### 3.2.2 Fixed-income markets

Compared to equity markets, there are few studies analyzing the impact of ESG screening on fixed-income markets. For example, Menz (2010) examined the relationship between the valuation of Euro corporate bonds and corporate social responsibility and concluded that “*CSR has apparently not yet been incorporated into the pricing of corporate bonds.*” Similarly, a neutral or slightly positive effect of socially responsible investing was found by Derwall and Koedijk (2009) when they compared the performance of SRI and conventional bond funds. This overall neutrality, sometimes associated with a lack of maturity in the integration of ESG information into the bond market, was also highlighted by Goldreyer et al. (1999), Bauer et al. (2005) and Cortez et al. (2009). In the CAPM approach, active management performance is captured by measuring the alpha. However, Lin et al. (2019) constructed industry and credit-rating controlled quintile portfolios and found no significant evidence that ESG factors contribute to positive alpha in the bond market. On the contrary, Oikonomou et al. (2014) found that corporate social performance is rewarded in the corporate debt market. Results of Leite and Cortez (2016) are slightly positive, but highly country-specific. For example, they conclude that “*French SRI bond funds match the performance of their conventional peers, German funds slightly outperform and UK funds significantly underperform conventional funds.*” Polbennikov et al. (2016) also found a slight outperformance of high ESG-rated bonds over low ESG-rated bonds after controlling for different risk exposures. More recently, Gerard (2019) and Pereira et al. (2019) found no relationship between ESG and performance in corporate bond markets.

#### Simulated results

There are three main reasons why the relationship between ESG and bond returns may differ from the relationship between ESG and equity returns:

1. The first is ESG sensitivity. In fact, ESG criteria are very important to a shareholder because ESG risks can affect long-term business risk and have a strong impact on the value of equity. In the case of a bondholder, his main objective is to manage default risk, which is more of a short-term risk. Therefore, the concept of active ownership does not really apply to fixed-income instruments. For example, the absence of voting rights dramatically reduces the impact of engagement policies when ESG investors focus on the bond market. From a theoretical perspective, it is generally accepted that shareholders are more sensitive to ESG factors than bondholders. This may explain the lower integration of ESG in fixed-income markets. Other factors may also explain the difference in ESG sensitivity. For example, equity prices react faster and more strongly than bond prices to negative events, news flows and market sentiment, while bond prices are mainly driven by long-term fundamentals. In addition, the difference in liquidity between the two markets and the buy-and-hold strategy imply that bonds are traded less frequently than stocks. In this context, investors may feel that fixed-income markets do not price in ESG issues.

2. The second is the impact of ESG investment flows. In the case of equities, they put upward pressure on share prices. In the case of bonds, we see two effects. Investment flows can obviously affect the dynamics of credit spreads and then put pressure on bond prices, but they also have an impact on the primary market by reducing or increasing the coupon. ESG investment flows then have an impact on carry, and a high ESG score generally implies a reduction in carry due to the supply/demand balance.

3. The third reason is related to the correlation between ESG ratings and credit ratings. Some extra-financial information is already incorporated into credit ratings. In a bond investment
universe, the conditional probability distribution of ESG scores then differs from the unconditional probability distribution of ESG scores. In most cases, we distinguish between IG and HY bonds. Since we observe a positive correlation between ESG and credit ratings, it follows that there are more worst-in-class issuers in the HY universe than in the IG universe. So we observe a bias in the ESG scores, which are no longer sector-neutral in fixed-income markets. Moreover, the average ESG score is certainly not zero. It is positive for IG bonds and negative for HY bonds<fnref n="28" />.

These various reasons explain why ESG scoring is more integrated in equity portfolio management, while ESG integration in bond portfolio management is generally limited to exclusions. Moreover, the development of pure ESG securities has generally led to a segmentation in the construction of bond portfolios. On the one hand, we find a core portfolio, which corresponds to a global aggregate fixed-income strategy and uses minimum ESG criteria without really promoting ESG analysis. On the other hand, a satellite portfolio is invested in green and sustainability bonds with the aim of implementing an impact investing strategy.

<figure><img src="image_95.png" /><figcaption>Figure 3.31: Probability density function of ESG scores</figcaption></figure>

Source: Ben Slimane et al. (2019b).

**Sorted portfolios** Ben Slimane et al. (2019b) has applied the sorted and optimized approach of Bennani et al. (2018) to the universe of bonds from the Intercontinental Exchange Bank of America Merrill Lynch large cap investment grade corporate bond index. The study period is from January 2010 to August 2019. Each month, the bonds are ranked according to their ESG score and five

<footnote n="28">²⁸In Figure 3.31, we show the estimated density function of Amundi ESG z-scores within the IG and HY universes. The empirical standard deviation of the z-scores is equal to 1.01 regardless of the bond universe. On the contrary, we find that the empirical mean is equal to 0.02 and −0.38 for IG and HY bonds, respectively.</footnote>
quintile portfolios are created. Portfolio $Q_1$ corresponds to the 20% best-ranked bonds, whereas portfolio $Q_5$ corresponds to the 20% worst-rated bonds. By construction, the sorted portfolios are sector-neutral. However, the authors do some clustering because some sectors are small. Sorted portfolios are then constructed using the same weighting structure as the benchmark, with the selected bonds equally weighted within a sector. In addition, each portfolio is rebalanced on a monthly basis, meaning that the portfolio is invested on the first trading day of the month and held for the entire month. In Figure 3.32, we show the difference in returns between the best-in-class portfolio and the worst-in-class portfolio. We use two performance measures. Total return (TR) corresponds to the mark-to-market return of the portfolio, including bond price changes and coupon effects. Credit return or excess return (ER) is the return above the total return of a risk-matched basket of government bonds or interest rate swaps, thus neutralizing the interest rate and yield curve risk and isolating the portion of performance due solely to credit and optionality risks. Focusing on the total return measure, all $Q_1 - Q_5$ EUR-denominated portfolios had a positive performance over both the 2010–2013 and 2014–2019 periods. Looking at the credit return measure, ESG and Ⓐ long/short portfolios had negative performance before 2014, while all portfolios had a positive performance in the second period. Looking at the universe of USD-denominated investment grade corporate bonds, the worst-in-class portfolio generally outperformed the best-in-class portfolio between 2010 and 2013, with the exception of the governance pillar.

<figure><img src="image_96.png" /><figcaption>Figure 3.32: Annualized return in bps of the long short $Q_1 - Q_5$ strategy (IG, 2010–2019)</figcaption></figure>

Source: Ben Slimane et al. (2019b).

We can draw the following conclusions from the previous results. First, the quintile portfolios show some strong bias in terms of duration risk. Therefore, the results based on credit return measures are not consistent with those based on total return measures. Second, these results confirm that the period 2014–2019 has generated better performance in terms of ESG investing, but only
for EUR-denominated corporate bonds. For USD-denominated corporate bonds, the performance remains negative. Finally, we need to be careful about the feasibility of capturing ESG alpha. For instance, if we consider the long/short strategy $Q_1 - Q_5$, the credit return is equal to 36.8 bps in the second period. We can decompose this ESG alpha into the long leg and the short leg. In Figure 3.33, we report the raw performance of sorted portfolios. The carry statistics in bps are:

<table><thead><tr><th>Period</th><th>Q<sub>1</sub></th><th>Q<sub>5</sub></th><th>Q<sub>1</sub> - Q<sub>5</sub></th></tr></thead><tbody><tr><td>2010-2013</td><td>175</td><td>192</td><td>-17</td></tr><tr><td>2014-2019</td><td>113</td><td>128</td><td>-15</td></tr></tbody></table>

Therefore, the positive credit return of the long/short $Q_1 - Q_5$ cannot be explained by the carry exposure. It is due to the mark-to-market component and the dynamics of credit spreads and bond prices. This implies that an ESG strategy has a short carry position. On the one hand, buy-and-hold ESG investors may suffer from this structural exposure, but they can increase the credit risk of their buy-and-hold portfolio to compensate for the lower carry. On the other hand, active ESG investors can only outperform if they are able to rebalance their portfolio. Liquidity issues in the corporate bond market are then a barrier to generating ESG alpha in the fixed-income universe. In particular, when we impose turnover constraints on the construction of the previously simulated portfolios, we observe a decrease in the alpha return.

<figure><img src="image_97.png" /><figcaption>Figure 3.33: Annualized credit return in bps of ESG sorted portfolios (EUR IG, 2010–2019)</figcaption></figure>

Source: Ben Slimane et al. (2019b).

**Remark 39** The above results suggest that the annualized credit return may not be the most appropriate measure to compare the performance of ESG and business-as-usual investments. The Sharpe ratio or a risk-adjusted measure using the duration-times-spread factor may be more relevant.
**Optimized portfolios** In the sorted portfolios, there is no control over duration or credit spread. Therefore, it is difficult to know whether the alpha return is explained by the ESG scoring or by the duration/spread bias. In fact, the sorted portfolio methodology is not really relevant when applied to the fixed-income universe. A better method is to consider the optimization approach, where the portfolio manager imposes some constraints on the active risk he can take with respect to the benchmark index. Ben Slimane et al. (2019b) define the active risk measure as the weighted average of duration risk and credit risk (see Box 3.7). They then implement an optimization program that consists of minimizing active risk while controlling the ESG excess score of the tilted portfolios. Starting from an ESG excess score of zero, they gradually increase the ESG score of the optimized portfolio until it reaches one. They find that the relationship between the ESG excess score and the ex-post tracking error volatility is approximately linear. On average, targeting an excess score of one requires accepting a tracking error of 25 bps.

Using the ICE (BofAML) Large Cap IG EUR Corporate Bond index, Figures 3.34 and 3.35 show the impact of ESG integration on the excess credit return of optimized portfolios for the periods 2010–2013 and 2014–2019. In the first period, the excess return of ESG-optimized portfolios is negative, meaning that ESG investors were penalized. This is particularly true when optimized portfolios targeted high excess scores. For example, an ESG excess score of +1 resulted in an underperformance of –35 bps per year. In the second period, we observe a small positive outperformance, which peaks at +4 bps when the ESG tilt is set to +1. We also observe that the relationship between the ESG excess score and the excess credit return is increasing. Looking at the individual pillars, the **E**, **S** and **G** optimized portfolios underperform over the period 2010–2013. Among the three pillars, environmental is the best pillar and its excess return falls to –22 bps when the target excess score is set to +1. Governance is the worst pillar and its excess return reaches –49 bps for the same tilt. After 2014, excess returns range from –3 to +9 bps. Social is the winning pillar and shows significant outperformance, peaking at +9 bps. The most recent period is then more favorable for ESG investors than before 2014.

If we look at the universe of USD investment grade corporate bonds, the results are different from those for EUR-denominated corporate bonds. ESG investing did not generate positive alpha over the entire 2010–2019 period (see Figures 3.36 and 3.37). However, the significant underperformance in the period 2010–2013 has been dramatically reduced since 2014. For example, the excess return is close to zero for the social pillar between 2014 and 2019. Another interesting observation is the behavior of the governance pillar. In many academic studies, the link between ESG and corporate financial performance is through the governance transmission channel. The results of Ben Slimane et al. (2019a) show that the governance pillar is not necessarily the most important factor and that investing in bonds with a good governance score is not fundamentally better than using the other pillars.

**Remark 40** The authors wonder whether the transatlantic divide really concerns the currency of the bonds issued or whether it is more of a regional issue. For example, a EUR-denominated bond can be issued by a European company, but also by a company based outside Europe. Similarly, a USD-denominated bond can be issued by an American company, but also by a company based outside America. Ben Slimane et al. (2019b) calculated the contribution of the different regions (Europe, North America and others) to credit return. They found that Europe had a systematic positive contribution, while North America had a systematic negative contribution, regardless of the currency (EUR and USD). Therefore, this transatlantic divide shows that ESG investing was a source of outperformance when it came to IG bonds of European issuers, but a source of underperformance when it came to IG bonds of American issuers.
<figure><img src="image_98.png" /><figcaption>Figure 3.35: Annualized excess return in bps of ESG optimized portfolios (EUR IG, 2014–2019)</figcaption></figure>

<figure><img src="image_99.png" /></figure>

Source: Ben Slimane et al. (2019b).
<figure><img src="image_100.png" /><figcaption>Figure 3.37: Annualized excess return in bps of ESG optimized portfolios (USD IG, 2014–2019)</figcaption></figure>

<figure><img src="image_101.png" /></figure>

Source: Ben Slimane et al. (2019b).
Box 3.7: Building an optimized ESG portfolio in the fixed-income universe

The ESG score of the portfolio $w = (w_1, \dots, w_n)$ is the weighted average of the individual scores: $\mathbf{S}(w) = \sum_{i=1}^n w_i \mathbf{S}_i$. If we consider a benchmark $b = (b_1, \dots, b_n)$, we deduce that the ESG excess score of portfolio $w$ relative to benchmark $b$ is equal to:

$$ \mathbf{S}(w | b) = \sum_{i=1}^{n} (w_i - b_i) \mathbf{S}_i = \mathbf{S}(w) - \mathbf{S}(b) $$

When we use z-scores, we observe that $\mathbf{S}(b) \approx 0$ because there is no reason for a capitalization-weighted index to have a positive or negative ESG score. In general, it is a neutral ESG portfolio. On the contrary, an ESG portfolio $w$ aims to have a better ESG score than the benchmark: $\mathbf{S}(w | b) > 0$. Of course, when building an ESG-optimized portfolio, there is a trade-off between the ESG excess score $\mathbf{S}(w | b)$ and the active or tracking risk $\mathcal{R}(w | b)$ relative to the benchmark. For example, if the active risk is zero, the ESG excess score will be zero. If we consider a high ESG score (e.g., greater than 1.5), we must also take a high active risk. Therefore, the optimization problem becomes:

$$ w^*(\gamma) = \arg\min \frac{1}{2} \mathcal{R}(w | b) - \gamma \mathbf{S}(w | b) $$

If $\gamma$ is set to zero, the optimized portfolio $w^*(0)$ is the benchmark portfolio $b$. If $\gamma$ is set to infinity, the optimized portfolio $w^*(\infty)$ is the bond with the highest z-score. The parameter $\gamma$ can then be calibrated to target a given excess score $\mathbf{S}^*$. The problem is the choice of the tracking risk metric. In the case of bonds, we generally use two measures. First, we can track the modified duration of the sectors. It follows that the modified duration risk of portfolio $w$ relative to benchmark $b$ is:

$$ \mathcal{R}_{\text{MD}}(w | b) = \sum_{j=1}^{n_S} \left( \left( \sum_{i \in \text{Sector}(j)} w_i \text{MD}_i \right) - \left( \sum_{i \in \text{Sector}(j)} b_i \text{MD}_i \right) \right)^2 $$

where $n_S$ is the number of sectors and $MD_i$ is the modified duration of bond $i$. An alternative is to use the DTS risk measure:

$$ \mathcal{R}_{\text{DTS}}(w | b) = \sum_{j=1}^{n_S} \left( \left( \sum_{i \in \text{Sector}(j)} w_i \text{DTS}_i \right) - \left( \sum_{i \in \text{Sector}(j)} b_i \text{DTS}_i \right) \right)^2 $$

where $\text{DTS}_i$ is the duration-times-spread (DTS) factor of bond $i$. We can also define a hybrid approach where the risk measure is an average of the MD and DTS active risks:

$$ \mathcal{R}(w | b) = \mathcal{R}_{\text{MD}}(w | b) + \mathcal{R}_{\text{DTS}}(w | b) $$

In fact, we can interpret $\mathcal{R}_{\text{MD}}(w | b)$ as a measure of interest rate risk and $\mathcal{R}_{\text{DTS}}(w | b)$ as a measure of credit risk, while $\mathcal{R}(w | b)$ is an integrated measure of both interest rate and credit risk.
<table><caption>Table 3.18: Performance of ESG bond indexes</caption><thead><tr><th rowspan="2">Year</th><th colspan="3">FTSE WGBI</th><th colspan="3">FTSE EGBI</th><th colspan="3">Bloomberg Euro Aggregate Corporate</th></tr><tr><th>Return</th><th>ESG</th><th>Alpha</th><th>Return</th><th>ESG</th><th>Alpha</th><th>Return</th><th>ESG</th><th>Alpha</th></tr><tr><th></th><th>CW</th><th></th><th></th><th>CW</th><th></th><th></th><th>CW</th><th></th><th></th></tr></thead><tbody><tr><td>2010</td><td>4.61</td><td>4.31</td><td>-30</td><td>0.61</td><td>4.14</td><td>353</td><td>3.07</td><td>2.93</td><td>2.96</td><td>-13</td><td>-10</td></tr><tr><td>2011</td><td>6.35</td><td>7.05</td><td>69</td><td>3.41</td><td>7.31</td><td>391</td><td>1.49</td><td>1.17</td><td>1.43</td><td>-32</td><td>-5</td></tr><tr><td>2012</td><td>1.65</td><td>3.06</td><td>141</td><td>10.65</td><td>7.39</td><td>-326</td><td>13.59</td><td>13.99</td><td>12.96</td><td>40</td><td>-63</td></tr><tr><td>2013</td><td>-4.00</td><td>-2.95</td><td>105</td><td>2.21</td><td>-1.40</td><td>-362</td><td>2.37</td><td>2.49</td><td>2.36</td><td>12</td><td>-1</td></tr><tr><td>2014</td><td>-0.48</td><td>-0.22</td><td>26</td><td>13.19</td><td>11.44</td><td>-175</td><td>8.40</td><td>8.31</td><td>8.49</td><td>-8</td><td>10</td></tr><tr><td>2015</td><td>-3.57</td><td>-4.85</td><td>-128</td><td>1.65</td><td>0.39</td><td>-126</td><td>-0.56</td><td>-0.59</td><td>-0.50</td><td>-3</td><td>6</td></tr><tr><td>2016</td><td>1.60</td><td>1.02</td><td>-59</td><td>3.20</td><td>4.00</td><td>81</td><td>4.73</td><td>4.60</td><td>4.44</td><td>4.60</td><td>-13</td><td>-29</td></tr><tr><td>2017</td><td>7.49</td><td>8.16</td><td>67</td><td>0.15</td><td>-0.47</td><td>-62</td><td>2.41</td><td>2.47</td><td>2.48</td><td>2.47</td><td>6</td><td>6</td></tr><tr><td>2018</td><td>-0.84</td><td>-1.41</td><td>-57</td><td>0.88</td><td>1.65</td><td>78</td><td>-1.25</td><td>-1.12</td><td>-1.11</td><td>-1.12</td><td>13</td><td>14</td></tr><tr><td>2019</td><td>5.90</td><td>5.56</td><td>-34</td><td>6.72</td><td>4.45</td><td>-227</td><td>6.24</td><td>6.01</td><td>5.92</td><td>6.01</td><td>-24</td><td>-32</td></tr><tr><td>2020</td><td>10.11</td><td>10.90</td><td>79</td><td>5.03</td><td>4.11</td><td>-92</td><td>2.77</td><td>2.69</td><td>2.70</td><td>2.52</td><td>-8</td><td>-7</td></tr><tr><td>2021</td><td>-6.97</td><td>-7.15</td><td>-17</td><td>-3.54</td><td>-3.76</td><td>-21</td><td>-0.97</td><td>-0.96</td><td>-0.99</td><td>-0.99</td><td>1</td><td>-2</td></tr><tr><td>2022</td><td>-18.26</td><td>-20.00</td><td>-173</td><td>-18.52</td><td>-19.06</td><td>-54</td><td>-13.65</td><td>-13.62</td><td>-13.48</td><td>-13.48</td><td>3</td><td>16</td></tr><tr><td>2023</td><td>5.19</td><td>5.69</td><td>50</td><td>7.90</td><td>6.32</td><td>-158</td><td>8.19</td><td>8.16</td><td>8.00</td><td>7.99</td><td>-3</td><td>-18</td></tr><tr><td>3Y</td><td>-7.18</td><td>-7.75</td><td>-57</td><td>-5.34</td><td>-6.09</td><td>-74</td><td>-2.56</td><td>-2.55</td><td>-2.56</td><td>-2.56</td><td>0</td><td>0</td></tr><tr><td>5Y</td><td>-1.39</td><td>-1.67</td><td>-29</td><td>-1.01</td><td>-2.07</td><td>-106</td><td>0.20</td><td>0.15</td><td>0.13</td><td>0.11</td><td>-6</td><td>-7</td></tr><tr><td>7Y</td><td>-0.09</td><td>-0.29</td><td>-20</td><td>-0.58</td><td>-1.32</td><td>-74</td><td>0.31</td><td>0.29</td><td>0.28</td><td>0.26</td><td>-1</td><td>-2</td></tr><tr><td>10Y</td><td>-0.31</td><td>-0.62</td><td>-31</td><td>1.32</td><td>0.59</td><td>-74</td><td>1.44</td><td>1.40</td><td>1.41</td><td></td><td>-3</td><td>-3</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Year</th><th colspan="4">Bloomberg US Corporate</th><th colspan="3">Bloomberg Global High Yield</th></tr><tr><th>CW</th><th>Return</th><th>SRI</th><th>S-SRI</th><th>Alpha</th><th>SRI</th><th>SUS</th></tr></thead><tbody><tr><td>2019</td><td></td><td></td><td></td><td></td><td></td><td>2.30</td><td>2.20</td><td></td></tr><tr><td>2020</td><td></td><td></td><td></td><td></td><td></td><td>5.73</td><td>6.49</td><td>6.67</td><td>76</td><td>94</td></tr><tr><td>2021</td><td>-1.04</td><td>-1.55</td><td>9.56</td><td>2.34</td><td>-51</td><td>1 060</td><td>338</td><td></td><td></td><td></td></tr><tr><td>2022</td><td>-15.76</td><td>-15.12</td><td>-1.10</td><td>-13.86</td><td>64</td><td>1 467</td><td>190</td><td></td><td></td><td></td></tr><tr><td>2023</td><td>8.52</td><td>8.44</td><td></td><td></td><td>-8</td><td></td><td></td><td></td><td></td><td></td></tr></tbody><tfoot><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>13.66</td><td>14.14</td><td>13.59</td><td>48</td><td>-7</td></tr></tfoot></table>

Source: FTSE, Bloomberg & Author's calculations.
Bond indexes

In Table 3.18, we compare the annualized returns of some well-known bond indexes (FTSE World Government Bond Index or WGBI, FTSE EMU Government Bond Index or EGBI, Bloomberg Euro Aggregate Corporate Total Return Index, Bloomberg US Corporate Total Return Index and Bloomberg Global High Yield Total Return Index) with the annualized returns of their ESG counterparts<sup>29</sup>. The results for bond indexes are close to those for equity indexes. Indeed, ESG investing has its ups and downs. However, we find that the results are less balanced than before. It seems that ESG investing generates positive alpha less often in the bond market, even in the case of European bonds (Figure 3.38). In fact, the main reason is that ESG bond indexes have less carry because they have better credit ratings<sup>30</sup>.

<figure><img src="image_102.png" /><figcaption>Figure 3.38: Alpha returns of European ESG bond indexes (in bps)</figcaption></figure>

Source: FTSE, Bloomberg & Author's calculations.

