Box 15: Non-monetary value of health impacts

The Global Burden of Disease (GBD) defines three non-monetary values of health effects:

* Years of life lost (YLL)

The YLL measures the burden of premature mortality. For an individual, it is calculated by comparing the age at which a person dies and the standard life expectancy. For example, if a person in a population dies at the age of 50 and the standard life expectancy is 75, the years of life lost are 25. For a homogeneous population we have:

$$YLL = \sum_{i=1}^{n} YLL_{i} = \sum_{i=1}^{n} (L_{i} - L^{*})$$

where $L_i$ is the age of the deceased, $L^*$ is the standard life expectancy of the population and $n$ is the number of deaths.

* Years lost/lived with disability (YLD)

The YLD measures the burden of living with a health condition that causes disability or reduced quality of life but does not result in death. For an individual, it is calculated as the product of the disability weight (DW) and the average duration of the condition. The disability weight is a value between 0 (for perfect health) and 1 (equivalent to death) that reflects the severity of the health condition. For a population and a given disease, the YLD is calculated as the sum of individual YLD values, which is the number of cases of the disease multiplied by the disability weight of the disease and the average duration of the disease.

* Disability-adjusted life years (DALY)

The DALY is the sum of YLL and YLD. It provides a single, comprehensive measure of health loss because it is a combined measure of both premature mortality and non-fatal health loss.

Another interesting study is the World Bank report, which uses data from the Global Burden of Disease<fnref n="120" /> 2019, but focuses only on particulate matter PM₂.₅, both outdoor (ambient air pollution) and indoor (household air pollution, i.e., the use of fuels for cooking and heating). Global pollution exposure to ambient PM₂.₅ was 43 µg/m³ in 2019 compared to the WHO recommended level of 5 µg/m³. World Bank (2022, page xiii) distinguishes two types of costs:

1. Death and premature mortality;

2. Morbidity due to illness and disability.

For premature deaths, World Bank (2022) reported that 6.45 million premature deaths in 2019 were attributed to exposure to PM₂.₅ pollution. PM₂.₅ was responsible for about 8.1% of global mortality. Globally, 64.2% of all PM₂.₅ deaths were due to ambient air pollution (outdoor pollution), while 35.8% were due to household air pollution from the use of solid fuels (indoor pollution). About 95% of these deaths occurred in low- and middle-income countries, with 27.7% in China, 24.6% in India, 3.6% in Pakistan, 3.1% in Nigeria, and 2.9% in Indonesia. In terms of morbidity, World Bank (2022) estimated that air pollution has caused 21 million years lived with disability (YLD) and 93 billion days lived with illness (DLI) in 2019. The YLD figures are calculated by the Global Burden

<footnote n="120">¹²⁰This is the same data used by Fuller et al. (2022) (see Footnote 117 on page 130).</footnote>
of Disease (GBD) using the methodology described in Box 15, while the DLI figures are estimated by the World Bank using this formula:

$$DLI = \frac{365 \times YLD}{DW}$$

where DW is the disability weight. The breakdown of the morbidity impact is shown in Table 35. Type 2 diabetes accounts for 33% of days lived with illness, COPD (chronic obstructive pulmonary disease) for 24%, stroke for 11%, cataracts for 12%, ischaemic heart disease (IHD) for 18%, and low respiratory infections (LRI) for 1%.

<table><caption>Table 35: Global burden of morbidity from PM<sub>2.5</sub> exposure in 2019</caption><thead><tr><th rowspan="2">Disease</th><th colspan="2">YLD</th><th colspan="2">DLI</th><th>DW</th></tr><tr><th>(in mn)</th><th>(in %)</th><th>(in bn)</th><th>(in %)</th><th>(in %)</th></tr></thead><tbody><tr><td>Type 2 diabetes</td><td>6.653</td><td>31.34</td><td>30.252</td><td>32.52</td><td>8.03</td></tr><tr><td>COPD</td><td>5.831</td><td>27.47</td><td>22.780</td><td>24.49</td><td>9.34</td></tr><tr><td>Stroke</td><td>5.028</td><td>23.68</td><td>10.497</td><td>11.29</td><td>17.48</td></tr><tr><td>Cataracts</td><td>2.143</td><td>10.10</td><td>11.370</td><td>12.22</td><td>6.88</td></tr><tr><td>IHD</td><td>1.248</td><td>5.88</td><td>16.654</td><td>17.90</td><td>2.74</td></tr><tr><td>LRI</td><td>0.198</td><td>0.93</td><td>1.214</td><td>1.31</td><td>5.95</td></tr><tr><td>Lung cancer</td><td>0.099</td><td>0.47</td><td>0.214</td><td>0.23</td><td>16.92</td></tr><tr><td>Neonatal disorders</td><td>0.019</td><td>0.09</td><td>0.035</td><td>0.04</td><td>20.32</td></tr><tr><td>Other</td><td>0.008</td><td>0.04</td><td>0.000</td><td>0.00</td><td></td></tr></tbody><tfoot><tr><td>Total</td><td>21.229</td><td>100.00</td><td>93.016</td><td>100.00</td><td>8.33</td></tr></tfoot></table>

Source: World Bank (2022, Table 3.6, page 22) & Author's calculations.

Understanding the health effects of pollution remains a complex and multifaceted challenge for researchers and policy makers. While significant progress has been made in certain areas, such as the study of the effects of particulate matter, many other forms of pollution remain poorly understood. PM<sub>2.5</sub> has been the subject of extensive scientific investigation due to its well-documented and profound effects on human health. A substantial body of literature, including the seminal work of Pope and Dockery (2006), has elucidated the biological mechanisms by which PM<sub>2.5</sub> contributes to a range of adverse health outcomes, such as cardiovascular and respiratory disease. Subsequent studies, such as those by Lelieveld et al (2015) and Thangavel et al. (2022), have expanded our understanding, highlighting the risk factors associated with long-term exposure to this pollutant and its contribution to global mortality. Despite this progress, our knowledge of the health effects of other forms of pollution is less comprehensive, leaving significant gaps in our understanding. Research conducted by the Lancet Commission has highlighted areas known as Zone 2 and Zone 3, which include emerging and less studied pollutants. These zones represent critical frontiers in pollution research that require further exploration and scientific investigation. For example, soil contamination by heavy metals such as lead and mercury has long been recognized as a public health concern, but its broader impacts on ecosystems and human health need to be better understood. Similarly, the effects of black carbon, a component of soot produced by incomplete combustion of fossil fuels and biomass, are increasingly understood to have both direct and indirect health effects. Another prominent example is glyphosate, a widely used herbicide that has generated considerable public and scientific debate. While some studies suggest potential health risks (Van Bruggen et al., 2018), such as links to cancer or endocrine disruption, others argue that these findings remain inconclusive due to methodological limitations and conflicting evidence. This controversy underscores the broader challenge of assessing the health effects of complex chemical exposures,
particularly in the absence of robust long-term studies. Moreover, the health effects of pollution are not limited to morbidity and mortality. Impacts on other dimensions of health must also be considered, such as cognitive and physical development, mental health, skin conditions, and more. Addressing these broader dimensions is critical to developing a comprehensive understanding of how pollution affects human well-being.

**Global economic costs** Estimating the economic costs of pollution poses significant challenges due to complex methodological issues and considerable data uncertainty. The time horizon is particularly important, as the inclusion of long-term costs yields very different results than the analysis of short-term impacts. Consequently, the following results should be interpreted with considerable caution. When examining such estimates, orders of magnitude and relative rankings tend to be more reliable indicators than absolute figures. This explains the considerable variation in results among the scientific research publications on this topic. In the following, we focus mainly on two reports: Quantifying the Economic Costs of Air Pollution from Fossil Fuels (Myllyvirta, 2020) and The Global Health Cost of PM<sub>2.5</sub> Air Pollution: A Case for Action Beyond 2021 (World Bank, 2022). But there are other reports that can be used. For example, the Lancet Commission estimated that welfare losses due to pollution are more than $4.6 trillion per year, or 6.2% of global economic output, and that 81.5% of these economic losses are due to ambient air pollution and household air pollution, with the remainder explained by lead exposure (9.8%) and water pollution (8.7%) (Landrigan et al., 2018, Table 5, page 487). These figures already give an idea of the global economic cost of pollution.

Myllyvirta (2020) proposes a simple method for estimating the economic cost of pollution. He divides the impacts of pollution into various health and economic outcomes. A common health outcome of air pollution is premature death, but pollution also causes other health issues such as childhood asthma, preterm births, illnesses, or disabilities, as well as economic outcomes like work absences or productivity losses. The total cost is calculated as the sum of the economic costs of these different health and economic outcomes:

$$C = \sum_{j=1}^{m} C_j = \sum_{j=1}^{m} n_j c_j$$

where m is the number of health or economic outcomes, $n_j$ is the number of cases of the jth outcome, and $c_j$ is the average unit cost of the jth outcome. Consider the example of a hypothetical country. If the average unit cost of a lost workday is $150 and pollution is responsible for 2.5 million lost workdays, the pollution-related cost of lost workdays is $2.5 \times 10^6 \times 150 = \$375 mn$. If the average unit cost of a preterm birth (including initial hospitalization and long-term health effects) is $30 000 and pollution is responsible for 2 000 preterm births, the pollution-related cost of preterm birth is $30 \times 10^3 \times 2 000 = \$60 mn$. The total cost of these two health and economic outcomes is then $435 million. Using published concentration-response functions (to estimate $n_j$) and unit costs by country (to assess $n_j$), and considering six health and economic outcomes, Myllyvirta (2020) estimated that air pollution from fossil fuels resulted in a global economic cost equivalent to 3.3% of GDP in 2018:

> "The economic costs of air pollution from fossil fuels are estimated at $2.9 trillion in 2018, or 3.3% of global GDP [...] An estimated 4.5 million people died in 2018 due to exposure to air pollution from fossil fuels. On average, each death was associated with a loss of 19 years of life. [...] Fossil fuel PM<sub>2.5</sub> pollution was responsible for 1.8 billion days of work absence, 4 million new cases of child asthma and 2 million preterm births, among other health impacts that affect healthcare costs, economic productivity and welfare." (Myllyvirta, 2020, page 2).
The distribution of this total cost is as follows: 84% is attributed to adult deaths, followed by disability due to chronic diseases (7%), sick leave (3.5%), preterm births (3.15%), child deaths (1.75%), and asthma (0.6%). The global figure of 3.3% masks disparities between countries, as shown in Table 36. China has the highest costs (6.6% of GDP), followed by Bulgaria and Hungary (6.0% of GDP). On the contrary, Brazil and Spain have low costs, less than 2% of their GDP. However, these figures do not reflect the full reality. In fact, by measuring costs by GDP, the analysis favors high-income countries because of their high GDP. Myllyvirta (2020) provided the economic cost of air pollution per capita. In this case, the country with the highest costs is Luxembourg ($2600 per capita), followed by the US ($1900 per capita), Switzerland ($1900 per capita), Austria ($1700 per capita) and Germany ($1700 per capita). Interestingly, the cost in France is $800 per capita.

<table><caption>Table 36: Economic costs of air pollution from fossil fuels (% of GDP, 2018)</caption><thead><tr><th>Country</th><th>Cost</th><th>Country</th><th>Cost</th></tr></thead><tbody><tr><td>China</td><td>6.6%</td><td>Bulgaria</td><td>6.0%</td></tr><tr><td>Hungary</td><td>6.0%</td><td>Ukraine</td><td>5.8%</td></tr><tr><td>Serbia</td><td>5.8%</td><td>Belarus</td><td>5.4%</td></tr><tr><td>India</td><td>5.4%</td><td>Romania</td><td>5.3%</td></tr><tr><td>Bangladesh</td><td>5.1%</td><td>Moldova</td><td>5.0%</td></tr><tr><td>Poland</td><td>4.9%</td><td>Russia</td><td>4.1%</td></tr><tr><td>Germany</td><td>3.5%</td><td>South Korea</td><td>3.4%</td></tr><tr><td>USA</td><td>3.0%</td><td>Japan</td><td>2.5%</td></tr><tr><td>UK</td><td>2.3%</td><td>France</td><td>2.0%</td></tr><tr><td>Spain</td><td>1.7%</td><td>Brazil</td><td>0.8%</td></tr></tbody></table>

Source: Myllyvirta (2020, page 6) & Centre for Research on Energy and Clean Air (CREA).

The report of the World Bank focuses only on the particulate matter PM<sub>2.5</sub>, and distinguishes two types of cost: (1) death and premature mortality and (2) morbidity due to illness and disability. The economic cost of premature deaths is estimated using the approach based on the value of statistical life (VSL), while the economic cost of morbidity is estimated by the years lost with disability (YLD) (see Box 16). The global economic cost of mortality and morbidity was estimated at $8.1 trillion, or 6.1% of global GDP in 2019, with the following breakdown: 85% from premature mortality and 15% from morbidity. Low- and middle-income countries bear a disproportionate share of these costs, accounting for more than 90% of the global health burden of PM<sub>2.5</sub> pollution. The most affected regions are South Asia, and East Asia and Pacific, with costs equivalent to 10.3% and 9.3% of GDP, respectively (Table 37). Table 38 lists the top three countries in each region ranked by percentage of GDP. For example, China ranks first in East Asia & Pacific (EAP) with an economic cost equivalent to 12.9% of GDP. In Europe, Serbia is the leading country with a cost of 18.9% of GDP. Table 39 provides the top 15 countries in terms of absolute cost in billions of dollars. For each country, we also indicate the breakdown between outdoor and indoor pollution, and the proportion attributed to mortality and morbidity. For instance, China has a total cost of $3029 billion, of which 79% arises from outdoor pollution, and 12% is attributed to morbidity.
<table><caption>Table 37: Annual cost of health damages from PM<sub>2.5</sub> by region (% of GDP, 2019)</caption><thead><tr><th>Region</th><th>Outdoor</th><th>Indoor</th><th>Mortality</th><th>Morbidity</th><th>Total</th></tr></thead><tbody><tr><td>East Asia and Pacific (EAP)</td><td>7.3%</td><td>2.0%</td><td>8.1%</td><td>1.2%</td><td>9.3%</td></tr><tr><td>Europe and Central Asia (ECA)</td><td>4.4%</td><td>0.2%</td><td>4.0%</td><td>0.6%</td><td>4.6%</td></tr><tr><td>Latin America and Caribbean (LAC)</td><td>2.7%</td><td>0.7%</td><td>2.9%</td><td>0.5%</td><td>3.4%</td></tr><tr><td>Middle East and North Africa (MNA)</td><td>5.5%</td><td>0.0%</td><td>4.7%</td><td>0.8%</td><td>5.5%</td></tr><tr><td>North America (NA)</td><td>1.7%</td><td>0.0%</td><td>1.3%</td><td>0.4%</td><td>1.7%</td></tr><tr><td>South Asia (SA)</td><td>5.9%</td><td>4.3%</td><td>8.3%</td><td>2.0%</td><td>10.3%</td></tr><tr><td>Sub-Saharan Africa (SSA)</td><td>3.6%</td><td>2.4%</td><td>5.2%</td><td>0.8%</td><td>6.0%</td></tr><tr><td>Low-income countries</td><td>1.3%</td><td>4.6%</td><td>5.0%</td><td>0.9%</td><td>5.9%</td></tr><tr><td>Lower-middle-income countries</td><td>5.4%</td><td>3.6%</td><td>7.5%</td><td>1.5%</td><td>9.0%</td></tr><tr><td>Upper-middle-income countries</td><td>7.1%</td><td>1.8%</td><td>7.8%</td><td>1.1%</td><td>8.9%</td></tr><tr><td>High-income non OECD countries</td><td>4.3%</td><td>0.2%</td><td>4.0%</td><td>0.5%</td><td>4.5%</td></tr><tr><td>High-income OECD</td><td>2.8%</td><td>0.0%</td><td>2.3%</td><td>0.5%</td><td>2.8%</td></tr></tbody></table>

Source: World Bank (2022, Figures 3.13 & 3.14, pages 20 & 21) & Author's calculations.

<table><caption>Table 38: Annual cost of health damages from PM<sub>2.5</sub> by country (% of GDP, 2019)</caption><thead><tr><th>Region</th><th>Top 1 country</th><th>Top 2 country</th><th>Top 3 country</th></tr></thead><tbody><tr><td>EAP</td><td>China</td><td>Papua New Guinea</td><td>Myanmar</td><td>11.4%</td></tr><tr><td>ECA</td><td>Serbia</td><td>Bulgaria</td><td>North Macedonia</td><td>15.9%</td></tr><tr><td>LAC</td><td>Barbados</td><td>Haiti</td><td>Trinidad/Tobago</td><td>7.8%</td></tr><tr><td>MNA</td><td>Egypt</td><td>Morocco</td><td>Tunisia</td><td>6.5%</td></tr><tr><td>NA</td><td>USA</td><td>Canada</td><td></td><td></td></tr><tr><td>SA</td><td>India</td><td>Nepal</td><td>Pakistan</td><td>8.9%</td></tr><tr><td>SSA</td><td>Burkina Faso</td><td>Mali</td><td>Central African Republic</td><td>8.7%</td></tr></tbody></table>

Source: World Bank (2022, Table 3.5, page 21).

<table><caption>Table 39: Annual cost of health damages from PM<sub>2.5</sub> by country in 2019 (Top 15)</caption><thead><tr><th>Country</th><th>Total (in $ bn)</th><th>Total (in % of GDP)</th><th>Outdoor (in %)</th><th>Indoor (in %)</th><th>Mortality (in %)</th><th>Morbidity (in %)</th></tr></thead><tbody><tr><td>China</td><td>3 029</td><td>12.9</td><td>79</td><td>21</td><td>88</td><td>12</td></tr><tr><td>India</td><td>1 022</td><td>10.6</td><td>60</td><td>40</td><td>81</td><td>19</td></tr><tr><td>United States</td><td>373</td><td>1.7</td><td>100</td><td>0</td><td>78</td><td>22</td></tr><tr><td>Russia</td><td>241</td><td>5.7</td><td>97</td><td>3</td><td>91</td><td>9</td></tr><tr><td>Indonesia</td><td>220</td><td>6.6</td><td>56</td><td>44</td><td>85</td><td>15</td></tr><tr><td>Japan</td><td>210</td><td>3.8</td><td>100</td><td>0</td><td>82</td><td>18</td></tr><tr><td>Germany</td><td>178</td><td>3.8</td><td>100</td><td>0</td><td>81</td><td>19</td></tr><tr><td>Turkey</td><td>134</td><td>5.8</td><td>99</td><td>1</td><td>85</td><td>15</td></tr><tr><td>Italy</td><td>132</td><td>5.0</td><td>99</td><td>1</td><td>83</td><td>17</td></tr><tr><td>Poland</td><td>127</td><td>9.8</td><td>91</td><td>9</td><td>86</td><td>14</td></tr><tr><td>South Korea</td><td>114</td><td>5.1</td><td>100</td><td>0</td><td>83</td><td>17</td></tr><tr><td>Egypt</td><td>105</td><td>8.6</td><td>100</td><td>0</td><td>87</td><td>13</td></tr><tr><td>Mexico</td><td>104</td><td>4.0</td><td>78</td><td>22</td><td>84</td><td>16</td></tr><tr><td>Saudi Arabia</td><td>96</td><td>5.7</td><td>100</td><td>0</td><td>89</td><td>11</td></tr><tr><td>Pakistan</td><td>94</td><td>8.9</td><td>48</td><td>52</td><td>82</td><td>18</td></tr></tbody></table>

Source: World Bank (2022, Table A.3, page 40) & Author's calculations.
Box 16: A basic economic model of pollution costs

Dechezleprêtre et al. (2019) consider a classical output function that uses capital and labor as factors of production, but also incorporates pollution as an additional variable: $Y = F(K, L, P)$, where Y is economic output, K is capital, L is labor, and P is pollution. The labor input L can be expressed as $L = N \cdot A_L \cdot T_L$ where N is the workforce size (or the population), $A_L$ is labor productivity, $T_L = \tau - \zeta$ is the time individuals spend working, which is the difference between the total endowment of labor time $\tau$ and sick time $\zeta$. It is assumed that all three variables ($N$, $A_L$, and $\zeta$) depend on the pollution P. Consequently, the output function can be rewritten as:

$$Y = F(K, N(P) A_L(P) (\tau - \zeta(P)), P)$$

From this, we deduce the derivative of the logarithm of output with respect to pollution:

$$\frac{d \ln Y}{dP} = \frac{\partial \ln Y}{\partial \ln L} \frac{\partial \ln L}{\partial P} + \frac{\partial \ln Y}{\partial P} = \epsilon_L \frac{\partial \ln L}{\partial P} + \frac{\partial \ln Y}{\partial P}$$

where $\epsilon_L \ge 0$ is the elasticity of output with respect to labor. Breaking this down further:

$$\frac{d \ln Y}{dP} = \epsilon_L \left( \frac{\partial \ln N}{\partial P} + \frac{\partial \ln A_L}{\partial P} + \frac{\partial \ln (\tau - \zeta(P))}{\partial P} \right) + \frac{\partial \ln Y}{\partial P}$$

Expanding the third term:

$$\frac{\partial \ln (\tau - \zeta(P))}{\partial P} = -\frac{1}{\tau - \zeta(P)} \frac{\partial \zeta(P)}{\partial P} = -\frac{\zeta(P)}{\tau - \zeta(P)} \frac{\partial \ln \zeta(P)}{\partial P}$$

Substituting this into the equation and introducing the notation $\theta = \frac{\zeta}{\tau - \zeta}$, which represents the ratio of sick time to effective labor time, we get:

$$\frac{d \ln Y}{dP} = \epsilon_L \underbrace{\left( \frac{\partial \ln N}{\partial P} + \frac{\partial \ln A_L}{\partial P} - \theta \frac{\partial \ln \zeta(P)}{\partial P} \right)}_{\text{Pollution-related labor impact}} + \frac{\partial \ln Y}{\partial P}$$

This equation can be expressed in the following compact form:

$$\beta_P = \epsilon_L \beta_{L,P} + \beta_{L,P}$$

Therefore, pollution has an indirect impact on output through the labor factor, which operates through three dimensions:

1. Pollution increases mortality, reducing the size of labor force: $\frac{\partial \ln N}{\partial P} < 0$.

2. Pollution increases morbidity, decreasing labor productivity: $\frac{\partial \ln A_L}{\partial P} < 0$.

3. Pollution increases morbidity, leading to more work absences: $\frac{\partial \ln \zeta(P)}{\partial P} > 0$.
Box 16: A basic economic model of pollution costs (Continued from previous page)

The labor-related impact of pollution also depends on the labor-output elasticity. A higher value of $\epsilon$ results in a higher cost of pollution, especially in labor-intensive industries (Graff Zivin and Neidell, 2013). Consequently, the labor-related impact of pollution is always negative: $\epsilon_L \beta_{L,P} \le 0$. In contrast, the direct impact of pollution on output can be either positive or negative: $\beta_{L,P} = \frac{\partial \ln Y}{\partial P} \le 0$. When pollution levels are low, increasing pollution can have a positive impact because GDP is boosted by an increase in energy supply. However, when pollution levels are very high, increasing pollution has a negative impact due to the negative externalities (e.g., infrastructure damage, reduced agricultural yields, water stress) that affect the production system. Therefore, the direct relationship follows a bell curve: $\beta_{L,P}$ is initially positive, but becomes negative as pollution increases. The aggregation of these two effects is not straightforward. While $\beta_P$ may be positive when pollution levels are low, it is certain to become negative when pollution levels are high.

