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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.   

How Indonesia’s electric mobility transition is being held back by financial system inertia — not engineering limits.

By Diana – Marina Florea and Prof. Kristjan Jespersen

Indonesia has declared an electric future. By 2030, the government wants two million electric cars and twelve million electric two-wheelers on its roads. It has invested in battery production, courted EV manufacturers, and rolled out an incentive package designed to shift consumer behaviour. On paper, the transition is underway, in practice, adoption is stalling – and the received explanation does not hold up. The conventional diagnosis blames technology: too few charging stations, range anxiety, uncertain battery lifetimes. Our research tells a different story. In Indonesia’s largest digital ride-hailing ecosystem, the barrier to EV adoption is not what engineers have built. It is what financial institutions have failed to build alongside it.

The EV is not unbankable because the technology is unreliable. It is unbankable because the financial system has not caught up.

The Canary in the Coal Mine: GoTo’s Electrification Commitment

GoTo is Indonesia’s largest digital platform ecosystem – the parent of Gojek, Tokopedia, and GoPay. With millions of driver-partners and a 2030 commitment to full fleet electrification, it is the single most important test case for EV uptake among the country’s ‘gig economy’ workers. The commitment is genuine. GoTo has signed partnership agreements with EV manufacturers, invested in Electrum, a battery swapping joint venture, and deployed EVs across pilot markets. And yet, despite all of this, uptake among driver-partners remains persistently low.

The drivers are not resistant to electric vehicles. Many actively want them: lower fuel costs, reduced maintenance, and access to Jakarta’s odd-even traffic exemptions make EVs economically attractive. The problem is they cannot get financing. And the reason they cannot get financing is structural, not individual.

When Uncertainty Becomes Unbankable

Traditional vehicle lending was calibrated on decades of internal combustion engine data. Lenders know how a 3-year-old Toyota depreciates. They know its resale market, its repair costs, its default risk profile. That knowledge is embedded in pricing models, underwriting criteria, and collateral assessments built up over generations. Electric vehicles disrupt every one of those assumptions simultaneously.

When a lender looks at an EV, especially from a newer manufacturer, they face compounding uncertainties: battery degradation is non-linear and hard to predict, resale markets are shallow and volatile, technological obsolescence is rapid, and the long-term keeps residual values volatile, which keeps lenders cautious, which maintains the very conditions that make ownership inaccessible.

The trap:  Rental models exist because ownership financing is broken. But rental models also perpetuate the conditions that keep ownership financing broken.

Data Is the Missing Infrastructure

If financial institutions cannot price EV risk with confidence, the most powerful intervention is not a subsidy but better data.

GoTo sits on exactly the kind of data that could unlock EV financing at scale. The platform knows how far each driver rides per day, how consistently they generate income, how their earnings fluctuate across seasons, how reliably they service existing obligations. This granular, real-time dataset is arguably more informative about creditworthiness than a traditional credit bureau report – and it exists for millions of drivers who have no formal credit history at all.

Internationally, similar data-driven models have already been deployed successfully. In Southeast Asia and Sub-Saharan Africa, fintech lenders using ride-hailing platform data have extended EV financing with default rates materially below those of traditional subprime auto lending. The model is proven. The barrier in Indonesia is not technical but instead it is about data sharing, regulatory frameworks, and institutional trust between platforms and lenders.

Battery passports offer a second, complementary tool. By tracking battery health, charge cycles, maintenance records, and degradation patterns across the vehicle’s lifetime, a battery passport can make an EV’s core asset – its battery – transparent, comparable, and verifiable. That transparency stabilises residual values. When a lender can look at a battery passport and see that a specific unit has experienced 12% degradation over 80,000 kilometres, they can price that risk. When they cannot see it, they assume the worst.

Together, platform-level income data and vehicle-level battery passports could do more to unlock EV financing in Indonesia than any subsidy programme. They reduce information asymmetry rather than papering over it.

Policy Has Been Treating Symptoms, Not the Disease

Indonesia’s EV incentive package is not insignificant. Value-added tax reductions, luxury tax exemptions, import duty waivers, subsidised fast-charging tariffs, and traffic restriction exemptions in Jakarta and when taken together, these measures do lower the cost of EV ownership and make EVs more attractive for higher-income buyers.