<footnote n="29"><sup>29</sup>For the FTSE WGBI and EGBI, we use the FTSE ESG World Government Bond and FTSE ESG Select EMU Government Bond indexes. For the Bloomberg Euro Aggregate Corporate TR Index, we use the MSCI Euro Corporate SRI TR Index (SRI), the Bloomberg MSCI Euro Corporate Sustainable SRI TR Index (S-SRI), and the Bloomberg MSCI Euro Corporate ESG Sustainability SRI (ESG-S). For the Bloomberg Euro Aggregate Corporate TR Index, we use the MSCI Euro Corporate SRI TR Index (SRI), the Bloomberg MSCI Euro Corporate Sustainable SRI TR Index (S-SRI), and the Bloomberg MSCI Euro Corporate ESG Sustainability SRI (ESG-S). For the Bloomberg US Corporate TR Index, the comparison is done to the Bloomberg MSCI US Corporate SRI Select Index (SRI), the Bloomberg MSCI US Corporate Sustainability SRI Index (S-SRI) and the Bloomberg MSCI US Corporate ESG Sustainability SRI (ESG-S). Finally, the Bloomberg Global High Yield Total Return Index is compared to the Bloomberg MSCI Global High Yield SRI Index (SRI) and the Bloomberg MSCI Global High Yield Sustainability Index (SUS).</footnote>

<footnote n="30"><sup>30</sup>The relationship between ESG and credit ratings is explored on page 213.</footnote>
## 3.3 Cost of capital

While the previous two sections focused on the impact of ESG investing on returns, here we focus on the cost of capital. The cost of capital is the cost to a company of financing its operations. It represents the required rate of return that investors expect to receive for providing capital to the company, whether in the form of debt or equity. It is therefore the minimum rate of return that a company must achieve to justify its investments. The cost of capital has two components. The cost of debt is the interest rate a company pays on its borrowed funds, while the cost of equity is the return on equity investors require to compensate them for the risk they take by investing in the company's stock. In general, the weighted average cost of capital (WACC) is the most common method of determining a company's total cost of capital, taking into account the proportional weight of debt and equity in its capital structure:

$$WACC = \frac{E}{E+D}C_E + \frac{D}{E+D}C_D(1-\tau_c)$$

where WACC is the weighted average cost of capital, $C_E$ is the cost of equity, $C_D$ is the cost of debt, $E$ is the market value of the firm's equity, $D$ is the market value of the firm's debt, and $\tau_c$ is the corporate tax rate.

The question is whether there is a relationship between ESG investing and the cost of capital. In fact, we can assume that there are several channels, at least two. The first is that the extra-financial information contained in ESG scores is value-added information that helps distinguish best-in-class companies from worst-in-class companies in terms of corporate financial performance. In this context, companies with higher ESG scores must have a lower cost of capital. The second channel is again the supply/demand balance between issuers and investors. The preference of ESG investors is to finance issuers with higher ESG scores, implying that ESG investors require a lower cost of capital for best-in-class companies than non-ESG investors. To answer the previous question, we postulate a relationship between the cost of capital and some ESG metrics. In general, we use linear regression:

$$y_{i,t} = \alpha + \underbrace{\beta x_{i,t}}_{\text{ESG metric}} + \underbrace{\sum_{k=1}^{n_z} \gamma_k z_{i,t}^{(k)}}_{\text{Control variables}} + \varepsilon_{i,t} \quad (3.19)$$

where $y_{i,t}$ is the endogenous variable that depends on the cost of capital of company $i$ at time $t$, $x_{i,t}$ is the ESG metric and $z_{i,t}^{(k)}$ is a set of control variables. The previous model can be extended to multiple ESG metrics and we obtain the following multi-factor regression model:

$$y_{i,t} = \alpha + \underbrace{\sum_{j=1}^{n_x} \beta_j x_{i,t}^{(j)}}_{\text{ESG variables}} + \underbrace{\sum_{k=1}^{n_z} \gamma_k z_{i,t}^{(k)}}_{\text{Control variables}} + \varepsilon_{i,t} \quad (3.20)$$

To test that the cost of capital is a decreasing function of the ESG variable $x_{i,t}^{(j)}$, we assume that $\beta_j < 0$. The choice of the dependent variable, the ESG variables, and the control variables is important and varies across studies.

### 3.3.1 Cost of equity

Sharfman and Fernando (2008) is a landmark study in the area of whether CSR performance has an impact on a company's cost of capital. Using a sample of 267 S&P 500 companies, the authors test three hypotheses:
$\mathcal{H}_1$ The higher the level of environmental risk management, the lower the firm's cost of debt capital for a given level of debt.

$\mathcal{H}_2$ The higher the level of environmental risk management, the lower the cost of equity capital.

$\mathcal{H}_3$ The higher the level of environmental risk management, the lower the firm's weighted average cost of capital.

The cost of equity capital is estimated using the CAPM:

$$C_E = r + \beta (\mu_m - r)$$

where $\pi_m = \mu_m - r$ is the market risk premium, $r$ is the risk-free return and $\beta$ is the systematic risk coefficient (firm's beta with respect to the market portfolio). The cost of debt is estimated using the firm's marginal cost of borrowing from the Bloomberg Financial dataset. The ESG variable is the level of environmental risk management, which is given by an environmental score from the ESG rating agency KLD<fnref n="31" />. Finally, Sharfman and Fernando (2008) use three control variables, which are the firm's financial leverage, the logarithm of market capitalization, and industry membership (dummy variables). The linear regression is then:

$$y_{i,t} = \alpha + \beta \mathbf{S}_{i,t} + \gamma_1 L_{i,t} + \gamma_2 M_{i,t} + \gamma_3 I_{i,t} + \varepsilon_{i,t}$$

where $y_{i,t}$ is one of the cost of capital measures ($C_E$, $C_D$ and WACC), ($L_{i,t}$, $M_{i,t}$, $I_{i,t}$) are the three control variables, and $\mathbf{S}_{i,t}$ is the environmental score. Sharfman and Fernando (2008) found that $\hat{\beta}$ has a positive sign for $C_D$ and a negative sign for $C_E$ and WACC. They concluded that the level of environmental risk management increases the cost of debt and decreases the cost of equity and the weighted average cost of capital.

Another seminal study is El Ghoul et al. (2011), who test the relationship on a larger investment universe: 12 915 observations representing 2 809 unique firms between 1992 and 2007. They use four different models to estimate the cost of equity<fnref n="32" />: Claus-Thomas, Gebhardt-Lee-Swaminathan, Ohlson-Juettner-Nauroth, and Easton. The control variables include the stock's beta, size (log of total assets), book-to-market ratio, leverage (ratio of total debt to market value of equity), average long-run growth forecast, and dispersion of analyst forecasts. For the ESG variables, they use a large panel of KLD scores: (1) community, (2) diversity, (3) employee relations, (4) environment, (5) human rights and (6) product. They also consider an aggregate score, the CSR score, which is the sum of the community, diversity, employee relations, environment, human rights, and product characteristics scores. In addition to these ESG variables, they also construct controversial dummy variables related to alcohol, gambling, tobacco, firearms, military and nuclear activities. The univariate regression analysis is performed on the aggregate CSR score. El Ghoul et al. (2011) found that the average cost of equity for firms with a high CSR score is 56 basis points lower than for firms with a low CSR score. These results suggest that firms with better CSR scores have significantly lower cost of equity capital. They then conduct a multivariate regression analysis including the six dimensions of KLD social performance. They find that the employee relations, environmental policy, and product strategy dimensions contribute to lower firms' cost of equity, while the other three dimensions (community, diversity, and human rights) do not. In terms of controversies, they confirm the findings of Hong and Kacperczyk (2009) that the tobacco and nuclear power industries have higher equity financing costs.

<footnote n="31">³¹See page 26 for more details on KLD.</footnote>

<footnote n="32">³²The cost of equity formulas are described in Box 3.8.</footnote>
Box 3.8: Cost of equity measurement

The cost of equity is the “expected rate of return that the market requires in order to attract funds to a particular investment” (Pratt and Grabowski, 2008). There are several ways to calculate it. One approach is to calculate the expected return $E[R_t]$ of the stock from an asset pricing model (e.g., CAPM, Fama-French model) and use $E[R_t]$ as a proxy for $C_E$. The second approach is to use a valuation model, such as the discounted cash flow (DCF) model or the dividend discount model (DDM). Recall that the price of a financial asset is the expected value of the stochastic discounted value of the cash flow leg. Under some assumptions (Roncalli, 2020a, chapter 3), we can show that:

$$P(t) = \sum_{t_m > t} B(t, t_m) E[CF(t_m) | \mathcal{F}_t]$$

where $\mathcal{F}_t$ is the filtration under the risk-neutral probability measure $\mathbb{Q}$, $B(t, t_m)$ is the discount factor for the maturity date $t_m$ and $\{\text{CF}(t_m), t_m \ge t\}$ is the stream of stochastic cash flows. Let $\varrho$ be the constant discount rate such that:

$$P(t) = \sum_{t_m > t} \frac{E[\text{CF}(t_m) | \mathcal{F}_t]}{(1 + \varrho)^{(t_m - t)}}$$

In the case of stocks, $\text{CF}(t)$ is the earnings or dividends per share, and $C_E$ is then equal to the discount rate $\varrho$. Below are some common formulas for calculating the cost of equity:

* In the CAPM, the cost of equity is equal to the expected rate of return:

$$C_E = r + \beta(\mu_m - r) \quad (3.21)$$

where $\pi_m = \mu_m - r$ is the market risk premium, $r$ is the risk-free rate and $\beta$ is the beta of the stock return relative to the market portfolio return. Once we have assumed a value for $\pi_m$, we can easily calculate $C_E$ using the estimated beta coefficient.

* The Gordon growth model uses the dividend discount model and assumes that $\text{DPS}(t_m) = (1 + g)^{(t_m - t)} \text{DPS}(t)$ is the dividend per share at time $t_m$ and $g$ is the growth rate. We deduce that:

$$P(t) = \sum_{t_m=t+1}^{\infty} \frac{\text{DPS}(t_m)}{(1 + \varrho)^{(t_m - t)}} = \text{DPS}(t) \sum_{t_m=t+1}^{\infty} \frac{(1 + g)^{(t_m - t)}}{(1 + \varrho)^{(t_m - t)}} = \text{DPS}(t) \frac{1 + g}{\varrho - g}$$

Since $\text{DPS}(t+1) = (1 + g) \text{DPS}(t)$, we conclude that:

$$C_E := \varrho = g + \text{EDY} \quad (3.22)$$

where $\text{EDY} = \text{DPS}(t+1)/P(t)$ is the expected one-year ahead dividend yield.

* The previous model can be extended by considering expected cash flows instead of dividends up to time $t^*$ and assuming a period of constant growth after $t^*$:

$$P(t) = \sum_{t_m=t+1}^{t^*} \frac{\text{CF}(t_m)}{(1 + \varrho)^{(t_m - t)}} + \frac{(1 + g) \text{CF}(t^*)}{(\varrho - g)(1 + r)^{(t^* - t)}} \quad (3.23)$$
Box 3.8: Cost of equity measurement (continued)

* Claus and Thomas (2001) develop a residual income model by assuming that CF (t + n) = EPS (t + n) - ϱ BPS (t + n - 1) and BPS (t + n) = BPS (t + n - 1) + EPS (t + n) - DPS (t + n) where EPS (t + n), BPS (t + n) and DPS (t + n) are expected n-year ahead earnings per share, book value per share and dividend per share, respectively. By assuming a dividend policy, for example DPS (t + n) = δ EPS (t + n) where δ ≥ 0 is the dividend payout ratio, we can calculate all quantities and find the numerical solution ϱ of the following equation:

$$P(t) = \text{BPS}(t) + \sum_{t_m=t+1}^{t^*} \frac{\text{CF}(t_m)}{(1+\varrho)^{(t_m-t)}} + \frac{(1+g)\text{CF}(t^*)}{(\varrho-g)(1+r)^{(t^*-t)}} \quad (3.24)$$

* The model defined by Gebhardt et al. (2001) is close to the one proposed by Claus and Thomas (2001). We have:

$$P(t) = \text{BPS}(t) + \sum_{t_m=t+1}^{t^*} \frac{(\text{ROE}(t_m) - \varrho) \text{BPS}(t_m - 1)}{(1 + \varrho)^{(t_m - t)}} + \frac{(\text{ROE}(t^* + 1) - \varrho) \text{BPS}(t^*)}{\varrho (1 + \varrho)^{(t^* - t)}} \quad (3.25)$$

where ROE (t + n) is the expected return on equity and BPS (t + n) is the expected book value per share satisfying the recurrence relationship BPS (t + n) = BPS (t + n - 1) + EPS (t + n) - DPS (t + n).

* Ohlson and Juettner-Nauroth (2005) suggest valuing stocks using the following formula:

$$P(t) = \frac{\text{EPS}(t+1)}{\varrho} + \frac{\text{EPS}(t+2) - \text{EPS}(t+1) - \varrho(\text{EPS}(t+1) - \text{DPS}(t+1))}{\varrho(\varrho - g)}$$

where EPS (t + n) and DPS (t + n) are the expected n-year ahead earnings per share and dividends per share. Gode and Mohanram (2003) show that the cost of equity is equal to:

$$C_E = A + \sqrt{A^2 + \frac{\text{EPS}(t+1)}{P(t)} (g' - g)} \quad (3.26)$$

where $A = \frac{1}{2} \left( g + \frac{\text{DPS}(t+1)}{P(t)} \right)$ and $g' = \frac{\text{EPS}(t+2) - \text{EPS}(t+1)}{\text{EPS}(t+1)}$. If $g' = g$, we retrieve the cost of capital in the Gordon model:

$$C_E = 2A = g + \frac{\text{DPS}(t+1)}{P(t)}$$

* Using the model by Ohlson and Juettner-Nauroth (2005), Easton (2004) assumes that $g = 0$:

$$P(t) = \frac{\text{EPS}(t+2) + \varrho \text{DPS}(t+1) - \text{EPS}(t+1)}{\varrho^2} \quad (3.27)$$
Since the publication of these two seminal articles<fnref n="33" />, much research has been done to confirm or refute these findings. A comprehensive review can be found in Coqueret (2022). In most cases, academics find a negative relationship between extra-financial risks and the cost of equity (Ng and Rezaee, 2015; Gupta, 2018; Albuquerque et al., 2019). However, there are a few studies that conclude that there is no relationship or a weak relationship (Humphrey et al., 2012; Gregory et al., 2014). Focusing on the most recent studies, the vast majority suggest that a good ESG score reduces the cost of equity, but there are some differences across the three pillars (Gonçalves et al., 2022; Khanchel and Lassoued, 2022; Ramirez et al., 2022). There is strong evidence that the relationship is negative for the E and G pillars, but not necessarily for the S pillar. For the environmental pillar, the carbon footprint measures used by investors also show such a negative relationship (Trinks et al., 2022a).

### 3.3.2 Cost of debt

The cost of debt is the effective interest rate that a company pays on its debt. It represents the cost to the company of borrowing funds through debt instruments such as bonds, loans, or lines of credit. There are several ways to measure it, but the most popular are the yield to maturity and credit spread for bonds, the marginal cost of borrowing for loans and lines of credit, and the credit rating. We have already seen that Sharfman and Fernando (2008) found a positive relationship between environmental risk management scores and the cost of debt. This finding is confirmed by Menz (2010), who examined the relationship between the valuation of Euro corporate bonds and corporate social responsibility (CSR) and concluded that “CSR has apparently not yet been incorporated into the pricing of corporate bonds”. This overall neutrality, sometimes associated with a lack of maturity in the integration of ESG information into the bond market, has also been highlighted by Izzo and Magnanelli (2012).

On the contrary, Oikonomou et al. (2014) found that corporate social performance is rewarded in the corporate bond market. These authors use a procedure similar to the one described on page 209. They consider three dependent variables: the credit spread, the credit rating, and a dummy variable indicating whether the bond is speculative grade or not. The ESG variables are again several KLD scores. The control variables are divided into three categories: firm characteristics (firm size, interest coverage ratio, leverage, liquidity, market-to-book ratio, return on assets, research and development intensity, speculative dummy), bond characteristics (amount issued, convexity, duration, maturity) and sector characteristics (sector fixed effects). Bauer and Hann (2010), Attig et al. (2013), and Cooper (2015) also find a negative relationship between CSR and the cost of debt. Focusing on the most recent research, empirical studies generally conclude that firms with higher ESG scores benefit from lower cost of debt relative to firms with weaker ESG scores (Eliwa et al., 2021; Raimo et al., 2021; Apergis et al., 2022).

Let us now see how we calculate the cost of debt in detail. According to Ben Slimane et al. (2019a), a typical linear regression model would look like this:

$$ \ln s_{i,t} = \alpha_t + \beta \mathbf{S}_{i,t} + \gamma_{md} \text{MD}_{i,t} + \sum_{k=1}^{n_{\mathbf{S}_{i,t}}} \gamma_{\text{sector}}(k) \mathbf{s}_{i,t}(k) + \sum_{k=1}^{n_{\mathbf{R}_{i,t}}} \gamma_{\text{rating}}(k) \mathbf{R}_{i,t}(k) + \varepsilon_{i,t} \quad (3.28) $$

where $s_{i,t}$ is the yield spread of bond i at time t, $\mathbf{S}_{i,t}$ is the ESG score, MD$_{i,t}$ is the modified duration, $\mathbf{s}_{i,t}(k)$ is the dummy variable associated with the kth sector, $\mathbf{R}_{i,t}(k)$ is the dummy variable associated

<footnote n="33">³³These are not the first two studies on the relationship between ESG and the cost of equity capital. For example, Skaife et al. (2004) and Derwall and Verwijmeren (2007) already showed that good governance reduces the cost of equity capital. Unfortunately, these two studies were never published.</footnote>
<figure><img src="image_103.png" /></figure>

Source: Ben Slimane et al. (2019b, Figure 11, page 17).

with the $k^{th}$ rating and $\epsilon_{i,t} \sim N(0, \sigma^2)$. In this regression model, the control variables are modified duration and ratings (bond characteristics) and sectors (firm characteristics). It is important to include explanatory variables for the rating because there is a positive relationship between ESG ratings and credit ratings. To illustrate, Figure 3.39 shows the average ESG score by rating class<fnref n="34" />. Therefore, bonds with a good credit rating have a better ESG score than bonds with a poor credit rating. We reiterate that the ESG z-scores are between -3 and +3, which means that the mean of the ESG scores is zero while the standard deviation is one (Table 3.19). A Student's t-test shows that ESG scores by rating are statistically different from zero, except for the BB credit rating. ESG ratings and credit ratings are not independent. This is normal as rating agencies also include extra-financial risks in their credit ratings.

<table><caption>Table 3.19: ESG score relative to credit rating (2010–2019)</caption><thead><tr><th>Rating</th><th>Mean</th><th>Median</th><th>Stdev</th><th>Skewness</th><th>t-statistic</th></tr></thead><tbody><tr><td>AA</td><td>0.384</td><td>0.452</td><td>1.058</td><td>-0.020</td><td>3.266</td></tr><tr><td>A</td><td>0.090</td><td>0.088</td><td>1.056</td><td>0.011</td><td>1.676</td></tr><tr><td>BBB</td><td>0.156</td><td>0.101</td><td>0.997</td><td>0.122</td><td>3.945</td></tr><tr><td>BB</td><td>-0.048</td><td>-0.109</td><td>1.002</td><td>0.131</td><td>-0.795</td></tr><tr><td>B</td><td>-0.328</td><td>-0.414</td><td>0.957</td><td>0.196</td><td>-4.534</td></tr><tr><td>CCC</td><td>-0.381</td><td>-0.394</td><td>1.095</td><td>0.129</td><td>-2.525</td></tr><tr><td>All ratings</td><td>0.046</td><td>0.000</td><td>1.031</td><td>0.092</td><td>1.804</td></tr></tbody></table>

Source: Ben Slimane et al. (2019a, Table 11, page 22).

<footnote n="34">³⁴We use the Amundi ESG z-scores for the period 2010–2019.</footnote>
<table><caption>Table 3.20: Results of the panel data regression model (EUR IG corporate bonds, 2010–2019)</caption><thead><tr><th rowspan="2"></th><th colspan="4">2010–2013</th><th colspan="4">2014–2019</th></tr><tr><th>ESG</th><th>E</th><th>S</th><th>G</th><th>ESG</th><th>E</th><th>S</th><th>G</th></tr></thead><tbody><tr><th>ℝ<sup>2</sup> (in %)</th><td>60.0</td><td>59.4</td><td>59.5</td><td>60.3</td><td>66.3</td><td>65.0</td><td>65.2</td><td>64.6</td></tr><tr><th>Δℝ<sup>2</sup> (in %)</th><td>0.6</td><td>0.0</td><td>0.2</td><td>1.0</td><td>4.0</td><td>2.6</td><td>2.9</td><td>2.3</td></tr><tr><th>β̂ (in %)</th><td>-4.8</td><td>-1.1</td><td>-2.1</td><td>-6.7</td><td>-8.9</td><td>-7.9</td><td>-7.6</td><td>-7.7</td></tr><tr><th>t-statistic</th><td>-31.7</td><td>-7.3</td><td>-15.6</td><td>-38.8</td><td>-123.9</td><td>-98.4</td><td>103.9</td><td>-91.8</td></tr></tbody></table>

Source: Ben Slimane et al. (2019b, Table 2, page 18).

In Table 3.20, we report the results for the EUR IG universe obtained by Ben Slimane et al. (2019b). The coefficient of determination $ℝ^2$ calculates the explanatory power of the model. It is relatively high at around 60% while the number of observations is around 192 000! We can also see that it has increased by an average of 6% over the period 2014–2019. The excess contribution represents the difference in $ℝ^2$ between the regression with ESG score and the regression without ESG score. We observe that this excess contribution becomes significant after 2014. For example, it is +4.0% for the ESG score. Testing that the ESG score has a significant impact on the yield spread is equivalent to accepting the null hypothesis $H_0: β < 0$. For the ESG score and the period 2010–2013, we have $β̂ = -4.8\%$ and a t-statistic of -31.7. We therefore accept the null hypothesis. We also report the results for the three pillars (E, S and G). All estimated betas are negative and significant at the 99% confidence level. The negative relationship between the score and the yield spread has also increased over the 2014–2019 period.

<table><caption>Table 3.21: Results of the panel data regression model (USD IG corporate bonds, 2010–2019)</caption><thead><tr><th rowspan="2"></th><th colspan="4">2010–2013</th><th colspan="4">2014–2019</th></tr><tr><th>ESG</th><th>E</th><th>S</th><th>G</th><th>ESG</th><th>E</th><th>S</th><th>G</th></tr></thead><tbody><tr><th>ℝ<sup>2</sup> (in %)</th><td>52.7</td><td>52.8</td><td>52.8</td><td>53.4</td><td>60.6</td><td>60.5</td><td>60.3</td><td>60.9</td></tr><tr><th>Δℝ<sup>2</sup> (in %)</th><td>0.0</td><td>0.2</td><td>0.2</td><td>0.7</td><td>0.3</td><td>0.2</td><td>0.0</td><td>0.7</td></tr><tr><th>β̂ (in %)</th><td>-0.3</td><td>2.7</td><td>2.6</td><td>-7.5</td><td>-3.7</td><td>-3.1</td><td>-0.0</td><td>-6.3</td></tr><tr><th>t-statistic</th><td>-2.1</td><td>19.3</td><td>20.6</td><td>-43.0</td><td>-47.7</td><td>-39.6</td><td>-0.4</td><td>-73.2</td></tr></tbody></table>

Source: Ben Slimane et al. (2019b, Table 3, page 18).

