PPTs - TAIEX TSI MNB-OECD-EC Launch Event: Technical implementation of the Supervisory Framework for Assessing Nature-related Financial Risks to the Hungarian financial sector, June 2024.

Presentations from the TAIEX TSI MNB-OECD-EC Launch Event: Technical implementation of the Supervisory Framework for Assessing Nature-related Financial Risks to the Hungarian financial sector, 7 June 2024.

1. Overview of the ProjectI
Ms. Geraldine Ang, Team Lead, Financial Systems for Biodiversity, Environment Directorate, OECD
Ms. Fatos Koc, Team Lead, Financial Markets, Financial and Enterprise Affairs, OECD

2. An integrated approach to nature-related risks
2
•Biodiversityloss,climatechange,and
broadernaturedegradationallinteractto
exacerbatetherisks.
•Therearefourwaysclimatechange,
biodiversityloss,andbroadernature
degradationmayinteract:
I.Climatechangeasadriverofnature
degradation;
II.Unintendedconsequencesofclimate
changemitigationoradaptationasa
driver;
III.Naturedegradationasadriverof
climatechange;
IV.Naturerestorationasamitigatorof
climatechange.
Climate
Change
Biodiversity
Loss
Broader
Nature
Degradation
Economic
and
Financial
Risks
e.g., weather variability
e.g., deforestation
e.g., water exploitation

3. Conceptual framework of nature-related financial risks
3Adapted from Svartzman et al., 2021 & NGFS, 2023Risk Origination Risk sources
Physical risks
Transition risks
Transmission channels
Direct and Indirect
Microeconomic Impacts
Direct and Indirect
Macroeconomic Impacts
Financial risk
Credit risk
Market risk
Underwriting risk
Liquidity risk
Operational risk
Endogenous risk Feedback loops
Financial contagion
Climate Change
Biodiversity loss
Broader nature
degradation

4. Project Overview
4
Phase 1: Research and Development (11 months)
PROJECT OVERVIEW
Output 1: Mapping
Existing Tools and
Metrics
Output 2: Developing
a Supervisory
Framework
Phase 2: Implementation (11 months)
Output 3: Applying
the Framework
Implementation of the
Supervisory Framework
to the Hungarian
financial sector
Output 4: Capacity
Building and
Knowledge Sharing
Capacity building and
knowledge sharing with
key stakeholders

5. OECD supervisory framework overview
5Financial risk channels
•Credit risk
•Market risk
•Liquidity risk
•Other risks
Impact and dependency
assessment
Direct economic risk
Economic sector
identification
Ecosystem identification
Sources and magnitude Geographic location Time horizon
Indirecteconomic risk
Microeconomic and macroeconomic impacts
Feedback loops
Additional risks
•Financial system interaction
•Feedbacks between the financial system and the real economy
Recommendations for approaches to
climate-related risks
Applicability of climate-related financial
risks to nature-related financial risks
Consideration of approaches for nature-
related financial risks
1
2
3
4
Risk identification
and prioritisation
Economic risk
assessment
Financial risk
assessment
Supervisory
considerations
Following a four-step approach, the framework aims to enable financial authorities to identify,
conceptualise, and assess nature-related financial risks.

6. Remarks on protecting biodiversity in Hungary
Technical implementation of the Supervisory Framework for Assessing Nature-related
Financial Risks to the Hungarian financial sector
Dr. Magyar Gábor
June 7, 2024
1

7. 2
Fénykép: Hargitai L.-
től.
III. A végrehajtás eszközei és megoldásai (17–19. célkitűzés)

8. Number of species
3
Fénykép: Hargitai L.-
től.
group World Hungary
1. Mammals 4 100 83
2. Birds 8 700 370
3. Reptiles 6 300 15
4. Amphibians 3 000 15
5. Fish 22 900 81
I. Vertebrates 45 000 540
6. Crustacea 20 000 1 058
7. Molluscs 85 000 202
8. Insects 1 100 000 40 200
II. Invertebrates 1 205 000 41 460
Animals (total) 1 250000-30 255 700 42000 (43 560)

9. Number of protected species in Hungary
4
Fénykép: Hargitai L.-
től.
Protected
Strictly
protected
Total
Plants (and mushrooms) total 463 52 515
Inveretebrates 389 - 389
Vertebrates 382 84 466
Animals total 771 84 855
Grand total 1234 136 1370

10. Why to conserve nature?
5
Fénykép: Hargitai L.-
től.
Preserve the resilience of nature (including non-protected species) to impacts
- dynamic balance of ecosystems
- diversity of species
- diversity in biomass
- genetic diversity within species
Environmental pollution
Climate change
Invasive alien species (ecological threat + agricultural threat)
Emerging infectious diseases

11. Ecosystem services
ecosystem means the dynamic ensemble of plant, animal and microorganism
communities, as well as their non-liveenvironment in onefunctional unit
'ecosystem services' means the direct and indirect contributions of ecosystems to
human wellbeing (REGULATION 1143/2014/EU, Art. 3. 6)
•- pollination by bees
•- carbon absorption of trees but also by wetlands!
•- oxygen production, and air pollution control by trees
•- liveable world (recreation, sports, tourism, etc.)
•- etc., etc.
International law
•EU habitats and birds directives – non-compliance results in infringement
procedures (fine, freezing in available funds)
•International conventions (Ramsar Convention, CBD, CMS, Berne Convention,
CITES, etc.)
Why to protect nature –
ecosystem sevices & international commitments
6
Fénykép: Hargitai L.-
től.

