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MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 1

ANALYSISJANUARY 2021

Prepared by

Juan [email protected] Director

Petr [email protected] Director

Chris [email protected]

Janet [email protected]

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Climate Risk Macroeconomic ForecastingINTRODUCTION

As early as 2021, many regulators across the world will require financial institutions to provide a self-assessment or to stress-test their balance sheets with respect to climate change risk. Constructing climate change scenarios starts with a trajectory for carbon dioxide emissions, the necessary policies to reduce these emissions, and the corresponding change in global temperatures. Moody’s Analytics is expanding its capabilities to enable institutions to assess risks posed by climate change. The physical and transition impacts on the economy of temperature change are determined using our model of the global economy. Our scenarios are consistent with Orderly, Disorderly, and Hot House World scenarios by the Network of Central Banks and Supervisors for Greening the Financial System. We employ our modelling framework to generate climate change scenarios for the U.S. and the U.K.

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Climate Risk macroeconomic ForecastingBY JUAN LICARI, PETR ZEMCIK, CHRIS LAFAKIS, AND JANET LEE

As early as 2021, many regulators across the world will require financial institutions to provide a self-assessment or to stress-test their balance sheets with respect to climate change risk. Constructing climate change scenarios starts with a trajectory for carbon dioxide emissions, the necessary policies to reduce these emissions, and the

corresponding change in global temperatures. Moody’s Analytics is expanding its capabilities to enable institutions to assess risks posed by climate change. The physical and transition impacts on the economy of temperature change are determined using our model of the global economy. Our scenarios are consistent with Orderly, Disorderly, and Hot House World scenarios by the Network of Central Banks and Supervisors for Greening the Financial System. We employ our modelling framework to generate climate change scenarios for the U.S. and the U.K.

More specifically, we first provide an over-view of the relevant regulatory landscape, focusing on the U.K. and the U.S. We then sum-marize our methodological approach, which is complementary to scenarios produced by Integrated Assessment Models (see Table 1 for the list of acronyms). The approach leverages our modelling framework initially designed for standard macroeconomic forecasting and for producing stress-testing scenarios for various regulatory purposes. We then discuss how the framework is employed to account for the long-term physical risk associated with climate change and then altered to incorporate risks linked to the transition to a carbon-neutral economy. The newly constructed transition mechanism block includes a carbon dioxide tax in the system of simultaneous equations. Finally, we generate climate change scenar-ios for the U.K. and the U.S. consistent with published NGFS scenarios using the updated modelling setup.

Regulatory landscapeThe Network for Greening the Financial

System and the Task Force on Climate-Related Financial Disclosures are key organizations leading the global effort to assess the financial impact of climate risk. The NGFS is a group of central banks and regulatory agencies worldwide established at the Paris One Planet

Summit in December 2017.1 Members include the Bank of England, Banque De France, the European Central Bank and the European Banking Authority, The People’s Bank of China, and the European Insurance and Occupational Pensions Authority.2 In December 2020, the Federal Reserve joined the NGFS as well. The TCFD is a taskforce set up by the Financial Sta-bility Board, composed of over 785 influential organizations from around the world. They have been urging businesses across industries

1 See https://www.oneplanetsummit.fr/en.2 The full list of members and observers is at https://www.

ngfs.net/en/about-us/membership.

to voluntarily measure and disclose their vul-nerabilities to climate risk. Scenario-based risk analysis is an integral part to both the NGFS and TCFD’s action plans.

The U.K. government has made significant steps towards assessment of the financial impact of climate risk. It has committed to ending coal sales, taxing carbon, and reducing emissions to net zero by 2050. It also closely follows recommendations of the U.K. Climate Change Committee and sets up five-year car-bon budgets required by the Climate Change Act. In addition, the country will host the 26th United Nations Climate Change conference in Glasgow in November 2021. The key financial

Table 1: List of AcronymsAcronym Full titleBoE Bank of EnglandBES Biennial Exploratory ScenarioECB European Central BankGCAM Global Change Analysis ModelIAM Integrated Assessment ModelIPCC Intergovernmental Panel for Climate Change JFSA Japanese Financial Services AgencyMAgPIE Model of Agricultural Production and Its Impacts on the EnvironmentNGFS Network of Central Banks and Supervisors for Greening the Financial SystemORSA Own Risk and Solvency Assessment RCP Reactive Concentration Pathways REMIND Regional Model for Investment and Development TCFD Task Force on Climate-Related Financial DisclosuresSource: Moody’s Analytics

https://www.oneplanetsummit.fr/en

https://www.ngfs.net/en/about-us/membership

https://www.ngfs.net/en/about-us/membership


regulator, the Prudential Regulatory Authority (a part of the Bank of England), already pub-lished the contours of climate risk scenarios in its Insurance Stress Test guidelines in 2019. The climate change scenarios contain elements of both physical and transition climate change risks.3 Following the Insurance Stress Test, the PRA announced its intention to test the U.K. fi-nancial system’s resilience to the financial risks from climate change as part of the 2021 Bienni-al Exploratory Scenario. The plan was outlined in the July 2019 Financial Stability Report.4 Finally, the U.K. Financial Conduct Authority has announced premium-listed companies to be subject to more detailed disclosures regard-ing how climate risk affects their business from January 2021. This announcement is consistent with the recommendations of TCDF. These are linked to environmental, social and governance factors for individual companies. The environ-mental factor reflects the vulnerability of com-panies to climate risk.5

The Bank of England indicated that it will use NGFS scenarios as the foundation for the Biennial Exploratory Scenario. In December 2020, it published an updated methodology of a 2019 discussion paper describing its intend-ed approach.6 The BoE will supply integrated climate and macrofinancial variables. These will include projections for temperature, emis-sions and climate policies that incorporate the underlying physical and transition risks. The macrofinancial variables should be used to as-sess the impact of climate change. There will be NGFS scenarios characterizing outcomes under assumptions of the earlier and later policy actions, as well as the scenario with no policy change. These scenarios are discussed in detail in the subsequent sections of this paper.

While the U.S. has not been very active in terms of climate risk regulation in the last de-cade, Fed Chair Jerome Powell indicated the Fed would like to engage with NGFS in November

3 SS3/19: Enhancing banks’ and insurers’ approaches to man-aging the financial risks from climate change April 2019.

4 https://www.bankofengland.co.uk/financial-stability-re-port/2019/july-2019.

5 Moody’s Analytics has acquired Vigeo-Eiris, a company that provides ratings for the ESG for some 5,000 companies. This will be enlarged to over 10,000 in 2021. Moody’s Analytics will leverage on the existing ratings to estimate ESG scores for 100,000 non-rate companies.

6 The updated information is at https://www.bankofengland.co.uk/climate-change and the 2019 discussion paper can be found at https://www.bankofengland.co.uk/paper/2019/biennial-exploratory-scenario-climate-change-discus-sion-paper.

2020. The point man for financial regulation, Randal Quarles, echoed these comments later. On 15 December 2020, the Federal Reserve formally joined NGFS. The Financial Stability Report from November 2020 already listed climate risk as one of the risks to financial stability in the near future. It mentioned both chronic and acute physical hazards af-fecting the value of financial and nonfinancial assets, as well as volatility in sentiment.7 Earlier in September 2020, an advisory panel to the Commodity Futures Trading Commission re-leased a report entitled “Managing Climate Risk in the U.S. Financial System.” It is the first of its kind from a federal financial regulator and im-plies that proper pricing of carbon emissions in the U.S. will be required.

Other financial regulators across the globe have been active in climate risk regulation as well. The European Central Bank published its guide on climate-related and environmental risks for banks on 27 November 2020. In 2021, the banks regulated by the ECB will be required to conduct a self-assessment of climate risk exposure and formulate action plans reflect-ing the outcome of this assessment. A full supervisory review of banks’ practices will be conducted in 2022. The European Insurance and Occupational Pensions Authority started a consultation process regarding the use of cli-mate change risk scenarios in the Own Risk and Solvency Assessment in October 2020. Else-where, the Japanese regulator Financial Services Agency plans to include climate risk scenarios in a stress-test pilot for the country’s five big-gest banks. Similarly, the Monetary Authority of Singapore intends to include climate change scenarios in its stress-testing exercise within the next two years.

Climate risk scenariosThe climate change scenarios are similar

in principle to the classical stress-testing ones

7 See the statement by Governor Lael Brainard at https://www.federalreserve.gov/publications/brainard-com-ment-20201109.htm.