But they leave the financing problem entirely untouched. The short-lived subsidy for electric two-wheelers, which expired in early 2025, illustrates the gap precisely. The subsidy reduced purchase prices. It did not address rapid EV depreciation, the absence of functioning secondary markets, or lenders’ structural viability of some OEMs remains unclear. The rational response, from within a risk management framework designed for ICE assets, is exactly what we observe: higher interest rates, shorter tenors, tighter collateral requirements, or outright refusal.

For GoTo’s driver-partners – many of whom lack formal credit histories, stable income documentation, or collateral beyond the vehicle itself – this is not a minor friction. It is a hard stop.

Key insight:  Drivers in the gig economy are precisely the borrowers most dependent on EV financing, and precisely the borrowers the existing system is least equipped to serve.

The Technology Gap Has Closed. The Finance Gap Has Not

Indonesia’s public debate still treats charging infrastructure and battery performance as the dominant barriers. This framing is now outdated, and it matters because it directs policy attention away from the actual constraint.

Charging infrastructure is expanding rapidly, driven by state-owned utility PLN, private partnerships, and GoTo’s own Electrum network. Battery performance under tropical conditions, which is a legitimate concern two years ago, is improving with each new generation of cells. Evidence from more mature EV markets already suggests that battery lifetimes, on average, are broadly comparable to ICE vehicle lifetimes, especially for two-wheelers used in urban ride-hailing.

Uncertainty persists, particularly for intensive ride-hailing use, which stress-tests battery cells more aggressively than private commuting does. But these are questions of degree, not of fundamental viability. The EV works. What does not yet work is the financial architecture built to support it.

Technology improves on engineering timelines. Financial infrastructure moves on institutional timelines – and institutions are slower.

Trapped in a Rental Equilibrium

In the absence of conventional financing, the market has developed a workaround: rental and subscription models. EV manufacturers and fleet operators bundle the vehicle, maintenance, battery swapping, and sometimes insurance into a single daily or weekly payment. This lowers the barrier for drivers with no upfront capital required, no residual value risk and has allowed EV usage to grow even as ownership has stagnated.

The workaround, however, has a structural ceiling.

Rental models require manufacturers or platform operators to carry large vehicle fleets on their own balance sheets. Capital is absorbed that could otherwise be recycled into new production. Scaling requires proportional increases in equity or debt financing that smaller EV manufacturers, in particular, cannot easily access. And because drivers do not own their vehicles, the secondary market for EVs remains underdeveloped, which, in turn, keeps residual values volatile, which keeps lenders cautious, which maintains the very conditions that make ownership inaccessible.

The trap:  Rental models exist because ownership financing is broken. But rental models also perpetuate the conditions that keep ownership financing broken.

Data Is the Missing Infrastructure

If financial institutions cannot price EV risk with confidence, the most powerful intervention is not a subsidy but better data.

GoTo sits on exactly the kind of data that could unlock EV financing at scale. The platform knows how far each driver rides per day, how consistently they generate income, how their earnings fluctuate across seasons, how reliably they service existing obligations. This granular, real-time dataset is arguably more informative about creditworthiness than a traditional credit bureau report – and it exists for millions of drivers who have no formal credit history at all.

Internationally, similar data-driven models have already been deployed successfully. In Southeast Asia and Sub-Saharan Africa, fintech lenders using ride-hailing platform data have extended EV financing with default rates materially below those of traditional subprime auto lending. The model is proven. The barrier in Indonesia is not technical but instead it is about data sharing, regulatory frameworks, and institutional trust between platforms and lenders.

Battery passports offer a second, complementary tool. By tracking battery health, charge cycles, maintenance records, and degradation patterns across the vehicle’s lifetime, a battery passport can make an EV’s core asset – its battery – transparent, comparable, and verifiable. That transparency stabilises residual values. When a lender can look at a battery passport and see that a specific unit has experienced 12% degradation over 80,000 kilometres, they can price that risk. When they cannot see it, they assume the worst.

