# Subsidies against Nature: A multidimensional framework for biodiversity-aligned national budgets

Morgane Gonon, Améline Vallet, Vincent Deschamps, Amélie Le Mieux, Aurélien Oosterlinck, Hélène Soubelet, Louise Dupuis, Harold Levrel

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## To cite this version:

Morgane Gonon, Améline Vallet, Vincent Deschamps, Amélie Le Mieux, Aurélien Oosterlinck, et al. "Subsidies against Nature: A multidimensional framework for biodiversity-aligned national budgets." *Ecological Economics*, 2025, 235, pp.108661. 10.1016/j.ecolecon.2025.108661. hal-05074919

HAL Id: hal-05074919

https://agroparistech.hal.science/hal-05074919v1

Submitted on 20 May 2025

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# Subsidies against Nature: A multidimensional framework for biodiversity-aligned national budgets

Morgane Gonon<sup>a, *</sup>, Améline Vallet<sup>a,b</sup>, Vincent Deschamps<sup>a</sup>, Amélie Le Mieux<sup>c</sup>, Aurélien Oosterlinck<sup>a</sup>, Hélène Soubelet<sup>d</sup>, Louise Dupuis<sup>d</sup>, Harold Levrel<sup>a</sup>

## ARTICLE INFO

### Keywords:

- Biodiversity

- Green budgeting

- Harmful subsidies

- Sustainable pathways

- Biodiversity financing

- Public finance

## ABSTRACT

Governments provide more than USD 800 billion annually in environmentally harmful subsidies at the global level despite international commitments. This paper introduces a novel and replicable framework for identifying biodiversity-harmful subsidies within national budgets. Our multidimensional approach is based on the five drivers of biodiversity loss: land use change, resource exploitation, climate change, pollution, and invasive species. Our framework evaluates subsidies across seven economic sectors — transport, housing, industry, agriculture, livestock, power generation, and digital deployment— based on their expected biodiversity impact (harmful, positive, mixed, neutral, or unclassifiable). We apply this framework to the French national budget and find that, in 2022, €27.14 billion were allocated to subsidize harmful activities. Pollution is the most financially supported driver of biodiversity loss. Our analysis also reveals significant trade-offs, with 25% of climate-positive subsidies exacerbating land use pressures. The study calls for a sector-specific approach to subsidy reform. By codeveloping this framework with biodiversity experts and public authorities, we provide a decision-support tool to align public investments with pathways towards sustainability.

## 1. Introduction

Governments provide more than USD 800 billion annually in environmentally harmful subsidies at the global level (OECD, 2021a). Unrequited payments provided by governments to benefit producers in certain sectors or industries—granted “on the basis of the levels of their production activities or the quantities or values of the goods or services which they produce, sell, or import” (OECD, 2008)—include direct financial transfers, tax expenditures, and price support mechanisms, all of which can incentivize activities that drive climate change and biodiversity loss (Segerson et al., 2024). In this regard, governments should undertake an impact assessment of existing subsidies, direct and indirect, and across all ministries (Deutz et al., 2020). “Subsidy accountability” (Dempsey et al., 2020) is a necessary foundation to advance transformative economic change and to foster democratic debate on environmental policy.

In 2022, parties to the Convention on Biological Diversity agreed to reduce or reform biodiversity-harmful subsidies (BHS) by at least USD

500 billion annually by 2030 (Target 18) (Convention on Biological Diversity, 2022). Similar commitments have been made in the past: In 2010, parties agreed to eliminate, phase out, or reform public incentives harmful to biodiversity by 2020 (Aichi Target 3). These reforms face major implementation challenges. It is widely acknowledged that they have made little progress since then (Cottrel et al., 2021; Matthews and Karousakis, 2022). Nineteen countries only reported improvements in reforming BHS in 2018. Success stories include the reduction of subsidies for chemical fertilizers in Bangladesh and France and the elimination of subsidies for wetland draining in Denmark (Secretariat of the Convention on Biological Diversity, 2014). Examples of monitoring and tracking biodiversity-related expenditures in the national budget system, and not only climate mitigation and adaptation, are rare. France (Sainteny et al., 2011), Germany (Bretschneider and Burger, 2021), and Switzerland (Gubler et al., 2020) are noteworthy examples. The European regulation establishing the Recovery and Resilience Facility (Commission, 2021) is also an important step towards the implementation of systematic biodiversity impact assessment, as it states that

<footnote><sup>a</sup> Université Paris-Saclay, AgroParisTech, CNRS, Ecole des Ponts ParisTech, Cirad, EHESS, UMR CIRED, 94130 Nogent-sur-Marne, France</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systematique et Evolution, 91190 Gif-sur-Yvette, France</footnote>

<footnote><sup>c</sup> Office français de la biodiversité (OFB), 12 Cr Louis Lumière, 94300 Vincennes, France</footnote>

<footnote><sup>d</sup> Fondation pour la recherche sur la biodiversité, 195 rue Saint Jacques, 75005 Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: morgane.gonon@agroparistech.fr (M. Gonon).</footnote>
no measure (i.e. no reform and no investment) should lead to significant harm to any of the six environmental objectives of the European Taxonomy including protection and restoration of biodiversity and ecosystems. The lack of a standardized methodology for identifying, monitoring, and reporting BHS remains a major barrier to scaling up these initiatives.

