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Climate-Related Financial Risks for Kenyan Banks

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Reuben Muhindi Wambui

4. Indices analysis

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1The standardised reporting of banks’ lending, key sectoral measures already analysed across banks, and GHG emissions mapped to sectoral lending is all the necessary information for the main analysis of interest: construction of two climate-relevant indices, Emissions Exposure Index (EEi) and Emissions Funding Index (EFi) for each bank i.

4.1 Definition of the indices

4.1.1 Emissions Exposure Index (EEi)

2This is a measure of a bank’s portfolio climate risk exposure. The index answers the question “How much is each bank exposed to climate risk through its sectoral loan composition, given the GHG emissions represented by its loan portfolio?”

Recall that GLij denotes the gross loans by each bank i to each economic sector j and Sj is the share of emissions attributable to each sector. Also, we defined Wij as the share of each bank’s loan portfolio to each sector, such that Image 100002010000011A0000004C7A4C3720D00D3160.pngwhere the index k runs over the set of all sectors and where ΣWij = 1 for each bank.

3The Emissions Exposure Index (EEi) is the sum of the relative exposures of each bank i across all economic sectors j weighted by the relative share of emissions attributable to each sector Sj, that is:

EEi = ΣjSjWij (3)

4For example, assume Bank A has its entire lending concentrated on agriculture (WA,AGR = 1) and Bank B directs its lending entirely to financial services (WB,FIN = 1); since the share of emissions in the agriculture sector is higher than in financial services, i.e. SAGR > SFIN, then EEA > EEB in this case.

5Thus, EEi captures the GHG emissions intensity of the loan portfolio of each bank according to the emission shares represented by its sectoral loan composition.

4.1.2 Emissions Funding Index (EFi)

6This is a measure of the climate risk funded by the bank’s lending portfolio. The index answers the question “How much of the climate risk attributed to GHG emissions does each bank fund through its lending relative to other banks?

Again, GLij denotes the gross loans by each bank i to each economic sector j and Sj is the share of emissions attributable to each sector. In addition to Wij, we also defined Mij as the market share of each bank in each sector, such that Image 100002010000011F0000004C50BE640699CDA077.pngwhere the index k runs over the set of all banks and where ΣMij = 1 for each sector.

7The Emissions Funding Index (EFi) is the sum of the market shares of each bank i across all economic sectors j weighted by the relative share of emissions attributable to each sector Sj, that is:

EFi = ΣjSjMij (4)

8ΣEFi = 1 across all banks.

9For example, assume there are only two Banks, A and B, that lend entirely to the transport sector with market shares of 40% and 60% respectively (MA,TRAN = 0.4, MB,TRAN = 0.6); since the share of transport sector emissions STRAN is the same for both banks, the difference in market shares means EFA < EFB as Bank B funds a larger proportion of emissions.

10Thus, EFi captures the relative funding of climate risk and is a measure of climate risk importance of each bank, given the emissions funded by its lending relative to other banks.

11As noted by Monasterolo et al. (2017) in their novel development of climate-relevant indices for the euro area, these two indices are complementary and help capture two very important aspects of climate risk for Kenyan banks: the climate risk exposure represented by banks’ portfolios and their relative funding of the climate risk through their lending. No such related indices have been constructed for banks in any other emerging economies in Africa. Further, no available research on climate-related disclosures for banks has mapped GHG emissions to lending sectors using the hybrid approach applied in this study.

4.2 Results and interpretation

12Figure 1 below reports the values of EEi and EFi calculated for each bank across all sectors.

