Bringing the economics of biodiversity into policy and decision-making: A target and cost-based approach to pricing biodiversity

TL;DR

The paper presents the Target and Cost Analysis (TCA) framework to mainstream biodiversity into public decision-making by introducing the Marginal Biodiversity Recovery Cost (MBRC) curve and a target-aligned shadow price. By analogizing to carbon pricing, it defines MBRC through cost-effective restoration sequences and links biodiversity targets to monetary prices via MBRC = c_j / MB_j, enabling consistent CBA across sectors. It demonstrates the approach with Europe-wide extinction-risk targets, a UK species-richness case, and a local Biodiversity Net Gain context, highlighting spatial heterogeneity, metric choices, and sensitivity to key parameters. The work provides policy-ready methods to internalize biodiversity impacts in government appraisals, aligns with Green Book and ENCA guidelines, and underscores the need for targets, data, and governance to realize biodiversity mainstreaming in decision-making.

Abstract

Given ongoing, human-induced, loss of wild species we propose the Target and Cost Analysis (TCA) approach as a means of incorporating biodiversity within government appraisals of public spending. Influenced by how carbon is priced in countries around the world, the resulting biodiversity shadow price reflects the marginal cost of meeting government targets while avoiding disagreements on the use of willingness to pay measures to value biodiversity. Examples of how to operationalize TCA are developed at different scales and for alternative biodiversity metrics, including extinction risk for Europe and species richness in the UK. Pricing biodiversity according to agreed targets allows trade-offs with other wellbeing-enhancing uses of public funds to be sensibly undertaken without jeopardizing those targets, and is compatible with international guidelines on Cost Benefit Analysis.

Figures (5)

• Figure 1: Global Marginal Abatement Cost (MAC) curve for greenhouse gas abatement measures. Note, negative values appear due to the positive financial returns associated with some efficiency measures, such as insulation and fuel efficiency. This MAC curve largely ignores the transaction costs of implementation technologies. Note: $^1$ GtCO$_2$e = gigaton of carbon dioxide equivalent; The abatement measures shown are for investments beyond “business as usual” based on emissions growth driven mainly by increasing demand for energy and transport around the world and by tropical deforestation. $^2$ tCO$_2$e = ton of carbon dioxide equivalent. $^3$ Measures costing more than €40 a ton were not the focus of this study. $^4$ Atmospheric concentrations of all greenhouse gases recalculated into CO$_2$ equivalents; ppm = parts per million. $^5$ Marginal cost of avoiding emissions of 1 ton of CO$_2$ equivalents in each abatement demand scenario. Source: Adapted from Enkvist2007CostCurve
• Figure 2: Marginal Biodiversity Recovery Cost (MBRC) curve. An example of the sequence of technologies that could be used to achieve a biodiversity target.
• Figure 3: Cost effective reductions in extinction risk and the Marginal Biodiversity Recovery Cost (MBRC) curve: Fig. \ref{['fig:3a']} shows the cost-effective plots that could be targeted to reduce extinction risk in the EEA zone using the relationship Persistence $p=(H/OH)^{z}$, under the assumption that $z = 0.25$ (see Equation 1 in Methods). The red areas are more cost effective than blue ones (see legend). Fig. \ref{['fig:3b']} shows the associated MBRC for the EEA, colour-coded in the same way as for Fig. \ref{['fig:3a']}, with the low cost interventions in red and higher cost interventions in blue and the X-axis measuring persistence, noting that persistence $= 1 -$ extinction risk (e.g. Strassburg2020RestorationPriorities). The shadow price for biodiversity can be read off the Y-axis of Fig. \ref{['fig:3b']} for any target set on the X-axis, where the current level of average persistence for the EEA area is $0.75$ when $z = 0.25$. Fig. \ref{['fig:3b']} also shows sensitivity to the parameter $z$ with the upper and lower curves using $z = 0.35$ and $z = 0.15$, respectively the upper and lower bound of the 95% confidence interval used in Strassburg2020RestorationPriorities.
• Figure 4: Spatial and technological variation in the marginal biodiversity restoration cost (MBRC) across England. Delivering the biodiversity associated with species-rich semi-natural grassland habitat by (a) conversion from arable farmland, or (b) destocking grassland. While the spatial pattern is relatively similar across technologies, restoration costs for technology (b) are generally (but not always) lower cost than for (a). Costs and biodiversity response were calculated using the land use models given by Day2024NaturalCapitalPolicies.
• Figure 5: An empirical Marginal Biodiversity Recovery Cost (MBRC) curve. The cost curves are calculated across three 'technologies': arable to species rich grassland; arable to forest/woodland; and livestock to species rich grassland (destocking). Separate MBRCs for each technology illustrate the heterogeneity of marginal costs (measured as opportunity costs of land). The 'combined' MBRC curve reflects all three technologies ordered in terms of the cost of a percentage change in average species richness across the UK (see Methods). Costs and biodiversity response calculated using the land use models given by Day2024NaturalCapitalPolicies.