The importance of identifying and reforming harmful subsidies has long been recognized in environmental and ecological economics (Pearce and Barbier, 2000; Barbier, 2022a; Barbier, 2022b). However, research has mostly focused on the biodiversity impact of sector-specific policies such as the European common agricultural policy (Wier et al., 2002; Schmid et al., 2007; Barnes et al., 2016). A comprehensive approach to harmful subsidy identification must extend beyond agriculture and forestry to include sectors such as housing, transport, and energy, which also exert significant pressure on biodiversity. The assessment of the causal links between drivers, impacts, and funding poses significant methodological challenges as impacts are direct and indirect, strongly location dependent, delayed, and linked to all economic sectors (Schmid et al., 2007; O'Brien et al., 2024). Systematic identification of harmful subsidies requires not only quantifying or estimating biodiversity loss but also understanding the underlying economic drivers (Spangenberg, 2007; Dempsey et al., 2020). Addressing these barriers requires rigorous research to develop systematic methodologies for identifying environmentally harmful subsidies, prioritizing those with the most significant negative impacts, and assessing the trade-offs between environmental objectives and socioeconomic support mechanisms.

This paper addresses this research gap by introducing a new methodology, as well as a scoring framework to identify government budget items that directly subsidize activities impacting biodiversity. These items are then classified based on their harmfulness (harmful, positive, mixed, unclassifiable). Our approach is based on the French Green Budgeting initiative (FGB) and was built in close collaboration with the public administrations involved in the FGB. We extend the FGB methodology at three levels: (1) we consider multiple human-induced pressures that affect biodiversity (hereafter "direct drivers") and not only an aggregated score; (2) our scoring method accounts for cumulative impacts, i.e., the number of biodiversity loss drivers likely intensified by a given subsidy; (3) our overall biodiversity impact assessment reveals both positive and harmful subsidies, as well as the trade-offs and synergies that might appear between the drivers of biodiversity erosion within and between budget programs.

In the Budget Act for 2022, 10% of the budget appropriations and earmarked taxes and 22% of tax expenditures are associated with potential direct impacts on biodiversity, i.e., 11.7% of the total national budget. Programs with the highest potential for negative impact include those related to overseas France's living conditions, urban planning, agriculture and the food industry, energy, post-mining activities, and transport. However, our analysis indicates that the expenditures most likely to exacerbate multiple drivers of biodiversity loss are not necessarily those associated with the highest subsidies, highlighting the limitations of monetary indicators in capturing the full extent of harmful effects. Pollution emerged as the most financially supported driver of biodiversity loss, with €20.04 billion in funding across 109 budget lines. Finally, our assessment reveals strong trade-offs between environmental objectives, particularly in the case of climate change mitigation. 25% of the subsidies deemed beneficial for reducing greenhouse gas emissions may simultaneously contribute to land use change.

Section 2 outlines the method we employed to collaborate with biodiversity experts and practitioners in developing a new framework for assessing the French 2022 Budget Act. Section 3 presents our findings, detailing the amounts, programs, and sectors involved. It also includes a thorough analysis of the synergies and trade-offs among the IPBES drivers reflected in the budget. Finally, Section 4 discusses our findings, the replicability of our method, and its limitations.

# 2. Materials and methods

## 2.1. Study case: the French green budget initiative

The French government submits the following year's draft budget to Parliament each year in September. The budget takes the form of a single document that covers all government expenditures and revenue forecasts. It is based on a three-tier structure: Missions, which correspond to major policy objectives and span one or more government departments. Programs encompass appropriations for a particular action or a coherent set of actions under the responsibility of a single department. Sub-programs (actions) encompass appropriations for a particular purpose within a given program (Berland, 2019). In this study, we used the 2022 Program's performance reports that justify qualitatively each action and subaction when appropriate.

Following the OECD initiative, the "Paris Collaborative on Green Budgeting" (2017), and in accordance with Article 179 of the 2020 Budget act 2019-1479, the French government has submitted each year since 2020 to Parliament, as an appendix to the budget bill, a report on the environmental impact of the budget (FGB). FGB addresses the six environmental dimensions of the European Taxonomy framework (namely climate mitigation and adaptation, water resource management, the circular economy and waste, pollution abatement, biodiversity, and sustainable land use); and includes in the scope of the assessment earmarked taxes and tax expenditure in addition to budget appropriations (OECD, 2021b). The FGB is available online in open access.