**Economic costs in Europe** Studying the economic costs of air pollution in Europe is particularly valuable because of the availability of more reliable and comprehensive data. This allows researchers to consider additional pollutants beyond PM$_{2.5}$, such as heavy metals (e.g., arsenic) and organic pollutants (e.g., benzene). They can also examine trends in economic costs over time and assess the impact of policy regulations on air quality and associated costs. For instance, Dechezleprêtre et al. (2019) estimated that a 1 µg/m$^3$ increase in PM$_{2.5}$ concentration leads to a 0.8% reduction in real GDP within the same year. Notably, 95% of this impact is attributed to a decline in output per worker, which can result from higher absenteeism or reduced labor productivity. Since PM$_{2.5}$ pollution in Europe has decreased by 0.2µg/m$^3$ per year since 2000, reducing air pollution could increase real GDP by 1.6% per decade. Thus, this study shows that reducing air pollution generates economic growth through improved labor productivity.

More recently, Mejino-López and Oliu-Barton (2024) studied the allocation of EU funds among member states and the cost-effectiveness of European air pollution policies. Using the results of Dechezleprêtre et al. (2019), they estimated the costs of particulate matter PM$_{2.5}$ as follows$^{121}$:

$$C = \beta_P \cdot ([C] - AQG)^+ \cdot GDP = 0.8\% \cdot \max([C]_{PM_{2.5}} - 5 \mu g/m^3, 0) \cdot GDP$$

On a global basis, they found that costs have declined since 2014, but remain at high levels:

> "Despite significant progress, air pollution still causes €600 billion in losses each year in the European Union — equal to 4% of its annual GDP. These costs stem from productivity losses such as increased absenteeism, the reduction of in-job productivity and harm to ecosystems. Air pollution costs are disproportionately high in eastern Europe and Italy, where losses are projected to remain above 6% of GDP until 2030. The EU's 10% most-polluted regions suffer 25% of the burden of mortality attributable to air pollution." (Mejino-López and Oliu-Barton, 2024, page 1).

The large differences between countries are shown in Figure 54. For each country, Mejino-López and Oliu-Barton (2024) calculated the cost of air pollution for three seven-year periods$^{122}$: 2014–2020,

<footnote>$^{121}$Since we have $\beta_P = \Delta \ln Y/\Delta P$, we deduce that $\Delta Y = \beta_P \cdot \Delta P \cdot Y$.</footnote>

<footnote>$^{122}$For the 2024–2030 period, they used World Bank projections of GDP and extrapolated PM$_{2.5}$ concentrations based on linear regressions.</footnote>
<figure><img src="image_89.png" /><figcaption>Figure 54: Estimated cost of air pollution by country (in % of GDP)</figcaption></figure>

Source: Mejino-López and Oliu-Barton (2024, Figure 10, page 17).
2021–2027, and 2024–2030. Some countries, such as Bulgaria, Romania, Cyprus, Belgium, and the Netherlands have dramatically reduced air pollution when considering the gap 2014–2030. However, there are still some air pollution hotspots in Europe. Considering the 234 EU regions, Mejino-López and Oliu-Barton (2024) identified 16 hotspots: 5 in Bulgaria, 4 in Poland, 2 in Romania, and 1 each in Czechia, Croatia, and Hungary. These hotspots represent 7% of the EU population, but concentrate 14% of air pollution mortality.

EEA (2024a) uses a different methodology to estimate the economic costs of pollution. The analysis focuses on industrial facilities in Europe<fnref n="123" /> and considers the following groups of pollutants:

* Main air pollutants: particulate matter (PM<sub>2.5</sub>, PM<sub>10</sub>), sulfur dioxide (SO<sub>2</sub>), ammonia (NH<sub>3</sub>), nitrogen oxides (NO<sub>x</sub>), and non-methane volatile organic compounds (NM VOC);

* Greenhouse gases (GHG): carbon dioxide (CO<sub>2</sub>), methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O);

* Heavy metals: arsenic (As), cadmium (Cd), chromium VI (Cr), lead (Pb), mercury (Hg), and nickel (Ni);

* Organic pollutants: 1,3 butadiene, benzene, formaldehyde, polycyclic aromatic hydrocarbons, dioxins and furans.

The study examines only air pollution and estimates the external costs of these facilities, taking into account both health effects (mortality and morbidity) and non-health effects (damage to buildings, crops, and forests). Health effects for the main pollutants are quantified using concentration-response functions and unit costs based on the value of a statistical life (VSL) and the value of life year (VOLY). For greenhouse gases, negative externalities are assessed by calculating the marginal abatement cost of carbon emissions required to meet the Paris Agreement targets. The results, broken down by country, are presented in Table 40. EEA (2024a) estimates that the economic costs associated with the value of a statistical life and the value of a life year are 2.23% and 1.39% of GDP, respectively, for the EU-27. The most affected countries are Bulgaria, Poland, Greece, Romania and Estonia, while the top five Member States with facilities contributing the highest external costs are Germany, Poland, Italy, France and Spain. Table 41 shows the amount of negative externalities in millions of euro. In 2012, the costs for the EU-27 were 326.5 and 536.7 billion euros, depending on the approach (VOLY vs. VSL). In 2021, these figures become 219.4 and 352.7 billion euros, representing a decrease of 32.8% and 34.3% of total external costs, respectively. During the period 2012-2021, the cumulative externalities were 2.7 and 4.3 trillion euros, respectively.

We see that heavy metals and organic pollutants are responsible for less than 5% of the economic costs, and these costs are mainly concentrated on lead, followed by cadmium and mercury. For the group of major air pollutants, sulfur dioxide and nitrogen oxides are the main contributors. EEA (2024a) also finds that 129 facilities (out of about 9400 facilities) are responsible for about 50% of the total damage caused by air emissions:

> "It is worth noting that half (25) of the 50 most polluting facilities in 2021 were thermal power stations, with most of them located in Germany (nine) and Poland (six). Twenty-three of these 25 plants burn lignite or hard coal as their main fuel." (EEA, 2024a, page 10).

<footnote n="123">¹²³The study is based on the European Pollutant Release and Transfer Register (E-PRTR) dataset, a comprehensive database established under European Union Regulation 166/2006 to provide public access to environmental data from large industrial facilities across Europe. E-PRTR contains data reported annually by more than 30 000 industrial facilities in the EU, Iceland, Liechtenstein, Norway, Serbia, Switzerland and the United Kingdom from 65 economic activities and covers 91 pollutants, including emissions to air, water and land as well as waste transfers.</footnote>
<table><caption>Table 40: External costs of air pollution (2021)</caption><thead><tr><th rowspan="2">Country</th><th colspan="2">Relative cost</th><th colspan="2">Breakdown of external costs</th></tr><tr><th>VOLY/GDP</th><th>VSL/GDP</th><th>VOLY</th><th>VSL</th></tr></thead><tbody><tr><td>Austria</td><td>0.71%</td><td>1.07%</td><td>1.43%</td><td>1.34%</td></tr><tr><td>Belgium</td><td>1.59%</td><td>2.68%</td><td>3.96%</td><td>4.16%</td></tr><tr><td>Bulgaria</td><td>7.87%</td><td>16.11%</td><td>2.77%</td><td>3.53%</td></tr><tr><td>Croatia</td><td>1.72%</td><td>2.89%</td><td>0.50%</td><td>0.52%</td></tr><tr><td>Cyprus</td><td>2.89%</td><td>3.02%</td><td>0.34%</td><td>0.22%</td></tr><tr><td>Denmark</td><td>0.47%</td><td>0.64%</td><td>0.78%</td><td>0.66%</td></tr><tr><td>Estonia</td><td>3.35%</td><td>3.75%</td><td>0.52%</td><td>0.36%</td></tr><tr><td>Finland</td><td>2.12%</td><td>2.36%</td><td>2.63%</td><td>1.82%</td></tr><tr><td>France</td><td>0.68%</td><td>1.07%</td><td>8.39%</td><td>8.25%</td></tr><tr><td>Germany</td><td>1.54%</td><td>2.63%</td><td>27.41%</td><td>29.19%</td></tr><tr><td>Greece</td><td>2.93%</td><td>4.76%</td><td>2.63%</td><td>2.67%</td></tr><tr><td>Hungary</td><td>2.21%</td><td>4.27%</td><td>1.69%</td><td>2.03%</td></tr><tr><td>Ireland</td><td>0.50%</td><td>0.62%</td><td>1.06%</td><td>0.82%</td></tr><tr><td>Italy</td><td>1.06%</td><td>1.79%</td><td>9.35%</td><td>9.87%</td></tr><tr><td>Latvia</td><td>0.69%</td><td>1.05%</td><td>0.11%</td><td>0.11%</td></tr><tr><td>Luxembourg</td><td>0.35%</td><td>0.65%</td><td>0.13%</td><td>0.15%</td></tr><tr><td>Netherlands</td><td>1.37%</td><td>1.91%</td><td>5.81%</td><td>5.03%</td></tr><tr><td>Poland</td><td>5.63%</td><td>8.68%</td><td>16.06%</td><td>15.43%</td></tr><tr><td>Portugal</td><td>1.67%</td><td>2.18%</td><td>1.77%</td><td>1.45%</td></tr><tr><td>Romania</td><td>2.47%</td><td>4.55%</td><td>2.96%</td><td>3.38%</td></tr><tr><td>Slovenia</td><td>1.76%</td><td>2.99%</td><td>0.46%</td><td>0.48%</td></tr><tr><td>Spain</td><td>1.07%</td><td>1.77%</td><td>6.40%</td><td>6.58%</td></tr><tr><td>Sweden</td><td>1.07%</td><td>1.18%</td><td>2.86%</td><td>1.95%</td></tr><tr><td>EU-27</td><td>1.39%</td><td>2.23%</td><td></td><td></td></tr></tbody></table>

Source: EEA (2024b, Figure 4.2, Tables 4.2 & 4.3, pages 31–34), Author's calculations & icons taken from

https://icons8.comicons.
<table><caption>Table 41: External costs of air pollution in € mn (2021)</caption><thead><tr><th>Pollutants</th><th>2012</th><th>2021</th><th>Cumulative</th><th>2021 Breakdown</th></tr></thead><tbody><tr><td>Main air (VOLY)</td><td>119 042</td><td>59 728</td><td>834 066</td><td>SO<sub>2</sub>: 45.3%, NO<sub>x</sub>: 41.1%, NH<sub>3</sub>: 7.5%, PM<sub>10</sub>: 5.0%, NM VOC: 1.2%</td></tr><tr><td>Main air (VSL)</td><td>329 152</td><td>193 056</td><td>2 426 585</td><td>SO<sub>2</sub>: 45.7%, NO<sub>x</sub>: 40.5%, NH<sub>3</sub>: 7.7%, PM<sub>10</sub>: 5.0%, NM VOC: 1.0%</td></tr><tr><td>GHG</td><td>193 641</td><td>150 657</td><td>1 728 224</td><td>CO<sub>2</sub>: 94.5%, CH<sub>4</sub>: 4.9%, N<sub>2</sub>O: 0.6%</td></tr><tr><td>Heavy metals</td><td>13 803</td><td>8 924</td><td>120 622</td><td>Pb: 79.6%, Cd: 15.9%, Hg: 3.2%, As: 1.3%, Cr(VI): 0.1%, Ni: 0.0%</td></tr><tr><td>Organic</td><td>66</td><td>69</td><td>1 071</td><td>Dioxins: 82.6%, B(a)P: 15.9%, Benzene: 1.4%</td></tr><tr><td>Total (VOLY)</td><td>326 553</td><td>219 378</td><td>2 683 984</td><td>GHG: 68.7%, Main air: 27.2%, Heavy metals: 4.1%, Organic: 0.0%</td></tr><tr><td>Total (VSL)</td><td>536 663</td><td>352 707</td><td>4 276 503</td><td>Main air: 54.7%, GHG: 42.7%, Heavy metals: 2.5%, Organic: 0.0%</td></tr></tbody></table>

Source: EEA (2024b, Table 4.1, Figures 4.4–4.7, pages 28 & 35–37) & Author’s calculations.

The technical report contains an interesting analysis of marginal damage costs (MDC) for different pollutants and countries. The MDC represents the monetary value of the damage caused by emitting one additional tonne of a specific pollutant into the environment. For the main air pollutants, the average marginal costs for 2019, expressed in 2021 thousand euros per kg of pollutant emitted into the air, are as follows<fnref n="124" />:

<table><thead><tr><th>Pollutant</th><th>NO<sub>x</sub></th><th>SO<sub>2</sub></th><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>NM VOC</th><th>NH<sub>3</sub></th></tr></thead><tbody><tr><td>MDC (VOLY)</td><td>15.4</td><td>16.2</td><td>51.5</td><td>86.5</td><td>1.8</td><td>19.0</td></tr><tr><td>MDC (VSL)</td><td>43.0</td><td>38.3</td><td>141.1</td><td>237.1</td><td>4.5</td><td>52.3</td></tr></tbody></table>

while for heavy metals and organic compounds they find these values:

<table><thead><tr><th>Pollutant</th><th>Arsenic</th><th>Cadmium</th><th>Chromium VI</th><th>Lead</th><th>Mercury</th></tr></thead><tbody><tr><td>MDC</td><td>10.3</td><td>253.1</td><td>0.7</td><td>45.2</td><td>16.8</td></tr><tr><td>Pollutant</td><td>Nickel</td><td>1,3 Butadiene</td><td>Benzene</td><td>B(a)P</td><td>Dioxins</td></tr><tr><td>MDC</td><td>0.02</td><td>0.00</td><td>0.00</td><td>1.4</td><td>132 600</td></tr></tbody></table>

We read these numbers as follows. One additional tonne of NO<sub>x</sub> emitted to the air induces an additional cost of 15 400 euros in the VOLY method, while one additional tonne of PM<sub>2.5</sub> emitted to the air induces an additional cost of 237 100 euros in the VSL method. The pollutant with the highest marginal damage cost is dioxins with an MDC of 132.6 million euro per kg.

**Remark 12** Although the EEA report is certainly one of the most comprehensive, well-documented and data-driven studies of the economic costs of air pollution, it also shows that this is a complex task and that there are many uncertainties about these costs. Key assumptions include the specification of concentration-response functions and the modelling of PM<sub>2.5</sub> impacts. In addition, this report is far from exhaustive as it does not include some important activities that do not correspond to industrial installations. For example, the industrial facilities reported in the E-PRTR database cover

<footnote n="124">¹²⁴Source: EEA (2024a, Tables 3.1 & 3.4, pages 26 & 27).</footnote>
49% of SOx and 48% of mercury, but only 3% of PM10 and 10% of lead. Furthermore, the study only analyses outdoor pollution and does not consider indoor pollution, in particular the impact of emissions on workers inside the facilities.

Box 17: Monetary economic value of health impacts

In Box 16 on page 137, we have defined three non-monetary measures of health impact: years of life lost (YLL), years lived with disability (YLD) and disability-adjusted life years (DALY). To assess economic costs, we need to convert these physical measures into monetary ones. Some standard approaches are described below.

* The quality-adjusted life year (QALY) is a unit of measurement that combines both the quantity and quality of life lived into a single index. One QALY represents one year of perfect health, and zero QALYs represent one year of death. It can be used to evaluate the benefits of a policy<sup>a</sup>:

$$ \text{QALY} = \text{Years of Life Gained} \times \text{Average Quality of Life Weight} $$

This approach is called linear because it is equivalent to directly summing the annual quality of life weights:

$$ \text{QALY} = \sum_{u=t}^{T} Q(u) $$

where T is the maximum time horizon and $Q(t) \in [0, 1]$ is the quality life of weight at time t. According to Hammitt (2023, Equation 2.5), a non-linear approach is to calculate the quality-adjusted life expectancy (QALE)<sup>b</sup>:

$$ \text{QALE} = \frac{1}{S(t)} \int_{t}^{T} e^{-\varrho(u-t)} \mathbf{S}(u) \mathbb{E}[Q(u)] du $$

where $\mathbf{S}(t)$ is the survival function at time t, $\varrho$ is the discount rate and $\mathbb{E}[Q(t)]$ is the expected quality weight.

* The value of a statistical life (VSL) is the economic value placed on the benefit of avoiding a death. It is not the value of an individual's life per se, but rather a measure of society's collective willingness to pay for small reductions in mortality risk. As such, the VSL is closely related to the concept of willingness-to-pay. The VSL is based on the amount of money that individuals are willing to pay for a given reduction in the risk of premature death. Several approaches are used to estimate VSL, including revealed preferences (e.g., wage premiums for risky jobs) and stated preferences (e.g., surveys asking about risk reduction choices). Hammitt (2023) mathematically defines the VSL as follows:

$$ \text{VSL} = \frac{v}{\Delta L} $$

where v is the monetary value of the risk reduction and $\Delta L$ is the expected number

<footnote><sup>a</sup>When we want to evaluate the cost of a disease, we replace Years of Life Gained by Years of Life Lost.</footnote>

<footnote><sup>b</sup>T can be set to $\infty$, since $S(\infty) = 0$.</footnote>
Box 17: Monetary economic value of health impacts (Continued from previous page)

of lives saved. For example, if workers in an industry are paid an additional $1 000 per year to face a 1 in 10 000 increased risk of death, the VSL can be calculated as $VSL = \frac{1 000}{1/10 000} = \$10 mn$. In this example, the VSL is $10 million, reflecting the monetary value associated with risk reductions that collectively save one statistical life. In the case of the economic costs of air pollution, EEA (2024b, page 19) used a VSL of €4.2 million — for infant mortality, it is set to €6.5 million.

* The value per statistical life year (VSLY) — or the value of a life year (VOLY) — is equal to the value of a statistical life divided by the remaining expected life years Hammitt (2023, Equation 2.10):

$$VSLY = \frac{VSL}{\frac{1}{S(t)} \int_{t}^{T} e^{-\rho(u-t)} S(u) du}$$

It represents the economic value of extending the life of a population by one additional year. Using the previous example, if the average remaining expected life in the industry is 40 years, $VSLY = \frac{\$10 mn}{40} = \$250 000$. In the case of air pollution, EEA (2024b, page 19) used a VOLY of €111 470, which means that the remaining expected life years used for this study is 37.68 years.

* VSL, VSLY, and VOLY are not available for all countries, as their computation requires extensive data and statistical resources. These metrics are generally well documented for OECD countries, the United States, and Europe, where data availability and research capacity are higher. For other countries, country-specific figures can be estimated by adjusting a baseline statistic using a transfer function, as described by Hammitt and Robinson (2011) and Viscusi and Masterman (2017):

$$VSL_c = VSL_b \left( \frac{(Y/N)_c}{(Y/N)_b} \right)^{\epsilon}$$

where $VSL_c$ is the country-specific VSL, $VSL_b$ is the baseline VSL, $(Y/N)$ is the GDP per capita and $\epsilon$ is the income elasticity of VSL, which captures how VSL scales with income. For high-income countries, $\epsilon$ is typically less than 1, reflecting the diminishing marginal utility of income. For low-income countries, $\epsilon \ge 1$ because the value of risk reduction increase more sharply as income rises$^a$.

Other approaches are available, but most are less popular than QALY and VSL, with the exception of willingness-to-pay$^b$ and cost of illness (COI). COI includes direct medical costs (medical care, hospitalization), direct non-medical costs (transportation to medical care, home modification), and indirect costs (loss of productivity and income).

<footnote>$^a$In very poor countries, each additional dollar has extremely high utility for basic consumption. This makes people relatively less willing to trade income for risk reduction.</footnote>

<footnote>$^b$Willingness-to-pay is defined in Roncalli (2025, Box 5.2).</footnote>
## 3.4 Overexploitation and resource extraction

Overexploitation is the practice of harvesting renewable resources faster than they can be replenished. This unsustainable practice often leads to significant declines in species populations, ecosystem degradation, evolutionary consequences, and in some cases, extinction. It commonly occurs through activities such as overfishing, in which fish stocks are harvested at a rate that exceeds their ability to reproduce; deforestation, in which timber is harvested at a rate that exceeds forest regeneration; hunting and poaching, characterized by the excessive killing of animals for food, sport, or trade; and illegal wildlife trade, including the poaching of animals for their body parts or the pet trade, such as the ivory and rhino horn markets. The concept of overexploitation can also be applied to natural resources such as minerals (e.g., phosphorus and rare earth elements) and fossil fuels (e.g., oil and natural gas). Even if they are not expected to become extinct by 2100, many natural resources will face problems as high-grade deposits are rapidly depleted and easily accessible deposits become increasingly scarce. Cobalt and zinc are typical examples.

Overexploitation is neither a new nor a recent phenomenon. It has existed throughout history, with varying degrees of severity in different contexts:

> "Although there is considerable variation in detail, there is remarkable consistency in the history of resource exploitation: resources are inevitably overexploited, often to the point of collapse or extinction." (Ludwig et al., 1993, page 17).

Peres (2010) highlights numerous examples of overexploitation that underscore the profound impact of human activities on wildlife. One notable pattern is the extinction of large-bodied vertebrates, which has been largely attributed to human overhunting and overkilling in the post-Pleistocene. Evidence suggests that humans historically shifted their hunting practices from larger to smaller animals after depleting populations of larger species. Large animals are particularly attractive targets because their size provides a greater yield of meat, hides, and bones, making them economically valuable. However, these species often have slower reproductive rates, characterized by longer gestation periods and fewer offspring per birth. This makes it difficult for their populations to recover from significant declines caused by hunting or habitat destruction. As large species declined or went extinct, humans increasingly relied on hunting smaller animals to meet their needs. This pattern — overhunting of large animals followed by a shift to smaller species — has been documented across regions and historical periods. Svenning et al. (2021, Figure 1, page 3) found that among terrestrial mammals, only 11 of 57 species of megaherbivores (mean adult body mass greater than 1000 kg) survived to 1000 AD:

<table><thead><tr><th>Status</th><th>Cetartiodactyla</th><th>Cingulata</th><th>Diprotodontia</th><th>Litopterna</th><th>Notoungulata</th><th>Rhinocerotidae</th><th>Pilosa</th><th>Elephantidae</th><th>Proboscidea</th><th>Total</th></tr></thead><tbody><tr><th>Survivor</th><td>4</td><td></td><td></td><td></td><td></td><td>4</td><td>3</td><td></td><td></td><td>11</td></tr><tr><th>Extinct</th><td>4</td><td>4</td><td>1</td><td>2</td><td>3</td><td>4</td><td>16</td><td>7</td><td>5</td><td>46</td></tr></tbody></table>

Survivors include three species of elephant, four species of rhinoceros, the common hippopotamus, the giraffe, and two species of cattle. The extinct species include seven species of elephant, four species of rhinoceros, one species of hippopotamus, the mastodon, etc. Ripple et al. (2019) studied the threats to megafauna, which are large vertebrate species. They considered all species weighing more than 100 kg for mammals, ray-finned fish, and cartilaginous fish, and all species weighing more than 40 kg for amphibians, birds, and reptiles. They found that the proportion of threatened
species was significantly higher among megafauna than among all vertebrates (58.6% vs. 21.3%). Furthermore, 70% of megafauna species have declining populations, with birds and amphibians being particularly affected (100% of species in these groups have declining populations), followed by cartilaginous fish (89.7%) and ray-finned fish (83.8%). Focusing on specific species, Ripple et al. (2019) identified various threats to megafauna. They concluded that harvesting of megafauna for human consumption<fnref n="125" /> is the single most important threat across all classes studied (Table 42), far outweighing other factors. Harvesting is a threat in more than 95% of cases, while invasive species and habitat development are threats in less than 40% of cases. Interestingly, climate change was found to be a threat in less than 20% of cases.

