Box 10.2: Willingness to pay, willingness to accept and consumer surplus

Let us assume that the utility function is quasi-linear:

$$ \mathcal{U}(x, z) = x + V(z) $$

where $x$ is private consumption, $z$ is the level of the public good, and $V(z)$ is the utility derived from the public good, satisfying $V(0) = 0$, $\partial_z V(z) > 0$, and $\partial_z^2 V(z) < 0$. We assume that the consumer faces a budget constraint where he can trade off private consumption $x$ for the public good $z$ at some price $p$. The optimization problem is:

$$ \begin{array}{ll} \{x^*, z^*\} = & \arg\max, \mathcal{U}(x, z) \\ \text{s.t.} & x +pz = y \end{array} $$

where $y$ is the income of the consumer. The Lagrange function is $\mathcal{L}(x, z; \lambda) = \mathcal{U}(x, z) + \lambda(y - x - pz)$. The first-order conditions are:

$$ \begin{cases} \partial_x \mathcal{L}(x, z; \lambda) = 1 - \lambda = 0 \\ \partial_z \mathcal{L}(x, z; \lambda) = \partial_z V(z) - \lambda p = 0 \\ \partial_\lambda \mathcal{L}(x, z; \lambda) = y - x - pz = 0 \end{cases} $$

We deduce that $\lambda = 1$, $\partial_z V(z^*) = p$, and $x^* = y - pz^*$. The consumer surplus is the integral of the inverse demand function:

$$ W_{CS}(z^*) - W_{CS}(0) = \int_{0}^{z^*} (\partial_z V(z) - p) dz = V(z^*) - V(0) - pz^* = V(z^*) - pz^* $$

WTP is the maximum amount the consumer would pay to obtain the public good $z$ instead of having none of it. We have to solve:

$$ \begin{align*} \mathcal{U}(x - \text{WTP}, z) = \mathcal{U}(x, 0) &\Leftrightarrow x - \text{WTP} + V(z) = x + V(0) \\ &\Leftrightarrow \text{WTP} = V(z) \end{align*} $$

WTA is the minimum compensation required to give up the public good $z$:

$$ \begin{align*} \mathcal{U}(x + \text{WTA}, 0) = \mathcal{U}(x, z) &\Leftrightarrow x + \text{WTA} + V(0) = x + V(z) \\ &\Leftrightarrow \text{WTA} = V(z) \end{align*} $$

It follows that:

$$ \text{WTA} = \text{WTP} = V(z) - V(0) $$

This formula represents the total additional utility a consumer gets from having the public good $z$ instead of having none of it$^a$. Since we have $\text{WTP} = V(z^*)$ and $W_{CS}(z^*) - W_{CS}(0) = V(z^*) - pz^*$, we conclude that WTP represents the gross benefit from consuming the public good, while consumer surplus represents the net benefit after accounting for the price paid. If the public good is free, the willingness to pay of the consumer is exactly equal to his consumer surplus.

<footnote>$^a$This is a special case. In general, with non-linear utility functions, WTA ≠ WTP due to income effects, and typically WTA > W$_{CS}$ > WTP for normal goods.</footnote>
**Social welfare considerations** A social welfare function (SWF) is a rule that aggregates the individual utilities or preferences of all members of a society into a single, comprehensive measure of social welfare (Arrow, 1950). It reflects the collective preferences or social utility of society. Mathematically, a preference-based SWF maps individual preference orderings over social states into a social preference ordering:

$$ \succ_S = f(\succ_1, \succ_2, \dots, \succ_n) $$

where $\succ_S$ is the social preference relation, $f$ is the aggregation function, $\succ_i$ is the preference relation of individual $i$, and $n$ is the number of individuals in the society. In the case of a utility-based (or cardinal) SWF, the function aggregates individuals' utility levels<fnref n="19" />:

$$ W = f(\mathcal{U}_1, \mathcal{U}_2, \dots, \mathcal{U}_n) $$

where $W$ is the level of social welfare, $f$ is the aggregation function, $\mathcal{U}_i$ is the utility of individual $i$, and $n$ is the number of individuals in the society. There are various ways to aggregate individual utilities. The most common is the utilitarian social welfare function:

$$ W = \mathcal{U}_1 + \mathcal{U}_2 + \dots + \mathcal{U}_n $$

The objective in this case is to maximize the sum of individual utilities. This approach assumes that each unit of utility is equally valuable, regardless of who receives it. Since this approach focuses solely on maximizing aggregate satisfaction, equity and distribution are not considered. In other words, this approach is concerned with the size of the pie, not how it is divided. To address distributional concerns, we can use the weighted utilitarian social welfare function:

$$ W = \sum_{i=1}^{n} \omega_i \mathcal{U}_i $$

where $\omega_i$ are the weights assigned to individual $i$, reflecting the relative importance of their utility. This formulation was discussed earlier in the context of the RICE model, which uses Negishi welfare weights<fnref n="20" />. However, other weighting schemes may also be used. For instance, weights can be assigned that are inversely proportional to initial income or utility: $\omega_i \propto Y_i^{-1}$. Another well-known alternative to the utilitarian function is the Rawlsian social welfare function (Rawls, 1971), which prioritizes those who are worst off:

$$ W = \min(\mathcal{U}_1, \mathcal{U}_2, \dots, \mathcal{U}_n) $$

The objective in this case is to maximize the well-being of the poorest individual in society.

**Remark 124** The choice of a social welfare function influences policy recommendations and reflects underlying ethical judgments about equity and fairness. The utilitarian approach treats all individuals symmetrically. However, it may justify substantial inequality if doing so maximizes total welfare. Weighted utilitarian functions address this concern by giving greater priority to disadvantaged groups. Nevertheless, selecting the appropriate weights is often contentious and reflects political or philosophical values rather than objective criteria. The Rawlsian max-min criterion, by contrast, strongly prioritizes equality. It focuses exclusively on improving the position of the worst off, even at the cost of significant aggregate welfare gains. These fundamental trade-offs between efficiency and equity are central to policy design and underscore the importance of explicitly acknowledging the distributional assumptions embedded in any social welfare analysis (Rawls, 1971).

<footnote n="19">¹⁹It is sometimes referred to as the Bergson-Samuelson social welfare function, whereas the ordinal-based SWF that aggregates individual preference rankings is known as the Arrow social welfare function.</footnote>

<footnote n="20">²⁰See page 777.</footnote>
**Pareto efficiency, Kaldor-Hicks criterion and equity** Welfare analysis typically pursues two central objectives: efficiency and equity. The efficiency objective is commonly associated with Pareto optimality, in which an allocation is considered optimal if it cannot be improved without making someone worse off. In contrast, the equity objective relates to social justice and focuses on reducing poverty and inequality. In practice, however, these two goals often conflict, requiring governments to navigate complex trade-offs. For instance, a policy that increases national income could simultaneously decrease the income of the poorest individuals, thereby worsening inequality. To address these conflicts, economists sometimes turn to the Kaldor-Hicks compensation criterion, which is a more flexible alternative to Pareto optimality.

> "In all cases, therefore, where a certain policy leads to an increase in physical productivity, and thus of aggregate real income, the economist's case for the policy is quite unaffected by the question of the comparability of individual satisfactions; since in all such cases it is possible to make everybody better off than before, or at any rate to make some people better off without making anybody worse off. There is no need for the economist to prove — as indeed he never could prove — that as a result of the adoption of a certain measure nobody in the community is going to suffer. In order to establish his case, it is quite sufficient for him to show that even if all those who suffer as a result are fully compensated for their loss, the rest of the community will still be better off than before." (Kaldor, 1939, page 550).

> "The main practical advantage of our line of approach is that it fixes attention upon the question of compensation. Every simple economic reform inflicts a loss upon some people; the reforms we have studied are marked out by the characteristic that they will allow of compensation to balance that loss, and they will still show a net advantage. Yet when such reforms have been carried through in historical fact, the advance has usually been made amid the clash of opposing interests, so that compensation has not been given, and economic progress has accumulated a roll of victims, sufficient to give all sound policy a bad name. [...] If, as will often happen, the best methods of compensation feasible involve some loss in productive efficiency, this loss will have to be taken into account. In practice, it is not unlikely that we shall have to reject on these grounds many measures which would be approved of by the traditional analysis, but which would only be reckoned by that analysis as offering a small gain." (Hicks, 1939, pages 711 & 712).

Under the Kaldor-Hicks criterion, a policy is considered an improvement if the gains to those who benefit are sufficient to compensate those who lose out, even if no actual compensation occurs. This expands the range of desirable policies by allowing for efficiency-enhancing changes that may still create losers.

In the context of a cost-benefit analysis, a typical Kaldor-Hicks test applies when the aggregate willingness to pay of those who benefit exceeds the aggregate willingness to accept of those who are harmed:

$$ \sum_{i=1}^{n} \text{WTP}_i > \sum_{j=1}^{m} \text{WTA}_j $$

Here, actual compensation is not required. The test simply asks whether compensation would be feasible in principle while still yielding net gains. The Kaldor-Hicks criterion clearly reflects a utilitarian perspective, prioritizing aggregate improvements in welfare, even if it results in increased inequality.
Consider the general SWF: $W = f(\mathcal{U}_1, \mathcal{U}_2, \dots, \mathcal{U}_n)$. This function can be locally approximated using a differential expression:

$$dW = \sum_{i=1}^{n} \frac{\partial f}{\partial \mathcal{U}_i} \frac{\partial \mathcal{U}_i}{\partial Y_i} dY_i$$

where $Y_i$ denotes the income of individual i, and $dW$ is the marginal change in social welfare resulting from income changes $dY_i$ where:

* $\frac{\partial f}{\partial \mathcal{U}_i} \ge 0$ is the marginal social weight on individual i's utility, indicating how much society values an increase in utility $\mathcal{U}_i$;

* $\frac{\partial \mathcal{U}_i}{\partial Y_i} \ge 0$ is the marginal utility of income for individual i, indicating how much utility they gain from an additional unit of income.

This decomposition allows us to evaluate how changes in income affect social welfare by considering societal preferences and individual responsiveness. We can rewrite the expression $dW$ by categorizing individuals as winners (those with $dY_i > 0$) or losers (those with $dY_i < 0$):

$$dW = \sum_{i \in \text{Winners}} \frac{\partial f}{\partial \mathcal{U}_i} \frac{\partial \mathcal{U}_i}{\partial Y_i} |dY_i| - \sum_{i \in \text{Losers}} \frac{\partial f}{\partial \mathcal{U}_i} \frac{\partial \mathcal{U}_i}{\partial Y_i} |dY_i|$$

In a standard cost-benefit analysis, we assume that $\partial_{\mathcal{U}_i} f \cdot \partial_{Y_i} \mathcal{U}_i = 1$ for all $i$. Under the Kaldor-Hicks compensation principle, a policy is justified if the gains to the winners are large enough to hypothetically compensate the losers, regardless of whether the compensation occurs. It follows that:

$$dW \ge 0 \Leftrightarrow \begin{cases} \sum_{i \in \text{Winners}} |dY_i| - \sum_{i \in \text{Losers}} |dY_i| = \sum_{i=1}^{n} dY_i \ge 0 \\ \partial_{\mathcal{U}_i} f = \partial_{\mathcal{U}_i} f \\ \partial_{Y_i} \mathcal{U}_i = \partial_{Y_i} \mathcal{U}_j \end{cases}$$

This implies that all individuals are treated equally in terms of social weights and marginal utility of income. Thus, the Kaldor-Hicks compensation criterion implicitly reflects a utilitarian perspective, assuming that distributional concerns and fairness can be addressed independently through compensation mechanisms<fnref n="21" />.

**Example 48** Consider two individuals with the following utility function:

$$\mathcal{U}(Y) = \sqrt{Y}$$

We assume that their initial incomes are $Y_1 = 1000$ and $Y_2 = 500$, respectively. We evaluate the efficiency of the following policies using the utilitarian social welfare function, the Rawlsian SWF, and the Kaldor-Hicks criterion:

* Policy #1: $dY_1 = +50$ and $dY_2 = -25$

* Policy #2: $dY_1 = +40$ and $dY_2 = -30$

<footnote n="21">²¹In the case of the utilitarian SWF, we have $\partial_{\mathcal{U}_i} f = 1$. For the weighted utilitarian SWF, we get $\partial_{\mathcal{U}_i} f = \omega_i$. The Rawlsian social welfare function corresponds to $dW = d\mathcal{U}_{poorest}$.</footnote>
The marginal utility with respect to income is:

$$ \frac{\partial \boldsymbol{u}_i}{\partial Y_i} = \frac{1}{2\sqrt{Y_i}} $$

The differential change in social welfare is then given by:

$$ dW = \frac{\omega_1}{2\sqrt{Y_1}} dY_1 + \frac{\omega_2}{2\sqrt{Y_2}} dY_2 $$

In the Rawlsian SWF, only the change in utility of the worst-off individual (in this case, individual 2, since $Y_2 < Y_1$) is considered. In the Kaldor-Hicks criterion, we calculate the total income gains and losses. We obtain the following results<fnref n="22" />:

<table><thead><tr><th>SWF</th><th>Formula</th><th>Value</th><th>Welfare improving?</th></tr></thead><tbody><tr><td>Utilitarian</td><td>1/(2√Y<sub>1</sub>)dY<sub>1</sub> + 1/(2√Y<sub>2</sub>)dY<sub>2</sub></td><td>#1: +0.232<br>#2: -0.038</td><td>✓</td></tr><tr><td>Rawlsian</td><td>1/(2√Y<sub>2</sub>)dY<sub>2</sub></td><td>#1: -0.559<br>#2: -0.671</td><td>✗</td></tr><tr><td>Kaldor-Hicks</td><td>dY<sub>1</sub> + dY<sub>2</sub></td><td>#1: +25<br>#2: +10</td><td>✓<br>✓</td></tr></tbody></table>

## Economic and financial valuation

**Cost and benefit measurement** Cost-benefit analysis involves the measurement of both financial and economic costs and benefits. While financial costs and benefits are related to direct monetary flows for individuals or entities, economic analysis captures broader societal impacts that often differ from financial considerations alone. For instance, economic costs include opportunity costs, which can differ significantly from financial outlays due to market distortions, taxes, unemployment, and other factors. Similarly, economic benefits, such as environmental improvements, public health gains, and time savings, often have no observable market price. The valuation process in CBA typically follows a structured sequence. First, all relevant impacts are identified. Then, the physical magnitude of each impact is estimated. Next, appropriate monetary values are assigned. Finally, the timing and distribution of the impacts are considered. As Atkinson et al. (2018) noted, economists have developed techniques to quantify non-market impacts, particularly in environmental contexts (Figure 10.8). These techniques include revealed preference methods, which infer values from observed behavior. Examples include the travel cost method and the averting behavior approach. They also include stated preference methods, such as contingent valuation and discrete choice experiments.

$$ \begin{aligned} \Delta W &= \omega_1 (\boldsymbol{u}_1 (Y_1 + dY_1) - \boldsymbol{u}_1 (Y_1)) + \omega_2 (\boldsymbol{u}_2 (Y_2 + dY_2) - \boldsymbol{u}_2 (Y_2)) \\ &= \omega_1 (\sqrt{Y_1 + dY_1} - \sqrt{Y_1}) + \omega_2 (\sqrt{Y_2 + dY_2} - \sqrt{Y_2}) \end{aligned} $$

We obtain the following results:

<table><thead><tr><th>SWF</th><th>Formula</th><th>Value</th><th>Welfare improving?</th></tr></thead><tbody><tr><td>Utilitarian</td><td>√Y<sub>1</sub> + dY<sub>1</sub> - √Y<sub>1</sub> + √Y<sub>2</sub> + dY<sub>2</sub> - √Y<sub>2</sub></td><td>#1: +0.215<br>#2: -0.055</td><td>✓</td></tr><tr><td>Rawlsian</td><td>√Y<sub>2</sub> + dY<sub>2</sub> - √Y<sub>2</sub></td><td>#1: -0.566<br>#2: -0.681</td><td>✗</td></tr></tbody></table>

<footnote n="22">²²Another approach is to use the exact value of the utility function:</footnote>
<figure><img src="image_481.png" /><figcaption>Figure 10.8: Valuation methods in cost-benefit analysis</figcaption></figure>

The stated preference approach uses surveys to directly ask people about their preferences in hypothetical scenarios. For example, contingent valuation surveys ask respondents about their willingness to pay for environmental improvements. Discrete choice experiments present trade-offs between options with different attributes and prices. A simpler version of this approach is paired comparisons, in which respondents choose between two alternatives to reveal their preferences. The revealed preference approach uses actual market behavior to infer values. The market price method uses existing prices directly when they are available. The hedonic pricing method examines how attributes such as proximity to green spaces affect property values. The travel cost method estimates the value of recreational sites based on how much people spend to visit them. Averting behavior examines spending intended to avoid negative environmental impacts. Replacement cost estimates the cost of replacing damaged environmental services. The value transfer approach applies valuation estimates from previous studies to new contexts. In unit value transfer, the mean or median per-unit value from a similar study is used. Function transfer extrapolates an entire valuation function, such as a demand curve, from a source study. This allows for adjustments based on site-specific characteristics. The most refined version, meta-analytic function transfer, uses data from multiple studies to create a statistical model that predicts values in new contexts.

Source: Atkinson et al. (2018) & Author's research.
These methods use survey responses to determine individuals' willingness to pay in hypothetical situations. Value transfer methods are an alternative when primary data collection is infeasible. These methods apply existing valuation estimates from similar contexts.

It's important to acknowledge that these diverse methods are a patchwork of approaches, and their results aren't always perfectly consistent. Each method has specific assumptions, advantages, and limitations. For example, contingent valuation elicits hypothetical willingness to pay, while revealed preference methods derive values from actual choices and behaviors. As previously discussed, the statistical value of life quantifies society's willingness to pay for reductions in mortality risk by considering hedonic wage studies or stated preference surveys. Finally, specialized techniques such as benefit transfer, choice modeling, and replacement cost approaches are valuable tools for addressing context-specific valuation challenges, especially when market data are unavailable or incomplete.

**Discounting and present value analysis** To assess the cost-benefit of a project, the most widely used metric is the net present value (NPV), which is the difference between the present value of future benefits and the present value of future and current costs:

$$NPV(\rho) = \sum_{t=0}^{T} \frac{\mathcal{B}_t - \mathcal{C}_t}{(1 + \rho)^t}$$

where $\rho$ is the discount rate, $\mathcal{B}_t$ are the benefits at time $t$, and $\mathcal{C}_t$ are the costs at time $t$. If there are no current benefits ($\mathcal{B}_0 = 0$), we get$^{23}$:

$$NPV(\rho) = -\mathcal{C}_0 + \sum_{t=1}^{T} \frac{\mathcal{B}_t - \mathcal{C}_t}{(1 + \rho)^t}$$

The Kaldor-Hicks compensation test accepts a project if $NPV(\rho) > 0$. If we use willingness to pay metrics, then we have $\mathcal{B}_t = \sum_{i \in \text{Winners}} \text{WTP}_{i,t}$ and $\mathcal{C}_t = \sum_{i \in \text{Losers}} \text{WTP}_{i,t}$ (or $\mathcal{C}_t = \sum_{i \in \text{Losers}} \text{WTA}_{i,t}$). It follows that the project passes the Kaldor-Hicks test if the present value of the WTP of the winners exceeds the present value of the WTP (or WTA) of the losers.

Two critical parameters determine the net present value calculation: the time horizon $T$ and the discount rate $\rho$. Selecting an appropriate time horizon can be challenging because the impact of policies often extends across decades. Consider, for example, a school construction project. Should the benefits be considered over 10, 25, or 50 years? Similar temporal complexities arise when assessing vaccination programs, nuclear waste management, hydropower projects, and other long-term public investments. Time horizons vary considerably across sectors based on the nature and duration of the expected impacts. Infrastructure and public health interventions typically use 10–30 year time horizons, reflecting their medium-term benefits. In contrast, energy projects and climate policies require timeframes of 40 to 100 years to account for their extended operational lives and environmental consequences. Nuclear power presents the most extreme case. Analyses span over 100 years to account for the full lifecycle, including waste storage, decommissioning, and intergenerational effects. The discount rate poses significant challenges and has a profound influence on NPV calculations. It reflects the rate at which future benefits and costs are discounted relative to present values. The discount rate reflects society's time preference and the opportunity cost of capital. Higher discount rates diminish the present value of future cash flows more severely, potentially making long-term projects appear less attractive. In environmental and climate policy

<footnote>$^{23}$In many cases, the exact future benefits and costs are uncertain. In such situations, $\mathcal{B}_t$ and $\mathcal{C}_t$ refer to the expected values of the corresponding random variables representing future benefits and costs.</footnote>
contexts, selecting an appropriate discount rate is controversial because small adjustments, such as changing from 3% to 7%, can dramatically alter project rankings and policy recommendations. Government agencies often mandate specific discount rates, but economists debate whether lower rates should apply to environmental projects with intergenerational impacts<fnref n="24" />. This two-dimensional issue of time horizon and discount rate is especially important when comparing projects with different benefit profiles over time.

If we consider two competing projects $P_1$ and $P_2$ with respective net present values $NPV_1(\rho)$ and $NPV_2(\rho)$, the preferred project is the one with the higher net present value:

$$NPV_1(\rho) > NPV_2(\rho) \Rightarrow P_1 > P_2$$

However, this decision rule does not account for the size and efficiency of the project. For instance, how should we compare project $P_1$, which has an initial cost $C_0 = 100$ and a net present value $NPV_1(\rho) = 500$ with project $P_2$, which has an initial cost $C_0 = 10$ and a net present value $NPV_1(\rho) = 300$? Although $P_1$ has a higher NPV, $P_2$ may be more efficient in terms of return relative to its cost. In such cases, it may be more appropriate to compute the internal rate of return (IRR), which is defined as the discount rate $\rho$ at which the net present value is zero:

$$IRR = \min \{\rho : \text{NPV}(\rho) \ge 0\}$$

The IRR can be interpreted as the breakeven rate of return, i.e., the discount rate at which a project neither gains nor loses value in present terms. The Kaldor-Hicks compensation test accepts a project if $IRR > 0$. In finance, the decision rule for investing in a project is to ensure that $IRR > r^*$, where $r^*$ is the required rate of return or the cost of capital. For instance, if the IRR equals the risk-free rate, there is no incentive to invest in a risky project since the return does not compensate for the additional risk. In the case of two competing projects, the IRR-based decision rule becomes:

$$IRR_1 > IRR_2 \Rightarrow P_1 > P_2$$

**Alternative assessment criteria** Other metrics, such as the benefit-cost ratio and the profitability index, are better suited for comparing competing projects. Let $PV(B; \rho) = \sum_{t=0}^{T} (1 + \rho)^{-t} B_t$ and $PV(C; \rho) = \sum_{t=0}^{T} (1 + \rho)^{-t} C_t$ be the present value of benefits and costs. The benefit-cost ratio is calculated as:

$$BCR(\rho) = \frac{PV(B; \rho)}{PV(C; \rho)}$$

If this ratio exceeds one, the project is considered worthwhile. The profitability index provides a measure of return per unit of initial investment, calculated as the ratio of net present value to the initial cost:

$$PI(\rho) = \frac{NPV(\rho)}{C_0}$$

Projects with a profitability index greater than one are generally considered efficient. These metrics are particularly useful when capital is limited because they allow projects to be ranked based on their efficiency in generating net benefits per unit of investment (total or initial cost). The decision rule becomes $BCR_1(\rho) > BCR_2(\rho) \Rightarrow P_1 > P_2$ and $PI_1(\rho) > PI_2(\rho) \Rightarrow P_1 > P_2$.