If we look at the universe of USD IG corporate bonds, we get the results shown in Table 3.21. First, we see that the excess $ℝ^2$ is low even for the most recent period. Second, only the G pillar has a negative sign in the first period. This is not the case for the most recent period. Indeed, the relationship between the score and the yield spread is negative and significant, except for the S pillar. Nevertheless, the relationship for USD IG corporate bonds is weaker that observed for EUR IG corporate bonds. These results illustrate that we can obtain a significant negative beta with a quasi zero variation of $ℝ^2$. Therefore, we have to be careful in concluding that a negative value of $β$ implies a lower cost of debt.

The next step is to calculate the cost of debt, which is equal to the expected yield spread<fnref n="35" />:

$$C_D = \mathbb{E}[s_i]$$

<footnote n="35">³⁵We omit here the yield to maturity of the non-defaultable bond. This is not a problem as we generally analyze the cost of debt in terms of difference.</footnote>
Using Equation (3.28), we get:

$$C_D = C_D(\mathrm{MD}_i, \mathbf{s}_i, \mathcal{R}_i) C_D(\mathbf{S}_i)$$

where:

$$C_D(\mathrm{MD}_i, \mathbf{s}_i, \mathcal{R}_i) = \exp \left( \hat{\alpha} + \hat{\gamma}_{md} \mathrm{MD}_i + \hat{\gamma}_{sector}(\mathbf{s}_i) + \hat{\gamma}_{rating}(\mathcal{R}_i) + \frac{1}{2} \hat{\sigma}^2 \right)$$

and:

$$C_D(\mathbf{S}_i) = \exp(\hat{\beta} \mathbf{S}_i)$$

The previous expression defines the cost of debt as the product of two factors. The first factor $C_D(\mathrm{MD}_i, \mathbf{s}_i, \mathcal{R}_i)$ is related to the control variables, while the second factor $C_D(\mathbf{S}_i)$ measures the impact of the ESG score. If the score is zero, $C_D(\mathbf{S}_i) = 1$ and $C_D = C_D(\mathrm{MD}_i, \mathbf{s}_i, \mathcal{R}_i)$. In Figure 3.40, we show the relationship between the ESG score and the cost of debt when the base value is $C_D(\mathrm{MD}_i, \mathbf{s}_i, \mathcal{R}_i) = 4\%$. Very quickly, the impact of the ESG score is significant even if $\hat{\beta}$ is equal to -1%. Suppose two companies issue two bonds with the same characteristics (same maturity, same sector, same rating), but they have two different ESG scores, then the difference in terms of cost of debt is equal to:

$$\Delta C_D = \bar{C}_D (e^{\hat{\beta}\mathbf{S}_1} - e^{\hat{\beta}\mathbf{S}_2})$$

where $\bar{C}_D$ is the average cost of debt (or the cost of debt if the ESG score is zero). For example, if one company has an ESG score of -2 and another company has an ESG score of +1, the difference in cost of debt is$^{36}$ 36 bps if $\hat{\beta} = -3\%$ and $\bar{C}_D = 4\%$.

<figure><img src="image_104.png" /><figcaption>Figure 3.40: Relationship between ESG score and cost of debt ($\bar{C}_D = 4\%$)</figcaption></figure>

<footnote>$^{36}$We have $\Delta C_D = 4\% \times (\exp(2 \times 3\%)) - \exp(-1 \times 3\%)) = 0.0036$.</footnote>
**Remark 41** This methodology has been used by Ben Slimane et al. (2019a). For EUR IG corporate bonds, they found that one unit of ESG score implies an average reduction in yield spread of 9 bps after neutralizing the effects of credit rating, duration and sector. This means that the difference in the yield spread between a best-in-class company and a worst-in-class company is 31 bps on average. For USD IGs, the spread is 15 bps, which is half the spread observed for EUR IGs.

### 3.3.3 The case of sovereign issuers

ESG rating agencies provide extra-financial information that may seem completely different from the financial information provided by credit rating agencies. However, the opposition between extra-financial and fundamental analysis is more complicated than it sounds, as we have seen in the case of corporate bonds. Rather than opposing each other, ESG and credit analysis can be seen as complementary. This is even more true in the case of sovereign risk, because a country's creditworthiness is highly dependent on its economic growth, political environment, willingness to pay its debts, social stability, etc., and we can easily relate these factors to ESG pillars. In fact, there is a large body of economic literature that examines the interrelationship between sovereign credit risk, economic development, environmental resources, social welfare, and governance (Alesina et al., 1996). For example, country risk and sovereign credit risk are interrelated and highly dependent on political risk, which can be measured by governance (and social) indicators. In the academic literature, many research articles have been written to compare economic growth between two regions. Known as “A Tale of Two Countries”, they generally highlight how institutions, governance, and political uncertainties affect economic development and financial performance (Lucas, 1993; Henry and Miller, 2009). Similarly, we can relate country risk to some social issues such as education, social infrastructure, democratic institutions, social inequality, etc. Finally, the environmental pillar also plays an important role due to climatic conditions, natural resources, water stress or climate change. These extra-financial risks affect not only a country's economy, but also its funding cost<fnref n="37" />.

To assess the impact of sustainability on sovereign borrowing costs, analyses have often been conducted using either **E**, **S**, and **G** scores or the global ESG score. For example, Crifo et al. (2017) and Capelle-Blancard et al. (2019) found that countries with higher ESG ratings face lower bond yield spreads. However, there is no consensus in the literature on the “winning pillar”. On the one hand, Capelle-Blancard et al. (2019) found that the environmental pillar is not priced in, while governance is more important than social in reducing borrowing costs. On the other hand, Martellini and Vallée (2016) found a negative relationship between environmental rating and bond yield spread. However, many studies focus mainly on developed markets. Using a larger set of countries, Dudás and Naffa (2020) found a negative relationship between ESG rating and cost of debt, highlighting the time-varying importance of ESG factors in their results, as well as the different effects across country income groups. In fact, they demonstrated the increasing importance of the environmental pillar in recent years, although the social and governance pillars remain the most important drivers of sovereign risk premia, except for low-income countries where some environmental variables dominate. Regarding the social pillar alone, Semet (2020) showed that income inequality tends to increase sovereign yield spreads, although the effect may be peculiar to developed countries. Martellini and Vallée (2016) also reported that a good social rating is associated with a reduction in emerging market bond spreads. Working with CDS spreads on a sample of developed and emerging markets, Hübel (2022) finds that higher ESG ratings are associated with lower CDS spreads. He also highlights that governance has a greater impact on a sovereign's short-term creditworthiness than the environmental and social pillars that are priced into longer-term CDS.

<footnote n="37">³⁷Semet et al. (2021) provide a literature review on these various topics.</footnote>
Semet et al. (2021) uses data from Verisk and supplements it with databases from the World Bank and the United Nations. In total, they test 269 ESG indicators across 26 themes, of which 100 are related to the environmental pillar, 83 to the social pillar and 86 to the governance pillar. The 26 themes are:

E biodiversity, climate change, commitment to environmental standards, energy mix, natural hazard, natural hazard outcome, non-renewable energy resources, temperature, water management;

S civil unrest, demographics, education, gender, health, human rights, income, labour market standards, migration, water and electricity access;

G business environment and R&D, governance effectiveness, infrastructure and mobility, international relations, justice, national security, political stability.

On the financial data side, they consider generic 10 year government bond yields from Bloomberg and Eikon-Datastream, while macroeconomic variables are extracted from the IMF and World Bank databases. The analysis covers the period 2015-2020 and 67 countries. Using the pooling method, they estimate the following baseline model<fnref n="38" />:

$$ \ln(s_{i,t} + 1\%) = \underset{(0.1349)}{1.9477^{***}} + \underset{(0.0125)}{0.0838^{***}} g_{i,t} + \underset{(0.0065)}{0.0524^{***}} \pi_{i,t} - \underset{(0.0007)}{0.0006} d_{i,t} - \underset{(0.0054)}{0.0332^{***}} ca_{i,t} + \underset{(0.0082)}{0.0275^{***}} ra_{i,t} - \underset{(0.0012)}{0.0166^{***}} R_{i,t} + \varepsilon_{i,t} \quad (3.29) $$

where $s_{i,t}$ is the bond yield spread of country i at time t, $g_{i,t}$ corresponds to the growth rate of the gross domestic product, the inflation variable $\pi_{i,t}$ refers to the percentage change of the consumer price index, the debt variable $d_{i,t}$ measures the ratio between the gross debt and the GDP of the country, $ca_{i,t}$ is the current account balance in percent of GDP, $ra_{i,t}$ corresponds to the reserve adequacy and $R_{i,t}$ is a composite credit score computed as the average credit rating from three credit rating agencies (S&P, Moody's and Fitch). The baseline model has a total $\mathfrak{R}_c^2$ of 70% and can be interpreted as the result of fundamental analysis.

The previous model can be cast in Models (3.19) and (3.20) by setting $y_{i,t} = \ln(s_{i,t} + 1\%)$ and $\sum_{k=1}^{6} \gamma_k z_{i,t}^{(k)} = \gamma_1 g_{i,t} + \gamma_2 \pi_{i,t} + \gamma_3 d_{i,t} + \gamma_4 ca_{i,t} + \gamma_5 ra_{i,t} + \gamma_6 R_{i,t}$. Since the list of the 269 ESG variables is large, Semet et al. (2021) first performs a single-factor analysis using linear regression (3.19) and selects the ESG variables that have an estimated value $\hat{\beta}$ that is significant at the 99% confidence level. They find 184 significant variables. In Table 3.22, we report the seven most relevant variables by pillar. $\Delta\mathfrak{R}_c^2$ indicates the difference in $\mathfrak{R}_c^2$ between the regression with the ESG variable and the regression without the ESG variable. For example, the most relevant variable is freedom of assembly, which adds 8.74% of explanatory variable, which means that the coefficient of determination $\mathfrak{R}_c^2$ of the linear regression with the 7 variables is 78.74%. We recall that $\mathfrak{R}_c^2$ is 70% for the baseline model with the six control variables.

This single-factor analysis shows that all 26 ESG themes selected represent metrics that have an impact on determining a country's creditworthiness, confirming that investors integrate extra-financial criteria into bond pricing. However, some themes are more relevant than others (Table 3.23). For example, three themes that have been extensively studied and highlighted by academics as important factors for their impact on the cost of sovereign debt are less relevant. These are income, education, and political stability.

<footnote n="38">³⁸Below each estimate $\hat{\gamma}_k$, the corresponding standard error $\sigma(\hat{\gamma}_k)$ is reported in parentheses.</footnote>
<table><caption>Table 3.22: The 7 most relevant variables of the single-factor analysis by pillar</caption><thead><tr><th>Pillar</th><th>Theme</th><th>Variable</th><th>ΔR<sup>2</sup><sub>c</sub></th></tr></thead><tbody><tr><td rowspan="7"></td><td>Climate change</td><td>Climate change vulnerability (acute)</td><td>5.51%</td></tr><tr><td>Climate change</td><td>Climate change exposure (extreme)</td><td>4.80%</td></tr><tr><td>Water management</td><td>Agricultural water withdrawal</td><td>4.02%</td></tr><tr><td>E Climate change</td><td>Climate change sensitivity (acute)</td><td>3.95%</td></tr><tr><td>Biodiversity</td><td>Biodiversity threatening score</td><td>3.53%</td></tr><tr><td>Climate change</td><td>Climate change exposure (acute)</td><td>3.39%</td></tr><tr><td>Climate change</td><td>Climate change vulnerability (average)</td><td>3.11%</td></tr><tr><td rowspan="3"></td><td>Human rights</td><td>Freedom of assembly</td><td>8.74%</td></tr><tr><td>Human rights</td><td>Extent of arbitrary unrest</td><td>8.04%</td></tr><tr><td>Human rights</td><td>Extent of torture and ill treatment</td><td>7.63%</td></tr><tr><td rowspan="4">S Labour market standards</td><td>Human rights</td><td>Severity of working time violations</td><td>7.21%</td></tr><tr><td>Human rights</td><td>Forced labour violations (extent)</td><td>6.10%</td></tr><tr><td>Human rights</td><td>Child labour (extent)</td><td>5.83%</td></tr><tr><td>Human rights</td><td>Vulnerability of migrant workers</td><td>5.83%</td></tr><tr><td rowspan="3"></td><td>National security</td><td>Severity of kidnappings</td><td>6.80%</td></tr><tr><td>Business environment and R&D</td><td>Ease of access to loans</td><td>6.77%</td></tr><tr><td>Infrastructure and mobility</td><td>Roads km</td><td>6.45%</td></tr><tr><td rowspan="4">G Business environment and R&D</td><td>Business environment and R&D</td><td>Capacity for innovation</td><td>5.65%</td></tr><tr><td>Business environment and R&D</td><td>Ethical behaviour of firms</td><td>5.37%</td></tr><tr><td>National security</td><td>Frequency of kidnappings</td><td>5.27%</td></tr><tr><td>Infrastructure and mobility</td><td>Physical connectivity</td><td>4.94%</td></tr></tbody></table>

Source: Semet et al. (2021, Section 3.3, pages 12-24).

<table><caption>Table 3.23: Summary of the single-factor analysis</caption><thead><tr><th>Relevance</th><th>E</th><th>S</th><th>G</th></tr></thead><tbody><tr><th>High</th><td>Temperature<br>Climate change<br>Natural hazard outcome</td><td>Labour market standards<br>Human rights<br>Migration</td><td>Infrastructure and mobility<br>National security<br>Justice</td></tr><tr><th>Low</th><td>Water management<br>Energy mix</td><td>Income<br>Education<br>Water and electricity access</td><td>Political stability</td></tr></tbody></table>

Source: Semet et al. (2021).

The statistical analysis shows that the 184 significant variables are highly correlated, especially variables within a given theme. These colinearity problems and the large number of explanatory variables do not allow the construction of a multivariate regression model. Therefore, the authors perform a lasso regression with the 184 variables in order to keep the 7 more meaningful variables per pillar in a multivariate framework. The results are presented below in order of importance:

* **E** total GHG emissions (non-renewable energy resources), biodiversity threatening score (biodiversity), severe storm hazard (high extreme) (natural hazard), temperature change (temperature), fossil fuel intensity of the economy (non-renewable energy resources), drought hazard (high extreme) (natural hazard), Paris Agreement (commitment to environmental standards);
S vulnerability of migrant workers (migration), projected population change (5 years) (demographics), frequency of civil unrest incidents (civil unrest), index of labor standards (labor market standards), right to join trade unions (protection) (labor market standards), food import security (human rights), average monthly wage (income);

G exporting across borders (cost) (international relationships), ethical behaviour of firms (business environment and R&D), severity of kidnappings (national security), capacity for innovation (business environment and R&D), physical connectivity (infrastructure and mobility), air transport departures (infrastructure and mobility), rail lines km (infrastructure and mobility).

Note that these selected variables are not necessarily the same as the selected variables in Table 3.22. The reason is that the multi-factor regression model takes into account the correlation patterns between the ESG variables. Therefore, it may be interesting to combine two or three ESG variables with lower explanatory power than to keep the best ESG variable. Finally, Semet et al. (2021) obtain the multi-factor model shown in Table 3.24. In addition to the six control variables, 7 ESG variables are significant in explaining the sovereign yield spread. Moreover, they have the right sign, which means that a higher value of these ESG variables reduces the cost of debt of countries. In terms of explanatory power, the coefficient of determination $R^2$ of this integrated model is 83.5%. The difference $\Delta R^2$ from the baseline model is then 13.5%.

<table><caption>Table 3.24: Final multi-factor model of sovereign bond yields</caption><thead><tr><th>Variable</th><th>β</th><th>t-student</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept α</td><td>2.834</td><td>15.72***</td><td>0.00</td></tr><tr><td>GDP growth g<sub>i,t</sub></td><td>0.017</td><td>1.37</td><td>0.17</td></tr><tr><td>Inflation π<sub>i,t</sub></td><td>0.048</td><td>6.64***</td><td>0.00</td></tr><tr><td>Debt ratio d<sub>i,t</sub></td><td>-0.001</td><td>-1.71*</td><td>0.08</td></tr><tr><td>Current account balance ca<sub>i,t</sub></td><td>-0.012</td><td>-2.45**</td><td>0.01</td></tr><tr><td>Reserve adequacy ra<sub>i,t</sub></td><td>0.005</td><td>0.74</td><td>0.45</td></tr><tr><td>Rating score R<sub>i,t</sub></td><td>-0.013</td><td>-9.08***</td><td>0.00</td></tr><tr><td>Exporting across borders (cost)</td><td>4.05e-4</td><td>4.11***</td><td>0.00</td></tr><tr><td>Severe storm hazard (absolute high extreme)</td><td>-0.015</td><td>-1.66*</td><td>0.09</td></tr><tr><td>Capacity for innovation</td><td>-0.004</td><td>-4.99***</td><td>0.00</td></tr><tr><td>Ethical behavior of firms</td><td>-0.061</td><td>-2.79***</td><td>0.00</td></tr><tr><td>Temperature change</td><td>-0.149</td><td>-3.50***</td><td>0.00</td></tr><tr><td>Severity of kidnappings</td><td>-0.032</td><td>-4.25***</td><td>0.00</td></tr><tr><td>Drought hazard (absolute high extreme)</td><td>3.33e-8</td><td>2.60***</td><td>0.00</td></tr></tbody></table>

Source: Semet et al. (2021, Table 29, page 64).

While the single analysis shows that **S** and **G** variables are more important than **E** variables, the multi-factor analysis when the three pillars are combined shows a different picture. In fact, governance and environmental issues dominate social issues in a multivariate framework. However, this analysis does not take into account the heterogeneity of the 67 countries in terms of economic development. Semet et al. (2021) repeat the analysis by distinguishing between high-income and middle-income countries.

Focusing on high-income countries, the authors select the 21 most important ESG variables using the same lasso procedure as in the global analysis above. Figure 3.41 shows the pillar importance of these 21 factors. The graph shows that the winning pillar for the high-income group is the **E**
<figure><img src="image_105.png" /><figcaption>Figure 3.41: ESG pillar importance for high-income countries</figcaption></figure>

Source: Semet et al. (2021, Figure 11, page 40).

For example, if we had to choose only 9 indicators to explain the variation in government bond yields while controlling for economic effects, we would choose 3 governance indicators over 6 environmental indicators. The full set of environmental indicators is included in the model when the number of selected variables reaches 14 out of 21. The **C** pillar is also relevant for the sample of high-income countries. The first governance indicator selected in the model ranks fourth. Finally, we see that the **S** pillar is lagging behind, with none of the social indicators remaining in the list of seven variables. As a result, none of the social indicators seems to be relevant in explaining sovereign yield spreads in high-income countries. Given the set of indicators used in this analysis, these results suggest that environmental and governance issues dominate the ESG landscape in bond pricing. In Table 3.25, we present the first ten variables selected for the high-income group. The first ESG variable selected is the fossil fuel intensity of the economy. This result provides evidence of market integration of transition risk. The next two variables included in the model, temperature change and average annual cooling degree days, have odd curvatures that make them difficult to interpret. The fourth variable selected measures innovation capacity. This result suggests that countries that invest in R&D are rewarded by the market. This is the most critical governance indicator in the ESG landscape for sovereign bonds issued by high-income countries. The fifth and sixth selected variables are heat stress from the temperature theme and the severity of kidnappings, which can be understood as a proxy for a safe environment. Thus, national security is important to investors and ranks as the second most important issue in the governance pillar for high-income countries. Finally, the threat to biodiversity is also included in the model. This result suggests that the loss of biodiversity is under scrutiny by investors. Countries that act to preserve biodiversity may be rewarded with lower bond yield spreads.
<table><caption>Table 3.25: The most relevant variables of the multi-factor analysis (high-income vs. middle-income analysis)</caption><thead><tr><th>Category</th><th>Rank</th><th>Pillar</th><th>Variable</th></tr></thead><tbody><tr><td rowspan="10">High income countries</td><td>1</td><td>E</td><td>Fossil fuel intensity of the economy</td></tr><tr><td>2</td><td>E</td><td>Temperature change</td></tr><tr><td>3</td><td>E</td><td>Cooling degree days annual average</td></tr><tr><td>4</td><td>G</td><td>Capacity for innovation</td></tr><tr><td>5</td><td>E</td><td>Heat stress (future)</td></tr><tr><td>6</td><td>G</td><td>Severity of kidnappings</td></tr><tr><td>7</td><td>E</td><td>Biodiversity threatening score</td></tr><tr><td>8</td><td>G</td><td>Efficacy of corporate boards</td></tr><tr><td>9</td><td>E</td><td>Total GHG emissions</td></tr><tr><td>10</td><td>S</td><td>Significant marginalized group</td></tr><tr><td rowspan="10">Middle income countries</td><td>1</td><td>E</td><td>Tsunami hazard</td></tr><tr><td>2</td><td>E</td><td>Transport infrastructure exposed to natural hazards</td></tr><tr><td>3</td><td>G</td><td>Severity of kidnappings</td></tr><tr><td>4</td><td>S</td><td>Discrimination based on LGBT status</td></tr><tr><td>5</td><td>G</td><td>Air transport departures</td></tr><tr><td>6</td><td>G</td><td>Exporting across borders (cost)</td></tr><tr><td>7</td><td>S</td><td>Index of labour standards</td></tr><tr><td>8</td><td>S</td><td>Vulnerability of migrant workers</td></tr><tr><td>9</td><td>E</td><td>Paris Agreement</td></tr><tr><td>10</td><td>G</td><td>Military expenditure (% of GDP)</td></tr></tbody></table>

Source: Semet et al. (2021).