<table><caption>Table 42: Current threats to megafauna</caption><thead><tr><th></th><th>Megafauna</th><th>n</th><th>Animal husbandry</th><th>Climate</th><th>Cropping</th><th>Development</th><th>Harvesting</th><th>Invasives</th><th>Pollution</th><th>Transportation</th></tr></thead><tbody><tr><td></td><td>Cartilaginous fish</td><td>38</td><td>3%</td><td>11%</td><td></td><td>13%</td><td>100%</td><td></td><td>13%</td><td>8%</td></tr><tr><td></td><td>Mammals</td><td>73</td><td>53%</td><td></td><td>61%</td><td>56%</td><td>98%</td><td>54%</td><td></td><td>51%</td></tr><tr><td></td><td>Ray-finned fish</td><td>29</td><td></td><td></td><td></td><td>14%</td><td>100%</td><td>31%</td><td>40%</td><td>28%</td></tr><tr><td></td><td>Reptiles</td><td>20</td><td></td><td>40%</td><td>25%</td><td>40%</td><td>90%</td><td>65%</td><td></td><td>25%</td></tr></tbody></table>

Source: Ripple et al. (2019, Figure 2, page 5).

As explained by Peres (2010), overexploitation can lead to the rapid collapse of species. In some cases, just a few years or decades are sufficient, and it does not necessarily take centuries or millennia for a species to become nearly extinct. One of the best known examples is the North American buffalo (*Bison bison*), which experienced a dramatic collapse in less than 40 years:

> "Prior to European exploration and settlement of North America, the buffalo or American bison inhabited vast stretches of the continent. [...] At its greatest moment, the total numbers for the continent may have been as high as 25 to 30 million before white settlement. On the Great Plains, where the bison were most suited and most plentiful, its population is estimated to have been 20 million as late as 1800. Even by 1850, substantially more than 10 million bison roamed the plains. Yet, by 1890, these plains held just 1000 bison." (Lueck, 2002, page 609).

After 1890, the story of the American bison took a more hopeful turn, thanks to conservation efforts. A few individuals established small herds on private ranches to preserve the species. The US government protected a small population of wild bison in Yellowstone, providing an important sanctuary. Laws were also passed prohibiting the hunting of bison. Over time, through the combined efforts of individuals, conservation groups, and the government, the bison population began to recover. Today, bison are no longer endangered. There are approximately 400 000 bison in North America, but most are managed as livestock on private ranches. In fact, only about 30 000 are wild bison<fnref n="126" />.

<footnote n="125">¹²⁵The unsustainable hunting of wild animals for food is called the bushmeat crisis.</footnote>

<footnote n="126">¹²⁶Source: https://bisoncentral.com/bison-by-the-numbers.</footnote>
Box 18: The Tragedy of the Commons

Published in Science in 1968, *The Tragedy of the Commons* by Garrett Hardin is a seminal essay that explores the conflict between individual interests and the common good in the context of shared resources. The article focuses on how individuals, acting in their own self-interest, can deplete or degrade shared resources (referred to as ‘*commons*’), even when it is not in the collective best interest to do so. Hardin (1968) illustrates this dilemma with a hypothetical example of a shared pasture (the commons) where each herder individually benefits by adding more cattle to the pasture. Each herder reasons that adding one more animal will bring him personal gain, while the negative consequences (overgrazing) are shared by all users of the commons. Therefore, from an individual perspective, it’s rational to add more animals. However, if every herder follows this logic, the collective overuse of the pasture leads to its destruction, as the resources become insufficient to sustain the community. This illustrates the dilemma of individual gain versus collective ruin.

The essay applies this principle to various domains, including national parks, pollution, and environmental issues. It had a profound and far-reaching impact across multiple fields, influencing academic thought, public policy, and environmental management. However, the article has also faced significant criticism. For instance, Nobel laureate Elinor Ostrom provided empirical evidence demonstrating that communities can often self-organize to manage commons effectively, challenging the assumption that the tragedy is inevitable. More recently, the concept of the tragedy of the commons has resonated with contemporary challenges such as climate change. It has been extensively revisited, notably inspiring the idea of the ‘*tragedy of the horizon*’ (Carney, 2015), which highlights the short-term focus of financial markets and policy-making in addressing long-term environmental risks.

Beyond species extinction, overexploitation occurs in other areas and can take different forms. One important form is the depletion and impoverishment of natural resources. We often think of excessive logging for timber, overgrazing by livestock, and unsustainable harvesting of medicinal plants, all of which involve the depletion of plants that reduce soil fertility and lead to erosion. However, natural resource depletion also includes other essential resources such as water, minerals, and energy resources. In this context, IPBES provides a highly critical assessment of resource extraction:

> "Extraction of living biomass and nonliving materials is increasing as both populations and per capita consumption increased sixfold from 1970 to 2010, while the demand for materials used in construction and industry quadrupled during that time. [...] Materials for construction and industry increased 4-fold, with the most dramatic increases for lower-middle (7-fold) and upper-middle income countries (11-fold) and the Asia and the Pacific region (10-fold for whole region) and, generally, the growing economies. The use of biomass, fossil fuels, metal ores and non-metallic minerals doubled from 2005 (26.3 billion tons) to 2015 (46.4 billion tons), growing an annual rate of 6.1%." (IPBES, 2019, page 121).

The root causes of overexploitation lie in the expanding size and growth of the human population, coupled with rising standards of living. These driving forces are described by various terms, such as ‘*the scale of the human enterprise*’ (Ehrlich, 1995; Gaston and Spicer, 2004), ‘*the great acceleration*’ (Steffen et al., 2015a), and others. Collectively, they underscore the profound impact of human activities on the planet’s natural systems.
3.4.1 An example with freshwater

Overexploitation of water resources includes both the overdrawing of groundwater from aquifers for irrigation or consumption, and the unsustainable use of surface water, such as river diversions and lake exploitation. Figure 55 shows the annual freshwater withdrawals measured in trillion m³. We observe four main periods. From 1900 to 1950, the trend is relatively slow. Then there is a sharp acceleration in freshwater withdrawals until 1980, followed by a first slowdown from 1980 to 2010 and a second slowdown since 2010. This graph illustrates ‘*The Great Acceleration*’ described by Steffen et al. (2015a), which refers to the dramatic, rapid increase in human activity and its profound impact on Earth’s systems that began after World War II.

<figure><img src="image_90.png" /><figcaption>Figure 55: Global freshwater withdrawals (in trillion m³ per year)</figcaption></figure>

Source: Flörke et al. (2013), Steffen et al. (2015a), World Bank & Author’s calculations.

Freshwater resources and withdrawals are unevenly distributed. In Table 43, we report several statistics for different countries: (1) the total annual freshwater withdrawals in billion cubic meters in 2000, (2) the total annual freshwater withdrawals in billion cubic meters in 2021, (3, 4, 5) the distribution among agriculture, industry, and domestic use, (6) the renewable internal freshwater resources<fnref n="127" /> in billion cubic meters in 2021, (7) the renewable internal freshwater resources per capita, and (8) the water stress. The total volume of renewable freshwater resources worldwide is approximately 42.8 trillion cubic meters, with more than 50% concentrated in just seven countries: Brazil, Canada, China, India, Indonesia, Russia, and the United States. Some countries have very limited renewable freshwater resources. For instance, Egypt, Libya, and Saudi Arabia each have less than 2 billion cubic meters of freshwater resources, despite their large land areas. These disparities

<footnote n="127">¹²⁷Renewable internal freshwater resource flows measure the total volume of freshwater generated by natural processes within a country's borders. This includes water from precipitation (rain, snow) that contributes to rivers, lakes, and groundwater but excludes any water that flows in from other countries.</footnote>
<table><caption>Table 43: Freshwater withdrawals by country</caption><thead><tr><th rowspan="2">Country</th><th colspan="2">Total</th><th colspan="2">Agriculture</th><th colspan="2">Industry</th><th colspan="3">Domestic</th><th colspan="3">Renewable resources</th><th>Water stress</th></tr><tr><th>2000<br>(in bn m³)</th><th>2021<br>(in bn m³)</th><th>2021<br>(in %)</th><th>2021<br>(in %)</th><th>2021<br>(in bn m³)</th><th>2021<br>(in m³)</th><th>2021<br>(in %)</th><th>2021<br>(in bn m³)</th><th>2021<br>(in m³)</th><th>2021<br>(in %)</th><th>2021<br>(in bn m³)</th><th>2021<br>(in m³)</th><th>2021<br>(in %)</th></tr></thead><tbody><tr><td>Algeria</td><td>5.6</td><td>9.8</td><td>63.8</td><td>1.8</td><td>34.4</td><td></td><td></td><td>11</td><td>251</td><td>137.9</td><td></td><td></td><td></td></tr><tr><td>Argentina</td><td>30.4</td><td>37.7</td><td>73.9</td><td>10.6</td><td>15.5</td><td></td><td></td><td>292</td><td>6444</td><td>10.5</td><td></td><td></td><td></td></tr><tr><td>Australia</td><td>21.7</td><td>11.4</td><td>67.8</td><td>18.1</td><td>14.0</td><td></td><td></td><td>492</td><td>19155</td><td>4.6</td><td></td><td></td><td></td></tr><tr><td>Bangladesh</td><td></td><td>35.9</td><td>87.8</td><td>2.1</td><td>10.0</td><td></td><td></td><td>105</td><td>626</td><td>5.7</td><td></td><td></td><td></td></tr><tr><td>Brazil</td><td>56.1</td><td>67.3</td><td>61.3</td><td>14.5</td><td>24.2</td><td></td><td></td><td>5661</td><td>27015</td><td>1.5</td><td></td><td></td><td></td></tr><tr><td>Canada</td><td>41.9</td><td>36.3</td><td>11.4</td><td>74.2</td><td>14.4</td><td></td><td></td><td>2850</td><td>74530</td><td>3.7</td><td></td><td></td><td></td></tr><tr><td>China</td><td>550.9</td><td>568.5</td><td>62.1</td><td>17.7</td><td>20.1</td><td></td><td></td><td>2813</td><td>1992</td><td>41.5</td><td></td><td></td><td></td></tr><tr><td>Congo (DRC)</td><td>0.6</td><td>0.7</td><td>10.5</td><td>21.5</td><td>68.0</td><td></td><td></td><td>900</td><td>9077</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>Egypt</td><td>57.0</td><td>77.5</td><td>79.2</td><td>7.0</td><td>13.9</td><td></td><td></td><td>1</td><td>9</td><td>141.2</td><td></td><td></td><td></td></tr><tr><td>France</td><td>32.7</td><td>24.7</td><td>13.9</td><td>64.3</td><td>21.7</td><td></td><td></td><td>200</td><td>2948</td><td>21.6</td><td></td><td></td><td></td></tr><tr><td>Gabon</td><td>0.1</td><td>0.1</td><td>29.0</td><td>10.1</td><td>60.9</td><td></td><td></td><td>164</td><td>69003</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>Germany</td><td>44.9</td><td>25.8</td><td>4.2</td><td>54.3</td><td>41.5</td><td></td><td></td><td>107</td><td>1286</td><td>35.4</td><td></td><td></td><td></td></tr><tr><td>Iceland</td><td>0.2</td><td>0.3</td><td>0.1</td><td>71.1</td><td>28.7</td><td></td><td></td><td>170</td><td>456351</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>India</td><td>610.4</td><td>647.5</td><td>90.4</td><td>2.2</td><td>7.4</td><td></td><td></td><td>1446</td><td>1022</td><td>66.5</td><td></td><td></td><td></td></tr><tr><td>Indonesia</td><td>113.3</td><td>222.6</td><td>85.2</td><td>4.1</td><td>10.7</td><td></td><td></td><td>2019</td><td>7294</td><td>29.7</td><td></td><td></td><td></td></tr><tr><td>Iran</td><td>88.5</td><td>93.0</td><td>92.2</td><td>1.2</td><td>6.6</td><td></td><td></td><td>128</td><td>1453</td><td>81.3</td><td></td><td></td><td></td></tr><tr><td>Italy</td><td>45.1</td><td>33.6</td><td>50.2</td><td>22.7</td><td>27.1</td><td></td><td></td><td>182</td><td>3086</td><td>29.6</td><td></td><td></td><td></td></tr><tr><td>Kazakhstan</td><td>21.0</td><td>24.6</td><td>62.7</td><td>18.5</td><td>18.8</td><td></td><td></td><td>64</td><td>3259</td><td>34.1</td><td></td><td></td><td></td></tr><tr><td>Korea, Rep.</td><td>27.7</td><td>29.2</td><td>58.9</td><td>16.4</td><td>24.6</td><td></td><td></td><td>65</td><td>1253</td><td>85.2</td><td></td><td></td><td></td></tr><tr><td>Libya</td><td>4.3</td><td>5.7</td><td>83.2</td><td>4.8</td><td>12.0</td><td></td><td></td><td>1</td><td>98</td><td>817.1</td><td></td><td></td><td></td></tr><tr><td>Madagascar</td><td>13.4</td><td>13.5</td><td>95.9</td><td>1.2</td><td>2.9</td><td></td><td></td><td>337</td><td>11350</td><td>11.3</td><td></td><td></td><td></td></tr><tr><td>Malaysia</td><td>5.6</td><td>6.7</td><td>45.6</td><td>29.9</td><td>24.5</td><td></td><td></td><td>580</td><td>16918</td><td>3.4</td><td></td><td></td><td></td></tr><tr><td>Mexico</td><td>68.2</td><td>89.9</td><td>75.7</td><td>9.5</td><td>14.8</td><td></td><td></td><td>409</td><td>3204</td><td>45.0</td><td></td><td></td><td></td></tr><tr><td>Netherlands</td><td>8.4</td><td>7.9</td><td>1.0</td><td>73.5</td><td>25.5</td><td></td><td></td><td>11</td><td>627</td><td>16.1</td><td></td><td></td><td></td></tr><tr><td>Nigeria</td><td>10.3</td><td>12.5</td><td>44.2</td><td>15.8</td><td>40.1</td><td></td><td></td><td>221</td><td>1011</td><td>9.7</td><td></td><td></td><td></td></tr><tr><td>Pakistan</td><td>172.6</td><td>264.2</td><td>94.0</td><td>0.8</td><td>5.3</td><td></td><td></td><td>55</td><td>230</td><td>162.1</td><td></td><td></td><td></td></tr><tr><td>Philippines</td><td></td><td>89.0</td><td>76.2</td><td>13.4</td><td>10.3</td><td></td><td></td><td>479</td><td>4235</td><td>27.2</td><td></td><td></td><td></td></tr><tr><td>Qatar</td><td>0.2</td><td>0.2</td><td>33.3</td><td>4.3</td><td>62.4</td><td></td><td></td><td>0</td><td>22</td><td>431.0</td><td></td><td></td><td></td></tr><tr><td>Russia</td><td>75.9</td><td>64.8</td><td>28.8</td><td>44.8</td><td>26.5</td><td></td><td></td><td>4312</td><td>29790</td><td>4.1</td><td></td><td></td><td></td></tr><tr><td>Saudi Arabia</td><td>19.7</td><td>23.4</td><td>81.6</td><td>5.4</td><td>13.1</td><td></td><td></td><td>2</td><td>78</td><td>974.2</td><td></td><td></td><td></td></tr><tr><td>South Africa</td><td>12.7</td><td>20.9</td><td>61.3</td><td>21.2</td><td>17.4</td><td></td><td></td><td>45</td><td>728</td><td>66.9</td><td></td><td></td><td></td></tr><tr><td>Spain</td><td>36.1</td><td>29.0</td><td>65.3</td><td>19.0</td><td>15.7</td><td></td><td></td><td>111</td><td>2345</td><td>43.3</td><td></td><td></td><td></td></tr><tr><td>Sudan</td><td></td><td>26.9</td><td>96.2</td><td>0.3</td><td>3.5</td><td></td><td></td><td>4</td><td>83</td><td>118.7</td><td></td><td></td><td></td></tr><tr><td>Ukraine</td><td>18.3</td><td>9.5</td><td>31.0</td><td>40.9</td><td>28.1</td><td></td><td></td><td>55</td><td>1244</td><td>12.3</td><td></td><td></td><td></td></tr><tr><td>United Kingdom</td><td>12.5</td><td>8.4</td><td>14.0</td><td>12.0</td><td>74.0</td><td></td><td></td><td>145</td><td>2163</td><td>14.4</td><td></td><td></td><td></td></tr><tr><td>United States</td><td>473.5</td><td>444.4</td><td>39.7</td><td>47.2</td><td>13.1</td><td></td><td></td><td>2818</td><td>8487</td><td>28.2</td><td></td><td></td><td></td></tr><tr><td>Viet Nam</td><td>71.8</td><td>81.9</td><td>94.8</td><td>3.7</td><td>1.5</td><td></td><td></td><td>359</td><td>3633</td><td>18.1</td><td></td><td></td><td></td></tr><tr><td>World</td><td>3679.8</td><td>3948.7</td><td>71.6</td><td>15.1</td><td>13.1</td><td></td><td></td><td>42809</td><td>5429</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: https://databank.worldbank.org/source/world-development-indicators.
are even more pronounced when considering renewable internal freshwater resources per capita. For example, Canada has more than 70 000 m³ of freshwater per capita, while Egypt has less than 10 m³. In addition, a worrying downward trend in available freshwater resources has been observed (Figure 56). Over the past 60 years, the global volume of renewable internal freshwater resources has declined by about 60%. In India, this decline is even more severe, approaching 70%. This downward trend is primarily driven by high withdrawal rates. Globally, about 4 trillion cubic meters of freshwater are withdrawn each year. Agriculture is the largest contributor, accounting for 71% of total withdrawals, followed by industry at 15%. Domestic use accounts for 13% of total withdrawals worldwide. However, domestic water use has grown much faster than agricultural and industrial use, increasing sevenfold over the past 50 years. In some countries, domestic freshwater use accounts for more than 40% of total withdrawals, such as in many African countries, as well as in Germany and the United Kingdom. In Table 43, we also report the ratio of total freshwater withdrawals to total renewable freshwater resources, referring specifically to internal freshwater resources. This ratio is commonly known as water stress. Water stress is considered high when the ratio exceeds 75% and critical when it exceeds<fnref n="128" /> 100%. We observe that some countries are experiencing critical water stress, including Algeria, Egypt, Libya, Pakistan, Qatar, Saudi Arabia, and Sudan. In fact, as of 2021, there are 25 countries with water stress levels exceeding 75%.

<figure><img src="image_91.png" /><figcaption>Figure 56: Renewable internal freshwater resources per capita (base 100 in 1961)</figcaption></figure>

Source: https://databank.worldbank.org/source/world-development-indicators & Author's calculations.

<footnote n="128">¹²⁸A water stress level greater than 100% means that a country is withdrawing more freshwater annually than the total amount of renewable internal freshwater resources. This indicates overexploitation of additional water sources beyond what is naturally replenished within the country. For example, a country may rely on non-renewable groundwater, external freshwater resources that originate outside its borders and flow in through rivers and lakes (e.g., the Nile and the Danube), or desalination, which involves the conversion of seawater into freshwater (e.g., in Qatar and Saudi Arabia).</footnote>
### 3.4.2 Mathematical models of population and resource ecology with harvesting

Historically, the Malthusian growth model is considered the first mathematical population model. In this case, the population growth rate is constant:

$$ \frac{dN(t)}{dt} = \delta N(t) $$

where $N(t)$ is the number of individuals, $N(t_0) = N_0$ is the initial population size and $\delta = \lambda - \mu$ is the difference between the birth rate and the death/mortality rate. Malthus (1798) believed that the population tends to grow exponentially$^{129}$, whereas he believed that food production and other resources increase at a linear rate due to the finite nature of land and labor. Therefore, Malthus predicted that the world would eventually face a crisis where resources would not be sufficient to support the growing population. However, his conclusions were later criticized by Verhulst (1838), who highlighted the role of the law of diminishing returns. Verhulst provided numerous examples demonstrating that population growth is inherently limited and cannot continue indefinitely. To model this, he proposed that population size follows the nonlinear differential equation:

$$ \frac{dN(t)}{dt} = \delta N(t) - \varphi(N(t)) $$

By considering the specific case where $\varphi(x) = \eta x^2$, Verhulst derived the well-known logistic population model:

$$ N(t) = \frac{\delta N_0 e^{\delta(t-t_0)}}{\delta + \eta N_0 (e^{\delta(t-t_0)} - 1)} $$

This model illustrates how population growth slows as it approaches a finite carrying capacity, offering a more realistic depiction of population dynamics compared to Malthus' original exponential growth predictions. In the 1920s, mathematician Vito Volterra introduced a class of population dynamics models describing multiple species competing for the same food or resources, or interacting as predators and prey (Volterra, 1928). During the same period, biophysicist Alfred Lotka independently developed similar equations to analyze predator-prey interactions (Lotka, 1925). These equations are now known as the Lotka-Volterra equations and form a pair of first-order nonlinear differential equations:

$$ \left\{ \begin{aligned} \frac{dx(t)}{dt} &= ax(t) - bx(t)y(t) \\ \frac{dy(t)}{dt} &= cx(t)y(t) - dy(t) \end{aligned} \right. \qquad (32) $$

where $x(t)$ is the prey population, $y(t)$ is the predator population, $a$ is the intrinsic growth rate of the prey (in the absence of predators), $b$ is the predation rate coefficient, $c$ is the reproduction rate of predators per prey consumed, and $d$ is the natural mortality rate of predators. These three models (Malthus, Verhulst, and Lotka-Volterra) form the basis for modeling population and resource dynamics with harvesting.

In the following, $x(t)$ represents the size of a population, the amount of a resource or the biomass stock of a species in a finite environment. When the stock $x(t)$ is small, the size of the environment has no impact, and $x(t)$ can grow at an exponential rate $\delta$, also known as the intrinsic growth rate. However, beyond a certain threshold, the density of the stock or population becomes too high to sustain the growth rate, and the regenerative rate of the stock decreases. This suggests the existence of a threshold $\kappa$ at which $x(t)$ remains constant. Therefore, we assume that the stock

<footnote>$^{129}$The solution of the differential equation is an exponential curve: $N(t) = N_0 e^{\delta(t-t_0)}$.</footnote>
variation is determined by the product of three factors: the constant intrinsic growth rate $\delta$ (or biotic potential), the regenerative rate$^{130}$ $\xi(t) = \frac{\kappa - x(t)}{\kappa}$ (or biotic resistance), which depends on the distance between the current stock $x(t)$ and the threshold $\kappa$ which is called the carrying capacity, and the current stock $x(t)$. This results in the following differential equation:

$$ \frac{dx(t)}{dt} = \delta \left( \frac{\kappa - x(t)}{\kappa} \right) x(t) $$

This model corresponds to the logistic model formulated by Verhulst (1838), where $\eta = \delta/\kappa$. We deduce that the solution is:

$$ x(t) = \frac{\kappa N_0 e^{\delta(t-t_0)}}{\kappa + N_0 (e^{\delta(t-t_0)} - 1)} $$

We verify that $x(t)$ tends asymptotically to $\kappa$ as $t \to \infty$. This model can be easily modified by considering exploitation:

$$ \frac{dx(t)}{dt} = \delta x(t) \left(1 - \frac{x(t)}{\kappa}\right) - h(x(t)) $$

where $h(x(t))$ is the harvest, catch or removal rate. This model has been considered by Gordon (1954) and Schaefer (1954) in the case of fisheries management. The maximum sustainable yield (MSY) is the largest harvest rate of a renewable resource that can be sustained indefinitely without causing the population to decline. The sustainable harvest rate is given by:

$$ h(x(t)) = \delta x(t) \left(1 - \frac{x(t)}{\kappa}\right) $$

The maximum value of $h(x)$ is reached when $h'(x) = 0$ or $\delta \left(1 - \frac{x}{\kappa}\right) - \delta \frac{x}{\kappa} = 0$. We deduce that the maximum sustainable yield is achieved when the stock is at half the carrying capacity: $x(t) = \frac{\kappa}{2}$. We deduce that:

$$ \text{MSY} := \max h(x(t)) = h\left(\frac{\kappa}{2}\right) = \frac{\delta}{4\kappa} $$

Figure 57 shows the function $g(x) = \delta x \left(1 - \frac{x}{\kappa}\right) - h$ when $\delta = 30\%$, $\kappa = 200$ and $h = \epsilon \cdot \text{MSY}$. The stability analysis demonstrates that the number of stable equilibria depends on the harvest rate$^{131}$. If $h = 0$, we have a stable equilibrium at $x^* = \kappa$ and an unstable equilibrium at $x^* = 0$. If $0 < h < \frac{\delta}{4\kappa}$, the two equilibria lie between 0 and $\kappa$. Among them, only the equilibrium $x^* = \frac{\delta\kappa + \sqrt{\delta\kappa(\delta\kappa - 4h)}}{2\delta}$

$$ \frac{dx(t)}{dt} = g(x) = 0 \Leftrightarrow -\delta x^2 + \delta\kappa x - h\kappa = 0 $$

We have $\Delta = \delta\kappa(\delta\kappa - 4h)$. If $h > \text{MSY}$, then there is no root and $\frac{dx(t)}{dt} < 0$. It follows that $x(t)$ converges to the stable equilibrium $x_1^* = 0$. If $h = \text{MSY}$, there is a root: $x' = \kappa/2$. Therefore, we have two stable equilibria: $x_1^* = 0$ and $x_2^* = \kappa/2$. If $h < \text{MSY}$, we have $\sqrt{\Delta} < \delta\kappa$ and there are two roots: $x' = (\delta\kappa - \sqrt{\Delta}) / (2\delta)$ and $x'' = (\delta\kappa + \sqrt{\Delta}) / (2\delta)$. Therefore, we have two stable equilibria $x_1^* = 0$ and $x_3^* = (\delta\kappa + \sqrt{\Delta}) / (2\delta)$ and one unstable equilibrium $x_2^* = (\delta\kappa - \sqrt{\Delta}) / (2\delta)$.