**Remark 125** A cost-effectiveness analysis (CEA) evaluates projects based on their ability to achieve specific outcomes. The goal is to treat the desired outcome as a given and compare the costs of different options for achieving it. CEA is similar to cost-benefit analysis, but unlike CBA, CEA does not quantify benefits.

<footnote n="24">²⁴See the Nordhaus-Stern controversy regarding the social cost of carbon on Section 8.3.1 page 771.</footnote>
**Example 49** We consider two competing projects $P_1$ and $P_2$, whose costs and benefits are described below:

<table><thead><tr><th></th><th>t</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th></tr></thead><tbody><tr><th>P<sub>1</sub></th><td><b>C</b><sub>t</sub></td><td>100</td><td>25</td><td>25</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td><b>B</b><sub>t</sub></td><td></td><td>10</td><td>30</td><td>30</td><td>50</td><td>50</td><td>50</td><td>50</td><td>50</td></tr><tr><th>P<sub>2</sub></th><td><b>C</b><sub>t</sub></td><td>80</td><td>15</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td><b>B</b><sub>t</sub></td><td></td><td>10</td><td>20</td><td>30</td><td>40</td><td>40</td><td>40</td><td>40</td><td>40</td></tr></tbody></table>

For each project $j$, we compute the net present value $NPV_j(\varrho)$, the benefit-cost ratio $BCR_j(\varrho)$, and the profitability index $PI_j(\varrho)$ for different values of the discount factor $\varrho$. The results are given in Table 10.3. We also calculate the internal rate of return$^{25}$ IRR$_j$. For small values of $\varrho$, we find that $NPV_1(\varrho) > NPV_2(\varrho)$. However, when $\varrho \ge 4.3661\%$, $NPV_1(\varrho) < NPV_2(\varrho)$, indicating that project $P_2$ becomes the more favorable option. Regarding the three other criteria, project $P_2$ dominates project $P_1$ for all values of $\varrho$.

<table><caption>Table 10.3: Net present value, benefit-cost ratio, profitability index and internal rate of return of project $P_1$ and $P_2$ (Example 49)</caption><thead><tr><th rowspan="2">ρ</th><th colspan="5">P<sub>1</sub></th><th colspan="5">P<sub>2</sub></th></tr><tr><th>0%</th><th>1%</th><th>3%</th><th>5%</th><th>7%</th><th>0%</th><th>1%</th><th>3%</th><th>5%</th><th>7%</th></tr></thead><tbody><tr><th>NPV<sub>j</sub>(ρ)</th><td>160.00</td><td>144.90</td><td>117.73</td><td>94.09</td><td>73.45</td><td>150.00</td><td>137.50</td><td>114.96</td><td>95.29</td><td>78.06</td></tr><tr><th>BCR<sub>j</sub>(ρ)</th><td>2.07</td><td>1.97</td><td>1.80</td><td>1.64</td><td>1.51</td><td>2.36</td><td>2.26</td><td>2.06</td><td>1.88</td><td>1.73</td></tr><tr><th>PI<sub>j</sub>(ρ)</th><td>1.60</td><td>1.45</td><td>1.18</td><td>0.94</td><td>0.73</td><td>1.88</td><td>1.72</td><td>1.44</td><td>1.19</td><td>0.98</td></tr><tr><th>IRR<sub>j</sub></th><td></td><td></td><td>17.42%</td><td></td><td></td><td></td><td></td><td>21.66%</td><td></td><td></td></tr></tbody></table>

NPV, IRR, BCR and PI are CBA measures that are not specific to policy assessment and are common metrics in business management. They are generally present in different CBA guides written by governments and international bodies. When comparing public policies, more specific metrics can be used to evaluate the efficiency and desirability of public policies, especially taxes and transfers:

* Marginal excess burden (MEB)

It measures the additional deadweight loss resulting from raising an extra dollar in tax revenue:

$$MEB = \frac{\partial \text{DWL}}{\partial R}$$

* Net social benefit (NSB)

It is the difference between total social benefits and total social costs:

$$NSB = \text{Total social benefits} - \text{Total social costs}$$

* Benefit-cost (social) ratio (BCR)

It compares the monetary value of social benefits to the monetary value of social costs:

$$BCR = \frac{\text{Total social benefits}}{\text{Total social costs}}$$

$$IRR = \frac{1}{\inf \{x_i^* : \text{Re}(x_i^*) = 0\}} - 1$$

<footnote>$^{25}$To solve the generic equation $\sum_{t=0}^{T} (1 + \varrho)^{-t} CF_t = 0$ where $CF_t$ is a series of cash flows, we consider the associated polynomial function $f(x) = \sum_{t=0}^{T} CF_t x^t$ and compute all the roots $x_i^*$ of the equation $f(x) = 0$. If all roots have a nonzero imaginary part, then no real solution exists for the internal rate of return. Otherwise, the internal rate of return is given by:</footnote>
* Marginal value of public funds (MVPF)

It measures the welfare benefits received for every dollar spent by the government, while taking into account the cost of raising that revenue (Hendren and Sprung-Keyser, 2020):

$$MVPF = \frac{\text{Willingness to pay for the policy}}{\text{Net cost to the government}}$$

**Remark 126** A cost-benefit analysis goes beyond simple net present value calculations. It incorporates critical principles, such as risk and uncertainty analysis, irreversibility, and the precautionary principle. Uncertainty and probabilistic risks can be incorporated through expected utility theory, which allows for more sophisticated welfare adjustments than relying solely on the expected values of benefits and costs. However, this approach requires complex assumptions about utility functions and risk probabilities. Atkinson et al. (2018, Chapter 9, page 231) argue that sensitivity analysis and Monte Carlo simulations offer greater practical value, particularly for applications such as nuclear power project assessments. Similarly, quasi-option value (QOV) provides a formal framework for applying the precautionary principle under conditions of uncertainty and irreversibility. QOV shows that postponing decisions can generate value by allowing for better information gathering. This highlights the potential benefits of delaying irreversible commitments until greater certainty is achieved (Atkinson et al., 2018, Chapter 10).

## Shadow pricing

In economics, a shadow price is an estimated price for goods, services, or resources that do not have a direct market price, usually because they are not bought or sold in traditional markets. In previous chapters, we encountered two well-known shadow prices: the social cost of carbon (SCC) and the value of a statistical life (VSL). However, shadow pricing is not limited to environmental and biodiversity economics. It can also be applied to various areas of public economics, including education, transportation, commuting, and national defense. As illustrated in Figure 10.8 on page 975, there are many methods for calculating shadow prices. This section focuses on Lagrange functions and the theory of duality. Next, we present applications and examples of shadow pricing.

**Lagrange function and duality** According to Dréze and Stern (1987), shadow prices are the social marginal cost or benefit of goods or inputs that may not be traded in competitive markets. Shadow prices can be used to correct for market failures, but they are not necessarily market prices since they can also reflect opportunity costs to society. The main approach to calculate shadow prices is to consider constrained optimization problems, and derive shadow prices from Lagrange multipliers. We consider the following minimization problem:

$$\begin{array}{l} x^* = \arg \min f(x) \\ \text{s.t.} \quad g(x) \le y \end{array}$$

where $f: \mathbb{R}^n \to \mathbb{R}$ is a smooth convex objective function, $g: \mathbb{R}^n \to \mathbb{R}^m$ is a set of constraint functions and $y \in \mathbb{R}^m$ is a parameter vector. For instance, $f(x)$ could represent a cost function, $g(x)$ could be a set of constraints on production factors, and $y$ could denote a bundle of commodity allowances. We define the value function $v(y) = \inf_x \{f(x) : g(x) \le y\}$. Under appropriate regularity conditions, the shadow price $\lambda^* \in \mathbb{R}^m$ is defined as the subgradient of $v(y)$, i.e., $\lambda^* \in \partial v(y)$. If $v(y)$ is differentiable, then we have:

$$\lambda_j^* = \frac{\partial v(y)}{\partial y_j}$$
This tells us how the optimal value decreases per unit relaxation of the j^th^ constraint — or equivalently, how much the optimal value increases per unit tightening. The corresponding Lagrange function is:

$$ \mathcal{L}(x; \lambda) = f(x) + \lambda^{\top}(g(x) - y) $$

and the dual function is:

$$ h(\lambda) = \inf_x \mathcal{L}(x; \lambda) = \inf_x \{f(x) + \lambda^{\top}(g(x) - y)\} $$

We deduce that the dual problem is defined as follows:

$$ \begin{array}{l} \lambda^* = \arg\max h(\lambda) \\ \text{s.t.} \quad \lambda \ge \mathbf{0}_m \end{array} $$

At optimality, the Lagrange function satisfies:

$$ \mathcal{L}(x^*; \lambda^*) = f(x^*) + \sum_{j=1}^{m} \lambda_j^* (g_j(x^*) - y_j) $$

Suppose now that we increase y by a vector ε ≥ 0m. Then the Lagrange function becomes:

$$ \begin{align*} \mathcal{L}_{\varepsilon}(x^*; \lambda^*) &= f(x^*) + \sum_{j=1}^{m} \lambda_j^* (g_j(x^*) - (y_j + \varepsilon_j)) \\ &= \mathcal{L}(x^*; \lambda^*) - \sum_{j=1}^{m} \lambda_j^* \varepsilon_j \end{align*} $$

Hence, relaxing the j^th^ constraint by a small value ε_j decreases the objective by approximately λ_j^*ε_j. If λ_j^* = 0, there is no incentive to change y_j, since it does not affect the minimum. But if λ_j^* > 0, relaxing y_j reduces the objective value. Suppose we need to purchase an additional allowance ε_j at a price p_j per unit. To make such a purchase worthwhile, we require p_j ≤ λ_j^*, so that the benefit (in terms of reduced cost) exceeds the expense. Therefore, λ_j^* represents the maximum willingness to pay to increase y_j by one unit. This is why λ_j^* is called the shadow price or dual price of the j^th^ constraint.

**Remark 127** In Appendix A.1.2 on pages 1140–1144, we introduce the linear programming method and apply the duality approach to this class of optimization problems. We demonstrate how to calculate shadow prices using the dual formulation and provide two examples of classical resource allocation problems to illustrate the methodology.

The previous formulation of shadow prices can be extended to optimal control theory and dynamic programming. In this context, Lagrange multipliers are time-dependent, and shadow prices can be derived using the Hamiltonian function and Pontryagin's maximum principle. Consider the following optimization problem:

$$ \begin{array}{l} v^*(t) = \arg\max \int_0^T f(t, x(t), v(t)) \, dt \\ \text{s.t.} \quad \frac{dx(t)}{dt} = g(t, x(t), v(t)) \end{array} $$
where $x(t)$ is the state variable and $v(t)$ is the control variable. The Hamiltonian function is defined as follows:

$$ \mathcal{H}(x, v, \lambda) = f(x, v) + \lambda^\top g(x, v) $$

where $\lambda$ is the vector of costate variables (Lagrange multipliers) associated with the dynamic constraint. The dynamics of $\lambda(t)$ are given by the adjoint equation:

$$ \frac{d\lambda(t)}{dt} = -\frac{\partial \mathcal{H}(x(t), v(t), \lambda(t))}{\partial x} $$

At the optimum, Pontryagin's principle states that the following necessary conditions hold:

$$ \left\{ \begin{array}{l} \frac{dx^*(t)}{dt} = \frac{\partial \mathcal{H}(x^*(t), v^*(t), \lambda^*(t))}{\partial \lambda} = g(t, x^*(t), v^*(t)) \\ \frac{d\lambda^*(t)}{dt} = -\frac{\partial \mathcal{H}(x^*(t), v^*(t), \lambda^*(t))}{\partial x} \\ v^*(t) = \arg \max_v \mathcal{H}(x^*(t), v, \lambda^*(t)) \end{array} \right. $$

In this framework, the shadow price of a state variable $x_j(t)$ is given by the corresponding costate variable $\lambda_j(t)$. It reflects the marginal value of relaxing the dynamic constraint on $x_j(t)$ at time $t$.

**Shadow price calculation** Shadow pricing can be applied to many microeconomic problems, including profit maximization, cost minimization, and utility maximization under wealth constraints. It can also be used to solve problems for consumers and social planners.

We consider a classical abatement cost minimization problem in environmental economics. The economy consists of $n$ firms, each choosing an abatement level $a_i \ge 0$, measured in tonnes of CO$_2$. The economy must collectively meet a minimum emission reduction target $E_{\min}$, which imposes the constraint $\sum_{i=1}^n a_i \ge E_{\min}$. The objective is to minimize the total abatement cost, which is given by the quadratic function $C(a_1, \dots, a_n) = \frac{1}{2} \sum_{i=1}^n \beta_i a_i^2$, where $\beta_i$ is the marginal abatement cost parameter for firm $i$, expressed in $$/tCO$_2$. The Lagrange function associated with the constrained minimization problem is:

$$ \begin{aligned} \mathcal{L}(a; \lambda) &= \frac{1}{2} \sum_{i=1}^{n} \beta_i a_i^2 - \lambda \left( \sum_{i=1}^{n} a_i - E_{\min} \right) \\ &= \frac{1}{2} a^\top \mathrm{diag}(\beta) a - \lambda \left( a^\top \mathbf{1}_n - E_{\min} \right) \end{aligned} $$

where $a = (a_1, \dots, a_n)$ and $\beta = (\beta_1, \dots, \beta_n)$. The first-order conditions are:

$$ \left\{ \begin{array}{l} \partial_a \mathcal{L}(a; \lambda) = \mathrm{diag}(\beta) a - \lambda \mathbf{1}_n = \mathbf{0}_n \\ \partial_\lambda \mathcal{L}(a; \lambda) = E_{\min} - a^\top \mathbf{1}_n = 0 \end{array} \right. $$

We have $a = \lambda \mathrm{diag}(\beta)^{-1} \mathbf{1}_n$. Therefore, we deduce the shadow price $\lambda^*$:

$$ \begin{align*} \mathbf{1}_n^\top a = E_{\min} &\Leftrightarrow \mathbf{1}_n^\top \lambda^* \mathrm{diag}(\beta)^{-1} \mathbf{1}_n = E_{\min} \\ &\Leftrightarrow \lambda^* = \frac{E_{\min}}{\mathbf{1}_n^\top \mathrm{diag}(\beta)^{-1} \mathbf{1}_n} \\ &\Leftrightarrow \lambda^* = \frac{E_{\min}}{\sum_{i=1}^n \beta_i^{-1}} \end{align*} $$
while the optimal solution $a^*$ is given by:

$$a^* = \lambda^* \operatorname{diag}(\beta)^{-1} \mathbf{1}_n = \frac{E_{\min}}{\sum_{i=1}^{n} \beta_i^{-1}} \beta^{-1}$$

$\lambda^*$ is the uniform carbon price across all firms expressed in $/tCO$_2$. $\lambda^*$ increases with a more stringent emission target $E_{\min}$, and higher marginal abatement costs $\beta_i$.

We consider a highly simplified version of the DICE model proposed by Nordhaus (1980). The welfare optimization problem is:

$$c^*(t) = \arg \max W(t) = \int_{0}^{\infty} e^{-\rho t} \mathcal{U}(c(t)) dt \\ \text{s.t.} \quad \left\{ \begin{array}{l} \frac{d\mathcal{C}\mathcal{C}(t)}{dt} = \beta \mathcal{C}\mathcal{E}(t) - \delta \mathcal{C}\mathcal{C}(t) \\ c(t) = F(\mathcal{C}\mathcal{E}(t)) - D(\mathcal{C}\mathcal{C}(t)) \end{array} \right.$$

where $W(t)$ denotes welfare, $c(t)$ is per capita consumption, $\mathcal{C}\mathcal{C}(t)$ represents the atmospheric concentration of CO$_2$ and $\mathcal{C}\mathcal{E}(t)$ is the rate of carbon emissions in the atmosphere. The economy has no technological progress, and no capital or labor inputs. The functions $F(x)$ and $D(x)$ capture the relationships between real consumption $c(t)$, emissions $\mathcal{C}\mathcal{E}(t)$ and CO$_2$ concentration $\mathcal{C}\mathcal{C}(t)$. $F(x)$ can be interpreted as the production function, while $D(x)$ represents the damage function$^{26}$. The expression of the Hamiltonian is$^{27}$:

$$\mathcal{H}(\mathcal{C}\mathcal{C}(t), c(t), \lambda(t)) = \mathcal{U}(c(t)) + \lambda(t)(\beta\mathcal{C}\mathcal{E}(t) - \delta\mathcal{C}\mathcal{C}(t)) \\ = \mathcal{U}(F(\mathcal{C}\mathcal{E}(t)) - D(\mathcal{C}\mathcal{C}(t))) + \lambda(t)(\beta\mathcal{C}\mathcal{E}(t) - \delta\mathcal{C}\mathcal{C}(t))$$

The first-order condition for optimal control is:

$$\frac{\partial \mathcal{H}(\mathcal{C}\mathcal{C}(t), \mathcal{C}\mathcal{E}(t), \lambda(t))}{\partial \mathcal{C}\mathcal{E}(t)} = \mathcal{U}'(c(t)) F'(\mathcal{C}\mathcal{E}(t)) + \lambda(t) \beta = 0$$

We deduce that:

$$\lambda(t) = -\mathcal{U}'(c(t)) \frac{F'(\mathcal{C}\mathcal{E}(t))}{\beta} \le 0$$

$\lambda(t)$ is the shadow price of atmospheric CO$_2$ concentration. Since an increase in CO$_2$ concentration reduces welfare, $\lambda(t)$ is negative. The costate equation is:

$$\begin{aligned} \frac{d\lambda(t)}{dt} &= \varrho\lambda(t) - \frac{\partial \mathcal{H}(\mathcal{C}\mathcal{C}(t), c(t), \lambda(t))}{\partial \mathcal{C}\mathcal{C}(t)} \\ &= (\varrho + \delta)\lambda(t) + \mathcal{U}'(c(t)) D'(\mathcal{C}\mathcal{C}(t)) \end{aligned}$$

At steady state, we have:

$$\frac{d\lambda^*(t)}{dt} = 0 \Leftrightarrow \lambda^*(t) = -\mathcal{U}'(c(t)) \frac{D'(\mathcal{C}\mathcal{C}(t))}{(\varrho + \delta)} \le 0$$

$$\left\{ \begin{array}{l} \frac{dx^*(t)}{dt} = \frac{\partial \mathcal{H}(x^*(t), v^*(t), \lambda^*(t))}{\partial \lambda} \\ \frac{d\lambda^*(t)}{dt} = \varrho\lambda(t) - \frac{\partial \mathcal{H}(x^*(t), v^*(t), \lambda^*(t))}{\partial x} \\ v^*(t) = \arg\max_v \mathcal{H}(x^*(t), v, \lambda^*(t)) \end{array} \right.$$

<footnote>$^{26}$This model assumes no saving.</footnote>

<footnote>$^{27}$When discounting is included in the objective function, the Pontryagin conditions are modified only in the costate equation:</footnote>
At equilibrium, the following condition must hold:

$$F'(\mathcal{CE}^*) = D'(CC^*) \left(\frac{\beta}{\varrho + \delta}\right)$$

where $CC^*$ and $\mathcal{CE}^*$ are the equilibrium levels of atmospheric concentration and carbon emissions, respectively. This is the relationship found by Nordhaus (1980, Equation (4), page 18). We now turn to determining the social cost of carbon. Recall that SCC ($t$) represents the present value of all future damages caused by emitting one additional unit of carbon at time $t$. If one additional tonnes of CO$_2$ is emitted ($\Delta\mathcal{CE}$ ($t$) = +1), atmospheric concentration increases by $\beta$ units ($\Delta CC$ ($t$) = $\beta\Delta\mathcal{CE}$ ($t$)). It follows that:

$$\Delta W(t) = \beta\lambda(t) \leq 0$$

The total welfare decreases by $\beta\lambda(t)$ dollars per tonne of CO$_2$. We deduce that:

$$\begin{aligned}
\mathrm{SCC}(t) &= -\frac{\partial W(t)}{\partial \mathcal{CE}(t)} \\
&= -\frac{\partial W(t)}{\partial CC(t)} \cdot \frac{\partial CC(t)}{\partial \mathcal{CE}(t)} \\
&= -\lambda(t)\beta \\
&= \mathcal{U}'(c(t)) F'(\mathcal{CE}(t))
\end{aligned}$$

The social cost of carbon is equal to the marginal utility of consumption multiplied by the marginal productivity of emissions. This represents the marginal benefit of emissions, which must be balanced against the marginal social damage in the optimal solution. At equilibrium, this relationship is characterized by:

$$\mathrm{SCC}^* = \left( \frac{\beta}{\varrho + \delta} \right) D'(CC^*) \qquad (10.7)$$

The social cost of carbon is proportional to the present value of marginal damages, discounted at the composite rate $\varrho + \delta$.

The previous model can be related to the model of fossil fuel depletion developed by Hoel and Kverndokk (1996). The optimization problem is:

$$c^*(t) = \arg\max W(t) = \int_0^\infty e^{-\varrho t} (\mathcal{U}(x(t)) - \mathcal{C}'(A(t))x(t) - D(S(t))) dt \\ \text{s.t.} \quad \left\{ \begin{array}{l} \frac{dA(t)}{dt} = x(t) \\ \frac{dS(t)}{dt} = x(t) - \delta S(t) \end{array} \right.$$

where $x(t)$ is the extraction of all fossil fuels in carbon units, $A(t)$ is the accumulated extraction, and $S(t)$ is the atmospheric stock of carbon. The parameter $\delta$ is the depreciation rate of the stock. $\mathcal{C}'(A(t))$ is the marginal extraction cost, and $D(S(t))$ is the damage function. The Hamiltonian is:

$$\mathcal{H}(A, S, x, \lambda, \mu) = \mathcal{U}(x(t)) - \mathcal{C}'(A(t))x(t) - D(S(t)) + \lambda(t)x(t) + \mu(t)(x(t) - \delta S(t))$$

The first-order condition for optimal control is:

$$\frac{\partial \mathcal{H}(A, S, x, \lambda, \mu)}{\partial x(t)} = \mathcal{U}'(x(t)) - \mathcal{C}'(A(t)) + \lambda(t) + \mu(t) = 0$$
The costate equations are:

$$ \frac{d\lambda(t)}{dt} = \varrho\lambda(t) - \frac{\partial\mathcal{H}(A, S, x, \lambda, \mu)}{\partial A(t)} = \varrho\lambda(t) + \mathbf{C}''(A(t))x(t) $$

and:

$$ \frac{d\mu(t)}{dt} = \varrho\mu(t) - \frac{\partial\mathcal{H}(A, S, x, \lambda, \mu)}{\partial S(t)} = \varrho\mu(t) + D'(S(t)) + \delta\mu(t) $$

We deduce that the solutions are:

$$ \lambda(t) = - \int_t^\infty e^{-\varrho(s-t)} \mathbf{C}''(A(s)) x(s) \, ds $$

and:

$$ \mu(t) = - \int_t^\infty e^{-(\varrho+\delta)(s-t)} D'(S(s)) \, ds $$

In Walrasian theory, recall that the consumer price of fossil fuels is equal to marginal utility: $p(t) = \mathbf{U}'(x(t))$. Using the first-order condition, we get (Hoel and Kverndokk, 1996, Equation (14), page 120):

$$ \begin{aligned} p(t) &= \mathbf{C}'(A(t)) - \lambda(t) - \mu(t) \\ &= \underbrace{\mathbf{C}'(A(t)) + \int_t^\infty e^{-\varrho(s-t)} \mathbf{C}''(A(s)) x(s) \, ds}_{\text{Producer price}} + \underbrace{\int_t^\infty e^{-(\varrho+\delta)(s-t)} D'(S(s)) \, ds}_{\text{Social cost of carbon}} \end{aligned} $$

The producer price is the sum of the marginal extraction cost and the scarcity rent. The consumer price is the sum of the producer price and the social cost of carbon. According to Hoel and Kverndokk (1996), the social cost of carbon or the optimal carbon tax “is always equal to discounted future negative externalities due to the accumulation of carbon in atmosphere.” At steady state, we have:

$$ \mathrm{SCC}^* = -\mu^* = \frac{D'(S^*)}{\varrho + \delta} \qquad (10.8) $$

The SCC is then the present value of future marginal damages. Equations (10.7) and (10.8) are identical<fnref n="28" />.