(see Chart 1). They leverage existing macro-economic models to generate projections of economic variables of interest. Following the Global Financial Crisis, the stress-test scenarios have now been fully embedded in the meth-odology toolkits of financial institutions across the world. The macroeconomic scenarios have been embraced by regulators, including central banks such as the Federal Reserve, the Bank of England, and the European Central Bank together with the European Banking Authority, and countless others. The stress scenarios have also been used for accounting standards for the Current Expected Credit Losses by the Financial Accounting Standards, and for the International Financial Reporting Standard by the Interna-tional Accounting Standards Board. Due to the extensive employment of these scenarios, they have been standardized to some extent. The forecast horizon ranges from five years for the standard stress scenarios to 30 years for ac-counting standards, although most of the stress typically occurs during the first one to three years of the scenario.

However, there are several differences and corresponding challenges. First, the time hori-zon needs to be extended by decades. Moody’s Analytics Global Macroeconomic Model, hosted on the web-based platform Scenario Studio, already generated projections for 30 years to accommodate accounting standards. However, the time horizon needs to be ex-tended to 2100, which involves extending the projections of potential GDP paths and paying close attention to long-term demographic trends. A key attribute of climate risk model-ling is the inclusion of the trajectory for carbon dioxide emissions. This projection is translated into a temperature path. Different tempera-ture paths generate different physical risk and

1

Chart 1: Economic vs.Climate Risk ScenariosDifferentiation btw standard forecasts and climate risk scenarios

Economic scenarios Climate risk scenarios• 5- to 30-yr forecast

horizon• Used to measure capital

and assess risk• Shock inputs are

provided by Moody’s Analytics, clients or regulators

• Stable economic relationships

• Shock inputs do not depend on carbon dioxide trajectory

• 30- to 80-yr forecast horizon• Used to assess risk• Impact channels must be translated into shock

inputs• Increased uncertainty over how impact

channels translate into economic inputs• Increased uncertainty over how the economy

transitions from fossil fuels to renewables• Shock inputs depend on carbon dioxide

trajectory• Requires new forecast variables and model

equations

Source: Moody’s Analytics

https://www.bankofengland.co.uk/financial-stability-report/2019/july-2019

https://www.bankofengland.co.uk/financial-stability-report/2019/july-2019

https://www.bankofengland.co.uk/climate-change

https://www.bankofengland.co.uk/climate-change

https://www.bankofengland.co.uk/paper/2019/biennial-exploratory-scenario-climate-change-discussion-paper



https://www.federalreserve.gov/publications/brainard-comment-20201109.htm




2

Chart 2: Transition and Physical Risk


CO2pathway

Carbon tax, other climate

policy

Financial impact

Climate risk scenarios

Temperature pathway

Physical risk shock inputs

3

Start with regulators’ parametersExpand scenario to extrapolate additional variables

using global macro model with climate risk components

Carbon price pathwaysEmissions pathways

Commodity & energy prices; energy mix

Global & regional temperature pathwaysHealth & productivity effects

Sea level riseEnergy demand & tourism

GDP & unemploymentInflation & central bank rates

Corporate profits & household incomeResidential & commercial property prices

Physical variables Macroeconomic variables

Transition variablesGovernment & corporate bond yields

Equity indexesExchange rates & bank rates

Financial market variables

Climate risk variables Macrofinancial variables

Chart 3: Constructing Climate Risk Scenarios


the subsequent impact on economic drivers. In addition, various government policies such as a carbon tax affect the speed with which the carbon dioxide paths are altered. These gener-ate transition risks associated with each path.

Chart 2 illustrates how the transition and physical risks associated with climate change are incorporated into the macroeconomic modelling. The pathway for carbon dioxide determines the temperature pathway. Phys-ical risk refers to the physical consequences of changing climate patterns, including rising sea levels, changing precipitation patterns, and changes in the magnitude and frequency of extreme weather events. These climate changes are the consequence of rising carbon dioxide emissions driving up global tempera-ture. Transition risk associated with climate

change mitigation policies is embedded in the path for carbon taxes and other policies. This is combined with a financial impact linked to the timeline, according to which asset markets incorporate the climate risk in asset prices. This is often referred to as the Minsky moment8 as it can lead to instability and cri-sis. Once the physical, transition and financial impact is considered, we can generate the climate risk scenarios.

Note that the Moody’s approach does not explicitly model either the conversion of the carbon dioxide pathway into the tem-perature pathway or the conversion of the temperature path into physical risks. These steps are typically addressed via a climate

8 According to economist Hyman Philip Minsky, who studied the stability of financial systems.

module that is part of the Integrated As-sessment Models. For example, one of the scenarios generated by NGFS is produced by the Global Change Analysis Model that uses an Earth System Module – Hector v2.0. The Moody’s approach differs from the IAM, as the objective is to provide a projection of the carbon tax consistent with a particular pathway of the carbon dioxide emissions. Moody’s aims to construct forecasts of standard economic drivers consistent with various climate risk assumptions and the corresponding temperature pathways, us-ing its Global Macroeconomic Model. Box 1 discusses in some detail the IAMs and elaborates on how the Moody’s approach to climate risk complements the IAM modelling philosophy.

Box 1: integrated assessment modelsOur approach to constructing climate scenarios shares some features of the commonly used IAMs. For example, the GCAM employed by NGFS spans across socioeconomics; energy; agriculture, land use and bioenergy; water; climate; emissions; economic choice; trade and technology; and policies and costs.9 Another example of IAM is the Regional Model for Investment and Development, a Ram-sey-type macroeconomic general equilibrium growth energy-economy model. It is combined with the Model of Agricultural Produc-tion and Its Impacts on the Environment.

These topics are captured by modules connected to describe how greenhouse gas emissions affect climate and how climate change affects the economy. The energy system serves as the conduit through which environmental and economic variables in-teract. Most IAM energy systems are detailed representations of the sources of energy supply, which subsequently determine emissions. Assumptions regarding population, labor productivity, technology characteristics and policies are used to provide out-puts on emissions, prices, energy supply and demand, temperature, agricultural production, land use, and water use, among others. IAMs produce forecasts for these outputs in five-year intervals. Moody’s Analytics uses output from these IAMs as an input into its scenario construction process. The main IAM inputs are fossil fuel consumption by source, temperature pathways and carbon prices.

9 See http://jgcri.github.io/gcam-doc/toc.html.

http://jgcri.github.io/gcam-doc/toc.html


Chart 3 provides an overview of the construction of climate risk scenarios. The climate risk variables consist of physical variables such as the temperature pathways and the transition variables, including car-bon dioxide emissions. The macroeconomic variables include core economic drivers such as GDP components, labor market metrics,

and key interest rates and prices. These are then used to produce projections of a wide range of financial market variables. In the Moody’s approach, we assess the impact of the chronic physical risk separately, mainly via its impact on productivity and other vari-ables. Newly, we have constructed variables to model and quantify the transition risk. The

macroeconomic variables are generated by the Moody’s Macroeconomic Model hosted on the web platform Scenario Studio (see Box 2). The macroeconomic variables are used as inputs together with additional assumptions regarding the timing of the Minsky moment to produce projections of a wide range of fi-nancial market variables.

Box 2: moody’s global macroeconomic model Generation of the climate risk scenarios relies on the Moody’s Analytics Global Macroeconomic Model hosted on the web-based platform Scenario Studio. The model forecasts more than 15,000+ time series across 100 countries that collectively consti-tute more than 95% of global GDP. Model equations are specified based on economic theory, and they feature shock properties that are essential in scenario construction, including the creation of economic forecasts consistent with different climate change as-sumptions. The model captures both short-term business cycle dynamics and long-run trends. Short-term forecasts are deter-mined by fluctuations in aggregate demand, whereas long-term forecasts are determined by an economy’s labor force and labor force productivity growth. The model captures both interconnect-edness among economic regions and country-specific idiosyncra-cies. The linkages among countries and regions are characterized by trade and financial flows. The cross-country linkages include the impact of global prices and exchange rates on economic per-formance. While the model structure is similar across countries, the framework allows for country-specific variations of key equations and for the inclusion of tailpipe equations for variables important for some countries (see Chart 4).10

10 For more details please see “Moody’s Analytics Global Macroeconomic Model Methodology”, M. Hopkins, Moody’s Analytics March 2018. https://tinyurl.com/y2ao8jga