Together, platform-level income data and vehicle-level battery passports could do more to unlock EV financing in Indonesia than any subsidy programme. They reduce information asymmetry rather than papering over it.

Policy Has Been Treating Symptoms, Not the Disease

Indonesia’s EV incentive package is not insignificant. Value-added tax reductions, luxury tax exemptions, import duty waivers, subsidised fast-charging tariffs, and traffic restriction exemptions in Jakarta and when taken together, these measures do lower the cost of EV ownership and make EVs more attractive for higher-income buyers.

But they leave the financing problem entirely untouched. The short-lived subsidy for electric two-wheelers, which expired in early 2025, illustrates the gap precisely. The subsidy reduced purchase prices. It did not address rapid EV depreciation, the absence of functioning secondary markets, or lenders’ structural reluctance to extend affordable credit to low-income gig workers. When the subsidy ended, sales collapsed and many drivers reverted to rentals.

Durable EV adoption requires a different category of policy instrument where one that works at the financial system level rather than the consumer level:

• Public or blended risk-sharing facilities for EV loan portfolios, structured to de-risk first-loss tranches for participating lenders
• Residual value guarantees on selected EV models, backed by public or OEM balance sheets, to create floor prices in the secondary market
• Technical assistance and co-investment to help domestic financial institutions build EV-specific credit models, using platform data and battery performance analytics
• Regulatory clarity on data sharing between platforms, OEMs, and lenders – establishing the legal and privacy framework that makes data-driven credit feasible

A Broader Lesson for Sustainable Finance

The GoTo case is an instance of a pattern that repeats across sustainable finance. Technological transitions create new asset classes with solar farms, offshore wind, green buildings, EVs being established financial systems were not designed to evaluate. The assets work. The technology is often commercially viable. But financial institutions, calibrated to historical data and familiar risk profiles, hesitate or misprice.

In these transitions, the financial system is not passive infrastructure. It is an active constraint. When it cannot accurately price new assets, capital flows are distorted, promising technologies stall at the pilot phase, and the costs of transition fall disproportionately on those, such as Indonesia’s gig workers, have the least capacity to bear them.

Closing that gap requires more than incremental adjustments to existing frameworks. It requires building financial institutions, data systems, and risk-sharing mechanisms that are purpose-built for the transition economy – not adapted from frameworks designed for an economy we are trying to leave behind.

Indonesia’s EV transition will not be won on the motorway. It will be won, or lost, in the credit committees of its financial institutions.

About the Authors  

Diana-Marina Florea is pursuing an MSc in Technology Entrepreneurship at DTU and spent her third semester on exchange at Copenhagen Business School (CBS), focusing on circular economy, sustainability, and leadership in multinational enterprises. Her work sits at the intersection of entrepreneurship, strategy, and business transformation, with a particular interest in how market structures, organizational decision-making, and financial systems shape the pace and direction of the green transition.

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 Ebbe Barsøe Nielsen, Emil Cramer Monk, Jonathan Vinther Højgaard and Prof. Kristjan Jespersen

For years, biodiversity has been called the next frontier of sustainable finance. That frontier is no longer distant. Policymakers, investors, and companies increasingly recognise that nature loss is not just an ecological emergency; it is a material financial risk. Freshwater availability, soil health, stable agricultural supply chains, and functioning ecosystems underpin large parts of the real economy. As a result, biodiversity has moved rapidly from the margins of ESG into the regulatory core, driven by the Corporate Sustainability Reporting Directive (CSRD), European Sustainability Reporting Standard E4 (ESRS E4), and the Taskforce on Nature-related Financial Disclosures (TNFD). 

And yet, for all this momentum, biodiversity remains genuinely difficult to integrate into investment analysis. Unlike climate change (where carbon emissions provide a single, globally comparable metric), biodiversity is multidimensional, deeply local, and non-linear. It spans water use, land conversion, pollution, ecosystem degradation, and supply-chain exposure, varying sharply across industries and geographies. No single number captures it. 

This creates a structural challenge for markets. ESG scores increasingly incorporate biodiversity-related indicators, yet often reward disclosure maturity rather than real-world ecological pressure or genuine preparedness. Biodiversity is important, yet still poorly understood in practice. 