For the middle income group, the importance of the pillars is shown in Figure 3.42. We can clearly see the perfect balance between the pillars, as each colored bar gradually increases along a similar path. For example, if we only need to select 9 variables, we will take 3 from the **E** pillar, 3 from the **S** pillar, and 3 from the **G** pillar. In Table 3.25 we present the first ten variables selected for the middle income group found by Semet et al. (2021). The first indicator entered into the model is tsunami hazard, which represents the most significant physical risk to the creditworthiness of middle-income countries<fnref n="39" />. The second variable included in the model is transportation infrastructure exposed to natural hazards. Transportation disruptions caused by natural hazards can paralyze a country for months. Based on these two environmental indicators, we observe a gap between the two income groups. While the yields of high-income countries are sensitive to measures taken to address climate change, the yields of middle-income countries are more sensitive to the occurrence of extreme weather events and resilience to natural hazards. The third indicator selected by the lasso is the severity of kidnapping, the only metric common to both middle-income and high-income groups. It confirms the dominance of national security in the ESG landscape. The first social variable to enter the model is ranked fourth and measures the extent of discrimination based on a country's LGBT status. It reflects a fair integration of human rights in the country. The fifth variable selected is air travel. This would suggest that countries that

<footnote n="39">³⁹The burden of tsunami hazard is borne mainly by countries in the Pacific and South Asian regions, where a significant proportion of middle-income countries are located.</footnote>
rely on air travel as a means of exchange may face a higher risk of transition in the future. The sixth variable is the cost of exporting across borders. For middle-income countries, the lower the cost of exporting a container across borders, the lower the rate of return. This is a measure of a country's trade efficiency. The seventh variable is a labor standards index. Fair labor conditions are thus scrutinized by investors, reflecting an important ESG element for middle-income countries. We can also argue that labor rights violations in middle-income countries can lead to civil unrest and political violence, which can weaken creditworthiness.

<figure><img src="image_106.png" /><figcaption>Figure 3.42: ESG pillar importance for middle-income countries</figcaption></figure>

Source: Semet et al. (2021, Figure 13, page 43).

On page 213, we saw that ESG ratings and credit ratings are correlated when we look at companies. There is no reason why this should be any different when looking at countries. The rationale is as follows. We can assume that although rating agencies focus on financial criteria to assess a country's creditworthiness, they may inherently assess ESG performance as it can have a material impact on a country's solvency. Thus, extra-financial indicators could complement financial criteria in assessing a country's creditworthiness. This suggests that credit risk analysis cannot be separated from ESG considerations, thus invalidating the dichotomy between fundamental and extra-financial analysis. It could therefore be argued that social progress, environmental mitigation or governance effectiveness are cardinal factors in a country's creditworthiness. Semet et al. (2021) explores this issue by inferring credit ratings solely from extra-financial criteria to determine which ESG indicators are priced in by credit ating agencies. They model the probability $p_{i,t}$ of a country to be rated upper-grade by a logistic model:

$$p_{i,t} = \Pr\{\mathcal{R}_{i,t} \in \mathcal{UG}\} = \mathbf{F}\left(\beta_0 + \sum_{j=1}^{n_x} \beta_j x_{i,t}^{(j)}\right) \quad (3.30)$$

where $\mathcal{UG}$ is the set of upper grading ratings (from AAA to A-), $\mathcal{R}_{i,t}$ is the rating of country $i$ at
time t, $\mathbf{F}(z) = \frac{e^z}{1+e^z}$ is the cumulative function of the logistic distribution, and $x_{i,t}^{(j)}$ is the $j$th selected ESG variable. The coefficients $\beta_j$ are estimated using the maximum likelihood method. If the predicted probability $\hat{p}_{i,t}$ is greater than 50%, the country is classified in the upper-grade category. More specifically, Semet et al. (2021) use the following correspondence:

<table><thead><tr><th>Category</th><th>Rating</th><th>Probabilty range</th></tr></thead><tbody><tr><td rowspan="3">Upper-grade</td><td>AAA</td><td>83% - 100%</td></tr><tr><td>AA</td><td>67% - 82%</td></tr><tr><td>A</td><td>50% - 66%</td></tr><tr><td rowspan="4">Lower-grade</td><td>BBB</td><td>39% - 49%</td></tr><tr><td>BB</td><td>29% - 38%</td></tr><tr><td>B</td><td>11% - 28%</td></tr><tr><td>CCC</td><td>0% - 10%</td></tr></tbody></table>

For each pillar, the authors perform a lasso-logistic analysis to determine which seven indicators are more relevant in predicting a country's credit rating. They find the following variables<sup>40</sup>:

* **E** biodiversity threatening score, climate change vulnerability (average), domestic regulatory framework, energy self-sufficiency, wastewater treatment index, water import security (average), water intensity of the economy;

* **S** base pay/value added per worker, basic food stuffs net imports per person, food import security, health expenditure per capita, mean years of schooling of adults, public dissatisfaction with water quality, urban population change (5 years);

* **G** customs efficiency, enforcing a contract (time), getting electricity (time), government effectiveness index, paying tax (process), R&D expenditure (% of GDP), venture capital availability.

The coefficient of determination $R^2$ is 49.1%, 65.6% and 67.9% for the pillars **E**, **S** and **G**, respectively.

Combining the set of significant ESG indicators from the three pillars yields the results shown in Table 3.26. The parameters are reported in odds format ($\hat{\theta}_j = \exp(\hat{\beta}_j)$), while the $t$-student and $p$-value statistics correspond to the raw parameters $\hat{\beta}_j$. When the three pillars are mixed together, we note that the coefficient of determination increases significantly and reaches 91.1%. We also observe that some variables do not remain significant. In fact, only five (respectively seven) variables are statistically significant at 1% (respectively 5%). The environmental indicators disappear, confirming the weak power of the **E** pillar to predict sovereign ratings. These results confirm that governance and social dimensions are certainly at the forefront of rating agencies' analysis. We find that the indicator assessing (1) the average years of schooling of adults is particularly relevant to the analysis, as it has the highest $t$-student. The other important dimensions are (2) access to electricity, (3) urban population change, (4) availability of venture capital, and (5) food import security.

Figure 3.43 shows the model accuracy in predicting credit ratings for each rating group. Semet et al. (2021) note that the accuracy of the **G** pillar in predicting ratings is higher than that of the **E** or **S** pillars. They also note the perfect accuracy of the models in predicting sovereign ratings for the highest rating (AAA and AA), while BBB is the most difficult rating to predict.

<footnote n="40"><sup>40</sup>5 variables are not significant at the 99% confidence level: energy self-sufficiency, wastewater treatment index, water intensity of the economy, customs efficiency and R&D expenditure (% of GDP).</footnote>
<table><caption>Table 3.26: Estimated logistic model with the ESG selected variables</caption><thead><tr><th>Pillar</th><th>Variable</th><th>θ̂<sub>j</sub></th><th>t-student</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="4">E</td><td>Domestic regulatory framework</td><td>2.881</td><td>1.44</td><td>0.14</td></tr><tr><td>Climate change vulnerability (average)</td><td>0.275</td><td>-1.17</td><td>0.24</td></tr><tr><td>Water import security (average)</td><td>0.717</td><td>-0.50</td><td>0.61</td></tr><tr><td>Biodiversity threatening score</td><td>1.029</td><td>0.14</td><td>0.88</td></tr><tr><td rowspan="6">S</td><td>Health expenditure per capita</td><td>0.998</td><td>-1.10</td><td>0.26</td></tr><tr><td>Public dissatisfaction with water quality</td><td>1.332</td><td>1.41</td><td>0.15</td></tr><tr><td>Mean years of schooling of adults</td><td>68.298</td><td>3.37***</td><td>0.00</td></tr><tr><td>Base pay/value added per worker</td><td>0.000</td><td>-1.07</td><td>0.28</td></tr><tr><td>Urban population change (5 years)</td><td>3.976</td><td>2.95***</td><td>0.00</td></tr><tr><td>Basic food stuffs net imports per person</td><td>0.990</td><td>-2.07**</td><td>0.03</td></tr><tr><td rowspan="5">G</td><td>Food import security</td><td>0.803</td><td>-2.59***</td><td>0.00</td></tr><tr><td>Government effectiveness index</td><td>1.751</td><td>2.37**</td><td>0.01</td></tr><tr><td>Venture capital availability</td><td>1.099</td><td>2.93***</td><td>0.00</td></tr><tr><td>Enforcing a contract (time)</td><td>0.999</td><td>-0.31</td><td>0.75</td></tr><tr><td>Paying tax (process)</td><td>0.846</td><td>-1.47</td><td>0.14</td></tr><tr><td></td><td>Getting electricity (time)</td><td>0.882</td><td>-2.95***</td><td>0.00</td></tr></tbody></table>

Source: Semet et al. (2021).

<figure><img src="image_107.png" /><figcaption>Figure 3.43: Prediction accuracy of the logistic model</figcaption></figure>

Source: Semet et al. (2021, Figure 13, page 43).
<table><caption>Table 3.27: Summary of the results found by Semet et al. (2021)</caption><thead><tr><th>What is directly priced in by the market?</th><th>What is indirectly priced in by credit rating agencies?</th></tr></thead><tbody><tr><td>● High-income countries<br>Transition risk ≻ Physical risk</td><td>● E metrics are second-order variables:<ul><li>Environmental standards</li><li>Water management</li><li>Biodiversity</li><li>Climate change</li></ul></td></tr><tr><td>● Middle-income countries<br>Physical risk ≻ Transition risk</td><td>● S matters for middle-income countries, especially for Gender inequality, Working conditions and Migration</td></tr><tr><td>National security, Infrastructure and mobility and International relationships are the relevant G metrics</td><td>● Education, Demographics and Human rights are prominent indicators for the S pillar<br>Government effectiveness, Business environment and R&D dominate the G pillar</td></tr><tr><td>Fundamental analysis: R<sub>c</sub><sup>2</sup> ≈ 70%<br>Extra-financial analysis: ΔR<sub>c</sub><sup>2</sup> ≈ 13.5%</td><td>Accuracy &gt; 95%<br>AAA, AA, B, CCC ≻ A ≻ BB ≻ BBB</td></tr></tbody></table>

Table 3.27 summarizes the results of Semet et al. (2021). The three pillars are not equally important when looking at the sovereign bond pricing. In fact, **G** and **E** dominate **S**. By refining the analysis to separate high- and middle-income countries, they conclude that environmental issues are indeed at the forefront of investors' concerns when assessing sovereign risk. However, they find a divergence between the two income groups. While sovereign yields in high-income countries are more related to the transition risk of climate change, they are more sensitive to the physical risk of climate change in middle-income countries. For high-income countries, **S** is chosen well after **E** and **G**. For middle-income countries, **S** is as important as **G**. They conclude that “improvements in the identified ESG metrics induce lower borrowing costs for the sovereign issuer, but the importance of each pillar is a function of the country's level of development.” Their analysis also shows that credit rating agencies integrate several classic **S** and **G** dimensions that are well documented in the academic literature. The authors point out that the set of metrics selected to predict ratings differs significantly from the set used to explain bond yields. This suggests that “the market selects ESG metrics that are not already embedded in credit ratings to avoid double counting.”
## 3.4 Exercises

### 3.4.1 Equity portfolio optimization with ESG scores

We consider the CAPM model:

$$R_i - r = \beta_i (R_m - r) + \varepsilon_i$$

where $R_i$ is the return of asset $i$, $R_m$ is the return of the market portfolio $w_m$, $r$ is the risk-free asset, $\beta_i$ is the beta of asset $i$ with respect to the market portfolio and $\varepsilon_i$ is the idiosyncratic risk of asset $i$. We have $R_m \perp \varepsilon_i$ and $\varepsilon_i \perp \varepsilon_j$. We note $\sigma_m$ the volatility of the market portfolio. Let $\tilde{\sigma}_i$, $\mu_i$ and $S_i$ be the idiosyncratic volatility, expected return and ESG score of asset $i$, respectively. We use a universe of 6 assets with the following parameter values:

<table><thead><tr><th>Asset <i>i</i></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>β<sub><i>i</i></sub></td><td>0.10</td><td>0.30</td><td>0.50</td><td>0.90</td><td>1.30</td><td>2.00</td></tr><tr><td>σ̃<sub><i>i</i></sub> (in %)</td><td>17.00</td><td>17.00</td><td>16.00</td><td>10.00</td><td>11.00</td><td>12.00</td></tr><tr><td>μ<sub><i>i</i></sub> (in %)</td><td>1.50</td><td>2.50</td><td>3.50</td><td>5.50</td><td>7.50</td><td>11.00</td></tr><tr><td>S<sub><i>i</i></sub></td><td>1.10</td><td>1.50</td><td>2.50</td><td>-1.82</td><td>-2.35</td><td>-2.91</td></tr></tbody></table>

and $\sigma_m = 20\%$. The risk-free return $r$ is set to 1% and the expected return of the market portfolio $w_m$ is $\mu_m = 6\%$.

1. We assume that the CAPM is valid.

(a) Compute the vector $\mu$ of expected returns.

(b) Compute the covariance matrix $\Sigma$. Deduce the volatility $\sigma_i$ of asset $i$ and find the correlation matrix $C = (\rho_{i,j})$ between asset returns.

(c) Compute the tangency portfolio $w^*$. Calculate $\mu(w^*)$ and $\sigma(w^*)$. Deduce the Sharpe ratio and ESG score of the tangency portfolio.

(d) Compute the beta coefficient $\beta_i(w^*)$ of the six assets with respect to the tangency portfolio $w^*$, and the implied expected return $\tilde{\mu}_i$:

$$\tilde{\mu}_i = r + \beta_i(w^*) (\mu(w^*) - r)$$

(e) Deduce the market portfolio $w_m$. Comment on the results.

2. We consider long-only portfolios and we also impose a minimum threshold $S^*$ for the portfolio's ESG score:

$$S(w) = w^\top S \geq S^*$$

(a) Let $\gamma$ be the risk tolerance. Write the mean-variance optimization problem.

(b) Find the QP form of the MVO problem.

(c) Compare the efficient frontier when (1) there is no ESG constraint ($S^* = -\infty$), (2) we impose a positive ESG score ($S^* = 0$) and (3) the minimum threshold is set to 0.5 ($S^* = 0.5$). Comment on these results.

(d) For each previous cases, find the tangency portfolio $w^*$ and the corresponding risk tolerance $\gamma^*$. Then compute $\mu(w^*)$, $\sigma(w^*)$, $\text{SR}(w^* | r)$ and $S(w^*)$. Comment on these results.
(e) Draw the relationship between the minimum ESG score $\mathbf{S}^*$ and the Sharpe ratio SR ($w^* | r$) of the tangency portfolio.

(f) We assume that the market portfolio $w_m$ is the tangency portfolio when $\mathbf{S}^* = 0.5$.

i. Compute the beta coefficient $\beta_i(w_m)$ and the implied expected return $\tilde{\mu}_i(w_m)$ for each asset. Then derive the alpha return $\alpha_i$ of asset $i$. Comment on these results.

ii. Consider the equally-weighted portfolio $w_{ew}$. Compute its beta coefficient $\beta(w_{ew} | w_m)$, its implied expected return $\tilde{\mu}(w_{ew})$ and its alpha return $\alpha(w_{ew})$. Comment on these results.

3. The investor's goal is twofold. He wants to manage the tracking error risk of his portfolio relative to the benchmark $b = (15\%, 20\%, 19\%, 14\%, 15\%, 17\%)$ and have a better ESG score than the benchmark. However, this investor faces a long-only constraint because he cannot leverage his portfolio and he cannot short the assets.

(a) What is the ESG score of the benchmark?

(b) Assume the investor's portfolio is $w = (10\%, 10\%, 30\%, 20\%, 20\%, 10\%)$. Compute the excess score $\mathbf{S}(w | b)$, the expected excess return $\mu(w | b)$, the tracking error volatility $\sigma(w | b)$ and the information ratio $IR(w | b)$. Comment on these results.

(c) Same question with the portfolio $w = (10\%, 15\%, 30\%, 10\%, 15\%, 20\%)$.

(d) In the following, we assume that the investor does not have a return objective. In fact, the investor's objective is to improve the ESG score of the benchmark and to control the tracking error volatility. Let $\gamma$ be the risk tolerance. Give the corresponding esg-variance optimization problem.

(e) Find the matrix form of the corresponding QP problem.

(f) Plot the esg-variance efficient frontier ($\sigma(w^* | b), \mathbf{S}(w^* | b)$) where $w^*$ is an optimal portfolio.

(g) Find the optimal portfolio $w^*$ when we target a given tracking error volatility $\sigma^*$. The values of $\sigma^*$ are 0%, 1%, 2%, 3% and 4%.

(h) Find the optimal portfolio $w^*$ when we target a given excess score $\mathbf{S}^*$. The values of $\mathbf{S}^*$ are 0, 0.1, 0.2, 0.3 and 0.4.

(i) We want to compare the efficient frontier obtained in Question 3.f with the efficient frontier when we implement a best-in-class selection or a worst-in-class exclusion. The selection strategy consists of investing only in the three best ESG assets, while the exclusion strategy implies no exposure to the worst ESG asset. Plot the three efficient frontiers. Comment on these results.

(j) What minimum tracking error volatility must the investor accept to implement the best-in-class selection strategy? Give the corresponding optimal portfolio.
# Chapter 4

## Sustainable Financial Products

In the introduction, we saw that the market for ESG investments reached approximately $30 trillion in 2022, according to the Global Sustainable Investment Alliance (GSIA, 2023). This figure represents all ESG investments managed by asset owners and managers, regardless of the type or format of the investment. Therefore, a large portion of these investments represent assets managed internally by asset owners or private mandates given by institutional investors to asset managers. In this chapter, we focus on sustainable financial products that are distributed through public investment vehicles in the financial markets. These investments fall into three main families of financial products. The first category corresponds to sustainable mutual funds. While mutual funds are well defined from a legal perspective, sustainable mutual funds are more of a category within the mutual fund industry. This means that there is no official definition of a sustainable mutual fund. Therefore, the market for sustainable mutual funds will vary from one investment research firm to another. The second category is the market for sustainable securities, such as green bonds or social bonds. This category is better defined than the previous one. It is now the main instrument used by governments and corporations to finance the transition to a low-carbon economy. Finally, the third category is an extension of the first when the assets under management are not listed public securities. This is the market of sustainable real assets, broadly speaking, assets that do not correspond to public equities and bonds. This category consists of four main categories: sustainable private equity, sustainable private debt, sustainable infrastructure and sustainable real assets.

### 4.1 ESG mutual funds

A mutual fund is an investment vehicle that pools money from many investors to invest in a portfolio of securities such as stocks, bonds, money market instruments, or a combination of these assets. These funds are managed by investment management firms, called asset managers, who make investment decisions on behalf of the fund's investors. Asset managers receive a mandate to manage the portfolio from asset owners (e.g., retail clients, banking networks, insurance companies, or institutional investors) in exchange for a management fee. In order to meet the heterogeneous objectives of different investors, asset managers highly structure and specialize mutual funds. The categories depend on the asset class, and we distinguish equity funds, bond funds, money market funds, balanced funds, etc. Another important criterion is the region in which the fund manager invests. For example, investors can buy an equity fund that specializes in European stocks, American stocks, or stocks of a particular country (e.g., French stocks, Spanish stocks, Indian stocks). Other criteria may be used to classify mutual funds, such as the level of active risk relative to a benchmark. For example, the objective of an index fund is to replicate a benchmark index, while the objective of
an actively managed fund is to generate positive alpha relative to a benchmark. If the investment strategy is complex, involves the use of leverage or short selling, or invests in less liquid assets, the fund is generally restricted to accredited investors and is referred to as a hedge fund or alternative investment fund. Because of these different capabilities, the potential for misalignment between investor and fund manager, and mis-selling issues, mutual funds are highly regulated. This is also the case for ESG funds in Europe with the sustainable finance disclosure regulation (SFDR). In other countries, regulation of ESG funds is more heterogeneous or non-existent because the status of ESG funds has not yet been defined.

### 4.1.1 Regulation

#### Sustainable finance disclosure regulation

The SFDR has already been introduced in section 1.4.3 on page 36. We reiterate that all products must be classified as falling under either Article 6, 8 or 9:

<table><thead><tr><td>Article 6</td><td>Article 8</td><td>Article 9</td></tr></thead><tbody><tr><td>These funds do not integrate or promote sustainability factors in their investment decisions. This means that they can invest in assets regardless of their ESG practices. They do not face ESG constraints. Article 6 is the default category. It can be seen as the traditional investment approach before the advent of ESG.</td><td>These funds integrate environmental, social and governance factors into their investment decisions. This means that their investment policy depends on some ESG criteria. For example, they may exclude some assets from their investment universe because of their poor extra-financial score. However, financial performance remains the primary objective of these funds, ahead of sustainability.</td><td>These funds have sustainable investing as their primary objective. Like Article 8 funds, they incorporate ESG criteria. The difference is that they allocate capital based on the impact of these assets on the development of economic sustainability. In simple terms, these funds can be assimilated to the impact investing category of the ESG strategy classification.</td></tr></tbody></table>

To maintain their Article 9 designation, funds must meet three requirements on an ongoing basis:

1. Positive impact

The fund's investments must actively contribute to a specific environmental or social objective. This objective should be consistent with the overall objectives of the fund and pursued through a clear and transparent investment strategy.

2. Do no harm

The fund must carefully assess and mitigate any potential negative social or environmental impacts of the companies in which it invests.

3. Strong governance

Companies in the fund's portfolio must demonstrate strong governance practices. This ensures accountability and ethical behavior by the companies in which the fund invests.

While the social objective was originally included in the definition of Article 9 funds, this objective has lost momentum. It now seems that it cannot be the primary objective of an Article 9 fund, certainly because the social taxonomy has not yet been developed. Clearly, the environment is the primary objective of Article 9 funds, as evidenced by the pre-contractual disclosure in Box 4.1.
Box 4.1: Pre-contractual information disclosure for the Article 9 financial products

* What is the sustainable investment objective of this product?

- How does this product measure that the sustainable investment objective will be met?

- What investments are not sustainable, what is their purpose and are there any minimum environmental or social safeguards?

* Has a reference benchmark been designated for the purpose of ensuring consistency with the sustainable investment objective of the product and how the consistency is monitored?

- How does the benchmark used differ from a relevant broad market index?

* What type of investments does this product make and what is the minimum proportion of sustainable investments?

- What are the objectives of the sustainable investments?

- How is significant harm to the environment and people avoided by the sustainable investments made?

* What is the minimum proportion of EU Taxonomy-aligned investments?

* Does this product commit to consider the most significant negative impacts of its investments on the environment and people (principal adverse impacts)?

* Does this product aim to decrease the greenhouse gas (GHG) emissions from the activities the product invests in?

- What is the greenhouse gas emission reduction target of the product?

- Is the greenhouse gas emission reduction target of the product compatible with the objective to limit global warming to 1.5°C?

Source: ESMA (2023) & https://www.esma.europa.eu/document/final-report-draft-rts-review-pai-and-financial-product-disclosures-sfdr-delegated.

According to Morningstar (2024b, Exhibit 11, page 15), the largest Article 9 fund is Nordea 1 – Global Climate and Environment Fund with €9.1 bn in assets under management as of December 2023. To understand the sustainability objective of the fund, we can analyze the ESG report and the SFDR report<fnref n="1" />. In the latter report, we can read:

* The fund will make at least 85% sustainable investments with an environmental objective in economic activities that qualify and do not qualify as environmentally sustainable under the EU taxonomy.

* The fund invests a minimum proportion of 2% of its total investments in activities that meet the technical criteria defined in the EU taxonomy.