12. 7
Fénykép: Hargitai L.-
től.
Source: Ron Milo (https://wis-
wander.weizmann.ac.il/environmen
t/weight-responsibility-biomass-
livestock-dwarfs-wild-mammals)

13. Protection of biodiversity - biomass
8
Fénykép: Hargitai L.-
től.
Cattle alone represent 420 million tons
Terrestrial mammals: 20 million tons
(40% by 10 species: wild-tailed deer, wild boar,
African elephant, etc. )
1200 species of bats account for 10% of the
biomass of terrestrial mammals

14. Thank you for your attention!

15. TECHNICAL IMPLEMENTATION OF THE SUPERVISORY
FRAMEWORK FOR ASSESSING NATURE-RELATED FINANCIAL
RISKS TO THE HUNGARIAN FINANCIAL SECTOR
Launch Event at the MNB Headquarters
7
th
of June 2024

16. Identification and Prioritisation of
Nature-related Financial Risks
I
Mr. Gürcan Zeren Gülersoy, Policy Analyst, Directorate for Financial and Enterprise Affairs, OECD

17. Step 1: Identification and Prioritisation
3
Key Risk
Identification
Ecosystem
Identification
Economic
Sector
Identification
Impacts and
Dependencies
Assessment
•Identification of direct
and indirect economic
activities.
•Domestic and foreign
exposures.
•Geographic location of
ecosystems.
•Assessment of current
and expected future
state of ecosystems.
•Link financial assets to
economic activities.
•Exposure to impacts
and dependencies on
ecosystem services.
•Identification of key
ecosystem services
and sectors.
A three-phase approach to identify and prioritise the most relevant risks for financial materiality.

18. Sectoral breakdown of Hungary’s financial system exposure
40
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Share of total investment Number of instruments
Note:ThefigureincludesexposuresofHungarianbankstononfinancialcorporatesandindividualsidentifiedasprimaryagriculturalproducersat
theendof2022.Theseinstrumentsrepresentapproximately70%(EUR56.5billion)ofthetotalvaluewithintheHungarianfinancialsystem
(EUR81.2billion).

19. Direct materiality ratings using ENCORE
5
Materiality ratings
VL 0 –0.2
L 0.2 –0.4
M 0.4 -0.6
H 0.6 -0.8
VH 0.8 –1

20. Transition risk vs. Physical risk
60%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High Very High High Very High
Impacts Dependencies
Portfolio share
1 ecosystem 2 ecosystems 3 ecosystems 4 ecosystems 5 ecosystems > 5 ecosystems
Portfolio share distribution by number of ecosystem services impacted and dependedon
OutofthetotalcorporatelendingbyHungarianbankstononfinancialcorporates,77%oflendinghighlyimpactsoneormore
driversofecosystemchangewhile39%ishighlydependentononeormoreecosystemservices.Thisunderscoresagreater
exposureoftheHungarianbankingsystemtotransitionrisksoverphysicalrisksduetoalargerproportionoflendingassociated
withindustriesthatimpactecosystemservices,comparedtothosethatdependonthem.

21. 7
Direct impactsof the portfolio

22. 8
Direct dependencies of the portfolio

23. Direct and upstream –cautious–materiality ratings of the portfolio
9
0 0,05 0,1 0,15 0,2 0,25 0,3
Dilution by atmosphere and ecosystems
Bio-remediation
Mediation of sensory impacts
Filtration
Genetic materials
Ventilation
Maintain nursery habitats
Animal based energy
Buffering and attenuation of mass flows
Pollination
Pest control
Disease control
Water quality
Fibres and other materials
Soil quality
Mass stabilisation and erosion control
Waterflow maintenance
Flood and storm protection
Climate regulation
Ground water
Surface water
Impacts
Dependencies
0 0,10,20,30,40,50,60,70,8
Other resource use
Marine ecosystem use
Biological interferences/alterations
Freshwater ecosystem use
Disturbances
Non-GHG air pollutants
Solid waste
Soil pollutants
Water pollutants
Terrestrial ecosystem use
Water use
GHG emissions
TheresultsoftheidentificationandprioritisationstepdemonstratethatHungary'sbankingsystemishighlyexposedtotransition
risksfromsectorssignificantlycontributingto“GHGemissions”(EUR27billion,48%ofcorporatelending)througha
combinationofdirectandupstreamchannels.Forphysicalrisks,thebankingsystemisexposedtosectorsheavilydependent
on“surfacewater”and“groundwater”(EUR24.4billion,43%andEUR23.3billion,41%ofcorporatelendingrespectively),
highlightingsignificantphysicalrisksfromwaterdisruptions.