<footnote>$^{130}$The regenerative rate $\xi(t)$ is positive if $x(t) < \kappa$, zero at $x(t) = \kappa$, and negative if $x(t) < \kappa$. In the latter case, the stock is declining because the population density is too high to sustain it.</footnote>

<footnote>$^{131}$We obtain a second order polynomial:</footnote>
<figure><img src="image_92.png" /><figcaption>Figure 57: Stability analysis of the logistic model with harvesting</figcaption></figure>

is stable. If $h = \text{MSY} = \frac{\delta}{4}\kappa$, the equilibrium at $x^* = \frac{\kappa}{2}$ is unstable. Indeed, if $x(t) > x^*$, $\frac{dx(t)}{dt} < 0$ and $x(t)$ tends toward $x^*$, but if $x(t) < x^*$, $\frac{dx(t)}{dt} < 0$ and $x(t)$ tends to 0. Finally, if $h > \text{MSY}$, $x(t)$ tends asymptotically to 0. These results are summarized in Table 44. In Figure 58, we have simulated the model with three different starting values $x_0$ and four values of the parameter $\epsilon$. This shows the stability of each equilibrium. In particular, when $h = \text{MSY}$, we obtain an equilibrium at $x^* = \kappa/2$ only when the initial stock is already larger than the equilibrium.

<table><caption>Table 44: Sign table of $\frac{dx(t)}{dt}$</caption><thead><tr><th>x</th><th>0</th><th>δκ - √(δκ(δκ - 4h)) / 2δ</th><th>κ / 2</th><th>δκ - √(δκ(δκ - 4h)) / 2δ</th><th>κ</th><th>∞</th></tr></thead><tbody><tr><td>h = 0</td><td>0</td><td>+</td><td>+</td><td>+</td><td>+</td><td>0</td></tr><tr><td>0 < h < δ/4κ</td><td>-</td><td>-</td><td>0</td><td>+</td><td>+</td><td>-</td></tr><tr><td>h = δ/4κ</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0</td><td>-</td></tr><tr><td>h > δ/4κ</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
<figure><img src="image_93.png" /><figcaption>Figure 58: Simulation of the logistic model with harvesting</figcaption></figure>

Figure 59 illustrates the equilibrium $x^*$ and the harvest rate $h$ as a function of the coefficient $\epsilon$. By construction, $x^*$ decreases with respect to $\epsilon$. When $\epsilon = 1$ or $h = \text{MSY}$, we verify that the equilibrium is unstable, indicating that $h = \text{MSY}$ is indeed a critical and undesirable harvest rate. To prevent overexploitation, it is essential that the absolute harvest rate remains below the maximum sustainable yield. Consequently, the relative harvest rate, defined as the ratio of the absolute harvest rate to the carrying capacity, must be less than one quarter of the intrinsic growth rate:

$$ \eta = \frac{h}{\kappa} < \eta^* = \frac{\delta}{4} $$

For example, if $\delta = 30\%$ per year, then $\eta^* = 7.5\%$, meaning that the relative harvest rate must be less than 7.5% per year. Conversely, if $\delta = 2\%$, the relative harvest rate must be much lower, less than 0.5% per year ($\eta^* = 0.5\%$). Here are some typical values for various species:

* **Fish species**

In general, small and fast growing fish species such as sardines and anchovies can have rates of 100% or higher, while large and slow growing fish species such as sharks and tuna often have rates below 10%. Many commercial fish species, such as cod and haddock, have intrinsic growth rates between 10% and 50% per year.

* **Mammals**

Large mammals such as whales and elephants typically have low rates (5%–20% per year) because they reproduce slowly and have long gestation periods, while small mammals such as rodents and rabbits can have higher rates (50%–200% per year), reflecting their ability to reproduce rapidly.

* **Bacteria**
<figure><img src="image_94.png" /><figcaption>Figure 59: Equilibrium $x^*$ and harvest rate $h$</figcaption></figure>

Bacteria can have very high intrinsic growth rates, sometimes doubling several times a day. For example, Escherichia coli (E. coli) can have rates of 20+ per day under optimal conditions.

Because large, slow-growing animals (such as many large fish species and mammals) have low intrinsic growth rates, they are highly vulnerable to overharvesting.

<table><caption>Table 45: Proportion in % of remaining buffalo under different harvest rate assumptions</caption><thead><tr><th>η (in %)</th><th>1.00</th><th>2.00</th><th>3.00</th><th>4.00</th><th>4.10</th><th>4.20</th><th>4.30</th><th>4.40</th></tr></thead><tbody><tr><td>10 years</td><td>93.55</td><td>86.81</td><td>79.77</td><td>72.40</td><td>71.65</td><td>70.89</td><td>70.12</td><td>69.36</td></tr><tr><td>20 years</td><td>90.88</td><td>80.64</td><td>68.95</td><td>55.35</td><td>53.85</td><td>52.34</td><td>50.79</td><td>49.22</td></tr><tr><td>30 years</td><td>89.71</td><td>77.32</td><td>61.63</td><td>40.10</td><td>37.45</td><td>34.68</td><td>31.78</td><td>28.74</td></tr><tr><td>40 years</td><td>89.18</td><td>75.41</td><td>55.85</td><td>21.32</td><td>16.04</td><td>10.18</td><td>3.62</td><td>0.00</td></tr></tbody></table>

Consider the dramatic collapse of the American buffalo. Suppose the intrinsic growth rate is 10% per year (Lueck, 2002; Jolles, 2007), and the initial buffalo population is at its carrying capacity ($x_0 = \kappa$). Table 45 shows the proportion of the remaining buffalo population under different relative harvest rate assumptions after 10, 20, 30, and 40 years.

* If the relative harvest rate $\eta$ is 1%, the remaining buffalo population after 10, 20, 30, and 40 years is 93.6%, 90.9%, 89.7%, and 89.2%, respectively.

* If the relative harvest rate is 4%, these figures decrease to 72.4%, 55.4%, 40.1%, and 21.3%.

* At a relative harvest rate of 4.4%, the buffalo population is completely wiped out (0% remaining) after 40 years.
The previous model can be extended in several directions (Quinn and Deriso, 1999). Gilpin et al. (1976) introduced a parameter $\theta \ge 0$ to control the asymmetry of the growth curve:

$$ \frac{dx(t)}{dt} = \delta x(t) \left( 1 - \left( \frac{x(t)}{\kappa} \right)^{\theta} \right) - h(x(t)) $$

In this formulation, $\theta$ allows the inflection point of the growth curve to vary between 0 and $\kappa$. When $\theta < 1$ (resp. $\theta > 1$), the maximum growth occurs for $x(t) < \kappa/2$ (resp. $x(t) < \kappa/2$). The case $\theta < 1$ is generally observed when resources are limited. Another extension is to consider different parameterizations of the harvest function. According to Begon and Townsend (2021), the three most popular functions $h(x)$ are:

1. Fixed quota (or constant catch) management

This specifies a predetermined, fixed number of animals that can be harvested. In this case, $h(x) = q$ is a constant, corresponding to the case we have already studied.

2. Fixed proportion harvesting

This specifies a proportion $e$ of animals that can be harvested, rather than a specific number:

$$ h(x) = ex $$

where $e$ is the exploitation rate expressed as a percentage.

3. Fixed escapement (or constant escapement rule)

This specifies not the number of animals to be harvested, but rather the number of animals to remain unharvested. In this approach, harvest occurs only when the population exceeds a threshold $x_{min}$, ensuring a minimum escapement:

$$ h(x) = e(x - x_{min})^{+} $$

Figure 60 shows the impact of the parameter $\theta$ and different harvesting management strategies on population size<fnref n="132" />. We note that the parameter $\theta$ has a significant impact when a fixed quota is used, whereas its effect is less pronounced under fixed proportion harvesting. In the latter case, the population dynamics follow the equation:

$$ \frac{dx(t)}{dt} = (\delta - e)x(t) - \delta \frac{x(t)^2}{\kappa} \left( \frac{x(t)}{\kappa} \right)^{\theta-1} $$

Here, the term $x(t)^2/\kappa$ has a greater magnitude than $(x(t)/\kappa)^{\theta-1}$, particularly when $\theta \ge 1$. We also observe that fixed escapement is the safest exploitation approach, as it ensures that $x^* > x_{min}$ — the limit case is obtained as $e \to \infty$.

Overexploitation can also be studied using a second class of multi-species models based on the Lotka-Volterra formulation. In the two-dimensional case, the system is given by:

$$ \left\{ \begin{aligned} \frac{dx(t)}{dt} &= ax(t) - bx(t)y(t) - h_x(x(t), y(t)) \\ \frac{dy(t)}{dt} &= cx(t)y(t) - dy(t) - h_y(x(t), y(t)) \end{aligned} \right. $$

<footnote n="132">¹³²The intrinsic growth rate $\delta$ is set to 30%, while $x_0$ and $\kappa$ are both equal to 100.</footnote>
<figure><img src="image_95.png" /><figcaption>Figure 60: Impact of the harvest function and the inflection point</figcaption></figure>

where $h_x(x, y)$ and $h_{x,y}(y)$ are the harvest functions of the prey and predator species, respectively. The classical Lotka-Volterra model is obtained by setting $h_x(x) = h_y(y) = 0$. This model has been extensively studied in both population dynamics modeling and the mathematical field of nonlinear systems, because it can be linked to the theory of deterministic chaos (characterized by sensitive dependence on initial conditions, strange attractors, and bifurcations). We can show that the classical Lotka-Volterra model has an unstable equilibrium at $(0,0)$ and a stable equilibrium at $\left(\frac{d}{c}, \frac{a}{b}\right)$. Let us assume that $a = 2$, $b = 3$, $c = 2$ and $d = 4$. Figure 61 shows the solutions $x(t)$ and $y(t)$ when the initial biomass values are $x_0 = 0.5$ and $y_0 = 1.5$ tonnes. Since there are initially too many predators and not enough prey, the predator population declines in the first phase. This allows the prey population to recover and reproduce, leading to an increase in prey biomass. In the second phase, as the prey population grows, the predators have more food available and begin to increase in number. Ultimately, this predator-prey system generates cyclical dynamics, as illustrated by the vector field analysis of the dynamical system (Figure 62). In this example, the cycle lasts 2.70 years. Figure 63 shows the phase portrait of the Lotka-Volterra model<fnref n="133" />, depicting the orbits or limit cycles generated by the system of differential equations.

$$ \frac{dy}{dx} = \frac{cxy - dy}{ax - bxy} $$

It follows that $(ax - bxy) dy = (cxy - dy) dx$. Dividing by $xy$, we get $c dx - d \frac{dx}{x} + b dy - a \frac{dy}{y} = 0$. We deduce that the solution is:

$$ cx - d \ln x + by - a \ln y = C \quad (33) $$

where $C$ is a constant. The phase portrait is the set of solutions $(x, y)$ that satisfy Equation (33) for a given value of $C$.

<footnote n="133">¹³³We have:</footnote>
<figure><img src="image_96.png" /><figcaption>Figure 62: Vector field representation of the Lotka-Volterra model ($a = 2, b = 3, c = 2, d = 4$)</figcaption></figure>

<figure><img src="image_97.png" /></figure>
<figure><img src="image_98.png" /><figcaption>Figure 64: Simulation of the Lotka-Volterra model with harvesting ($a = 2, b = 3, c = 2, d = 4, e_y = 5, y_{min} = 1$)</figcaption></figure>

<figure><img src="image_99.png" /></figure>
Now let us consider exploitation. There are many ways to specify the harvest functions $h_x(x, y)$ and $h_y(x, y)$. If we aim to protect the prey species, we can set $h_x(x, y) = 0$ and $h_y(x, y) = e_y(y - y_{min})^+$, where $e_y$ is the exploitation rate of the predator species above the threshold $y_{min}$. This leads to a dynamic in which the biomass of both prey and predator species becomes more stable (Figure 64). Another approach is to protect the prey species when its population falls below a threshold $x_{min}$. In this case, for example, we can define the harvest function as $h_y(x, y) = e_y y (x_{min} - x)^+$.

**Remark 13** The Lotka-Volterra model can also be used to study the impact of invasive species, which can be considered as another form of overexploitation. In this context, $x$ represents the native species, while $y$ represents the invasive species (Roques et al., 2015).

After its publication, the Lotka-Volterra model was rapidly extended in many directions. In reviewing these developments, Solomon (1949) introduced the concepts of functional and numerical responses:

1. The ‘functional response’ describes how the predation rate (i.e., the number of prey consumed per predator) varies with prey density. It quantifies the efficiency of individual predators in capturing and consuming prey.

2. The ‘numerical response’ describes how predator population density changes in response to prey density. It reflects the total population-level effect of prey availability on predator numbers, including factors such as predator consumption, reproduction and migration, but excluding natural mortality.

From a mathematical point of view, we can write:

$$ \begin{cases} \frac{dx(t)}{dt} = \delta(t)x(t) - f(x(t), y(t))y(t) \\ \frac{dy(t)}{dt} = g(x(t), y(t))y(t) - \mu(t)y(t) \end{cases} $$

where $\delta(t)$ is the growth rate of the prey species, $f(x, y)$ is the functional response, $g(x, y)$ is the numerical response, and $\mu(t)$ is the mortality rate of the predator species. The classical Lotka-Volterra model is obtained by setting $\delta(t) = a$, $f(x, y) = bx$, $g(x, y) = cx$ and $\mu(t) = d$. In the late 1950s, Holling (1959a,b) conducted a comprehensive review of predation theory and proposed a classification of predation models based on the form of their functional responses. These types describe how predation rate varies with prey density:

* **Type I (linear)**
Predation rate increases linearly with prey density until a maximum is reached. This simple, though somewhat unrealistic, model assumes that predators can process prey immediately upon encounter. It's often used in simplified models.

* **Type II (decelerating)**
Predation rate still increases with prey density, but the rate of increase slows as prey becomes more abundant. This reflects factors such as predator handling time (the time it takes to consume each prey item) or satiation (when the predator becomes full).

* **Type III (sigmoidal)**
Predation rate follows a sigmoidal curve, i.e., a slow initial increase at low prey densities, followed by an accelerated increase at moderate densities, and finally saturation at high densities.
This pattern often results from more complex predator behavior, such as learning, improved search efficiency, or the presence of prey refugia. It suggests that predators initially struggle to find or handle prey, but become more efficient over time.

Since Holling's publication, a fourth type has been added. Type IV responses are not monotonic. They increase to a maximum rate and decrease for higher values of x. The decrease can be explained by resource toxicity or predator confusion (Gentleman et al., 2003). These responses can be found for some bacterial processes. Below, we give some examples of functional responses:

<table><thead><tr><th>Type</th><th>I</th><th>II</th><th>III</th><th>IV</th></tr></thead><tbody><tr><td>f(x, y)</td><td>cx</td><td>αx / (β + x)</td><td>αx<sup>2</sup> / (β + x<sup>2</sup>)</td><td>αx / (β + x + γx<sup>2</sup>)</td></tr></tbody></table>

These responses are illustrated<fnref n="134" /> in Figure 65. Type II responses are the most well-known, as they are associated with the Michaelis-Menten-Monod equation.

<figure><img src="image_100.png" /><figcaption>Figure 65: Holling functional responses</figcaption></figure>

Tanner (1975) used the Holling Type II functional response and assumed that both prey and predator populations follow a logistic growth model. This leads to the Holling-Tanner predator-prey model:

$$ \begin{cases} \frac{dx(t)}{dt} = \delta x(t) \left(1 - \frac{x(t)}{\kappa}\right) - \frac{\alpha x(t)}{\beta + x(t)} y(t) \\ \frac{dy(t)}{dt} = \sigma y(t) \left(1 - \frac{y(t)}{\gamma x(t)}\right) \end{cases} $$

<footnote n="134">¹³⁴We have used the following parameters: c = 0.2 (type I), α = β = 1 (types II and III), α = 4, β = 1 and γ = 2.5 (type IV).</footnote>
where $x(t)$ is the prey population and $y(t)$ is the predator population. Here, the carrying capacity of the predator population is related to the size of the prey population: $\kappa_y = \gamma x(t)$. The interpretation of the parameters is the following: $\delta$ is the intrinsic growth rate of the prey, $\kappa$ is the carrying capacity of the prey, $\alpha$ is the maximum predation rate, $\beta$ is the half-saturation constant that determines how prey abundance affects predation, $\sigma$ is the intrinsic growth rate of the predators, and $\gamma$ is the maximum predator-prey ratio. This model is more complex than the single logistic growth model because it can have four critical points<fnref n="135" />. Figure 66 shows the simulation<fnref n="136" /> of the Holling-Tanner predator-prey model when the initial values are $x_0 = 8$ and $y_0 = 5.5$, and the parameters are $\delta = 10\%$, $\kappa = 100$, $\alpha = 4$, $\beta = 20$, $\sigma = 20\%$, and $\gamma = 10\%$.

<figure><img src="image_101.png" /><figcaption>Figure 66: Simulation of the Holling-Tanner predator-prey model</figcaption></figure>

Previous functional responses depend only on the prey population. In a seminal paper, Arditi and Ginzburg (1989) challenge the traditional view of predator-prey interactions in which the predator's consumption rate depends solely on prey density. They propose an alternative approach in which

$$ \frac{dy(t)}{dt} = 0 \Leftrightarrow y(t) = 0 \land y(t) = \gamma x(t) $$

and:

$$ \frac{dx(t)}{dt} = 0 \Leftrightarrow \delta x(\kappa - x)(\beta + x) - \alpha \gamma \kappa x^2 = 0 $$

We can show that there can be four equilibria: $(0, 0)$, $(\kappa, 0)$, $(x_-, \gamma x_−)$ and $(x_+, \gamma x_+)$ where:

$$ x_{\pm} = \frac{(\delta\kappa - \delta\beta - \alpha\gamma\kappa) \pm \sqrt{(\delta\kappa - \delta\beta - \alpha\gamma\kappa)^2 + 4\beta\delta^2\kappa}}{2\delta} $$

<footnote n="135">¹³⁵The stability analysis implies that:</footnote>

<footnote n="136">¹³⁶It corresponds to the blue curve.</footnote>
the predator's consumption rate depends on the ratio of prey to predator densities, introducing the concept of ratio-dependent predation $r = x/y$:

$$f(x, y) = \frac{\alpha r}{\beta + r} = \frac{\alpha x}{\beta y + x}$$

In prey-dependent models (Holling type II), when prey is rare, predation is still significant. In ratio-dependent models, when prey is rare relative to predators, predation decreases dramatically, which is more realistic in nature. Figure 66 shows the effect of using a ratio-dependent model instead of a prey-dependent model<fnref n="137" />.

**Remark 14** The introduction of harvesting in the Holling-Tanner predator-prey model is the same as in the Lotka-Volterra model. We can then derive the equilibria (Diz-Pita and Otero-Espinar, 2021), analyze the effects of harvesting policy and define the maximum sustainable yield (Ghosh and Kar, 2013; Kar and Ghosh, 2013).

While predator-prey models describe interactions in which one species benefits at the expense of another, competition models describe interactions in which both species suffer from their coexistence due to competition for shared resources such as food, space, or other ecological resources (Polis et al., 1989). The most well-known competition model is the Lotka-Volterra multi-species competition model, which is mathematically similar to the predator-prey model but with negative interactions for both species (Case and Gilpin, 1974):

$$\frac{dx_i(t)}{dt} = \delta_i x_i(t) \left( 1 - \frac{\sum_{j=1}^{n} \omega_{i,j} x_j(t)}{\kappa_i} \right) \quad (34)$$

where $i = 1, \dots, n$ is the species index, $n$ is the number of species, $\delta_i$ and $\kappa_i$ are the intrinsic growth rate and the carrying capacity of species $i$, and $\omega_{i,j} \ge 0$ is the competition coefficient (how much species $j$ affects species $i$). Another interference competition model consists in considering that each species is a prey for the other species. In this case, we consider a multi-species logistic growth model and include Lotka-Volterra penalties:

$$\frac{dx_i(t)}{dt} = \delta_i x_i(t) \left(1 - \frac{x_i(t)}{\kappa_i}\right) - \sum_{j \neq i} \alpha_{i,j} x_i(t) x_j(t) \quad (35)$$

where $\alpha_{i,j} \ge 0$ is the coefficient of interaction between species $i$ and $j$. More generally, interference competition models combine the logistic growth model and Holling functional responses:

$$\frac{dx_i(t)}{dt} = \delta_i x_i(t) \left(1 - \frac{x_i(t)}{\kappa_i}\right) - \sum_{j \neq i} f_{i,j}(x_1(t), \dots, x_n(t)) x_j(t) \quad (36)$$

Model (35) is a special case of model (36) when we use a Holling type I linear response: $f_{i,j}(x_1, \dots, x_n) = \alpha_{i,j}x_i$. For Holling type II responses, we can write:

$$f_{i,j}(x_1, \dots, x_n) = \frac{\alpha_{i,j} x_i}{\beta_{i,j} + x_i}$$

These different models can exhibit chaotic behavior, even in low dimension. For instance, Vano et al.

<footnote n="137">¹³⁷We use exactly the same parameters and initial values as in the classical Holling-Tanner prey-dependent model. The blue curves correspond to the prey-dependent model, while the red curves correspond to the ratio-dependent model.</footnote>
Box 19: Symbiosis and interspecific interactions

The term symbiosis, derived from Greek roots meaning ‘together’ and ‘living’, describes a close and long-term biological interaction between two different organisms. An ecological community includes all the populations of different species that coexist in a given area. The interactions between these species are called interspecific interactions. They can be categorized as positive (+), negative (−), or neutral (0) based on their effects on the species involved (Polis et al., 1989). There are seven broad types of symbiosis:

1. Amensalism (−/0)

One species is harmed and the other is unaffected, often through the release of a chemical compound.

2. Commensalism (+/0)

One species benefits while the other remains unaffected. A classic example is bar-nacles that attach themselves to whales for transportation, gaining a mobile habitat while the whale remains indifferent.

3. Competition (−/−)

Both species are negatively affected by the limited availability of shared resources such as food, water, or nesting sites. For example, the swift fox (*Vulpes velox*) and coyotes (*Canis latrans*) competing for the same resources may both experience reduced reproductive success (Kitchen et al., 1999).

