managers disclose physical climate risk metrics, and just 29% of infrastructure investors report them publicly. Unwritten’s 2025 analysis of 70 private market reports exposes a parallel failure in ongoing monitoring: 67% of firms incorporate climate risk in pre investment due diligence, but only 13% extend that monitoring through the full asset lifecycle.

The market is moving fast, and the bar is rising. NBIM, through its 2030 Climate Action Plan published in October 2025, commits to quantify all physical climate risk at the asset level across its unlisted real assets, to integrate climate and nature risks directly in its portfolio adjustments, and to deploy AI driven analytics on proprietary data. When allocators of that scale operationalize such detailed risk assessment, they signal precisely where the market is heading and what will be expected of every manager they allocate to. For the manager that still governs climate and nature risk softly, we therefore ask you: what happens when your largest LP can price the risk in your portfolio more precisely than you can?

Two things are missing in current practice: first, the quantification of climate and nature risks in financial terms; and second, a shared understanding of those risks across investment functions. The framework proposed here addresses both, with a clear ambition: climate and nature risks should be treated like any other financial risk exposure, not siloed as an “ESG” concern, but owned by the same people and subject to the same scrutiny.

Proposition: A Risk Taxonomy for Translating Climate and Nature Value at Stake

Premise and Assumptions

The proposition is conceptually elegant: climate and nature exposures are treated as value adjustments to an existing baseline, expressed as “Value at Stake” (VaS). The framework assumes that each asset carries a base case valuation metric (NPV/NAV/IRR) without explicit climate or nature adjustments, and that both risks and opportunities can be monetized as changes to that baseline. Risks reduce present value; opportunities add to it. Once VaS is expressed at the asset level, figures aggregate upward to the fund and manager level using economic exposure weights such as NAV or invested capital shares. Drawing inspiration from statistical VaR percentiles used in market risk, VaS is best understood as the expected effect of applied assumptions across specific climate and nature scenarios.

Proposed Framework and Core Methodology

The taxonomy is structured in three layers: Category, Sub category, and Risk type, all anchored in TCFD (2017) and TNFD (2023) terminology. At the highest level, it distinguishes between Climate Risks and Nature Risks, each divided into physical risks, transition risks, and opportunities, with explicit risk types beneath each. The full structure is illustrated in Figure 1.

Figure 1: Risk Taxonomy for Climate and Nature Risk. Source: Authors’ own conceptual model based on TCFD (2017) and TNFD (2023).*Attribution of total VaS to Risk Taxonomy segments should follow a standalone sensitivity approach with proportional allocation of interaction effects.

Asset level Climate Value at Stake (CVaS) and Nature Value at Stake (NVaS) treat climate and nature as explicit value adjustments to an existing base case valuation, expressed in changes in NPV, NAV, or IRR terms. In practice, assessments begin at the asset level. Risk signals relevant to each asset’s technology and location are translated into expected financial effects through existing budget items: revenue, OpEx, CapEx, and the Discount Rate. Each asset is assumed to have a baseline valuation without climate or nature considerations, with CVaS and NVaS representing the monetised change to that baseline driven by risks and opportunities. CVaS reflects the combined effect of physical and transition climate risks alongside climate related opportunities, while NVaS reflects the effect of physical and transition nature risks that reduce value and nature related opportunities that increase value. In both cases, value impacts are decomposed into three blocks: physical risks, transition risks, and opportunities. Physical risks are divided into acute and chronic components, while transition risks are structured across policy and legal, technology, market, and reputation channels. Climate opportunities follow established categories such as resource efficiency, energy sources, products and services, and market resilience, while nature opportunities are structured around business performance and sustainability performance.

Asset level climate or nature value at stake is the negative NPV impact of physical and transition climate or nature risks plus the positive NPV impact of climate or nature related opportunities for asset. A translation to the fund level would then follow using economic weights of all fund assets and their CVaS and NVaS, and subsequently to the manager level. Once hazards are classified consistently across assets, exposures become comparable across assets and funds. Governance improves because functions operate from a shared classification, and outputs become decision useful for pricing, portfolio steering, and risk appetite.

Figure 2: Implementation process in assessing climate and nature related risk from assets to portfolio level. Source: Authors’ own conceptual model

Integration in Practice: A Four Level Roadmap
A taxonomy without integration into pricing, monitoring, and governance is little more than a concept. To close that gap, a four level integration pathway is proposed. Each level builds the capability required for the next, progressively embedding the management of climate and nature risk across the full organization.

Figure 3: Organisational maturity pathway for climate and nature risk integration. Source: Authors’ own conceptual model.

Level 1: Internal Pricing. At the first stage, managers quantify Climate Value at Stake (CVaS) and Nature Value at Stake (NVaS) at the asset level using the taxonomy. These adjustments remain internal and are not yet incorporated into reporting or investor communication. Even at this stage, they serve as a powerful internal risk valuation tool, establishing baseline exposure and supporting due diligence, monitoring, and mitigation strategies such as insurance.

Level 2: TCFD/TNFD Reporting. Once internal quantification becomes consistent and repeatable, transparency naturally follows. Climate risk disclosure aligned with TCFD signals analytical maturity to investors, and TNFD aligned nature disclosure is expected to follow as organizational capability deepens. While LP expectations for climate reporting remain more advanced, expectations on nature are accelerating rapidly. At Level 2, disclosure is not simply about voluntary compliance: it communicates to LPs that risks are systematically identified, structured, and governed. It reduces informational asymmetry between GP and LP, strengthens fundraising credibility, and responds to rising expectations for governance standards.