Physical riskClimate change poses both physical and

transition risks. Physical risk refers to the physical consequences of changing climate patterns, including rising sea levels, changing precipitation patterns, and changes in the magnitude and fre-quency of extreme weather events. Physical risks can be separated into chronic and acute. There are six primary components of chronic physical risk:

» Sea level rise

» Human health effects

» Heat effect on labor productivity

» Agricultural productivity effects

» Tourism effects

» Energy demand effects

Moody’s Analytics already provides chronic physical risk scenarios that are available for all countries in the Global Macroeconomic Model through 2048 for four Reactive Concentration Pathways (RCP) by the Intergovernmental Panel for Climate Change (IPCC) (see Chart 5).11

We create three new scenarios that are consistent with NGFS projections and modify our framework to incorporate chronic phys-ical risk into our new scenarios. The most recent NGFS projections12 are available for three scenarios: Orderly, Disorderly, and Hot House World. The Orderly and Disorderly sce-narios assume that climate change policies

11 Fornarrativesforthesescenarios,see“TheEconomicIm-plicationsofClimateChange”,ChrisLafakis,LauraRatz,Em-ilyFazio,andMariaCosma,Moody’sAnalyticsJune2019. https://www.moodysanalytics.com/-/media/article/2019/economic-implications-of-climate-change.pdf

12 NGFSClimateScenariosforcentralbanksandsupervisors,NGFS,June2020.

are adopted to limit the global warming to below 2°C. In the case of the Disorderly sce-nario, the transition starts only after 2030. The Hot House World scenario ssumes no ad-aptation occurs. Stylized world-level carbon prices and emissions are iluustrated in Charts 6 and 7. The corresponding temperature path-ways are depicted in Chart 8.

Acute physical risk refers to weather events that could become increasingly likely or severe because of climate change. There are four primary components of acute physical risk:

» Heat waves and cold snaps

» Droughts and wildfires

» Flooding

» Tropical cyclones

4

Chart 4: Our Global Macroeconomic Model

Specification choice» Theoretical reasoning versus

statistical propertiesIn-sample equation fit» R-squared, RMSE, information

criteria» Fitted values and residualsForecasting performance» Back-testing: Conditional and

unconditional evaluation» Benchmarking during important

past episodesSensitivity to shocks» Forecasts across scenarios» Response to individual shocks

100+ country modules linked via trade and finance


https://tinyurl.com/y2ao8jga

https://www.moodysanalytics.com/-/media/article/2019/economic-implications-of-climate-change.pdf

https://www.moodysanalytics.com/-/media/article/2019/economic-implications-of-climate-change.pdf


Two approaches could be taken to mod-el acute physical risk. The first is to quan-tify the link between rising temperatures and the economic cost of natural disasters by country. These costs could then be modelled over time as global tempera-tures rise in the upcoming BoE scenarios. This approach does not model an explicit natural disaster, but rather smoothes their cost over the forecast horizon. The BoE (as mentioned in its discussion paper) will also provide characterization of a distribution of key physical variables to capture changes in the frequency and severity of climate events at a granular regional level. The sec-ond approach is to model an explicit natu-ral disaster occurring in a specific geogra-phy at a specific time that carries a specific intensity. Explicit modelling of the propen-sity towards acute physical risk requires cli-mate risk data at a regional and postcode level. Moody’s acquired a majority stake in Four Twenty Seven Inc., a provider of data,

intelligence and analysis related to physical climate risks. Four Twenty Seven Inc. trans-lates climate models into actionable intel-ligence for clients that include some of the world’s largest investors, asset managers, commercial banks, development finance institutions, corporations, and government agencies. The “427” analytics engine lever-ages a wide range of data from various sources to generate climate risk impact data. This information will be incorporated in downstream models for regional drivers and credit risk assessment.

The physical risk impact estimates are synthesized by Roson and Sartori (2016),13 who report the impact on GDP for the six components of physical risk by country for increases in °C of +1, +2, +3, +4, and +5 for years 2050 and 2100. The sea level

13 Roberto Roson and Martina Sartori, Estimation of Climate Change Damage Functions for 140 Regions in the GTAP9 Database, World Bank Group Policy Research Working paper 7728, June 2016.

impact is reflected in private real con-sumption FC$_GEO; agricultural and labor productivity, jointly with human health effects, impact real potential productivity FPROD$_POT_IGEO; tourism demand affects net exports FNETEX$_I_GEO; and energy demand impacts the Brent oil price FCPIFICEBOIU_US. Charts 9 and 10 sum-marize the impact channels for the phys-ical risk as well as the remaining climate risk modelling components. Real con-sumption and net exports are expenditure components of GDP. Potential GDP and real average wage depend on real poten-tial productivity, and many energy prices take oil prices into account. There are several options for incorporating the im-pact of the physical risk using the Moody’s Global Macroeconomic Model. Two key ones are creating a series for add factors and exogenizing the series. As the physical impact is long term, we opt for the latter approach in this case.

Climate Change 6

Car

bon

pric

e

Time

No additional policy actionLate policy actionEarly policy action

Carbon price, US$2010/metric ton CO2

Chart 6: Carbon Price (World)

Sources: Network for Greening the Financial System, Moody’s Analytics

Climate Change 7

Time

No additional policy actionLate policy actionEarly policy action

Emis

sion

s

Chart 7: CO2 Emissions (World)


8

Chart 8: NGFS Scenarios

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

05 15 25 35 45 55 65 75 85 95

Immediate 2C with CDR (Orderly, Rep) Delayed 2C with limited CDR (Disorderly, Rep) Current policies (Hot house world, Rep)(Hot House World, Rep)


World temperature pathway (°C relative to 1850-1900)

5

Chart 5: IPCC ScenariosGreenhouse gas concentration levels define four distinct scenarios

Temper-aturerises in tandem with the emission level

High carbon intensity

Emission-curbing policies

RCP 2.6The Paris Accord

RCP 4.5Moderate Warming

RCP 6Late-Century Warming

RCP 8.5No Mitigation

DecarbonizationGlobal coordination

4.1 C Temperature increase

2.4 C Temperature increase1.9 C

Temperature increase

No mitigating policies

No policy change, “business as intended”

+1 C

Corrective transition responses

More transition risks Climate hazard intensifies

Representative Concentration Pathways created by the Intergovernmental Panel on Climate Change to represent plausible long-run greenhouse gas emission pathways.



Transition riskCharts 9 and 10 include the key com-

ponents of the transition risk modelling, in addition to the previously described approach, to take account of the aggregate physical risk. The first step in determining the impact of introducing the carbon tax in an economy is setting dummy variables for the carbon divi-dend and carbon tax in a country model em-bedded in the Moody’s Global Macroeconomic Model. Using the U.K. model as an example, Appendix Table provides additional informa-tion for the blocks of equations described in the Modelling Framework in Charts 9 and 10. The table includes the mnemonics for these and other carbon tax-related variables. Fol-lowing the standard employed in the Scenario Studio platform, which hosts the Moody’s Global Macroeconomic Model, equations are stochastic, exogenous or identities. Stochastic equations contain an error term to capture randomness, and the projections are endoge-nously solved for in the system of all simulta-neous equations in the Moody’s Global Macro-economic Model. Identities characterize basic relationships, and exogenous variables enable the user to set up a path based on information outside of the model.

Appendix Table also includes the relevant units and upstream and downstream de-pendence. For example, government expen-ditures and household disposable income depend on the dividend dummy variable. The impact of the transition is triggered by imposing taxes on carbon dioxide for the key sources of energy such as coal, natural gas and petroleum. The usage of the tax rate is indicated via the carbon tax dummy and the corresponding tax rate. Prices of coal, natu-

ral gas and petroleum depend on the carbon tax rate and so does government revenue for the three fossil fuels. The carbon tax rate is imposed exogenously based on assumptions regarding a government’s climate change policy. Imposing the carbon tax rate results in the corresponding revenue. The equation for the revenue is a simple identity, where the revenue equals the dummy variable multiplied by the tax rate and the amount of emissions. This revenue is also added to the government budget.

The subsequent block of transition risk equations characterizes energy prices. Specif-ically, these include the average price of coal, the effective domestic natural gas price, and the effective domestic oil price. All three en-ergy prices depend on the dummy variable for the carbon tax rate as well as the carbon tax rate. The price of natural gas also depends on the exchange rate, and the price of oil on the price of Brent crude. Consumption of all three fossil fuels and consumer prices for energy and electricity reflect the energy prices. The price of oil also affects gross value added in industries such as electricity, gas, steam and air-conditioning, and mining and quarrying. Imports and the producer price index depend on oil prices as well. Besides energy prices, energy consumption depends on industrial production for natural gas, and household disposable income and motor vehicle regis-tration for passenger cars. Energy consump-tion mainly determines the emissions for each of the fossil fuels plus global demand for petroleum. There is essentially a one-to-one correspondence between the growth rates of energy consumption and emissions. These are captured by a simple regression equa-

tion which also includes autocorrelation in error terms.