The biodiversity data problem is worse than you think 

The core challenge is not simply that biodiversity data are scarce. It is that the data which do exist are fragmented, inconsistent, and easy to misread. Most biodiversity-relevant information is scattered across heterogeneous ESG indicators that differ in scale, meaning, and materiality. Governance commitments, intensity metrics, recycling rates, and controversy events are fundamentally different kinds of signal, yet they are routinely treated as interchangeable inputs and aggregated into a single composite score. This flattens complexity and masks the trade-offs that matter most for investors. 

Disclosure bias compounds the problem. Firms with limited direct ecosystem exposure often score well simply because fewer biodiversity indicators are material to their business models. Conversely, companies in agriculture, mining, or manufacturing may score poorly even when actively managing biodiversity risks, because their core activities generate unavoidable environmental pressure. Missing data create a further trap: mechanically penalising non-disclosure conflates reporting gaps with weak performance, while ignoring missing values risks overstating the preparedness of firms that simply disclose less. 

Finally, biodiversity risk is inherently place-based. Ecosystem impacts occur locally: at the watershed, the forest edge, the coral reef. Corporate reporting is almost always aggregated at the firm or group level. Translating site-specific ecological realities into comparable firm-level signals is an unsolved challenge that no index has yet resolved. Together, these issues explain why biodiversity has struggled to gain analytical clarity within ESG frameworks, despite its growing regulatory prominence. 

Our goal, therefore, is not to perfectly measure biodiversity impact (no tool can do that today). It is to construct a credible, scalable methodology that helps investors distinguish between firms that are better or worse prepared for the risks of a nature-constrained economy. 

Introducing the BNPi: building a better signal 

To address this gap, we developed the Biodiversity and Nature Performance Index (BNPi): a structured framework that translates fragmented ESG data into an interpretable, actionable biodiversity performance signal. The BNPi does not ask whether companies are “nature-positive”. That question, while important, cannot yet be answered reliably at scale. Instead, it assesses relative preparedness, governance quality, and environmental pressure, given current disclosure constraints. 

The methodology begins with indicator selection. From more than 870 ESG data fields in LSEG Workspace, we identified 36 quantitative indicators with direct relevance to biodiversity and nature. These capture both biophysical pressure: water use intensity, waste generation, and pollutant levels, as well as organisational readiness: biodiversity commitments, risk assessments, targets, and supply-chain due-diligence processes. Indicator selection follows the logic embedded in ESRS E3/E4 and TNFD.  

Next, indicators are harmonised. Because biodiversity data come in incompatible formats, all indicators are mapped onto a common 0 to 100 scale. Intensity metrics are inverted so that lower pressure corresponds to higher scores, while binary indicators reflect the presence or absence of governance structures. Crucially, firms are not penalised for missing data; indicators contribute to the index only when data are available, supported by a separate coverage ratio¹ to maintain transparency about how much of a firm’s result is grounded in actual disclosure versus assumed defaults. 

Figure 1: Illustration of how ESG indicators are transformed into an overall index. Source: own creation.  

The final step and the core analytical contribution is structure. Rather than relying on a single aggregate score, the BNPi is deliberately designed as a five-pillar framework, reflecting the fact that biodiversity performance is not one-dimensional. 

The Governance and Policy pillar captures organisational readiness: does the firm have the institutional capacity to identify and manage biodiversity-related risks through formal commitments, risk assessments, and supplier oversight? The Targets and Actions pillar evaluates forward-looking intent, examining whether governance structures are actually translated into measurable, time-bound objectives addressing biodiversity and environmental pressure. 

The Pressure and Intensity pillar reflects firms’ direct biophysical footprint, capturing how business models depend on or exert pressure on natural systems. This is complemented by the Circularity and Efficiency pillar, which focuses on resource stewardship and firms’ capacity to reduce that pressure over time. Finally, the Outcomes and Controversies pillar provides a backward-looking validity check by accounting for realised biodiversity- and environment-related incidents. 