The previous analysis demonstrated how to calculate the shadow price of externalities, such as the social cost of carbon. This framework is then used to estimate the optimal tax needed to correct these externalities. Shadow pricing can also be applied to goods and services. In such cases, the relationship is given by:

$$ \text{Shadow price of good} = \text{Market price of good} + \sum_{j=1}^{m} \text{Shadow price of } j^{\text{th}} \text{ externality} $$

The shadow price of a good or service equals its market price (also referred to as the producer price) plus the sum of the shadow prices of all externalities it generates. In practice, however, this additive approach is often replaced by a multiplicative formulation:

$$ \text{Shadow price} = \text{Market price} \times \text{Conversion factor} $$

<footnote n="28">²⁸The parameter $\beta$ in Equation (10.7) vanishes in Equation (10.8) because all metrics are expressed in carbon units. This differs from the first model, which considers the concentration rate.</footnote>
The conversion factor reflects the extent of market distortion and varies by sector and context. Depending on the nature and magnitude of these distortions, conversion factors may be greater than, less than, or equal to one. For regulated prices, conversion factors typically account for the difference between regulated prices and competitive market prices. In markets with significant government intervention, regulated prices may not reflect true scarcity values or the full social costs of goods and services.

## 10.2 Carbon pricing mechanisms

In this section, we follow the study by Dao et al. (2024), which provides a comprehensive overview of carbon pricing practices worldwide. A common initial distinction is between carbon taxes and emissions trading systems (ETS), including cap-and-trade mechanisms. However, these instruments are closely related, and countries rarely rely on just one approach. In practice, most jurisdictions implement a combination of multiple instruments. Figure 10.9 presents an overview of the various carbon pricing mechanisms currently in place. As of December 2023, there were 125 carbon pricing initiatives worldwide, though only 75 were actively in effect. Some schemes have been discontinued, while others remain under development. Accordingly, our analysis considers both types of external carbon pricing instruments. In recent years, carbon credit markets have also emerged. These operate within either voluntary or compliance frameworks, in which credits are generated through emission reduction projects and purchased by entities aiming to offset their emissions. Since 2020, the World Bank has incorporated the analysis of carbon crediting mechanisms into its *State and Trends of the Carbon Market* report series<fnref n="29" />. Following this precedent, we dedicate a subsection to carbon credits. The final part of this section explores internal carbon pricing practices, which are voluntarily adopted by organizations to account for their own emissions.

<figure><img src="image_482.png" /><figcaption>Figure 10.9: Global network of carbon pricing mechanisms as of December 2023</figcaption></figure>

Source: Dao et al. (2024, Figure 1, page 6).

<footnote n="29">²⁹The publication of the series began in December 2003.</footnote>
### 10.2.1 Definition

Carbon pricing is the primary tool for implementing public climate policies aimed at reducing CO₂ emissions:

> "Carbon pricing is an instrument that captures the external costs of greenhouse gas (GHG) emissions — the costs of emissions that the public pays for, such as damage to crops, health care costs from heat waves and droughts, and loss of property from flooding and sea level rise — and ties them to their sources through a price, usually in the form of a price on the carbon dioxide emitted." (World Bank, 2023).

There are various forms of direct carbon pricing, including a carbon tax, an emissions trading scheme (ETS), and a carbon credit mechanism. The underlying idea is that the largest emitters of greenhouse gases pay higher taxes or face higher costs. This encourages them to change their practices and reduce their emissions. These mechanisms also promote green business development and stimulate market innovation by increasing the cost of carbon-intensive activities. Additionally, carbon pricing generates revenue for governments, which can finance the transition to a low-carbon economy.

#### Carbon tax

A carbon tax is a direct price imposed by governments on carbon emissions. It sets a fixed charge per tonne of CO₂e emitted, making carbon-intensive products more expensive and less accessible. This pricing shift aims to encourage consumers to choose cleaner alternatives. As a price-based instrument, a carbon tax reveals and stabilizes the cost of emissions. However, the actual quantity of emissions may fluctuate based on market responses. Therefore, while the policy provides cost certainty, it leaves environmental outcomes uncertain. The effectiveness of a carbon tax hinges on how producers and consumers respond to increased costs. These responses are influenced by market structure, the elasticity of substitution between goods, and the level of regulatory support.

The primary objective of a carbon tax is to send a clear price signal throughout the economy. Raising the price of fossil fuels, such as coal, oil, and natural gas, changes the relative prices throughout the supply chain. This makes carbon-intensive goods and services more expensive than their low-carbon alternatives. For example, coal-generated electricity becomes less competitive than solar or wind power, encouraging utilities to transition to renewable energy sources. A secondary objective is to generate public revenue to support the transition to a low-carbon economy. This revenue can subsidize clean energy technologies, invest in sustainable infrastructure, or support affected households and industries. Some jurisdictions opt for revenue-neutral carbon taxes, redistributing the proceeds through tax cuts or direct rebates to households. Others channel the funds into climate mitigation and adaptation projects. How the revenue is recycled plays a critical role in determining the tax's economic efficiency and political acceptability.

Carbon tax rates are usually calculated based on the carbon content of different fuels and expressed as the cost per tonne of CO₂e emitted. We have:

$$C = \tau \cdot CE$$

where $\tau$ is the carbon tax expressed in $/tCO₂$ (or $/tCO₂e$) and $CE$ is the carbon emissions of the product expressed in tCO₂ (or tCO₂e). For example, if the carbon tax is $50 per tonne of CO₂ and the emission factor is 2.3 kg of CO₂ per liter of gasoline, the tax is approximately 11.5 cents per liter of gasoline:

$$C = \frac{\$50}{1000 \text{ kg}} \times \frac{2.3 \text{ kg}}{1 \text{ liter}} = \$0.115 \text{ per liter}$$
Carbon taxes can be implemented at various points in the supply chain: upstream (producers such as oil refineries, coal mines, and gas importers are taxed at the point of extraction or import), mid-stream: (taxes target fuel distributors and wholesalers), and downstream (taxes are levied directly on end users, such as consumers or firms). A fourth category, called point source, can also be used. In this case, the tax targets specific emitters from a fixed locations, such as power plants, industrial facilities, and refineries. Most governments favor upstream or point source implementation due to their administrative simplicity.

<table><caption>Table 10.4: Carbon taxes worldwide ranked by 2024 price (in $/tCO$_2$)</caption><thead><tr><th>Country</th><th>2015</th><th>2020</th><th>2022</th><th>2023</th><th>2024</th><th>2025</th><th>Coverage</th></tr></thead><tbody><tr><td>Uruguay</td><td></td><td></td><td>137.30</td><td>155.87</td><td>167.17</td><td>158.76</td><td>5%</td></tr><tr><td>Liechtenstein</td><td>37.17</td><td>99.44</td><td>129.86</td><td>130.81</td><td>132.12</td><td>136.04</td><td>72%</td></tr><tr><td>Switzerland</td><td>61.96</td><td>99.44</td><td>129.86</td><td>130.81</td><td>132.12</td><td>136.04</td><td>35%</td></tr><tr><td>Sweden</td><td>129.81</td><td>119.43</td><td>129.89</td><td>125.56</td><td>123.70</td><td>144.62</td><td>40%</td></tr><tr><td>Norway</td><td>68.32</td><td>63.28</td><td>96.31</td><td>94.49</td><td>107.78</td><td>133.92</td><td>14%</td></tr><tr><td>Finland</td><td>62.38</td><td>84.21</td><td>85.10</td><td>83.74</td><td>99.98</td><td>66.89</td><td>39%</td></tr><tr><td>Netherlands</td><td></td><td></td><td>46.14</td><td>60.83</td><td>71.48</td><td>94.83</td><td>1%</td></tr><tr><td>British Columbia</td><td>23.79</td><td>28.14</td><td>39.96</td><td>48.03</td><td>58.94</td><td></td><td></td></tr><tr><td>Canada</td><td></td><td>21.10</td><td>39.96</td><td>48.03</td><td>58.94</td><td></td><td></td></tr><tr><td>Northwest Territories</td><td></td><td>14.07</td><td>31.97</td><td>48.03</td><td>58.94</td><td></td><td></td></tr><tr><td>Ireland</td><td>21.50</td><td>28.52</td><td>45.69</td><td>53.19</td><td>52.57</td><td>68.50</td><td>30%</td></tr><tr><td>Luxembourg</td><td></td><td></td><td>43.35</td><td>45.00</td><td>49.91</td><td>58.55</td><td>72%</td></tr><tr><td>France</td><td>15.59</td><td>48.77</td><td>49.29</td><td>48.50</td><td>47.94</td><td>48.11</td><td>41%</td></tr><tr><td>Iceland</td><td>16.21</td><td>30.74</td><td>35.61</td><td>36.11</td><td>36.51</td><td>60.06</td><td>36%</td></tr><tr><td>Denmark</td><td>24.26</td><td>25.67</td><td>26.37</td><td>26.29</td><td>28.40</td><td>108.43</td><td>44%</td></tr><tr><td>Portugal</td><td></td><td>13.79</td><td>26.17</td><td>25.75</td><td>25.45</td><td>72.70</td><td>40%</td></tr><tr><td>United Kingdom</td><td>26.71</td><td>22.28</td><td>23.65</td><td>22.28</td><td>22.62</td><td>23.24</td><td>0%</td></tr><tr><td>Tamaulipas</td><td></td><td></td><td>13.10</td><td>17.25</td><td>19.66</td><td>16.67</td><td>0%</td></tr><tr><td>Slovenia</td><td>18.58</td><td>18.92</td><td>19.12</td><td>0.00</td><td>18.60</td><td>33.28</td><td>46%</td></tr><tr><td>Singapore</td><td></td><td>3.51</td><td>3.69</td><td>3.77</td><td>18.48</td><td>18.62</td><td>71%</td></tr><tr><td>Latvia</td><td>3.76</td><td>9.84</td><td>16.58</td><td>16.31</td><td>16.12</td><td>16.18</td><td>2%</td></tr><tr><td>Spain</td><td>21.51</td><td>16.40</td><td>16.58</td><td>16.31</td><td>16.12</td><td>16.18</td><td>2%</td></tr><tr><td>Zacatecas</td><td></td><td>10.21</td><td>12.60</td><td>13.86</td><td>15.09</td><td>12.28</td><td>0%</td></tr><tr><td>Albania</td><td></td><td></td><td></td><td></td><td>13.11</td><td>13.69</td><td>73%</td></tr><tr><td>South Africa</td><td></td><td>7.06</td><td>9.84</td><td>8.93</td><td>10.09</td><td>12.85</td><td>82%</td></tr><tr><td>Colombia</td><td></td><td>4.24</td><td>5.01</td><td>5.04</td><td>6.68</td><td>6.54</td><td>20%</td></tr><tr><td>Chile</td><td></td><td>5.00</td><td>5.00</td><td>5.00</td><td>5.00</td><td>5.00</td><td>55%</td></tr><tr><td>Mexico</td><td>3.12</td><td>2.33</td><td>3.20</td><td>3.79</td><td>4.31</td><td>3.94</td><td>13%</td></tr><tr><td>Estonia</td><td>2.15</td><td>2.19</td><td>2.21</td><td>2.17</td><td>2.15</td><td>26.97</td><td>9%</td></tr><tr><td>Japan</td><td>1.47</td><td>2.50</td><td>2.20</td><td>2.01</td><td>1.91</td><td>1.93</td><td>80%</td></tr><tr><td>Argentina</td><td></td><td>6.34</td><td>5.00</td><td>3.22</td><td>0.81</td><td>5.33</td><td>38%</td></tr><tr><td>Ukraine</td><td></td><td>0.38</td><td>1.03</td><td>0.82</td><td>0.77</td><td>0.73</td><td>32%</td></tr><tr><td>Poland</td><td>0.08</td><td>0.07</td><td>0.08</td><td></td><td>0.08</td><td>0.10</td><td>24%</td></tr></tbody></table>

Source: World Bank Carbon Pricing Dashboard (2025), carbonpricingdashboard.worldbank.org.

Table 10.4 shows how carbon taxes have changed over time in various countries. However, it is difficult to compare these figures because they do not cover the same sectors. For example, the carbon tax was $167 in Uruguay, $124 in Sweden, $16 in Spain, and $2 in Japan in 2024. The percentage of greenhouse gas emissions covered by the tax varies by country: 5% in Uruguay, 40% in Sweden, 2%
in Spain, and 80% in Japan. Therefore, there is no global homogeneity among countries. In some countries, carbon taxation is the primary climate policy, while in others, it supplements mechanisms such as emissions trading systems. Portugal's carbon tax, for example, supplements the EU ETS. The tax primarily applies to CO₂ emissions from industry, buildings, and transportation, including some sectors covered by the EU ETS. Sectors that do not use fossil fuels are exempt from paying the carbon tax. Another significant difference is that the carbon tax can be paid by either producers or consumers. In France, for example, the carbon tax<fnref n="30" /> is added to the final price of gasoline, diesel, heating oil, and natural gas, which individuals and businesses then pay. In this context, it is difficult to compare carbon tax levels across countries.

## Emissions trading system

When carbon emissions are regulated using a quantity-based policy instrument, the mechanism typically takes the form of an emissions trading system. In an ETS, participants trade emission allowances within a regulated carbon market to comply with their emissions targets. An ETS functions by setting an aggregate cap on total allowable emissions across all participating entities, which are usually firms or industrial sectors. This cap is expressed as the following constraint:

$$ \sum_{j \in \text{ETS}} \mathcal{C}\mathcal{E}_j \leq \mathcal{C}\mathcal{E}_{\text{ETS}}^{+} $$

where $j \in \text{ETS}$ denotes an entity covered by the ETS, $\mathcal{C}\mathcal{E}_j$ is the carbon emissions of entity $j$, and $\mathcal{C}\mathcal{E}_{\text{ETS}}^{+}$ is the overall emissions cap imposed by the system regulator. To operationalize this limit, a corresponding number of emission allowances (or permits) are issued or auctioned to entities. Each allowance typically grants the right to emit one tonne of CO₂e. The distribution of allowances must also respect the cap:

$$ \sum_{j \in \text{ETS}} \mathcal{E}\mathcal{A}_j \leq \mathcal{C}\mathcal{E}_{\text{ETS}}^{+} $$

where $\mathcal{E}\mathcal{A}_j$ denotes the emissions allowances allocated to entity $j$. In order to comply, each entity must surrender a number of allowances that equals its actual emissions. The system operates under the following logic:

* If $\mathcal{C}\mathcal{E}_j < \mathcal{E}\mathcal{A}_j$, the entity has surplus allowances and may sell the excess ($\mathcal{E}\mathcal{A}_j - \mathcal{C}\mathcal{E}_j$) on the carbon market.

* If $\mathcal{C}\mathcal{E}_j > \mathcal{E}\mathcal{A}_j$, the entity faces a shortage and must purchase additional allowances or face regulatory penalties.

When the supply of allowances meets the demand, the ETS establishes a market price for greenhouse gas emissions. This price creates a financial incentive for firms to reduce emissions efficiently. The lower their emissions, the more allowances they can sell to generate revenue or reduce compliance costs.

<footnote n="30">³⁰The French carbon tax, officially called the *contribution climat-énergie* (CCE), was introduced in 2014. It is integrated into existing domestic excise duties on energy consumption as a carbon-related surcharge on the TICPE (*taxe intérieure de consommation sur les produits énergétiques*). The value of the CCE is directly linked to the carbon content of various fuels. The CCE broadens the tax base by targeting energy products, including fuels, heating oil, natural gas, and other fossil-based energy sources. The CCE is a complementary measure to the EU ETS, each mechanism addresses different yet overlapping segments of greenhouse gas emissions. Ultimately, these taxes are embedded in the final retail prices of energy products, such as gasoline, diesel, heating oil, and natural gas, meaning the cost is borne by households and businesses alike.</footnote>
An ETS is essentially synonymous with a cap-and-trade system, with “cap” referring to the emissions ceiling and “trade” referring to the market for allowances. Depending on the policy design, allowances can be allocated based on historical emissions or benchmarks, or they can be auctioned. Another variant of an ETS is the baseline-and-credit system, which can coexist with cap-and-trade systems. In this system, each entity is assigned a baseline level of emissions. If a firm emits below its baseline, it earns credits. If it exceeds its baseline, it must acquire credits from others. As with cap-and-trade, this system creates a market price for emissions through the interaction of supply and demand for credits. Both systems use market mechanisms to ensure emissions are reduced in a cost-effective manner.

<table><caption>Table 10.5: ETS worldwide ranked by 2024 carbon price (in $/tCO$_2$)</caption><thead><tr><th>Country</th><th>2015</th><th>2020</th><th>2022</th><th>2023</th><th>2024</th><th>2025</th><th>Coverage</th></tr></thead><tbody><tr><td>EU</td><td>7.69</td><td>18.54</td><td>86.53</td><td>96.30</td><td>61.30</td><td>70.37</td><td>40%</td></tr><tr><td>Switzerland</td><td>12.91</td><td>19.85</td><td>65.59</td><td>93.81</td><td>59.17</td><td>64.75</td><td>13%</td></tr><tr><td>Alberta</td><td>11.89</td><td>21.10</td><td>39.96</td><td>48.03</td><td>58.94</td><td>66.21</td><td>59%</td></tr><tr><td>British Columbia</td><td></td><td></td><td>19.98</td><td>18.47</td><td>58.94</td><td>66.21</td><td>26%</td></tr><tr><td>Canada</td><td></td><td></td><td>39.96</td><td>48.03</td><td>58.94</td><td>66.21</td><td>3%</td></tr><tr><td>New Brunswick</td><td></td><td></td><td>39.96</td><td>48.03</td><td>58.94</td><td>66.21</td><td>54%</td></tr><tr><td>Newfoundland and Labrador</td><td></td><td></td><td>39.96</td><td>48.03</td><td>58.94</td><td>66.21</td><td>36%</td></tr><tr><td>Nova Scotia</td><td></td><td></td><td>23.10</td><td>20.87</td><td>58.94</td><td>66.21</td><td>36%</td></tr><tr><td>Ontario</td><td></td><td></td><td>31.97</td><td>48.03</td><td>58.94</td><td>66.21</td><td>26%</td></tr><tr><td>Saskatchewan</td><td></td><td></td><td>39.96</td><td>48.03</td><td>58.94</td><td></td><td></td></tr><tr><td>Austria</td><td></td><td></td><td></td><td>35.34</td><td>48.37</td><td>48.55</td><td>36%</td></tr><tr><td>Germany</td><td></td><td></td><td>33.16</td><td>32.62</td><td>48.37</td><td>48.55</td><td>39%</td></tr><tr><td>United Kingdom</td><td></td><td></td><td>98.56</td><td>88.13</td><td>45.06</td><td>57.23</td><td>27%</td></tr><tr><td>California</td><td>12.49</td><td>15.30</td><td>30.82</td><td>29.84</td><td>38.59</td><td>29.27</td><td>76%</td></tr><tr><td>Quebec</td><td>12.49</td><td>21.75</td><td>38.58</td><td>29.84</td><td>38.59</td><td>41.54</td><td>76%</td></tr><tr><td>Tokyo</td><td>37.52</td><td>5.58</td><td>4.42</td><td>5.24</td><td>36.91</td><td>4.01</td><td></td></tr><tr><td>New Zealand</td><td>4.93</td><td>14.30</td><td>52.62</td><td>34.20</td><td>35.10</td><td>32.01</td><td>44%</td></tr><tr><td>Montenegro</td><td></td><td></td><td></td><td></td><td>25.80</td><td>25.89</td><td>43%</td></tr><tr><td>Washington</td><td></td><td></td><td></td><td>22.20</td><td>25.75</td><td>50.00</td><td>71%</td></tr><tr><td>Australia</td><td></td><td></td><td></td><td></td><td>21.90</td><td>21.82</td><td>26%</td></tr><tr><td>RGGI</td><td>5.90</td><td>5.13</td><td>13.89</td><td>15.39</td><td>17.64</td><td>23.32</td><td>14%</td></tr><tr><td>Beijing</td><td>8.21</td><td>12.20</td><td>6.53</td><td>12.96</td><td>14.51</td><td>12.15</td><td>17%</td></tr><tr><td>China</td><td></td><td></td><td>9.20</td><td>8.15</td><td>12.57</td><td>11.76</td><td>51%</td></tr><tr><td>Shanghai</td><td>4.73</td><td>5.07</td><td>9.28</td><td>8.72</td><td>10.06</td><td>10.80</td><td>21%</td></tr><tr><td>Shenzhen</td><td>5.98</td><td>2.38</td><td>0.64</td><td>8.77</td><td>8.96</td><td>6.53</td><td>37%</td></tr><tr><td>Guangdong</td><td>5.49</td><td>4.13</td><td>12.51</td><td>12.34</td><td>8.94</td><td>5.41</td><td>12%</td></tr><tr><td>South Korea</td><td>9.10</td><td>32.79</td><td>18.75</td><td>11.24</td><td>6.30</td><td>6.45</td><td>79%</td></tr><tr><td>Chongqing</td><td>3.91</td><td>5.31</td><td>5.66</td><td>4.66</td><td>5.99</td><td>5.51</td><td>14%</td></tr><tr><td>Hubei</td><td>4.18</td><td>3.57</td><td>7.24</td><td>6.96</td><td>5.62</td><td>5.27</td><td>20%</td></tr><tr><td>Tianjin</td><td>4.21</td><td>2.82</td><td>4.40</td><td>4.60</td><td>4.71</td><td>5.30</td><td>18%</td></tr><tr><td>Fujian</td><td></td><td>1.28</td><td>1.83</td><td>4.66</td><td>3.75</td><td>4.73</td><td>16%</td></tr><tr><td>Massachusetts</td><td></td><td>8.20</td><td>0.50</td><td>12.05</td><td>2.25</td><td>9.30</td><td></td></tr><tr><td>Kazakhstan</td><td></td><td>1.11</td><td>1.08</td><td>1.12</td><td>1.06</td><td>0.93</td><td>43%</td></tr></tbody></table>

Source: World Bank Carbon Pricing Dashboard (2025), carbonpricingdashboard.worldbank.org.