Figure 1: Results of EEi and EFi analysis

Image 10000000000003A5000001D6F7E17C5821150B2A.jpg

Note: Barclays has since rebranded to Absa Kenya; CBA and NIC have since merged to form NCBA Bank; NBK has since been acquired by KCB Bank

Emissions Exposure Index, EEi

13From the main results in Figure 1 above, all banks except one have EEi values around a narrow range of 0.06–0.09. Given the index definition, this means that the banks have a fairly similar exposure to GHG emissions based on their sectoral lending composition. CFC Stanbic has a relatively higher EEi of 0.14. As the sample is representative of the industry, this implies a fairly similar concentration of climate risk exposure across Kenyan banks given their sectoral loan composition. The relatively higher exposure by CFC Stanbic can be explained by the high concentration of its loan portfolio in the high-emission agriculture and real estate sectors as seen in Table 7 (collectively accounting for 51% of the bank’s gross loans).

14The key finding from the Emissions Exposure Index (EEi) analysis can be summed up as follows: Kenyan banks, with the exception of one outlier, have fairly similar exposure to climate risk through their loan portfolios, given the GHG emissions represented by their sectoral lending.

Emissions Funding Index, EFi

15From the main results in Figure 1, there is a differentiation in the values of EFi across the banks which is fairly proportional to their market share of gross loans. Given the index definition, this means that the banks have differentiated funding of climate risk through their lending that is fairly proportional to their market shares. CFC Stanbic has a high emissions funding relative to its size (EFi of 16% similar to the biggest bank KCB) and this can be explained by its relatively high climate risk exposure EEi seen earlier. The differentiation of emissions funding that mimics market shares is not unexpected given that banks have a fairly similar concentration of exposure as concluded from the EEi analysis. Thus, bigger (smaller) banks have higher (lower) funding of climate risk.

16The key finding from the Emissions Funding Index (EFi) analysis can be summed up as follows: Kenyan banks, with the exception of one outlier, have differentiated funding of climate risk attributed to their loan portfolios that is fairly proportional to their market shares of gross loans, and this is expected given the similarity in their similar exposure profiles.

Rescaling emissions funding to 100%

17From the main results in Figure 1, values of EFi add up to 0.89, i.e. the funding of sectoral emissions through the banks’ loan portfolios add up to 89% of emissions attributed to the sectors collectively. This is because there are emissions within the economy that are not directly linked to any of the bank lending sectors. These are mostly the “other economic activities” identified in the GDP reporting that are not directly linked to any of the distinct lending sectors. They include economic activities in the following GDP categories: public administration; professional, administration and social services; education; health; and other services.

18The calculation of EEi and EFi is repeated this time rescaling the emissions sectoral contribution such that the lending sectors account for 100% of emissions funding within the banking industry. Figure 2 uses the rescaled emissions, i.e. ΣEFi = 1 to report the values of EEi and EFi. As the relative weighting of emissions across sectors is unchanged, the interpretation and conclusion is the same as in Figure 1: Kenyan banks, with the exception of one outlier, have differentiated funding of climate risk attributed to their loan portfolios that is fairly proportional to their market shares of gross loans.

Figure 2: Results of EEi and EFi analysis (rescaled emissions=100%)

Image 10000000000003A6000001D95124E4A87726E32E.jpg

Note: Barclays has since rebranded to Absa Kenya; CBA and NIC have since merged to form NCBA Bank; NBK has since been acquired by KCB Bank

19From Figure 2, the non-sampled banks account for 13% of emissions funding implying that the sample of banks representing 82% of industry gross loans account for 87% of emissions holdings.

4.3 Forward-looking scenario analysis

20The values of EEi and EFi are known for each bank, together with the respective interpretation of the two indices for the banking industry. To enhance the analysis, I am interested in a forward-looking view of the expected values of the indices in 2030. This helps enable a futuristic view of the climate risk exposure across the banks. Two scenarios are of interest:

  • a business-as-usual (BAU) scenario that uses the emissions forecasts for 2030;

  • a transition scenario of abating emissions by 30% relative to 2030 BAU.

21For both scenarios, I repeat the analysis of EEi and EFi using the BAU emissions forecasts and the transition scenario 30% emissions reduction target as shown in column 1 and column 2 respectively in Table 12 below. For both cases, the futuristic view is determined with regard to the emissions (and not the sectoral loan composition), and thus we use the current sectoral loan composition for each bank but with the 2030 emissions forecasts for each scenario.

