How to Budget for Biodiversity Impact Measurement?

The Missing Discussion

The focus on delivering positive biodiversity impact has steadily gained momentum in the investment community in recent years, particularly following the adoption in 2022 of the Kunming–Montreal Global Biodiversity Framework (GBF).

While funders, investors, fund managers, and project implementers increasingly seek demonstrable biodiversity outcomes, measuring and evidencing those impacts on the ground remains methodologically complex, logistically demanding, and costly.

Methodological challenges have been partially addressed through the development of numerous frameworks, tools, metric catalogues, and technologies that are improving the accuracy of data collection and impact reporting (IFC, 2023; IFC, 2024; ICMA, 2025; Nature Positive Finance). Comparative analyses of biodiversity risk-assessment and impact-measurement tools also aim to guide financiers toward the most appropriate options (Finance for Biodiversity Foundation et al., 2025).

Although cost-efficiency is frequently referenced in this literature and in related discussions, limited attention has been paid to a fundamental question: who should bear the costs of impact measurement, and how should fund manager budgets account for them? Practitioners highlight a growing discrepancy between investors’ rapidly increasing expectations for high-integrity social and environmental impact and their willingness to provide the additional resources required for robust data collection (Knowledge at Wharton, 2022). Because “impact” as a budget line is relatively new for traditional fund management, this gap often forces fund managers to devise ad hoc, case-by-case solutions (Dichter & Bourke, 2024).

This challenge is particularly acute for biodiversity-focused investments, where impacts typically require long time horizons to materialize. The absence of dedicated medium- and long-term budgets diminishes funds’ ability to assess impact during and after investment and to capture accurate, ecosystem-level trends and outcomes. Instead, scarce resources tend to be directed toward pre-investment risk assessments or to measuring inputs, activities, and outputs—approaches that provide only a partial picture (Defra & Pegasys, 2022; Trinomics & Rio Impact, 2025). This blog seeks to start a discussion on strategic approaches to funding and budgeting for biodiversity impact measurement. Drawing on insights from funders, fund managers, and project developers, it explores how these costs are currently reflected in investor and fund budgets—and how current practice could be improved.


Budgeting Models

1. Investee or Project Developer Budget

Use cases: Carbon credits with biodiversity co-benefits; biodiversity credits

Budget model: For commercially viable carbon or biodiversity credit projects, the costs of monitoring, reporting, and verification (MRV) are typically embedded in the final credit price and therefore passed on to buyers or outcome payers. The cost differential associated with biodiversity is especially evident in carbon credits that deliver biodiversity co-benefits and command a price premium. As the carbon markets place greater emphasis on high integrity, this model creates a clearer link between buyers’ biodiversity expectations and the financial resources allocated to measurement: the higher the expectations, the higher the price. While a price premium does not always fully cover the additional MRV required for biodiversity benefits, it signals buyer recognition of those added costs. Because these expenses are usually paid upfront by project developers, many seek to build internal expertise in impact measurement as a cost-efficiency strategy. However, when carbon prices fall, developers may be forced to absorb part of the MRV costs, potentially undermining project viability or even leading to closure.


2. Technical Assistance Budget

Use cases: Private investment funds; blended finance funds providing company-level finance

Budget model: Technical assistance (TA) facilities attached to blended-finance or impact-seeking funds can be used to support investee impact measurement. Typically grant-funded by development agencies or philanthropies, TA provides the flexibility needed to accompany investees along their impact journey.

Yet only a limited number of blended-finance investors—primarily development finance institutions—have the mandate and resources to provide such grants. Scarcity of TA funding, combined with competing needs to build other investee capabilities, means this option is available to relatively few impact-first funds and often covers only selected investees or specific stages of measurement (Dichter & Bourke, 2024). In practice, TA resources must frequently be divided between investee-level measurement and fund-level frameworks, policies, data aggregation, and standardization. To reduce costs, many funds rely on proxy indicators—such as drivers of impact—rather than on detailed, field-level data.


3. Management Fees

Use cases: Private investment funds; blended finance funds providing company-level finance

Budget model:Investment funds traditionally allocate around 2% per year of committed capital to management fees covering general operating costs. Some fund managers use a portion of this budget for impact measurement (Dichter & Bourke, 2024).

However, depending on portfolio size, the share available for impact measurement is often insufficient to meet investors’ expectations for rigor. Raising management fees to finance higher-quality measurement is rarely viable, as investors tend to view such increases skeptically. For small, nature-focused funds, measurement costs can be disproportionately high relative to management fees; in these cases, impact-first investors may accept higher rates or encourage cost-sharing between management fees and investee budgets.


Potential Avenues for Improvement

These three models illustrate the options currently available to fund managers and project developers seeking to finance high-quality impact measurement. The first model offers a relatively straightforward solution by embedding costs in the price of carbon or biodiversity outcomes. The other two would benefit from more strategic processes to align expectations with delivery.

Stakeholder feedback and the wider literature highlight three key elements that could help balance investor expectations with available budgets:

  1. Address budgeting early in negotiations: Fund managers and investors should establish a dedicated budget category for impact measurement from the outset. Discussions must reflect the long timeframes required for biodiversity outcomes to emerge, with funding allocated accordingly (WCMC, 2023). Investors should recognize that high-quality projects are inherently more expensive and support them through higher management fees, earmarked TA budgets, or a distinct budget line. The Social Finance Impact First Fund, which treats impact measurement and management (IMM) as an explicit allowable expense within LP agreements (Dichter & Bourke, 2024), offers a model that could be adapted to biodiversity.
  2. Balance ambition with feasibility: Fund managers need to calibrate the complexity of their impact frameworks against costs, investee effort, and the practical utility of data. Excessive monitoring demands risk alienating investees, while overly modest expectations expose investors to greenwashing accusations and undermine genuine accountability.
  3. Explore innovative incentive structures: Investors could introduce performance fees linked to specific impact targets, supplementing standard management fees. These additional revenues could then be earmarked for impact measurement, creating a stronger alignment between financial incentives and biodiversity outcomes.

Robust biodiversity impact measurement is essential for credible nature-positive finance, yet it will not materialize without deliberate and adequately resourced budgeting. Moving from ad hoc solutions to structured, transparent funding models is a necessary next step for the sector.

Copy edited with support from ChatGPT by OpenAI.

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