4. Mutualism (+/+)

Both species benefit from the interaction. A common example is the relationship between bees and flowers, where bees receive food (nectar) and flowers receive pol-lination assistance.

5. Neutralism (0/0)

Neither species is affected by the interaction. Although theoretically possible, true neutralism is difficult to prove definitively in complex ecosystems. For example, a butterfly and a snake living in the same habitat may appear neutral.

6. Parasitism (+/−)

One species (the parasite) benefits at the expense of the other (the host). Ticks that feed on the blood of a mammal are an example of this relationship, where the tick gains nourishment while the host experiences potential harm and irritation.

7. Predation (+/−)

One species (the predator) benefits by killing and consuming the other (the prey). A lion hunting a zebra is a clear example of predation.

(2006) studied model (34) with four species and the following parameters: $\delta = (1.00, 0.72, 1.53, 1.27)$, $\kappa = (10, 10, 10, 10)$ and:

$$ \Omega = (\omega_{i,j}) = \begin{pmatrix} 1 & 1.09 & 1.52 & 0 \\ 0 & 1 & 0.44 & 1.36 \\ 2.33 & 0 & 1 & 0.47 \\ 1.21 & 0.51 & 0.35 & 1 \end{pmatrix} $$
They showed that the chaotic attractor of this dynamical system corresponds to homoclinic orbits (Figure 67).

<figure><img src="image_102.png" /><figcaption>Figure 67: Phase portrait of the Lotka-Volterra four-species competition model</figcaption></figure>

Source: Vano et al. (2006, Figure 3, page 2386).

A second family of competition models explicitly introduces a set of resources. One of the first explicit resource competition models was proposed by MacArthur and Levins (1964). This model has been extensively used and extended by Robert MacArthur in several research papers and is now known as MacArthur consumer-resource model (Chesson, 1990). In MacArthur (1970), the model is formulated as follows:

$$ egin{cases} \frac{dx_i(t)}{dt} = \beta_i x_i(t) \left( \sum_{j=1}^{m} \alpha_{i,j} \omega_j y_j(t) \right) - \mu_i x_i(t) \\ \\ \frac{dy_j(t)}{dt} = \delta_j y_j(t) \left( 1 - \frac{y_j(t)}{\kappa_j} \right) - \sum_{i=1}^{n} \alpha_{i,j} x_i(t) \end{cases} $$

where $x_i(t)$ represents the population density of species i, $y_j(t)$ represents the population density of resource j (e.g., food resources), $\beta_i$ is a conversion factor that translates resource consumption into per capita growth rate, $\alpha_{i,j}$ is the rate at which species i captures resource j, $\omega_j$ is the value of a unit of resource j to the consumer (e.g., caloric energy), $\mu_i$ is the mortality rate of species i, and $\delta_j$ is the intrinsic growth rate of resource j. A variant of the MacArthur model assumes that resource availability depends on three factors: supply, depletion and consumption. In this case, we
Box 20: Robert H. MacArthur

Despite his tragically short life (1930–1972), Robert MacArthur was one of the most influential ecologists of the 20th century (Fretwell, 1975; Brown, 1999). He received his Ph.D. from Yale University in 1957 under the direction of G. E. Hutchinson and was a professor at the University of Pennsylvania and Princeton University. He is known for developing the theory of island biogeography, the study of limiting similarity (niche theory), consumer-resource theory, and his contributions to the complexity-stability debate. According to Brown (1999), “MacArthur’s influence stems not only from his substantial and frequently cited published works but also from his direct personal interactions and collaborations with contemporary scientists, especially young people”. He had a significant influence on his co-authors, all of whom went on to brilliant careers: Martin L. Cody, Joseph Hurd Connell, Jared Mason Diamond, Henry Stainken Horn, George Evelyn Hutchinson, James R. Karr, Peter Hubert Klopfer, Richard Levins, Robert McCredie May, Harry Frederick Recher, Eric Rodger Pianka, Michael L. Rosenzweig, and Edward Osborne Wilson.

<figure><img src="image_103.png" /></figure>

obtain the following Lotka-Volterra competition model with externally supplied resources (Cui et al., 2024):

$$ egin{cases} \frac{dx_i(t)}{dt} = \beta_i x_i(t) \left( \sum_{j=1}^{m} \alpha_{i,j} \omega_j y_j(t) \right) - \mu_i x_i(t) \\ \frac{dy_j(t)}{dt} = \kappa_j - \varrho_j y_j(t) - \left( \sum_{i=1}^{n} \alpha_{i,j} x_i(t) \right) y_j(t) \end{cases} $$

where $\kappa_j$ is the supply of resource j, $\varrho_j$ is the natural depletion or decay rate of resource j, and $\alpha_{i,j}$ is the rate at which species i consumes resource j. Along with Robert MacArthur, David Tilman is another prolific researcher on competition models and their impact on community structure. In his book, Tilman (1982) used the following general formulation:

$$ egin{cases} \frac{dx_i(t)}{dt} = f_i(y_1(t), \dots, y_m(t)) x_i(t) - \mu_i(t) x_i(t) \\ \frac{dy_j(t)}{dt} = g_j(y_j(t)) - \sum_{i=1}^{n} \alpha_{i,j} (y_1(t), \dots, y_m(t)) f_i(y_1(t), \dots, y_m(t)) x_i(t) \end{cases} $$

where $f_i(y_1, \dots, y_m)$ is the growth function of consumer i, $\mu_i(t)$ is the mortality rate function of consumer i, $g_j(y_j)$ is the growth function of resource j, and $\alpha_{i,j}(y_1, \dots, y_m)$ is the conversion factor function from resource j to consumer i. The MacArthur consumer-resource model and the Lotka-Volterra competition model with externally supplied resources are both special cases of Tilman's general model<fnref n="138" />.

<footnote n="138">¹³⁸The MacArthur consumer-resource model is obtained by setting $f_i(y_1, \dots, y_m) = \beta_i \sum_{j=1}^{m} \alpha_{i,j} \omega_j y_j$, $\mu_i(t) = \mu_i$,</footnote>
The competitive exclusion principle (or Gause's law) states that two species competing for exactly the same resource cannot stably coexist indefinitely. One will outcompete the other. For coexistence to occur, the species must occupy slightly different niches or use resources differently (niche theory). This traditional interpretation of competitive exclusion and niche theory has been challenged by much research. In fact, although there are many examples where the traditional interpretation is valid<fnref n="139" />, there are also many examples where multiple competitive species can coexist<fnref n="140" />. Moreover, Armstrong and McGehee (1980) demonstrated that under certain conditions, multiple species can coexist with fewer resources in a Lotka-Volterra model of multi-species competition. In fact, the relationship between biodiversity and competition is more complex, as demonstrated by Chesson (1990), who examined the processes that allow multiple species to coexist within the same ecological community and addressed the apparent paradox of high biodiversity despite competitive interactions. He categorized mechanisms that promote species coexistence into two primary types: equalizing mechanisms (which reduce average fitness differences between species, minimizing competitive inequalities) and stabilizing mechanisms (which increase negative intraspecific interactions relative to interspecific interactions, promoting coexistence by ensuring that species limit their own population growth more than they limit that of others). He also emphasized that environmental variability, both spatial and temporal, can facilitate coexistence. It follows that competition and harvesting are not equivalent. It is not obvious that competition dramatically reduces biodiversity, which is not the case with harvesting. However, the effects of harvesting strategies in multi-species consumer-resource metapopulations are not well known. For example, Strevens and Bonsall (2011) found that certain harvesting strategies, particularly fixed proportion harvesting, resulted in larger regional population sizes, fewer local extinctions, and higher yields compared to unharvested metapopulations. This counterintuitive result suggests that increasing local mortality through specific harvesting methods can increase overall population sizes — a phenomenon referred to as the 'hydra effect'. However, as Abrams (2009) points out, there is little empirical evidence for hydra effects in nature. Thus, there is a gap between previous theoretical and empirical results.

### 3.4.3 Overexploitation in aquatic systems

In the early 2000s, several important research papers alerted the scientific community to the threat of overfishing and the potential collapse of marine ecosystems. Jackson et al. (2001) examined how historical overfishing had led to the degradation and collapse of coastal ecosystems over time. Using a variety of historical data — including archaeological records, historical documents, and paleoecological studies — the authors described how the removal of top predators and key species (e.g., sharks, sea otters, cod, oysters) through overfishing could trigger cascading effects throughout the food web, leading to shifts in the abundance and distribution of other species. For example,

<footnote>$g_j(y_j) = \delta_j y_j (1 - \kappa_j^{-1} y_j)$, and $\alpha_{i,j} (y_1, \dots, y_m) = (\beta_i \sum_{j=1}^m \alpha_{i,j} \omega_j y_j)^{-1} \alpha_{i,j}$. For the Lotka-Volterra competition model with externally supplied resources, the generic functions for the resources are $g_j(y_j) = \kappa_j - \varrho_j y_j$, and $\alpha_{i,j} (y_1, \dots, y_m) = (\beta_i \sum_{j=1}^m \alpha_{i,j} \omega_j y_j)^{-1} \alpha_{i,j} y_j$.</footnote>

<footnote n="139">¹³⁹ A famous example is the competition between the flour beetles *Tribolium castaneum* and *Tribolium confusum* (Park, 1962; Pointer et al., 2021).</footnote>

<footnote n="140">¹⁴⁰ A well-known example that challenges the competitive exclusion principle is the plankton paradox, first described by Hutchinson (1961). In aquatic ecosystems, numerous species of phytoplankton coexist despite competing for a limited number of resources such as light, nitrogen, and phosphorus. According to the principle of competitive exclusion, only a few dominant species should survive because they would outcompete the others. In natural environments, however, dozens of phytoplankton species coexist in the same habitat. A second well-known example was described by MacArthur (1958), who studied five species of warblers (small songbirds) coexisting in the same forest areas and apparently feeding on the same insects. This apparent contradiction of competitive exclusion led to the development of the theory of niche differentiation.</footnote>
the loss of large predators led to an increase in prey species, which in turn overgrazed habitats. In addition, the destruction of habitat-forming species (e.g., oyster reefs, seagrass beds, corals) led to a decline in biodiversity and ecosystem functions. Pauly et al. (2002) introduced the concept of ‘fishing down marine food webs’, where fisheries increasingly target smaller, lower trophic level species as larger predatory fish become scarce. The authors emphasized that without significant intervention, global fisheries would continue to decline, putting marine biodiversity at risk. Pauly et al. (2005) built on these themes and expanded the discussion of the consequences of overfishing for marine ecosystems and global food security:

> “With the development of industrial fishing, and the resulting invasion of the refuges previously provided by distance and depth, our interactions with fisheries resources have come to resemble the wars of extermination that newly arrived hunters conducted 40 000–50 000 years ago in Australia, and 12 000–13 000 years ago against large terrestrial mammals in North America.” (Pauly et al., 2005, page 5).

Another milestone was reached with the publication of empirical research by Myers and Worm (2003) and Worm et al. (2006) in *Nature* and *Science*. Myers and Worm (2003) defined the following biomass time-trend model:

$$N_i(t) = N_i(0) \left( (1 - \varphi_i) e^{-r_i(t-t_0)} + \varphi_i \right)$$

where $N_i(t)$ is the biomass at time $t$, $N_i(0)$ is the initial biomass before industrialized exploitation, $r_i$ is the rate of decline and $\varphi_i$ is the fraction of the community that remains at equilibrium as $t \to \infty$. Using large predator biomass data from nine oceanic ecosystems (tropical/subtropical/temperate × Atlantic/Indian/Pacific) and four shelf ecosystems (Gulf of Thailand, South Georgia, Southern Grand Banks, and Saint Pierre Banks), they estimated the coefficients $\hat{r}_i$ and $\hat{\varphi}_i$ by using the method of maximum likelihood, and the parameters of the nonlinear mixed-effects models assuming that $r_i \sim N(\mu_r, \sigma_r^2)$ and $\varphi_i \sim N(\mu_\varphi, \sigma_\varphi^2)$. Figure 68 shows the results for the global region and three specific regions<fnref n="141" />. The authors concluded that industrialized fisheries typically reduce community biomass by 80% within 15 years of exploitation, and that the biomass of large predatory fishes is now only about 10% of pre-industrial levels. Worm et al. (2006) “analyzed local experiments, long-term regional time series, and global fisheries data to test how biodiversity loss affects marine ecosystem services across temporal and spatial scales.” They found that “rates of resource collapse increased and recovery potential, stability, and water quality decreased exponentially with declining diversity.” They emphasized that their findings highlight the societal consequences of the ongoing erosion of

<table><thead><tr><th>Region</th><th>Î</th><th>μ<sub>r</sub></th><th>φ̂<sub>i</sub></th><th>μ<sub>φ</sub></th></tr></thead><tbody><tr><td>Tropical Atlantic</td><td>16.6</td><td>16.7</td><td>12.1</td><td>11.9</td></tr><tr><td>Subtropical Atlantic</td><td>12.9</td><td>13.0</td><td>8.1</td><td>8.3</td></tr><tr><td>Temperate Atlantic</td><td>21.4</td><td>20.3</td><td>4.7</td><td>5.3</td></tr><tr><td>Tropical Indian</td><td>9.2</td><td>9.5</td><td>17.6</td><td>16.8</td></tr><tr><td>Subtropical Indian</td><td>6.5</td><td>6.8</td><td>8.2</td><td>9.2</td></tr><tr><td>Temperate Indian</td><td>30.7</td><td>27.7</td><td>5.5</td><td>6.3</td></tr><tr><td>Tropical Pacific</td><td>12.1</td><td>12.4</td><td>15.5</td><td>14.9</td></tr><tr><td>Subtropical Pacific</td><td>12.8</td><td>13.5</td><td>23.5</td><td>21.5</td></tr><tr><td>Temperate Pacific</td><td>20.8</td><td>20.4</td><td>8.2</td><td>8.5</td></tr><tr><td>Gulf of Thailand</td><td>25.6</td><td>22.2</td><td>9.3</td><td>9.8</td></tr><tr><td>South Georgia</td><td>166.6</td><td>30.8</td><td>20.9</td><td>16.0</td></tr><tr><td>Southern Grand Banks</td><td>4.0</td><td>5.7</td><td>0.0</td><td>10.0</td></tr><tr><td>Saint Pierre Banks</td><td>5.1</td><td>6.3</td><td>2.7</td><td>7.9</td></tr><tr><td>Global</td><td></td><td>16.0</td><td></td><td>10.3</td></tr></tbody></table>

<footnote n="141">¹⁴¹Below, we report the estimated values found by Myers and Worm (2003, Table 1, page 281):</footnote>
biodiversity, which appears to be accelerating globally. Moreover, they expressed serious concern about this trend, as their regression model predicted the global collapse of all commercially fished taxa by the mid-21st century (specifically, by 2048). Some newspapers and journalists have focused on this figure, interpreting it as a prediction that “*the oceans will be empty by 2048*”. For example, this claim was mentioned in the controversial documentary *Seaspiracy*, which premiered on Netflix in 2021.

<figure><img src="image_104.png" /><figcaption>Figure 68: Time trends of community biomass in oceanic and shelf ecosystems</figcaption></figure>

Source: Myers and Worm (2003, Figure 1, page 280) & Author's calculations.

As Boris Worm later explained, this period marked the peak of pessimism. Indeed, Worm's study was not the only one with a gloomy outlook — many studies in the 2000s warned that the collapse of global fisheries could become a reality. Thurstan et al. (2010) analyzed annual demersal fish landings from bottom trawls in the United Kingdom from 1889 to 2007 (Figure 69). Their results showed a dramatic decline in the commercial productivity of fisheries, with landings per unit of fishing power falling by 94% over the past 118 years. Another well-known example is the collapse of the Atlantic cod fishery in eastern Canada (Myers et al., 1997). According to Schijns et al. (2021), “*the fishery for Northern Atlantic cod ( Gadus morhua ) off Newfoundland and Labrador, Eastern Canada, presents the most spectacular case of an exploited stock crashed in a few decades by an industrial bottom trawl fishery under a seemingly sophisticated management regime after half a millennium of sustainable fishing. The fishery, which had generated annual catches of 100 000 to 200 000 tonnes from the beginning of the 16th century to the 1950s, peaked in 1968 at 810 000 tonnes, followed by a devastating collapse and closure 24 years later.*” Figure 70 illustrates the evolution of cod harvests in eastern Canada. We observe a continuous increase throughout the 18th and 19th centuries, with a sharp acceleration in the 20th century, especially after 1960. Schijns et al. (2021) estimated the maximum sustainable yield at 380 000 tonnes, a level well below the harvest volumes
<figure><img src="image_105.png" /><figcaption>Figure 69: Fish catch in the United Kingdom</figcaption></figure>

Source: Thurstan et al. (2010, Figure 1a, page 2) & www.ourworldindata.org/fish-and-overfishing.

<figure><img src="image_106.png" /><figcaption>Figure 70: Northern code catch in eastern Canada</figcaption></figure>

Source: Schijns et al. (2021, Figure 1, page 2679) & Author's calculations.
recorded between 1960 and 1990. We have also reported the estimated stock levels in Figure 70, which clearly shows the abrupt collapse in just thirty years<fnref n="142" />. On July 2, 1992, the Canadian government announced a moratorium on cod fishing. Originally intended to be a temporary two-year ban to allow the northern cod population to recover, the moratorium has effectively become permanent, as cod stocks have shown very limited signs of recovery even after more than 30 years. This decision had a devastating impact on coastal communities that depended on the fishery, resulting in the loss of approximately 30 000 jobs — one of the largest mass layoffs in Canadian history. Other notable examples of fishery collapses and management challenges include the Californian Pacific sardine collapse (which reached its lowest point in the mid-1950s), the Peruvian anchoveta crisis (1972-1973, triggered by an El Niño event combined with overfishing), the overexploitation of orange roughy (beginning in the 1980s, particularly severe in New Zealand and Australia), the significant decline of Pacific bluefin tuna (throughout the 20th century), and the Namibian sardine collapse (late 1960s to early 1970s). In some cases, however, these species have shown signs of recovery.

<figure><img src="image_107.png" /><figcaption>Figure 71: Global seafood production in Mt (1960-2022)</figcaption></figure>

Source: https://databank.worldbank.org/source/world-development-indicators & Author's calculations.

To better understand the current sustainability of fisheries in the world, we refer to the FAO report on world fisheries and aquaculture. According to FAO (2024b), fish production (including algae production) reached a new record high, with a total production<fnref n="143" /> of 223.2 million tonnes

$$x(t+1) = x(t) + \delta x(t) \left(1 - \frac{x(t)}{\kappa}\right) - h(t)$$

where $x(t)$ is the biomass stock in year $t$, $\delta$ is the intrinsic growth rate, $\kappa$ is the carrying capacity and $h(t)$ is the fish harvest in year $t$.

<footnote n="142">¹⁴²The biomass stock is estimated using the discrete version of the logistic growth population model with harvest:</footnote>

<footnote n="143">¹⁴³63% of this production is finfish, 17% algae, 11% molluscs and 8% crustaceans.</footnote>
(Figure 71). Of this production, 89% is used for human consumption, equivalent to an estimated 20.7 kg per capita, while the remainder is mainly used for fishmeal and fish oil. The FAO estimates that 61.8 million people are employed in the primary production sector. In sixty years, fish production has increased by a factor of more than 5, while the growth rate between 1960 and 2022 is 2.98%. These figures can be explained by two factors: an increase in the world's population and an increase in the share of seafood in the human diet.

<figure><img src="image_108.png" /><figcaption>Figure 72: Global capture fisheries and aquaculture in Mt (1960–2022)</figcaption></figure>

Source: https://databank.worldbank.org/source/world-development-indicators & Author's calculations.

An annual growth rate of 3% means a multiplication factor of 19 over one century and 369 over two centuries! In this context, it is understandable that some people predict that sooner or later the oceans will be empty. However, if we examine the components of the production, namely capture fisheries and aquaculture<fnref n="144" />, we observe two distinct phases (Figure 72):

* From 1960 to 1990, production growth was driven primarily by capture fisheries.

* Since 1990, capture fisheries have remained constant, while aquaculture has experienced impressive growth.

In 1960, capture fisheries accounted for 95% of seafood production, while today aquaculture contributes about 60% of total seafood production. As a result, fish production has shifted from an

<footnote n="144">¹⁴⁴Here are the definitions used by the FAO. Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes. Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption. Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included.</footnote>
industry based on the capture of wild fish to one based on aquaculture. Table 46 shows seafood

<table><caption>Table 46: Fisheries production by country (1960–2022)</caption><thead><tr><th>Country</th><th>1960</th><th>1980</th><th>2000</th><th>2010</th><th>2020</th><th>2022</th></tr></thead><tbody><tr><td colspan="7">Capture fisheries production</td></tr><tr><td>World (in Mt)</td><td>31.6</td><td>58.1</td><td>94.1</td><td>87.7</td><td>90.3</td><td>88.0</td></tr><tr><td>China</td><td>7.0</td><td>5.4</td><td>15.8</td><td>17.2</td><td>14.9</td><td>15.0</td></tr><tr><td>Indonesia</td><td>2.2</td><td>2.8</td><td>4.4</td><td>6.1</td><td>7.7</td><td>8.4</td></tr><tr><td>India</td><td>3.5</td><td>3.6</td><td>4.0</td><td>5.4</td><td>5.2</td><td>6.3</td></tr><tr><td>Peru</td><td>11.1</td><td>4.7</td><td>11.3</td><td>4.9</td><td>6.3</td><td>6.1</td></tr><tr><td>Russia</td><td>0.0</td><td>0.0</td><td>4.3</td><td>4.6</td><td>5.6</td><td>5.7</td></tr><tr><td>United States</td><td>8.6</td><td>6.4</td><td>5.1</td><td>5.0</td><td>4.7</td><td>4.8</td></tr><tr><td>Vietnam</td><td>1.4</td><td>0.8</td><td>1.7</td><td>2.6</td><td>3.9</td><td>4.1</td></tr><tr><td>Japan</td><td>18.7</td><td>17.3</td><td>5.5</td><td>4.8</td><td>3.6</td><td>3.4</td></tr><tr><td>Chile</td><td>1.1</td><td>5.0</td><td>4.8</td><td>3.5</td><td>2.4</td><td>3.1</td></tr><tr><td>Norway</td><td>4.4</td><td>4.4</td><td>3.1</td><td>3.2</td><td>2.9</td><td>3.0</td></tr><tr><td colspan="7">Aquaculture production</td></tr><tr><td>World (in Mt)</td><td>2.0</td><td>7.6</td><td>43.0</td><td>78.0</td><td>122.8</td><td>126.9</td></tr><tr><td>China</td><td>46.8</td><td>40.8</td><td>69.2</td><td>61.3</td><td>57.4</td><td>59.4</td></tr><tr><td>Indonesia</td><td>3.9</td><td>3.0</td><td>2.3</td><td>8.0</td><td>12.1</td><td>11.5</td></tr><tr><td>India</td><td>2.2</td><td>4.8</td><td>4.5</td><td>4.9</td><td>7.0</td><td>8.1</td></tr><tr><td>Vietnam</td><td>1.8</td><td>1.3</td><td>1.2</td><td>3.5</td><td>3.8</td><td>4.1</td></tr><tr><td>Bangladesh</td><td>2.4</td><td>1.2</td><td>1.5</td><td>1.7</td><td>2.1</td><td>2.2</td></tr><tr><td>Philippines</td><td>3.0</td><td>4.4</td><td>2.6</td><td>3.3</td><td>1.9</td><td>1.9</td></tr><tr><td>Norway</td><td>0.1</td><td>0.1</td><td>1.1</td><td>1.3</td><td>1.2</td><td>1.3</td></tr><tr><td>Egypt</td><td>0.2</td><td>0.2</td><td>0.8</td><td>1.2</td><td>1.3</td><td>1.2</td></tr><tr><td>Chile</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.9</td><td>1.2</td><td>1.2</td></tr><tr><td>Myanmar</td><td>0.0</td><td>0.0</td><td>0.2</td><td>1.1</td><td>0.9</td><td>0.9</td></tr><tr><td colspan="7">Total production</td></tr><tr><td>World (in Mt)</td><td>33.6</td><td>65.5</td><td>136.7</td><td>165.0</td><td>211.9</td><td>213.6</td></tr></tbody></table>

Source: https://databank.worldbank.org/source/world-development-indicators & Author's calculations.

production by country across different decades<fnref n="145" />. In 1960, fish production was led by Japan, Peru, China, and the United States, with production shares of 18.6%, 10.4%, 9.5%, and 8.4%, respectively. These countries were followed by Norway (4.1%), India (3.5%), the United Kingdom (3.0%), Canada (2.8%), Spain (2.7%), South Africa (2.6%), Germany (2.3%), France (2.3%), Indonesia (2.3%), and Iceland (1.9%). In 2022, seafood production had become dominated by China with a production share of 41.5%, followed by Indonesia (10.3%), India (7.4%), Vietnam (4.1%), Peru (2.6%), Russia (2.5%), Bangladesh (2.2%), the United States (2.2%), Norway (2.0%), and Chile (2.0%). This represents a complete transformation of global seafood production over sixty years. For example, in 2022, former major producers had dropped significantly in rank: Japan (13th, 1.83%), Spain (23rd, 0.51%), Canada (25th, 0.41%), France (30th, 0.34%), and Germany (54th, 0.10%). Today, 70% of production is located in Asia, while South America, Europe, Africa and North America represent 9%, 9%, 7% and 3%, respectively. We also observe a long-term upward trend in per capita consumption of aquatic food products. Between 1961 and 2021, consumption rises from 9.1 kg to 20.6 kg.