Including the carbon dioxide tax has im-plications for government finances. Overall tax revenue depends on tax income reflecting the GDP level and the revenue from the car-bon tax. Expenditures depend on the carbon dividend dummy as well as the carbon tax revenue. Many other factors have an impact on government finances, including the current interest expense on existing government debt and the debt-to-GDP ratio. The government budgetary metrics determine government spending as a part of GDP, which completes the endogenous loop for the simultaneous equations model.

The consumer energy price index de-pends on the prices of oil, natural gas and electricity. The electricity price depends on the price of gas and coal. The overall price index depends on energy prices as well as on a variety of other factors such as the unemployment rate and inflation expecta-tions. The producer price index depends on the oil price.

The key variables that are impacted by the block of climate transition risk equations are real imports via the effective domestic oil price; disposable income via the carbon diox-ide tax revenue and indirectly via government expenditures; indirectly the exchange rate and gross value added for industries. The GVA is one of the drivers of employment in each of the 20 industries.

Moody’s Analytics NGFS scenariosIn this section, we show how we are cali-

brating to the NGFS scenarios and discuss the key results generated by the Moody’s Analytics

9

Impact channel Energy consumption

CO2 taxesCO2 emissions

Chart 9: Modelling Framework 1/2

Mnemonics1. Coal FCOALCONQ_IGEO2. Natural gas FNGASCONQ_IGEO3. Petroleum and othe liquid FPETCONQ_IGEO

Mnemonics1. Coal FCOALCO2EQ_IGEO2. Natural gas FNGASCO2EQ_IGEO3. Oil and petroleum products FPETCO2EQ_IGEO

Mnemonics1. Dividend dummy DUM_CARBONDIV_IGEO2. Carbon tax dummy DUM_CARBONTAX_IGEO3. Carbon tax rate FCARBONTAX_IGEO4. Carbon tax revenue FCARBONREV_IGEO

4-i. Carbon tax revenue: Coal FCOALREV_IGEO4-i. Carbon tax revenue: Natural gas FNGASREV_IGEO4-i. Carbon tax revenue: Petroleum FPETREV_IGEO

Energy prices

Mnemonics1. Sea level rise FC$_GEO2. Agricultural productivity FPROD$_POT_GEO3. Heat stress effect on labor productivity FPROD$_POT_GEO4. Human health effects FPROD$_POT_GEO5. Tourism FNETEX$_I_GEO6. Energy demand FCPIFICEBOIU_US

Government financesMnemonics

1. Total revenue FGGREV_IGEO2. Total expense FGGEXP_IGEO3. Expenditure intermediate term FGGEXP_I_IGEO4. Expenditure residual FGGEXP_RESID_IGEO

Mnemonics1. Coal FPCCOALDOM_IGEO2. Natural gas FPCNGASDOM_IGEO3. Oil FPCOILDOM_IGEO


10

Price indexes Employment by industry

Other

Chart 10: Modelling Framework 2/2

Mnemonics1. All items ex energy, food FCPIX_IGEO

2. Food, alcohol & tobacco FCPIF_IGEO

3. Energy FCPIE_IGEO

4. Electricity FCPIELEC_IGEO5. Total FCPI_IGEO

6. Producer price FPPI_IGEO

Mnemonics

1. Real imports FIM$_IGEO

2. Disposable income FYPD_IGEO

3. Exchange rate FTFXIUSA_IGEO

4. GVA of services industry FGDPSERV$_IGEO

Mnemonics1. Arts; entertainment and recreation FEAE_I_IGEO2. Agriculture; forestry and fishing FEAF_I_IGEO3. Administrative and support service activities FEAS_I_IGEO4. Construction FECN_I_IGEO5. Education FEED_I_IGEO6. Electricity; gas; steam and air conditioning supply FEEG_I_IGEO7. Accommodation and food service activities FEFA_I_IGEO8. Financial and insurance activities FEFI_I_IGEO9. Activities of households as employers FEHH_I_IGEO10. Human health and social work activities FEHS_I_IGEO11. Information and communication FEIC_I_IGEO12. Manufacturing FEMF_I_IGEO13. Mining and quarrying FEMQ_I_IGEO14. Public administration and defence; compulsory social security FEPD_I_IGEO15. Professional; scientific and technical activities FEPS_I_IGEO16. Real estate activities FERE_I_IGEO17. Other service activities FESO_I_IGEO18. Transportation and storage FETS_I_IGEO19. Wholesale and retail trade; repair of motor vehicles FEWR_I_IGEO20. Water supply; sewerage; waste management and remediation FEWW_I_IGEO



Global Macro Model. In general, we also follow guidelines by the BoE in its BES discussion pa-per mentioned above, although we go beyond these in a number of ways. For example, the BoE forecast horizon is 2050 while it is 2100 in our case. The BoE leverages on the NGFS projections at a five-year frequency, while we have produced datapoints at quarterly frequency to allow time series analysis with standard macrofinancial variables.

The forecasted series shown here all include the impacts from both transition and chronic physical risk, derived using the methodology discussed in detail in the earlier sections. The NGFS publishes results for three representative scenarios and five alternative scenarios using three different IAMs: GCAM 5.2, REMIND-MAgPIE 1.7-3.0, and MEASSAGE ix-GLOBIOM 1.0. This cre-ates a challenge in the calibration process. First, the population and GDP figures are different in the historical period of 2005 to 2020 for different models. Therefore, the

starting point across scenarios will not be the same if we calibrate to the NGFS mark-er scenarios. Second, NGFS suggested cal-culating the mitigation cost expressed as a loss of GDP between two scenarios by sub-tracting GDP in one scenario from the other for REMIND-MAgPIE and MESSAGEix-GLO-BIOM. Calibrating to the marker scenarios will not allow us to calculate the mitigation cost as suggested by NGFS. Finally, only for the REMIND-MAgPIE 1.7-3.0 IAM model does the NGFS publish results for all three of its representative scenarios—Hot House World, Disorderly, and Orderly. The NGFS therefore recommends that all scenario analysis exercises be conducted using the same model. Following this recommenda-tion, and for the sake of consistency across scenarios, we have used the NGFS-provid-ed output from REMIND-MAgPIE 1.7-3.0 to construct our climate risk scenarios.

Chart 11 summarizes the adopted process to produce scenarios consistent with NGFS.

We match energy consumption translated into fuel emissions by source. For the phys-ical risk, we apply the Moody’s Analytics approach including the assumptions with re-spect to projections of population and GDP. For transition risk, we have opted to match energy consumption and emissions, as they are well defined, while the GDP paths pub-lished by NGFS depend on assumptions with respect to population and other variables that are inconsistent with ours. We also use the NGFS carbon price trajectories and our model to produce forecasts for domestic energy prices, which simultaneously inter-act with other model variables to produce our full set of macroeconomic forecasts. Ultimately, the ranking of our GDP paths is similar to those of the NGFS, though there are differences in absolute levels. The end product is a set of macroeconomic sce-narios consistent with NGFS assumptions on fossil fuel usage, carbon emissions, and carbon prices.