Table 10.5 shows how carbon prices have changed across different ETS jurisdictions. The EU ETS has demonstrated substantial price volatility. Prices increased from $7.69 in 2015 to a peak of $96.30 in 2023, then dropping to $70.37 in 2025. Figure 10.10 shows the daily changes in EU
ETS carbon allowance prices from 2008 to July 2025, confirming the significant price fluctuations. Switzerland follows a similar trajectory, closely aligning with EU prices due to the linkage between the two systems. The United Kingdom's ETS also exhibits significant volatility. In North America, Canadian provinces and territories have shown price convergence since 2022, reflecting the influence of federal carbon pricing coordination. California and Quebec, which have a linked ETS, have maintained relatively low but stable prices, averaging around $30–$40 in 2025. A clear divide is evident between ETS in developed and developing economies. While EU and North American systems generally maintain prices in the $40–$70 range per tonne, most of China's regional pilot programs remain below $15. These differences highlight variations in market maturity and regulatory stringency.

<figure><img src="image_483.png" /><figcaption>Figure 10.10: Evolution of the EU ETS carbon allowance price</figcaption></figure>

Carbon crediting mechanisms

Carbon crediting mechanisms are broader systems designed to generate carbon credits from emission reduction or removal projects, usually outside an ETS. A more formal definition is:

> "Under a carbon crediting mechanism, tradable credits (each representing 1tCO2e) are generated through voluntary mitigation activities that avoid (preventing GHGs from being released, such as capturing methane from landfills) or remove (extracting GHG from the atmosphere, for instance through afforestation that sequesters carbon) emissions. These credits are issued following established protocols designed to ensure that each credit corresponds to a real and measurable reduction or removal." (World Bank, 2025, page 74).

Although carbon crediting mechanisms generate credits, they are only effective if there is a demand for those credits, providing a financial incentive to reduce emissions. The main sources of credit
Box 10.3: The European Union Emissions Trading System

The EU ETS is the world's first and largest carbon market. Since its launch in 2005, it has undergone significant transformations, evolving through four distinct phases aimed at improving market functioning, and alignment with EU climate objectives.

*   **Phase I (2005–2007)—The pilot phase**
    The first phase of the EU ETS was a pilot period intended to test the system and gain experience with emissions trading. During this phase, the system primarily covered power and heat generation. Allowances were distributed for free using a grandfathering method based on historical emissions. However, due to a lack of verified emissions data and the absence of a harmonized cap across member states, over-allocation quickly became apparent, causing the carbon price to plummet to nearly zero by 2007.

*   **Phase II (2008–2012)—Kyoto alignment period**
    The second phase expanded participation to include all member states. Although the cap was more centralized than in the first phase, and banking of allowances into Phase III was permitted, approximately 90% of the allowances were still allocated for free. A key innovation was the introduction of mechanisms that permitted the use of international offset creditsª from the Clean Development Mechanism (CDM) and Joint Implementation (JI). However, the 2008 financial crisis coupled with continued over-allocation generated a surplus of approximately 0.82 GtCO₂e (Dao et al., 2024, page 14), raising concerns about market oversupply and weakening the price signal.

*   **Phase III (2013–2020)—Structural reform**
    Phase III involved a significant restructuring of the EU ETS. A key innovation was replacing national caps with a single, EU-wide cap that decreased annually by 1.74%. The scope of the EU ETS expanded to include additional sectors, such as intra-EU aviation and aluminum production. The allocation method also shifted, with auctioning becoming the default rule. Where free allocation remained, the grandfathering approach was replaced by benchmarking, with allowances based on the performance of the 10% most efficient installations in each sector. For the first time, verified emissions exceeded allocated allowances, reversing the trend of over-allocation. This resulted in an under-allocation of 0.89 GtCO₂e, leading to a negative cumulative surplus of –0.07 GtCO₂e by the end of the phase (Dao et al., 2024, Table 4, page 15). In 2019, the market stability reserve (MSR) was introduced to address surplus allowances and improve market resilience.

*   **Phase IV (2021–2030)—Enhanced climate ambition**
    The current phase has accelerated decarbonization. The linear reduction factor has increased to 4.3% annually over the period 2024–2027, and to 4.4% annually from 2028. The EU ETS cap aims to reduce emissions by 62% by 2030 compared to 2005 levels. International credits have been eliminated, and the maritime sector was included in 2024. Phase IV not only tightens the existing system but also expands its scope by introducing a separate ETS (ETS 2) in 2027 that will cover buildings and road transport.

<footnote>ªThese mechanisms are presented on page 727.</footnote>
supply include: (1) governmental crediting mechanisms, (2) independent (or voluntary) crediting mechanisms, and (3) international crediting mechanisms. The main sources of demand for carbon credits are: (1) domestic compliance, such as businesses regulated under an ETS or carbon tax regimes that permit offsets, (2) international compliance, notably under CORSIA<fnref n="31" />, (3) achievement of nationally determined contributions (NDC), where countries (e.g., Singapore and Switzerland) purchase credits to meet international climate goals, and (4) voluntary commitments, where private companies buy credits to meet self-imposed net zero or carbon neutrality targets.

According to World Bank (2025, Figure 13, page 50), the most important categories of credit projects under crediting mechanisms are as follows, in order of importance:

* **Forestry and land use**
  Increasing the volume of emissions removed from the atmosphere or avoiding emissions being released through changes to terrestrial sinks.

* **Renewable energy**
  Emissions avoided by integrating renewable energy into the energy supply in the place of fossil fuel power.

* **Household/community devices**
  Emissions reductions achieved through the distribution of clean cooking stoves, water purification systems, and other household technologies that replace traditional practices with lower-emission alternatives.

* **Chemical process/industrial manufacturing**
  Avoiding emissions produced by industrial facilities including manufacturing, metal production, fertilizer production. Includes emissions from fuel used for energy in these facilities as well as emissions from industrial processes.

* **Waste disposal**
  Reductions in emissions from waste management activities, including the capture and utilization of landfill gas, composting programs, waste-to-energy projects, and the capture of methane from wastewater treatment facilities.

* **Agriculture**
  Reducing emissions from any activities in the agricultural sector.

* **Energy efficiency/fuel switching**
  Avoiding emissions from the participant's energy use through either reducing the amount of energy the participant consumes or switching to a less emissions-intensive energy source.

* **Carbon capture and storage/carbon capture and utilization**
  Removals achieved through carbon capture and storage or carbon capture and utilization.

* **Fugitive emissions**
  Avoiding the release (intentional or unintentional) of GHGs during the extraction, processing, transformation, and delivery of fossil fuels to the point of final use.

<footnote n="31">³¹CORSIA (Carbon Offsetting and Reduction Scheme for International Aviation) is a global, market-based program designed to reduce carbon emissions from international aviation. Developed by the International Civil Aviation Organization (ICAO), CORSIA is the first sector-wide climate initiative of its kind. It applies to international flights between participating countries and targets airlines that emit more than 10 000 tonnes of CO₂ annually from such operations. This effectively covers approximately 80% of international aviation emissions.</footnote>
* Transport
  Reduction of emissions resulting from fuels burned for energy in the service of moving people or goods (e.g., road, rail), including for aviation.

In 2024, the total demand for carbon credit retirements was approximately 250 MtCO₂e, with 76% for voluntary purposes and 24% for compliance purposes. The main sources of compliance-related retirements were the California cap-and-trade program (36%), the Colombia carbon tax (29%), the Quebec cap-and-trade program (23%) and the South Africa carbon tax (7%). As of April 2025, there were 33 governmental crediting mechanisms. Examples include:

* Alberta Emission Offset System (www.alberta.ca/alberta-emission-offset-system)

* Australia Carbon Credit Unit Scheme (https://cer.gov.au/schemes/australian-carbon-credit-unit-scheme)

* California Compliance Offset Program (www.arb.ca.gov/our-work/programs/compliance-offset-program)

* China GHG Voluntary Emission Reduction Program

* Portuguese Voluntary Carbon Market (https://mvcarbono.pt)

* UK Woodland Carbon Code (www.woodlandcarboncode.org.uk)

There are fewer independent crediting mechanisms. The most prominent are the Verified Carbon Standard (https://verra.org/programs/verified-carbon-standard) and the Gold Standard (www.goldstandard.org).

<table><caption>Table 10.6: Price evolution of carbon credits (in $)</caption><thead><tr><th>Category</th><th>2020</th><th>2021</th><th>2022</th><th>2023</th><th>2024</th></tr></thead><tbody><tr><td>Forestry and land use</td><td>5.5</td><td>5.9</td><td>10.1</td><td>10.0</td><td>9.3</td></tr><tr><td>Agriculture</td><td>10.4</td><td>8.8</td><td>11.0</td><td>6.5</td><td>7.7</td></tr><tr><td>Household/community devices</td><td>4.1</td><td>5.4</td><td>8.6</td><td>7.7</td><td>7.3</td></tr><tr><td>Waste disposal</td><td>2.8</td><td>3.7</td><td>7.2</td><td>7.5</td><td>6.7</td></tr><tr><td>Chemical processes</td><td>2.1</td><td>3.1</td><td>5.1</td><td>4.1</td><td>3.7</td></tr><tr><td>Transport</td><td>0.6</td><td>1.2</td><td>4.4</td><td></td><td>3.2</td></tr><tr><td>Energy efficiency/fuel switching</td><td>1.0</td><td>2.2</td><td>5.4</td><td>3.6</td><td>3.0</td></tr><tr><td>Renewable energy</td><td>1.1</td><td>2.2</td><td>4.2</td><td>4.1</td><td>2.7</td></tr><tr><td>Overall</td><td>2.6</td><td>4.1</td><td>7.4</td><td>6.7</td><td>6.3</td></tr></tbody></table>

Source: Ecosystem Marketplace (2025), Global Carbon Markets, https://hub.ecosystemmarketplace.com/landing.

As shown in Table 10.6, carbon credit prices remained relatively low across all project categories, even during the peak in 2022. In 2024, forestry and land use projects had the highest average price, at $9.30 per tCO₂e. Agriculture projects followed with an average price of $7.70. Several categories, such as renewable energy and energy efficiency, traded below $3 per tCO₂e. These prices are significantly lower than those in emissions trading systems and carbon tax regimes. On average, carbon credit prices are five to ten times lower than compliance-based prices. This price gap reflects the structural differences between markets. Voluntary and offset credit markets often face oversupply, heterogeneous quality standards, and weaker regulatory oversight.
### 10.2.2 Panorama of existing direct pricing mechanisms

Figure 10.11 shows the global status of ETS and carbon taxes, including implemented systems, those under development, and those under consideration. This policy landscape changes continuously as new instruments are introduced, suspended policies are revived, and existing systems are occasionally reformed or abandoned.

#### Scale and ambition of carbon pricing policies

How should different ETS and carbon taxes be ranked? This is a non-trivial question, as it involves multiple dimensions. One key metric is the volume of GHG emissions covered by the instrument. Another important dimension is the amount of revenue generated. Table 10.7 presents the top 15 largest carbon pricing instruments worldwide, ranked according to these two criteria. When ranked by emissions coverage, the China national ETS is by far the largest, covering 15.29% of global emissions, followed by the EU ETS with 2.36%, and the Japan carbon tax at 1.57%. However, when ranked by revenue generated in 2024, the EU ETS is in the lead with $41.70 billion, followed by the German ETS with $13.93 billion and the Canadian federal fuel charge with $8.86 billion. Together, these ETS and carbon tax instruments cover 28.58% of global GHG emissions and generate $102.19 billion in revenue.

<table><caption>Table 10.7: Top 15 largest carbon pricing instruments worldwide in 2024</caption><thead><tr><th>Rank</th><th>Instrument name</th><th>Share (in %)</th><th>Instrument name</th><th>Revenue (in $ bn)</th></tr></thead><tbody><tr><td>1</td><td>China national ETS</td><td>15.29</td><td>EU ETS</td><td>41.70</td></tr><tr><td>2</td><td>EU ETS</td><td>2.36</td><td>Germany ETS</td><td>13.93</td></tr><tr><td>3</td><td>Japan carbon tax</td><td>1.57</td><td>Canada federal fuel charge</td><td>8.86</td></tr><tr><td>4</td><td>Korea ETS</td><td>1.10</td><td>France carbon tax</td><td>7.84</td></tr><tr><td>5</td><td>South Africa carbon tax</td><td>0.81</td><td>California CaT</td><td>4.40</td></tr><tr><td>6</td><td>Indonesia ETS</td><td>0.59</td><td>UK ETS</td><td>3.25</td></tr><tr><td>7</td><td>California CaT</td><td>0.56</td><td>Sweden carbon tax</td><td>2.31</td></tr><tr><td>8</td><td>Mexico ETS</td><td>0.54</td><td>BC carbon tax</td><td>1.89</td></tr><tr><td>9</td><td>Germany ETS</td><td>0.50</td><td>Norway carbon tax</td><td>1.61</td></tr><tr><td>10</td><td>Taiwan carbon fee</td><td>0.46</td><td>RGGI</td><td>1.46</td></tr><tr><td>11</td><td>Canada federal fuel charge</td><td>0.44</td><td>Japan carbon tax</td><td>1.45</td></tr><tr><td>12</td><td>Alberta TIER</td><td>0.30</td><td>Switzerland carbon tax</td><td>1.43</td></tr><tr><td>13</td><td>France carbon tax</td><td>0.29</td><td>Finland carbon tax</td><td>1.38</td></tr><tr><td>14</td><td>Kazakhstan ETS</td><td>0.28</td><td>Portugal carbon tax</td><td>1.27</td></tr><tr><td>15</td><td>Australia Safeguard Mechanism</td><td>0.28</td><td>Austria ETS</td><td>1.26</td></tr><tr><td>Total</td><td></td><td>28.58</td><td></td><td>102.19</td></tr></tbody></table>

Source: World Bank Carbon Pricing Dashboard (2025), carbonpricingdashboard.worldbank.org.

Figure 10.12 shows the nationwide coverage of GHG emissions through emissions trading systems and carbon taxes in 2022, according to data from Dao et al. (2024). Some countries, including Iceland, Ukraine, Luxembourg, South Africa, and Japan, achieved high levels of coverage, regulating around 80% of their national emissions. In contrast, some countries are still in the early stages of implementing carbon pricing. For instance, current policies in the United States and Uruguay cover less than 10% of their emissions. Several major economies, including Australia, Brazil, India, Indonesia, and Turkey, do not have national carbon pricing frameworks. The median national GHG
<figure><img src="image_484.png" /><figcaption>Figure 10.11: Global map of ETS and carbon taxes implemented, under development, or under consideration</figcaption></figure>

Source: World Bank (2025, Figure 2, page 22).
<figure><img src="image_485.png" /><figcaption>Figure 10.12: Share (%) of national GHG emissions covered by carbon pricing mechanisms in 2022</figcaption></figure>

Source: World Bank (2023) & Dao et al. (2024, Figure 2, page 7).

coverage across all countries with carbon pricing mechanisms in 2022 is estimated at 50%, regardless of the instrument type. The choice between an ETS and a carbon tax is relatively balanced, though there is a slight preference for ETS adoption. Seven countries rely solely on carbon taxes, 22 countries have only implemented an ETS, and 19 countries use both instruments simultaneously. Thus, ETS are more widespread in terms of country adoption. However, the average national emissions coverage is slightly higher under carbon taxes (33%) than under an ETS (30%). Nevertheless, ETS generated nearly four times more revenue than carbon taxes in 2024.

If the depth of emissions coverage is essential for evaluating the scale of a carbon pricing instrument, the price signal it delivers remains the central benchmark for assessing its stringency. The explicit carbon price is the rate formally announced or applied by the policy. In the case of an ETS, the price is typically defined as the clearing price at which allowances are sold at auction on the primary market. For a carbon tax, it is the statutory tax rate applied per tonne of CO₂e emissions. However, comparing instruments based solely on their explicit prices can be misleading due to the heterogeneous design of carbon pricing mechanisms. Prices may apply to different greenhouse gases, economic sectors, or points in the supply chain, which complicates direct comparisons. To address this challenge, Desnos et al. (2023) propose using an implicit carbon price, which normalizes revenues by the quantity of emissions covered. This approach allows for more consistent comparisons across instruments with varying scopes. They define the implicit carbon price CP as follows:

$$CP = \frac{R_{\text{gov}}}{s^* CE}$$

where R_gov is the government revenue from the carbon pricing instrument (in $ mn), s\* is the share of national emissions covered by the instrument, and CE is the total national GHG emissions (in MtCO₂e).
<figure><img src="image_486.png" /><figcaption>Figure 10.13: Implicit and explicit carbon price in 2022 (in $/tCO2e)</figcaption></figure>

Source: World Bank (2023) & Dao et al. (2024, Figure 3, page 12).
Figure 10.13 shows the implicit and explicit carbon prices (in dollars per tCO2e) for each type of instrument. In general, implicit prices are lower than explicit prices, though there are a few exceptions. For ETS programs, explicit carbon prices range from $5 to $100 per tCO2e, while implicit prices range from $0 to $70. For carbon tax programs, explicit prices range from $0 to $135, and implicit prices range from $0 to $115. The difference between the two is due to tax exemptions that effectively cover priced emissions. This difference is even more pronounced with ambitious carbon pricing. The higher the price, the higher the exemption. For example, the Swiss ETS has an explicit carbon price of nearly $65/tCO2e, yet it only covers 10% of national emissions<fnref n="32" />. Similarly, the Norwegian carbon tax reached $90/tCO2e in 2022. However, the effective tax rate was reduced to $7/tCO2e for liquefied petroleum gas (LPG) and natural gas used in greenhouses.

Several programs have an implicit carbon price close to or below $1, including most Chinese pilot programs. Since these programs do not auction their allocations, they do not generate revenue. This category also includes carbon taxes in South Africa and Ukraine. These two countries have nearly 80% nationwide emissions coverage. More broadly, there appears to be a global imbalance between emissions coverage and carbon prices. In other words, even the most ambitious regulations may lack the necessary coverage or price to be considered effective game changers. Estimates suggest that over 50% of emissions covered by instruments are implicitly priced below $10, with nearly 26% priced below $1 and over 90% priced between $0 and $25. This range of prices falls far short of the recommendations in the Stiglitz and Stern report (Stiglitz et al., 2017). The authors stated that, to achieve the goals of the Paris Agreement, the price of carbon should be between $40 and $80 per tCO2e by 2020, increasing to between $50 and $100 by 2030. However, as of 2022, less than 1% of global emissions are priced above $40/tCO2e.

On average, the carbon price is higher under carbon taxes than under emissions trading systems. While the difference between the two types of prices is relatively small at $3, the difference between implicit and explicit prices is substantial at $10.70. In fact, carbon taxes generate much higher revenue per tCO2e than ETS. This shortfall is mainly due to the distribution of free allowances, which generate no revenue by definition.

## Carbon pricing revenue

In addition to their environmental benefits, carbon pricing instruments generate substantial revenue. Revenue generation is an important aspect of these mechanisms. However, Carl and Fedor (2016) noted that environmental benefits may take a long time to materialize and can be affected by policies and regulations outside the scope of these mechanisms. Consequently, environmental impact alone may not justify the adoption of carbon pricing policies or sustain public support<fnref n="33" />. Therefore, revenues generated by carbon pricing and how these revenues are managed can provide an anchor for public support and tangible evidence of policy effectiveness. Some countries have embraced carbon pricing primarily for its revenue-raising potential. Ireland and Iceland, for example, have viewed carbon taxation as a means of raising funds during periods of fiscal constraint. Scandinavian countries have also expressed interest in using carbon taxes to enable broader tax reductions.

According to I4CE (2025), global revenues from carbon pricing mechanisms reached $103 billion in 2024. Figure 10.14 shows revenue estimates for each program and how these funds are allocated. Revenue generation depends on the carbon price and the scope of emissions coverage. For emissions trading systems, revenue generation also depends on the proportion of emissions subject to auctions. The EU ETS was the most prolific program, accounting for 41% of global revenues. It was followed

<footnote n="32">³²Only 13% of these emissions are covered by auctioned allowances (ICAP, 2023).</footnote>

<footnote n="33">³³One may recall the main slogan of the Yellow Vest protests: “End of the month before the end of the world.”</footnote>
<figure><img src="image_487.png" /><figcaption>Figure 10.14: 2024 carbon pricing revenue generation and use (in $ bn)</figcaption></figure>

Source: I4CE (2025, page 11).

by Germany's national ETS (14%) and Canada's carbon tax (9%). According to I4CE (2025), only ten jurisdictions accounted for 86% of total global carbon revenues. As previously emphasized, carbon taxes tend to generate significantly more revenue per tCO2e than emissions trading systems (Carl and Fedor, 2016). Nevertheless, the size of the EU ETS explains the dominant share of ETS programs in global revenues. The transition from free allocation to auctioning also contributed to significant revenue growth. By 2024, the ETS accounted for 67% of all carbon pricing revenues, more than twice the amount generated by carbon taxes.

A clear distinction emerges in how governments use revenues from the two types of carbon pricing mechanisms. These uses fall into four categories: direct transfers, tax exemptions and credits, earmarking, and allocation to the general budget. Direct transfers involve redistributing funds directly to households or businesses, often as compensation for increased energy costs. Tax exemptions and credits provide targeted relief or incentives to offset specific economic burdens. Earmarking dedicates funds to specific green initiatives, such as climate investments. Revenues allocated to the
general budget are used for broader public expenditures, including tax cuts. Overall, carbon pricing revenues are significantly earmarked, primarily due to the role of ETS programs in funding green investments. According to Dao et al. (2024), approximately 77.7% of ETS revenues are earmarked, even when the EU ETS is excluded. The EU ETS Directive stipulates that at least 60% of auction revenues must be allocated to climate and energy initiatives. This reflects the European Union's dual objective of reducing demand through pricing while facilitating economic transformation. Between 2013 and 2019, over 78% of EU ETS revenues were earmarked for climate and energy investments. Consequently, the share of ETS revenues allocated to direct transfers is minimal. In contrast, carbon tax revenues primarily go to the general budget (53%), with a significant portion (32.5%) allocated to direct transfers. Finally, I4CE (2025) estimates that eliminating the free allocation of emissions allowances could generate an additional $75 billion in 2024, on top of the $53.5 billion already raised by cap-and-trade systems.

## Sector analysis

The sectoral coverage of carbon pricing instruments varies significantly from one program to another. For example, emissions from agriculture<fnref n="34" /> and international aviation — which account for 12.7% and 2% of global emissions, respectively — are not covered by any current instrument. As shown in Tables 10.8 and 10.9, the sectoral coverage of existing carbon taxes and emissions trading systems differs across countries, primarily due to the use of non-overlapping policy combinations. Typically, emissions covered by a carbon market are exempt from carbon taxes, and vice versa. In Europe, for instance, most countries that apply carbon taxes have limited overlap with the EU ETS, except for the United Kingdom, the Netherlands, and Estonia (World Bank, 2023). As a result, industrial production is the sector most likely to be fully or partially covered by carbon taxes, followed by the energy sector. Nevertheless, the transport and buildings sectors are among the most comprehensively covered sectors, with approximately 60% of carbon tax programs fully encompassing these emissions. In contrast, emissions from waste management and aviation are generally less covered because both are considered downstream sources.