<footnote n="145">¹⁴⁵These figures differ from previous ones because they exclude some forms of algae production.</footnote>
kg per person, an average annual increase of about 1.4%. The growth in fish production can then be attributed equally to demographic expansion and changes in dietary preferences.

FAO (2024b) assesses the sustainability of fisheries by comparing the biomass stock $x(t)$ with the maximum sustainable yield (MSY) and defines three categories:

<table><thead><tr><th>Category</th><th>Overfished</th><th>Maximally sustainably fished</th><th>Underfished</th></tr></thead><tbody><tr><td>x(t)/MSY</td><td>[0, 0.8[</td><td>[0.8, 1.2]</td><td>]1.2, +∞)</td></tr><tr><td>Unsustainable</td><td>✓</td><td></td><td></td></tr><tr><td>Sustainable</td><td></td><td>✓</td><td>✓</td></tr></tbody></table>

The FAO analysis shows that the proportion of fishery stocks within biologically sustainable levels declined from 94% in 1974 to 62% in 2021¹⁴⁶. This means that 38% of fishery stocks are not sustainable. This figure masks a large discrepancy between the 15 major FAO fishing regions<fnref n="147" />. Indeed, four areas have more than 50% of their fish stocks at unsustainable levels: the eastern central Atlantic (51.3%), the northwest Pacific (56%), the Mediterranean and Black Sea (62.5%), and the southeast Pacific (66.7%). In contrast, four areas have more than 75% of fisheries stocks that are sustainable: the eastern central Pacific (84.2%), the northeast Atlantic (79.4%), the northeast Pacific (76.5%), and the southwest Pacific (75.9%). According to FAO (2024b, Figure 20, page 45), three distinct patterns emerge:

1. Areas with a continuously increasing trend in landings since 1950

This group includes four areas: the eastern central Atlantic, the eastern and western Indian Ocean, and the western central Pacific.

2. Areas with landings oscillating around a globally stable value since 1990, associated with the dominance of pelagic, short-lived species

This group includes three areas: the northwest, northeast and eastern central Pacific.

3. Areas with an overall declining landing trend following historical peaks

This group includes the remaining eight regions.

With fisheries production expected to increase by 10% over the next decade, FAO has proposed a process to achieve this goal and improve sustainability: the *FAO Blue Transformation Roadmap 2022-2030*. This roadmap focuses on three main pillars: expanding sustainable aquaculture, improving fisheries management, and strengthening value chains and market access (FAO, 2022). Since we have reached a limit in terms of harvesting wild fish, the growth of fisheries production would be mainly through aquaculture. It is also important to improve fisheries management. For example, there is an urgent need to reduce discards or bycatch:

> "Discards, or discarded catch is that portion of the total organic material of animal origin in the catch, which is thrown away, or dumped at sea for whatever reason. It does not include plant materials and post-harvest waste such as offal. The discards may be dead, or alive." (Pérez Roda et al., 2019, page 1).

<footnote n="146">¹⁴⁶Each fishery stock is weighted equally in this assessment. When the stocks are weighted by the volume of landings, the 62% figure increases to 77%.</footnote>

<footnote n="147">¹⁴⁷These are the Atlantic (eastern central, northeast, northwest, southeast, southwest, western central), the Indian Ocean (eastern, western), the Mediterranean and Black Sea, and the Pacific (eastern central, northeast, northwest, southeast, southwest, western central).</footnote>
<figure><img src="image_109.png" /><figcaption>Figure 73: Estimated fish discards in Mt (1950–2018)</figcaption></figure>

Source: FishStat & www.ourworldindata.org/grapher/fish-discards.

This happens for various reasons, such as the fish are too small or young, they're not the target species, the fish have low commercial value, the catch exceeds the storage capacity of the vessel, the fish are damaged, etc. Precise estimates of global fish discards are difficult to obtain, but approximate values are available. Zeller et al. (2018) estimated that discards increased from 5 million tonnes per year in the early 1950s to a peak of 18.8 million tonnes in 1989, before declining to less than 10 million tonnes per year in recent years. This represented 10–20% of the total fish catch until 2000 and now represents less than 10% of the total annual catch. Similarly, Pérez Roda et al. (2019) estimated that annual discards averaged 9.1 million tonnes between 2010 and 2014, representing 10.8% of the total annual catch during this period. From a regional perspective, the northwest Pacific and northeast Atlantic together accounted for 39% of global discards. Among fisheries, those targeting crustaceans had the highest discard rates. In fact, the problem is not just bycatch, but fish waste in general. For example, Coppola et al. (2021) report that about two thirds of the total amount of fish is discarded. This explains why fish and seafood products provide about 1% of the total calories in the human diet at the global level. However, fish remains an important source of essential nutrients, providing omega-3 fatty acids, vitamins (A, B12 and D) and minerals such as iron (Fe), iodine (I), zinc (Zn) and calcium (Ca), which are essential for human health. Moreover, many local communities rely heavily on fisheries for their livelihoods. In this global context, the call of Worm et al. (2009) to rebuild global fisheries remains highly relevant (Duarte et al., 2020).

**Remark 15** To go further, the most prominent fisheries databases<sup>148</sup> include the FAO FishStat database, now called FishStatJ (data on fish production, trade and consumption), the RAM Legacy

<footnote n="148"><sup>148</sup>The websites are www.fao.org/fishery/en/statistics/software/fishstatj, www.ramlegacy.org and www.seaaroundus.org.</footnote>
Stock Assessment database (a global compilation of detailed stock assessment data), and Sea Around Us (a research initiative that provides global fisheries catch reconstructions, including estimates of illegal, unreported and unregulated fishing).

### 3.4.4 Overexploitation in tropical forests

By definition, overexploitation in tropical forest ecosystems is related to deforestation and habitat degradation. However, we need to distinguish between the two concepts: deforestation and overexploitation (Wilkie et al., 2011). According to Peres (2010), overexploitation of tropical forests involves three main issues:

* Timber extraction refers to the process of harvesting trees for commercial purposes, such as logging for wood, paper, and construction materials;

* Tropical forest vertebrates have been hunting in tropical forests for over 100 000 years, but their consumption increased during the 20th century;

* Non-timber forest products are biological resources such as plants and raw materials.

Rice et al. (1997) questioned the feasibility and effectiveness of sustainable forest management in tropical regions. They argued that sustainable logging in tropical forests is often ineffective because it yields less timber than conventional logging. Empirical studies support this (Pearce et al., 2003). Another important challenge is the lack of financial incentives for loggers to harvest at sustainable levels and invest in forest regeneration, as economic principles suggest that trees should be harvested when their volume growth rate falls below the prevailing interest rate (Peres, 2010). Delaying harvesting beyond this point entails an opportunity cost, as profits from immediate harvesting could be reinvested elsewhere for higher returns. Reynolds and Peres (2006) used a study of Bolivian mahogany by Raymond Gullison to illustrate this problem. Despite legal restrictions, trees as small as 40 cm in diameter were harvested — well below the legal limit — because at this size, mahogany trees grow in volume at a rate of 4% per year, and their market value increases at about 1% per year due to rising timber prices, while Bolivia's real interest rates averaged 17% in the mid-1990s. This significant gap between tree growth rates and high interest rates creates strong economic pressure to harvest trees as soon as they have market value, rather than waiting for them to mature.

Let us formalize the opportunity cost problem. If we denote by $\Delta p$ and $\Delta v$ the annual changes in timber prices and tree volume relative to a reference age $t_0$, the market value $W(t)$ of the trees is given by:

$$W(t) = p(t)v(t) = p_0v_0(1 + \Delta p)^{t-t_0}(1 + \Delta v)^{t-t_0}$$

where $p_0$ and $v_0$ are the price level and the volume of trees at age $t_0$. Now assume that trees are harvested at age $\tau \le t$ and the proceeds are invested at the risk-free rate $r$. The resulting wealth conditional on this harvest is equal to:

$$W(t | \tau \le t) = p(\tau)v(\tau)(1+r)^{t-\tau}$$

The economic objective is to maximize the conditional wealth $W(t | \tau \le t)$ or to find the optimal harvest age $\tau$ that maximizes the return. Figure 74 illustrates the economic problem with the following assumptions: $t_0 = 20$ years, $p_0 = \$500/m^3$, $v_0 = 2 m^3$, $\Delta p = 1\%$ and $\Delta v = 4\%$. If the tree is harvested at age 20, the income is $W(20) = 500 \times 2 = \$1000$. If the harvest is delayed by 30 years, the income increases to $W(50) = (500 \times 1.01^{30}) \times (2 \times 1.04^{30}) = \$4371.6$. Now suppose the
<figure><img src="image_110.png" /><figcaption>Figure 74: Opportunity cost of logging in tropical forests</figcaption></figure>

interest rate is 3%, the income from logging at age 20 and reinvesting would be $W(50 | \tau = 20) = 1000 \times 1.03^{30} = \$2427.3$. Since $W(50 | \tau = 20) < W(50)$, there is a financial gain from waiting another 30 years before harvesting. However, if the interest rate is 7%, we have $W(50 | \tau = 20) = 1000 \times 1.07^{30} = \$7612.3$. This implies a significant opportunity cost for delaying the harvest$^{149}$. In general, there is an opportunity cost when the interest rate is relatively high, especially when $r \ge \Delta p + \Delta v$. According to Rice et al. (1997), there is no economic solution to this problem, and they suggest that protected areas are the only viable way to manage tropical forests sustainably. Twenty-five years later, the debate about selective logging and its impact on biodiversity remains open (Bicknell et al., 2015; Burivalova et al., 2021).

Hunting wild animals is a practice with deep historical roots, initially driven by the basic need for food. Today, food security in developed countries rarely depends on hunting. Conversely, it remains a vital source of food in many developing countries. In addition to providing food for personal consumption, hunting can be an important source of income for hunters and their families. In these cases, harvested animals are sold not only for food but also for other purposes, such as the production of leather goods and medicinal products. There is much evidence of overexploitation over the last fifty years, especially as the trade of wild animals has increased. This has two major consequences. First, hunting of wild animals causes biodiversity loss. For example, Peres and Palacios (2007) provided a meta-analysis of changes in population density for 30 species of mammals, birds, and

$$C = \underbrace{W(50 | \tau = 20)}_{\text{Income from 1st harvest}} + \underbrace{W(30 | \tau = 20)}_{\text{Income from 2nd harvest}} + \underbrace{W(10)}_{\text{3rd tree at age 10}} - \underbrace{W(50)}_{\text{Tree at age 50}}$$

<footnote>$^{149}$This opportunity cost is underestimated because two harvests could occur within 50 years. Therefore, the opportunity cost is given by:</footnote>
Box 21: The bushmeat crisis

Bushmeat is the meat of wild animals that are hunted for food (Nasi et al., 2008; Fa et al., 2002). The meat is either eaten by the hunter or sold to make money. In the media, the term bushmeat is generally used to refer to the illegal hunting of protected animals in Africa. This includes various species such as antelopes, monkeys, rodents and other wild animals. Bushmeat has historically been a vital protein source in parts of Africa, Latin America and South Asia. In remote rural areas of West and Central Africa, it constitutes 80–90% of animal protein intake. While subsistence hunting by local communities isn’t the main conservation issue, growing commercial demand is problematic. The market for bushmeat is now increasingly driven by urban consumers and diaspora communities, leading to unsustainable harvesting in several African regions.

Estimating the global impact of bushmeat harvesting is challenging. However, according to Nasi et al. (2011), “*the harvest of animals such as tapir, duikers, deer, pigs, peccaries, primates and larger rodents, birds and reptiles [...] represents around 6 million tonnes of animals extracted yearly [...] with an estimated yearly extraction rate in the Congo Basin of 4.5 million tonnes.*”. While more than 1 000 animal species are affected by bushmeat hunting, Ripple et al. (2016) estimated that approximately 300 of these terrestrial mammal species are threatened with extinction. This also means that the majority of mammal species (70%) are not listed as threatened on the IUCN Red List.

The term ‘bushmeat crisis’ refers to this paradox: the need for local people to hunt wild animals to improve their food security and well-being, while at the same time this practice has a significant negative impact on biodiversity. In this context, trade-offs and dilemmas are more difficult to resolve, as the study by Cawthorn and Hoffman (2015) shows.

reptiles in 101 forest sites. Populations declined by up to 75% in more intensively hunted areas compared to less intensively hunted areas. Of the 30 species studied, 22 declined significantly at high levels of hunting. As expected, body size significantly influenced the magnitude of changes in abundance, with large-bodied species declining faster in overhunted areas. Considering the 12 most hunted species, mean total biomass decreased almost elevenfold from 980 kg/km² in unhunted areas to only 89 kg/km² in highly hunted areas (Peres, 2010, page 111). The second consequence is the threat to the food security of local populations that depend on wildlife hunting. Indeed, hunting has become more sophisticated, systematic, and industrialized. For example, several studies indicate that African bushmeat has become an organized luxury market in African urban centers and restaurants (Gluszek et al., 2021), as well as in Europe (Gombeer et al., 2021), with porcupines, pangolins, antelopes, and snakes being the most sought-after products. For instance, Chaber et al. (2023) estimate that an average of 3.9 tonnes of bushmeat is smuggled through Brussels Airport each month. While the wildlife trade provides short-term financial benefits to hunters and their families, most do not make significant profits. Moreover, the ongoing loss of biodiversity today means food insecurity for these families in the future (Nasi et al., 2011).

Another way to understand the impact of overexploitation on biodiversity loss is the growth of trafficking in protected species of wild fauna and flora. According to Traffic (www.traffic.org), “*the illegal trade in wild species is one of the most profitable criminal activities in the world, estimated to be worth up to $23 billion each year.*” The UNODC (United Nations Office on Drugs and Crime) published the third edition of the World Wildlife Crime Report in 2024. This report shows that despite two decades of concerted action at the international and national levels and the entry into
Box 22: Operation Thunder 2024

On 4 February 2025, INTERPOL announced that nearly 20 000 live animals, all endangered or protected species, were seized in a global operation against wildlife and forestry trafficking networks jointly coordinated by INTERPOL and the World Customs Organization (WCO). *Operation Thunder 2024* took place in 138 countries between 11 November and 6 December 2024. Authorities arrested 365 suspects and identified six transnational criminal networks suspected of trafficking in animals and plants protected by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In addition to live animals, participating countries seized hundreds of thousands of protected animal parts and derivatives, trees, plants, marine life and arthropods. Timber cases represented the most significant seizures, occurring primarily in maritime container shipments, while most other seizures took place at airports and postal hubs. More than 100 companies involved in the trade of protected species were also identified. Significant seizures included:

* 134 tonnes of timber headed to Asia via ocean freight (Indonesia);

* 41 tonnes of exotic timber headed to Asia via ocean freight (Kenya);

* 4 472 kg of pangolin scales (Nigeria);

* 6 500 live songbirds discovered during a vehicle inspection at the Syrian border (Turkey);

* 5 193 live red-eared slider turtles concealed in passenger suitcases arriving from Malaysia at Chennai Airport (India);

* 3 700 protected plants intercepted en route from Ecuador (Peru);

* 8 rhino horns found in a suspect's luggage while transiting from Mozambique to Thailand (Qatar);

* One tonne of sea cucumbers, considered a seafood delicacy, smuggled from Nicaragua (United States);

* 973 kg of dried shark fins originating from Morocco seized at the airport (Hong Kong);

* 8 tigers, aged between two months and two years, discovered in a suspected illegal breeding facility (Czech Republic);

* 846 pieces of reticulated python skin, from the world's longest snake species, concealed onboard a ship (Indonesia).

Source: INTERPOL (2025), https://www.interpol.int/News-and-Events.
force in 1975 of CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora), wildlife trafficking continues worldwide. Moreover, the report estimates that “wildlife crime is interconnected with the activities of large and powerful organized crime groups operating in some of the most fragile and diverse ecosystems from the Amazon to the Golden Triangle<fnref n="150" />.” UNDOC (2024, page 46) reports that from 2015 to 2021, more than 140 000 seizures of wildlife products were made in 162 countries, involving 4 000 different species. Analysis of seizure records shows that coral pieces were the most common item found in the illegal wildlife trade, accounting for 16% of the total number of seizures. Other seizures included crocodiles (9%), elephants (6%), bivalves and carnivores (5% each), parrots and cockatoos (4%), orchids (4%), and many other species. Figure 75 illustrates the most affected species based on the standardized seizure index, which serves as a proxy for the market value of the seized goods. In this case, seizures of rhinos (29%) rank first, followed by pangolins (28%) and elephants (15%).

<figure><img src="image_111.png" /><figcaption>Figure 75: Percentage share by species group aggregated by standardized seizure index (2015–2021)</figcaption></figure>

Source: UNDOC (2024, Figure 2.3, page 63).

The case of the rhinoceros is emblematic, as hunting has had a devastating impact on rhinoceros populations, pushing several species to the brink of extinction since 1900. The number of black rhinos declined by over 95% from around 100 000 in 1960 to fewer than 2 500 in the 1990s due to hunting and poaching. Although conservation efforts have helped, they remain critically endangered with only about 6 200 animals recorded in 2021. White rhinos were nearly extinct, with the southern population falling to fewer than 100 individuals in the early 20th century. They have been saved by rigorous conservation and breeding programs. As of 2021, there are approximately 16 000 individuals.

<footnote n="150">¹⁵⁰The Golden Triangle refers to a mountainous region in Southeast Asia where the borders of Thailand, Laos and Myanmar meet.</footnote>
In contrast, the northern white rhino has virtually disappeared, with only two females remaining in 2021. Indian rhinos were reduced to less than 100 individuals in 1900 due to hunting, but have since recovered thanks to conservation efforts and now number around 4 000. Javan and Sumatran rhinos were already rare in 1900, but continued to decline due to hunting and habitat loss, and both species now number fewer than 100 individuals. In Figure 76, we show the evolution of the number of rhinos in the world. We observe that only three of the six species are really significant, and that the recovery since 1990 is mainly due to the expansion of the white rhino in southern Africa<fnref n="151" />.

<figure><img src="image_112.png" /><figcaption>Figure 76: Number of rhinoceros in the world (1960–2021)</figcaption></figure>

Source: www.ourworldindata.org/rhino-populations & Author's calculations.

Two decades ago, Bengis et al. (2004) and Kruse et al. (2004) raised concerns about the growing risk of zoonotic pandemics and the role of biodiversity loss, particularly deforestation and habitat alteration. Zoonoses are diseases transmissible from animals to humans caused by pathogens — viruses, bacteria, fungi, or parasites — that circulate naturally in animal populations<fnref n="152" />. In a landmark study, Taylor et al. (2001) analyzed 1 415 human pathogenic species and found that 61% were zoonotic. Since then, the emergence of new diseases and pandemics, many of which are linked or suspected to be linked to the wildlife trade, has underscored these concerns. Examples include avian influenza (H5N1), COVID-19, Ebola, H1N1 influenza, MERS, SARS, and Zika. In addition, research has established or strongly suggested a zoonotic origin for diseases such as bubonic plague, chikungunya, dengue fever, HIV/AIDS, Lyme disease, the 1918 Spanish flu, salmonellosis, and rabies. It is also now widely accepted that hunting and the bushmeat trade have become important factors in the transmission of zoonotic diseases (Hilderink and De Winter, 2021). The case of SARS-CoV-2 (COVID-19) is emblematic. While many hypotheses were initially proposed, it is now widely

<footnote n="151">¹⁵¹As of 2021, the numbers of rhinoceros are as follows: 15 940 southern white rhinos, 2 northern white rhinos, 6 195 black rhinos, 4 014 Indian rhinos, 76 Javan rhinos, and 41 Sumatran rhinos.</footnote>

<footnote n="152">¹⁵²The list of major zoonotic diseases can be found in Rahman et al. (2020).</footnote>
accepted that bats and pangolins likely played a role as reservoirs or intermediate hosts in the transmission of the virus (Zhang et al., 2021). However, Bernstein et al. (2022) identified three key areas for pandemic prevention: regulation of wildlife trade and hunting, mitigation of agricultural intensification and expansion, and conservation of tropical forests. The study also estimated that the cost of primary pandemic prevention measures is significantly less than the economic and human losses caused each year by emerging viral zoonoses.

The overexploitation of tropical forests is not limited to timber and wild animals. According to Peres (2010), it also includes plant products such as fruits, nuts, oil seeds, latex, resins, gums, medicinal plants, spices, dyes, ornamental plants, and raw materials such as firewood, climbing palms, bamboo, and rattan. Palm oil is a prominent example, and wild orchids are another<sup>153</sup>.

## 3.5 Climate change

The case of climate change is examined in detail in Part 2 of Roncalli (2025). For example, Chapter 8 explains the concept and economic implications of climate change, while Chapter 12 discusses the physical risk of climate change. Climate change and biodiversity are inextricably linked. A rise in temperature, more intense precipitation, and an increase in natural disasters will naturally affect biodiversity. Animals such as butterflies, insects and birds are typical examples. Similarly, climate change has significant impacts on many aspects of non-animal biodiversity, including agriculture, forests, freshwater ecosystems and soil health.

# 4 Biodiversity measurement

Measuring biodiversity is essential for governments, regions, municipalities, companies, financial institutions and investors for two primary reasons. First, they operate within regulatory frameworks that increasingly require biodiversity risk monitoring<sup>154</sup>. Second, they must make decisions that either affect or are affected by biodiversity, a concept known as double materiality. For example, a company's corporate social responsibility strategy must include a biodiversity dimension, project financing should consider the potential for biodiversity loss, and governments need biodiversity metrics to develop effective conservation strategies and policies. Similar to ESG and climate risk assessment, measuring biodiversity risk requires the collection of relevant data. This data is then used within standardized methodologies to calculate biodiversity metrics, such as mean species abundance. In addition, a variety of commercial and open source solutions are available to assess biodiversity risk at various scales, including geospatial points, regional, national, and company-specific levels.