Taken together, this architecture renders trade-offs explicit rather than hiding them. A firm may exhibit strong governance alongside high ecological pressure. Another may have a low current footprint but weak forward-looking commitments. By maintaining these dimensions as analytically distinct, the BNPi avoids collapsing biodiversity performance into an opaque composite where the weaknesses of one dimension are quietly offset by the strengths of another. 

Figure 2: Visualisation of our methodology, with the five-pillar structure of the BNPi. Source: Own creation. 

What we found: patterns across Europe 

Applying the BNPi framework to more than 2,000 listed companies across the EU-27 and the UK produces several clear and striking insights. First, the index meaningfully differentiates firms. Scores are widely distributed rather than clustered near the mean: biodiversity-related performance is far from uniform, even within the same industry. This is precisely what a useful risk-screening tool should show. 

Second, geography matters, though not deterministically. Southern and Western European companies, particularly in Spain, Portugal, and France, tend to score above the European average, while several smaller Eastern and South-Eastern economies lag behind. But high- and low-performing firms exist in nearly every country, suggesting that national context shapes outcomes without fully determining them. 

Third, industry structure plays a dominant role. Real-economy sectors with tangible environmental interfaces (energy, materials, utilities, food production) often score higher than finance- and knowledge-intensive sectors. Importantly, this does not necessarily imply superior ecological outcomes; in many cases it reflects stronger governance traditions and more mature reporting practices. This is precisely the kind of nuance a multi-pillar framework makes visible. 

Finally, governance and forward-looking targets emerge as the strongest drivers of overall BNPi performance. Firms that score well on these pillars also tend to perform better on pressure, circularity, and outcomes, consistent with the logic embedded in CSRD and TNFD: building the organisational capacity to manage nature risks is a precondition for managing them well. 

What this means for investors 

The BNPi is not a buy-or-sell signal, nor does it claim to measure true ecological impact. What it does, and what investors urgently need, is to function as a credible screening and risk-identification tool. It helps investors identify where biodiversity-related risks may be concentrated across portfolios, industries, and geographies, and where disclosure gaps may mask underlying exposure. 

Interpreting scores requires care. A high BNPi score does not imply nature-positive impact, and a low score does not automatically indicate ecological harm. Coverage matters: firms with limited disclosure may appear stronger than warranted, while transparent firms may expose weaknesses more clearly. These are features of the index, not flaws: they reflect the messy, incomplete reality of today’s biodiversity data landscape. Used thoughtfully, the BNPi supports targeted engagement with portfolio companies, prioritised due diligence on high-risk exposures, and alignment with the rapidly evolving expectations of CSRD, TNFD, and institutional investors increasingly asking hard questions about nature. 

About the Authors  

Ebbe Barsøe Nielsen is finalizing his MSc in General Management and Analytics at Copenhagen Business School. During his studies, he completed the Minor in Environmental, Social, Governance (ESG): Metrics, Reporting and Sustainable Investments, where he worked extensively with ESG data, regulatory frameworks, and empirical analysis – including the LSEG-based project that forms the foundation of this blog post. Alongside his studies, Ebbe works within Strategy, PMO & Process at Everllence, where he is responsible for a portfolio management and reporting project.

Emil Cramer Monk is finalizing his MSc in General Management and Analytics at Copenhagen Business School. During his studies, he completed the Minor in Environmental, Social, Governance (ESG): Metrics, Reporting and Sustainable Investments, gaining extensive hands-on experience with ESG data, regulatory frameworks, and empirical analysis, including the LSEG-based project that forms the foundation of this blog post. Alongside his studies, Emil works within Strategy, PMO & Process at Everllence, where he supports large-scale programs and strategic initiatives across program governance and stakeholders.

Jonathan Vinther Højgaard is completing his MSc in Business Administration & Economics (General Management and Analytics) at Copenhagen Business School. Like the two others, he pursued the Minor in Environmental, Social and Governance (ESG): Metrics, Reporting and Sustainable Investments. Through the collaboration with LSEG and the use of the WorkSpace platform, he focused on translating complex ESG datasets into structured financial insights, primarily focused on investors – an approach that underpins this blog post. Alongside his studies, Jonathan works in Finance & Controlling at Everllence, where he engages with performance measurement, cost structures, and financial reporting. He has a strong interest in the intersection of finance and sustainability, particularly in how ESG data can inform investment decisions and long-term capital allocation.