Due to concerns about competitiveness and dispersed emissions, carbon taxes only partially apply to certain sectors. For instance, Sweden exempts exported fuels and those used in industrial processes. Additionally, the number of sectors covered by a carbon tax does not necessarily correspond to a jurisdiction's total greenhouse gas emissions. Singapore's carbon tax, for instance, only applies to the energy, industry, and mining sectors, which collectively account for nearly 80% of the country's emissions. Along with South Africa and Liechtenstein, Singapore has one of the most comprehensive carbon tax in terms of emissions coverage. Compared to ETS programs, sub-national carbon taxes are not as common. However, some regions in Canada (e.g., British Columbia, Northwest Territories, and Prince Edward Island) and Mexico (e.g., Zacatecas) have considered the price mechanism to correct GHG emissions. ETS programs primarily target emitters in the power generation, industrial production, and mining sectors (see Table 10.9). Over 70% of ETS initiatives fully cover emissions from these sectors. All ETS programs address energy-related emissions, either partially or fully. However, unlike carbon tax schemes, ETS programs generally exclude the transportation and buildings sectors. In fact, 60% of them exclude these sectors altogether. The ETS program of South Korea is the most comprehensive in terms of emissions coverage, encompassing over 70% of national emissions. It is also the first mandatory, nationwide ETS program in East Asia. Within the EU ETS, energy-related emissions, particularly those from fuel combustion, constitute over 60% of covered emissions.

<footnote n="34">³⁴New Zealand is the only country that is required to cover GHG emissions from agricultural practices. However, these emissions will not be priced until 2026.</footnote>
<table><caption>Table 10.8: Sector and emission coverage of carbon tax programs</caption><thead><tr><th>Carbon tax program</th><th>Energy</th><th>Industry</th><th>Buildings</th><th>Mining</th><th>Transport</th><th>Aviation</th><th>Forestry</th><th>Waste</th><th>Agriculture</th><th>Coverage</th></tr></thead><tbody><tr><td>Argentina</td><td>●</td><td>●</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td><td>○</td><td>20%</td></tr><tr><td>Canada federal fuel charge</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td><td>○</td><td>30%</td></tr><tr><td>Chile</td><td>●</td><td>●</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td><td>○</td><td>29%</td></tr><tr><td>Colombia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td><td>23%</td></tr><tr><td>Denmark</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>○</td><td>40%</td></tr><tr><td>Estonia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>7%</td></tr><tr><td>Finland</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>36%</td></tr><tr><td>France</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>35%</td></tr><tr><td>Iceland</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>55%</td></tr><tr><td>Ireland</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Japan</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>75%</td></tr><tr><td>Latvia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>5%</td></tr><tr><td>Liechtenstein</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>81%</td></tr><tr><td>Luxembourg</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>65%</td></tr><tr><td>Mexico</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>44%</td></tr><tr><td>Netherlands</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>52%</td></tr><tr><td>Norway</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>63%</td></tr><tr><td>Poland</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>4%</td></tr><tr><td>Portugal</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Singapore</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>80%</td></tr><tr><td>South Africa</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>80%</td></tr><tr><td>Spain</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>2%</td></tr><tr><td>Sweden</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Switzerland</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>33%</td></tr><tr><td>United Kingdom</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>24%</td></tr><tr><td>Ukraine</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>71%</td></tr><tr><td>Uruguay</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>11%</td></tr><tr><td>Canada</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>British Columbia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>70%</td></tr><tr><td>Northwest Territories</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>79%</td></tr><tr><td>Prince Edward Island</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>56%</td></tr><tr><td>Mexico</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Baja California</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td></tr><tr><td>Tamaulipas</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td></tr><tr><td>Zacatecas</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>50%</td></tr></tbody></table>

● Full coverage, ○ Partial coverage, ○ No coverage

Source: World Bank (2023) & Dao et al. (2024).
<table><caption>Table 10.9: Sector and emission coverage of ETS programs in 2022</caption><thead><tr><th>ETS program</th><th>Energy</th><th>Industry</th><th>Buildings</th><th>Mining</th><th>Transport</th><th>Aviation</th><th>Forestry</th><th>Waste</th><th>Agriculture</th><th>Coverage</th></tr></thead><tbody><tr><td>Austria</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Canada federal OBPS</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>1%</td></tr><tr><td>China national</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>31%</td></tr><tr><td>EU ETS</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>38%</td></tr><tr><td>Germany</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Indonesia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>26%</td></tr><tr><td>Kazakhstan</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>46%</td></tr><tr><td>Mexico pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Montenegro</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td></tr><tr><td>New Zealand</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>49%</td></tr><tr><td>South Korea</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>74%</td></tr><tr><td>Switzerland</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>11%</td></tr><tr><td>United Kingdom</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>28%</td></tr><tr><td>Canada</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alberta TIER</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>58%</td></tr><tr><td>New Brunswick</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>50%</td></tr><tr><td>Newfoundland/Labrador</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>43%</td></tr><tr><td>Nova Scotia</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>87%</td></tr><tr><td>Ontario EPS</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>25%</td></tr><tr><td>Québec</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>77%</td></tr><tr><td>Saskatchewan OBPS</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>13%</td></tr><tr><td>China</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Beijing pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>24%</td></tr><tr><td>Chongqing pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>51%</td></tr><tr><td>Fujian pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>51%</td></tr><tr><td>Guangdong pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>40%</td></tr><tr><td>Hubei pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>27%</td></tr><tr><td>Shanghai pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>36%</td></tr><tr><td>Shenzhen pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>30%</td></tr><tr><td>Tianjin pilot</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>35%</td></tr><tr><td>Japan</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Saitama</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>17%</td></tr><tr><td>Tokyo</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>20%</td></tr><tr><td>USA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>California</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>74%</td></tr><tr><td>Massachusetts</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>8%</td></tr><tr><td>Oregon</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>43%</td></tr><tr><td>RGGI</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>14%</td></tr><tr><td>Washington</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>●</td><td>70%</td></tr></tbody></table>

● Full coverage, ○ Partial coverage, ○ No coverage

Source: World Bank (2023), ICAP (2023) & Dao et al. (2024).
### 10.2.3 Internal carbon price

#### Definition

An increasing number of companies have adopted internal carbon pricing (ICP) as a strategic tool for monitoring transition risks. ICP is a non-binding mechanism that helps organizations anticipate and manage the financial risks of operating in a carbon-constrained world. It involves internalizing the social cost of carbon to inform business decisions and strengthen resilience to future climate-related regulations. As a risk management instrument, ICP evaluates the potential effects of strict carbon pricing on business operations. It also supports corporate decarbonization strategies by identifying carbon-intensive activities and facilitating the reallocation of resources toward low-carbon alternatives. There are three main ICP approaches:

1. Shadow price

Shadow pricing is the process of assigning a hypothetical carbon price to evaluate the long-term environmental and financial impacts of investments. It is primarily used in capital budgeting, acquisitions, and R&D to incorporate climate considerations into decision-making processes. It is particularly prevalent in high-emitting sectors, such as energy, chemicals, and manufacturing, where future carbon costs could significantly affect the viability of projects.

2. Internal carbon fee

The internal carbon fee approach is similar to a self-imposed carbon tax because it also imposes a charge on greenhouse gas emissions. Departments or business units are financially responsible for their emissions, increasing their operational expenditures. The generated revenue is often allocated to transition funds or sustainability projects. This method is typically used in sectors with lower Scope 1 and 2 emissions, where direct operational emissions are more manageable.

3. Implicit price

The implicit carbon price represents the actual cost incurred by a company when implementing emission reduction projects. It is calculated retrospectively, based on the abatement cost of past emissions (e.g., dollars spent per tonne of CO2 reduced). While it does not directly incentivize emissions reductions, it serves as a benchmark for setting climate targets and evaluating the cost-effectiveness of decarbonization pathways. CDP recognizes it as a valid ICP method.

Therefore, the implicit carbon price represents the actual cost a company incurs when implementing emission reduction projects, the internal carbon fee is a real charge applied within the company, typically paid by individual departments based on their emissions, while the shadow price is a hypothetical estimate of the cost of carbon abatement, reflecting the potential financial impact of future carbon regulations or market-based mechanisms that could require companies to pay for their emissions.

#### The rationale for internal carbon pricing adoption

As carbon pricing policies tighten around the world, some companies have taken the lead in voluntarily internalizing carbon prices into their operations. In order to encourage and expand the adoption of such practices, it is important to understand the motivations behind them. Thus, several empirical studies focusing on ICP have attempted to identify the main reasons for the adoption of this practice in private organizations. A common argument is related to the risk management narrative. Companies view internal carbon pricing as a way to manage transition risk by incorporating the price of carbon as an additional cost into the business model. In other words, ICP implementation
primarily helps to assess how sensitive and vulnerable the company's activities are to strict envi-
ronmental regulations (Bento et al., 2021). Indeed, for the company's long-term strategic plan, the
impact of the carbon price on the business model in certain sectors of activity is critical (Bento
and Gianfrate, 2020) and may be the consequence of stakeholder pressure (Harpankar, 2019). In
general, companies adopt ICP to anticipate the introduction of a constraining external carbon price,
which would inevitably lead to a reduction in carbon intensity. Thus, it is not uncommon to find a
strong correlation between ICP adoption and exposure to external carbon pricing (Harpankar, 2019;
Bento and Gianfrate, 2020; Trinks et al., 2022b). For example, Trinks et al. (2022b) found that
firms exposed to external carbon pricing have a 10-13 percentage point higher predicted probability
of adopting some form of ICP. Bento and Gianfrate (2020) suggested that the ICP is significantly
higher for companies headquartered in countries with high GDP and an external carbon policy in
place. It's easy to see why a company would be encouraged to use an ICP if it expects to be subject
to an external carbon price in the near future, but why continue to support such a practice if such a
regulation is already in place? Trinks et al. (2022b) explained that the company may be anticipating
rising external prices, may be using different ICP systems to meet personal targets, or may have
simply implemented ICP before the regulation was implemented.

While managing transition risk appears to be the most justifiable reason for adopting an ICP,
according to World Bank (2023), one-third of companies that report using an ICP do not expect
regulatory pressure in the coming years. For example, Ben Amar et al. (2022) found that companies
exposed to climate change are driving ICP adoption. Although these adopters may justify ICP
programs for their own purposes, companies may also use them as a communication tool rather than
to effectively initiate the decarbonization of their operations. In this way, companies can reassure
concerned stakeholders while maintaining a healthy corporate image (Harpankar, 2019). Since then,
the suspicion of voluntary and dubious claims made by carbon-intensive companies forces us to think
about the growing risk of greenwashing. While this suspicion has already been noted in a number
of articles (Bento et al., 2021; Trinks et al., 2022b), very few have actually investigated the link
between ICP and carbon emission reduction. Therefore, one of the remaining questions is whether
this additional constraint efficiently improves the environmental impact of the firm.

Data analysis

Data on internal carbon pricing remains difficult to obtain. The CDP database is currently the
only centralized, consistent source of company-level ICP data. Table 10.10 provides examples of
ICP as reported in the CDP questionnaire database. For each company, we include the minimum
and maximum prices disclosed. For example, Adidas reports an ICP range of $10 to $100, whereas
Alphabet applies a uniform price of $51 per tonne of CO2e. However, these figures are difficult to
interpret or use in a standardized way because ICP varies across three key dimensions:

*   Height (price level)
    This refers to the monetary value assigned to one tonne of CO2. What is the price per tonne?

*   Width (carbon scope of application)
    This refers to the types of emissions covered. Does it apply only to Scope 1 emissions, or does it also include Scope 2 and 3?

*   Depth (business scope of application)
    This refers to how broadly the ICP is applied across the organization. Is it implemented company-wide, or is it limited to specific business units or geographic locations?
<table><caption>Table 10.10: Examples of internal carbon pricing in 2023</caption><thead><tr><th>Company</th><th>Country</th><th>Min price</th><th>Max price</th></tr></thead><tbody><tr><td>Adidas</td><td>Germany</td><td>10</td><td>100</td></tr><tr><td>Air Liquide</td><td>France</td><td>50</td><td>100</td></tr><tr><td>Alphabet</td><td>USA</td><td>51</td><td>51</td></tr><tr><td>American Airlines</td><td>USA</td><td>4</td><td>25</td></tr><tr><td>AstraZeneca</td><td>UK</td><td>100</td><td>100</td></tr><tr><td>AXA</td><td>France</td><td>11</td><td>16</td></tr><tr><td>Bank of Montreal</td><td>Canada</td><td>20</td><td>30</td></tr><tr><td>BASF</td><td>Germany</td><td>5</td><td>400</td></tr><tr><td>Bayer</td><td>Germany</td><td>100</td><td>100</td></tr><tr><td>BBVA</td><td>Spain</td><td>25</td><td>25</td></tr><tr><td>BNP Paribas</td><td>France</td><td>9</td><td>10</td></tr><tr><td>Canadian Pacific Railway</td><td>Canada</td><td>50</td><td>50</td></tr><tr><td>Danone</td><td>France</td><td>35</td><td>35</td></tr><tr><td>Delta Air Lines</td><td>USA</td><td>107</td><td>157</td></tr><tr><td>Deutsche Lufthansa</td><td>Germany</td><td>5</td><td>125</td></tr><tr><td>Dow Chemical</td><td>USA</td><td>41</td><td>105</td></tr><tr><td>easyJet</td><td>UK</td><td>69</td><td>78</td></tr><tr><td>EDF</td><td>France</td><td>38</td><td>175</td></tr><tr><td>Engie</td><td>France</td><td>119</td><td>119</td></tr><tr><td>Eni</td><td>Italy</td><td>45</td><td>250</td></tr><tr><td>Ford Motor</td><td>USA</td><td>87</td><td>87</td></tr><tr><td>Heineken</td><td>Netherlands</td><td>50</td><td>90</td></tr><tr><td>Hermes International</td><td>France</td><td>40</td><td>40</td></tr><tr><td>Iberdrola</td><td>Spain</td><td>0</td><td>113</td></tr><tr><td>Intel</td><td>USA</td><td>73</td><td>73</td></tr><tr><td>Johnson &amp; Johnson</td><td>USA</td><td>30</td><td>50</td></tr><tr><td>Kellogg</td><td>USA</td><td>98</td><td>110</td></tr><tr><td>LVMH</td><td>France</td><td>30</td><td>30</td></tr><tr><td>McCain Foods</td><td>Canada</td><td>100</td><td>100</td></tr><tr><td>McKinsey</td><td>USA</td><td>50</td><td>50</td></tr><tr><td>Mercedes-Benz</td><td>Germany</td><td>475</td><td>475</td></tr><tr><td>Novartis</td><td>Switzerland</td><td>100</td><td>100</td></tr><tr><td>Ørsted</td><td>Denmark</td><td>750</td><td>750</td></tr><tr><td>PepsiCo</td><td>USA</td><td>50</td><td>50</td></tr><tr><td>Pernod Ricard</td><td>France</td><td>80</td><td>80</td></tr><tr><td>Pfizer</td><td>USA</td><td>20</td><td>20</td></tr><tr><td>Pirelli</td><td>Italy</td><td>90</td><td>90</td></tr><tr><td>Repsol</td><td>Spain</td><td>70</td><td>100</td></tr><tr><td>SAAB</td><td>Sweden</td><td>142</td><td>142</td></tr><tr><td>Schneider Electric</td><td>France</td><td>50</td><td>130</td></tr><tr><td>Siemens</td><td>Germany</td><td>46</td><td>46</td></tr><tr><td>Sodexo</td><td>France</td><td>20</td><td>100</td></tr><tr><td>Suncor Energy</td><td>Canada</td><td>65</td><td>170</td></tr><tr><td>Swisscom</td><td>Switzerland</td><td>120</td><td>120</td></tr><tr><td>TotalEnergies</td><td>France</td><td>100</td><td>100</td></tr><tr><td>Volkswagen</td><td>Germany</td><td>20</td><td>20</td></tr><tr><td>Walt Disney</td><td>USA</td><td>10</td><td>20</td></tr><tr><td>Whirlpool Corporation</td><td>USA</td><td>300</td><td>300</td></tr></tbody></table>

Source: CDP Questionnaire database (2023).
While the CDP database captures carbon scope relatively well, it does not provide consistent and standardized information on the depth of ICP implementation. Another potential source of information is corporate sustainability reports or company websites<fnref n="35" />. However, such disclosures are often incomplete or unclear. This is partly because many companies have not yet implemented internal carbon pricing and partly because those that have may be reluctant to disclose detailed information due to concerns about accusations of greenwashing or reputational risk.

<table><caption>Table 10.11: Breakdown of companies with respect to external and internal carbon pricing mechanisms (2023 CDP Questionnaire)</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Internal carbon pricing</th><th rowspan="2">Total</th></tr><tr><th>NA</th><th>No</th><th>In the future</th><th>Yes</th></tr></thead><tbody><tr><td rowspan="10">External carbon pricing</td><td>NA</td><td>69</td><td>219</td><td>108</td><td>141</td><td>537</td></tr><tr><td>No</td><td>12</td><td>1726</td><td>650</td><td>350</td><td>2738</td></tr><tr><td>In the future</td><td>3</td><td>246</td><td>426</td><td>257</td><td>932</td></tr><tr><td>Yes</td><td>3</td><td>352</td><td>454</td><td>756</td><td>1565</td></tr><tr><td>CT</td><td>0</td><td>189</td><td>216</td><td>211</td><td>616</td></tr><tr><td>ETS</td><td>2</td><td>123</td><td>188</td><td>389</td><td>702</td></tr><tr><td>Both</td><td>0</td><td>39</td><td>50</td><td>155</td><td>244</td></tr><tr><td>NA</td><td>1</td><td>1</td><td>0</td><td>1</td><td>3</td></tr><tr><td>Total</td><td></td><td>87</td><td>2543</td><td>1638</td><td>1504</td><td>5772</td></tr></tbody></table>

In the case of an external carbon pricing system, the *No* and *In the future* tags correspond to the following responses: “No, and we do not anticipate being regulated in the next three years” and “No, but we anticipate being regulated in the next three years”. In the case of an internal carbon pricing system, the responses are: “No, and we do not currently anticipate doing so in the next two years” and “No, but we anticipate doing so in the next two years”. NA means we don’t have the information.

Source: CDP Questionnaire database (2023) & Dao et al. (2024, Table 5, page 20).

Since 2002, CDP has sent its questionnaire annually to corporate boards worldwide. In 2023, the questionnaire was sent to 14170 companies. Approximately 40% (5772 companies) completed it, while 60% (8398 companies) did not respond. Dao et al. (2024) analyzed the database as of October 2023, focusing on various sections, particularly Section C11, which addresses carbon pricing. Of the 5772 companies that responded to the CDP questionnaire, Dao et al. (2024) found that 1565 are regulated by an external carbon pricing scheme, while 1504 use an internal carbon pricing scheme (Table 10.11). This represents 27% and 26% of respondents, respectively. Only 756 of the regulated companies (or 13% of respondents) reported that they are also implementing internal carbon pricing, meaning that half of the regulated companies are not cascading external carbon costs internally. Additionally, 932 companies expect to be regulated in the next three years and 1638 companies plan to implement an internal carbon pricing scheme in the next two years. Two main factors explain this asymmetry. First, 30% of regulated companies (454 out of 1565) have the ambition to align internal interests with the external cost of carbon. Second, internal carbon pricing is a voluntary decision. Therefore, non-regulated companies can easily participate and implement internal carbon pricing schemes, especially companies that have announced net-zero targets.

<footnote n="35">³⁵For instance, the Repsol website states: “In the absence of global measures, we have established our own internal carbon price that we apply to all new investments we make. Within the European Union, values are set at $100/tCO₂e in 2025 and go up to $110/tCO₂e after 2030. In other countries, we’ve set the price at $60/tCO₂e after 2025.”</footnote>
<table><caption>Table 10.12: How do CDP reporting companies use internal carbon pricing?</caption><thead><tr><th>Question</th><th>#</th><th>%</th></tr></thead><tbody><tr><td colspan="3">What is the type of internal carbon price?</td></tr><tr><td>Implicit price</td><td>211</td><td>13.3</td></tr><tr><td>Internal fee</td><td>214</td><td>13.5</td></tr><tr><td>Shadow price</td><td>1078</td><td>67.9</td></tr><tr><td>Other</td><td>84</td><td>5.3</td></tr><tr><td colspan="3">How is the internal carbon price determined?</td></tr><tr><td>Alignment with the price of a carbon tax</td><td>409</td><td>26.0</td></tr><tr><td>Alignment with the price of allowances under an ETS</td><td>544</td><td>34.5</td></tr><tr><td>Benchmarking against peers</td><td>274</td><td>17.4</td></tr><tr><td>Cost of required measures to achieve emissions reduction targets</td><td>378</td><td>24.0</td></tr><tr><td>Price with material impact on business decisions</td><td>188</td><td>11.9</td></tr><tr><td>Price/cost of voluntary carbon offset credits</td><td>326</td><td>20.7</td></tr><tr><td>Social cost of carbon</td><td>151</td><td>9.6</td></tr><tr><td>Other</td><td>212</td><td>13.5</td></tr><tr><td colspan="3">What is the goal of implementing this internal carbon price?</td></tr><tr><td>Change internal behavior</td><td>973</td><td>61.1</td></tr><tr><td>Drive energy efficiency</td><td>971</td><td>61.0</td></tr><tr><td>Drive low-carbon investment</td><td>1175</td><td>73.8</td></tr><tr><td>Identify and seize low-carbon opportunities</td><td>829</td><td>52.1</td></tr><tr><td>Navigate GHG regulations</td><td>545</td><td>34.2</td></tr><tr><td>Reduce supply chain emissions</td><td>215</td><td>13.5</td></tr><tr><td>Set a carbon offset budget</td><td>101</td><td>6.3</td></tr><tr><td>Stakeholder expectations</td><td>506</td><td>31.8</td></tr><tr><td>Stress test investments</td><td>368</td><td>23.1</td></tr><tr><td>Other</td><td>83</td><td>5.2</td></tr><tr><td colspan="3">Which Scope of carbon emissions is covered?</td></tr><tr><td>Scope 1</td><td>1414</td><td>89.2</td></tr><tr><td>Scope 2</td><td>1263</td><td>79.6</td></tr><tr><td>Scope 3 (upstream)</td><td>438</td><td>27.6</td></tr><tr><td>Scope 3 (downstream)</td><td>309</td><td>19.5</td></tr><tr><td>Other</td><td>0</td><td>0.0</td></tr><tr><td colspan="3">What is the spatial variance approach to internal carbon pricing?</td></tr><tr><td>Differentiated</td><td>312</td><td>20.1</td></tr><tr><td>Uniform</td><td>1211</td><td>78.1</td></tr><tr><td>Other</td><td>31</td><td>2.0</td></tr><tr><td colspan="3">What is the temporal variance approach to internal carbon pricing?</td></tr><tr><td>Evolutionary</td><td>873</td><td>56.1</td></tr><tr><td>Static</td><td>636</td><td>40.8</td></tr><tr><td>Other</td><td>50</td><td>3.2</td></tr></tbody></table>

Source: CDP Questionnaire database (2023) & Dao et al. (2024, Table 19, page 36).
Many believe that internal carbon pricing is being increasingly adopted by companies worldwide. This perception is supported by the CDP report, which highlighted ICP adoption as its first key finding (CDP, 2021). In 2022, 1504 companies reported using ICP, up from just 548 in 2017 — an apparent annual growth rate of 22.4%. However, a closer examination reveals that the trend may be less widespread than it appears. Dao et al. (2024) showed that 80% of this growth is attributable to the expansion of companies reporting to CDP, rather than a broader uptake of ICP. When measured as a share of total CDP respondents, adoption rose modestly from 21.9% and 25.52% in 2017 and 2018, to 25.32% and 26.1% in 2022. Their analysis concludes that, once adjusted for reporting biases and changes in the respondent base, the progress in ICP adoption is less substantial than raw figures suggest.