24. Sectoral disaggregation: Impacts
10
#Sectors with highest impact scores
1Manufacturing
2Real Estate Activities
3Transportation and StorageDescription
Agriculture, Forestry And Fishing
Mining And Quarrying
Manufacturing
Electricity, Gas, Steam
Water Supply Construction
Wholesale And Retail Trade Transportation And Storage
Accommodation And Food Service
Information And Communication
Financial And Insurance
Real Estate
Professional, Scientific And Technical Administrative And Support Service Public Administration And Defence
Education
Human Health And Social Work
Arts, Entertainment And Recreation
Other Service Activities
Activities Of Households As Employers Extraterritorial Organisations and Bodies
Portfolio score
Impacts
Disturbances
Freshwater ecosystem use
GHG emissions
Marine ecosystem use
Non-GHG air pollutants
Other resource use
Soil pollutants
Solid waste
Terrestrial ecosystem use
Water pollutants
Water use
Biological interferences/alterations
Total impact score

25. Sectoral disaggregation: Dependencies
11
#
Sectors with highest dependency
scores
1Agriculture, Forestry And Fishing
2Manufacturing
3Real Estate ActivitiesDescription
Agriculture, Forestry And Fishing
Mining And Quarrying
Manufacturing
Electricity, Gas, Steam
Water Supply Construction
Wholesale And Retail Trade Transportation And Storage
Accommodation And Food Service
Information And Communication
Financial And Insurance
Real Estate
Professional, Scientific And Technical
Administrative And Support Service Public Administration And Defence
Education
Human Health And Social Work
Arts, Entertainment And Recreation
Other Service Activities
Activities Of Households As Employers Extraterritorial Organisations and Bodies
Portfolio score
Dependencies
Animal based energy
Bio-remediation
Buffering/ attenuation of mass flows
Climate regulation
Dilution by atmosphere and ecosyst.
Disease control
Fibres and other materials
Filtration
Flood and storm protection
Genetic materials
Ground water
Maintain nursery habitats
Mass stabilisation/ erosion control
Mediation of sensory impacts
Pest control
Pollination
Soil quality
Surface water
Ventilation
Waterflow maintenance
Water quality
Total dependency score

26. Economic sector identification and geographical breakdown
12
#Findings
1
Manufacturing is the sector with the highest output, highest foreign-reliancefor direct intermediate inputs, and second highest portfolio
share.
2
Agriculture, Forestry and Fishing, Mining and Quarrying, and Constructionare the sectors most directly linked to nature, both in terms
of impacts and dependencies, whereas Activities of Households, and Accommodation and Food Service Activities are the most
indirectly linked.
3
Among the sectors identified as having high impacts or dependencies, Agriculture, Forestry, and Fishing is the most critical sector in terms
of its position within value chains.
Total exposure Manufacturing Agriculture

27. Ecosystem identification: Nature-related Transition and Physical Risk Drivers
13
#Transition risks
1Policy Developments: Impact of international and domestic environmental policies, particularly on agriculture and manufacturing.
2Sectoral Impact: Agriculture, Forestry, and Fishing most exposed, followed by Manufacturing.
3Financial Exposure:Significant proportion of banking system’s lending portfolio linked to these sectors.
4Consumer & Market Sentiment:Limited but growing awareness of biodiversity issues.
#Physical risks
1Water Dependency: 95% of surface water sourced from beyond borders, high transboundary risk.
2Ecosystem Vulnerability: Droughts, changes in precipitation patterns, soil degradation, and reduced soil fertility.
3
Sectoral Dependence: High water usage in agriculture and manufacturing, increasing vulnerability to regulatory and physical water-related
risks.
4Biodiversity Loss: Lower biodiversity and natural land cover compared to neighboring countries, driven by historical land use change.
#Future outlook
1Increased regulatory stringency and potential economic impacts on land-intensive sectors.
2Uncertainty in ecosystem resilience and stability, with potential exacerbation of risks due to climate change.

28. Economic Risk AssessmentII
Mr. Hugh Miller, Policy Analyst, Environment Directorate, OECD
Mr. Gabriel Santos Carneiro, Consultant, OECD

29. Step 2: Economic risk assessment
15Risk sources
Physical risk
Loss of ecosystem services:
-Acute (e.g., extreme events) or
chronic (e.g., soil degradation)
-Interactions with other
ecological issues
Transition risk
Adjustments aiming to restore
nature through:
-Regulation/policy
-Technology
-Consumer and investor
sentiment
Economic risk and transmission channels
Direct Impacts Indirect Impacts
Feedback loops
Microeconomic Impact
-Earnings decline
-Increased cost of capital
-Stranded assets/capital degradation
-Land use restrictions
-Raw material price volatility
-Value chain disruption, lower profitability
Macroeconomic Impact
-Structural changes
-Public finance deterioration
-Productivity changes
-Socioeconomic changes
-Inflation
-Trade and capital flows
Theeconomicriskassessmentfocusesonadomestic“casestudy”shockscenarioinHungary,examiningthedirectandindirect
impactsonthedomesticeconomybasedonevidence-drivenassumptions.
Additionally,thesectionanalyzespotentialforeignrisksthatcouldimpacttheHungarianeconomythroughinternationaltrade
linkages.