Similarly to the FGB, we evaluated the likely impacts of €586.60 billion of fiscal and budgetary expenditures (1427 budget lines), including budget appropriations, tax expenditures (i.e. the reduction of taxes for different actors or activities) and earmarked taxes (i.e. the taxes directly allocated to a public or private operator). Hereafter the term "subsidies" refers to all three types of public incentives mentioned previously. The budget assessed includes the expenditures governed by the discretionary expenditures rule (€302.50 billion in the 2022 budget bill in nominal terms) and the levies on revenue paid to the European Union and transfers to local governments. The scope also covers expenditures under the Invest for the Future Program (PIA), along with expenditures on debt service, debt reduction and pensions (Policy Programs 741 and 742), and other earmarked accounts (French Government, 2021). We only included in our analysis the credits open under payment appropriations (annualized), and not those under commitment authorizations (multi-annual).

It should be recalled that the scope we used does not consider the expenditures that have become "tax norms", as is the case for the tax exemption on paraffin and maritime transport imposed by the energy directive (2003/96/EC of 27 October 2003). Other tax expenditures do not appear in the budget documents, for example, the difference in taxation between diesel and petrol.

## 2.2. Biodiversity impact assessment: introducing a new analytical framework

We co-developed with a panel of experts an analytical framework (Fig. 1) to score the different subsidies regarding each of the five IPBES drivers of biodiversity erosion: land use change, natural resource use and direct exploitation, climate change, pollution and invasive species (Appendix 2). These scores were then aggregated into an overall biodiversity impact score (weighted sum, the weights describing the relative importance/severity of the IPBES drivers). Based on this overall score, each subsidy was classified into one of the following categories: positive, harmful, mixed, neutral, and unclassifiable.

### 2.2.1. Co-developing the framework with biodiversity experts and practitioners

The framework was co-developed by academics and practitioners
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** A multidimensional analytical framework based on the five IPBES drivers of biodiversity loss (1) applied to the 2022 state budget (2) allowed us to identify positive and harmful subsidies among the budget lines (3) through the weighted ($c_i$) sum of the scores ($s_i$) obtained by each driver.</figcaption></figure>

(Appendix 1 Table A1). The co-production activities were led by the French Biodiversity Agency, which established a working group consisting of individuals who are co-authors of this paper. The working group conducted interviews with experts of the French Biodiversity Agency, the French Sustainable Development Agency (CGDD) and the Directorate-General for Maritime Affairs regarding the biodiversity impact of various economic activities and the drivers of biodiversity loss (Appendix Table A1). Experts were selected based on their specific knowledge on economic sectors or drivers of biodiversity loss. They regularly evaluated and suggested updates to the framework.

The framework used to assess biodiversity impacts is based on IPBES drivers of biodiversity loss rather than on the six axes of the European taxonomy. This decision is motivated by the need to focus on the bio-physical consequences of human-induced actions in assessing public action. It is crucial for biodiversity impact assessments to reflect those causal linkages and, therefore, to select the most appropriate framework (D'iaz et al., 2015). The working group considered that the IPBES driver framework allowed for a more direct link between human activities and degradation than the taxonomy framework, which includes both pressures and public policy trajectories (circular economy, adaptation to climate change). In addition, major international initiatives recommend using the IPBES framework for measuring the biodiversity impact of financial institutions, such as Finance for Biodiversity Pledge (Finance for biodiversity (2022)).

In the first step of our analytical framework (Fig. 1), the activities and practices specific to various economic sectors were reviewed by the working group, and their potential direct impacts on the five IPBES drivers (rather positive or negative) were scored. We built on the work by Cherrier et al. (2021) used for territorial diagnosis to draw connections between anthropic activities and biodiversity losses through IPBES drivers.

Similarly to the FGB, we only consider the direct impact i.e. clear cause and effect relationship between the practice and an element of the environment in a short period of time; we do not include indirect impacts produced away from or as a result of complex pathways. Indirect impacts concern, for instance, many state subsidies related to education,

culture, and innovation, which are therefore out of scope.

We build the analytical framework for seven economic sectors: transport, housing & buildings, industry and manufacturing, agriculture and forestry, livestock & fisheries, power generation, and digital deployment. This scoring process is based on a preliminary literature review by the working group on the main impacts of different economic sectors on biodiversity. When existing literature provided clear indications of an impact, it served as a guiding reference for the scoring process.

The working group's task was then to rank, within each sector, the least harmful or most beneficial ways of fulfilling essential needs (such as food, transportation, and housing) regarding each driver (Fig. 1). In other words, the scoring was conducted in relative terms (i.e. by comparing the impacts of activities from a specific sector) rather than absolute impact. A positive score (coded as +1 in the database) characterized the activities that mitigated the IPBES drivers or were less detrimental while answering an essential need, while a negative score (coded -1 in the database) corresponded to those that maintained or exacerbated them (i.e. increased pressure). For instance, although public transportation exacerbates drivers of biodiversity loss in absolute terms, the working group rated it positively on numerous drivers because it creates an incentive for people to abandon their private cars (Islam et al., 2016), reducing numerous pressures on biodiversity: a decrease in the number of vehicles, less chemical and noise pollution (pollution), and fewer collisions with wildlife (direct exploitation). A neutral score, represented as 0 in the database, indicates that the activity funded by the expenditure does not have a direct impact on biodiversity. For example, this may include areas such as education, research, and health, which cannot be classified under any specific sector of the analytical framework. In cases where the literature review was inconclusive, these uncertainties were discussed during expert meetings. Non consensual decision led to an "unclassifiable" notation; unclassifiable shows a lack of information in the literature or consensus on the nature of the impact.

