The Economics of Biodiversity Additionality (BIOADD)

BIOADD: The Economics of Biodiversity Additionality

BIOADD addresses a central challenge in biodiversity conservation and nature‑based climate policy: how to define, measure, price, and deliver genuinely additional environmental benefits in settings where land‑use change, economic incentives, and policy interventions interact dynamically. Against a backdrop of declining confidence in voluntary carbon markets (VCM) and growing concern that biodiversity is systematically disadvantaged by existing appraisal frameworks, the project develops an integrated economic and empirical approaches to additionality that are policy‑ready and operational. Funded by NERC UKRI ref: NE/X002292/1.

Background: Nature‑based solutions are increasingly relied upon to meet climate and biodiversity goals, yet persistent concerns about non‑additionality, leakage, location bias, and impermanence undermine their credibility. Biodiversity outcomes are particularly disadvantaged: they lack a clear accounting price, depend on poorly aligned metrics, and are crowded out by carbon‑focused incentives. Existing approaches to additionality are often static and backward‑looking, failing to capture dynamic land‑use pressures, strategic behaviour, and long‑run welfare effects. BIOADD was designed to address these failures through an integrated programme spanning theory, empirics, policy design, and tools.

Headlines

A unified framework for pricing biodiversity and assessing additionality across policies and markets.
New empirical tools to target conservation/restoration where additional carbon and nature benefits are largest.
Direct policy traction, including ongoing work with DEFRA and contributions to HM Treasury’s Green Book.
Practical decision‑support tools for policy makers and business, and capacity building beyond academia.

Policy Implications & Recommendations

Treat additionality as a dynamic, forward‑looking concept that accounts for risk and strategic behaviour.
Establish a clear accounting price and metric framework for biodiversity to level the playing field with carbon.
Target conservation using ex‑ante risk and value information rather than low‑pressure locations.
Require transparency and systematic ex post evaluation in voluntary carbon markets.
Integrate biodiversity shadow pricing and ecosystem scarcity effects into standard appraisal guidance.

Findings

Figure 1. Target and Cost-Based Approach to Biodiversity Pricing: Species Richness Example.

WP1

Pricing additionality for biodiversity [1, 2, 3, 4]. The project establishes the theoretical foundations for biodiversity shadow pricing using a target‑and‑cost approach analogous to carbon pricing. Preference elicitation with the public, experts, and financial practitioners shows that widely used metrics are misaligned with societal values, which prioritise species richness, extinction risk, and genetic distinctiveness. New empirical work links land‑use change to extinction risk, enabling marginal biodiversity recovery cost curves to be estimated and the concept of the ‘cost of a statistical species’ to be identified for policy purposes. Together with recommendations on ecosystem pricing (see Ecosystem Pricing tool below) this framework is now being developed with DEFRA and will feed into revisions of HM Treasury’s Green Book and the ENCA guidelines.

Figure 2. Probabilities of Survival (a,c,e) and Regrowth (b,d,f) and Additionality

WP2

Measuring additionality in dynamic landscapes [5, 6, 7, 8, 9, 10]. BIOADD develops complementary approaches to measure additionality. Spatial survival models forecast deforestation and regrowth pathways, translated into carbon values using the Social Value of Offsets (See Tools Section: SVO tool), and into net present values to guide targeting. Market level work on transparency and ex post evaluation provides recommendations to restore credibility in the VCM to combat the historical absence of additionality in the VCM for tropical forest carbon. Policy additionality is evaluated by estimating the impact of Protected Areas (PAs) while controlling for baseline deforestation risk, removing location bias. Additionality for biodiversity (using the LIFE metric) is also shown to increase by risk of deforestation.

Theoretical / conceptual work investigates the strategic additionality that arises in contested landscapes and shows that that additionality is not purely marginal: strategic interactions imply that areas with low apparent additionality today may be optimal from a long run welfare perspective. Typical strategies followed in pursuit of additionality (interventions where threats are highest for instance) do not always serve additionality objectives well. The Conservation Strategy Game (See Tools Section below), allows users to participate in this strategic game with an automated adversary and demonstrate these findings.

Figure 3. Overlapping Policies in Indonesia

WP3

Policy interactions and sequencing [10–11]. We develop a new and unique dataset of overlapping area-based policies in Indonesia charting the roll-out of Protected Areas (PA), in principle a stronger form of protection, since the 1960s. PAs are layered on top of land designated as ‘protection forest’. Using these data we provide empirical evidence to suggest that the strategic modelling demonstrated in WP2, that identified pre-emptive protection holds true in the Indonesian context, where land is contested between Palm Oil on ‘production forest’ and PAs. Preliminary results indicate that conservation outcomes depend on the interaction and ordering of policies under development pressure. Overlapping protections can generate additional forest conservation, but static threat‑based or cost‑effectiveness rules systematically underperform in contested environments. Simple value‑first or pre‑emptive strategies are more robust once strategic responses by developers are accounted for.

In addition, WP3 charts the impact of reforestation policies in the Philippines on development, ecosystems and carbon sequestration by looking at a unique reforestation policy roll-out. The research finds that bundling large-scale tree planting with conditional PES can generate large reductions in poverty The poverty reduction co-benefits of tree planting may extend beyond the payment window when communities are given managerial control over productive forest assets. Third, the detection of positive spillovers implies that programme evaluations based solely on treated units may underestimate aggregate social returns.

Figure 4. Carbon Removal Portfolios: Mean, S.D and Forests

WP4

Tools and capacity building [see tools below]. The project delivers industry-led operational tools, including SVO [1] and Carbon at Risk (CaR) [13] calculators (See Tools Section Below) . The latter replaces binary permanence classifications with probabilistic risk assessment for carbon removal portfolios, the former the value of temporary carbon removals equivalence. An interactive ecosystem‑services valuation tool calculates a Relative Price Change rule [12], supporting policy analysis and sensitivity testing and informing discussions in the UK Treasury’s discount‑rate review. Additionality mapping tools and collaboration with SDSN Bolivia has supported leakage analysis, spillover assessment, and training of local researchers in collaboration with policymakers.

References

[1] Groom, B. & Venmans, F. (2023). The social value of offsets. Nature.
[2] Groom, B. & Venmans, F. (2025). The social value of temporary carbon removals and delayed emissions. Environmental and Energy Policy and the Economy.
[3] Groom, B. et al. (2025). Bringing the economics of biodiversity into policy and decision-making: A target- and cost-based approach to pricing biodiversity. Working paper.
[4] Meier, S. et al. (2026). Public and expert preferences for biodiversity metrics and the implications for shadow pricing. Working paper.
[5] Inkinen, A. et al. (2026). Dynamic additionality in forest conservation. Working paper.
[6] Delacote, P. et al. (2025). Restoring credibility in carbon offsets through systematic ex post evaluation. Nature Sustainability.
[7] Delacote, P. et al. (2024). Strong transparency required for carbon credit mechanisms. Nature Sustainability.
[8] SDSN Bolivia (2023). Map of agricultural potential in Bolivia.
[9] Sileci, L. et al. (2026). Additionality of overlapping conservation policies. Working paper.
[10] Weinhold, D. & Andersen, L. (2026). Conservation strategies in contested environments. Working paper.
[11] Drupp, M. A. et al. (2024). Accounting for the increasing benefits from scarce ecosystems. Science.
[12] Drupp, M. A. et al. (2025). Global evidence on income elasticity of willingness to pay and relative price changes. Environmental and Resource Economics.
[13] Lee, B. et al. (2025). The Carbon at Risk measure can unlock financial markets for large-scale carbon removal. Nature.