30. Domestic Scenario: initial shock
16Waterflow MaintenanceGround Water Surface Water Climate Regulation
Water Extraction Climate Change Biodiversity Loss
WaterEcosystems
Threedriversareidentifiedtocontributetowardsfuturedrought-relatedrisksinHungary:(i)climatechange;(ii)lossofnaturalland
cover;and(iii)demandsonwaterextraction.
TheHungarianeconomyandbankingsystemareexposedtophysicalnature-relatedrisksstemmingfromdisruptionsto
“groundwater”,“surfacewater”,“waterflowmaintenance”and“climateregulation”.Threedroughtscenariosarederivedfrom
thisinformation,withdifferinglevelsofseverityrangingfrom1-4-monthshutdownperiods.

31. Domestic Scenario: percentage reduction in GDP growth
17-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
GDP Growth
GDP Growth Rate for Hungary 1-month shutdown 2-month shutdown 4-month shutdown
OverallGDPreducesbetweenEUR6billiontoEUR11.3billionfromtheleasttomostseverescenario,whichwouldequatetoafall
intheGDPlevelbetween3.8%and7.1%.

32. Domestic Scenario: output reduction in selected sectors
180
2000
4000
6000
8000
10000
12000
ManufacturingAgriculture, Forestry and
Fishing
Wholesale and Retail TradeElectricity, Gas, SteamTransportation and Storage
EUR millions
1-month shutdown 2-month shutdown 4-month shutdown
Reduction in selected sectoral output under three drought scenarios0%
10%
20%
30%
40%
50%
60%
Agriculture, Forestry
and Fishing
Water SupplyElectricity, Gas and
Steam
Mining and QuarryingManufacturingWholesale and Retail
Trade
1-month shutdown 2-month shutdown 4-month shutdown
Absolute
Percentage
Theshock,inabsoluteterms,isprimarilycontainedwiththeManufacturingandAgriculture,ForestryandFishingsectors,with
sectoraloutputreducedbyEUR10.6billionandEUR5.9billion,underthemostseverescenario,respectively.
Onarelativebasis,asashareoftotalsectoroutput,Agriculture,Forestry,andFishingincursarelativereductionbetween27.6%
to53.5%inoutput,whereasManufacturingonlyincursbetween3.5%to8.6%reduction,underthedifferentscenarios.

33. Domestic Scenario: price increase in selected sectors and inflationary pressures
19
Average relative price increase of outputs by sectorValue loss in foreign currency generation by sector and total0%
5%
10%
15%
20%
25%
30%
Agriculture, Forestry and
Fishing
Water Supply ManufacturingElectricity, Gas and SteamMining and Quarrying
1-month shutdown 2-month shutdown 4-month shutdown -6000
-5000
-4000
-3000
-2000
-1000
0
1-Month 2-Month 4-Month
Base (M.EUR): Shock (M.EUR)
-3000
-2500
-2000
-1500
-1000
-500
0
ManufacturingAgriculture, Forestry,
and Fishing
Transportation and
Storage
EUR millions
1-Month 2-Month 4-Month
Agriculture, Forestry, and Fishing exhibit the greatest relative price increases, between 13.6% to 23.9% under the three
scenarios. This can be an indicator of cost-push inflationary pressures in the economy.
Thetotalnetforeigncurrencygenerationdeclines,rangingfromalossofEUR3.08billionundertheleastsevere
scenariotoEUR5.3billionunderthemostseverescenario.

34. Domestic Scenario: price increase in selected sectors and inflationary pressures
200%
10%
20%
30%
40%
50%
60%
70%
WheatOil seedsMeat
animals nec
Animal
products nec
Cereal gains
nec
1-Month 2-Month 4-Month
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
2014201520162017201820192020202120222023
PPI Crops Animals
Year-on-year historical relative price change for agricultural commodities, and relative
price change under three drought scenarios
Therelativepricechangesunderthe
scenariosreflectthoserecorded
duringtheseveredroughtin2022
foragriculturalproducts,withrelative
pricechangefortheproducerprice
indexforagriculturaloutputwas49.7%,
witharelativechangeof51.5%and
45.6%forcropsandanimals,
respectively.
Similarly,themostaffected
agriculturalcommoditieswithinour
scenariosdisplayarangeofrelative
pricechangesbetween31.5%and
59.9%,underthemostsevere
scenario.

35. Foreign Exposure Analysis: Share of Hungarian foreign trade exposed
210%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Share of total foreign trade
Exports Imports 0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Share of total foreign trade
Exports Imports
to dependencies on ecosystem servicesto different nature-related impact drivers
Theresultsindicatearound40%ofHungarianimportsand35%ofitsexportsarecomposedofactivitiesthatarehighly
intensivein“waterpollutants”.Atleast25%ofHungarianforeigntradeisexposedtotransitionrisksstemmingfrom
activitiesthatimpactnatureintheformof“GHGemissions”,“soilpollutants”,“solidwaste”,and“wateruse”.
Converselyforphysicalrisks,theresultsindicatethataround10%ofHungarianforeigntradeactivityisdependenton“ground
water”and“surfacewater”.