### 2.2.2. Applying the analytical framework to the French 2022 Budget Act

Once the framework was established, it was applied to assess the
state budget of France in 2022 (see Fig. 1, step 2). As budget documents do not allow the identification of the exact activities and practices assessed in the previous step, we defined ratios or shares within a single budget item whenever possible to more accurately fit our framework. The relevant administrative authorities, mainly the French Sustainable Development Agency (CGDD) and the Treasury, provided details on beneficiaries and funding conditions for each budget item, and insights from previous environmental assessments of specific expenditures (Appendix A1).

The definition of ratios aims to reduce the number of unclassifiable items by identifying at least one (and eventually more) practices reviewed in the first step of the analytical framework. For instance, within the agricultural sector, we used ratios between conventional and organic farming (using information on their relative surface). Similarly, the share between access to home ownership and support for rental investment was estimated using the ratio between collective and individual housing, on one hand, and new constructions and old building on the other hand (INSEE, 2017). Other sources reviewed to define the sectorial ratios include: senatorial reports (Sénat, 2022), public studies (INSEE, 2017), activity reports (Agence de l'eau Rhône Méditerranée Corse, 2021), state-operators contracts (Office national des forêts, 2022).

### 2.2.3. Computing an overall biodiversity impact score

An overall biodiversity impact score was computed in order to synthesize the impact on the five IPBES drivers. Mathematically, it was defined as the weighted sum of the scores obtained by each driver, divided by the number of drivers considered in the analysis (Appendix C). The weights described the relative importance of each IPBES driver. IPBES (2019), ranked the drivers as follows (descending order of importance): Land use (LU), Direct exploitation (DE), Climate change (CC), pollution (PLL), and invasive species (IS). However, this might not reflect local or national environmental concerns and issues. It is, therefore, important, in the calculation of the overall biodiversity score, to use weights that correctly translate these territorial specificities. For the application of the framework to the French 2022 Budget Act, the experts panel and the working group determine each driver's weight. The working group and the panel of experts collectively decided that in mainland France, land use change and pollution were twice as important as the other drivers, which resulted in weights of 0.29 for the former and 0.14 for the latter.

In the end, the overall biodiversity score obtained varied from -1 to 1, with -1 indicating a subsidy that exacerbates all the drivers of biodiversity loss and +1 one that reduces the pressure on all the drivers. This score was used to classify each subsidy depending on its harmfulness: subsidies with a negative overall score are classified as harmful subsidies, those with a positive overall score as positive subsidies, and those with an overall score of zero were either considered as neutral subsidies (if all drivers had an individual score of zero) or as mixed subsidies (if some of the drivers scores were non-null). Subsidies with more than 50% of weighted drivers scored as unclassifiable were considered as unclassifiable, otherwise, only the drivers with explicit scores (i.e. -1, 0 or 1) were used to calculate their overall biodiversity score and harmfulness category (Appendix C). The data processing and calculations were performed with R and the tidyverse package (Wickham et al., 2019).

We finally conducted a sensitivity analysis in order to analyze how the drivers' weights might affect the overall harmfulness category of each budget line. We listed all the 3876 possible combinations of weights by 5% increments, thanks to the iterpc, foreach and parallel R packages (Lai, 2020; Microsoft and Weston, 2020; Hvitfeldt, 2021). For each budget line, we computed the number of combinations that resulted in each harmfulness category and identified the lines with the highest variability (hereafter "swing lines"). Mathematically, swing lines were those with the lowest standard deviation (i.e. those with very similar numbers of combinations over harmfulness category) and whose

overall harmfulness category was likely to "swing" from one weights' combination to another. Swing lines were also identified by comparing the most frequent harmfulness category to the category obtained with the weights elicited by the group of experts. Quite logically, we expect these swing lines to affect the overall evaluation of the 2022 Budget act, as they will influence the total amounts of subsidies with positive, harmful, mixed or unclassifiable impacts. The amounts of the budget lines were summed up per category of harmfulness in order to provide a general overview of the 2022 Budget Act impact. The same totals were also calculated for each of the weights' combination we tested during the sensitivity analysis, which allowed us to discuss the biodiversity impact independently of the weights' customization. Budget lines were also summed up by budgetary program and sectors, in order to highlight the subsidies which, combine high levels of expenditure with very poor biodiversity scores. This information is crucial for the decision-makers as it can be used to prioritize the programs or sectors to reform.