## 4.1 Essential biodiversity variables

Measuring biodiversity is complex due to its multifaceted nature and numerous dimensions. Moreover, even when focusing on a single dimension, data availability is often limited. However, these data are critical for assessing progress towards biodiversity goals. Mace and Baillie (2007) emphasized the importance of developing effective biodiversity indicators that are relevant to policy objectives and simple enough to facilitate comparisons between regions and countries. They discussed the distinction between pressures, status and responses, and called for urgent action to define a set of biodiversity indicators that are truly relevant for measuring biodiversity risks. Nearly twenty years

<footnote n="153"><sup>153</sup>According to UNODC (2024), the five most trafficked plant species are cedar and other sapindales, rosewood, agarwood and other myrtales, golden chicken fern, and orchids. Palm oil is not listed in CITES.</footnote>

<footnote n="154"><sup>154</sup>This is discussed further in the following section on biodiversity governance and regulation.</footnote>
later, significant progress has been made, but biodiversity measurement remains a challenge due to the lack of consistent and clear indicators. The article by Mace and Baillie was written in the context of the 2010 Biodiversity Target adopted by the Convention on Biological Diversity (CBD), whose objective was to “halt the loss of biological diversity at all levels by the year 2010.” The monitoring of progress was defined with respect to 22 headline indicators<fnref n="155" />. In fact, a number of these indicators were vague, for example the indicators (8) Ecological footprint and related concepts, (10) Trends in invasive alien species and (15) Incidence of human-induced ecosystem failure.

<table><caption>Table 47: EBV classes and names</caption><thead><tr><th>#</th><th>EBV class</th><th>EBV theme</th></tr></thead><tbody><tr><td>1</td><td>Genetic composition</td><td>Genetic diversity (richness and heterozygosity), genetic differentiation (number of genetic units and genetic distance), effective population size, inbreeding</td></tr><tr><td>2</td><td>Species populations</td><td>Species distributions, species abundances</td></tr><tr><td>3</td><td>Species traits</td><td>Morphology, physiology, phenology, movement, reproduction</td></tr><tr><td>4</td><td>Community composition</td><td>Community abundance, taxonomic/phylogenetic diversity, trait diversity, interaction diversity</td></tr><tr><td>5</td><td>Ecosystem functioning</td><td>Primary productivity, ecosystem phenology, ecosystem disturbances</td></tr><tr><td>6</td><td>Ecosystem structure</td><td>Live cover fraction, ecosystem distribution, ecosystem vertical profile</td></tr></tbody></table>

Source: https://geobon.org/ebvs/what-are-ebvs.

The lack of clear indicators led Pereira et al. (2013) to introduce the concept of essential biodiversity variables (EBVs), with the aim of establishing a standardized framework for monitoring biodiversity change worldwide:

> “Despite progress in digital mobilization of biodiversity records and data standards, there is insufficient consistent national or regional biodiversity monitoring and sharing of such information. Along with inadequate human and financial resources, a key obstacle is the lack of consensus about what to monitor. Many initiatives collect data that could be integrated into an EBV global observation network, though important gaps remain. Different organizations and projects adopt diverse measurements, with some important biodiversity dimensions, such as genetic diversity, often missing.” (Pereira et al., 2013, page 277).

The essential biodiversity variables are maintained by the Group on Earth Observations Biodiversity Observation Network<fnref n="156" /> (GEO BON). Table 47 shows the 6 EBV classes and the associated EBV themes<fnref n="157" />. Schmeller et al. (2018) proposed to focus on the eight candidate EBVs given in Table 48. Many other indicators have been proposed by the scientific community, the latest and most promising indicators are certainly the remote sensing geospatial patterns measured by satellites (Skidmore et al., 2021). All these indicators can be used to build a biodiversity scoring system. However, the lack of consensus and standardization remains an obstacle to biodiversity monitoring.

<footnote n="155">¹⁵⁵For instance, the first three indicators were (1) Trends in extent of selected biomes, ecosystems, and habitats, (2) Trends in abundance and distribution of selected species and (3) Coverage of protected areas.</footnote>

<footnote n="156">¹⁵⁶The website is www.geobon.org, while the EBV datasets are available at https://portal.geobon.org/datasets.</footnote>

<footnote n="157">¹⁵⁷Here are some examples of EBV indicators promoted by GEO-BON: Biodiversity Habitat Index (BHI), Genetic Diversity Indicator (GDI), Global Ecosystem Restoration Index (GERI), Local Biodiversity Intactness Index (LBII),</footnote>
<table><caption>Table 48: Eight essential biodiversity variables (Schmeller et al., 2018)</caption><thead><tr><td>Indicator</td><td>Definition</td></tr></thead><tbody><tr><td>Abundance</td><td>Abundance is the number of individuals of a species within a local population.</td></tr><tr><td>Allelic diversity</td><td>Allelic diversity is the average number of alleles per locus in a population of a given species.</td></tr><tr><td>Body mass</td><td>Body mass scaled by body size, or the body mass index (BMI), indicates the condition and energy reserves of animals.</td></tr><tr><td>Ecosystem heterogeneity</td><td>Ecosystem heterogeneity describes the amount of variability in space and time of ecosystems.</td></tr><tr><td>Phenology</td><td>Phenology is defined as annually recurring life-cycle events, such as the timing of migration or flowering.</td></tr><tr><td>Range dynamics</td><td>Range dynamics are changes in species distributions through time, space and shape. This EBV is derived from the species distribution EBV for detecting critical ecological change early.</td></tr><tr><td>Size at first reproduction</td><td>Size at first reproduction is the individual body size (length) reached by an organism at the time when its first reproduction occurs.</td></tr><tr><td>Survival rates</td><td>Survival rate is the average probability that an organism will stay alive between two time points.</td></tr></tbody></table>

Source: Schmeller et al. (2018).

## 4.2 Biodiversity metrics

We have already mentioned the Living Planet Index and the Red List Index<fnref n="158" />. These indices focus on the extinction risk of species, and the better geographic resolution we have is the country. Below we present alternative simple measures of biodiversity that are available at higher resolutions.

### 4.2.1 Mean species abundance (MSA)

Mean species abundance is one of the most popular biodiversity indicators because it is easy to understand and has been widely popularized by GLOBIO (www.globio.info). Let $n_i$ be the abundance of species $i$ in an area $A$, $n_i^*$ be the abundance of species $i$ in a reference state, and $S_A$ be the number of native species in the area. The MSA of area $A$ is calculated as follows:

$$MSA = \frac{1}{S_A} \sum_{i=1}^{S_A} \min \left( \frac{n_i}{n_i^*}, 1 \right)$$

By construction we have $MSA \in [0, 1]$. An MSA of 1 corresponds to an undisturbed area, while a low MSA value indicates a highly disturbed area, where many native species have disappeared or experienced significant population declines. Consider the example illustrated in Figure 77. There are four native species in the reference area. In the disturbed area, the parrots have disappeared and have been replaced by rodents. The MSA is then calculated as follows:

$$MSA = \frac{1}{4} \left( \frac{0}{2} + \frac{2}{4} + \frac{3}{4} + \frac{1}{2} \right) = 43.75\%$$

Protected Area Representativeness & Connectedness indices (PARC), Invasive Alien Species Rate (IAS), Species Habitat Index (SHI), Species Protection Index (SPI), and Species Status Information Index (SSII).

<footnote n="158">¹⁵⁸See Roncalli (2025, Chapter 2).</footnote>
<figure><img src="image_113.png" /><figcaption>Figure 77: Computation of the MSA</figcaption></figure>

In GLOBIO, the MSA is calculated for different pressures: land use, climate change, roads, nitrogen deposition, hunting. An overall MSA is calculated by combining the MSA of the different pressures (Alkemade et al., 2009):

$$MSA = \prod_{j=1}^{p} MSA_{(j)}$$

where $MSA_{(j)}$ is the MSA for pressure $j$ and $p$ is the number of pressures (between 1 and 5). According to Schipper et al. (2020), the contribution of pressure $j$ to biodiversity loss is equal to:

$$C_{(j)} = \frac{(1 - MSA_{(j)})}{\sum_{j=1}^{p} (1 - MSA_{(j)})} (1 - MSA)$$

where $1 - MSA_{(j)}$ is the biodiversity loss for pressure $j$.

Figure 78 shows global MSA values for the year 2015. Notably, MSA values approach one in polar regions and deserts. This pattern requires careful interpretation because the MSA does not measure biodiversity richness, but rather the intactness of biodiversity relative to an undisturbed reference state (Schipper et al., 2020). In naturally species-poor ecosystems such as deserts, tundra, and polar regions, where reference biodiversity is already low, even limited human disturbance results in high MSA values. This reflects minimal deviation from the natural state rather than high biodiversity. In essence, when the reference biodiversity of a region is naturally low, the potential for biodiversity loss is limited, resulting in MSA values close to one in these ecosystems despite their relatively low species richness. This illustrates an important characteristic of the MSA metric: it quantifies the relative preservation of native ecological communities rather than absolute biodiversity.

**Remark 16** Since the release of the first version of GLOBIO in 2000, the model has been improved and enriched over the years. Three extensions of GLOBIO have been developed to cover additional relevant aspects of nature. GLOBIO-ES models changes in global ecosystem services by assessing current conditions, trends and future scenarios of nature's benefits to humans, such as food, water, climate regulation, soil health and health support. GLOBIO-Aquatic models the health of freshwater ecosystems by quantifying biodiversity (MSA) and water quality (algal blooms) based on human pressures such as land use, flow alteration, eutrophication and temperature. GLOBIO-Species models how human pressures affect the distribution and population size of individual vertebrate species, enabling the calculation of biodiversity indicators such as the RLI and LPI metrics to track progress towards biodiversity goals.
<figure><img src="image_114.png" /><figcaption>Figure 78: Mean species abundance values for 2015 (GLOBIO 4)</figcaption></figure>

Source: Schipper et al. (2020, Figure 4(a), page 766).

### 4.2.2 Potentially disappeared fraction (PDF)

The potentially disappeared fraction is a metric used to assess the biodiversity loss due to environmental change, particularly human activities such as pollution, habitat destruction, and climate change. Originally developed by Müller-Wenk (1998), it has been widely used in life cycle assessment (LCA) models to evaluate the environmental footprint of products and activities. PDF represents the fraction of species that have disappeared or are expected to disappear from a region due to environmental pressures such as land use, climate change, or pollution<fnref n="159" />. The PDF of area A is:

$$PDF = \frac{S_0 - S_A}{S_0} = \frac{1}{S_0} \sum_{j=1}^{S_0} 1 \{j \notin A\}$$

where $S_0$ is the undisturbed species richness of A, $S_A$ is the current species richness of A, and j is the species index. PDF equals zero if no species disappear (no biodiversity loss), while PDF equals one if all species in the considered ecosystem disappear (total biodiversity loss). PDF is equal to 25% for the area shown in Figure 77, because 25% of the species in this area have disappeared (parrot species have disappeared, while frogs, trees, and owls remain).

The PDF approach has been popularized by the ReCIPe LCA model (Goedkoop et al., 2008), which provides characterization factors (CFs) that translate human interventions in the environment (emissions, resource use, land use) into potential impacts on biodiversity, often expressed as PDF values. More complex approaches have been developed to calculate PDFs and CFs, such as the model of Kuipers et al. (2021), which uses the species-area relationship and defines new characterization factors<fnref n="160" />. The unit PDF.m².yr is often used. It represents the potential area (in square meters) where all species are potentially lost per year. For instance, 10 PDF.m².yr means that 10 m² has lost all its species during a year.

<footnote n="159">¹⁵⁹We have already encountered the PDF measure when studying the species-area relationship. The PDF measure is a generalization of the metric $Loss_{species}$ when the pressures are not limited to habitat loss (see Equation (16) on page 78).</footnote>

<footnote n="160">¹⁶⁰CF and PDF values can be found in the Supplementary Data available online (Tables S2 and S3).</footnote>
### 4.2.3 Biodiversity intactness index (BII)

A biodiversity intactness index is a metric that aims to measure how much of a region's natural biodiversity remains, despite human impacts. For instance, if we measure intactness at the species level, we can define the BII from the PDF: $BII = 1 - PDF$. Scholes et al. (2005) proposed the following empirical estimator of the biodiversity intactness index:

$$BII = \frac{\sum_{i} \sum_{j} \sum_{k} S_{i,j} A_{j,k} I_{i,j,k}}{\sum_{i} \sum_{j} \sum_{k} R_{i,j} A_{j,k}} \quad (37)$$

where *i* is the taxon index, *j* is the ecosystem index, *k* is the land use index, $S_{i,j}$ is the species richness of taxon *i* in ecosystem *j*, $A_{j,k}$ is the area of land use *k* in ecosystem *j*, and $I_{i,j,k}$ is the population impact or relative population of taxon *i* (compared to the reference state) under land use *k* in ecosystem *j*. We generally assume that $I_{i,j,k} \le 1$, but there may be special situations where the population has increased, meaning that $I_{i,j,k}$ may be greater than 1. At first glance, Formula (37) seems complex, but it is simply a weighted average of the various population impacts:

$$BII = \sum_{i} \sum_{j} \sum_{k} w_{i,j,k} I_{i,j,k}$$

where $w_{i,j,k} = \frac{S_{i,j} A_{j,k}}{\sum_{i} \sum_{j} \sum_{k} R_{i,j} A_{j,k}}$ and the sum of weights is equal to one — $\sum_{i} \sum_{j} \sum_{k} w_{i,j,k} = 100\%$. If there is only one ecosystem and one land use, Formula (37) reduces to the weighted average of the population impacts where the weights are proportional to the species richness $S_i$ of taxon *i*. For example, assuming there are three taxonomic groups (birds, mammals, and plants), the species richness is 100 bird species, 50 mammal species, 200 plant species, and the population impacts are 50%, 80%, and 90%, respectively, we get:

$$BII = \frac{100 \times 50\% + 50 \times 80\% + 200 \times 90\%}{100 + 50 + 200} = \frac{270}{350} = 77.14\%$$

<table><caption>Table 49: Biodiversity intactness index in % of tropical forests (2001–2012)</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Metric</th><th colspan="9">ISO code</th></tr><tr><th>ARG</th><th>BOL</th><th>BRA</th><th>CHL</th><th>COL</th><th>CUB</th><th>PER</th><th>PRY</th></tr></thead><tbody><tr><td rowspan="3">South America</td><td>2001 BII</td><td>48.3</td><td>72.8</td><td>82.7</td><td>14.6</td><td>81.2</td><td>18.0</td><td>88.2</td><td>58.2</td></tr><tr><td>2012 BII</td><td>46.4</td><td>71.2</td><td>80.7</td><td>16.1</td><td>80.0</td><td>19.3</td><td>87.5</td><td>53.0</td></tr><tr><td>Change (in %)</td><td>-3.8</td><td>-2.2</td><td>-2.4</td><td>10.2</td><td>-1.4</td><td>7.8</td><td>-0.8</td><td>-8.9</td></tr><tr><td rowspan="3">Africa</td><td>2001 BII</td><td>57.0</td><td>79.7</td><td>84.2</td><td>89.7</td><td>87.9</td><td>72.6</td><td>60.2</td><td>58.7</td></tr><tr><td>2012 BII</td><td>41.9</td><td>79.3</td><td>83.2</td><td>88.8</td><td>87.3</td><td>72.8</td><td>57.2</td><td>57.7</td></tr><tr><td>Change (in %)</td><td>-26.4</td><td>-0.5</td><td>-1.2</td><td>-1.1</td><td>-0.7</td><td>0.3</td><td>-4.9</td><td>-1.8</td></tr><tr><td rowspan="3">South Asia</td><td>2001 BII</td><td>38.6</td><td>41.2</td><td>75.8</td><td>13.3</td><td>64.5</td><td>79.6</td><td>31.0</td><td>39.5</td></tr><tr><td>2012 BII</td><td>36.4</td><td>34.5</td><td>70.4</td><td>13.0</td><td>61.3</td><td>70.5</td><td>29.7</td><td>38.2</td></tr><tr><td>Change (in %)</td><td>-5.5</td><td>-16.2</td><td>-7.1</td><td>-1.5</td><td>-4.9</td><td>-11.4</td><td>-4.2</td><td>-3.4</td></tr></tbody></table>

Source: De Palma et al. (2021), https://doi.org/10.5519/5wriutqz & Author's calculations.

The calculation of the biodiversity intactness index requires the collection of population impacts for different pressures and taxa. Data sets can be found on various websites such as www.nhm.ac.
uk/our-science/services/data/biodiversity-intactness-index.html. In Table 49 we report the values of the biodiversity intactness index in tropical forests calculated by De Palma et al. (2021) between 2001 and 2012. We find high heterogeneity even within the same region. For example, Peru had a BII of 88% in 2012, while it was only 19% in Argentina. Côte d'Ivoire experienced the largest decline (−26%), while the intactness of biodiversity in Chile improved by 10% between 2011 and 2012.

### 4.2.4 Species threat abatement and restoration (STAR)

The species threat abatement and restoration (STAR) metric is a global framework designed to quantify and guide efforts to reduce biodiversity loss. It measures the potential contribution of specific conservation actions (reducing pressure on species and restoring habitats) to improving the status of threatened species. According to Mair et al. (2021), for a given location *i* and a given threat *j*, the STAR threat-abatement score is calculated as a weighted average of the species IUCN Red List status:

$$T_{i,j} = \sum_{s=1}^{S} T_{i,j}^{(s)} = \sum_{s=1}^{S} \pi_{i,s} w_s c_{j,s}$$

where $\pi_{i,s} \in [0, 1]$ is the current area of habitat (AOH) of species *s* within location *i* — expressed as a percentage of the current global AOH of the species, $w_s \in \{1, 2, 3, 4\}$ is the IUCN Red List category weight<fnref n="161" /> of species *s*, $c_{j,s}$ is the relative contribution of threat *j* to the extinction risk of species *s*, and *S* is the species richness at location *i*. The STAR restoration score for a given location *i* and a given threat *j* is calculated as:

$$R_{i,j} = \sum_{s=1}^{S} R_{i,j}^{(s)} = \sum_{s=1}^{S} \varphi_{i,s} w_s c_{j,s} m_{i,s}$$

where $\varphi_{i,s} \in [0, 1]$ is the amount of restorable AOH for species *s* at location *i* — expressed as a percentage of the current global AOH of the species, and $m_{i,s} > 0$ is a multiplier appropriate for the habitat at location *i* to discount restoration results — the default value is 29%. The scores $T_{i,j}$ and $R_{i,j}$ can be decomposed to obtain the contribution $T_{i,j}^{(s)}$ and $R_{i,j}^{(s)}$ of each species. By construction, the contributions are between 0 and 1, while the scores are between 0 and the species richness *S*. Below, we provide an example to compute $T_{i,j}$ and $R_{i,j}$ and their species contribution $T_{i,j}^{(s)}$ and $R_{i,j}^{(s)}$:

<table><thead><tr><th>Species</th><th>Red List</th><th>w<sub>s</sub></th><th>π<sub>i,s</sub></th><th>c<sub>j,s</sub></th><th>φ<sub>i,s</sub></th><th>m<sub>i,s</sub></th><th>T<sub>i,j</sub><sup>(s)</sup></th><th>R<sub>i,j</sub><sup>(s)</sup></th></tr></thead><tbody><tr><td>Salmo salar (salmon)</td><td>NT</td><td>1</td><td>25%</td><td>90%</td><td>10%</td><td>29%</td><td>0.225</td><td>0.026</td></tr><tr><td>Phengaris teleius (butterfly)</td><td>VU</td><td>2</td><td>50%</td><td>90%</td><td>25%</td><td>29%</td><td>0.900</td><td>0.131</td></tr><tr><td>Conraua goliath (frog)</td><td>EN</td><td>3</td><td>80%</td><td>80%</td><td>25%</td><td>29%</td><td>1.920</td><td>0.174</td></tr><tr><td>Pericallis malvifolia (magnolia)</td><td>CR</td><td>4</td><td>75%</td><td>80%</td><td>20%</td><td>29%</td><td>2.400</td><td>0.186</td></tr><tr><td>Total</td><td></td><td></td><td></td><td></td><td></td><td></td><td>5.445</td><td>0.516</td></tr></tbody></table>

The site contains four species, each belonging to a different IUCN Red List category. Salmon occupy 25% of the habitat, indicating this is either a marine site or one with rivers. The threat contribution is high at 90%, meaning this threat is responsible for 90% of the extinction risk to salmon. Potential habitat restoration is 10%, suggesting we can reduce extinction risk by that amount. This yields an absolute contribution of 0.225 for salmon, representing 4.1% of the total threat-abatement score.

<footnote n="161">¹⁶¹$w_s$ takes the values 1, 2, 3, and 4 for near threatened, vulnerable, endangered, and critically endangered, respectively.</footnote>
In contrast, magnolias represent 44% of the threat-abatement score due to two main factors: they cover a larger habitat area, and the species *Pericallis malvifolia* is classified as critically endangered on the IUCN Red List.

<figure><img src="image_115.png" /><figcaption>Figure 79: Global STAR threat-abatement score for amphibians, birds and mammals (at a 50-km grid cell resolution)</figcaption></figure>

Source: Mair *et al.* (2021, figure 2).

Figure 79 illustrates the global STAR threat-abatement scores for amphibians, birds, and mammals as calculated by Mair *et al.* (2021). The color gradient ranges from yellow (low scores) to green (high scores). The distribution shows that biodiversity conservation priorities are geographically concentrated in some regions, particularly South America, South Asia, and southern Africa, suggesting that effective biodiversity risk management requires targeted approaches in these hotspots:

> "While every nation can contribute towards halting biodiversity loss, Indonesia, Colombia, Mexico, Madagascar and Brazil combined have stewardship over 31% of total STAR values for terrestrial amphibians, birds and mammals. Among actions, sustainable crop production and forestry dominate, contributing 41% of total STAR values for these taxonomic groups. Key Biodiversity Areas cover 9% of the terrestrial surface but capture 47% of STAR values." (Mair *et al.*, 2021, page 836).

### 4.2.5 Comparison of these methodologies

Which methodology is best? There is no definitive answer. First, in addition to the widely accepted approaches, there are numerous other valuable methodologies that receive less attention but provide important insights into biodiversity assessment. Second, these methodologies target different aspects of biodiversity — from species abundance and richness to density and ecosystem integrity — making direct comparisons problematic. Third, while all of these methodologies require substantial data inputs, they often present simplified perspectives by focusing on single dimensions of biodiversity. In particular, they focus primarily on species populations, which represent only one of the six essential biodiversity variables. This explains why these methods are so closely related (Rossberg,
2022; Rossberg et al., 2023). Fourth, a major limitation of these approaches is their general failure to incorporate ecosystem services — a critical dimension for a comprehensive understanding of biodiversity and its relationship to human well-being.

## 4.3 Commercial solutions

In addition to the established biodiversity measurement practices seen previously, a wide range of commercial approaches has emerged, offering diverse tools such as datasets, analytical software, calculation methods and comprehensive assessment services. In their Update Report 5 for the EU Business & Biodiversity Platform, De Ryck et al. (2024) documented 37 different approaches, indicating a significant increase in demand for biodiversity advisory services. However, concerns remain about the robustness and long-term viability of these tools. This pattern mirrors the evolution of ESG measurement, which has experienced rapid expansion followed by a period of consolidation. Critically, despite widespread agreement on the need for standardization and alignment, neither regulatory nor voluntary disclosure frameworks mandate specific measurement approaches. This is a significant barrier.