13Climate Risk Macroeconomic Forecasting 13

Chart 13: U.S. Carbon Dioxide Tax Rate

0

2,000

4,000

6,000

8,000

10 20F 30F 40F 50F 60F 70F 80F 90F 100F

NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)

$ per metric ton, NSA



Chart 14: U.K. Effective Domestic Price: Coal

1,000

10,000

100,000

15 25F 35F 45F 55F 65F 75F 85F 95F


1/100 £ per #, log scale, NSA

Sources: U.K. Department for Business, Energy & Industrial Strategy, Moody’s Analytics


Chart 12: U.K. Carbon Dioxide Tax Rate

0

1,000

2,000

3,000

4,000

10 20F 30F 40F 50F 60F 70F 80F 90F 100F


£ per metric ton, NSA


Climate Risk Macroeconomic Forecasting 11

Chart 11: NGFS Consistent ScenariosEnergy consumption

REMIND-MAgPIE 1.7-3.0 IAMTranslate into fuel emissions

by source

Physical riskMA Approach

MA population and GDP assumptions

OutputGDP paths consistent with assumptions regarding physical and transition risk Industrial detail projections

Energy prices & price indexesCO2 tax set to match emissionsPrices reflect the taxes

START FINISH



Charts 12 and 13 summarize the U.K. and U.S. carbon tax rate until 2100 for the three NGFS representative scenarios. For each sce-nario, we calibrate the carbon tax pathway to match the carbon tax trajectory generated by the REMIND-MAgPie IAM model used by NGFS. In the Orderly scenario, the carbon tax is put into effect starting in the third quarter of 2021, and the carbon tax rate rises overtime, with the increase significantly in-tensifying in the second half of the century. In the Disorderly scenario, the carbon tax is not implemented until the first quarter of 2030, and because of the late start, the carbon tax rate needs to be higher than the immediate scenario in order to make up for the lost time. In the Hot House World scenario, the carbon tax rate is zero since no additional future ac-tion will be taken to mitigate climate risks.

The carbon tax will raise the effective domestic energy prices of fossil fuels tremen-dously. Charts 14 and 15 show that prior to 2030, energy prices for U.K. coal and U.S.

natural gas in the Disorderly scenario are the same as in the Hot House World scenario, but they will rise rapidly and exceed the Orderly scenario starting in 2030. By 2100, the U.K. effective domestic coal price in the Orderly and Disorderly scenarios will be more than 15 times the Hot House World scenario, and a similar pattern holds for the U.S. ef-fective domestic natural gas price. Since the carbon tax is levied per metric ton of emis-sion, it will have a larger impact on coal than on natural gas because coal has a higher CO2 emission factor.

As a result of the very large carbon tax rate imposed in the Orderly and Disorderly scenarios, fossil fuels consumption will fall dramatically. Chart 16 shows that U.K. coal consumption will be driven to near zero in the Orderly and Disorderly scenarios, and without the carbon tax, U.K. coal consump-tion will continue its long-term decline, but will not fall to zero by 2100 in the Hot House World scenario. U.S. natural gas

consumption in Chart 17 is projected to rise steadily in the Hot House World scenario, but in the the Orderly and Disorderly sce-narios it is projected to decline by over 50% in 2100.

After calibrating to the NGFS carbon tax and fossil fuels consumption pathway, and adjusting for the effects of chronic physical risk for the U.K.14 and the U.S., we use the Moody’s Analytics Global Macro Model to generate the full scenario path-way for the U.K., U.S., and the rest of the global economy. The scenario outputs are the entire suite of economic and financial variables currently in the Moody’s Analytics Global Macro Model universe. Charts 18 and 19 show the projected percentage loss in real GDP between scenarios. Since the impacts of chronic physical risk are small and almost negligible for the U.K. and

14 Since the U.K. is not a separate region in the NGFS scenari-os, we calibrate the U.K. using the carbon tax rate and fossil fuels consumption pathway for the EU as a proxy.


Chart 15: U.S. Effective Domestic Price: NG

1

10

100

1,000

10 20F 30F 40F 50F 60F 70F 80F 90F 100F


$ per mmBTU, log scale, SA



Chart 16: U.K. Energy Consumption: Coal

1

10

100

1,000

15 25F 35F 45F 55F 65F 75F 85F 95F


BTU, tril, log scale, SAAR

Sources: U.S. Energy Information Administration, Moody’s Analytics


Chart 17: U.S. Energy Consumption: NG

0

8,000

16,000

24,000

32,000

40,000

15 25F 35F 45F 55F 65F 75F 85F 95F


Ths, short tons, SAAR



Chart 18: U.K. Real GDP Scen Comparison

-4

-3

-2

-1

0

1

10 20F 30F 40F 50F 60F 70F 80F 90F 100F

NGFS Delay (Disorderly)NGFS Immediate (Orderly)

% deviation from NGFS current




Chart 20: U.K. GVA Svcs Industry Scenario

-4

-3

-2

-1

0

1

10 20F 30F 40F 50F 60F 70F 80F 90F 100F





Chart 19: U.S. Real GDP Scen Comparison

-4

-3

-2

-1

0

1

10Q2 20Q2 30Q2 40Q2 50Q2 60Q2 70Q2 80Q2 90Q2 00Q2





Chart 21: U.S. GPO Mining Industry Scenario

-80

-60

-40

-20

0

20

10 20F 30F 40F 50F 60F 70F 80F 90F 100F





Chart 22: U.K. Employment Services Industry

-0.2

-0.1

0.0

0.1

0.2

0.3

10Q4 20Q4 30Q4 40Q4 50Q4 60Q4 70Q4 80Q4 90Q4 00Q4





Chart 23: U.S. Employment Mining Industry

-40

-30

-20

-10

0

10

10Q4 20Q4 30Q4 40Q4 50Q4 60Q4 70Q4 80Q4 90Q4 00Q4




24Climate Risk & ESG

Chart 24: Climate Risk SensitivitiesClimate change and credit risk


ST/IFRS 9/CECL ModelsProbability of default (PD)

» 427 climate risk data on physical risk score» The scores are differentiated based on geography (postcode

level) and type of asset, affecting the property valuePhys

ical

ris

k

Loss given default (LGD)

Climate change & physical risk estimates

Cre

dit

risk

» Moody’s climate change scenarios at national and sub-national level for key macroeconomic drivers

Clim

ate

scen

ario

s

PD – Physical risk & climate change adjusted

LGD – Physical risk & climate change adjusted


the U.S., losses in real GDP for these two countries are mostly due to transition risk alone. The percentage losses calculated from the Moody’s Analytics Global Macro Model are similar to the estimates from RE-MIND-MAgPie. For the U.K., the peak loss occurs at around 3% in 2100, and for the U.S., the peak loss occurs earlier in 2070 at almost 5%, and the loss becomes smaller after that. Other than the initial period in which the carbon tax is put into effect, real GDP is consistently lower in both the Or-derly and Disorderly scenarios when com-pared with the Hot House World scenario. In the initial period, the economy gets an immediate boost due to the collection and distribution of the carbon tax revenue, which raises real GDP. In the Orderly sce-nario, GDP loss occurs right away in 2021, and in the Disorderly, GDP loss occurs in 2030 after the carbon tax is implemented and exceeds the loss level in the Orderly scenario throughout the forecast period.

GDP loss at the aggregate level may give a false impression of the full impact from transition risk for individual indus-tries. In transitioning to a low carbon economy, there needs to be a substantial reallocation of resources, and the inflation pressures from energy prices will affect industries and sectors differently. High-risk industries such as mining and utilities will be hit much harder than low-risk indus-tries such as professional services. Charts 20 and 21 show the projected percentage losses in gross value added for the U.K. service-providing industry and the gross

product originating for the U.S. mining industry. Not surprisingly, the difference between industries is huge. The U.K. ser-vice-providing industry is expected to incur a peak loss in output of 3% by 2100, slighly lower than the percentage loss in aggregate GDP, whereas the U.S. mining industry is expected to suffer a peak loss of well over 60% when compared with the Hot House World scenario. Industry employment tells a similar story. The U.K. service-provid-ing industry is virtually unaffected, with employment loss hovering around 0%, while the U.S. mining industry is projected to experience a nearly 40% reduction in employment in both the Orderly and Disor-derly scenarios (see Charts 22 and 23).

SummaryThe financial industry has been gearing

up to address the challenges of assessing climate risk. The starting point of this assessment is the generation of climate change scenarios. Moody’s Analytics has developed a framework to complement and expand scenarios provided by various regulators and often produced using IAMs or other types of models with heavy em-phasis on climate modelling. The Moody’s framework leverages its well-tested Global Macroeconomic Model, which has been used to produce macroeconomic scenarios for stress-testing and other regulatory pur-poses. We have presented a methodology that enables us to include the long-term physical climate change risk in the scenario generation process. The Global Macroeco-

nomic Model has been enhanced by several blocks of equations at the country and in-dustry levels to incorporate transition risk to a zero-carbon economy. The transition risk approach is based on a path of the car-bon dioxide tax that affects energy prices and subsequently the overall price level. The simultaneous equations model of the global economy is then solved for all stan-dard macrofinancial variables, including the newly added transition drivers. We use this upgraded framework to produce NGFS con-sistent scenarios for the U.S. and the U.K. These scenarios are also in general con-sistent with the BoE BES that also employ NGFS climate change scenarios.