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 Elisa Hugary, Jakob Wadel, Maximilian Styra, Camilla Feldmann, Anna Brusco and Prof. Kristjan Jespersen

Offshore wind energy has become a cornerstone of Europe’s energy transition. Capital is flowing at scale, capacity is expanding rapidly, and the sector is widely regarded as one of the most promising sustainable investment opportunities available. Yet the story is more complicated than the capital deployment figures suggest. Offshore wind development generates real environmental pressures on marine ecosystems: underwater noise, seabed disturbance, bird collisions, and increased vessel traffic. These are not exceptional side effects. They are inherent features of how offshore wind farms are built and operated. In principle, such impacts are assessed through Environmental Impact Assessments (EIAs). In practice, EIAs function primarily as regulatory compliance tools. They are project specific, differ in scope and methodology across jurisdictions, and rarely allow meaningful comparison between projects. The result is that investors tend to engage with marine environmental impacts only when regulation forces them to. This raises a central question: how can environmental impacts be assessed in a way that is both ecologically meaningful and practically useful for investment decisions?

The limits of project level EIAs

EIAs contain extensive environmental information, but they are not built for comparability. Two offshore wind farms may generate similar environmental pressures while describing them using entirely different metrics, assumptions, and formats. From an investor’s perspective, this makes it nearly impossible to assess relative environmental risk across assets or portfolios. Interviews conducted for this research confirm what many practitioners already suspect: environmental impacts typically enter financial decision making only through regulatory channels. Biodiversity concerns may lead to permitting delays, additional mitigation requirements, or project redesigns, all of which carry direct financial consequences. But outside these regulatory triggers, environmental information remains largely disconnected from investment analysis. Closing this gap requires a more structured and consistent approach to assessing environmental impacts.

Thinking at the asset level

Environmental pressures do not arise at the “project level.” They originate from specific components and activities across the offshore wind lifecycle. Foundations, turbines, cables, and vessels each generate distinct pressures during construction, operation, and decommissioning. Adopting an asset level perspective makes it possible to identify recurring pressure patterns across offshore wind projects, even when individual EIAs describe them differently. When these cause-effect pathways are mapped systematically, a striking pattern emerges: a limited number of environmental pressures occur repeatedly across assets and lifecycle phases, and they are consistently supported by scientific evidence. This asset level mapping provides a clearer and more systematic understanding of how offshore wind infrastructure interacts with marine ecosystems.

Five recurring pressure categories

Across asset levels and lifecycle phases, five pressure categories emerge as both robust and material: bird collisions, noise pollution, seabed disturbance, vessel pressure, and water contamination. Each of these is generated by multiple assets and occurs across different stages of offshore wind projects. While they are partially addressed in existing regulation, they are not assessed in a standardised or comparable way. Identifying these recurring categories is a crucial step toward building a consistent environmental assessment framework.

From pressures to a comparable impact score

Translating environmental pressures into investment relevant information requires standardisation. This research develops a scoring mechanism for precisely this purpose: the Offshore Wind Environmental Impact Score (OWEIS). The logic is deliberately straightforward. Environmental pressures are first quantified using established models already commonly applied in EIAs. Because these impact values are expressed in different units and scales, they are then standardised to ensure comparability across pressure categories. Finally, the standardised values are weighted using the ENCORE scale according to their ecological materiality and aggregated into a single, transparent score. The OWEIS does not aim to replace detailed ecological analysis. Rather, it enables relative comparison across offshore wind assets, allowing investors to assess which projects are likely to face higher or lower environmental risk under comparable conditions.