Table 50 provides an overview of available biodiversity assessment solutions. De Ryck et al. (2024) proposes a framework for evaluating these tools based on eight key criteria: business context, biodiversity pressures, level of ambition, scope, metrics, data requirements, implementation effort, and sector applicability. This framework allows companies to select methodologies that meet their specific needs and capabilities<fnref n="162" />. The landscape of biodiversity assessment tools varies widely in specialization — some are tailored to specific industries such as finance, mining or agriculture, while others are broadly applicable. These solutions also vary in their measurement granularity, with capabilities ranging from localized assessments (site or project level) to broader assessments (product, company or entire supply chain analysis). They also address different dimensions of biodiversity, including species diversity, habitat integrity, and ecosystem services, while accounting for different environmental pressures, such as land-use change, climate impacts, pollution sources, and the spread of invasive species. A critical criterion for selecting an approach is the scope of the solution, which defines the boundaries of what is included when measuring impacts and dependencies. These scopes can be categorized as follows<fnref n="163" />:

1. Scope 1: Impacts generated within the entity's control and other impacts directly caused by the entity during the assessment period;

2. Scope 2: Impacts resulting from the generation of non-fuel energy (electricity, steam, heat and cooling) for use at the site level, including impacts from land use change, fragmentation and related factors;

3. Scope 3

(a) Scope 3 Upstream: Impacts that are a consequence of the company's activities, but are caused by sources not owned or controlled by the company within its upstream supply chain;

(b) Scope 3 Downstream: Impacts that are a consequence of the company's activities, but are caused by sources not owned or controlled by the company within its downstream consumption and waste processes.

<footnote n="162">¹⁶²See also Bailon et al. (2024) for a comparison of the different solutions.</footnote>

<footnote n="163">¹⁶³These scope definitions are based on the GHG Protocol and are analogous to the scope definitions used to measure GHG emissions (see Roncalli (2025, Section 9.1.3)).</footnote>
<table><caption>Table 50: List of commercial solutions for measuring biodiversity</caption><thead><tr><th>Sigle</th><th>Name</th><th>Sponsors</th><th>OS<sup>‡</sup></th></tr></thead><tbody><tr><td>ABD Index</td><td>AgroBioDiversity Index</td><td>Alliance of Bioversity International, CIAT</td><td></td></tr><tr><td>B-INTACT</td><td>Biodiversity Integrated Assessment Computation Tool</td><td>FAO</td><td>✓</td></tr><tr><td>BFFI</td><td>Biodiversity Footprint for Financial Institutions</td><td>ASN Bank, CREM, PRé Sustainability</td><td>✓</td></tr><tr><td>BFM</td><td>Biodiversity Footprint Methodology</td><td>Plansup</td><td></td></tr><tr><td>BIA-GBS</td><td>Biodiversity Impact Analytics – Global Biodiversity Score</td><td>CDC Biodiversité, Carbon4 Finance</td><td></td></tr><tr><td>BIAT</td><td>Biodiversity Impact Assessment Tool</td><td>ISS ESG</td><td></td></tr><tr><td>BII</td><td>Biodiversity Intactness Index</td><td>Natural History Museum (UK)</td><td></td></tr><tr><td>BIM</td><td>Biodiversity Impact Metric</td><td>Cambridge Institute for Sustainable Leadership</td><td></td></tr><tr><td>BRF</td><td>Biodiversity Risk Filter</td><td>WWF</td><td>✓</td></tr><tr><td>CBF</td><td>Corporate Biodiversity Footprint</td><td>Iceberg Datalab</td><td></td></tr><tr><td>ENCORE</td><td>Exploring Natural Capital Opportunities, Risks and Exposure</td><td>Global Canopy, UNEP FI, UNEP-WCMC</td><td>✓</td></tr><tr><td>GBS</td><td>Global Biodiversity Score</td><td>CDC Biodiversité</td><td></td></tr><tr><td>GBSFI</td><td>Global Biodiversity Score for Financial Institutions</td><td>CDC Biodiversité</td><td></td></tr><tr><td>GID</td><td>Global Impact Database</td><td>Impact Institute</td><td></td></tr><tr><td>GIST NBS</td><td>Nature & Biodiversity Suite (BIGER/SLAM/DIRO 360)</td><td>GIST Impact</td><td></td></tr><tr><td>IBAT</td><td>Integrated Biodiversity Assessment Tool</td><td>BirdLife International, Conservation International, IUCN, UNEP-WCMC</td><td></td></tr><tr><td>InVEST</td><td>Integrated Valuation of Ecosystem Services and Trade-offs</td><td>Natural Capital Project</td><td>✓</td></tr><tr><td>MBFM</td><td>MSCI Biodiversity Footprint Metrics</td><td>MSCI</td><td></td></tr><tr><td>NRP</td><td>Nature Risk Profile</td><td>S&P Global</td><td></td></tr><tr><td>NVE</td><td>Nature Value Explorer</td><td>Flemish Institute for Technological Research</td><td>✓</td></tr><tr><td>PBF</td><td>Product Biodiversity Footprint</td><td>I Care</td><td></td></tr></tbody></table>

<footnote>‡OS stands for Open Source.</footnote>
Comparison of biodiversity assessment solutions reveals a paradoxical landscape characterized by both significant uniformity and heterogeneity. On the one hand, there is considerable homogeneity in the basic approaches, with most methodologies primarily emphasizing metrics of species abundance and diversity. Many solutions are based on identical underlying data sets, which are simply presented through different interfaces or analytical frameworks. On the other hand, there is considerable heterogeneity in the maturity of these solutions. Established tools have been widely adopted by many institutions over long periods of time and have evolved into comprehensive, integrated platforms. Meanwhile, other solutions remain in earlier stages of development, functioning essentially as proofs of concept or experimental approaches with limited implementation history.

# 5 Biodiversity governance and regulation

Biodiversity risk, like climate risk, is subject to regulation, and its governance has evolved significantly in recent years. While the political agenda may lack clarity, its mere existence represents progress. In particular, the Conference of the Parties has recently received more media attention than in previous years.

## 5.1 Convention on Biodiversity (CBD)

The Convention on Biological Diversity was adopted at the 1992 Earth Summit in Rio de Janeiro and entered into force in 1993. The purpose of this international treaty is to address the global loss of biological diversity, with the following objectives:

1. The conservation of biological diversity;

2. The sustainable use of its components;

3. The fair and equitable sharing of the benefits arising out of the utilization of genetic resources.

Similar to the United Nations Framework Convention on Climate Change (UNFCCC), the Conference of the Parties (COP) serves as the governing body of the CBD, meeting every two years to make key decisions. Some of the major treaties and initiatives adopted under the CBD include:

* The International Coral Reef Initiative (1994, COP1, Bahamas), a partnership of nations and organizations to protect coral reefs;

* The Cartagena Protocol on Biosafety (2000), which regulates the international movement of living modified organisms to ensure biosafety;

* The Nagoya Protocol on Access and Benefit-Sharing (2010, COP10, Japan), which establishes a legal framework for the equitable sharing of benefits arising from the use of genetic resources, and adopts the Aichi Biodiversity Targets (2011–2020), which set 20 global targets for biodiversity conservation;

* The Pyeongchang Roadmap (2014, COP12, South Korea), which aims to accelerate global efforts to achieve the Aichi Biodiversity Targets;

* The Kunming-Montreal Global Biodiversity Framework, known as the GBF (2022, COP15), which includes the goal of protecting 30% of the Earth's land and marine areas by 2030 (the 30 × 30 target).
<table><caption>Table 51: Aichi Biodiversity Targets (2011–2020)</caption><tr><td>Strategic Goal A</td><td>Address the underlying causes of biodiversity loss by mainstreaming bio-diversity across government and society (Target 1–4)</td></tr><tr><td>Strategic Goal B</td><td>Reduce the direct pressures on biodiversity and promote sustainable use (Target 5–10)</td></tr><tr><td>Strategic Goal C</td><td>To improve the status of biodiversity by safeguarding ecosystems, species and genetic diversity (Target 11–13)</td></tr><tr><td>Strategic Goal D</td><td>Enhance the benefits to all from biodiversity and ecosystem services (Target 14–16)</td></tr><tr><td>Strategic Goal E</td><td>Enhance implementation through participatory planning, knowledge management and capacity building (Target 17–20)</td></tr><tr><td>Target 5</td><td>By 2020, the rate of loss of all natural habitats, including forests, is at least halved and, where feasible, brought close to zero, and degradation and fragmentation are significantly reduced</td></tr><tr><td>Target 7</td><td>By 2020 areas under agriculture, aquaculture and forestry are managed sustainably, ensuring conservation of biodiversity</td></tr><tr><td>Target 8</td><td>By 2020, pollution, including from excess nutrients, has been brought to levels that are not detrimental to ecosystem function and biodiversity</td></tr><tr><td>Target 9</td><td>By 2020, invasive alien species are identified, priority species are controlled or eradicated, and measures are in place to manage pathways to prevent their introduction and establishment</td></tr><tr><td>Target 11</td><td>By 2020, at least 17% of terrestrial and inland water areas and 10% of coastal and marine areas are protected by well-connected systems of protected areas</td></tr><tr><td>Target 12</td><td>By 2020, the extinction of known threatened species has been prevented, and their conservation status has been improved and maintained, especially for those species most in decline</td></tr><tr><td>Target 15</td><td>By 2020, the resilience of ecosystems has been enhanced, including restoration of at least 15% of degraded ecosystems</td></tr><tr><td>Target 20</td><td>By 2020, the goal is to significantly increase financial resources for effective implementation of the Strategic Plan for Biodiversity 2011–2020</td></tr></table>

Source: https://www.cbd.int/sp/targets.

### 5.1.1 Aichi Biodiversity Targets

The Aichi targets are a set of 20 global biodiversity goals adopted at the 10th Conference of the Parties in Japan in 2010. These 20 targets are grouped into five strategic goals, as shown in Table 51. The overall objective is to reduce the rate of biodiversity loss, as reflected in some of the targets listed in Table 51. However, there is broad consensus that the world has failed to achieve any of the Aichi targets. Several factors have contributed to this failure. First, many of the targets are vague and lack clear, enforceable commitments, making it difficult to hold countries accountable. Enforcement mechanisms are limited, and the wording of many targets leaves room for interpretation. For example, Target 7 states that “by 2020 areas under agriculture, aquaculture and forestry are managed sustainably, ensuring conservation of biodiversity.” But what does that mean in practice? What specific actions should be taken? What metrics should be used to assess sustainability in these sectors? The lack of clear definitions and measurable indicators has weakened the impact of the targets. The second factor is inadequate funding, which creates a significant gap between what is needed and what is available for biodiversity conservation projects. For example, Target 20 states
that “by 2020, at the latest, the mobilization of financial resources for effectively implementing the Strategic Plan for Biodiversity 2011–2020 from all sources, and in accordance with the consolidated and agreed process in the Strategy for Resource Mobilization, should increase substantially from the current levels.” McCarthy et al. (2012) examined the financial costs of Targets 11 and 12, which relate to protected areas and extinction prevention. For Target 11, they estimated that protecting and effectively managing all terrestrial areas of global importance for bird conservation would cost $65.1 billion annually, and adding areas for other taxa would increase this to $76.1 billion annually. For Target 12, they estimated that reducing the risk of extinction of all globally threatened bird species by at least one IUCN Red List category would cost between $875 million and $1.23 billion, representing only 12% of current funding. In another study, by comparing actual expenditures with expected levels, Waldron et al. (2013) identified 40 severely underfunded countries, with the top five being Iraq, Djibouti, Angola, Kyrgyzstan, and Guyana. Most of the countries in this ranking are developing countries, with the exception of five developed countries: Finland, Iceland, France, Australia and Austria. More recently, the Paulson Institute, the Nature Conservancy, and the Cornell Atkinson Center for Sustainability conducted a comprehensive review of global biodiversity finance and concluded that there is a huge funding gap:

> “This report determines that, in 2019, the total global annual flow of funds toward biodiversity protection amounted to approximately $124–143 billion per year against an estimated annual need of $722–967 billion to halt the decline in global biodiversity between now and 2030. Taken together, these figures reveal a biodiversity financing gap of $598–824 billion per year. [...] this report shows that annual governmental expenditures on activities harmful to biodiversity in the form of agricultural, forestry, and fisheries subsidies — $274–542 billion per year in 2019 — are two to four times higher than annual capital flows toward biodiversity conservation.” (Deutz et al, 2020, page 12).

Finally, the third factor contributing to the failure of the Aichi targets was the lack of government commitment, inadequate monitoring and reporting by the CBD, and low public awareness. These factors combined to weaken stakeholder engagement, resulting in limited societal pressure for action.

### 5.1.2 Kunming-Montreal Global Biodiversity Framework

The failure of the Strategic Plan for Biodiversity 2011–2020 and its Aichi Biodiversity Targets created an urgent need for a more effective global approach. At CBD COP15 in December 2022, 192 countries responded by adopting the Kunming-Montreal Global Biodiversity Framework (GBF). This new framework aims to re-launch global biodiversity conservation efforts by addressing the gaps in national ambition and commitment, as well as previous initiatives. The GBF text is divided into several sections, the most important of which are Section F (2050 vision and 2030 mission), Section G (global goals for 2050) and Section H (global targets for 2030). According to Convention on Biological Diversity (2022), the vision of the Kunming-Montreal Global Biodiversity Framework is “a world of living in harmony with nature where by 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people.” The four 2020 goals are to protect and restore (goal A), prosper with nature (goal B), share benefits fairly (goal C), and invest and collaborate (goal D). While the first three goals are easy to understand, the fourth goal is described as follows:

> “Adequate means of implementation, including financial resources, capacity-building, technical and scientific cooperation, and access to and transfer of technology to fully implement the Kunming-Montreal Global Biodiversity Framework are secured and equitably accessible to all Parties, especially developing country Parties, in particular the
<table><caption>Table 52: The 23 targets of the Kunming-Montreal Global Biodiversity Framework</caption><thead><tr><th>Target</th><th>Purpose</th></tr></thead><tbody><tr><td>1</td><td>Plan and manage all areas to reduce biodiversity loss</td></tr><tr><td>2</td><td>Restore 30% of all degraded ecosystems — terrestrial, inland water, and marine and coastal ecosystems</td></tr><tr><td>3</td><td>Conserve 30% of land, waters and seas — protected areas and other effective area-based conservation measures</td></tr><tr><td>4</td><td>Halt species extinction, protect genetic diversity, and manage human-wildlife conflicts</td></tr><tr><td>5</td><td>Ensure sustainable, safe and legal harvesting and trade of wild species (reducing the risk of pathogen spillover)</td></tr><tr><td>6</td><td>Reduce the introduction of invasive alien species by 50% and minimize their impact — eradicating or controlling invasive alien species, especially in priority sites, such as islands</td></tr><tr><td>7</td><td>Reduce pollution to levels that are not harmful to biodiversity — (a) reducing excess nutrients lost to the environment by at least half, (b) reducing the overall risk from pesticides and highly hazardous chemicals by at least half, (c) preventing, reducing, and working towards eliminating plastic pollution</td></tr><tr><td>8</td><td>Minimize the impacts of climate change on biodiversity and build resilience</td></tr><tr><td>9</td><td>Manage wild species sustainably to benefit people</td></tr><tr><td>10</td><td>Enhance biodiversity and sustainability in agriculture, aquaculture, fisheries, and forestry</td></tr><tr><td>11</td><td>Restore, maintain and enhance nature's contributions to people</td></tr><tr><td>12</td><td>Enhance green spaces and urban planning for human well-being and biodiversity</td></tr><tr><td>13</td><td>Increase the sharing of benefits from genetic resources, digital sequence information and traditional knowledge</td></tr><tr><td>14</td><td>Integrate biodiversity in decision-making at every level</td></tr><tr><td>15</td><td>Businesses assess, disclose and reduce biodiversity-related risks and negative impacts</td></tr><tr><td>16</td><td>Enable sustainable consumption choices to reduce waste and overconsumption</td></tr><tr><td>17</td><td>Strengthen biosafety and distribute the benefits of biotechnology</td></tr><tr><td>18</td><td>Reduce harmful incentives by at least $500 billion per year, and scale up positive incentives for biodiversity</td></tr><tr><td>19</td><td>Mobilize $200 billion per year for biodiversity from all sources, including $30 billion through international finance</td></tr><tr><td>20</td><td>Strengthen capacity-building, technology transfer, and scientific and technical cooperation for biodiversity</td></tr><tr><td>21</td><td>Ensure that knowledge is available and accessible to guide biodiversity action</td></tr><tr><td>22</td><td>Ensure participation in decision-making and access to justice and information related to biodiversity for all</td></tr><tr><td>23</td><td>Ensure gender equality and a gender-responsive approach for biodiversity action</td></tr></tbody></table>

Source: (Convention on Biological Diversity, 2022, pages 9-13).
least developed countries and small island developing States, as well as countries with
economies in transition, progressively closing the biodiversity finance gap of $700 billion
per year, and aligning financial flows with the Kunming-Montreal Global Biodiversity
Framework and the 2050 Vision for biodiversity." (Convention on Biological Diversity,
2022, page 9).

Table 52 shows the 23 targets. At first glance, one might get the impression that the Kunming-Montreal targets are close to the Aichi targets. This is true, and we also note that some targets are always vague. However, if the official text is not technical, the CBD has done some work since its publication to be more precise. In fact, the guidelines for these targets, the indicators for monitoring progress and the national targets submitted to the CBD Secretariat are available on the website www.cbd.int/gbf/targets. For each target, we can find the following guidance notes prepared by the Secretariat:

A. Why is this target important?

B. Explanation of the target and its elements

C. Links to other elements of the Kunming-Montreal GBF, and other frameworks and processes

D. Guiding questions to national-setting

E. Indicators

F. Relevant resources that can assist implementation

Concerning indicators, they are split into three categories: headline indicators<fnref n="164" />, component indi-
cators and complementary indicators. For example, if we consider Target 6 on invasive alien species,
there is one headline indicator (rate of invasive alien species establishment), three component indi-
cators (rate of invasive alien species impact and rate of impact; rate of invasive alien species spread;
number of invasive alien species introductions) and three complementary indicators (number of inva-
sive alien species on national lists; trends in abundance, temporal occurrence and spatial distribution
of non-indigenous species; Red List Index (impact of invasive alien species)).

A major challenge in implementing the Kunming-Montreal GBF is financing. Goal D of the GBF recognized a current biodiversity finance gap of $700 billion per year. To address this, Target 18 proposes to reduce incentives and subsidies that harm biodiversity by $500 billion per year by 2030, while

<footnote n="164">¹⁶⁴Here are all the headline indicators identified by the CBD Secretariat as of March 2025: 1.A.1 Red List of ecosystems; 1.A.2 Extent of natural ecosystems; 1.1 Per cent of land and seas covered by biodiversity-inclusive spatial plans; 2.2 Area under restoration; 3.1 Coverage of protected areas and OECMs; 4.A.3 Red list Index; 4.A.4 The proportion of populations within species with an effective population size > 500; 5.1 Proportion of fish stocks within biologically sustainable levels; 6.1 Rate of invasive alien species establishment; 7.1 Index of coastal eutrophication potential; 7.2 Pesticide environment concentration; 9.1 Benefits from the sustainable use of wild species; 9.2 Percentage of the population in traditional occupations; 10.1 Proportion of agricultural area under productive and sustainable agriculture; 10.2 Progress towards sustainable forest management; 11.B.1 Services provided by ecosystems; 12.1 Average share of the built-up area of cities that is green/blue space for public use for all; 13.C.1 Indicator on monetary benefits received; 13.C.2 Indicator on non-monetary benefits; 15.1 Number of companies reporting on disclosures of risks, dependencies and impacts on biodiversity; 18.1 Positive incentives in place to promote biodiversity conservation and sustainable use; 19.D.1 International public funding, including official development assistance for conservation and sustainable use of biodiversity and ecosystems; 19.D.2 Domestic public funding of conservation and sustainable use of biodiversity and ecosystems; 19.D.3 Private funding (domestic and international) of conservation and sustainable use of biodiversity and ecosystems; 21.1 Indicator on biodiversity information for monitoring the global biodiversity framework. The first number is the GBF target number. Note that headline indicators have not yet been identified for some targets.</footnote>
Target 19 specifies that at least $200 billion per year needs to be mobilized by 2030 to implement national biodiversity strategies. The latter target includes increasing financial resources from developed countries, providing development assistance to developing countries, leveraging private finance, promoting blended finance, encouraging impact funds, and promoting innovative schemes such as payments for ecosystem services, green bonds, biodiversity offsets and credits, and benefit-sharing mechanisms. As previously noted, Deutz et al (2020) estimated biodiversity funding at $124–143 billion per year in 2019, with the breakdown shown in Table 53. 57% and 20% are financed through domestic budgets and natural infrastructure, respectively. Green financial products, such as green bonds and nature-based solutions, account for less than 3%. Using the OECD’s category of potential biodiversity subsidies, the authors estimated global harmful subsidies to be between $274 and $542 billion per year in 2019.

<table><caption>Table 53: Global biodiversity conservation financing in 2019 (in $ bn)</caption><thead><tr><th>Financial flows</th><th>Lower limit</th><th>Upper limit</th><th>Midpoint</th><th>Percentage</th></tr></thead><tbody><tr><td>Domestic budgets and tax policy</td><td>74.6</td><td>77.7</td><td>76.2</td><td>57.1%</td></tr><tr><td>Natural infrastructure</td><td>26.9</td><td>26.9</td><td>26.9</td><td>20.2%</td></tr><tr><td>Biodiversity offsets</td><td>6.3</td><td>9.2</td><td>7.8</td><td>5.8%</td></tr><tr><td>Sustainable supply chains</td><td>5.5</td><td>8.2</td><td>6.8</td><td>5.1%</td></tr><tr><td>Official development assistance (ODA)</td><td>4.0</td><td>9.7</td><td>6.8</td><td>5.1%</td></tr><tr><td>Green financial products</td><td>3.8</td><td>6.3</td><td>5.0</td><td>3.8%</td></tr><tr><td>Philanthropy & conservation NGOs</td><td>1.7</td><td>3.5</td><td>2.6</td><td>2.0%</td></tr><tr><td>Nature-based solutions & carbon markets</td><td>0.8</td><td>1.4</td><td>1.1</td><td>0.8%</td></tr><tr><td>Total</td><td>123.6</td><td>142.9</td><td>133.3</td><td>100.0%</td></tr></tbody></table>

Source: Deutz et al (2020, Table 3.1, page 48).

<table><caption>Table 54: Global biodiversity conservation funding needs (in $ bn)</caption><thead><tr><th>Funding needs</th><th>Lower limit</th><th>Upper limit</th><th>Midpoint</th><th>Percentage</th></tr></thead><tbody><tr><td>Croplands</td><td>315</td><td>420</td><td>367.5</td><td>43.5%</td></tr><tr><td>Protected areas</td><td>149</td><td>192</td><td>170.5</td><td>20.2%</td></tr><tr><td>Rangelands</td><td>81</td><td>81</td><td>81.0</td><td>9.6%</td></tr><tr><td>Urban environments</td><td>73</td><td>73</td><td>73.0</td><td>8.6%</td></tr><tr><td>Invasive alien species</td><td>36</td><td>84</td><td>60.0</td><td>7.1%</td></tr><tr><td>Coastal</td><td>27</td><td>37</td><td>32.0</td><td>3.8%</td></tr><tr><td>Fisheries</td><td>23</td><td>47</td><td>35.0</td><td>4.1%</td></tr><tr><td>Forests</td><td>19</td><td>32</td><td>25.5</td><td>3.0%</td></tr><tr><td>Total</td><td>722</td><td>967</td><td>844.5</td><td>100.0%</td></tr></tbody></table>

Source: Deutz et al (2020, Figure 4.1, page 55).

Estimated funding needs are reported in Table 54. Croplands and protected areas account for 64% of total funding, while fisheries and forest conservation accounts for only 7% of total funding (about $60 billion per year). Deutz et al (2020) proposed nine financial and policy mechanisms to reduce the biodiversity financing gap. The optimistic estimate is a total positive financial flow of $632.5 billion, which is insufficient to close the gap (Table 55). While significant progress has been made since 2022 (establishment of the Global Biodiversity Framework Fund (GBFF), launch of the Cali Fund), we are still not on track to meet the 23 goals of the Kunming-Montreal Global Biodiversity Framework.