* A fundamental bottom-up research process identifies companies with significant future cash

<footnote n="1">¹Both reports are available at www.nordea.lu/en/professional/fund/climate-expertise.</footnote>
<figure><img src="image_108.png" /><figcaption>Figure 4.1: Reclassification of Article 6, 8 and 9 funds</figcaption></figure>

Source: Morningstar (2024b, Exhibit 8d, page 13).

flows from their contribution to environmental solutions such as resource efficiency, environmental protection and alternative energy solutions.

* Companies that are materially involved in the following activities are excluded from the investment universe: Pornography, Alcohol, Conventional weapons, Coal, Gambling, Natural gas, Nuclear weapons, Oil, Arctic drilling and oil sands, Tobacco and Controversial Weapons.

* The fund uses the Paris Aligned Fossil Fuel Policy (PAFF) developed by Nordea Asset Management to identify companies with credible transition plans.

Morningstar (2024b, Exhibit 10, page 14) considers that the largest Article 8 fund is Flossbach von Storch SICAV – Multiple Opportunities with €24.9 bn in assets under management as of December 2023. In the SFDR report<fnref n="2" />, we can read:

* The fund promotes environmental or social characteristics, but does not have a sustainable investment objective.

* Social and environmental exclusion criteria are implemented, while an engagement policy is implemented as part of the investment strategy.

These two examples show that the main difference between Article 8 and Article 9 funds is the fund management process. Most Article 8 funds only implement asset exclusion based on some ESG criteria, while most Article 9 funds also implement a selection process based on green intensity and carbon footprint criteria. However, this distinction is somewhat theoretical as the classification is not always obvious. This explains why we see an ongoing reclassification of European funds. According to Morningstar (2024b, page 12), 256 funds changed their SFDR status in the last quarter of 2023: "The majority of funds (218) moved to Article 8 from Article 6, while three upgraded to

<footnote n="2">²It is available at www.flossbachvonstorch.ch/en/funds/LU0945408952.</footnote>
Article 9 from Article 6, and eight funds moved to Article 9 from Article 8. Meanwhile, four funds downgraded to Article 8 from Article 9". In Figure 4.1, we reproduce the history of quarterly reclassifications calculated by Morningstar (2024b). There are two important stylized facts. First, many mutual funds are upgraded from Article 6 to Article 8 each quarter. Second, we observe a wave of downgrades from Article 9 to Article 8 between the third quarter of 2022 and the first quarter of 2023. This period of voluntary reclassifications begins in July 2022, when the European Commission provided additional guidance to Article 9 funds as part of the SFDR. The key point emphasized that only funds that meet 100% sustainable criteria can qualify under Article 9. However, the definition and criteria for sustainable investments remained unclear. Fears of greenwashing and a misunderstanding of the original SFDR explain this wave of downgrades. Even today, the definition of sustainable investments is left to the discretion of the asset manager, although certain guidelines have been published and specified in the Regulatory Technical Standards<fnref n="3" /> (ESMA, 2023). In Box 4.2, we have reported on Nordea Asset Management's definition of sustainable investment, which can be found in the pre-contractual SFDR document for the Nordea 1 - Global Climate and Environment Fund. We can compare this definition with the Pictet Asset Management's definition of sustainable investment, which can be found in the pre-contractual SFDR document for the Pictet Water Fund<fnref n="4" /> (Box 4.3). While these two definitions use common guidelines, they are not strictly equivalent. Each asset manager is therefore free to interpret what constitutes a sustainable investment. For example, unlike Article 8 products, Article 9 funds must ensure that many of the companies in which they invest are aligned with the EU taxonomy. However, the percentage of alignment may vary from one asset manager to another.

**Minimum proportion of sustainable investments** Figure 4.2 shows the distribution of minimum sustainable investment percentages for Article 8 and Article 9 funds<fnref n="5" />. The two distributions are very different, as almost all Article 8 funds have a minimum sustainable investment percentage below 60%, while almost all Article 9 funds have a minimum sustainable investment percentage above 70%. About 35% of Article 8 funds report a minimum sustainable investment of 0%, while about 45% of Article 9 funds report a minimum sustainable investment of over 90%. These are the two most common frequencies. Curiously, 1% of Article 9 funds report a minimum sustainable investment of 0%.

**Principal adverse sustainability impacts** Initially, the EU identified 64 adverse impact indicators to be calculated and reported (18 mandatory and 46 voluntary). In the latest version of the SFDR, there are 74 indicators, of which 20 are mandatory. Below we report on the comprehensive list of indicators, which can be found in ESMA (2023, pages 91-120). Several distinctions can be made. First, we can distinguish between indicators applicable to investments in investee companies, sovereigns and supranationals, and real estate assets. Second, we can distinguish between climate and environmental indicators and social indicators. Third, we can distinguish between mandatory (principal adverse impacts) and voluntary indicators. Count statistics are given in Table 4.1. 77% of the indicators concern companies. While mandatory environmental indicators are more important than mandatory social indicators, the opposite is true for voluntary indicators. Some (S) indicators can be classified as (G) indicators. This is for example the case for indicators 12 (gender diversity of

<footnote n="3">³The term "sustainable investment" appears 272 times in the Final Report on draft Regulatory Technical Standards, but it is never defined.</footnote>

<footnote n="4">⁴According to Morningstar (2024b, Exhibit 11, page 15), the Pictet Water Fund is the third largest Article 9 fund with €8.1 bn in assets under management as of December 2023.</footnote>

<footnote n="5">⁵The x-label 0 corresponds to 0%, the label 10 corresponds to 0.1-9.9%, the label 20 corresponds to 10-19.9%, while the label 100 corresponds to 90-100%.</footnote>
Box 4.2: Nordea Asset Management’s definition of sustainable investment

> "We take into consideration the regulatory framework for sustainable finance, as well as proprietary investment methodologies, to ensure that sustainable investments can be identified, invested in and measured. In order to select sustainable investments, we apply three tests. The investment should pass all three tests to qualify as a sustainable investment.

* Step 1: Good governance test — a company needs to have sufficient processes in place related to the four good governance topics specified by the SFDR: sound management structures, employee relations, remuneration of staff and tax compliance, and must not be subject to significant controversies related to any of these topics.

* Step 2: Do no significant harm test — a company or an issuer cannot perform negatively on selected principal adverse impact (PAI) indicators. Principal adverse impacts are the most significant negative impacts of investment decisions on sustainability factors relating to environmental, social and employee matters, respect for human rights, anticorruption and antibribery matters. The thresholds defining negative outliers and poor performance vary for each PAI indicator.

* Step 3: Environmental or Social Contribution — NAM’s proprietary process applies a pass/fail criteria on contribution to one or more of the UN SDGs or one or more of the environmental objectives in the EU Taxonomy, with a 20% threshold. Contribution may be measured on the proportion of revenue that can be linked to the above objectives. For sectors where capital expenditure or operating expenses or other relevant activity measures are more relevant, these will be used. Bonds that are issued under ICMA or equivalent frameworks (labelled bonds) are considered sustainable if their adherence under the relevant standard has been verified by an approved external auditor."

Source: https://www.nordeaassetmanagement.com/responsible-investment.

Box 4.3: Pictet Asset Management’s definition of sustainable investment

> "Pictet Asset Management uses a proprietary reference framework as well as the objectives of the EU Taxonomy to define sustainable investments. Sustainable investments are calculated on a pass/fail basis including labelled bonds(where relevant to the asset class),and securities from issuers with minimum 20% exposure (as measured by revenue, EBIT, enterprise value or similar metrics) to economic activities that contribute to environmental or social objectives. In the absence of an EU social taxonomy, Pictet has developed a proprietary social taxonomy framework. The framework is based on the Report on Social Taxonomy published by the EU Platform on Sustainable Finance in 2022. Eligible activities are defined as socially beneficial goods and services that substantially contribute to one of the following three social objectives: (1) inclusive and sustainable communities, (2) adequate living standards and well-being for end users and (3) decent work. Whilst the fund includes achieving a positive environmental or social objective, its investment policy does not target specifically investments with climate change mitigation and climate change adaptation objectives as defined by the technical screening criteria of the Taxonomy Regulation."

Source: https://am.pictet/en/uk/institutional/funds.
<figure><img src="image_109.png" /><figcaption>Figure 4.2: Distribution of minimum sustainable investment percentages (Article 8 vs. Article 9 funds, December 2023)</figcaption></figure>

Source: Morningstar (2024b, Exhibit 23, page 27 & Exhibit 26, page 30).

management and supervisory boards), 13 (non-cooperative tax jurisdictions) and 52 (excessive CEO pay ratio). Most indicators are measured using a single metric, with the exception of indicators 1, 26, 35, 39 and 51. For each negative sustainability indicator, the fund must report on the current impact, the impact over the past year, and the actions taken, actions planned, and targets set for the next reporting period. In addition to these quantitative indicators, we also note three qualitative indicators that describe policies for identifying and prioritizing the most significant negative impacts on sustainability factors, engagement policies, and references to international standards. A fourth and final qualitative indicator is a historical comparison of the quantitative indicators over the life of the fund. The general idea is to show the overall sustainability trend of the product.

<table><caption>Table 4.1: Number of indicators in the SFDR</caption><thead><tr><th></th><th>Corporates</th><th>Sovereigns</th><th>Real estate assets</th><th>Total</th></tr></thead><tbody><tr><td>Mandatory</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>9</td><td>1</td><td>2</td><td>12</td></tr><tr><td></td><td>7</td><td>1</td><td>0</td><td>8</td></tr><tr><td>Total</td><td>16</td><td>2</td><td>2</td><td>20</td></tr><tr><td>Voluntary</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>17</td><td>1</td><td>5</td><td>23</td></tr><tr><td></td><td>21</td><td>4</td><td>0</td><td>25</td></tr><tr><td></td><td>3</td><td>3</td><td>0</td><td>6</td></tr><tr><td>Total</td><td>41</td><td>8</td><td>5</td><td>54</td></tr><tr><td>Total</td><td>57</td><td>10</td><td>7</td><td>74</td></tr></tbody></table>

Source: ESMA (2023) & Author's calculations.
## Climate and other environment-related indicators

* Greenhouse gas emissions

1. GHG emissions

* Scope 1 GHG emissions of investee companies expressed in tonnes of CO₂ equivalent

* Scope 2 GHG emissions of investee companies expressed in tonnes of CO₂ equivalent

* Scope 3 GHG emissions of investee companies expressed in tonnes of CO₂ equivalent

* Total GHG emissions of investee companies expressed in tonnes of CO₂ equivalent

2. Carbon footprint

* Total GHG emissions expressed per million EUR invested

3. GHG intensity of investee companies

* GHG emissions per million EUR of revenue of investee companies

4. Exposure to companies active in the fossil fuel sector

* Share of investments in companies active in the fossil fuel sector

5. Share of non-renewable energy consumption and production

* Share of non-renewable energy consumption and non-renewable energy production of investee companies from non-renewable energy sources compared to renewable energy sources, expressed as share of total energy intensity

6. Energy consumption intensity per high impact climate sector

* Energy consumption in MWh per million EUR of revenue of investee companies, per high impact climate sector

* Biodiversity

7. Activities negatively affecting biodiversity-sensitive areas

* Share of investments in investee companies with sites/operations located in or near to biodiversity-sensitive areas where activities of those investee companies negatively affect those areas

* Water

8. Pollution of water

* Tonnes of pollution emitted into water generated by investee companies per million EUR invested

* Waste

9. Hazardous waste ratio

* Tonnes of hazardous waste generated by investee companies per million EUR invested

## Indicators for social and employee, respect for human rights, anti-corruption and anti-bribery matters

* Social and employee matters

10. Non-respect of OECD Guidelines for Multinational Enterprises or the UN Guiding Principles including the principles and rights set out in the eight fundamental conventions identified in the ILO Declaration and the International Bill of Human Rights

* Share of investments in investee companies that have been involved in non-respect of the OECD Guidelines for Multinational Enterprises, the UN Guiding Principles, including the principles and rights set out in the eight fundamental conventions identified in the ILO Declaration and the International Bill of Human Rights

11. Unadjusted gender pay gap between female and male employees

* Average gender pay gap between female and male employees of investee companies

12. Management and supervisory board gender diversity

* Average ratio of female to male management and supervisory board members in investee companies, expressed as a percentage of all board members
13. Amount of accumulated earnings in non-cooperative tax jurisdictions applying to investee companies where the total consolidated revenue on their balance sheet date for each of the last two consecutive financial years exceeds a total of EUR 750 M

* Amount of accumulated earnings at the end of the relevant financial year from investee companies where the total consolidated revenue on their balance sheet date for each of the last two consecutive financial years exceeds total of EUR 750M in jurisdictions that appear on the revised EU list of non-cooperative jurisdictions for tax purposes

14. Exposure to controversial weapons

* Share of investments in investee companies involved in the manufacture or selling of controversial weapons

15. Exposure to companies active in the cultivation and production of tobacco

* Share of investments in investee companies active in the cultivation and production of tobacco

16. Share of employees of investee companies earning less than the adequate wage

* Average percentage of employees in investee companies earning less than the adequate wage

## Indicators applicable to investments in sovereigns and supranationals

* Environmental

17. Sovereign GHG intensity

* GHG intensity of investee countries as a ratio of investee country's PPP-adjusted GDP

* Social

18. Investee countries subject to social violations

* Investments in investee countries subject to social violations from international treaties and conventions, United Nations principles and where applicable national laws or principles

## Indicators applicable to investments in real estate assets

* Fossil fuels

19. Exposure to fossil fuels through real estate assets

* Share of investments in real estate assets involved in the extraction, storage, transport, trade or manufacture of fossil fuels

* Energy efficiency

20. Exposure to energy inefficient real estate assets

* Share of investments in energy inefficient real estate assets

## Other indicators for principal adverse impacts on sustainability factors

* Description of policies to identify and prioritise principal adverse impacts on sustainability factors (Article 7)

* Engagement policies (Article 8)

* References to international standards (Article 9)

* Historical comparison (Article 10)

## Additional climate and other environment-related indicators
# Climate and other environment-related indicators

## Emissions

21. Emissions of inorganic pollutants

* Tonnes of inorganic pollutants equivalent per million EUR invested

22. Emissions of air pollutants

* Tonnes of air pollutants equivalent per million EUR invested

23. Emissions of ozone-depleting substances

* Tonnes of ozone-depleting substances equivalent per million EUR invested

24. Investments in companies without carbon emission reduction initiatives

* Share of investments in investee companies without carbon emission reduction initiatives aimed at aligning with the Paris Agreement

## Energy performance

25. Breakdown of energy consumption by type of non-renewable sources of energy

* Share of energy from non-renewable sources used by investee companies broken down by each non-renewable energy source

## Water, waste and material emissions

26. Water usage and recycling

* Average amount of water consumed by the investee companies (in cubic meters) per million EUR of revenue of investee companies

* Weighted average percentage of water recycled and reused by investee companies

27. Investments in companies without water management policies

* Share of investments in investee companies without water management policies

28. Exposure to areas of high water stress

* Share of investments in investee companies with sites located in areas of high water stress without a water management policy

29. Investments in companies producing pesticides and other agrochemical products

* Share of investments in investee companies the activities of which fall under Division 20.2 of Annex I to Regulation (EC) No 1893/2006

30. Land degradation, desertification, soil sealing

* Share of investments in investee companies the activities of which cause land degradation, desertification or soil sealing

31. Investments in companies without sustainable land/agriculture practices or policies

* Share of investments in investee companies, the activities of which involve land/agricultural activities without sustainable land/agriculture practices or policies

32. Investments in companies without sustainable oceans/seas practices or policies

* Share of investments in investee companies, the activities of which involve oceans, seas, coasts or inland water activities without sustainable oceans/seas practices or policies

33. Non-recycled waste ratio

* Tonnes of non-recycled waste generated by investee companies per million EUR invested

34. Radioactive waste ratio

* Tonnes radioactive waste generated by investee companies per million EUR invested

35. Natural species and protected areas

* Share of investments in investee companies whose operations affect threatened species

* Share of investments in investee companies without a biodiversity protection policy covering operational sites owned, leased, managed in, or adjacent to, a protected area or an area of high biodiversity value outside protected areas
36. Deforestation

* Share of investments in companies without a policy to address deforestation

* Green securities

37. Share of corporate debt securities not issued under Union legislation on environmentally sustainable bonds

* Share of securities in investments not issued under Union legislation on environmentally sustainable bonds

## Indicators applicable to investments in sovereigns and supranationals

* Green securities

38. Share of sovereign and supranational debt securities not issued under Union legislation on environmentally sustainable bonds

* Share of bonds not issued under Union legislation on environmentally sustainable bonds

## Indicators applicable to investments in real estate assets

* Greenhouse gas emissions

39. Real estate GHG emissions

* Scope 1 GHG emissions generated by real estate assets expressed in tonnes of CO₂ equivalent

* Scope 2 GHG emissions generated by real estate assets expressed in tonnes of CO₂ equivalent

* Scope 3 GHG emissions generated by real estate assets expressed in tonnes of CO₂ equivalent

* Total GHG emissions generated by real estate assets expressed in tonnes of CO₂ equivalent

* Energy consumption

40. Energy consumption intensity

* Energy consumption in GWh of owned real estate assets per square meter

* Waste

41. Waste production in operations

* Share of real estate assets not equipped with facilities for waste sorting and not covered by a waste recovery or recycling contract

* Resource consumption

42. Raw materials consumption for new construction and major renovations

* Share of raw building materials (excluding recovered, recycled and biosourced) compared to the total weight of building materials used in new construction and major renovations

* Biodiversity

43. Land artificialisation

* Share of non-vegetated surface area (surfaces that have not been vegetated in ground, as well as on roofs, terraces and walls) compared to the total surface area of the plots of all assets

## Additional indicators for social and employee, respect for human rights, anti-corruption and anti-bribery matters
Indicators applicable to investments in investee companies

• Social and employee matters

44. Investments in companies without workplace accident prevention policies or management systems

* Share of investments in investee companies without a workplace accident prevention policy or management system

45. Rate of recordable work-related accidents

* Rate of recordable work-related accidents in investee companies

46. Number of days lost to work-related injuries, accidents, ill health and fatalities

* Number of workdays lost to work-related injuries, accidents, ill health or fatalities of investee companies

47. Low coverage of collective bargaining agreements

* Share of employees in investee companies not covered by collective bargaining agreements

48. Lack of a supplier code of conduct

* Share of investments in investee companies without any supplier code of conduct (against unsafe working conditions, child labour and forced labour)

49. Lack of grievance/complaints handling mechanism related to employee matters

* Share of investments in investee companies without a grievance/complaints handling mechanism related to employee matters

50. Insufficient whistleblower protection

* Share of investments in entities without policies on the protection of whistleblowers

51. Incidents of discrimination and incidents of discrimination related to any type of discrimination leading to monetary and non-monetary sanctions in investee companies

* Number of incidents of discrimination

* Number of incidents of discrimination related to any type of discrimination leading to monetary and non-monetary sanctions in investee companies

52. Excessive CEO pay ratio

* Ratio within investee companies of the annual total compensation for the highest compensated individual to the median annual total compensation for all employees (excluding the highest-compensated individual)

53. Excessive use of non-guaranteed-hour employees in investee companies

* Share of non-guaranteed hour employees in investee companies as share of total employees

54. Excessive use of temporary contract employees in investee companies

* Share of temporary contract employees in investee companies as share of total employees

55. Excessive use of non-employee employees in investee companies

* Share of non-employee employees in investee companies as share of total employees

56. Insufficient employment of persons with disabilities within the workforce

* Share of persons with disabilities within the workforce of investee companies

• Human Rights

57. Lack of processes and compliance mechanisms to monitor compliance with OECD Guidelines for Multinational Enterprises or the UN Guiding principles, including the principles and rights set out in the eight fundamental conventions identified in the ILO Declaration and the International Bill of Human Rights

* Share of investments in investee companies without policies to monitor compliance with or grievance/complaints handling mechanisms to address violations of the OECD Guidelines for Multinational Enterprises or the UN Guiding principles, including the principles and rights set out in the eight fundamental conventions identified in the ILO Declaration and the International Bill of Human Rights

58. Lack of human rights policies
* Share of investments in entities without human rights policies

59. Lack of processes and measures for preventing trafficking in human beings

* Share of investments in investee companies without policies against trafficking in human beings

60. Operations and suppliers at significant risk of incidents of child labour

* Share of investments in investee companies exposed to operations and suppliers at significant risk of incidents of child labour in terms of geographic areas and/or type of operation

61. Operations and suppliers at significant risk of forced or compulsory labour

* Share of investments in investee companies exposed to operations and suppliers at significant risk of incidents of forced or compulsory labour in terms of geographic areas and / or the type of operation

62. Number of identified cases of severe human rights issues and incidents

* Number of cases of severe human rights issues and incidents connected to investee companies

63. Lack of remediation mechanism for affected communities relating to the operations of the investee companies

* Share of investments in investee companies without remediation mechanism for stakeholders materially affected by the operations of the investee companies

64. Lack of remediation handling mechanism for consumers/end-users of the investee company

* Share of investments in investee companies without remediation mechanism for consumers/end-users of the investee companies

* Anti-corruption and anti-bribery

65. Lack of anti-corruption and anti-bribery policies

* Share of investments in entities without policies on anti-corruption and anti-bribery consistent with the United Nations Convention against Corruption

66. Lack of action taken to address breaches of standards of anti-corruption and anti-bribery

* Share of investments in investee companies with no action taken to address breaches in procedures and standards of anti-corruption and anti-bribery

67. Convictions and fines for violation of anti-corruption and anti-bribery laws

* Share of investments in investee companies with convictions or fines for violations of anti-corruption and anti-bribery laws

## Indicators applicable to investments in sovereigns and supranationals

* Social

68. Average income inequality score

* The distribution income and economic inequality among the participant in a particular economy including a quantitative indicator explained in the explanation column

69. Average freedom of expression score

* Measuring the extent to which political and civil society organisations can operate freely including a quantitative indicator explained in the explanation column

70. Average human rights performance

* Measure of the average human right performance of investee countries using a quantitative indicator explained in the explanation column

* Human rights

71. Average corruption score

* Measure of the perceived level of public sector corruption using a quantitative indicator explained in the explanation column

* Governance

72. Investments in non-cooperative tax jurisdictions
* Investments in jurisdictions on the EU list of non-cooperative jurisdictions for tax purposes

73. Average political stability score

* Measure of the likelihood that the current regime will be overthrown by the use of force using a quantitative indicator explained in the explanation column

74. Average rule of law score

* Measure of the level of corruption, lack of fundamental rights, and the deficiencies in civil and criminal justice using a quantitative indicator explained in the explanation column

These 74 indicators can be thought of as the metrics of an ESG scoring system, as defined in Figure 2.7 on page 66. Since we have seen that ESG metrics are noisy, we might wonder if the same is true for PAI metrics. We recall that disclosure of these indicators is mandatory for financial market participants and financial products. In fact, disclosure at the fund level appears to be rare. Indeed, looking at the 20 largest Article 9 funds as of December 2023 (Morningstar, 2024b, Exhibit 11, page 15), we found only two funds with a comprehensive PAI report available on the fund's website. These are Nordea 1 – Global Climate and Environment Fund and Handelsbanken Global Index Criteria Fund. Table 4.2 shows the figures for these two funds for the 20 mandatory principal adverse impacts. The case of financial market participants is easier because we generally found the PAI report on their website<fnref n="6" />. For example, we have reported on the PAI report of 6 asset managers in Table 4.2. Several comments can be made. First, the report concerns the legal entity and not the asset manager. For example, the report of Amundi Luxembourg covers all the Amundi funds located in Luxembourg, but not the Amundi funds located in Ireland, France, Germany, Austria, etc. Second, there are few indicators for which we observe consistent figures across reports. This may be the case for the eleventh indicator (gender pay gap). It is certainly not the case for greenhouse gas intensity, non-renewable energy share<fnref n="7" />, energy consumption intensity<fnref n="8" />, biodiversity impact, etc. For example, the GHG intensity of Amundi Luxembourg and AXA IM Paris are 286.9 and 1109 tCO₂e/€ mn, respectively. The share of non-renewable energy is 0.83% (production) for JP Morgan AM Europe and 80.99% (consumption and production) for Schroder IM Europe. Emissions to water are 0.00 and 256.2 t/€ mn for BlackRock Ireland and Amundi Luxembourg, respectively. A third comment is that these large differences can only be explained by asset managers using different approaches or databases. For instance, the GHG intensity by Amundi Luxembourg includes Scope 1, 2 and 3 first-tier upstream GHG emissions, while this by AXA IM Paris considers a comprehensive coverage of Scope 3 upstream and downstream GHG emissions. Mirova's PAI report provides two GHG intensity figures: 3317, tCO₂e/€ mn (without double counting correction) and 796, tCO₂e/€ mn (with double counting correction). These examples show that we are still a long way from harmonising measures between investors. Yet these 20 metrics are the most frequently encountered.