The climate change scenarios are used as input and the first step in the credit risk assessment of portfolios of financial insti-tutions. They are further combined with facility-level data such as the previously discussed physical risk scores by 427 (see Chart 24). The data requirements are sim-ilar to standard stress-testing and/or IFRS 9/CECL type calculations. These data inputs are used to generate projections of risk parameters such as probability of default, loss given default, and the corresponding expected credit losses. The analysis of instrument-level performance based on the portfolio data snapshots, combined with facility-level climate risk score from 427, will result in adjustment factors for PDs and LGDs to account for the climate change risk. The output includes a variety of instrument-level metrics combined with the adjustment factors.


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le In

com

e - G

ross

)


2. C

arbo

n ta

x du

mm

yD

UM

_CAR

BON

-TA

X_I

GBR

Dum

my

varia

ble

for

carb

on ta

x:

1=ta

x ap

plie

d;

0=no

tax

appl

ied

Exog

enou

sBo

olea

n, N

SAN

A

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in P

ence

- C

oal)

FCO

ALR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

coa

l)

FNG

ASR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

nat

ural

gas

)

FPET

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e fro

m p

etro

leum

)

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

nat

ural

gas

pric

e)

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic o

il pr

ice)

3. C

arbo

n ta

x ra

teFC

ARBO

NTA

X_

IGBR

Car

bon

diox

-id

e ta

x ra

teEx

ogen

ous

GBP

per

met

ric to

n,

NSA

NA

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in P

ence

- C

oal)

FCO

ALR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

coa

l)

FNG

ASR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

nat

ural

gas

)

FPET

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e fro

m p

etro

leum

)

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

nat

ural

gas

pric

e)

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic o

il pr

ice)

4. C

arbo

n ta

x re

venu

eFC

ARBO

NR

EV_

IGBR

Car

bon

diox

-id

e ta

x re

venu

e - T

otal

Iden

tity

Bil.

GBP

, SAA

R

FCO

ALR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

coa

l)FG

GEX

P_I_

IGBR

(Gov

ernm

ent F

inan

ce: E

xpen

ditu

re -

Tota

l [I

nter

med

iate

term

])

FNG

ASR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

nat

ural

gas

)FG

GR

EV_I

GBR

(Gov

ernm

ent fi

nanc

e: G

ener

al g

over

nmen

t - T

otal

re

venu

e)

FPET

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e fro

m p

etro

leum

)FY

PD_I

GBR

(Hou

seho

ld D

ispos

able

Inco

me

- Gro

ss)

4

-i. C

arbo

n ta

x

re

venu

e: c

oal

FCO

ALR

EV_I

GBR

Car

bon

diox

-id

e ta

x re

venu

e fro

m c

oal

Iden

tity

Bil.

GBP

, SAA

R

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)FC

ARBO

NR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

- Tot

al)

FCAR

BON

TAX

_IG

BR (C

arbo

n di

oxid

e ta

x ra

te)

FCO

ALC

O2E

Q_I

GBR

(Car

bon

diox

ide

emiss

ions

- C

oal a

nd c

oke)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


4

-i. C

arbo

n ta

x

re

venu

e:

n

atur

al g

asFN

GAS

REV

_IG

BR

Car

bon

diox

-id

e ta

x re

venu

e fr

om n

atur

al

gas

Iden

tity

Bil.

GBP

, SAA

R

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)FC

ARBO

NR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

- Tot

al)

FCAR

BON

TAX_

IGBR

(Car

bon

diox

ide t

ax ra

te)

FNG

ASC

O2E

Q_I

GBR

(Car

bon

diox

ide

emiss

ions

- N

atur

al G

as)

4

-i. C

arbo

n ta

x

reve

nue:

pet

role

umFP

ETR

EV_I

GBR

Car

bon

diox

ide

tax

reve

nue

from

pe

trole

um

Iden

tity

Bil.

GBP

, SAA

R

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)FC

ARBO

NR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

- Tot

al)

FCAR

BON

TAX

_IG

BR (C

arbo

n di

oxid

e ta

x ra

te)

FPET

CO

2EQ

_IG

BR (C

arbo

n di

oxid

e em

is-sio

ns -

Petro

leum

and

oth

er li

quid

s)

3 - E

nerg

y pr

ices

1. C

oal

FPC

CO

ALD

OM

_IG

BRAv

erag

e pr

ice

in P

ence

- C

oalSt

ocha

stic

1/10

0 G

BP

per #

, NSA

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in

Penc

e - C

oal)

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in P

ence

- C

oal)

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)FC

OAL

CO

NQ

_IG

BR (C

oal c

onsu

mpt

ion)

FCAR

BON

TAX_

IGBR

(Car

bon

diox

ide t

ax ra

te)

FCPI

E_IG

BR (C

onsu

mer

Pric

e In

dex:

Goo

ds -

Ener

gy)

FCPI

ELEC

_IG

BR (C

onsu

mer

pric

e in

dex:

Ele

ctric

ity)

2. N

atur

al g

asFP

CN

GAS

DO

M_

IGBR

Effec

tive

do-

mes

tic n

atur

al

gas p

rice

Stoc

hasti

cIn

dex

2010

=100

, SA

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

na

tura

l gas

pric

e)FC

PIE_

IGBR

(Con

sum

er P

rice

Inde

x: G

oods

- En

ergy

)

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)FC

PIEL

EC_I

GBR

(Con

sum

er p

rice

inde

x: E

lect

ricity

)

FCAR

BON

TAX_

IGBR

(Car

bon

diox

ide t

ax ra

te)

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

nat

ural

gas

pric

e)

FTFX

IUSA

_IG

BR (N

omin

al B

ilate

ral

Exch

ange

Rat

e)FN

GAS

CO

NQ

_IG

BR (N

atur

al g

as c

onsu

mpt

ion)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


3. O

ilFP

CO

ILD

OM

_IG

BREff

ectiv

e do

mes

tic o

il pr

ice

Iden

tity

GBP

per

bbl

, NSA

FCPI

FIC

EBO

IU_U

S (F

utur

es P

rice:

Bre

nt

crud

e oi

l 1-m

onth

forw

ard

[fob]

)FC

PIE_

IGBR

(Con

sum

er P

rice

Inde

x: G

oods

- En

ergy

)

FTFX

IUSA

_IG

BR (N

omin

al B

ilate

ral E

x-ch

ange

Rat

e)FG

DPE

G$_

I_IG

BR (G

ross

Val

ue A

dded

[GVA

] - E

lect

ricity

; gas

; ste

am

and

air c

ondi

tioni

ng su

pply

[Int

erm

edia

te te

rm])

DU

M_C

ARBO

NTA

X_I

GBR

(Dum

my

varia

ble

for c

arbo

n ta

x: 1

=tax

app

lied;

0=n

o ta

x ap

plie

d)

FGD

PMQ

$_I_

IGBR

(Gro

ss V

alue

Add

ed [G

VA] -

Min

ing

and

quar

ryin

g [I

nter

med

iate

term

])

FCAR

BON

TAX

_IG

BR (C

arbo

n di

oxid

e ta

x ra

te)

FIM

$_IG

BR (N

atio

nal A

ccou

nts:

Real

Impo

rts o

f Goo

ds a

nd S

ervi

ces)

FPET

CO

NQ

_IG

BR (P

etro

leum

con

sum

ptio

n)

FPPI

_IG

BR (P

rodu

cer P

rice

Inde

x: In

put P

rices

[mat

eria

ls an

d fu

el])

4 - E

nerg

y co

nsum

ptio

n

1. C

oal

FCO

ALC

ON

Q_I

GBR

Coa

l con

-su

mpt

ion

Stoc

hasti

cTr

il. B

TU

, SAA

RFP

CC

OAL

DO

M_I

GBR

(Ave

rage

pric

e in

Pe

nce

- Coa

l)FC

OAL

CO

2EQ

_IG

BR (C

arbo

n di

oxid

e em

issio

ns -

Coa

l and

cok

e)

2. N

atur

al g

asFN

GAS

CO

NQ

_IG

BRN

atur

al g

as

cons

umpt

ion

Stoc

hasti

cTr

il. B

TU

, SAA

R

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

na

tura

l gas

pric

e)FN

GAS

CO

2EQ

_IG

BR (C

arbo

n di

oxid

e em

issio

ns -

Nat

ural

Gas

)

FIP_

IGBR

(Ind

ustr

ial P

rodu

ctio

n: T

otal

)

3. P

etro

leum

and

oth

er

li

quid

FPET

CO

NQ

_IG

BRPe

trole

um

cons

umpt

ion

Stoc

hasti

cTr

il. B

TU

, SAA

R

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic o

il pr

ice)

FPET

CO

2EQ

_IG

BR (C

arbo

n di

oxid

e em

issio

ns -

Petro

leum

and

oth

er

liqui

ds)

FYPD

$_IG

BR (H

ouse

hold

Disp

osab

le In

com

e - G

ross

)FE

IAPD

GQ

_US

(EIA

: Pet

role

um- G

loba

l Dem

and)

FREG

_IG

BR (N

ew M

otor

Veh

icle

Reg

istra

-tio

ns: P

asse

nger

Car

s)

5 - C

O2

emiss

ions

1. C

oal

FCO

ALC

O2E

Q_

IGBR

Car

bon

diox

-id

e em

issio

ns -

Coa

l and

cok

eSt

ocha

stic

Mil.