In Table 10.12, we report summary statistics calculated by Dao et al. (2024) from the CDP questionnaire on how companies use internal carbon pricing, using checkbox responses<fnref n="36" />. The most common type of internal carbon price is the shadow price, used by 67.9% of companies, far surpassing the use of internal fees and implicit prices, which were cited by approximately 13% of respondents. A small fraction (5.3%) reported using other types. Companies most often determine the internal carbon price based on alignment with the price of allowances under an emissions trading system (ETS) (34.5%) or a carbon tax (26.0%). Other frequently cited methods include estimating the cost of emission reduction measures (24.0%) and the cost of voluntary carbon offset credits (20.7%). Benchmarking against peers (17.4%) and using the social cost of carbon (9.6%) are less common. The primary goals of implementing internal carbon pricing reflect strong environmental and strategic priorities. The leading motivations are driving low-carbon investment (73.8%), changing internal behavior (61.1%), and improving energy efficiency (61.0%). Over half of companies also aim to identify and seize low-carbon opportunities (52.1%). Regulatory motivations are less common, including navigating greenhouse gas regulations (34.2%) and stress testing investments (23.1%). Most internal carbon pricing schemes cover Scope 1 (89.2%) and Scope 2 (79.6%) emissions. However, coverage of Scope 3 emissions is significantly lower, at 27.6% for upstream emissions and 19.5% for downstream emissions. This illustrates the difficulty of incorporating value chain emissions into pricing mechanisms. In terms of implementation strategies, 78.1% of companies apply a uniform internal carbon price across business units, while 20.1% differentiate the price by unit or region. In terms of temporal flexibility, the majority of companies (56.1%) report using an evolutionary pricing approach, adjusting the price over time. In contrast, 40.8% maintain a static price.

In 2022, CDP respondents reported a median internal carbon price of 51.6$/tCO₂e. The minimum and maximum median prices were, on average, 38.1$/tCO₂e and 65.0$/tCO₂e, respectively (see Table 10.13). These figures highlight significant heterogeneity in internal carbon pricing driven by factors such as country of operation and industry sector. For example, the median carbon price in the biotechnology and health care sector is almost three times higher than in the hospitality sector. Among the most carbon-intensive sectors — fossil fuels, transportation, and materials — the median internal price is around 65$/tCO₂e. In contrast, despite its high emissions, the electricity generation sector reports one of the lowest median prices at 25$/tCO₂e. Dao et al. (2024) examined trends over time and found that the median internal carbon price increased from 19.3 to 51.6$/tCO₂e between 2017 and 2022, representing an average annual growth rate of 21.7%.

**Remark 128** Since Donald Trump's election in 2024, the future of internal and external carbon pricing has become increasingly uncertain. A potential rollback of federal climate policies, reduced regulatory pressure, and a shift in international climate diplomacy could weaken the incentive for

<footnote n="36">³⁶Companies may select multiple answers to questions about internal carbon pricing type and implementation goals, so the response frequencies do not sum to 100%.</footnote>
<table><caption>Table 10.13: Median value of minimum and maximum internal carbon prices by industry (in $/tCO₂e)</caption><thead><tr><th rowspan="2">Industry</th><th colspan="2">Minimum</th><th colspan="2">Maximum</th><th>Average</th></tr><tr><th>#</th><th>Q<sub>50%</sub></th><th>#</th><th>Q<sub>50%</sub></th><th>Q<sub>50%</sub></th></tr></thead><tbody><tr><td>Biotech & health care</td><td>50</td><td>60.2</td><td>49</td><td>89.1</td><td>74.7</td></tr><tr><td>Fossil fuels</td><td>72</td><td>41.6</td><td>69</td><td>100.0</td><td>70.8</td></tr><tr><td>Transportation services</td><td>71</td><td>51.3</td><td>71</td><td>85.6</td><td>68.4</td></tr><tr><td>Materials</td><td>282</td><td>50.0</td><td>277</td><td>76.3</td><td>63.1</td></tr><tr><td>Manufacturing</td><td>301</td><td>45.0</td><td>300</td><td>75.0</td><td>60.0</td></tr><tr><td>Food & agriculture</td><td>82</td><td>45.8</td><td>82</td><td>57.0</td><td>51.4</td></tr><tr><td>Infrastructure</td><td>139</td><td>32.1</td><td>135</td><td>64.2</td><td>48.2</td></tr><tr><td>Apparel</td><td>15</td><td>42.8</td><td>15</td><td>48.8</td><td>45.8</td></tr><tr><td>Retail</td><td>73</td><td>29.0</td><td>74</td><td>52.1</td><td>40.6</td></tr><tr><td>Services</td><td>343</td><td>24.2</td><td>341</td><td>47.3</td><td>35.7</td></tr><tr><td>Power generation</td><td>67</td><td>16.7</td><td>61</td><td>34.0</td><td>25.3</td></tr><tr><td>Hospitality</td><td>7</td><td>20.0</td><td>7</td><td>26.0</td><td>23.0</td></tr></tbody><tfoot><tr><td>Total</td><td>1502</td><td>38.1</td><td>1481</td><td>65.0</td><td>51.6</td></tr></tfoot></table>

Source: CDP Questionnaire database (2023) & Dao et al. (2024, Table 20, page 38).

companies to adopt or maintain ambitious carbon pricing mechanisms. Consequently, while some firms may continue to align with long-term decarbonization targets or respond to investor expectations, others may scale back their internal efforts in response to a less stringent policy environment.

## 10.3 Stranded assets

### 10.3.1 Definition

The concept of stranded assets has a long and evolving history in economic thought. During the 1980s and 1990s, the term was used primarily in reference to “stranded costs” or “stranded investments”. These were infrastructure or financial obligations that became economically unrecoverable due to shifts in policy, market dynamics, technology, or regulatory frameworks. This issue gained particular prominence in North America amid the deregulation of the electricity sector in the early 1990s:

> “Through the end of this century, the most critical regulatory issue facing electric utilities will be stranded costs, which can be defined as those costs that the utilities currently are permitted to recover through their rates but whose recovery may be impeded or prevented by the advent of competition in the industry. These costs represent expenditures incurred by a utility in the past in meeting its obligation to serve all customers within the area in which it held an exclusive franchise, granted to it under the traditional regulatory regime. [...] These outlays have been approved by the regulatory agencies, and many were imposed on the utilities by those agencies. They have also served to hold down prices to electricity customers in the past. However, the entry of competitors who are not burdened by such inherited expenses can prevent the utilities from recovering those costs. The desirability of such a scenario has been questioned on the basis of both equity and economic efficiency.” (Baumol and Sidak, 1994, page 835).

Early debates about stranded costs framed the issue as a regulatory and economic dilemma: how and whether to compensate incumbent utilities for investments made under monopoly conditions
that became unprofitable due to market liberalization. These foundational discussions paved the way for the broader use of the term *stranded assets*, which is now commonly associated with climate risk, fossil fuel investments, and the global energy transition.

The modern usage of stranded assets in the context of climate change and fossil fuels was popularized by the Carbon Tracker Initiative through its seminal January 2011 report “*Unburnable Carbon — Are the World’s Financial Markets Carrying a Carbon Bubble?*”. This publication introduced the concept to financial markets by arguing that a significant portion of fossil fuel reserves would become “*unburnable*” if climate targets were to be met, thereby creating substantial stranded asset risk for investors (Carbon Tracker Initiative, 2011). Following this groundbreaking work, the concept gained widespread traction among regulators, central banks, and academics. Ben Caldecott was a key figure in this academic development and is considered a pioneer in stranded assets research. In 2012, he founded the Stranded Assets Programme at the University of Oxford’s Smith School of Enterprise and the Environment, establishing the first academic research center dedicated to this emerging field. While the Carbon Tracker Initiative popularized the concept in financial markets, Caldecott and his team systematized the academic study of stranded assets through numerous influential publications and research initiatives (Caldecott, 2017). Caldecott’s work notably expanded the scope of stranded assets research beyond fossil fuels to include agriculture, water, mining, and other sectors vulnerable to climate change (Caldecott et al., 2013, 2016b). His definition of stranded assets is certainly one of the most widely used in the finance community and has formed the backbone of most other definitions of stranded assets (Daumas, 2024, Section 2.1.2, pages 604–606):

“*Stranded assets are assets that have suffered from unanticipated or premature write-downs, devaluations, or conversion to liabilities.*” (Caldecott et al., 2013, 2016a, pages 7 and 8).

Other authors have proposed related but broader definitions, for example:

“[*...] we define stranded assets as assets that lose economic value well ahead of their anticipated useful life, whether that is a result of changes in legislation, market forces, disruptive innovation, societal norms, or environmental shocks and we define stranded resources as resources which are considered uneconomic or cannot be developed or extracted as a result of technological, spatial, regulatory, political or market limitations, or changes in social and environmental norms.*” (Bos and Gupta, 2019, pages 1-2).

Other authors have framed asset stranding explicitly in financial terms:

“*Asset stranding describes the unexpected write-down in the value of an asset. Since financial valuations are based on forward-looking indicators, such as discounted cash flow modelling, a change in expectations about the future ability of an asset to generate cash flow after investment has taken place, can (partially) strand the asset. Underlying such financial revaluations are real-world processes, such as the premature shutting down of fossil fuel extraction sites due to a lack of demand.*” (Hartley et al., 2025, page 4).

Taken together, these definitions reveal both common ground and important differences in their emphasis on causality, temporal aspects, and sectoral scope. The first definition focuses on financial outcomes, such as the actual occurrence of write-downs, devaluations, or liability conversion. Its emphasis on “*unanticipated or premature*” outcomes underscores the unexpected nature of asset stranding. The second definition significantly broadens the scope by categorizing the drivers of asset stranding and introducing the concept of stranded resources. It includes causation mechanisms such as legislation, market forces, innovation, societal norms, and environmental shocks. It also
extends beyond physical assets to include undeveloped resources. The third definition emphasizes the valuation process and expectations. It links financial theory — specifically, discounted cash flow modeling — directly to real-world operational impacts. This approach highlights the forward-looking nature of asset stranding, where changes in future cash flow expectations can cause assets to become stranded before physical operations cease.

Daumas (2024) identifies three main categories of stranded assets in the context of the low-carbon transition:

1. Stranded resources

This category refers to raw materials and natural assets that cannot be economically exploited or commercialized due to changes in regulations (e.g., climate policy), market shifts, environmental constraints, or evolving social norms. The economic value of these materials is effectively stranded in the ground or in their natural state. Examples include fossil fuel reserves (coal, oil, and gas) that must remain unburned to meet the climate targets of the Paris Agreement; protected forests and natural areas subject to conservation policies; undeveloped agricultural lands that are designated for conservation, rewilding, or biodiversity preservation rather than commercial use; and mineral deposits in environmentally sensitive areas that become uneconomical to mine due to new environmental laws, collapsed demand, or a lack of social license to operate.

2. Stranded capital

This category refers to physical infrastructure, machinery, assets, and long-lived equipment that become obsolete or unprofitable before the end of their expected economic lifetime, causing them to lose market value. This is often the most visible form of stranding. Examples include fossil fuel power plants (e.g., coal or gas-fired stations) that are shut down prematurely; industrial facilities and factories that use carbon-intensive technologies, such as steel or cement factories, which cannot be economically decarbonized; transportation assets that depend on fossil fuels; energy-inefficient buildings ("non-retrofitted" buildings and houses) that fail to meet new energy performance standards and lose rental or market value; and manufacturing equipment that is incompatible with clean production processes.

3. Stranded financial assets

This category represents the financial aspect of stranding. It refers to financial assets, or the "paper" on balance sheets, whose value is derived from stranded resources and capital. When physical assets become stranded, the financial claims on them also plummet in value. Examples include drops in the equity value of stocks in companies heavily invested in stranded resources or capital (such as oil majors, utility companies, and airlines); loan defaults to projects or companies that are no longer economically viable; nonperforming loans to high-carbon companies; downgrading of corporate bonds issued by carbon-intensive industries; and losses on financial instruments tied to these at-risk assets, such as derivatives or insurance policies.

According to Daumas (2024), these three categories form an interconnected cascade of risk. Stranded resources can lead to stranded capital, which can result in stranded financial assets. This progression has the potential to amplify systemic vulnerabilities throughout the financial system during the climate transition. For this reason, climate risk is often considered one of the two main sources of systemic risk, the other being liquidity risk.
10.3.2 Drivers of asset stranding

Stranded assets can arise from a variety of factors, most of which overlap with the drivers of cli-
mate transition risk. The three most significant factors are climate policy and legal risks, shifts in
stakeholder preferences, and rapid technological change (Daumas, 2024, page 606).

Government intervention is one of the most immediate and powerful causes of asset stranding. Regulatory measures, such as carbon pricing, stricter emissions standards, and renewable energy mandates, can quickly reshape the economic landscape by phasing out fossil fuels. These measures can make once-profitable, carbon-intensive assets unviable. Legal risks further compound these policy pressures. Companies operating high-emission assets are increasingly facing the prospect of climate litigation, regulatory penalties, and compensation claims for environmental damage. Together, these factors increase financial risk and hasten the obsolescence of carbon-intensive investments. However, the recent evolution of climate policy in the United States has, for now, tempered these risks both domestically and abroad, as many countries remain cautious about imposing regulations that could place their industries at a competitive disadvantage.

Stakeholder risks arise from shifts in consumer behavior, investor expectations, and broader societal pressures that affect asset valuations. For example, consumers are increasingly favoring sustainable products and renewable energy solutions, which reduces the demand for carbon-intensive goods and services. Meanwhile, the financial sector's growing focus on ESG criteria has caused significant capital to leave high-emission industries. Investors are not only divesting from fossil fuel companies, but also demanding credible decarbonization strategies. These changes make it harder and more costly for carbon-intensive businesses to obtain financing. Reputational risks compound these pressures. Companies perceived as climate laggards face erosion of their social license to operate. This can result in reduced market access, difficulty attracting talent, community opposition, and consumer boycotts. These factors can render assets economically stranded, even when they remain technically profitable.

Perhaps the most transformative driver of asset stranding is the accelerating innovation of clean
technologies. Rapid advances in renewable energy, battery storage, electric vehicles, green hydrogen,
and carbon capture are reshaping entire industries at remarkable speed. As these alternatives reach
cost parity with traditional, carbon-intensive technologies, competitive pressures emerge that can
swiftly render existing assets unviable. Solar and wind power, for example, are already the most
affordable sources of electricity in some regions, undermining the economic viability of fossil fuel
generation. Technological change also shortens the useful life of carbon-intensive infrastructure.
Assets built to operate for decades may be forced into early retirement as cleaner, more efficient
options dominate the market.

10.3.3 Scope and sector analysis

As noted by Daumas (2024), the stranded assets literature initially focused on coal, oil, and gas extraction companies. For example, the report “Unburnable Carbon”, published by Carbon Tracker Initiative (2011), examined fossil fuel reserves, by assessing the most exposed stock exchanges and the top 200 companies ranked by estimated carbon holdings. While fossil fuels remain the sector most naturally exposed to stranding risks, the pioneering work of Ben Caldecott and his co-authors demonstrated that stranded assets extend far beyond the energy sector. They argued that the debate had become too narrowly centered on fossil fuels, contending that environmental risks are systemic and affect a wide range of industries. In this context, the 2013 agriculture report authored by Caldecott et al. (2013), “Stranded Assets in Agriculture: Protecting Value from Environment-Related Risks”, was particularly groundbreaking. By applying the analytical framework of stranded assets
to agriculture, they sought to demonstrate the concept's broader applicability while highlighting specific, under-appreciated vulnerabilities in the global food system. This seminal work established that stranded asset risk transcends the energy sector and is a relevant phenomenon across the entire economy in the face of environmental change. Below, we provide an overview of sectors exposed to stranded assets, identifying the main assets at risk and the scenarios under which they may become stranded.

<table><thead><tr><td>Sector</td><td>Assets at risk</td><td>Scenario</td></tr></thead><tbody><tr><td>Fossil fuel extraction and reserves</td><td>Coal mines and processing facilities, oil and gas reserves, drilling rigs, offshore platforms, pipelines, refineries, petrochemical complexes, and associated transportation infrastructure</td><td>Coal is being rapidly phased out due to climate policies, air quality regulations, and competition from cheaper renewable energy sources. Stringent climate targets aimed at limiting global warming to 1.5°C or 2°C could render oil and gas reserves unburnable carbon. Peak oil demand scenarios, carbon pricing mechanisms, and fossil fuel divestment campaigns are accelerating the devaluation of these assets. As demand declines and investment shifts to clean alternatives, the supporting infrastructure will become obsolete.</td></tr><tr><td>Power generation (utilities)</td><td>Coal-fired power plants, natural gas peaking plants, combined-cycle gas turbines, aging nuclear reactors, and associated transmission infrastructure designed for centralized generation</td><td>In terms of levelized cost, renewable energy sources are becoming more competitive with fossil fuels. Meanwhile, carbon pricing, emissions standards, and air quality regulations are raising operational costs. The accelerating shift toward distributed generation, battery storage, and smart grids makes large, inflexible thermal plants economically infeasible. Trends in grid modernization and electrification favor flexible, low-carbon technologies over legacy baseload capacity.</td></tr><tr><td>Heavy industry (steel, cement, chemicals, aluminum)</td><td>Coal-fired blast furnaces, clinker-based cement kilns, steam crackers for petrochemicals, primary aluminum smelters, and carbon-intensive manufacturing facilities</td><td>The emergence of low-carbon alternatives, such as hydrogen-based steel production, alternative cement chemistries, and bio-based chemicals, poses a threat to existing production processes. Carbon border adjustments and industrial emissions standards increase the cost of high-carbon production. Retrofitting existing facilities with carbon capture technology or modifying processes may be uneconomical compared to investing in new, low-carbon facilities, especially as technology costs decrease and regulatory pressure increases.</td></tr></tbody></table>
<table><thead><tr><td>Sector</td><td>Assets at risk</td><td>Scenario</td></tr></thead><tbody><tr><td>Mining and minerals</td><td>Thermal coal mines, fossil fuel extraction equipment, mining operations in environmentally sensitive areas, and processing facilities dependent on cheap fossil energy</td><td>Demand for thermal coal has plummeted as the power generation sector transitions to renewable energy sources. Furthermore, carbon pricing increases the cost of energy-intensive processing. However, the increased demand for minerals used in clean energy technologies could benefit certain mining operations. Environmental regulations and community opposition are increasingly restricting mining operations in sensitive ecosystems.</td></tr><tr><td>Transport and mobility</td><td>Internal combustion engine vehicle manufacturing plants, oil-dependent shipping fleets, conventional aviation assets, diesel locomotives, gasoline and diesel refueling infrastructure, and automotive supply chains focused on ICE components</td><td>The rapid adoption of electric vehicles (EVs), fueled by declining battery costs and growing charging infrastructure, poses a threat to the production of internal combustion engine (ICE) vehicles. Government mandates to phase out ICEs are accelerating this transition. Meanwhile, the aviation and shipping industries are under increasing pressure to decarbonize using sustainable fuels, hydrogen, and electric propulsion. As transportation becomes more electrified and alternative fuels gain market share, gas stations, oil distribution networks, and ICE-focused suppliers risk becoming obsolete.</td></tr><tr><td>Agriculture and land use</td><td>Deforestation-linked agricultural land (soy, palm oil, cattle ranching), intensive livestock operations, monoculture plantations, water-intensive crops in drought-prone regions, and agricultural infrastructure dependent on fossil fuel inputs</td><td>Due diligence regulations and zero-deforestation commitments within the supply chain generate compliance costs and limit market access. Growing consumer demand for plant-based alternatives and cultivated meat poses a threat to traditional livestock operations. Climate change alters agricultural productivity zones, which could make land and infrastructure in certain regions unsuitable. Carbon pricing and incentives for nature-based solutions could encourage converting agricultural land into forests and conservation areas, thereby reducing its value.</td></tr></tbody></table>
<table><thead><tr><td>Sector</td><td>Assets at risk</td><td>Scenario</td></tr></thead><tbody><tr><td>Real estate and built environment</td><td>Energy-inefficient office buildings, older residential housing stock, fossil fuel-heated properties, buildings in climate-vulnerable locations (flood zones, extreme heat areas), and real estate dependent on carbon-intensive industries</td><td>Stricter building energy codes, mandatory emissions disclosure requirements, and rising tenant expectations for sustainable properties are resulting in <i>brown discounts</i>, which decrease the value of inefficient buildings. Physical climate risks, such as flooding and extreme weather, also threaten property values in vulnerable locations. Retrofit costs often exceed the economic benefits for older properties. Meanwhile, the adoption of green building standards in new construction is creating a two-tier market.</td></tr><tr><td>Tourism</td><td>Ski resorts dependent on natural snow, coastal tourism infrastructure vulnerable to sea-level rise, desert resorts facing water scarcity, and destinations dependent on high-carbon transportation access</td><td>Climate change directly impacts tourism destinations by altering weather patterns, triggering extreme weather events, and affecting the availability of resources. Movements such as flight shaming and carbon taxes on aviation could reduce long-haul tourism. Furthermore, shifting consumer preferences toward sustainable travel options pose a threat to high-carbon tourism models.</td></tr><tr><td>Financial sector</td><td>Loans and credit facilities to carbon-intensive industries, equity holdings in fossil fuel companies, insurance policies covering climate-vulnerable assets, sovereign bonds from fossil fuel-dependent economies, and investment funds concentrated in brown sectors</td><td>As the value of stranded assets decreases across sectors, banks, insurers, and asset managers are facing cascading risks that could result in loan defaults, equity write-downs, and insurance claims. Climate stress testing and prudential regulations are increasingly requiring financial institutions to evaluate these risks and allocate funds accordingly. ESG investment mandates and fiduciary duty considerations are prompting the divestment of high-risk assets, creating liquidity pressures and valuation discounts.</td></tr></tbody></table>

### 10.3.4 Measurement of financial risks

According to Daumas (2024), the measurement of stranded assets falls into three main categories. The first category involves non-monetary estimates of physical assets or reserves that may become economically unviable due to climate transition. The second category focuses on monetary estimates, providing financial valuations of potential losses and impairments. These two categories use a bottom-up approach and are usually applied to corporations and non-financial entities. The third category uses a top-down approach to evaluate transition risks via financial networks, climate stress testing, and scenario-based methods. This category is better suited to financial institutions.
<figure><img src="image_488.png" /><figcaption>Figure 10.15: 2011 & 2013 Carbon Tracker Initiative *Unburnable Carbon* reports</figcaption></figure>

## Non-monetary estimates

The 2011 report by Carbon Tracker Initiative (2011) was the first publication to explicitly measure stranded assets and introduce the concept of *Unburnable Carbon*. This work was followed by an updated publication in 2013 that refined and expanded upon the original analysis (Carbon Tracker Initiative, 2013). Together, these two publications have significantly influenced subsequent academic studies and established modern approaches to measuring stranded assets (Figure 10.15).