**Remark 42** As far as the voluntary indicators are concerned, each financial market participant is spoilt for choice among the 54 remaining indicators. However, two of them are very often chosen by FMPs:

24. Investments in companies without carbon emission reduction initiatives

58. Lack of human rights policies

<footnote n="6">⁶The PAI report is generally called “statement on principal adverse impacts of investment decisions on sustainability factors”.</footnote>

<footnote n="7">⁷If there are two numbers, the first measures the share of non-renewable energy consumption, while the second measures the share of non-renewable energy production. If there is only one number, there is no distinction between consumption and production.</footnote>

<footnote n="8">⁸If there are two numbers, they represent the minimum and maximum intensity across all NACE sectors. If there is only one number, it corresponds to the weighted average of all NACE sectors.</footnote>
<table><caption>Table 4.2: Statement on principal adverse impacts of investment decisions on sustainability factors</caption><thead><tr><th>Indicator</th><th>AXA<br>IM Paris<br>July 2023</th><th>Amundi<br>Luxembourg<br>June 2023</th><th>BlackRock<br>Ireland<br>June 2023</th><th>DWS<br>GmbH<br>2023</th><th>JP Morgan<br>AM Europe<br>June 2023</th><th>Schroder<br>IM Europe<br>June 2023</th><th>Nordea 1<br>GCE Fund<br>Q4 2023</th><th>Handelsbanken<br>GIC Fund<br>2023</th></tr></thead><tbody><tr><td>1. Total GHG emissions</td><td>60887445</td><td>16688741</td><td>233064529</td><td>117087045</td><td>88112702</td><td>41176662</td><td>739486</td><td>1773122</td></tr><tr><td>2. Carbon footprint</td><td>497</td><td>91.6</td><td>271</td><td>489.4</td><td>221</td><td>374.77</td><td>83.3</td><td>305.33</td></tr><tr><td>3. GHG intensity</td><td>1109</td><td>286.9</td><td>597</td><td>1087.9</td><td>864</td><td>1014.03</td><td>290.5</td><td>860.39</td></tr><tr><td>4. Fossil fuel exposure (corporates)</td><td>5.43%</td><td>11.7%</td><td>9%</td><td>17.07%</td><td>1.2%</td><td>6.59%</td><td>8.1%</td><td>2.64%</td></tr><tr><td>5. Non-renewable energy share</td><td>60.01%</td><td>77.7/64.2%</td><td>64/60%</td><td>75.15%</td><td>0.97/0.83%</td><td>80.99%</td><td>77.6%</td><td>68.24/15.14%</td></tr><tr><td>6. Energy consumption intensity</td><td>0.15-3.80</td><td>0.2/2.6</td><td>0.7</td><td>0.21-14.26</td><td>0-1.39</td><td>29.22</td><td>2.4</td><td>0.17/1117</td></tr><tr><td>7. Impact on biodiversity</td><td>4.86%</td><td>0.0%</td><td>2%</td><td>0.08%</td><td>1.12%</td><td>0.06%</td><td>0.0%</td><td>4.73%</td></tr><tr><td>8. Emissions to water</td><td>0.02</td><td>256.2</td><td>0</td><td>238.59</td><td>1.57</td><td>2.29</td><td>0.1</td><td>0.25</td></tr><tr><td>9. Hazardous waste</td><td>5.72</td><td>22.6</td><td>57</td><td>8.29</td><td>2.64</td><td>3.52</td><td>0.4</td><td>33.36</td></tr><tr><td>10. Non-respect for human rights</td><td>0.07%</td><td>0.7%</td><td>10%</td><td>0.17%</td><td>0.03%</td><td>1.03%</td><td>0.0%</td><td>0.0%</td></tr><tr><td>11. Gender pay gap</td><td>14.87%</td><td>9.6%</td><td>15%</td><td>14.27%</td><td>8.29%</td><td>12.74%</td><td>6.7%</td><td>15.13%</td></tr><tr><td>12. Board gender diversity</td><td>36%</td><td>29.4%</td><td>32%</td><td>32.94%</td><td>4.92%</td><td>35.39%</td><td>33.0%</td><td>30.35%</td></tr><tr><td>13. Non-cooperative tax jurisdictions</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>14. Exposure to controversial weapons</td><td>0%</td><td>0.0%</td><td>&lt;0.01%</td><td>0%</td><td>0.01%</td><td>0%</td><td>0.0%</td><td>0.04%</td></tr><tr><td>15. Tobacco exposure</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>16. Lack of adequate wage</td><td>387</td><td>451.6</td><td>51</td><td>294.7</td><td>32.01</td><td>362.68</td><td></td><td></td></tr><tr><td>17. Sovereign GHG intensity</td><td></td><td>8</td><td>4</td><td>6</td><td>1</td><td>2</td><td></td><td></td></tr><tr><td>18. Country social violations</td><td>1</td><td>0%</td><td>0%</td><td>0%</td><td></td><td></td><td></td><td></td></tr><tr><td>19. Fossil fuel exposure (real estate)</td><td></td><td>44.90%</td><td>0%</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>20. Energy-inefficient real estate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Asset managers' websites & Author's calculations.
UK sustainability disclosure requirements

On 28 November 2023, the Financial Conduct Authority (FCA) published its final rules on sustain-
ability disclosure requirements (SDRs) and investment labels (Policy Statement PS23/16). PS23/16
includes<fnref n="9" />:

* An anti-greenwashing rule for all firms authorised by the FCA to reinforce that sustainability-related claims must be fair, clear and not misleading.

* Rules on the naming and marketing of investment products to ensure that the use of sustainability-related terms is accurate.

* Four labels to help consumers navigate the investment product landscape and increase consumer confidence.

* Consumer-facing information to provide consumers with better, more accessible information to help them understand the key sustainability features of a product.

* Detailed information targeted at institutional investors and consumers seeking more information in pre-contractual, ongoing product-level, and entity-level disclosures.

* Requirements for distributors to ensure that product-level information (including labels) is made available to consumers.

The current UK SDRs only apply to UK domiciled funds, but may be extended in the future to cover overseas funds, such as UCITS funds. They will come into force between May 2024 and December 2026.

With the creation of the sustainability labels, UK funds will now be divided into three categories: (1) sustainability labeled funds that use one of the four labels; (2) unlabeled ESG funds that are marketed as ESG funds; and (3) non-ESG funds that are not marketed as ESG funds. The four voluntary sustainability labels are:

1. Sustainability Focus funds invest in assets that are environmentally or socially sustainable, determined by a robust, evidence-based standard of sustainability.

2. Sustainability Improvers funds invest in assets that have the potential to become more sus-
tainable over time, determined by their potential to meet a robust, evidence-based standard
of sustainability over time.

3. Sustainability Impact funds seek to achieve a predefined, positive, measurable environmental and/or social impact.

4. Sustainability Mixed Goals funds invest in assets that meet or have the potential to meet a robust, evidence-based standard for sustainability, and/or invest with an aim to achieve positive impact.

To qualify for one of the four labels, a product must meet both general and specific criteria. The five general criteria are (1) sustainability objectives, (2) investment policy and strategy, (3) key performance indicators, (4) resources and governance, and (5) stewardship. Products must seek to achieve positive environmental and/or social outcomes as part of their investment objectives

<footnote n="9">⁹The link is www.fca.org.uk/publication/policy/ps23-16.pdf.</footnote>
and disclose any potential negative outcomes<fnref n="10" />. At least 70% of the product's assets should be aligned with its sustainability objective, based on a robust, evidence-based standard. Firms must define KPIs to track progress toward sustainability goals. Firms must also have adequate resources, governance and organizational arrangements to support their sustainability objectives and outline their stewardship strategy to support their sustainability objectives, detailing expected activities and outcomes.

**Remark 43** The SDRs can be seen as the UK version of the EU SFDR. However, they differ in terms of reporting, as the SDR endorses the ISSB reporting framework, which implies a single materiality approach rather than a double materiality approach.

## Other regulations

With the exception of the UK SRDs, there are no equivalent regulations for financial products outside the European Union. In fact, most regulations relate to green taxonomies<fnref n="11" /> and corporate ESG disclosures. Asset managers must then comply with these disclosure reports, but not the financial products and investment funds. Below is a list of the few initiatives in other countries.

**United States** On September 20, 2023, the SEC introduced new rules aimed at preventing the misleading use of ESG terms in mutual fund names and combating greenwashing. These rules update the Investment Company Act's “Names Rule” to ensure that fund names accurately reflect their investment strategies. Previously, funds were required to invest at least 80% of their assets in the areas suggested by their names. The new rules extend this requirement to ESG strategies, ensuring that fund names have a clear and established meaning. This extension will avoid situations where funds technically meet the 80% rule, but their remaining holdings contradict their names, for example a fund labeled fossil fuel free that holds fossil fuel investments in its remaining 20%.

**Japan** On March 2023, the Financial Services Agency of Japan issued new guidelines<fnref n="12" /> for ESG investment funds: Comprehensive Guidelines for Supervision of Financial Instruments Business Operators, etc. Again, the aim of these guidelines is to combat greenwashing. They provide instructions and standards for the supervision of financial instruments business operators, including securities firms, investment advisors and other entities involved in the financial services industry in Japan. The guidelines cover compliance requirements, risk management, customer protection and market integrity. For example, funds that don't follow these guidelines are not allowed to use ESG-related terms such as “green” or “sustainable” in their names.

**Malaysia** On February 2023, the Securities Commission of Malaysia released a revised version of the Guidelines for Sustainable and Responsible Investment Funds, which sets out the reporting and disclosure requirements for a fund to qualify as a Sustainable and Responsible Investment (SRI) fund and an ASEAN Sustainable and Responsible Fund (SRF). SRI funds are now required to invest at least two-thirds of their net asset value in securities that meet their SRI policies and strategies. They are also encouraged to have a clear strategy for achieving their policies, such as ESG integration, ethical and faith-based investing, impact investing, etc.

<footnote n="10">¹⁰However, this does not mean that the SDRs adopt the DNSH principle.</footnote>

<footnote n="11">¹¹For example, green taxonomies are already active in Bangladesh, China, Colombia, Indonesia, Japan, Malaysia, Mexico, Norway, Russia, Singapore, South Africa, South Korea, and Thailand, while some 20 other countries are developing similar frameworks (e.g., Brazil, Canada, India, Turkey, and the United Kingdom).</footnote>

<footnote n="12">¹²The English version can be downloaded from https://www.fsa.go.jp/common/law/guide/kinyushohin_eng.pdf.</footnote>
4.1.2 Sustainable labels

A label refers to a designation given to a particular type of product and is used to indicate certain characteristics or features of the product to help investors understand its nature and the risks associated with it. When applied to sustainable finance, an ESG label indicates that the products meet certain environmental, social or governance criteria. However, many products fall into this category. For example, Article 8 and 9 funds have an ESG stamp, but Article 8 and 9 cannot be considered as two SFDR labels because the classification of the fund is decided by the fund itself, so it is a self-designation. A label implies an external authority that certifies that the product complies with the sustainability rules of the label.

<figure><img src="image_110.png" /><figcaption>Figure 4.3: The jungle of sustainability labels</figcaption></figure>

Source: www.kpmg.com/ch/en/home/insights/2023/03/sustainability-eco-label.html.

According to Ecolabel Index<fnref n="13" /> and Standards Map<fnref n="14" />, there are approximately hundreds labels in the world related to ESG or environmental considerations. This large number raises the question of how companies and people can navigate what KPMG<fnref n="15" /> calls the “jungle of sustainability and eco-labels”. This is especially true for consumers, who have a choice of many labels without knowing their characteristics, but who ultimately trust them (Testa et al., 2015). In the financial sector, the problem is less significant because there are fewer players. For example, according to Megaeva et al. (2021, 2023), there are nine major labels in Europe with published and public criteria (Table 4.3).

<footnote n="13">¹³More than 400 eco-labels can be found at www.ecolabelindex.com/ecolabels.</footnote>

<footnote n="14">¹⁴The website www.standardsmap.org/en/identify lists ESG labels according to several dimensions: SDGs, sector, sustainability themes, etc.</footnote>

<footnote n="15">¹⁵KMPG (2023). Sustainability Standards and Labels: Navigating the Jungle. Report, October, 16 pages.</footnote>
<table><caption>Table 4.3: Main sustainable labels</caption><thead><tr><th>Name</th><th>website</th><th>Type</th><th>Country</th><th>Sponsor</th></tr></thead><tbody><tr><td>FNG-Siegel</td><td>www.fng-siegel.org</td><td>ESG</td><td>Austria, Germany, Switzerland</td><td>FNG: Forum Nachhaltige Geldanlagen</td></tr><tr><td>Label Greenfin</td><td>www.ecologie.gouv.fr/label-greenfin</td><td>Green</td><td>France</td><td>French government</td></tr><tr><td>Label ISR</td><td>www.lelabelisr.fr</td><td>ESG</td><td>France</td><td>French government</td></tr><tr><td>LuxFLAG Climate Finance</td><td>www.luxflag.org/labels//climate-finance</td><td>ESG</td><td>Luxembourg</td><td>LuxFLAG</td></tr><tr><td>LuxFLAG Environment</td><td>www.luxflag.org/labels/environment</td><td>ESG</td><td>Luxembourg</td><td>LuxFLAG</td></tr><tr><td>LuxFLAG ESG</td><td>www.luxflag.org/labels/esg</td><td>ESG</td><td>Luxembourg</td><td>LuxFLAG</td></tr><tr><td>LuxFLAG Microfinance</td><td>www.luxflag.org/labels/microfinance</td><td>ESG</td><td>Luxembourg</td><td>LuxFLAG</td></tr><tr><td>Nordic Swan Ecolabel</td><td>www.nordic-swan-ecolabel.org</td><td>Green</td><td>Nordics</td><td>Nordic Council</td></tr><tr><td>Towards Sustainability</td><td>www.towardssustainability.be</td><td>Green</td><td>Belgium</td><td>Febelfin</td></tr><tr><td>Umweltzeichen</td><td>www.umweltzeichen.at</td><td>ESG</td><td>Austria</td><td>Austrian government</td></tr></tbody></table>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>
Greenwashing issues

The concept of labels is closely related to the issue of greenwashing, which can be defined as follows:

* “The creation or propagation of an unfounded or misleading environmentalist image” (Oxford English Dictionary)

* “Behaviour or activities that make people believe that a company is doing more to protect the environment than it really is” (Cambridge Dictionary)

* “A term (combining green and whitewash) that environmentalists use to describe the activity (for example by corporate lobby groups) of giving a positive public image to practices that are environmentally unsound” (Oxford Reference)

* “The act or practice of making a product, policy, activity, etc. appear to be more environmentally friendly or less environmentally damaging than it really is” (Merriam-Webster Dictionary)

Therefore, greenwashing is any activity by a company or organization that is intended to make people think that it cares about the environment, even if its actual business is actually harmful to the environment. For example, a common form of greenwashing is to publicly claim a commitment to the environment while quietly lobbying to avoid regulation.

<figure><img src="image_121.png" /><figcaption>Figure 4.4: A typology of firms based on environmental performance and communication</figcaption></figure>

Source: Delmas and Burbano (2011, Figure 1, page 67).

According to Delmas and Burbano (2011), “a greenwashing firm engages in two behaviors simultaneously: poor environmental performance and positive communication about its environmental
*performance*” (Figure 4.4) and there are two ways by which a non-greenwashing firm can become a greenwashing firm:

* “First, a vocal firm can alter its environmental performance.”

* “Second, a brown firm can alter communication about its environmental performance.”

However, a firm’s environmental performance is relatively fixed and changes very smoothly. Therefore, greenwashing is mainly explained by the communication axis. In this context, Delmas and Burbano (2011) identify four drivers of greenwashing:

1. Market external drivers: consumer demand, investor demand, and competitive pressure;

2. Non-market external drivers: lax and uncertain regulatory environment, and activist, NGO and media monitoring;

3. Individual psychological drivers: optimistic bias, narrow decision framing, and hyperbolic intertemporal discounting;

4. organizational drivers: firm characteristics, incentive structure and culture, effectiveness of intra-Firm communication, and organizational inertia.

Greenwashing does not only concern firms, but also products and services. This is why Delmas and Burbano (2011) distinguish two types of greenwashing: firm-level greenwashing and product-level greenwashing.

<figure><img src="image_122.png" /><figcaption>Figure 4.5: Count of unique entities with at least one ESG risk incident linked to both environmental footprint and misleading communication</figcaption></figure>

Source: RepRisk (2023, Figure 2).
RepRisk (2023) has developed a methodology to capture greenwashing events through the inter-section of two ESG groups: an environmental issue (or controversy) and a misleading communication. From September 2018 to September 2023, RepRisk listed 4237 risk incidents linked to ESG pillars and misleading communication broken down as follows: 30.1% for environmental risk events, 36.4% for environmental and social risk events, 26.3% for social risk events and 7.1% for governance risk events. In Figure 4.5, we show the evolution of greenwashing by region. We note that the risk of greenwashing has accelerated in Europe and North America. Table 4.4 gives the breakdown of sectors when considering greenwashing events related to climate change, GHG emissions, and global pollution. The sectors most exposed to greenwashing risk are oil and gas, banking and financial services, utilities and industrials and construction.

<table><caption>Table 4.4: Breakdown of sectors linked to climate greenwashing (in %)</caption><thead><tr><td>Sector</td><td>2018-2022</td><td>2023</td></tr></thead><tbody><tr><td>Banking and financial services</td><td>13.1</td><td>15.5</td></tr><tr><td>Food and beverage</td><td>9.2</td><td>8.2</td></tr><tr><td>Industrials and construction</td><td>8.1</td><td>9.5</td></tr><tr><td>Mining</td><td>5.0</td><td>3.9</td></tr><tr><td>Oil and gas</td><td>26.1</td><td>19.3</td></tr><tr><td>Retail, personal, and household goods</td><td>7.7</td><td>6.8</td></tr><tr><td>Travel and airlines</td><td>5.4</td><td>9.7</td></tr><tr><td>Utilities</td><td>11.6</td><td>10.0</td></tr><tr><td>Other</td><td>13.7</td><td>17.2</td></tr></tbody></table>

Source: RepRisk (2023, Figure 3).

When applied to the financial sector, greenwashing refers to “a practice whereby sustainability-related statements, declarations, actions, or communications do not clearly and fairly reflect the underlying sustainability profile of an entity, a financial product, or financial services. This practice may be misleading to consumers, investors, or other market participants” (EBA, 2023, page 12). Again, greenwashing can involve a financial institution, such as an asset owner or manager (entity-level greenwashing), an investment vehicle, such as a mutual fund (product-level greenwashing), or a financial service, such as an advisory agreement (service-level greenwashing). There are also two types of greenwashing risk:

*   Explicit and intentional greenwashing

Explicit greenwashing involves deliberately misleading consumers by making false or exaggerated claims about a product's environmental benefits. Financial institutions typically engage in explicit greenwashing to gain a competitive advantage in the marketplace.

*   Unintentional greenwashing

Financial institutions may unintentionally misrepresent the environmental attributes of their products due to incomplete or inaccurate information, biased metrics and data, or lack of precise regulations. Unintentional greenwashing can occur despite a company's sincere commitment to sustainability because of the multiple interpretations of ESG dimensions.

An example of intentional greenwashing is DWS Group, which has been accused of misleading investors by marketing its funds as greener than they actually were. The story began when former head of sustainability Desiree Fixler said the company was struggling with its environmental, social and governance investment strategy and claimed it was not giving investors an accurate picture. In
October 2023, DWS agreed to pay a $19 million fine to settle the charges in the US<fnref n="16" />. In Germany, investigation is still ongoing. An example of unintentional greenwashing is the wave of reclassifications from Article 9 to Article 8 that will occur at the end of 2022. The main reason is that the definition of Article 9 was not precise. In 2022, the publication of new SFDR regulatory technical standards led many asset managers to voluntarily downgrade funds from Article 9 to Article 8 in order to meet the new minimum requirements. So if the regulation is too vague, the risk of unintentional greenwashing increases.

In fact, it is extremely difficult to identify greenwashing at the corporate level. It is not always black and white. In the financial sector, most examples involve the banking industry and the financing of the fossil fuel industry (EBA, 2023; RepRisk, 2023). But they also help finance the transition to a low-carbon economy and renewable energy. Of course, banks will communicate publicly about its green activities, not its brown activities. Does that mean they are involved in greenwashing? Again, this is a matter of balance and interpretation. Another reason for this difficulty is that there are several shades or forms of greenwashing. Planet Tracker identifies six types of greenwashing activities<fnref n="17" />:

1. "Greencrowding is built on the belief that you can hide in a crowd to avoid discovery; it relies on safety in numbers. If sustainability policies are being developed, it is likely that the group will move at the speed of the slowest.

2. Greenlighting occurs when company communications (including advertisements) spotlight a particularly green feature of its operations or products, however small, in order to draw attention away from environmentally damaging activities being conducted elsewhere.

3. Greenshifting is when companies imply that the consumer is at fault and shift the blame on to them.

4. Greenlabelling is a practice where marketers call something green or sustainable, but a closer examination reveals that their words are misleading.

5. Greenrinsing refers to when a company regularly changes its ESG targets before they are achieved.

6. Greenhushing refers to the act of corporate management teams under-reporting or hiding their sustainability credentials in order to evade investor scrutiny.”