### 2.2.4. Assessing tradeoffs and synergies between IPBES drivers

The correlation between drivers' individual scores was calculated using Spearman's rank correlation coefficient, to highlight the tradeoffs and synergies resulting from the public subsidies. We complemented this analysis by a measure of the similarity between IPBES drivers' scores, using the Goodman-Kruskal's Gamma statistic, which is particularly relevant to assess the strength of two variables' association when both variables are ordered (i.e. negative, neutral and positive). Finally, we also implemented a weighted version of Spearman's coefficient using the wCorr R package, to account for the amount of each budget line in the calculation of the correlation (Bailey and Emad, 2021).

For each pair of drivers, we also calculated the amounts corresponding to (1) positive synergies (i.e. the two drivers were scored with +1, meaning that the amounts of money spent on these budgetary lines simultaneously fought against both pressures); (2) negative synergies (both drivers scored -1, these budgetary lines maintained or exacerbated both pressures); (3) weak tradeoff (one driver scored -1, the other 0) and (4) strong trade-off (one driver scored -1 and the other 1, meaning that these lines had a positive effect for fighting against one driver, but exacerbated another). This decomposition of the budget subsidies into win and lose outcomes highlighted the potential conflicts between the environmental dimensions to be safeguarded and current budget priorities from an ecological perspective.

All the graphs presented in the following section were generated with the ggplot2, ggpubr, ggradar and the paletteer R packages (Wickham, 2016). The data and the code supporting this research are publicly available for reproducibility.

## 3. Results

### 3.1. Overall evaluation of the French 2022 Budget's impact on biodiversity

In total, 10% of the budget appropriations and earmarked taxes and 22% of tax expenditures were associated with a potential direct impact on biodiversity in 2022, i.e., 11.7% of the total national budget. Of this non-neutral spending, €27.14 billion (40%) was allocated to activities potentially harmful to biodiversity, while €24.76 billion (36%) supported subsidies aimed at reducing the intensity of biodiversity loss drivers. Unclassifiable expenditures represented €16.60 billion.

Tax expenditures subsidize harmful activities more than other budget categories: 11.8% of total tax expenditure was harmful to biodiversity i.e., €10.08 billion. Harmful subsidies come from the general budget for €15.33 billion and from earmarked taxes for €1.73 billion. Positive subsidies are mainly from budget appropriations (€14.42 billion), then followed by tax expenditures (€8.50 billion) and earmarked taxes (€3.60 billion). Unclassifiable expenditures corresponded to military equipment (€2.09 billion), urban policy, regional planning (€1.67 billion) and official development assistance funds
(€1.88 billion). Given the variety of available technologies and the absence of any environmental conditions associated to the funding, supporting to bioenergy and biogas (€1.29 billion) were considered unclassifiable.

In the sensitivity analysis, median values and 75th percentile of harmful and positive subsidies distributions were similar (respectively 22 and 24 billion euros), but the 25th percentile was lower for positive than for harmful subsidies (18 vs. 20 billion euros). The weights used for customization to the French context led to a low estimate of unclassifiable totals, and high estimates (> to 75th percentile) of harmful and positive subsidies (Fig. 2B). The swing lines (i.e., those whose overall harmfulness category was likely to “swing” from one weights combination to another) corresponded to green infrastructure and mobility, clean vehicles, decarbonization of industry, biofuels and biogas in transport, and maintenance of maritime and land routes (Appendix D Table D.2). Twenty budget lines were classified differently with the customized weights than in most combinations explored during the sensitivity analysis (Appendix D Table D.3). They were mainly in relation with offshore wind energy, commercial navigation on inland waterways, and road maintenance.

## 3.2. Zooming on harmful subsidies: which programs and sectors are concerned?

Eight programs were identified as including higher proportion of potentially harmful expenditures than others (Fig. 3): Overseas France condition of living (P123), Business development and regulation (P134), Urban planning (P135), Agriculture and food (p149), Equipment for the armed forces (P146), Energy, climate and post-mining (p174), Transport (P203), Energy public service (P345). Some of the most harmful budget lines also corresponded to high levels of subsidies, such as the reduction on the internal consumption tax on energy products in overseas France (Prog. 123).

Lines supporting rental investments (Prog. 135) and responsible for land use change and the creation of new buildings also received low scores. Program 203 (related to the funding of the public agency in charge of transport infrastructure) is by far predominant in harmful earmarked taxes, although some of the agency's actions, earmarked for certain public transport or maintenance of existing infrastructure, are also considered positive. Indeed, the harmful subsidies with the lowest scores were not necessarily the ones with the highest levels of subsidies, which might suggest that indicators based on the amount (in €) of harmful subsidies are not sufficient to illustrate the severity of the

impact.