Table 12: GHG emissions forecasts in 2030 – BAU and transition scenarios

Sector

GHG Emissions Forecasts, 2030

Current Baseline

(2015)

Baseline Scenario

(BAU)

Transition Scenario

(30% reduction)

Agriculture

32

39

36

Electricity generation

1

42

32

LULUCF

26

22

2

Transportation

9

21

18

Energy demand

7

10

4

Industrial processes

3

6

5

Waste

2

4

4

Total

80

143*

100*

Emissions reduction

30%

*The values in the rows add up to 144 and 101 respectively due to rounding.

4.3.1 2030 business-as-usual scenario

22Figure 3 below reports the values of EEi and EFi using the 2030 BAU emissions forecasts.

Figure 3: Results of EEi and EFi analysis – 2030 BAU scenario

Image 10000000000003A8000001D683AD0870C814A07D.jpg

Note: Barclays has since rebranded to Absa Kenya; CBA and NIC have since merged to form NCBA Bank; NBK has since been acquired by KCB Bank

23From the results in Figure 3 above using 2030 BAU emissions forecasts, most EEi values are still within a narrow range, 0.07–0.12 with CFC Stanbic still as the outlier. In addition, there is one more result of interest: there is an emissions exposure spike for I&M Bank and NIC Bank (which is now merged with CBA Bank to form NCBA). This EEi spike is explained by high lending exposure to the manufacturing, energy, and water sector (24% sector share by each of the two banks). The energy sector is forecast to have the highest increase in emissions by 2030, with a 12-fold increase from 4 MtCO2e in 2015 to 47 MtCO2e in 2030 (see Table 12), accounting for 63% of the increase in emissions over the 15-year period. This significant increase in the risk exposure of the two banks with a high concentration in lending to the manufacturing, energy, and water sector is associated with a small increase in the funding risk EFi. Overall, EFi results are still consistent with the earlier finding that larger (smaller) banks have higher (lower) funding of climate risk.

4.3.2. 2030 transition scenario

24The transition scenario is expected to have an effect on the values of the indices in 2030 given the increase in expected emissions but also the fact that different sectors have different abatement potential and thus this affects Sj, the relative share of emissions attributable to each sector in 2030. Like with the BAU scenario, I use the current sectoral loan composition for each bank but with the 2030 emissions forecasts with the 30% abatement target scenario. Figure 4 below reports the values of EEi and EFi using the 2030 transition scenario

Figure 4: Results of EEi and EFi analysis – 2030 transition scenario

Image 10000000000003A7000001D7E930799361E6ABBB.jpg

Note: Barclays has since rebranded to Absa Kenya; CBA and NIC have since merged to form NCBA Bank; NBK has since been acquired by KCB Bank

25From the results in Figure 4 above using 2030 transition emissions forecasts, most EEi values are still within a range, 0.05–0.11 with CFC Stanbic still as the outlier. In addition, just like in the 2030 BAU scenario, there is an emissions exposure spike for I&M Bank and NIC Bank (which is now merged with CBA Bank to form NCBA). This spike is still explained by the high lending exposure to the manufacturing, energy, and water sector, which has expected a significant increase in emission levels. While there is a 21% drop in the sector’s forecast emissions from 47 MtCO2e to 36 MtCO2e under the 2030 reduction targets, the latter still represents a significant increase in emissions from 4 MtCO2e in 2015. For contrast, the LULUCF sector has a forecast 93% emissions reduction from 26 MtCO2e in 2015 to 2 MtCO2e under the 2030 transition scenario. Thus, these two banks that have a high concentration in lending to the manufacturing, energy, and water sector are still expected to have a significant increase in risk exposure with an associated small increase in the funding risk. Overall, like with the 2030 BAU scenario, the EFi results are still consistent with the earlier finding that larger (smaller) banks have higher (lower) funding of climate-related risk.

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