In theory, product-level greenwashing is easier to detect when the rules of the game are clear and well-defined. For example, the minimum requirements for Article 9 funds are now better defined, but there is still a lot of room for interpretation. Moreover, the status of Article 8 and 9 funds is based on the asset manager's self-declaration. In this context, Article 8 and 9 funds cannot be considered a label, as a label requires a comprehensive set of criteria and certification by an external auditor. These two features are the real added value of a label and a way to avoid greenwashing, as it is not the fund that decides on its status, but an external authority. Certification is then central to having a label. For example, we cannot talk about CTB and PAB labels because even if there is a comprehensive set of criteria, there is no certification process. We have a similar situation with the Green Bond Principles, which are a set of recommendations, not mandatory rules. However, it is extremely difficult to launch a green bond without external verification and possible certification by an organization like the Climate Bonds Initiative (CBI).

<footnote n="16">¹⁶This is the largest greenwashing penalty ever imposed by the SEC on an asset manager.</footnote>

<footnote n="17">¹⁷Source: www.planet-tracker.org/wp-content/uploads/2023/01/Greenwashing-Hydra-3.pdf.</footnote>
## ESG labels

In the following we describe the different European labels. The underlying idea is not to give a comprehensive presentation, because it is relatively complex to list all the criteria, minimum requirements and also guidelines of each label. This task has already been done by Megaeva et al. (2021, 2023). The goal is to give a broad overview of the different labels and to have an idea about the main differences between them. In particular, there are two types of labels: those that use a pass-or-fail system and those that use a grading or bonus point system. In the first case, all requirements are mandatory and must be met at all times to qualify for the label. In the second case, each requirement is assigned a score, and the total score must exceed a minimum threshold.

**Umweltzeichen Ecolabel (Austria)** The Austrian eco-label (UZ 49) for sustainable financial products was established by the Austrian government in 2004 and is the oldest financial eco-label in Europe<fnref n="18" />. Certification is carried out by the Federal Ministry for Climate Protection (BMK), while the Austrian consumer association (VKI) is responsible for the development and administration of the UZ guidelines. The Austrian label is a pass-or-fail system for financial products in the financing sector (e.g., green bonds, sustainable savings products, current accounts and green loans) and a point system for investment products with portfolio characteristics (e.g., mutual funds). The maximum score is 66 for equity funds, 55 for bond funds, and 35 for real estate funds. The minimum threshold is set at 75% of the maximum total score. All criteria are listed in Chapter 2 of the guidelines. Below is a breakdown of the scores by fund category:

<table><thead><tr><th>Section</th><th>Title</th><th>Equity</th><th>Bond</th><th>Real estate</th><th>Bonus</th></tr></thead><tbody><tr><td>2.3.1</td><td>Selection criteria</td><td>30</td><td>33</td><td>18</td><td></td></tr><tr><td>2.3.2</td><td>Implementation of Selection criteria</td><td>22</td><td>10</td><td>12</td><td></td></tr><tr><td>2.3.4</td><td>Degree of selection investment universe</td><td>5</td><td>5</td><td>5</td><td></td></tr><tr><td>2.3.5</td><td>Requirements EU taxonomy</td><td></td><td></td><td></td><td>10%</td></tr><tr><td>2.3.6</td><td>Engagement</td><td>9</td><td>7</td><td>0</td><td></td></tr><tr><td>2.3.7</td><td>Bonus section</td><td></td><td></td><td></td><td>3%</td></tr><tr><td></td><td>Total</td><td>66</td><td>55</td><td>35</td><td></td></tr></tbody></table>

In addition, the fund must comply with mandatory criteria relating to exclusion, research process, transparency, investment fund business, compliance, and information and declaration. Exclusions relate to business sectors (nuclear energy, fossil fuels, weapons and armaments, genetic engineering and tobacco) and business practices (UN Global Compact).

**Label ISR (France)** Label ISR is an ESG label sponsored by the French government and certified by one of three external auditors: Afnor Certification, EY France and Deloitte. It was introduced in 2016 and covers equity, bond and real estate funds. It can be considered the successor of the ISR label launched by Novethic in 2009 and suspended in 2016 due to the existence of this new label. A first revision took place in 2020. The third revision came into force in March 2024. In previous versions of Label ISR, the exclusion policy was decided by the fund. With the new version, there is an explicit list of exclusions based on social criteria (e.g., controversial weapons, UN Global Compact, tobacco), environmental criteria (e.g., thermal coal, fossil fuels) or governance criteria (e.g., any company domiciled in a country on the Financial Action Task Force (FATF) blacklist or greylist). In addition to exclusions, the label offers asset managers two options:

<footnote n="18">¹⁸The latest version of the labeling guidelines is available at www.umweltzeichen.at/en/products/sustainable-finance. It corresponds to the Austrian Ecolabel Guideline UZ 49, Sustainable Financial Products, Version 6.0 of January 1st, 2024.</footnote>
1. They can reduce their ESG investable universe by 30% compared to the fund's original investment universe. This reduction involves eliminating the 30% of investments with the lowest ESG scores, along with any exclusions imposed by the fund.

2. Alternatively, they can ensure that the average weighted ESG score of the portfolio is significantly higher than that of the original investment universe. Under this option, the average weighted ESG score of the portfolio must never fall below the weighted ESG score of the fund's original investment universe or benchmark index, even after excluding the 30% of investments with the worst ESG scores and any exclusions imposed by the fund.

**LuxFLAG ESG (Luxembourg)** LuxFLAG (Luxembourg Finance Labelling Agency) is a non-profit association created in Luxembourg in July 2006 by seven founding partners to support sustainable finance including the Association of the Luxembourg Fund Industry (ALFI), the European Investment Bank, the Luxembourg Stock Exchange and the Government of Luxembourg. The LuxFLAG ESG label is launched in May 2014 and is a pass-or-fail system. It requires 100% of the portfolio to be screened against one of the ESG strategies. It has a standard exclusion policy and requires the fund to integrate at least 3 investment strategies into the investment process. The certification process is conducted by a LuxFlag committee dedicated to the label.

**FNG-Siegel (Germany)** Forum Nachhaltige Geldanlagen (FNG) is the Sustainable Investment Forum (SIF) in the German-speaking countries (Germany, Austria and Switzerland). It launched the FNG-Siegel, also known as the FNG label, in 2015. It is a grading system, but it also includes some mandatory criteria and minimum requirements. Exclusions are related to weapons, nuclear energy, fossil fuels and UN Global Compact. The weighting of the grading system for the different categories is as follows:

<table><thead><tr><td>Category</td><td>Weighting</td></tr></thead><tbody><tr><td>Institutional credibility</td><td>10%</td></tr><tr><td>Product standards</td><td>20%</td></tr><tr><td>Selection strategy</td><td>35%</td></tr><tr><td>Dialogue strategy</td><td>25%</td></tr><tr><td>ESG key performance indicators</td><td>10%</td></tr><tr><td>Total score</td><td>100%</td></tr></tbody></table>

The label grades awarded are Basic for scores up to 24.99%, One Star for scores between 25% and 49.99%, Two Star for scores between 50% and 69.99%, and Three Star for scores of 70.00% or higher. Prior to 2021, the certification was managed as a third-party funded project at the chair of Professor Timo Busch in Hamburg. Since 2021, the evaluation process has been delegated to the non-profit scientific association F.I.R.S.T., which is responsible for the FNG label certifications, and the university spin-off Advanced Impact Research (AIR).

**Towards Sustainability (Belgium)** In 2019, the Federation of the Belgian Financial Sector (Febelfin) developed a new ESG label called Towards Sustainability. It is now managed by the Central Labelling Agency (CLA), a non-profit association incorporated under Belgian law. The label is a pass-or-fail system. To qualify for the label, a sustainable financial portfolio must employ the following four strategies: ESG integration, normative screening, exclusion<fnref n="19" />, and an additional core strategy. The first three strategies ensure that the portfolio does not finance activities that cause

<footnote n="19">¹⁹Exclusions include weapons, tobacco, coal, unconventional oil and gas, and lagging electric utilities.</footnote>
significant harm to sustainability factors. The additional strategy either reinforces ESG principles or aligns with the portfolio's sustainability objectives. These strategies may vary in their approach: a) best-in-class/universe selection, b) theme-based sustainability investing, c) impact investing, d) the objective of outperforming a benchmark on one or more ESG indicators, e) any other strategy approved by the CLA that results in the selection of more sustainable issuers. In addition to the required minimum of four ESG strategies, the non-technical portion of the portfolio must also meet minimum requirements for greenhouse gas intensity, gender diversity, sustainable investments, and corporate engagement and shareholder activism. For the different strategies (the first three DNSH strategies and the next five additional strategies), the CLA clearly defines the eligibility criteria for compliance. For example, a sustainability themed investing strategy selects investments based on one or more clearly defined themes as outlined by recognized frameworks endorsed by the CLA, such as the EU Taxonomy, the SDGs, the EU Green Bond Standard, the ICMA Social Bond Principles, and others. It must also meet at least one of the following criteria:

* At least 70% of the portfolio's assets are related to the selected theme(s). A company is considered related to a theme if it derives at least 50% of its revenues from activities related to that theme.

* At least 50% of the total portfolio is allocated to economic activities that contribute to the selected theme.

* The portfolio uses a best-in-universe approach, targeting the top 25% of the highest rated issuers based on an appropriate ESG rating relevant to the theme.

* The portfolio qualifies as an SFDR Article 9 portfolio and includes sustainability themed investments as part of its strategy.

## Green labels

Previous labels have a clear ESG focus. Below we present European eco-labels that aim to support the transition to a low-carbon economy. For comparison with the SFDR framework, green labels are the equivalent of Article 9 funds, while ESG labels are the equivalent of Article 8 funds. However, the comparison stops here as we recall that the SFDR is not a label.

**Nordic Swan (Nordic countries)** The Nordic Swan label was established in 1989 by the Nordic Council of Ministers as a voluntary labeling scheme for the Nordic countries of Denmark, Finland, Iceland, Norway and Sweden. According to its website<fnref n="20" />, "it is today the official Ecolabel of the Nordic countries, supported by all Nordic governments and the most recognised environmental label in the region. The goal is to enable consumers and professional buyers to choose the environmentally best goods and services by giving an effective tool to help companies develop more sustainable products and services." The Nordic Swan covers 58 different product groups, from alternative dry cleaning to windows and exterior doors. For financial products, the category corresponds to Investment Funds and Investment Products 101. It includes equity funds, bond funds, balanced funds and investment products. Funds must meet requirements within four different sustainability strategies:

* Exclude the worst companies and industries in coal, oil, gas, nuclear, tobacco, weapons, and non-compliance with international standards.

<footnote n="20">²⁰The address of the web site is www.nordic-swan-ecolabel.org.</footnote>
* Include more sustainable companies by rewarding companies with strong sustainability performance from an ESG and EU taxonomy perspective. The criteria also pay particular attention to sectors with high greenhouse gas emissions and/or high risk of biodiversity impact.

* Exercise active ownership by engaging with companies where there is uncertainty as to whether they will meet international standards if the company is not divested.

* Disclose all holdings in the portfolio and publish an annual sustainability report.

The Nordic Swan label is an ESG grading system with extra points for environmental focus. It is therefore both an ESG label and a green label. Out of a maximum of 14 and 11 points for equity and bond funds respectively, a minimum of 6 points for equity funds and 5 points for bond funds is required to obtain the Nordic Swan label. Below is the weighting scheme for equity funds:

<table><thead><tr><td>Section</td><td>Title</td><td>Points</td></tr></thead><tbody><tr><td>Inclusion criteria</td><td>EU Taxonomy alignment</td><td>6</td></tr><tr><td>Inclusion criteria</td><td>Enhanced analysis and inclusion</td><td>2</td></tr><tr><td>Active ownership</td><td>Systematic and targeted engagement</td><td>3</td></tr><tr><td>Active ownership</td><td>Regular voting</td><td>3</td></tr></tbody></table>

For example, the portfolio alignment with the EU taxonomy is calculated using the formula:

$$ \mathcal{G} = \sum_{i=1}^{n} w_i \cdot \min \left( \frac{\mathcal{GT}_i + \mathcal{GC}_i + \mathcal{GO}_i}{T_i}, 1 \right) $$

where $w_i$ is the weight of holding i in the portfolio, $\mathcal{GT}_i$ is the green turnover of company i in the last year, $\mathcal{GC}_i$ is the highest annual green capex of company i in the last 3 years, $\mathcal{GO}_i$ is the highest annual green opex of company i in the last 3 years and $T_i$ is the turnover of company i in the last year. The number of points is 1 if $\mathcal{G} \ge 5\%$, 2 if $\mathcal{G} \ge 10\%$, 3 if $\mathcal{G} \ge 20\%$, 4 if $\mathcal{G} \ge 30\%$, 5 if $\mathcal{G} \ge 40\%$ and 6 if $\mathcal{G} \ge 50\%$.

**LuxFLAG Environment (Luxembourg)** Launched in July 2011, the LuxFLAG Environment label requires a portfolio of investments in environmentally related sectors<fnref n="21" /> representing at least 75% of the fund's total assets. A company is considered to be an environmental company if its turnover in environment-related sectors is at least 20% of its total turnover. The portfolio allocation in environmental companies, weighted by the proportion of each company's turnover in environment-related sectors, must be at least 33%. A second green label, the LuxFLAG Climate Finance label, was introduced in September 2016. This label is more stringent than the Environment label. For example, funds must be classified as Article 9 SFDR products to qualify for this label.

**Label Greenfin (France)** The Greenfin label is a public certification program launched in 2015 by the French Ministry of Ecological Transition and Territorial Cohesion. Its aim is to channel investments into environmental and energy projects that support a sustainable future. Like the French ISR label, the certification is guaranteed by three external auditors: Novethic, EY France and Afnor Certification. The activities eligible for the Greenfin label are initially based on the Climate Bond Initiative's taxonomy and cover 8 themes:

<footnote n="21">²¹Environmentally related sectors are defined in relation to a globally recognized classification system such as the FTSE Environmental Markets Classification System or the HSBC Climate Change Structure.</footnote>
1. Energy: solar energy, wind energy, nuclear energy, bioenergy, hydraulic energy, geothermal energy, other renewable energies, energy distribution and management, energy storage, carbon capture, services;

2. Building: low carbon buildings, energy efficiency, energy capture systems, services;

3. Circular economy: technologies and products, services, waste energy valorization, existing fossil fuels energy valorization, waste management;

4. Industry: energy-efficient products, energy-efficient systems and processes, co-generation, tri-generation, etc., waste heat recovery, reduction of GHGs not linked to energy production, reduction of pollution, eco-efficient industrial processes, services, organic agro-food industry;

5. Transport: freight and railway transport system, urban rail transport system, electric vehicles, hybrid vehicles, alternative fuel vehicles, bus rapid transit, maritime transport, bicycle transport, biofuels, aviation biofuel, transport logistics;

6. Information and communications technology: data centres running on renewable energies, low-carbon facilities, products and technologies running on smart grid, substitution technologies;

7. Agriculture & forestry: organic agriculture, sustainable agriculture, forestry activities emitting less carbon and linked to carbon sequestration, low GHG emission agriculture, sequestering carbon and climate resilient;

8. Adaptation: water adaptation, infrastructures.

Each activity has a detailed description and some eligibility criteria. Three issuer pockets are then defined based on the weight of environmental activities in their revenues: Type I issuers are companies that generate more than 50% of their revenues from environmental activities; Type II issuers are companies that generate between 10% and 50% of their revenues from environmental activities; Type III issuers are companies that generate less than 10% of their revenues from environmental activities. Funds investing in private equities and bonds must have an allocation to Type I issuers of at least 75%. Funds investing in public equities must have an allocation of at least 75% in Type I and Type II issuers, with at least 20% in Type I issuers.

In addition to the previous allocation restrictions, the Greenfin label requires the exclusion of three categories of issuers: companies developing new projects for the exploration, extraction or transportation of coal, oil, gas and fossil fuels; companies deriving more than 5% of their revenues from the fossil fuel value chain; companies deriving 30% or more of their revenues from some energy-related activities that generate significant greenhouse gas emissions, such as storage and landfill without greenhouse gas capture or incineration without energy recovery.

## EU Ecolabel

The EU Ecolabel, established in 1992, is a European Union label for green products and services. It can be seen as the equivalent of the Nordic Swan label for the countries of the European Union. As of March 2024, 95 758 products and services have been awarded the EU Ecolabel and 2 743 licenses have been granted to companies. In January 2018, the HLEG final report proposed the creation of an EU-wide label for green investment funds<fnref n="22" />. Since then, several proposals for an EU Ecolabel for retail financial products have been made but never adopted. The current status of

<footnote n="22">²²See Chapter 1, page 31.</footnote>
the EU Ecolabel for green funds is therefore uncertain, as we don't know whether the European Commission is not satisfied with the latest version or whether the European Commission does not want to create this label. In its first versions (Kofoworola et al., 2019), the EU Ecolabel was based on the pocket approach and was close to the Greenfin label system: the first pocket includes companies that generate more than 50% of their revenues from green activities; the second pocket corresponds to companies that generate between 20% and 50% of their revenues from green activities; the third pocket includes companies that generate less than 20% of their revenues from green activities. At least 60% of the portfolio value must be invested in the first and second pockets, with at least 20% invested in the first pocket. In its latest version (Konstantas et al., 2021), the EU Ecolabel is based on a green intensity approach and is close to the Nordic Swan label system. For equity funds, the greenness of the portfolio is defined as:

$$G = \sum_{i=1}^{n} w_i \left( \frac{3}{5} GRS_i + \frac{2}{5} \max(GC_i^{5Y}, GR_i^{5Y}) \right)$$

where $w_i$ is the weight of holding i in the portfolio, $GRS_i$ is the green revenue share of company i in the last year, $GC_i$ is the cumulative green capex (in %) of company i over the last 5 years and $GR_i$ is the estimated green revenue growth for the next 5 years. The minimum requirement is $G \ge 40\%$. For bond funds, at least 50% of the portfolio must be invested in green bonds. The latest version of the EU Ecolabel also includes other criteria, notably ESG exclusions and the quality of the engagement policy.

<figure><img src="image_123.png" /><figcaption>Figure 4.6: The EU Ecolabel logo</figcaption></figure>

### 4.1.3 The market of ESG funds

Analyzing the ESG fund market requires a definition of an ESG fund. Again, we are faced with the question of what an ESG fund is. We therefore consider three types of analysis. The first analysis looks only at labeled funds, the second analysis focuses on Article 8 and 9 funds, and the third analysis looks at sustainable funds. By construction, the first and second analyses can only be carried out for European mutual funds, as there is no equivalent outside the European Union. For the third analysis, we have to rely on the definition of a sustainable fund provided by a third-party data provider. In this last case, we decided to use Morningstar, which is known as a reference in the mutual fund industry.
## Labeled European funds

According to Novethic's annual report, there are 2733 European funds with an ESG label, with a total of 1307 billion euros in assets under management (Table 4.5). This represents about 7% of the European mutual fund market. More than 75% of these funds are Article 8, with the remainder being Article 9. Two labels dominate the market: France's ISR label and Belgium's Towards Sustainability. They represent 66.8% of the number of funds and 80.8% of assets under management. We note that some funds have several labels. In fact, if we add up the first column of the table, we obtain a total of 3183 funds. The difference between 3183 and 2733 funds represents the double counting due to multiple labels. 333 funds have at least two labels, but some funds have three or four labels. The green-labeled funds are a small fraction, representing 5.9% of funds and 2.5% of assets under management.

<table><caption>Table 4.5: Overview of European sustainable labels as of 31 July 2023</caption><thead><tr><th>Label</th><th>Number of funds</th><th>Multiple labels</th><th>AUM € bn</th><th>No. of funds (in %)</th><th>AUM (in %)</th></tr></thead><tbody><tr><td>Label ISR</td><td>1354</td><td>205</td><td>783</td><td>42.54%</td><td>47.83%</td></tr><tr><td>FNG-Siegel</td><td>291</td><td>39</td><td>94</td><td>9.14%</td><td>5.74%</td></tr><tr><td>LuxFLAG ESG</td><td>246</td><td>43</td><td>112</td><td>7.73%</td><td>6.84%</td></tr><tr><td>LuxFLAG Microfinance</td><td>28</td><td>1</td><td>4</td><td>0.88%</td><td>0.24%</td></tr><tr><td>Towards Sustainability</td><td>771</td><td>492</td><td>539</td><td>24.22%</td><td>32.93%</td></tr><tr><td>Umweltzeichen</td><td>304</td><td>192</td><td>64</td><td>9.55%</td><td>3.91%</td></tr><tr><td>Nordic Swan</td><td>60</td><td>8</td><td>18</td><td>1.89%</td><td>1.10%</td></tr><tr><td>LuxFLAG Environment</td><td>9</td><td>1</td><td>1</td><td>0.28%</td><td>0.06%</td></tr><tr><td>Label Greenfin</td><td>120</td><td>23</td><td>22</td><td>3.77%</td><td>1.34%</td></tr></tbody><tfoot><tr><td>Total</td><td>2733</td><td>333</td><td>1307</td><td></td><td></td></tr></tfoot></table>

Source: Menif et al. (2023), www.novethic.fr & Author's calculations.

According to Brito-Ramos et al. (2024a,b), labeled funds, especially those with multiple labels, are often recognized as top ESG funds by private rating providers. Government labels and multiple labels are associated with higher ESG ratings for equity funds. These labeled funds also tend to be more compliant with Article 9 of the SFDR and often include ESG terminology in their names. In addition, once a fund has received the label, it attracts additional flows compared to otherwise comparable funds. These results confirm the resilience of labeled funds in 2022. In fact, while the European mutual fund market experienced a global outflow of €273 bn in 2022, labeled funds attracted €28 bn during the same period (EFAMA, 2023a; Menif et al., 2023).

## Article 8 and 9 European investment funds

To analyze the market for Article 8 and 9 funds, we use two sources. The first is Morningstar (2024b), a commercial data provider. The second is EFAMA (2023b), the European Fund and Asset Management Association. The main difference between the two studies is that in the latter case the data collection is based on surveys of EFAMA member associations. Typically, EFAMA's
Box 4.4: UCITS and alternative investment funds

UCITS (Undertakings for Collective Investment in Transferable Securities) are investment funds that can be marketed to investors throughout the European Union. They meet specific regulatory standards designed to ensure investor protection and liquidity. UCITS funds can invest in a variety of assets, such as equities, bonds and derivatives, and must comply with diversification rules and leverage limits.