To clarify how stranded assets are calculated, let us begin with the first report. Here is the summary:

> "Research by the Potsdam Institute calculates that to reduce the chance of exceeding 2°C warming to 20%, the global carbon budget for 2000–2050 is 886 GtCO₂. Minus emissions from the first decade of this century, this leaves a budget of 565 GtCO₂ for the remaining 40 years to 2050. [...] The total carbon potential of the Earth’s known fossil fuel reserves comes to 2 795 GtCO₂. [...] This means that governments and global markets are currently treating as assets, reserves equivalent to nearly 5 times the carbon budget for the next 40 years. [...] The fossil fuel reserves held by the top 100 listed coal companies and the top 100 listed oil and gas companies represent potential emissions of 745 GtCO₂. [...] Given only 20% of the total reserves can be used to stay below 2°C, if this is applied uniformly, then only 149 of the 745 GtCO₂ held by listed companies can be used unabated. Investors are thus left exposed to the risk of unburnable carbon. If the 2°C target is rigorously applied, then up to 80% of declared reserves owned by the world’s largest listed coal, oil and gas companies and their investors would be subject to impairment as these assets become stranded." (Carbon Tracker Initiative, 2011, page 2).

To understand these figures, we detail below the calculation. According to Meinshausen et al. (2009), the carbon budget CB (2000, 2050) for the period 2000–2050 was estimated at 886 GtCO₂ for an 80% probability<fnref n="37" /> of limiting warming to 2°C, 1 000 GtCO₂ for a 75% probability, 1 158 GtCO₂ for a 66.6% probability and 1 437 GtCO₂ for a 50% probability. Given that 321 GtCO₂ had already

$$ \Pr\{T \geq 2^{\circ}\mathrm{C} \mid \mathrm{CB}(2000, 2050) \leq 886\} = 20\% $$

<footnote n="37">³⁷This means that the exceedance probability is 20%:</footnote>
been emitted during 2000–2010, the remaining carbon budget CB (2010, 2050) compatible with the 2°C scenario was 565 GtCO₂ at the 80% probability level. In contrast, the estimated carbon stock of proven fossil fuel reserves at that time was 2,795 GtCO₂. This imbalance illustrates the essence of the stranded assets problem, as the majority of these reserves cannot be exploited if climate targets are to be achieved. The implied stranded carbon under the 2°C scenario at the 80% probability level is therefore:

$$ 
\begin{aligned}
\text{Stranded (2010, 2050)} &= \mathbf{CS} (2010, 2050) - \mathbf{CB} (2010, 2050) \\
&= 2795 - 565 \\
&= 2230 \text{ GtCO}_2
\end{aligned}
$$

This implied stranded carbon then represents about 80% of the reserves:

$$ \frac{\text{Stranded (2010, 2050)}}{\mathbf{CS} (2010, 2050)} = \frac{2230}{2795} = 79.79\% $$

Figure 10.16 compares the global 2°C carbon budget with estimated fossil fuel reserves. The total emissions potential of these reserves is 2795 GtCO₂. Approximately 65% of this total comes from coal, 22% from oil, and 13% from natural gas. Therefore, exploiting coal or oil reserves is incompatible with achieving the 2°C target. Only about one-third of the oil reserves and all of the gas reserves could be used within the remaining budget of 565 GtCO₂.

<figure><img src="image_489.png" /><figcaption>Figure 10.16: Comparison of the global 2°C carbon budget with fossil fuel reserves CO₂ emissions potential (in GtCO₂)</figcaption></figure>

Source: Carbon Tracker Initiative (2011, Figure 1, page 6).

The first part of the *Unburnable Carbon* report estimates the value of stranded assets, while the second part attempts to answer the question of where these assets are located. According to Carbon Tracker Initiative (2011), global coal, oil and gas reserves are concentrated in a small number of countries. The top 10 countries represent 91.5%, 82.9% and 76.8% of the respective
<table><caption>Table 10.14: Top 5 countries with the largest reserves of coal, oil and gas</caption><thead><tr><th>Rank</th><th></th><th>Coal</th><th></th><th>Oil</th><th></th><th>Gas</th></tr></thead><tbody><tr><td>1</td><td>US</td><td>28.90%</td><td>Saudi Arabia</td><td>17.90%</td><td>Russia</td><td>23.70%</td></tr><tr><td>2</td><td>Russia</td><td>19.00%</td><td>Canada</td><td>12.00%</td><td>Iran</td><td>15.80%</td></tr><tr><td>3</td><td>China</td><td>13.90%</td><td>Venezuela</td><td>11.70%</td><td>Qatar</td><td>13.50%</td></tr><tr><td>4</td><td>Australia</td><td>9.20%</td><td>Iran</td><td>9.30%</td><td>Turkmenistan</td><td>4.30%</td></tr><tr><td>5</td><td>India</td><td>7.10%</td><td>Iraq</td><td>7.80%</td><td>Saudi Arabia</td><td>4.20%</td></tr><tr><td>Total</td><td></td><td>78.10%</td><td></td><td>58.70%</td><td></td><td>61.50%</td></tr></tbody></table>

Source: Carbon Tracker Initiative (2011, Figure 2, page 7).

reserves. Table 10.14 shows the top five countries for reserves of each fossil fuel. For example, the US holds around 30% of the world's coal reserves, while Russia holds 24% of the world's gas reserves. The report then analyzes reserves held by publicly traded companies. These proven reserves — defined as discovered and economically recoverable fossil fuel deposits — constitute critical assets that significantly influence company valuations as they represent expected future revenue streams. The report's central finding is that the proven reserves of the top 200 publicly listed coal, oil, and gas companies amount to approximately 745 GtCO₂. This represents a fundamental mismatch with climate constraints because these reserves far exceed the remaining carbon budget of 565 GtCO₂ consistent with limiting global warming to 2°C. Accordingly, at least 180 GtCO₂ of these reserves would become stranded assets under such a climate-constrained scenario.

<table><caption>Table 10.15: Top 20 listed companies with the largest reserves of coal, oil and gas</caption><thead><tr><th>#</th><th>Company</th><th>GtCO<sub>2</sub></th><th>Cumul.</th><th>#</th><th>Company</th><th>GtCO<sub>2</sub></th><th>Cumul.</th></tr></thead><tbody><tr><td>1</td><td>Severstal JSC</td><td>141.6</td><td>141.6</td><td>2</td><td>Lukoil Holdings</td><td>43.6</td><td>185.2</td></tr><tr><td>3</td><td>Exxon Mobil Corp.</td><td>41.0</td><td>226.2</td><td>4</td><td>BP PLC</td><td>34.6</td><td>260.8</td></tr><tr><td>5</td><td>Gazprom OAO</td><td>28.8</td><td>289.6</td><td>6</td><td>Chevron Corp.</td><td>21.2</td><td>310.8</td></tr><tr><td>7</td><td>ConocoPhillips</td><td>19.1</td><td>330.0</td><td>8</td><td>Total S.A.</td><td>18.0</td><td>348.0</td></tr><tr><td>9</td><td>Anglo American PLC</td><td>16.8</td><td>364.8</td><td>10</td><td>Royal Dutch Shell PLC</td><td>16.2</td><td>381.0</td></tr><tr><td>11</td><td>BHP Billiton</td><td>16.1</td><td>397.0</td><td>12</td><td>Shanxi Coking Co. Ltd.</td><td>15.0</td><td>412.0</td></tr><tr><td>13</td><td>Exxaro Resources Ltd.</td><td>13.4</td><td>425.4</td><td>14</td><td>Petrobras</td><td>11.6</td><td>437.0</td></tr><tr><td>15</td><td>Xstrata PLC</td><td>11.6</td><td>448.6</td><td>16</td><td>Datang International</td><td>11.2</td><td>459.8</td></tr><tr><td>17</td><td>Rosneft</td><td>10.8</td><td>470.6</td><td>18</td><td>Peabody Energy Corp.</td><td>10.2</td><td>480.8</td></tr><tr><td>19</td><td>Mechel OAO</td><td>8.9</td><td>489.7</td><td>20</td><td>ENI S.p.A.</td><td>8.0</td><td>497.8</td></tr></tbody></table>

Source: Carbon Tracker Initiative (2011, Figure 5, pages 13 & 14) & Author's calculations.

**Remark 129** In fact, fewer than 200 publicly listed companies account for nearly the entire remaining carbon budget of 565 GtCO₂, with the 40 largest companies alone holding proven reserves that exceed 600 GtCO₂. Table 10.15 highlights the top 20 listed companies, whose reserves amount to nearly 500 GtCO₂.

The 2013 report updated the 2011 figures and shifted focus toward investor exposures. It found that the proven reserves of the top 200 listed companies contained 762 GtCO₂ (revised from 745 GtCO₂), underpinning $4 trillion in market value and $1.5 trillion in debt. Under current capital expenditure trajectories, the embedded carbon of potential listed reserves was estimated at 1541 GtCO₂ (Figure 10.17). The report concluded that these investments constitute “wasted capital”, i.e., money committed to assets that may become stranded and fail to generate expected returns.
<figure><img src="image_490.png" /><figcaption>Figure 10.17: Comparison of listed reserves in GtCO₂ to probability pro-rata carbon budget</figcaption></figure>

Source: Carbon Tracker Initiative (2013, page 15).
These reports have given rise to a substantial body of literature on fossil fuel reserves that could become stranded assets. One notable contribution is the study by McGlade and Ekins, which reached the following key conclusion:

> "Our results suggest that, globally, a third of oil reserves, half of gas reserves and over 80 per cent of current coal reserves should remain unused from 2010 to 2050 in order to meet the target of 2°C. We show that development of resources in the Arctic and any increase in unconventional oil production are incommensurate with efforts to limit average global warming to 2°C." (McGlade and Ekins, 2015, page 187).

More recent studies have shifted their focus to scenarios aligned with the 1.5°C carbon budget:

> "By 2050, we find that nearly 60 per cent of oil and fossil methane gas, and 90 per cent of coal must remain unextracted to keep within a 1.5°C carbon budget. This is a large increase in the unextractable estimates for a 2°C carbon budget, particularly for oil, for which an additional 25 per cent of reserves must remain unextracted. Furthermore, we estimate that oil and gas production must decline globally by 3 per cent each year until 2050. This implies that most regions must reach peak production now or during the next decade, rendering many operational and planned fossil fuel projects unviable." (Welsby et al., 2021, page 230).

> "In scenarios limiting warming to 1.5°C with no or limited overshoot, global coal, oil, and natural gas supply (intended for all uses) decline on average by 95%, 62%, and 42%, respectively, from 2020 to 2050 [...] If CDR is constrained by limits derived from expert consensus, the respective modelled coal, oil, and gas reductions become 99%, 70%, and 84%." (Achakulwisut et al., 2023, page 1).

For instance, we reproduce below the results of Welsby et al. (2021). Coal shows the highest unextractable share, with nearly 90% required to remain underground by 2050. Regional variation is notable: over 95% in the United States and Russia, versus 70% in China and India. Oil and gas face lower but still substantial limits, with about 60% of oil and over half of gas reserves globally unextractable by mid-century. The Middle East is distinctive, with 100% of coal and roughly two-thirds of oil and gas reserves left unused, underscoring the exposure of fossil fuel-dependent economies to transition risks.

<table><caption>Table 10.16: Unextractable reserves of fossil fuels by region in 2050 and 2100 under a 1.5°C scenario</caption><thead><tr><th rowspan="2">Region</th><th colspan="3">2050</th><th colspan="3">2100</th></tr><tr><th>Coal</th><th>Oil</th><th>Gas</th><th>Coal</th><th>Oil</th><th>Gas</th></tr></thead><tbody><tr><td>Africa</td><td>86%</td><td>51%</td><td>49%</td><td>85%</td><td>44%</td><td>43%</td></tr><tr><td>Australia</td><td>95%</td><td>40%</td><td>29%</td><td>95%</td><td>40%</td><td>25%</td></tr><tr><td>Canada</td><td>83%</td><td>83%</td><td>56%</td><td>83%</td><td>83%</td><td>56%</td></tr><tr><td>China/India</td><td>76%</td><td>47%</td><td>29%</td><td>73%</td><td>36%</td><td>24%</td></tr><tr><td>Central and South America</td><td>84%</td><td>73%</td><td>67%</td><td>82%</td><td>62%</td><td>65%</td></tr><tr><td>Europe</td><td>90%</td><td>72%</td><td>43%</td><td>90%</td><td>72%</td><td>40%</td></tr><tr><td>Former Soviet Union</td><td>97%</td><td>38%</td><td>63%</td><td>97%</td><td>29%</td><td>55%</td></tr><tr><td>Middle East</td><td>100%</td><td>62%</td><td>64%</td><td>100%</td><td>38%</td><td>49%</td></tr><tr><td>Other Developing Asia</td><td>42%</td><td>36%</td><td>32%</td><td>39%</td><td>31%</td><td>25%</td></tr><tr><td>US</td><td>97%</td><td>26%</td><td>24%</td><td>97%</td><td>20%</td><td>24%</td></tr><tr><td>Global</td><td>89%</td><td>58%</td><td>56%</td><td>88%</td><td>42%</td><td>47%</td></tr></tbody></table>

Source: Welsby et al. (2021, Figure 1, page 232).
## Monetary estimates

Non-monetary measures are expressed in physical units. For instance, McGlade and Ekins (2015) and Welsby et al. (2021) quantify stranded assets as billions of tonnes of coal, billions of barrels of oil, and trillions of cubic meters of gas. These figures can then be converted into percentages of proven reserves. By contrast, monetary measures are expressed in dollars. Converting physical measures into monetary values may seem straightforward at first. One might simply multiply the quantities by the prices of the respective commodities. In practice, however, the process is far more complex. As Hansen (2022) points out, the outcome depends on factors such as the projected trajectory of commodity prices over time, the chosen discount rate, and other scenario-specific parameters.

In monetary terms, stranded assets are generally defined as the difference between the value of fossil fuel assets under a baseline scenario (e.g., business-as-usual scenario) and their value under a climate stabilization scenario (e.g., a 1.5°C pathway):

$$Stranded(t_0, T) = VA_{Baseline}(t_0, T) - VA_{Climate}(t_0, T)$$

Building on this framework, Hansen (2022) decomposes stranded assets into the sum of wealth losses from stranded reserves — $SR(t_0, T)$ — and from stranded capital — $SC(t_0, T)$ — each is computed using the scenario-difference approach:

$$Stranded(t_0, T) = \underbrace{(VR_{Baseline}(t_0, T) - VR_{Climate}(t_0, T))}_{SR(t_0, T)} + \underbrace{(VC_{Baseline}(t_0, T) - VC_{Climate}(t_0, T))}_{SC(t_0, T)}$$

Here, $VR(t_0, T)$ and $VC(t_0, T)$ denote the values of reserves and capital goods, respectively. For reserves, Hansen (2022) applies a discounted cash flow model:

$$VR(t_0, T) = \sum_{t=t_0}^{T} \frac{(P(t) - MC(t))Q(t) - C(t)}{(1 + \varrho)^{(t-t_0)}}$$

where $VR(t_0, T)$ is the present value of the expected net income from reserve production, $P(t)$ is the projected market price in year $t$, $MC(t)$ is the expected marginal cost of production, $Q(t)$ is the extraction volume<fnref n="38" />, $C(t)$ is the capital expenditure, and $\varrho$ is the discount rate. The value of capital goods is calculated analogously as:

$$VC(t_0, T) = \sum_{t=t_0}^{T} \frac{C(t)}{(1 + \varrho)^{(t-t_0)}}$$

**Example 50** Consider an oil company with proven reserves of 4 trillion barrels. Table 10.17 reports, for the period 2025-2040 and under both the baseline and the 1.5°C scenarios, the evolution of the oil price $P(t)$, the marginal cost of production $MC(t)$, the share of proven reserves expected to be extracted $q(t)$, and the capital expenditures $C(t)$.

We first compute stranded assets in non-monetary units. For this purpose, we define $Q_{Baseline}(t) = q_{Baseline}(t) Q_{Reserve}$ and $Q_{Climate}(t) = q_{Climate}(t) Q_{Reserve}$ where total proven reserves

<footnote n="38">³⁸That is the quantity of proven reserves expected to be extracted in year $t$.</footnote>
<table><caption>Table 10.17: Data for Example 50</caption><thead><tr><th rowspan="2">t</th><th colspan="4">Baseline scenario</th><th colspan="4">1.5°C scenario</th></tr><tr><th>P (t)<br>$/barrel</th><th>MC (t)<br>$/barrel</th><th>q (t)<br>%</th><th>C (t)<br>$ mn</th><th>P (t)<br>$/barrel</th><th>MC (t)<br>$/barrel</th><th>q (t)<br>%</th><th>C (t)<br>$ mn</th></tr></thead><tbody><tr><td>2025</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td></tr><tr><td>2026</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>59</td><td>30</td><td>4.75%</td><td>950</td></tr><tr><td>2027</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>58</td><td>31</td><td>4.50%</td><td>900</td></tr><tr><td>2028</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>57</td><td>32</td><td>4.25%</td><td>850</td></tr><tr><td>2029</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>56</td><td>32</td><td>4.00%</td><td>800</td></tr><tr><td>2030</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>55</td><td>32</td><td>3.75%</td><td>750</td></tr><tr><td>2031</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>54</td><td>33</td><td>3.50%</td><td>700</td></tr><tr><td>2032</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>53</td><td>34</td><td>3.25%</td><td>650</td></tr><tr><td>2033</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>52</td><td>34</td><td>3.00%</td><td>600</td></tr><tr><td>2034</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>51</td><td>34</td><td>2.75%</td><td>550</td></tr><tr><td>2035</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>50</td><td>35</td><td>2.50%</td><td>500</td></tr><tr><td>2036</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>49</td><td>36</td><td>2.25%</td><td>450</td></tr><tr><td>2037</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>48</td><td>36</td><td>2.00%</td><td>400</td></tr><tr><td>2038</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>47</td><td>36</td><td>1.75%</td><td>350</td></tr><tr><td>2039</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>46</td><td>37</td><td>1.50%</td><td>300</td></tr><tr><td>2040</td><td>60</td><td>30</td><td>5.00%</td><td>1000</td><td>45</td><td>38</td><td>1.25%</td><td>250</td></tr></tbody></table>

<table><caption>Table 10.18: Intermediate calculations (Example 50)</caption><thead><tr><th>t</th><th>Q<sub>Baseline</sub> (t)<br>mn barrels</th><th>Q<sub>Climate</sub> (t)<br>mn barrels</th><th>V<sub>R</sub><sub>Baseline</sub> (t)<br>$ mn</th><th>V<sub>R</sub><sub>Climate</sub> (t)<br>$ mn</th><th>ΔV<sub>R</sub> (t)<br>$ mn</th><th>ΔC (t)<br>$ mn</th></tr></thead><tbody><tr><td>2025</td><td>200</td><td>200</td><td>5 000</td><td>5 000</td><td>0</td><td>0</td></tr><tr><td>2026</td><td>200</td><td>190</td><td>5 000</td><td>4 465</td><td>535</td><td>50</td></tr><tr><td>2027</td><td>200</td><td>180</td><td>5 000</td><td>3 960</td><td>1 040</td><td>100</td></tr><tr><td>2028</td><td>200</td><td>170</td><td>5 000</td><td>3 485</td><td>1 515</td><td>150</td></tr><tr><td>2029</td><td>200</td><td>160</td><td>5 000</td><td>3 040</td><td>1 960</td><td>200</td></tr><tr><td>2030</td><td>200</td><td>150</td><td>5 000</td><td>2 625</td><td>2 375</td><td>250</td></tr><tr><td>2031</td><td>200</td><td>140</td><td>5 000</td><td>2 240</td><td>2 760</td><td>300</td></tr><tr><td>2032</td><td>200</td><td>130</td><td>5 000</td><td>1 885</td><td>3 115</td><td>350</td></tr><tr><td>2033</td><td>200</td><td>120</td><td>5 000</td><td>1 560</td><td>3 440</td><td>400</td></tr><tr><td>2034</td><td>200</td><td>110</td><td>5 000</td><td>1 265</td><td>3 735</td><td>450</td></tr><tr><td>2035</td><td>200</td><td>100</td><td>5 000</td><td>1 000</td><td>4 000</td><td>500</td></tr><tr><td>2036</td><td>200</td><td>90</td><td>5 000</td><td>765</td><td>4 235</td><td>550</td></tr><tr><td>2037</td><td>200</td><td>80</td><td>5 000</td><td>560</td><td>4 440</td><td>600</td></tr><tr><td>2038</td><td>200</td><td>70</td><td>5 000</td><td>385</td><td>4 615</td><td>650</td></tr><tr><td>2039</td><td>200</td><td>60</td><td>5 000</td><td>240</td><td>4 760</td><td>700</td></tr><tr><td>2040</td><td>200</td><td>50</td><td>5 000</td><td>125</td><td>4 875</td><td>750</td></tr></tbody></table>
are $Q_{\text{Reserve}} = 4$ trillion barrels. We then obtain$^{39}$:

$$
\begin{align*}
\text{Stranded (2025, 2040)} &= \sum_{t=2025}^{2040} (Q_{\text{Baseline}}(t) - Q_{\text{Climate}}(t)) \\
&= Q_{\text{Reserve}} \left( \sum_{t=2025}^{2040} q_{\text{Baseline}}(t) - \sum_{t=2025}^{2040} q_{\text{Climate}}(t) \right) \\
&= 4 \times (80\% - 50\%) \\
&= 1.2 \text{ trillion barrels}
\end{align*}
$$

Knowing that the combustion of one barrel of oil generates 0.430 tCO₂e, the equivalent stranded emissions are:

$$
\begin{align*}
\text{Stranded (2025, 2040)} &= 1.2 \text{ trillion barrels} \times 0.430 \text{ tCO}_2\text{e} \\
&= 516 \text{ MtCO}_2\text{e}
\end{align*}
$$

To compute stranded assets in monetary terms, we use the quantities reported in Table 10.18:

$$
\left\{
\begin{array}{l}
\mathcal{V}\mathcal{R}_{\text{Baseline}}(t) = (P_{\text{Baseline}}(t) - \text{MC}_{\text{Baseline}}(t)) Q_{\text{Baseline}}(t) - \mathcal{C}_{\text{Baseline}}(t) \\
\mathcal{V}\mathcal{R}_{\text{Climate}}(t) = (P_{\text{Climate}}(t) - \text{MC}_{\text{Climate}}(t)) Q_{\text{Climate}}(t) - \mathcal{C}_{\text{Climate}}(t) \\
\Delta\mathcal{V}\mathcal{R}(t) = \mathcal{V}\mathcal{R}_{\text{Baseline}}(t) - \mathcal{V}\mathcal{R}_{\text{Climate}}(t) \\
\Delta\mathcal{C}(t) = \mathcal{C}_{\text{Baseline}}(t) - \mathcal{C}_{\text{Climate}}(t)
\end{array}
\right.
$$

The net present value of these flows is then computed. For example:

$$
\mathcal{V}\mathcal{R}_{\text{Baseline}}(2025, 2040) = \sum_{t=2025}^{2040} \frac{\mathcal{V}\mathcal{R}_{\text{Baseline}}(t)}{(1 + \rho)^{(t-t_0)}}
$$

Finally, total stranded assets are given by:

$$
\begin{align*}
\text{Stranded (2025, 2040)} &= \mathcal{V}\mathcal{R}_{\text{Baseline}}(2025, 2040) - \mathcal{V}\mathcal{R}_{\text{Climate}}(2025, 2040) + \\
&\quad \mathcal{V}\mathcal{C}_{\text{Baseline}}(2025, 2040) - \mathcal{V}\mathcal{C}_{\text{Climate}}(2025, 2040)
\end{align*}
$$

Results are reported in Table 10.19. We obtain stranded asset values of $53.40 billion without discounting, and $22.23 billion when using a 10% discount rate.