36. Financial Risk Assessmentand
Supervisory Considerations
III
Mr. Riccardo Boffo, Policy Analyst, Environment Directorate, OECD

37. Step 3: Financial risk assessment
23
The Framework conceptualises financial risk channels including credit, market, liquidity and
underwriting risks, which may present new risks for the financial systemCredit risk
-Increase in defaults
-Collateral depreciation
Market risk
-Repricing of assets
-Fire sales
Liquidity risk
-Shortages of liquid assets
-Refinancing risk
Underwriting risk
-Increased insured losses
-Increased insurance gap
Operational risk
-Disruption of financial
institutions’ processes
Financial
System
Financial contagion
Economy
Feedbacksbetween financial system and the economy

38. Overview of the Hungarian bankingsystem
240
1
2
3
4
5
6
7
8
0
2
4
6
8
10
12
PercentEUR billions
Outstanding debt Interest rate 0
2
4
6
8
10
12
14
16
2023202420252026202720282029After 2030
EUR million
Corporate loansReal estate loansShort-term finCorporate leasingInvestment loansPersonal loans
0
2
4
6
8
10
12
14
2019 2020 2021 2022 2023
10-Year yield CB interest rate
Maturity structure
of debt
outstanding
Outstanding
debt and
interest rate by
sector49%
44%
6%
1%
HUF EUR USD RON Outstanding debt by currency

39. Credit risk
250%
1%
2%
3%
4%
5%
6%
7%
2022 NPLs 1-month scenario NPLs 2-months scenario NPLs 4-months scenario
NPLs 1-month scenario NPLs 2-months scenario NPLs 4-months scenario Baseline NPLs (%)
Ratio of non-performing loans in the Hungarian banking system
Underthescenariosdevelopedintheeconomicsection,NPLsintheHungarianbankingsystemcouldincurincreasesin
therangeofEUR730(1.3%)upto1461(2.6%)millionsofthetotaldebtoutstanding.
Primarysectorsaretheonesthataremostaffectedbyanature-relatedshocksgiventheirproximitytonaturalresources.While
Manufacturingisthesectorwiththelargestlossinabsoluteterms,itstillrepresentsabout3%ofthetotaldebt.0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Mining And
Quarrying
Agriculture,
Forestry And
Fishing
Electricity, Gas,
Steam And Air
Conditioning
3%
8%
2%
7%
18%
3%2%
1%1%
11%
0
50
100
150
200
250
300
350
EUR million
1-month scenario 2-months scenario 4-months scenario
Selected sectoral non-performing loans increases

40. Market risk
26
Public debt-to-GDP ratio under different scenariosAmount and maturity of sovereign debt held by Hungarian banks60%
65%
70%
75%
80%
85%
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022Scenarios
1-month scenario 2-months scenario 4-months scenario Debt to GDP ratio (%) 0
1
2
3
4
5
2023202420252026202720282029After 2030
EUR billions
Fixed rate Fixed in bands rate Index-linked rate Variable rate Zero coupon
0
5
10
15
20
25
HUF EUR USD Other
BillionsRatingsDomesticForeign
S&P BBB BBB
Moody'sBaa2Baa2
FitchBBB BBB
Underthescenarios,Hungarycouldexpectitssovereigndebttoincreasetoarangebetween76.8%and79.6%ofGDPdue
totheoutputshock.
HungarianfinancialinstitutionsholdingsignificantamountsofHungarianGovernmentbondsareexposedto
fluctuationsinthequalityofHungarianGovernmentissuances.Additionally,foreign-currency-denominateddebtholdings
standatabout20%oftotaldebt.

41. Financial system interaction and real economy feedback
27Sector A
(high biodiversity
exposure)
Sector B
(low biodiversity
exposure)
Bank A
Bank B
Nature-related
shock
Real Economy Financial Sector
Cascading impacts Financial contagion
Financial risks
Financial risks
Cascadingimpactsmayoccurthroughboththerealeconomy,viavaluechains,andinthefinancialsystemthrough
contagionriskbetweenfinancialinstitutions.Intheeventofanature-relatedshock,ifafinancialinstitutionhasalargeportionofits
portfolioconcentratedinaparticularsectorthatexperiencesadownturn,suchasAgriculture,ForestryandFishing,theinstitution's
overallfinancialhealthcouldbejeopardised.
IntheHungariancontext,morethan50%ofallinstrumentsdirectlyrelatedtoahighorveryhigh-risksectorareheldbyfive
banks.0%
10%
20%
30%
40%
50%
60%
Impacts Dependencies
Held by 5 banks Held by rest of the market
Share of direct exposure to high-risk sectors

42. Step 4: Supervisory considerations
28
Short term considerations
▪(i) leverage the resources provided by the OECD and TNFD to identify relevant available data and
metrics;
▪(ii) advance work on nature-related financial risks by further enhancement through the application of
advanced methodologies;
▪(iii) extended prudential approaches to address nature-related risks.
Building on MNB’s Green Programme, three pillars are assessed with respect to their applicability to nature:
1. Initiatives in the financial sector; 2. Development of MNB’s social and international relations; and 3.
Greening of MNB’s own operations

43. Step 4: Supervisory considerations
29
Medium term considerations
•(i) develop supervisory expectations for credit institutions to identify, assess and manage nature-related
risks into their governance structures, processes, and risk management controls;
•(ii) start to assess how to refine its approach to nature scenario analysis and stress testing

44. 30
Long term considerations
•(i) prioritize domestic nature-related risks to ensure that it can continue to meet its objectives in the
long-term;
•(ii) consider adjustments to monetary and prudential frameworks to reflect nature-related
considerations
Step 4: Supervisory considerations