Met

ric T

ons,

SAAR

FCO

ALC

ON

Q_I

GBR

(Coa

l con

sum

ptio

n)FF

FCO

2EQ

_IG

BR (C

arbo

n di

oxid

e em

issio

ns -

Foss

il fu

els)

FCO

ALR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

coa

l)

2. N

atua

l gas

FN

GAS

CO

2EQ

_IG

BR

Car

bon

diox

-id

e em

issio

ns

- Nat

ural

gas

Stoc

hasti

cM

il. M

etric

Ton

s, SA

AR

FNG

ASC

ON

Q_I

GBR

(N

atur

al g

as c

onsu

mpt

ion)

FFFC

O2E

Q_I

GBR

(Car

bon

diox

ide

emiss

ions

- Fo

ssil

fuel

s)

FNG

ASR

EV_I

GBR

(Car

bon

diox

ide

tax

reve

nue

from

nat

ural

gas

)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


3. O

il an

d pe

trole

um

p

rodu

cts

FPET

CO

2EQ

_IG

BR

Car

bon

diox

-id

e em

issio

ns -

Petro

leum

and

ot

her l

iqui

ds

Stoc

hasti

cM

il. M

etric

Ton

s, SA

AR

FPET

CO

NQ

_IG

BR

(Pet

role

um c

onsu

mpt

ion)

FFFC

O2E

Q_I

GBR

(Car

bon

diox

ide

emiss

ions

- Fo

ssil

fuel

s)

FPET

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e fro

m p

etro

leum

)

6 - G

ovt fi

nanc

es

1. T

otal

reve

nue

FGG

REV

_IG

BR

Gov

ernm

ent

finan

ce:

Gen

eral

gov-

ernm

ent -

Tot

al re

venu

e

Iden

tity

Bil.

GBP

, SAA

R

FGD

P_IG

BR (N

atio

nal A

ccou

nts:

Nom

inal

G

ross

Dom

estic

Pro

duct

[GD

P])

FGG

BAL_

IGBR

(Gov

ernm

ent fi

nanc

e: G

ener

al g

over

nmen

t - B

udge

t ba

lanc

e)

FGG

TAX

R_I

_IG

BR (E

ffect

ive

tax

rate

[Int

er-

med

iate

term

])

FCAR

BON

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e - T

otal

)

2. T

otal

exp

ense

FGG

EXP_

IGBR

Gov

ernm

ent

finan

ce:

Gen

eral

go

vern

men

t -

Tota

l exp

ense

Iden

tity

Bil.

GBP

, SAA

R

FGG

EXP_

RES

ID_I

GBR

(Gov

ernm

ent

expe

nditu

re re

sidua

l [In

term

edia

te te

rm])

FGG

BAL_

IGBR

(Gov

ernm

ent fi

nanc

e: G

ener

al g

over

nmen

t -

Budg

et b

alan

ce)

FGG

EXPI

NT

_IG

BR (G

over

nmen

t fina

nce:

G

ener

al g

over

nmen

t - In

tere

st ex

pens

e)

FG_I

GBR

(Nat

iona

l Acc

ount

s: N

omin

al G

ov-

ernm

ent C

onsu

mpt

ion

Expe

nditu

re)

3. E

xpen

ditu

re in

term

e

dia

te te

rmFG

GEX

P_I_

IGBR

Gov

ernm

ent

finan

ce: E

x-pe

nditu

re

- Tot

al [I

nter

-m

edia

te te

rm]

Stoc

hasti

cBi

l. G

BP, S

AAR

FGG

EXP_

I_IG

BR (G

over

nmen

t Fin

ance

: Ex

pend

iture

- To

tal [

Inte

rmed

iate

term

])FG

GEX

P_I_

IGBR

(Gov

ernm

ent F

inan

ce: E

xpen

ditu

re -

Tota

l [I

nter

med

iate

term

])

FCAR

BON

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e - T

otal

)FG

GEX

P_R

ESID

_IG

BR (G

over

nmen

t exp

endi

ture

resid

ual

[Int

erm

edia

te te

rm])

DU

M_C

ARBO

ND

IV_I

GBR

(Dum

my

varia

ble f

or U

.K. c

arbo

n di

vide

nd: 1

=div

iden

d;

0=no

div

iden

d)FG

DP_

POT

_IG

BR (N

atio

nal A

ccou

nts:

Pote

ntia

l Nom

inal

Gro

ss D

omes

tic P

rodu

ct)

FGG

DEB

TG

DP_

IGBR

(Gen

eral

gov

ernm

ent

debt

to G

DP

ratio

)

4. E

xpen

ditu

re re

sidua

lFG

GEX

P_R

ESID

_IG

BR

Gov

ernm

ent

expe

nditu

re

resid

ual

[Int

erm

edia

te

term

]

Iden

tity

Bil.

EUR

, SAA

R

FGG

EXP_

I_IG

BR (G

over

nmen

t Fin

ance

: Ex

pend

iture

- To

tal [

Inte

rmed

iate

term

])FG

GEX

P_IG

BR (G

over

nmen

t fina

nce:

Gen

eral

gov

ernm

ent -

Tot

al

expe

nse)

FGG

EXPI

NT

_IG

BR (G

over

nmen

t fina

nce:

G

ener

al g

over

nmen

t - In

tere

st ex

pens

e)FY

PD_I

GBR

(Hou

seho

ld D

ispos

able

Inco

me

- Gro

ss)

FG_I

GBR

(Nat

iona

l Acc

ount

s: N

omin

al G

ov-

ernm

ent C

onsu

mpt

ion

Expe

nditu

re)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


7 - P

rice

inde

xes

1. A

ll ite

ms e

x en

ergy

,

food

, alc

ohol

, tob

acco

FCPI

X_I

GBR

CPI

H:

Inde

x - S

peci

al

aggr

egat

es -

Al

l ite

ms;

ex e

nerg

y;

food

; alc

ohol

; to

bacc

o

Stoc

hasti

cIn

dex

2015

=100

, SA

FCPI

X_I

GBR

(CPI

H: I

ndex

- Sp

ecia

l ag

greg

ates

- Al

l ite

ms;

excl

udin

g en

ergy

; foo

d;

alco

hol &

toba

cco)

FCPI

XU

Q_I

GBR

(CPI

H: I

ndex

- Sp

ecia

l agg

rega

tes -

All

item

s; ex

clud

-in

g en

ergy

; foo

d; a

lcoh

ol &

toba

cco)

FEIN

FLAT

_IG

BR (I

nflat

ion

expe

ctat

ions

)FC

PIX

_IG

BR (C

PIH

: Ind

ex -

Spec

ial a

ggre

gate

s - A

ll ite

ms;

excl

udin

g en

ergy

; foo

d; a

lcoh

ol &

toba

cco)

FTFX

IEU

ZN

_IG

BR (N

omin

al B

ilate

ral

Exch

ange

Rat

e)FC

PI_I

GBR

(Con

sum

er P

rice

Inde

x: E

U H

arm

onize

d - T

otal

)

FCPI

E_IG

BR (C

onsu

mer

Pric

e In

dex:

G

oods

- En

ergy

)

FCPI

F_IG

BR (C

onsu

mer

Pric

e In

dex:

G

oods

- Fo

od; a

lcoh

olic

bev

erag

es &

toba

cco)

FLBR

_IG

BR (L

abor

For

ce S

urve

y:

Une

mpl

oym

ent R

ate)

FNAI

RU_I

GBR

(Non

-acc

eler

atin

g in

flatio

n ra

te o

f une

mpl

oym

ent [

NAI

RU])