What this means for investors

Environmental pressures become financially relevant through transition risk. Projects with higher environmental impacts face a greater likelihood of permitting delays, stricter mitigation requirements, reputational pressure, and future regulatory tightening. Each of these factors can affect project timelines, costs, and ultimately asset valuations. The reverse also holds. Projects with lower environmental impact profiles tend to be more resilient: less exposed to regulatory intervention, less prone to disruption, and better aligned with evolving sustainability expectations. From an investment perspective, this translates into more predictable cash flows and lower downside risk. By integrating asset level environmental data into due diligence and portfolio analysis, investors can move beyond compliance driven assessments and begin incorporating environmental risk more systematically into capital allocation decisions.

Bridging marine ecology and finance

Offshore wind is essential for decarbonisation. But its environmental footprint cannot be treated as an afterthought. Current assessment practices generate valuable ecological data, yet they fall short of what investors need: consistent, comparable information that can inform capital allocation. An asset level approach, combined with standardised scoring, offers a practical way forward. It does not resolve every tension between energy transition ambitions and marine conservation. But it does provide a foundation for more informed investment decisions and, ultimately, for a more honest accounting of what offshore wind development costs

About the Authors  

Elisa Hugary is finalizing her MSc in People and Business Development at Copenhagen Business School. During her electives she took a minor in Building Organizations for Sustainable Futures, working closely with researchers for the Making Oceans Count project, which built the foundation for this blog post. Alongside her studies Elisa is working as a Junior Sustainability Consultant in an international logistics company where she supports sustainability reporting and initiatives.

Jakob Wadel is currently completing his MSc in Management at WU Vienna. During his exchange semester at Copenhagen Business School (CBS), he focused on consulting, management, and digital transformation, contributing to the Making Oceans Count project underlying this blog post. Alongside his studies, he has gained experience in finance and energy-related roles, including supporting investment analyses, developing controlling systems for solar projects, and contributing to financial planning. Currently, he is gaining further first-hand experience in Austria’s energy industry, working in an in-house consulting environment.

Maximilian Styra is currently completing his MSc in Management at WU Vienna, after earning his Bachelor’s degree in Business Administration from the University of Münster. During his Erasmus semester at Copenhagen Business School (CBS), he focused on the strategic integration of sustainability into business models and contributed to the Making Oceans Count project underlying this blog post. Alongside his studies, he has been working in real estate project development in Germany, where he explores how long-term value creation can be aligned with sustainability and environmental responsibility.

Camilla Feldmann is currently pursuing her MSc in Business Administration and Organisational Communication at Copenhagen Business School (CBS). Alongside her core programme, she has chosen the International Business and Society (IBS) profile, where she has focused on transformation, sustainability, and international collaborations and responsibilities. Through her studies, Camilla has developed a strong interest in how businesses navigate complex societal challenges and balance commercial objectives with sustainable and responsible practices in a global context.

Anna Brusco is in the final semester of her MSc in Sustainability Management at the University of Southern Denmark. During her electives semester at Copenhagen Business School (CBS), she focused on sustainable investing and responsible business, contributing to the offshore wind consulting project featured in this blog post. She is currently an ESG Data Intern at Matter, working with sustainability data for investors. Her master’s thesis explores geospecific data integration in sustainable finance.

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 Walter Bachmann, Prof. Kristjan Jespersen 

For years, green bonds captured the imagination of investors and issuers alike, promising a genuine win-win outcome: capital would flow into projects with measurable environmental benefits, and the firms raising that capital could do so at a lower cost than in the conventional bond market. This attractive combination, widely referred to as the green premium or greenium, implied that investors were willing to accept slightly smaller yields in exchange for the satisfaction of financing climate solutions. The narrative proved compelling. Policymakers welcomed it as evidence that financial markets could help close the climate-finance gap, companies used it to signal corporate responsibility, and asset managers viewed it as a practical way to align portfolios with sustainability mandates. 

Early empirical studies appeared to confirm the promise. Analyses of primary-market data from the mid-2010s reported yield discounts of three to eight basis points for green instruments relative to comparable vanilla issues. As a result, the labelled market expanded rapidly. By 2023 outstanding green bonds totalled roughly six hundred billion dollars, and the structure migrated from being a niche offering to becoming a standard tool for investment-grade corporations, financial institutions, and sovereign treasuries. 