45. Biodiversity-related Financial Risks –why
it matters and how can we measure them?
Case study of Georgia
Authors: Salome Tvalodze and Elene Nikuradze
June,2024

46. www.nbg.gov.ge
NBG’s Sustainable Finance Framework
3
Support capacity building and increase awareness
Guide the financial flows towards sustainable development
Ensure the incorporation of ESG into risk management and
decision-making of financial institutions
Support transparency and market discipline
I
II
III
IV
❑The NBG started developing Sustainable Finance Framework in 2017.
❑In 2019, the NBG launched the Roadmap for Sustainable Finance in Georgia:
❑The ultimate goal of this roadmap is to provide a credible, predictable, and stable
regulatory framework and prepare the market for transitioning to sustainable finance.
❑Pillars of the Roadmap:

47. www.nbg.gov.ge
Biodiversity Profile of Georgia
Environmental Performance Index
-Georgia belongs to the 35 "priority
ecoregions" identified by the World
Wide Fund for Nature (WWF);
-Georgia is located in two of the 36
biodiversity hotspots recognized
by Conversation International;
-Forests cover around 40 percent
of the country’s territory and 95–98
percent of them are of natural
origin;
-About 400 different species of
trees and plants are represented
-The protected area (PA) system
consists of 94 PAs
Sources:BasedontheYaleEnvironmentalPerformanceIndex(2022) 4

48. www.nbg.gov.ge
Assessment of BRFR in Georgia -Methodology
❑The exposure of the financial sector to
the economic sectors were determined
on the basis of commercial banks’
lending data to legal entities obtained
from the NBG.
❑In order to link ENCORE to financial data,
GICS business activities were manually
re-classified to match the two-digit NACE
REV 2 nomenclature.
❑The analysis excluded economically
irrelevant activities for the Georgian
economy by manually filtering them out.
https://
Source:Author’sownelaborationofNBGdata(2022)
Georgian commercial banks lending portfolio
5

49. www.nbg.gov.ge
Assessment of BRFR in Georgia -Dependencies
Ecosystem Service Percent of
portfolio
Direct physical input
Animal based energy 0.10%
Fibres and other materials 1.59%
Genetic materials 0.81%
Ground water 7.9%
Surface water 8.12%
Enabling production
Maintain nursery habitats 0.49%
Pollination 0.20%
Soil quality 0.89%
Ventilation 0.13%
Water flow maintenance 3.13%
Water quality 1.42%
Mitigating direct impacts
Bio-remediation 0.57%
Dilution by atmosphere and
ecosystems 0.32%
Filtration 0.44%
Mediation of sensory impacts 0.77%
Protecting from disruption
Buffering and attenuation of mass
flows 0.68%
Climate Regulation 5.45%
Disease control 0.57%
Flood and storm protection 6.54%
Mass stabilisationand erosion
control 5.13%
Pest control 0.45%
Total medium, high, and very high dependencies 45.71%
Source:Authors’elaboration
Approximately, 46 percent of Georgian commercial banks' lending portfolio to legal entities could be exposed to biodiversity-
related physical risk.
The financial sector and ecosystem services dependencies

50. www.nbg.gov.ge
Assessment of Biodiversity-related Financial Risks in Georgia -Impacts
Source:Authors’elaboration
Natural assets
Percent of
portfolio
Disturbances 2.19%
Freshwater ecosystem use 3.15%
GHG emissions 7.67%
Marine ecosystem use 1.85%
Non-GHG air pollutants 2.54%
Other resource use 1.32%
Soil pollutants 5.57%
Solid waste 6.45%
Terrestrial ecosystem use 7.87%
Water pollutants 6.17%
Water use 8.88%
Sum 53.66%
Total portfolio 100%
Approximately, 54 percent of Georgian banks’ business lending portfolio could be exposed to sectors that strongly
impact ecosystem services and, thus, may face a high transition risk.
7
The impact on the biodiversity of financial business sector lending

51. www.nbg.gov.ge
Thank You!
Elene Nikuradze
Senior Sustainable Finance Specialist
Financial Stability Department
National Bank of Georgia
[email protected]
[email protected]
https://nbg.gov.ge/en/page/sustainable-finance

52. Samuel Borges (University of Paris-Saclay)
Nora Laurinaityte (Green Finance Institute at INVEGA and Vilnius University)
Assessing Nature-Related
Financial Risks:
The Case of Lithuania
Authors acknowledge the support from the Bank of Lithuania received for
this project. The views presented herein are of the authors and do not
necessarily represent those of the Bank of Lithuania.