The sectoral analysis (Fig. 4) illustrates the direct implementation of the sector-specific framework (Fig. 1) created by the working group, leading to results that are less impacted by the budget's structure. The results reflect the sustainability of public action regarding practices and activities within a sector that benefits from public funds. It makes it possible to question whether public funds accompany the ecological transition within each sector. The most represented sectors (in €) among the identified positive and harmful subsidies were “Transport” (€16.40 billion), “Housing & Building” (€9.99 billion) and “Power generation” (€6.99 billion) (Fig. 4). Some sectors cumulated high levels of both positive and harmful subsidies, such as “Transport”, “Power generation” and “Agriculture & forestry”. Specific state funds for livestock are primarily beneficial to ecosystems regarding the preservation of open landscapes and grasslands, whereas the whole livestock sector has significant negative impact (Ranganathan, 2011). Similarly, Housing & building sector showed positive score, as it covered actions such as housing renovation when urban planning and development are missions carried out at the local governments' level and so are beyond the scope. The subsidies likely to worsen multiple drives of biodiversity loss support conventional agriculture, and domestic consumption tax for natural gas and methane used for agricultural and forestry work.

The impact scores which cumulated the highest subsidies corresponded to the discretized impact score centered on 0.2 (i.e. and covering values between 0.1 and 0.3) (Fig. 4). The positive subsidies got discretized impact score centered on 0.2, when harmful subsidies distribution can be characterized as having a unimodal pattern with a peak –0.4 (€8.42 billion). This difference in the distribution of scores shows that the negative cumulative impact between drivers is more frequent, and that the expenditure considered positive is rather “slightly positive”. These “slightly positive” scores can mean that budget lines had both harmful and positive impacts on the IPBES drivers studied, and that the positive impact concerned only a few drivers of biodiversity loss. This score can be interpreted as a qualification for less harmful policy options rather than spending that supports biodiversity conservation. For instance, in the transport sector, it concerns mainly budget appropriations to support the acquisition of clean vehicles, the maintenance of the rail network, and the contribution to the equipment of local authorities for the improvement of public transport. Thus, these budget items match the swing lines obtained from the robustness test (Appendix D.).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Evaluation of the 2022 Budget act. A. Totals obtained with the weights customized to the French context by experts. B. Sensitivity analysis on weights. Red dots represent the totals obtained with weights customized to the French context and blue dots with equal weights for all drivers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** BHS by expenditure category and budgetary program (Budget act 2022 – in €M). Harmful subsidies are identified in four ways: their category (budget, earmarked taxes, and tax expenditure), the budget program to which they are attached, their amount in €, and the sum of the scores, giving the overall score of the subsidy. Each point represents a budget item (appropriation, tax expenditure or earmarked tax). The colored programs displayed in the legend meet at least one of the two following criteria: containing more than five harmful budget items or one of its harmful items is credited with more than €1 billion. These programs are: Overseas France condition of living (P123), Business development and regulation (P134), Urban planning (P135), Agriculture and food (p149), Equipment for the armed forces (P146), Energy, climate and post-mining (p174), Transport (P203), Energy public service (P345). The overall score is the result of the sum of impacts on the several drivers.</figcaption></figure>

### 3.3. Synergies and trade-offs between the IPBES drivers

The five IPBES drivers are not equally affected by the 2022 budget act. Climate change mitigation benefited the most from public subsidies (€25.2 billion and 141 budget lines), while €20.04 billion and 109 budget lines are associated with activities that could contribute to pollution, making it the most financially supported driver linked to biodiversity loss. Invasive alien species showed the highest totals of neutral subsidies, reflecting the difficulties of assessing the impacts of the national state budget on this specific driver. The working group and the expert panel considered these subsidies as primarily increasing the risk of invasions, rather than exerting a direct impact. Positive subsidies targeting land use change mainly related with the maintenance and renovation of existing infrastructures, considered by the working group as a promising lever to reduce land take.

All drivers were positively correlated, with climate change and pollution showing the strongest correlation (Appendix E Fig. E1). These results were confirmed by the Goodman-Kruskal's Gamma statistics, which also identified positive association between all drivers (Appendix E Fig. E2). Considering each budget item amount as weights in the calculation of correlation changed the nature and intensity of some relationships (for example the correlation between invasive species and direct exploitation) which reflects the most relevant pressures per euro spent (climate change, land use change, pollution). (Appendix E Fig. E3). With all approaches to correlation and association measurement, pollution cumulated the highest levels of correlation with other drivers. This suggests that pollution could be used as a simple proxy of the overall impact on biodiversity.

The overall positive correlations between drivers hide the fact that some budget items reduced a specific driver while negatively exacerbated others (tradeoffs) (Fig. 5A). Indeed, climate change cumulated the highest levels of strong tradeoffs, and especially with land use change (25% of the subsidies that were positive for climate change mitigation

were harmful regarding land use change) (Fig. 5B). The budget appropriations (€4.74 billion) that are mitigation options exacerbating land use change support rail construction, waterways opening, ports building and maintenance, onshore wind power installation, and solar farms (as opposed to rooftop PV). The tradeoffs observed between climate change mitigation and direct exploitation (€4.5 billion) are due to the collisions of wildlife with renewable energies and transportation infrastructures.