Whether the pricing advantage survived this remarkable expansion is the central question addressed here. This study assembles an issuance-level dataset covering almost fifty thousand bonds launched between 2015 and 2025 in Europe, North America, and China, of which around 2 thousand were formally labelled as green. These three regions dominate both global bond volumes and global greenhouse-gas emissions, so results carry relevance beyond local markets. Ordinary least squares regressions and issuer-fixed-effects panel models are employed to estimate the difference in initial yield spreads while controlling for rating, tenor, currency, coupon type, and issuer characteristics.  

Results from the cross-sectional ordinary least squares model reveal a clear trend. From 2015 through 2017 the coefficient on the green label is negative and statistically different from zero, confirming that green bonds were indeed issued at lower yields. Yet the absolute size of the discount narrows every year, falling from roughly six basis points in 2015 to a statistically negligible level by 2020. By 2024 the coefficient has turned positive, indicating that issuers of green bonds now pay a slightly more. Confidence intervals widen in the later years, suggesting growing dispersion in investor valuations as the market matures and supply becomes abundant. 

Panel estimates reinforce the narrative but add nuance. Once constant issuer characteristics are stripped out, the green-bond premium oscillates around zero with greater variability than in the pooled sample. The discount disappears roughly two years earlier than in the cross-sectional case, while the subsequent up-turn begins in 2021 and reaches significance in 2024. These findings imply that unobservable differences between repeat issuers played a role in the early period but faded as disclosure standards converged and investors gained experience with the product. Furthermore, sectoral breakdowns deepen the picture. When combined the high-emitting industries such as oil and gas, metals, and chemicals never achieve a material greenium. Their environmental projects may be essential for the transition, yet investors appear unconvinced that a green label overrides broader balance-sheet exposure to carbon risk. 

Three structural forces explain the vanishing greenium. First, supply has caught up with demand. When labelled bonds were scarce, impact-oriented investors competed for limited paper, pushing up prices. By the early 2020s the market had produced enough volume that scarcity ceased to be a driver. Second, transparency improved. Harmonised taxonomies, second-party opinions, and post-issuance allocation reports reduced information asymmetry, leaving little hidden value for investors to monetise. Third, macroeconomic conditions shifted. Rising interest rates, elevated inflation uncertainty, and renewed focus on credit fundamentals compressed risk premia across asset classes, making non-pecuniary benefits a luxury many investors were unwilling to fund through lower yields. 

The erosion of the pricing advantage has practical consequences. For issuers, the green label no longer reduces interest expense in a predictable way, but it still confers reputational capital and may help diversify the investor base. Regulators are also moving toward mandatory sustainability disclosure, implying that future issuance choices will be influenced as much by compliance considerations as by cost. For investors, the disappearance of the yield concession heightens the importance of rigorous impact assessment. In the contemporary market the premium that matters is credibility. Transactions backed by verifiable environmental metrics, stringent use-of-proceeds frameworks, and transparent governance continue to attract strong demand, even if they no longer command higher secondary-market prices. 

The green-bond market has transitioned from novelty to norm. The financial incentive that once spurred issuers has largely evaporated, replaced by intangible benefits linked to brand value, stakeholder expectations, and regulatory readiness. Yet the instrument retains strategic importance as a conduit for climate finance. Its standardised documentation, earmarked proceeds, and investor familiarity make it uniquely suited to mobilise private capital at scale. Policymakers seeking to revive any lost cost advantage may consider tax incentives, credit guarantees, or structured co-financing that reward measurable emissions reductions. Ultimately, the path from green label to green impact will depend less on marginal changes in coupon rates and more on the integrity and transparency of the projects being financed. 

About the Authors  

Walter Bachmann recently completed a MSc Finance and Investments with a Minor in Environment, Social, and Governance (ESG) at Copenhagen Business School (CBS). During his time as a student, he also worked as a research assistant for the Nordic ESG Lab and as M&A Analyst at LNP Corporate Finance. This September he will commence his second master’s degree at Imperial College Business School, where he will dive deeper into sustainability and financeWith a finance degree already in hand, the additional qualification from Imperial, combined with his prior ESG experience, positions him well to advance in sustainable finance. 

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.