53. •Overhalf of global gross domestic product (GDP) is highly or
moderately dependent on nature and ecosystem services
•Nature-related risks can be transmitted to the financial system via
the real economy, with the potential to trigger financial instability
•The impacts from the loss of ecosystem services are not uniform
across geographic regions and different income levels
•To assess and mitigate the potential impact of ecosystem service
loss on financial stability, it is crucial to identify and measure
nature-related financial risks locally.
Motivation

54. Wefollowtheapproachesofsimilarstudiesby
usingtheENCOREframework,withthemain
assumptionbeingthatthelevelofecosystem
servicedependencyofeachfinancialinstitution
canbeapproximatedbyexaminingallthe
companies(orsectors)thefinancialinstitution
hasonitsbalancesheetandcalculatingthe
combineddependenciesofallthesecompanies
(orsectors)onecosystemservices.
Methodology

55. Data and Methodology
•Lithuanianbanks’commercialloanportfolioinformation(loanamountoutstandingasofApril1,2023,
perNACEeconomicsectorperbank)wascollectedusingthesupervisoryfinancialreporting(FINREP)
framework.
•WeuseENCOREforinformationonabusinessprocessdependenciesonecosystemservices.
•ENCOREusestheGICSclassification.Therefore,tolinkbanklendingdatatotheENCOREframework,
GICSbusinessprocessweremanuallyre-mappedtomatchtheNACEnomenclature.
•Inassessingthedependencyofthecommercialbanksontheecosystemservices,twoassumptions
weremade:
•First,foreachsectorthebankslendto,allproductionprocessesarerepresentedproportionally.
•Second,eachproductionprocessdependsproportionallyoneachecosystemservicedefinedbythe
ENCOREdatabase.

56. Data and Methodology
•Beforecomputingthedependencyofthefinancialinstitutiononecosystemservices,weassessthe
ecosystem-dependencyscoreofeachactivitysectorthefinancialinstitutionlendsto.
•Tocomputeadependencyscoreforeachbank(orfortheentirebankingsector,inwhichcasethereis
nosubscriptbdenotingeachbank)oneachecosystemservice,wecalculateaweightedmeanas
follows:
•Wealsoaggregatebusinesslendingaccordingtotheborrower’sdegreeofdependenceonecosystem
servicesasanalternative.Wefocusonlyon“veryhigh”,“high”,and“medium”dependenciesdefined
bytheENCOREmethodology.Inthisway,weobtaintheshareofbank’sloansthatareexposedto
ecosystemservicesatdifferentlevelsofecosystemdependence.

57. Lithuanian Banks Commercial Lending
Sectoral Composition of Bank Commercial Lending in LithuaniaExposure of Lithuania’s Banks’ Loan Portfolios to Ecosystem Services

58. Ecosystem Services Dependence

59. When it comes to risks arising from
dependencies on ecosystem
services, it is important to
remember that dependency
cannot be equated with risk.
To accurately assess the
magnitude of financial risks
associated with the dependencies
on ecosystem services we
document for the Lithuanian banks’
corporate loan portfolios, it is
necessary to take into account the
geographical specificity of
Lithuania.

60. •Lithuania is one of the few countries in the world with abundant fresh ground water resources. Water stress
is very low in Lithuania compared to other OECD countries and water is not considered a natural resource
at risk.
•According to the assessment of the European Systemic Risk Board and the ECB, the level of physical climate
change risk attributed to Lithuania is one of the lowest in the EU countries. Lithuania is located in the
midlatitude zone, where there is a high natural potential for adaptation to climate change; thus, the risks
posed by the dependence on ecosystem services associated with climate regulation and prevention of
storms and floods are relatively lower than in other countries located in different climatic zones.
Specificity of Lithuania

61. •70.1% of Lithuanian banks’ commercial lending portfolio to be to firms
with a very high dependence on at least one ecosystem service
•Lithuanian banks’ commercial lending portfolio is highly dependent on
surface and ground water provisions and climate regulation.
•The risks to bank loan portfolios of depleting these ecosystem
resources are relatively lower in Lithuania than in many other countries.
Even though the nature-related financial risks are relatively
lower in Lithuania than in many other countries, it is still
necessary to continuously monitor and reevaluate these
risks.
Conclusion

62. Thank you for your attention!

63. Network for Greening the Financial System 1
Assessingnature-relatedrisks:
the NGFS workand perspective
MNB-OECD-EC Launch Event: Technical implementation of the Supervisory
Framework for Assessing Nature-related Financial Risks to the Hungarian
financial sector
7 June 2024
Marie Gabet| NGFS Secretariat| Banquede France

64. Network for Greening the Financial System 2
▪Establishedby8Centralbanksand
SupervisorsduringtheParisDec.2017
OnePlanetSummit
▪AsofMarch2024:morethan150
members/observers,covering5
continents
▪Coalitionofthewilling.NotaStandard
SettingBody
▪NGFSmembers’jurisdictionscover:
–Supervisionof100%oftheglobal
systemicallyimportantbanksand
80%oftheinternationallyactive
insurancegroups;
–Morethan85%ofglobal
greenhousegasemissions.
The Network for Greeningthe Financial System

65. Network for Greening the Financial System 3
1. The NGFS Taskforce on biodiversitylossand nature-relatedrisks
Objective:Helpmainstreamtheconsiderationofnature-relatedrisksacrosstheNGFS,together
withclimate-relatedrisks.
Thewaytoachievethisobjective:
▪Actasanincubatorthatexplores,develops,andharmonisesnature-relatedconsiderationsandefforts
▪Exploretheinterconnections,similaritiesanddifferencesbetweennature-relatedandclimate-relatedrisks.
▪LeveragethesignificantamountofworkdoneandcollaboratewithNGFSworkstreams,networksand
members,andrelevantexternalstakeholders(e.g.TNFD,G7…).
Key publications in 2023
A Conceptual Framework on nature-related risks
that central banks and supervisors can utiliseto
develop policies and actions on nature-related
issues
A Technical Document providing specific
recommendations towards the development of
nature-related scenarios