## 4. Discussion

### 4.1. Connecting the assessment methodology with the IPBES framework

We presented in this article a new approach for conducting BHS assessment from open-access data about public subsidies and applied it to the French 2022 budget act as a study case. The main originality is the use of the five drivers of IPBES to provide a multidimensional assessment of biodiversity impacts. The IPBES framework provides a more comprehensive assessment of biodiversity and gives it more prominence within the environmental assessment. In comparison with the European taxonomy, for instance, the IPBES typology only considers pressures affecting biodiversity, and not the policy actions that bring positive outcomes (e.g. circular economy and adaptation to climate change). This allowed us to highlight the existing tradeoffs and synergies in relation with biodiversity resulting from public expenses (for instance, 25% of support for climate change mitigation is detrimental regarding land use change in our study case) and the need for a truly multidimensional environmental assessment. Future work could further characterize IPBES drivers, and better distinguish between, noise and chemical pollution within the pollution driver, or the risk of collision of species and intensive exploitation of an ecosystem within the direct exploitation of natural resources driver. It could allow for a more comprehensive understanding of direct impact, as well as the incorporation of life-cycle assessment and value chains into the analysis.
<figure><img src="image_7.png" /><figcaption>Fig. 4. Distribution of subsidies over discretized categories of impact score. Subsidies are disaggregated by economic sectors. We do not distinguish between the general budget, tax expenditure and earmarked taxes. Unclassifiable subsidies are not considered in the analysis, nor budget lines classified as neutral.</figcaption></figure>

Finally, the overall biodiversity impact score we obtained by aggregating the individual drivers turned out to be a useful metric of the severity of the impact beyond the monetary unit. To calculate the overall impact score, we proposed a flexible approach, based on weights, that allows the customization of the indicator, and better reflect local biodiversity priorities.

## 4.2. Co-producing knowledge for the French application

We demonstrated the value of collaboration between practitioners and experts in building decision-support tools. Our approach builds on the FGB initiative and uses a similar budgetary scope for the 2022 assessment. However, we ended up with different amounts of positive (€24.76 vs. €29.5 billion) and especially harmful subsidies (€27.14 vs. €10.30 billion).

Broader perspective, focused on biodiversity, which led us to give overall negative scores to some carbon-neutral activities. Harmful subsidies in our analysis that the FGB noted positively (€1,65 billion) were primarily allocated to seaports and maritime fret, waterway opening, energy connection of isolated sites, decarbonation of heavy industry, and forest management. Support for research & development and new technologies resulted as neutral with our framework regarding the direct impact criteria while deemed positively by the DGT (€4.25 billion).

## 4.3. The challenges of impacts assessment

The methodology of the FGB is continuously being developed and improved (Cour des comptes, 2021), particularly to be compatible with initiatives at higher scales (European, international), which requires consistency in the methods and data used, and to reduce the share of expenditure considered as neutral. By remaining close to the methodological framework used by the administrations and in constant dialogue with them, we contributed in this consolidation effort.

We were able to estimate the impact of a small percentage of the budget of the 2022 finance act (11.7%). The impact of most subsidies remains impossible to determine. This result is consistent with other studies, and notably the FGB, and can be explained by (1) the complexity of evaluating impacts on biodiversity; (2) the structure of budgetary documents, which often lack sufficient detail on policies and

implemented actions; and (3) the methodological scope of our analysis, which focused solely on direct impacts.

The participatory design of the study, and the regular consultations with a group of experts, who collaboratively constructed the sectorial and activities references for impacts allowed us to better document certain impacts for which the literature is scarce and does not inform on the ranking of activities and practices. For example, the study was able to enhance energy ratings by conducting in-depth analyses and establishing ratios, while also examining expenditures related to housing and tourism. This suggests that with more efforts and more resources, other sectors could be further refined and improved with the help of relevant experts, targeting first the sectors and activities that are currently unclassifiable in our analysis. Regarding the structure of budget documents, we were strongly limited by the documents distributed in open access, which did not contain a precise description of the activities conducted under each budget item. Here again the participatory design of the study helped us to overcome this difficulty by providing us with the information required to refine the budget item description.