<table><caption>Table 10.19: Stranded assets (in $ bn) (Example 50)</caption>
  <thead>
    <tr>
      <th>ρ</th>
      <th>0%</th>
      <th>3%</th>
      <th>5%</th>
      <th>10%</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>V<sub>R</sub><sub>Baseline</sub> (2025, 2040)</td>
      <td>80.00</td>
      <td>64.69</td>
      <td>56.90</td>
      <td>43.03</td>
    </tr>
    <tr>
      <td>V<sub>R</sub><sub>Climate</sub> (2025, 2040)</td>
      <td>32.60</td>
      <td>29.02</td>
      <td>27.05</td>
      <td>23.19</td>
    </tr>
    <tr>
      <td>S<sub>R</sub> (2025, 2040)</td>
      <td>47.40</td>
      <td>35.67</td>
      <td>29.84</td>
      <td>19.84</td>
    </tr>
    <tr>
      <td>V<sub>C</sub><sub>Baseline</sub> (2025, 2040)</td>
      <td>16.00</td>
      <td>12.94</td>
      <td>11.38</td>
      <td>8.61</td>
    </tr>
    <tr>
      <td>V<sub>C</sub><sub>Climate</sub> (2025, 2040)</td>
      <td>10.00</td>
      <td>8.49</td>
      <td>7.70</td>
      <td>6.22</td>
    </tr>
    <tr>
      <td>S<sub>C</sub> (2025, 2040)</td>
      <td>6.00</td>
      <td>4.45</td>
      <td>3.68</td>
      <td>2.39</td>
    </tr>
    <tr>
      <td>Stranded (2025, 2040)</td>
      <td>53.40</td>
      <td>40.11</td>
      <td>33.53</td>
      <td>22.23</td>
    </tr>
  </tbody>
</table>

<footnote>$^{39}$80% of reserves are extracted in the baseline scenario over the period 2025–2040 and 50% in the 1.5 scenario.</footnote>
According to Daumas (2024), two main monetary metrics are commonly used to assess stranded assets:

1. Book loss refers to the immediate write-down of asset values on financial statements. It represents the difference between an asset's current book value and its revised valuation after stranding occurs. From an accounting perspective, this is a realized loss recorded directly on the balance sheet when assets are revalued due to the stranding event.

2. Foregone revenue denotes the income that would have been generated if the asset had not become stranded. This metric captures an opportunity cost, reflecting the loss of future earnings that will no longer materialize once the asset is devalued in the context of the low-carbon transition.

Fricaudet et al. (2024) apply these two metrics to the shipping industry by estimating the potential book losses and foregone revenues from ships carrying fossil fuels. The authors model two scenarios up to 2050, that are both aligned with a 1.5°C demand trajectory: (a) the no-further-ordering scenario, in which no new fossil fuel carriers are ordered after 2024; (b) the newbuild-until-2030 scenario, in which the ordering of fossil fuel carriers continues until 2030. They found the following results:

* More than one-third of global shipping capacity is dedicated to transporting fossil fuels, representing an estimated fleet value of around $875 billion, distributed as follows$^{40}$:

<table><thead><tr><th>Type</th><th>Bulk carriers</th><th>Oil tankers</th><th>LNG tankers</th><th>LPG tankers</th></tr></thead><tbody><tr><td>Value (in $ bn)</td><td>336</td><td>286</td><td>186</td><td>67</td></tr></tbody></table>

* Oil tankers face a higher stranding risk than bulk carriers, since demand for other bulk commodities may offset the decline in coal transport to some extent.

* Between 2024 and 2050, the projected foregone revenue is $214 billion in the no-further-ordering scenario and $286 billion in the newbuild-until-2030 scenario.

* Book losses are estimated to peak at $90–$108 billion by 2030 in the no-further-ordering scenario, and at $121–$147 billion in the newbuild-until-2030 scenario.

In Appendix A1, Daumas (2024) provides an extensive review of studies that estimate the monetary value of stranded assets. Tables A1 and A2 present global estimates of book losses and foregone revenues, while Tables A3 and A4 present these figures at the regional level. A key observation is that the results may vary widely across studies. For instance, Hansen (2022) estimates total book losses of $12.9 and $17.2 trillion, respectively, under the 1.8°C and 1.5°C scenarios from 2020 to 2040. These values are calculated using a 10% discount rate. Without discounting, these values rise to $31.1 and $40.6 trillion (Table 10.20). In contrast, Chester et al. (2024), accounting for both produced and human capital, estimate stranded capital to be between $117 and $557 trillion, depending on how quickly carbon-intensive investments are phased out. Their estimates are therefore considerably higher than those reported in other studies. Nevertheless, there is a certain coherence across the literature, as figures generally differ due to variations in scope, discount rates, and scenario assumptions. For example, Edwards et al. (2022) estimate book losses of $1.5 and $1 trillion for coal reserves under 2°C and 1.5°C scenarios, respectively. These values are broadly consistent with those obtained by Hansen (2022).

<footnote>$^{40}$LNG = liquefied natural gas, mainly methane; LPG = liquefied petroleum gas, primarily propane and butane.</footnote>
<table><caption>Table 10.20: Estimates of book losses from stranded reserves</caption><thead><tr><th rowspan="2">Scenario</th><th rowspan="2">ε</th><th colspan="5">(Values in $ trillion of 2018)</th><th colspan="5">(Breakdown in %)</th></tr><tr><th>0%</th><th>3%</th><th>5%</th><th>8%</th><th>10%</th><th>0%</th><th>3%</th><th>5%</th><th>8%</th><th>10%</th></tr></thead><tbody><tr><td rowspan="4">1.8°C</td><td>Oil</td><td>23.5</td><td>17.6</td><td>14.7</td><td>11.5</td><td>9.9</td><td>75.4</td><td>75.9</td><td>76.3</td><td>76.7</td><td>77.0</td></tr><tr><td>Gas</td><td>3.9</td><td>3.0</td><td>2.5</td><td>2.0</td><td>1.7</td><td>12.6</td><td>12.8</td><td>13.0</td><td>13.2</td><td>13.3</td></tr><tr><td>Coal</td><td>3.8</td><td>2.6</td><td>2.1</td><td>1.5</td><td>1.3</td><td>12.1</td><td>11.2</td><td>10.7</td><td>10.1</td><td>9.8</td></tr><tr><td>Total</td><td>31.1</td><td>23.2</td><td>19.3</td><td>15.0</td><td>12.9</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">1.5°C</td><td>Oil</td><td>27.1</td><td>20.4</td><td>17.2</td><td>13.5</td><td>11.6</td><td>66.9</td><td>67.1</td><td>67.3</td><td>67.5</td><td>67.6</td></tr><tr><td>Gas</td><td>8.4</td><td>6.4</td><td>5.5</td><td>4.4</td><td>3.8</td><td>20.6</td><td>21.2</td><td>21.5</td><td>21.9</td><td>22.1</td></tr><tr><td>Coal</td><td>5.1</td><td>3.6</td><td>2.9</td><td>2.1</td><td>1.8</td><td>12.5</td><td>11.7</td><td>11.2</td><td>10.7</td><td>10.3</td></tr><tr><td>Total</td><td>40.6</td><td>30.4</td><td>25.5</td><td>20.0</td><td>17.2</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Hansen (2022, Tables 9 & 10, page 8) & Author's calculations.

Table 10.21 presents estimates of foregone revenues from stranded reserves. One of the earliest studies in this area was conducted by Kepler Cheuvreux, a financial services company providing trading, advisory, research, and asset-management services. In 2014, they published a research paper evaluating stranded assets and their implications for investors:

> "Under a global climate deal consistent with a 2°C world, we estimate that the fossil-fuel industry would stand to lose $28 tn (in constant 2012 US dollars) of gross revenues over the next two decades, compared with business as usual. [...] The oil industry accounts for $19.3 tn of this, gas $4 tn, and coal $4.9 tn. The revenues most at risk would be concentrated in the high-cost, high-carbon sources of production. For the oil industry, this means, above all, deepwater, oil-sands, and shale-oil plays. [...] we think investors need more details on the breakdown of oil companies' assets by project type and on their capital-allocation processes in order to be able to better assess carbon risk and cost/revenue risk." (Lewis et al., 2014, page 1).

Notably, the Kepler Cheuvreux report was followed by a wave of similar analyses carried out by investment banks, asset managers, brokers, and insurance companies. Among the most influential were the UBS report in 2016 and the Lloyd's report in 2017 (Koehler and Bertocci, 2016; Caldecott et al., 2017b). On average, estimates of foregone revenues are relatively consistent, typically falling within the range of $10 to $30 trillion. One exception is the study of Linquiti and Cogswell (2016, Table 4, page 671), who estimated stranded assets at $200 trillion using a 3% discount rate. The main reason for this much higher figure is the use of an uncommon time horizon of 2014–2115, i.e., one century. Their calculations suggest that under a 2°C scenario, 58.2% of oil reserves, 2.8% of gas reserves, and 81.6% of coal reserves would remain unproduced during this time.

<table><caption>Table 10.21: Estimates of foregone revenues from stranded reserves</caption><thead><tr><th>Study</th><th>Scenario</th><th>Oil</th><th>Gas</th><th>Coal</th><th>Total</th></tr></thead><tbody><tr><td>Lewis et al. (2014)</td><td>2°C</td><td>19.3</td><td>4.0</td><td>4.9</td><td>28.2</td></tr><tr><td>Nelson et al. (2014)</td><td>2°C</td><td>18.5</td><td>3.0</td><td>3.0</td><td>24.6</td></tr><tr><td>Bauer et al. (2016)</td><td>2°C</td><td></td><td></td><td></td><td>17.5</td></tr><tr><td>Linquiti and Cogswell (2016)</td><td>2°C</td><td>138.0</td><td>36.6</td><td>26.1</td><td>200.8</td></tr><tr><td>Mercure et al. (2018)</td><td>2°C</td><td>8.2</td><td>3.2</td><td>1.0</td><td>12.4</td></tr><tr><td>Chen et al. (2023)</td><td>1.5°C</td><td>30.0</td><td></td><td>1.7</td><td>31.7</td></tr></tbody></table>

Source: Daumas (2024, Tables A2, pages 656–660) & Author's research.
Finance-oriented estimates

Daumas (2024) considers a third category of studies: finance-oriented approaches to transition risks. These studies are related to stranded assets because they measure transition risk for both the real economy and the financial system. However, they are not strictly studies of stranded assets. They typically estimate potential financial losses for institutions, such as banks and investors. However, these losses are not solely due to stranded assets. Rather, they encompass the entire scope of transition risk. Consequently, some of these studies include the costs of transitioning to a low-carbon economy, which do not constitute stranded assets per se. Daumas (2024) categorizes these studies into three groups:

• Mapping the risk exposures of financial agents

• Stress testing (static, network-based, and dynamic stress tests)

• Long-run vulnerability assessments (e.g., portfolio loss at the investor level, NGFS-based studies)

**Remark 130** In Section 8.3.3 on page 781, we have already seen how integrated assessment models can be used to produce transition scenarios. Chapter 11 presents different approaches to climate portfolio optimization, in particular how to incorporate climate metrics into portfolio construction, including some of the transition metrics introduced in Chapter 9. Finally, Chapter 13 focuses on climate stress testing. For this reason, we do not expand on this section here, as it is already covered in other parts of the handbook.

10.4 Decarbonization pathway

Transition risk is closely tied to transition scenarios, which are economic, industrial, and financial trajectories designed to guide societies toward a low-carbon economy. Essentially, transition scenarios are decarbonization pathways, i.e., structured plans that show how greenhouse gas emissions can be reduced across sectors over time. By design, there is no single decarbonization pathway. Several critical factors shape the various alternatives. The most decisive factor is the time horizon, such as achieving net-zero emissions by 2050 versus 2070. Other factors include the order in which sectors decarbonize, associated costs, how efforts are distributed across regions, and technological assumptions. In the following discussion, we focus primarily on the 1.5°C transition scenario by 2050. There are three reasons for this choice. First, the 1.5°C scenario can serve as a benchmark. Understanding its dynamics makes less ambitious alternatives, such as the 2.0°C pathway extending to 2070, easier to grasp. Second, the 1.5°C scenario by 2050 has received the most attention in research, policy, and industry debates, so it has a particularly robust evidence base. Third, this scenario provides the clearest reference point for assessing transition risks given its stringent requirements and widespread use in stress-testing exercises. Many organizations have published decarbonization roadmaps. In what follows, we primarily draw on a subset of widely regarded, robust, coherent, and complementary scenarios:

• Energy Transitions Commission, www.energy-transitions.org (Energy Transitions Commission, 2020, 2021, 2023a,b, 2024)

• International Energy Agency, www.iea.org (IEA, 2021, 2023a,b, 2024a,b, 2025)

• McKinsey, www.mckinsey.com (McKinsey, 2022, 2023, 2024b,c)
Box 10.4: International Energy Agency (IEA)

Established in 1974 in response to the oil crisis, the International Energy Agency (IEA) is an autonomous intergovernmental organization within the framework of the Organization for Economic Cooperation and Development (OECD). The IEA comprises 32 member countries, 13 associate countries, and four accession countries. Together, these countries represent 80% of global energy consumption, 64% of global energy production, and 87% of global clean energy investment. Initially, the IEA's mission was to ensure an oil supply for its member countries by coordinating strategic petroleum reserves and collective responses to potential disruptions. Over the decades, however, the IEA has significantly broadened its mandate, evolving into one of the most influential global authorities on energy policy, technology, and sustainability. Today, the agency plays a central role in shaping the international energy dialogue by providing governments, businesses, and institutions with data, forecasts, and policy recommendations. Its analytical reports, in particular the *World Energy Outlook* (WEO), are widely regarded as reference points in academic and policy debates (IEA, 2025).

The scope of the IEA has expanded beyond fossil fuel markets to include the entire global energy transition. The agency actively promotes clean energy technologies, renewable energy deployment, energy efficiency measures, and comprehensive electrification strategies in every economic sector. Its transition scenarios, particularly the *Net Zero Emissions by 2050* pathway (IEA, 2021), are among the most widely studied and frequently cited frameworks in academic literature, policy documents, and industry analyses of transition risks and decarbonization strategies. Though originally established with a primary focus on fossil fuel security and traditional energy markets, the IEA has gained significant authority and recognition in sustainable energy policy and clean technology analysis over the decades. The IEA's work on energy transitions complements the research of the International Renewable Energy Agency (IRENA). Founded in 2009, IRENA is an intergovernmental organization that promotes the adoption of renewable energy and supports countries in transitioning to sustainable energy systems worldwide.

10.4.1 Global analysis

Rationale for achieving a low-carbon economy

When discussing the requirements for achieving net-zero emissions, it is tempting to compile an endless list of necessary actions. However, such lists often obscure priorities, making it difficult to distinguish between essential measures and those with only a marginal impact. In practice, reaching net zero requires fundamental transformations in seven core energy and land use systems: agriculture, buildings, forestry and land use, industry, power generation (electricity and heat), transportation, and waste management (McKinsey, 2022). Figure 10.18 illustrates the distribution of emissions across these systems, showing that a significant share of global emissions is concentrated in only a few of them. An analysis of leading transition roadmaps<fnref n="41" /> indicates that the power sector is central to the net-zero transformation and requires changes in two interdependent areas:

1. Massive electrification of the global economy

2. Decarbonization of electricity generation

<footnote n="41">⁴¹See IEA (2021, 2023b) and Energy Transitions Commission (2021, 2023a).</footnote>
The first transformation is widespread electrification. Many high-emitting activities, such as road transportation, shipping, building heating, and industrial processes, will increasingly rely on electricity rather than burning fossil fuels. Consequently, the level of global greenhouse gas emissions will depend heavily on the carbon intensity of electricity. The second transformation is the decarbonization of power generation, which aims to reduce this intensity by phasing out fossil fuels and expanding clean energy sources. The ultimate goal is to produce no electricity from fossil fuels by 2050. If this goal is achieved, all electrified activities could operate without adding new emissions by mid-century, effectively decoupling much of the global economy from its dependency on carbon. In other words, achieving net zero requires restructuring the global value chain<fnref n="42" />. The main implication is that not all sectors are equally critical to the net-zero transition. Prioritizing high-impact sectors, particularly the power sector, is essential. However, allocating resources to areas with marginal contributions could dilute efforts and slow progress.

<figure><img src="image_491.png" /><figcaption>Figure 10.18: Distribution of GHG emissions by energy source (in %)</figcaption></figure>

Source: McKinsey (2022, Exhibit E3, page 10).

<footnote n="42">⁴²Figure 10.19 illustrates the dual transformation pathway from 2017 to 2050. The power and heat sector expands dramatically, reflecting the significant electrification of transportation, buildings, and industry as these sectors transition away from direct consumption of fossil fuels. At the same time, the composition of electricity generation shifts significantly from fossil fuels to renewable energy sources. By 2050, hydrogen emerges as a solution for sectors that are difficult to electrify, such as heavy industry and long-distance transportation. The 2030 intermediate scenario shows how this transition progresses. Initially, electrification accelerates while the grid still relies heavily on fossil fuels. Then, in subsequent decades, the power sector aggressively decarbonizes. This phased approach allows economic sectors to prepare for electrification as clean energy infrastructure expands. The visualization underscores the importance of prioritizing power sector transformation to enable economy-wide decarbonization.</footnote>
<figure><img src="image_492.png" /><figcaption>Figure 10.19: Transforming the global value chain into a net-zero economy (primary energy demand in petajoules)</figcaption></figure>

Source: McKinsey (2023, Exhibit 6B, page 12).
**Example 51** We estimate that in 2020, global electricity production was 30 000 TWh, with an average carbon intensity of 500 gCO₂/kWh. Under a net-zero scenario, electricity generation is projected to increase to 100 000 TWh by 2050.

In 2020, electricity generation emitted about 15 GtCO₂:

$$ 
\begin{aligned}
\mathcal{CE} &= 30\,000 \text{ TWh} \times 500 \frac{\text{gCO}_2}{\text{kWh}} \\
&= 30\,000 \times 10^9 \text{ kWh} \times 500 \frac{\text{gCO}_2}{\text{kWh}} \\
&= 1.5 \times 10^{16} \text{ gCO}_2 \\
&= 15 \text{ GtCO}_2
\end{aligned}
$$

By 2050, the emissions from electricity generation will depend on the average carbon intensity:

<table><tr><td>CT (in gCO₂/kWh)</td><td>10</td><td>20</td><td>30</td><td>50</td><td>100</td></tr><tr><td>CE (in GtCO₂)</td><td>1</td><td>2</td><td>3</td><td>5</td><td>10</td></tr></table>

If we assume that natural and engineered carbon sinks can absorb about 2 GtCO₂ per year by 2050, then the electricity sector's emissions must be reduced to this level or below. This implies that the average carbon intensity of electricity must fall from 500 gCO₂/kWh in 2020 to about 20 gCO₂/kWh in 2050, corresponding to a 25-fold reduction.

## Funding requirements

According to McKinsey (2022), achieving the transition to a low-carbon economy will require approximately $275 trillion in cumulative investment in physical assets from 2021 to 2050, or about $9.2 trillion per year. This represents an increase of about $3.5 trillion per year compared to current spending levels. More than 85% of this investment will go to the buildings, power, and transportation sectors (McKinsey, 2023). Regional priorities differ. In developed markets, transportation will require the largest share of investment, while emerging markets, including China and India, will emphasize energy infrastructure. These estimates are consistent with those of the Energy Transitions Commission (Energy Transitions Commission, 2023a). As illustrated in Figure 10.20, the power sector alone accounts for around 70% of global net-zero investments, broken down as follows: 38% in power generation (green electricity), 26% in power networks (infrastructure and grids), and 6% in power storage (efficiency and flexibility). Including buildings (14%) and transportation (8%) brings the total to 92%. The remaining 8% is allocated to carbon removals (waste management and recycling), hydrogen, and industry. This distribution shows that not all sectors contribute equally to net-zero investment requirements.

Energy Transitions Commission (2023a) distinguishes between two types of financing: capital investments in technologies and assets, and grants to fund measures lacking market incentives. Capital investments are undertaken by the private and public sectors and are usually expected to generate future returns. Grants, on the other hand, are necessary where there is no economic incentive to act. Examples include the early phase-out of coal, ending deforestation, or financing carbon sequestration. Of the $3.5 trillion in required annual financing, the majority will take the form of capital investment, with about $0.5 trillion allocated to fossil fuel assets that are destined to decline. A key challenge is scaling up investment in middle- and low-income countries. While efforts in developed markets need to double relative to current levels, investment in middle- and low-income countries must increase nearly sevenfold.
<figure><img src="image_493.png" /><figcaption>Figure 10.20: Net-zero capital investments</figcaption></figure>

Source: Energy Transitions Commission (2023a, page 9) & Author's calculations.

As noted above<fnref n="43" />, the power sector will require the largest share of financing, about $2.4 trillion per year, to support carbon-free power generation, transmission and distribution networks, and enhanced grid flexibility. Electrification will be central to the transition, with significant spillover effects across other sectors. Global electricity supply must expand from 27 000 TWh today to about 130 000 TWh by 2050. This expansion is driven by large increases in wind and solar power, supported by additional zero-carbon sources including hydropower. The building sector requires the second largest investment, at around $500 billion per year. The transportation sector comes in third with $280 billion per year. Other sectors include carbon capture and storage ($135 billion), hydrogen ($80 billion), and industry ($70 billion). While these investments are feasible from a macroeconomic perspective, a significant increase in overall capital is still required, especially in the 2020s. At the country level, high-income economies will need to invest approximately $1.25 trillion per year, which is about twice the current level. Meanwhile, middle-income countries will require $875 billion, while low-income countries will need around $25 billion. China alone could sustain an additional $700 billion in annual investment. Concessional funding will come from carbon markets (through companies purchasing credits), philanthropic contributions, and intergovernmental transfers. The ETC estimates that these payments will total approximately $300 billion per year by 2030. This includes $25–50 billion for coal phase-out, $130 billion to end deforestation, and $100 billion to support carbon sequestration (Energy Transitions Commission, 2023a).

### 10.4.2 Sector analysis

A transition to a low-carbon economy requires coordinated changes in production across the different sectors. While the global financing needs provide an overall picture of the transition, sector-specific analyses reveal unique technological pathways, investment priorities, and implementation challenges.

<footnote n="43">⁴³All figures in this paragraph are taken from Energy Transitions Commission (2023a).</footnote>