66. Network for Greening the Financial System 4
2. The NGFS Conceptual Framework on nature-related financial risks
▪A common science-based understanding of, and
language fornature-related financial risks
▪Aligned and complementary approach to the
technical guidance focus of the OECD Supervisory
Framework on nature
▪Adopts an integrated approach, meaning that
climate-related financial risks are within scope
▪Includes a principle-based risk assessment
framework
▪Released as beta version to be refined and
supplemented over time
Overview

67. Network for Greening the Financial System 5
2. Understanding nature-related risks: transmission channels

68. Network for Greening the Financial System 6
2. Principle-based risk assessment framework
▪Seeks to operationalise
the understanding of
nature-related financial
risk
▪Provides flexibilityfor:
▪emerging analytical
methodologies and
risk management
practices
▪differences between
jurisdictions
▪Contains guiding
questions to capture key
elements that central
banks and supervisors
could consider
Phase 1:
Identify sources of
physical and
transition risk
Phase 2:
Assess economic
risks
Phase 3:
Assess risk to, from
and within the
financial system
i.Initial exposure
analyses
ii.Forward-looking
tools
iii.Local and systemic
dimensions
iv.Climate-nature
nexus
i.Direct and indirect
economic effects
(relevance of value
chains)
ii.Micro level and macro
level effects, including
interactions between
them
iii.The relevance of
substitutability
i.Traditional financial
risk categories
ii.Contagion
iii.Endogenous risk

69. 7
3. The NGFS Technical document on nature scenarios -Overview
Providesrecommendationstowardsthedevelopmentofnature-
relatedscenarios,seekingasmuchsynergyaspossiblewiththe
NGFSclimatescenarioswhileaccountingforthespecificfeatures
relatedtonatureloss
1.Twomainobjectives:
▪Suggestavenuestodevelopconsistentnarrativesfor
physicalandtransitionhazards
▪Assesstheabilityofdifferentmethodologies,models,and
toolstoaccountfornature-relatedrisks
2.Mainchallengesidentified:
▪Complexityofecosystemfunctionsandprocessesatstake
▪Nosinglemetric(e.g.akintoCO
2)orpolicy/measure(e.g.
pricingnaturalcapital)
▪Existenceofalocal-globaltradeoff
▪Modellingexercisesneedtoreflecttransmissionchannels
throughwhichaspecificnature-relatedhazardcan
propagateintheeconomy
Network for Greening the Financial System

70. 3. Work undertaken and 3 key findings
8
1. The report suggests approaches to develop scenario narratives that could translate nature concepts and
trends into practical physical and transition risks for the financial system:
▪Physical risk : ESGAP and INCAF-Oxford tools
▪Transition risk: in-house review of nature-related policies and suggested two-step
approach to generate narratives
2. The report assessessix of the most commonly used «nature-economy» models and of 14 purely
«biophysical» models.
➔The mostcommonlyused«nature-economy»modelslikelyunderestimatenature-relatedrisks,
becausethey:
▪accountfor a limitedfraction of potentialhazards
▪useassumptionsthatmitigatethe consequencesof nature loss
3. It also studies the main features of MRIO models and tables and conduct of two case studies(drought,
policy against ‘imported deforestation’) linking narratives to MRIOs
➔MRIOsare particularlyusefulto studyimpacts alonga wholevalue chain, and have the advantageto
assumeno elasticitiesof substitution. Howevertheymostlyallowfor short to medium termanalyses and
mightnot allowfor enoughgranularity
Network for Greening the Financial System

71. 3. Recommendations for nature scenario development
9
Nosilverbullet,butmanywaysforwardtodesignscenarios:
Short-termresearchprogram:
▪Useinput-outputtablesandmodels(potentiallywithbiophysicalmodels)forshort-term
scenarios
▪Usesomeofthemoretraditionalmodelsassessed,butwithgreatcaution(e.g.doing
sensitivityanalysesonelasticitiesofsubstitutionandcommunicatingclearlyonthis)
Longer-termresearchprogram:
▪Representmoreecosystemservices,morepolicyandtechnologyoptions,andalsomore
issuesrelatedtovaluesofnatureand“transformativechanges”(IPBES,2019)
▪Consideralternativemacroeconomicmodelingassumptionssuchasnon-equilibrium
approaches(e.g.stock-flowconsistentmodelscombinedwithinput-outputmodels)
Network for Greening the Financial System

72. Network for Greening the Financial System 10
1.Nature scenario work
oShort/medium term: workon the Technicaldocument short-
termrecommendations, work on narratives;
oLonger term: work on the improvement of modelling tools,
development of capacity-building instruments.
2.Frameworkrefinement and implementation
3.Mainstreaming of nature-related considerationswithin
the NGFS
▪Work on suggestions for workstreamsto bridge
existing gaps
▪Capacity-building work
▪Start of work on nature in other workstreamsand
expert networks
What’s next for the NGFS ?

73. Thank you !
Network for Greening the Financial System 11