If this provided an efficient workaround for assessing the impact of the 2022 finance act, it is, however, not a long-term solution as it is not easily replicable (e.g. to other countries or administrative levels) nor generalizable (to other budget years). Our results therefore call for a change in the structure and the content of the budget documents. For reliable and more systematic environmental and biodiversity impact assessments, coherent over budget years, a consistent structure is essential to prevent changes in amounts from solely reflecting shifts in budget perimeters (Bhattacharya and Bhattacharya, 2019). Additionally, the budget structure should align more closely with on-the-ground activities rather than a cross-cutting performance approach. Annual updates require a sufficiently fine-tuned assessment to ensure that changes in practices are reflected and a significant amount of work to monitor the progress of regulations and the evolution of practices on the ground. Budget tagging can thus be coupled with a complementary bottom-up approach to trace the funds associated with the observed impacts and damages (Bhattacharya and Bhattacharya, 2019). It is necessary to recall that the common agriculture policy and common fisheries policy, as well as local government spending and state's financial holdings are beyond the scope, regardless of their direct impact.
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Synergies and tradeoffs between IPBES drivers. A. Amounts (in €M) involved in synergies and tradeoffs for each pair of IPBES drivers. Positive synergies mean that the budget items positively affected both drivers (i.e. they both received a score of 1). Negative synergies mean that the budget items negatively affected both drivers (i.e. they both received a score of -1). Weak tradeoffs means that one of the two drivers was negatively impacted (score of -1) while the other was neutral (score of 0). Strong tradeoffs means that one of the two drivers was negatively impacted (score of -1) while the other was positively impacted (score of 1). B. Disaggregated analysis of the strong tradeoffs involving climate change. The radar chart presents the percentage of positive subsidies for climate mitigation that are harmful regarding each of the other four drivers.</figcaption></figure>
### 4.4. From impacts assessments to alignment of public investments on a pathway towards sustainability

Environmental impact assessment is often considered to be the first step to an (in-depth) reform of public finance, whose objective is to redirect budgetary flows towards investments to achieve a sustainable future as outlined in the "Nature 2050" vision (Convention on Biological Diversity, 2022).

While OCDE Guidelines suggest identifying harmful subsidies as measures that, if discontinued, would improve environmental quality (Secretariat of the Convention on Biological Diversity, 2014; French Government, 2021) and others recommend considering the feasibility of the reform, such as social acceptability and economic impact (Withana et al., 2012), we propose defining harmful expenditure as not aligned with a green transition path. In this paper, we defend an alternative approach for the assessment of BHS, which relies on a relative ranking of activities and practices within each sector based on expert knowledge. In practice, BHS identification is used to evaluate whether the "business as usual" (BAU) scenario is harmful to biodiversity.

This facilitates the identification of potential transformation pathways towards sustainability, specific to each sector. We developed an operational tool to help prioritize economic sectors for action in the implementation of a "zero BHS strategy" that would affect public investment in transport, housing, and energy production. Indeed, we provide a first approach of what a phase-out of harmful subsidies looks like, and more broadly, of the potential transition risks such reforms may generate (Hadji-Lazaro et al., 2024). Reforming public finance towards more sustainable investments will also require, in addition to impact assessment initiatives (i.e. impact "labeling"), to quantify the costs associated with scaling up and scaling out practices and activities favorable to biodiversity and the environment in general (i.e., implementing a strategic vision of investment and objectives).

This is crucial as biodiversity preservation can no longer be limited to protected areas (Campbell et al., 2010) but must be mainstreamed into all economic sectors and all public policies, as suggested by the post-2020 global biodiversity framework.

## 5. Conclusion

The method developed in this article is part of an ongoing effort that aims to develop decision-making tool through a collaborative approach. This initiative leads to the identification of much higher adverse expenditure on biodiversity impacts than the initial FGB's results. In addition, our method offers looking at the impact of expenditure in two ways: its amount (1) and the score awarded about the number of IPBES drivers affected – either positively or negatively – by it (2). An adaptation of the method to the French case (higher weight for some pressures) highlights that it needs to be adjusted to the context of the countries that might use it for it to be effective. By identifying, within each sector, the most positive practices and those that, from a perspective of coherence, should no longer receive public support in the sense of the Aichi targets, this first study provides a perspective of what would be a transition towards a more biodiversity-friendly economy.

### CRediT authorship contribution statement

**Morgane Gonon:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Améline Vallet:** Writing – original draft, Visualization, Software, Resources, Methodology, Formal analysis.
**Vincent Deschamps:** Methodology, Data curation, Conceptualization.
**Amélie Le Mieux:** Project administration, Methodology, Funding acquisition, Data curation, Conceptualization.
**Aurélien Oosterlinck:** Project administration, Methodology, Funding acquisition, Data curation.
**Hélène Soubelet:** Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Louise Dupuis:** Validation, Methodology.
**Harold Levrel:** Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Morgane Gonon reports financial support and administrative support were provided by French Biodiversity Office. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This work has been initiated and conducted within the working group "Biodiversity and economics" of the Observatoire national de la biodiversité (ONB), co lead with the Ecological accounting chair (CIRED).

The authors would also like to thank Vincent Marcus (CGDD), Robert Benda (DGT), Eléonore Cécillon (DGT), Théo Alves Da costa (Data 4 good), Mathilde Salin and Clément Surun (CIRED) for their valuable assistance and Feedbacks.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolecon.2025.108661.

### Data availability

Data will be made available on request.

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