Alternative investment funds (AIFs), on the other hand, encompass a broader range of investment funds that fall outside the scope of UCITS regulation. They can include hedge funds, private equity funds, real estate funds and other alternative investments. AIFs are subject to the Alternative Investment Fund Managers Directive (AIFMD), which imposes rules on fund managers to enhance transparency and investor protection. AIFs may use leverage and short positions and are generally less liquid. While UCITS funds offer daily liquidity, AIFs may offer daily, weekly, monthly or quarterly liquidity (ESMA, 2024).

results are more comprehensive because they theoretically provide more complete coverage of the AIF market, while Morningstar's results are more focused on the UCITS market. This explains why EFAMA found that Article 8 and 9 funds represented €6.78 tn at the end of 2022, while Morningstar found total assets under management of €5.2 tn at the end of 2023. By the end of 2022, Article 8 and Article 9 funds represent 45% and 2.4% of the European fund market, respectively (Table 4.6). The progress between Q1 2021 and Q4 2022 is impressive for Article 8 funds, as their market share was only 25% in the first quarter of 2021. The progress of Article 9 funds is less significant due to the wave of reclassifications between July and December 2022. These figures are higher than Morningstar's (Table 4.7), which certainly reflects the differences between UCITS and AIFs. Indeed, we can assume that alternative funds, especially hedge funds, need fewer constraints, including ESG and climate constraints, to limit the impact on their financial performance.

<table><caption>Table 4.6: Market share of SFDR funds (UCITS and AIFs)</caption><thead><tr><th>Period</th><th>Article 6</th><th>Article 8</th><th>Article 9</th></tr></thead><tbody><tr><td>Q1 2021</td><td>72.7%</td><td>25.0%</td><td>2.3%</td></tr><tr><td>Q4 2022</td><td>52.6%</td><td>45.0%</td><td>2.4%</td></tr></tbody></table>

Source: EFAMA (2023b, pages 2 and 4) & Author's calculations.

<table><caption>Table 4.7: Number of funds and breakdown of assets under management at the end of 2023</caption><thead><tr><th>Statistics</th><th>Article 6</th><th>Article 8</th><th>Article 9</th></tr></thead><tbody><tr><td>Number of funds</td><td>52.5%</td><td>43.4%</td><td>4.0%</td></tr><tr><td>Assets under management</td><td>41.0%</td><td>55.5%</td><td>3.5%</td></tr></tbody></table>

Source: Morningstar (2024b, Exhibits 6a-6b, pages 8 and 9).

Table 4.8 shows the SFDR Article 8 and Article 9 market by country of domicile. The first two columns present the domestic market share of Article 8 and Article 9 funds by country of domicile. The next two columns provide the breakdown of Article 8 funds by country of domicile. The last column gives the market share by country at European market level (UCITS and AIF). Take Luxembourg, for example. On average, 53.8% and 4.3% of funds domiciled in Luxembourg are Article 8 and Article 9 funds respectively. The assets under management of Article 8 funds domiciled in Luxembourg amount to €2194 billion, representing 34.09% of Article 8 funds in Europe. For
Article 9 funds domiciled in Luxembourg, these figures are €175.4 billion and 51.36% respectively. To have a basis for comparison, we note that the market share of funds domiciled in Luxembourg is 30.50% at the European level. We conclude that Article 8 and 9 funds are over-represented in the Nordic countries (Finland, Norway and Sweden), France, Luxembourg and the Netherlands. Conversely, Article 8 and 9 funds are under-represented in Germany, Ireland and Spain.

<table><caption>Table 4.8: The SFDR Article 8 and Article 9 market by country of domicile</caption><thead><tr><th rowspan="2">Country</th><th colspan="2">Domestic breakdown</th><th colspan="2">Article 8 market</th><th colspan="2">Article 9 market</th><th rowspan="2">Market share<br>by country (in %)</th></tr><tr><th>Article 8</th><th>Article 9</th><th>in € bn</th><th>%</th><th>in € bn</th><th>%</th></tr></thead><tbody><tr><td>Austria</td><td></td><td>2.5</td><td>77.0</td><td>1.20</td><td>5.0</td><td>1.46</td><td>1.21</td></tr><tr><td>Belgium</td><td>65.6</td><td>5.2</td><td>120.0</td><td>1.86</td><td>9.6</td><td>2.81</td><td>1.11</td></tr><tr><td>Croatia</td><td>36.9</td><td>0.3</td><td>1.0</td><td>0.02</td><td>0.0</td><td>0.00</td><td>0.02</td></tr><tr><td>Denmark</td><td>40.3</td><td>2.5</td><td>114.0</td><td>1.77</td><td>7.1</td><td>2.08</td><td>1.71</td></tr><tr><td>Finland</td><td>87.0</td><td>10.4</td><td>120.0</td><td>1.86</td><td>14.4</td><td>4.22</td><td>0.84</td></tr><tr><td>France</td><td>50.3</td><td>3.4</td><td>903.0</td><td>14.03</td><td>61.3</td><td>17.95</td><td>12.71</td></tr><tr><td>Germany</td><td>15.8</td><td>0.2</td><td>410.0</td><td>6.37</td><td>5.0</td><td>1.46</td><td>15.71</td></tr><tr><td>Greece</td><td>3.9</td><td></td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.00</td><td>0.08</td></tr><tr><td>Hungary</td><td>2.3</td><td></td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.01</td><td>0.14</td></tr><tr><td>Ireland</td><td>36.1</td><td>0.9</td><td>1015.0</td><td>15.77</td><td>24.4</td><td>7.14</td><td>22.15</td></tr><tr><td>Italy</td><td>26.1</td><td>2.0</td><td>89.0</td><td>1.38</td><td>6.9</td><td>2.02</td><td>2.07</td></tr><tr><td>Liechtenstein</td><td>55.8</td><td>3.0</td><td>39.0</td><td>0.61</td><td>2.1</td><td>0.61</td><td>0.43</td></tr><tr><td>Luxembourg</td><td>53.8</td><td>4.3</td><td>2194.0</td><td>34.09</td><td>175.4</td><td>51.36</td><td>30.50</td></tr><tr><td>Malta</td><td>1.0</td><td>0.1</td><td>0.2</td><td>0.00</td><td>0.0</td><td>0.01</td><td>0.12</td></tr><tr><td>Netherlands</td><td></td><td>1.7</td><td>662.0</td><td>10.29</td><td>12.9</td><td>3.78</td><td>4.69</td></tr><tr><td>Norway</td><td>65.2</td><td>4.3</td><td>103.0</td><td>1.60</td><td>6.7</td><td>1.96</td><td>0.96</td></tr><tr><td>Poland</td><td>0.8</td><td>0.1</td><td>0.5</td><td>0.01</td><td>0.1</td><td>0.03</td><td>0.35</td></tr><tr><td>Portugal</td><td>47.7</td><td>0.3</td><td>14.0</td><td>0.22</td><td>0.1</td><td>0.03</td><td>0.18</td></tr><tr><td>Slovakia</td><td>5.1</td><td></td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.00</td><td>0.05</td></tr><tr><td>Slovenia</td><td>2.6</td><td></td><td>0.1</td><td>0.00</td><td>0.0</td><td>0.00</td><td>0.03</td></tr><tr><td>Spain</td><td>31.1</td><td>0.8</td><td>100.0</td><td>1.55</td><td>2.7</td><td>0.79</td><td>1.96</td></tr><tr><td>Sweden</td><td>95.6</td><td>1.6</td><td>473.0</td><td>7.35</td><td>7.8</td><td>2.28</td><td>3.00</td></tr></tbody><tfoot><tr><td>Total</td><td></td><td></td><td>6436.3</td><td>100.00</td><td>341.5</td><td>100.00</td><td>100.00</td></tr></tfoot></table>

Source: EFAMA (2023a,b) & Author's calculations.

In Tables 4.9 and 4.10, we list the 20 largest Article 8 and 9 funds as of the end of 2023, according to Morningstar (2024b). For each fund, we provide the name, category, assets under management in billions of euros, and whether the fund is active or passive. Among the top 20 Article 8 funds, we find 2 bond funds, 9 equity funds, 5 multi-asset funds, and 4 real estate funds. Three of the equity funds are sector-specific. Only one fund is passive. Among the top 20 Article 9 funds, we find 4 bond funds and 16 equity funds, of which 7 are sector-specific. Three funds are passive. On average, equity funds represent 50% of Article 8 funds and 70% of Article 9 funds.

The study by Becker et al. (2022) shows that EU funds directly affected by the SFDR became demonstrably more sustainable after its announcement. This suggests that the regulation is effective in directing investment towards greener options. In addition, the study finds that post-SFDR, investors prefer funds with a stronger ESG focus, resulting in increased capital allocation to these funds. SFDR has driven a significant portion of investment flows over the past two years, a conclusion shared by Morningstar (2024b).
<table><caption>Table 4.9: The 20 largest Article 8 funds (as of end 2023)</caption><thead><tr><th>Name</th><th>Category</th><th>AUM</th><th>Passive</th></tr></thead><tbody><tr><td>Flossbach von Storch SICAV - Multiple Opportunities</td><td>Allocation</td><td>24.9</td><td></td></tr><tr><td>Morgan Stanley Global Brands Fund</td><td>Global Equity</td><td>20.5</td><td></td></tr><tr><td>AB FCP I - American Income Portfolio</td><td>US Fixed Income</td><td>20.2</td><td></td></tr><tr><td>DWS Top Dividende</td><td>Global Equity</td><td>19.5</td><td></td></tr><tr><td>Fidelity Funds - Global Technology Fund</td><td>Technology Sector</td><td>18.6</td><td></td></tr><tr><td>Deka-ImmobilienEuropa</td><td>Real Estate</td><td>18.5</td><td></td></tr><tr><td>JPMorgan Global Income Fund</td><td>Allocation</td><td>17.5</td><td></td></tr><tr><td>hausInvest</td><td>Real Estate</td><td>17.3</td><td></td></tr><tr><td>UniImmo: Deutschland</td><td>Real Estate</td><td>16.7</td><td></td></tr><tr><td>UniImmo: Europa</td><td>Real Estate</td><td>14.8</td><td></td></tr><tr><td>DWS Concept Kaldemorgen</td><td>Allocation</td><td>14.0</td><td></td></tr><tr><td>iShares MSCI USA ESG Enhanced ETF</td><td>US Equity</td><td>13.3</td><td>✓</td></tr><tr><td>DWS Verműgensbildungsfonds I</td><td>Global Equity</td><td>13.0</td><td></td></tr><tr><td>AB FCP I - Global High Yield Portfolio</td><td>Global Fixed Income</td><td>12.9</td><td></td></tr><tr><td>Mercer Multi Asset Growth Fund</td><td>Allocation</td><td>12.6</td><td></td></tr><tr><td>BlackRock World Healthscience Fund</td><td>Healthcare Sector</td><td>11.9</td><td></td></tr><tr><td>Flossbach von Storch - Multiple Opportunities II</td><td>Allocation</td><td>11.9</td><td></td></tr><tr><td>Swedbank Robur Technology</td><td>Technology Sector</td><td>11.9</td><td></td></tr><tr><td>Morgan Stanley Global Opportunity Fund</td><td>Global Equity</td><td>11.6</td><td></td></tr><tr><td>Pictet - Global Megatrend Selection</td><td>Global Equity</td><td>11.3</td><td></td></tr></tbody></table>

Source: Morningstar (2024b, Exhibit 10, page 14).

<table><caption>Table 4.10: The 20 largest Article 9 funds (as of end 2023)</caption><thead><tr><th>Name</th><th>Category</th><th>AUM</th><th>Passive</th></tr></thead><tbody><tr><td>Nordea 1 - Global Climate and Environment Fund</td><td>Global Equity</td><td>9.1</td><td></td></tr><tr><td>Handelsbanken Global Index Criteria</td><td>Global Equity</td><td>8.7</td><td>✓</td></tr><tr><td>Pictet-Water</td><td>Thematic Equity</td><td>8.1</td><td></td></tr><tr><td>Pictet - Global Environmental Opportunities</td><td>Global Equity</td><td>7.4</td><td></td></tr><tr><td>BlackRock Global Funds - Sustainable Energy Fund</td><td>Energy Sector</td><td>6.1</td><td></td></tr><tr><td>Handelsbanken USA Index Criteria</td><td>US Equity</td><td>4.9</td><td>✓</td></tr><tr><td>Mirova Global Sustainable Equity Fund</td><td>Global Equity</td><td>4.7</td><td></td></tr><tr><td>Pictet - Clean Energy Transition</td><td>Energy Sector</td><td>4.6</td><td></td></tr><tr><td>Handelsbanken Norden Index Criteria</td><td>Europe Equity</td><td>3.9</td><td>✓</td></tr><tr><td>BNP Paribas Funds Aqua</td><td>Thematic Equity</td><td>3.7</td><td></td></tr><tr><td>BNP Paribas Aqua</td><td>Thematic Equity</td><td>3.6</td><td></td></tr><tr><td>RobecoSAM Smart Energy Equities</td><td>Energy Sector</td><td>3.4</td><td></td></tr><tr><td>RobecoSAM Sustainable Water Equities</td><td>Thematic Equity</td><td>3.2</td><td></td></tr><tr><td>DPAM L - Bonds Emerging Markets Sustainable</td><td>EM Fixed Income</td><td>3.2</td><td></td></tr><tr><td>BNP Paribas Funds Climate Impact</td><td>Global Equity</td><td>2.8</td><td></td></tr><tr><td>AB SICAV I - Sustainable Global Thematic Portfolio</td><td>Global Equity</td><td>2.8</td><td></td></tr><tr><td>Impact ES Actions Europe</td><td>Europe Equity</td><td>2.5</td><td></td></tr><tr><td>Goldman Sachs Green Bond</td><td>Europe Fixed Income</td><td>2.4</td><td></td></tr><tr><td>Goldman Sachs - Green Bond</td><td>Europe Fixed Income</td><td>2.4</td><td></td></tr><tr><td>Candriam Sustainable Bond Euro Corporate</td><td>Europe Fixed Income</td><td>2.3</td><td></td></tr></tbody></table>

Source: Morningstar (2024b, Exhibit 11, page 15).
## Sustainable funds

The previous section dealt with the SFDR market, which is a purely European market. To get a global view, we need to use criteria other than Article 8, Article 9 or even ESG labels. However, this type of exercise is not straightforward as there is no definition of what an ESG fund is and comparisons between countries and regions are not straightforward. How do we compare an ESG fund domiciled in China, the US, Canada, Singapore, Sweden or Italy? This is why we need to turn to an external data provider. In general, asset managers use three main databases to analyze sustainable investment funds: Broadridge (www.broadridge.com), LSEG Lipper (www.lseg.com) and Morningstar (www.morningstar.com). However, we must be careful because each data provider uses its own definition of sustainable funds, so the numbers are not comparable across data providers. For example, our calculations show that European mutual fund assets under management (excluding money market funds) are €7.3 tn if we use the Broadridge Responsible Investment funds category, €5.8 tn if we use the Morningstar Article 8 & 9 funds category, and €2.5 tn if we use the Morningstar sustainable funds category. In the following, we will use the latter database because it is the more restrictive one.

Box 4.5: Morningstar's definition of the global universe of sustainable funds

> "The global sustainable fund universe encompasses open-end funds and ETFs that, by prospectus or other regulatory filings, claim to focus on sustainability, impact, or environmental, social, and governance factors. Our universe of sustainable funds is based on intentionality rather than holdings. For example, a portfolio can score well on ESG metrics such as the Morningstar Sustainability Rating, but if ESG issues are not the focus of the fund's investment strategy, it will not be included in our universe. To identify intentionality, we relied on a combination of fund names (a strong indicator of intentionality) and information found in fund documents. The fund's documents should contain enough details to leave no doubt that ESG concerns figure prominently in the security-selection and portfolio construction process. The global sustainable fund universe does not contain the growing number of funds often referred to as ESG integrated funds, which formally consider ESG criteria in the investment process and engage with portfolio holdings but do not make ESG considerations the focus of the investment process. Furthermore, the global sustainable fund universe doesn't include funds that employ limited exclusionary screens [...] We however include ESG-screened passive funds in our universe as, typically for these, the exclusions are the sole purpose of the strategy. Finally, the global sustainable fund universe excludes money market funds, feeder funds, and funds of funds to avoid double counting and inflating flows and assets." (Morningstar, 2024a, page 43).

According to Morningstar (2024a), there are around 7500 sustainable funds in the world with a total of $3 trillion in assets under management (Table 4.11). 84% of these assets are in Europe, 11% in the US and 5% in the rest of the world. The average fund size is $500 million in Europe and the US, which is five times the average size of sustainable funds in Asia and Oceania. The market share of sustainable funds continues to grow, with inflows into sustainable funds of $161 billion in 2022 and $63 billion in 2023. However, there is a contrast between the US and the other regions, as we observe net redemptions in the US in 2022 and 2023.

In Table 4.12, we show the top asset managers by sustainable fund assets. Morningstar (2024a) provides the top 20 for different categories (overall, actively managed and passively managed) and different regions (global, Europe and US). We note that the sustainable asset management industry
<table><caption>Table 4.11: Global sustainable fund statistics (as of December 2023)</caption><thead><tr><th rowspan="2">Region</th><th colspan="2">Funds</th><th colspan="2">AUM</th><th rowspan="2">Fund size<br>$ mn</th></tr><tr><th>#</th><th>in %</th><th>$ bn</th><th>in %</th></tr></thead><tbody><tr><td>Europe</td><td>5 433</td><td>72.59</td><td>2 492</td><td>83.99</td><td>459</td></tr><tr><td>United States</td><td>647</td><td>8.64</td><td>324</td><td>10.92</td><td>501</td></tr><tr><td>Asia ex-Japan</td><td>595</td><td>7.95</td><td>62</td><td>2.09</td><td>104</td></tr><tr><td>Australasia</td><td>263</td><td>3.51</td><td>33</td><td>1.11</td><td>125</td></tr><tr><td>Japan</td><td>235</td><td>3.14</td><td>25</td><td>0.84</td><td>106</td></tr><tr><td>Canada</td><td>312</td><td>4.17</td><td>31</td><td>1.04</td><td>99</td></tr></tbody><tfoot><tr><td>Total</td><td>7 485</td><td>100.00</td><td>2 967</td><td>100.00</td><td>396</td></tr></tfoot></table>

Source: Morningstar (2024a, Exhibit 1, page 2) & Author's calculations.

<table><caption>Table 4.12: Top asset managers by sustainable fund assets (in $ bn)</caption><thead><tr><th rowspan="2">Firm</th><th colspan="3">Global</th><th colspan="3">Europe</th><th colspan="3">United States</th></tr><tr><th>Total</th><th>Active</th><th>Passive</th><th>Total</th><th>Active</th><th>Passive</th><th>Total</th><th>Active</th><th>Passive</th></tr></thead><tbody><tr><td>BlackRock</td><td>318.1</td><td>46.8</td><td>271.3</td><td>256.8</td><td>41.5</td><td>215.3</td><td>59.2</td><td>5.5</td><td>53.7</td></tr><tr><td>UBS</td><td>164.7</td><td>66.0</td><td>98.7</td><td>164.2</td><td>65.6</td><td>98.5</td><td></td><td></td><td></td></tr><tr><td>Amundi</td><td>161.0</td><td>84.1</td><td>76.9</td><td>164.0</td><td>76.3</td><td>87.7</td><td>8.5</td><td>8.5</td><td></td></tr><tr><td>DWS</td><td>93.1</td><td>56.0</td><td>37.2</td><td>89.7</td><td>57.4</td><td>32.2</td><td>5.2</td><td>4.9</td><td>0.3</td></tr><tr><td>BNP Paribas</td><td>85.8</td><td>54.4</td><td>31.4</td><td>85.8</td><td>54.4</td><td>31.4</td><td></td><td></td><td></td></tr><tr><td>Swisscanto</td><td>84.0</td><td>52.3</td><td>31.7</td><td>84.0</td><td>52.3</td><td>31.7</td><td></td><td></td><td></td></tr><tr><td>Natixis</td><td>74.5</td><td>72.0</td><td>2.5</td><td>73.8</td><td>71.3</td><td>2.5</td><td></td><td></td><td></td></tr><tr><td>Nordea</td><td>57.2</td><td>57.2</td><td></td><td>57.2</td><td>57.2</td><td></td><td></td><td></td><td></td></tr><tr><td>Pictet</td><td>49.7</td><td>49.7</td><td></td><td>49.7</td><td>49.7</td><td></td><td></td><td></td><td></td></tr><tr><td>Vanguard</td><td>47.2</td><td>2.8</td><td>44.4</td><td></td><td></td><td>13.1</td><td>30.5</td><td>1.4</td><td>29.0</td></tr><tr><td>Allianz GI</td><td>46.6</td><td>46.6</td><td></td><td>46.7</td><td>46.7</td><td></td><td></td><td></td><td></td></tr><tr><td>Parnassus</td><td>39.3</td><td>39.3</td><td></td><td></td><td></td><td></td><td>39.3</td><td>39.3</td><td></td></tr><tr><td>Eaton Vance</td><td>34.7</td><td>25.0</td><td>9.7</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Royal London</td><td>34.7</td><td>28.6</td><td>6.1</td><td>34.7</td><td>28.6</td><td>6.1</td><td></td><td></td><td></td></tr><tr><td>Calvert</td><td></td><td></td><td></td><td></td><td></td><td></td><td>34.7</td><td>25.0</td><td>9.7</td></tr><tr><td>Eurizon</td><td>34.2</td><td>34.2</td><td></td><td>34.3</td><td>34.3</td><td></td><td></td><td></td><td></td></tr><tr><td>Goldman Sachs</td><td>34.2</td><td>34.1</td><td>0.1</td><td>30.7</td><td>30.6</td><td>0.1</td><td></td><td></td><td></td></tr><tr><td>Handelsbanken</td><td>33.8</td><td>4.8</td><td>29.0</td><td>36.2</td><td>5.5</td><td>30.7</td><td></td><td></td><td></td></tr><tr><td>AXA</td><td>33.4</td><td>33.4</td><td></td><td>32.9</td><td>32.9</td><td></td><td></td><td></td><td></td></tr><tr><td>KBC</td><td>33.2</td><td>33.2</td><td></td><td>33.2</td><td>33.2</td><td></td><td></td><td></td><td></td></tr><tr><td>Union Investment</td><td>33.0</td><td>33.0</td><td></td><td>37.7</td><td>37.7</td><td></td><td></td><td></td><td></td></tr><tr><td>Deka</td><td></td><td></td><td></td><td>30.7</td><td>26.9</td><td>3.8</td><td></td><td></td><td></td></tr><tr><td>Robeco</td><td></td><td>27.4</td><td></td><td>27.3</td><td>27.3</td><td></td><td></td><td></td><td></td></tr><tr><td>Vontobel</td><td></td><td>27.3</td><td></td><td>27.2</td><td>27.2</td><td></td><td></td><td></td><td></td></tr><tr><td>Schroders</td><td></td><td>26.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Candriam</td><td></td><td></td><td></td><td></td><td>26.1</td><td></td><td></td><td></td><td></td></tr><tr><td>State Street</td><td></td><td></td><td>24.2</td><td></td><td></td><td>21.5</td><td></td><td></td><td>2.0</td></tr><tr><td>Northern Trust</td><td></td><td></td><td>22.6</td><td></td><td></td><td>20.6</td><td></td><td></td><td>2.0</td></tr><tr><td>Nuveen/TIAA</td><td></td><td></td><td>5.6</td><td></td><td></td><td></td><td>21.7</td><td>16.2</td><td>5.6</td></tr></tbody></table>

Source: Morningstar (2024a, Exhibit 1, page 2) & Author's calculations.