Level 3: Risk Adjusted Reporting. The third level marks a structural shift. Quantified risk adjustments enter quarterly reporting through a risk adjusted NAV presented alongside the base case valuation. A transparent bridge illustrates exactly how CVaS and NVaS affect fund level value. Internally, this elevates investment committee discussions by enabling portfolio comparisons that weigh resilience and risk concentration alongside expected return. Externally, it shifts LP conversations from qualitative assurances to structured valuation impact, establishing a common language between GPs and LPs around what is genuinely at stake.

Level 4: Full Financial Integration. The final stage is full integration from the outset. Climate and nature risks are embedded directly into base case assumptions during due diligence and underwriting: physical risks inform generation profiles, asset lifetimes, operating costs, and resilience CapEx, while transition risks shape regulatory assumptions, market outlooks, and discount rates. Opportunities are incorporated where credible and evidence supported. At this level, CVaS and NVaS are no longer separate analytical overlays. They are woven into acquisition pricing, portfolio construction, and exit strategies, and treated with the same rigor as market, credit, and liquidity risk.

A Fiduciary Imperative: This progression is fundamentally about fiduciary responsibility, not perfection. Climate and nature risk management does not require flawless models from day one. It requires active engagement with the risks, quantification of what can be quantified, transparent documentation of assumptions, and a commitment to refining them over time. Integration is iterative. But it must begin.

Implications for investors
Climate and nature risk management is rapidly becoming a gating criterion for capital allocation. Many managers already deploy quantitative tools to price and manage these risks. Those that remain reliant on qualitative narratives will fall behind, and the fundraising consequences will be real. Benchmarking is no longer limited to peers: leading LPs such as NBIM are building analytical capabilities that may soon price climate and nature risks more precisely than many GPs can internally. Managers that fail to keep pace with both peers and their most sophisticated LPs risk losing credibility and access to some of the world’s largest capital commitments. Sophisticated integration is not achieved overnight, and organizational complexity will inevitably slow adoption. A sequenced implementation approach is therefore proposed, with multiple viable paths to full integration. The direction, however, is unambiguous. LP expectations are rising, momentum is strong, and AI adoption across private market players and data vendors is accelerating the pace of geospatial risk assessment. Climate and nature risk management is a competitive advantage today. It will be a licence to operate tomorrow.

About the Authors 

Daniel Schou is completing his MSc in Finance and Strategic Management at Copenhagen Business School, with a minor in ESG. Alongside his studies, he works in Responsible Investments at Velliv, leading sustainability due diligence across private equity, infrastructure, and private credit. His work spans GP engagement, benchmarking within private markets, and sustainability assessments of funds. Beyond due diligence, he develops LLM-enabled workflows for company risk assessments and contributes to SFDR data collection, analysis, and reporting. Daniel also serves as Head of Operations at CBS Investment Club, coordinating partnerships and events with firms in the financial sector. What drives him is building the tools and analysis that make sustainability data actionable for investment decisions, and integrating AI into that toolkit.

Nelly Chi is completing her Master in Management at ESSEC Business School and recently completed the Minor in ESG during her exchange semester at Copenhagen Business School. Alongside her studies, she gained experience in Singapore within a private equity fund focused on renewable energy investments in Southeast Asia, supporting financial modelling, investment research, and portfolio monitoring. She also contributed to sustainable finance initiatives within a French banking group, assisting with a Green Bond Allocation Report, developing an Excel-based tool to assess regulatory impacts on investment portfolios, and supporting work on an internal ESG impact framework. She hopes to contribute to investment strategies that combine financial discipline with meaningful environmental and social considerations.

Sebastian Manfred Streyffert is completing his MSc in Finance and Strategic Management at Copenhagen Business School, with a minor in ESG. Alongside his studies, he founded and runs Manfred & Co., an import company specializing in biodynamic and organic German wines, working directly with producers on sustainable sourcing and long-term market development. His thesis research focuses on how SMEs are indirectly affected by ESG regulation through value chain pressures from larger firms. Additionally, he serves as head of events at CBS Investment Club. Motivated by bridging compliance and strategy, he aims to institutionalize ESG practices in smaller companies to unlock competitiveness, operational efficiency, and long-term value creation in evolving European regulatory environments. 

Christian Munch Jørgensen is completing his MSc in Finance and Strategic Management at Copenhagen Business School, with a minor in ESG. His master’s thesis investigates how sustainability-related incentives and ESG schemes are integrated into executive pay under the CSRD/ESRS regulatory framework across European countries and industries. Alongside his studies, he works at Cerius-Radius in Strategic Portfolio Management, supporting economic governance of investment projects across the electricity distribution grid by tracking budget changes, analyzing deviations, and consolidating asset and investment data. He also translates portfolio and budget data into decision-ready insights for senior management. Christian is driven by enabling the green transition through infrastructure and responsible investments that support a sustainable future.

Prof. Kristjan Jespersen is an Associate Professor in Sustainable Innovation and Entrepreneurship at the Copenhagen Business School (CBS). Kristjan is an Associate Professor at the Copenhagen Business School (CBS). As a primary area of focus, he studies the growing development and management of Ecosystem Services in developing countries. Within the field, Kristjan focuses his attention on the institutional legitimacy of such initiatives and the overall compensation tools used to ensure compliance. He has a background in International Relations and Economics.