FYPE

WS_

IGBR

(Per

sona

l Inc

ome:

Nom

inal

W

ages

and

Sal

arie

s)

FGD

P_IG

BR (N

atio

nal A

ccou

nts:

Nom

inal

G

ross

Dom

estic

Pro

duct

[GD

P])

2. F

ood,

alc

ohol

, tob

acco

FCPI

F_IG

BR

Con

sum

er

pric

e in

dex:

G

oods

-

Food

; alc

ohol

; to

bacc

o

Stoc

hasti

cIn

dex

2015

=100

, SA

FCPI

F_IG

BR (C

onsu

mer

Pric

e In

dex:

Goo

ds -

Food

; alc

ohol

ic b

ever

ages

& to

bacc

o)FC

PIX

_IG

BR (C

PIH

: Ind

ex -

Spec

ial a

ggre

gate

s - A

ll ite

ms;

excl

udin

g en

ergy

; foo

d; a

lcoh

ol &

toba

cco)

FPC

IF_I

WR

LD (C

omm

odity

pric

es: A

gric

ul-

ture

- Fo

od)

FCPI

_IG

BR (C

onsu

mer

Pric

e In

dex:

EU

Har

mon

ized

- Tot

al)

FTFX

IEU

ZN

_IG

BR (N

omin

al B

ilate

ral

Exch

ange

Rat

e)FC

PIF_

IGBR

(Con

sum

er P

rice

Inde

x: G

oods

- Fo

od; a

lcoh

olic

bev

erag

es

& to

bacc

o)

FCPI

E_IG

BR (C

onsu

mer

Pric

e In

dex:

G

oods

- En

ergy

)

3. E

nerg

yFC

PIE_

IGBR

Con

sum

er

pric

e in

dex:

G

oods

- En

-er

gy

Stoc

hasti

cIn

dex

2015

=100

, SA

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic o

il pr

ice)

FCPI

X_I

GBR

(CPI

H: I

ndex

- Sp

ecia

l agg

rega

tes -

All

item

s; ex

clud

ing

ener

gy; f

ood;

alc

ohol

& to

bacc

o)

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

na

tura

l gas

pric

e)FC

PI_I

GBR

(Con

sum

er P

rice

Inde

x: E

U H

arm

onize

d - T

otal

)

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in

Penc

e - C

oal)

FCPI

F_IG

BR (C

onsu

mer

Pric

e In

dex:

Goo

ds -

Food

; alc

ohol

ic b

ever

ages

&

toba

cco)

FCPI

ELEC

_IG

BR (C

onsu

mer

pric

e in

dex:

El

ectr

icity

)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


4. E

lect

ricity

FCPI

ELEC

_IG

BRC

onsu

mer

pr

ice

inde

x:

Elec

tric

itySt

ocha

stic

Inde

x 20

15=1

00, S

A

FPC

NG

ASD

OM

_IG

BR (E

ffect

ive

Dom

estic

na

tura

l gas

pric

e)FC

PIE_

IGBR

(Con

sum

er P

rice

Inde

x: G

oods

- En

ergy

)

FPC

CO

ALD

OM

_IG

BR (A

vera

ge p

rice

in

Penc

e - C

oal)

5. T

otal

FCPI

_IG

BR

Con

sum

er

pric

e in

dex:

EU

Har

mo-

nize

d - T

otal

Stoc

hasti

cIn

dex

2015

=100

, SA

FCPI

E_IG

BR (C

onsu

mer

Pric

e In

dex:

Goo

ds

- Ene

rgy)

Alm

ost a

ll co

untr

ies i

n th

e gl

obal

mod

el

FCPI

F_IG

BR (C

onsu

mer

Pric

e Ind

ex: G

oods

- Fo

od; a

lcoho

lic b

ever

ages

& to

bacc

o)FC

PIX

_IG

BR (C

PIH

: Ind

ex -

Spec

ial

aggr

egat

es -

All i

tem

s; ex

clud

ing

ener

gy; f

ood;

al

coho

l & to

bacc

o)

6. P

rodu

cer p

rice

FPPI

_IG

BR

Prod

ucer

pric

e in

dex:

Inpu

t pr

ices

[mat

eri-

als a

nd fu

el]

Stoc

hasti

cIn

dex

2010

=100

, NSA

FCPI

_IG

BR (C

onsu

mer

Pric

e In

dex:

EU

H

arm

onize

d - T

otal

)

FPD

IIM

_IG

BR (I

mpl

icit

Pric

e D

eflat

or:

Impo

rts o

f Goo

ds a

nd S

ervi

ces)

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic o

il pr

ice)

8 - O

ther

1. R

eal i

mpo

rts

FIM

$_IG

BR

Nat

iona

l ac

coun

ts: R

eal

impo

rts

of g

oods

and

se

rvic

es

Stoc

hasti

cBi

l. 20

18 G

BP, S

AAR

FNET

EX$_

IGBR

(Nat

iona

l Acc

ount

s: Re

al

Net

Exp

orts

of G

oods

and

Ser

vice

s)

FC$_

I_IG

BR (N

atio

nal A

ccou

nts:

Priv

ate

Con

sum

ptio

n [I

nter

med

iate

term

])

FIF$

_I_I

GBR

(Nat

iona

l Acc

ount

s: G

ross

Fi

xed

Cap

ital F

orm

atio

n [I

nter

med

iate

term

])

FEX

$_IG

BR (N

atio

nal A

ccou

nts:

Real

Exp

orts

of G

oods

and

Ser

vice

s)

FTFX

IUSA

_IG

BR (N

omin

al B

ilate

ral

Exch

ange

Rat

e)

FCPI

_IG

BR (C

onsu

mer

Pric

e In

dex:

EU

Har

mon

ized

- Tot

al)

FCPI

U_U

S (C

PI: U

rban

Con

sum

er -

All I

tem

s)

FGD

P$_P

OT

_IG

BR (N

atio

nal A

ccou

nts:

Pote

ntia

l Rea

l Gro

ss D

omes

tic P

rodu

ct)

FPC

OIL

DO

M_I

GBR

(Effe

ctiv

e do

mes

tic

oil p

rice)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


2. D

ispos

able

inco

me

FYPD

_IG

BR

Hou

seho

ld

disp

osab

le

inco

me

- G

ross

Stoc

hasti

cBi

l. G

BP, S

AAR

FYPD

$_IG

BR (H

ouse

hold

Disp

osab

le In

com

e - G

ross

)FH

HD

EBT

CC

SR_I

GBR

(Deb

t Ser

vice

Rat

io -

Nom

inal

Cre

dit C

ard

Deb

t to

Nom

inal

Disp

osab

le In

com

e)

FYPE

WS_

IGBR

(Per

sona

l Inc

ome:

Nom

inal

W

ages

and

Sal

arie

s)FH

HD

EBT

MSR

_IG

BR (D

ebt S

ervi

ce R

atio

- N

omin

al M

ortg

age

Deb

t to

Nom

inal

Disp

osab

le In

com

e)

FCAR

BON

REV

_IG

BR (C

arbo

n di

oxid

e ta

x re

venu

e - T

otal

)FH

HD

EBTO

TH

SR_I

GBR

(Deb

t Ser

vice

Rat

io -

Oth

er N

omin

al

Con

sum

er D

ebt t

o N

omin

al D

ispos

able

Inco

me)

DU

M_C

ARBO

ND

IV_I

GBR

(Dum

my

varia

ble

for U

.K. c

arbo

n di

vide

nd: 1

=div

iden

d;

0=no

div

iden

d)

FHH

DEB

TM

LNN

_IG

BR (H

ouse

hold

Deb

t: N

et m

ortg

age

lend

ing

to

indi

vidu

als)

FRM

P_IG

BR (I

nter

est r

ate:

Ban

k of

Eng

land

Ba

se R

ate)

FYPD

$_IG

BR (H

ouse

hold

Disp

osab

le In

com

e - G

ross

)

FPD

IC_I

GBR

(Im

plic

it Pr

ice

Defl

ator

: Priv

ate

Con

sum

ptio

n Ex

pend

iture

)FH

HD

PDO

M_I

GBR

(Hou

seho

lds a

nd n

on-p

rofit

insti

tutio

ns se

rvin

g ho

useh

olds

- D

epos

its w

ith U

K M

FIs [

AF22

1])

FGD

P$_I

GBR

(Nat

iona

l Acc

ount

s: Re

al G

ross

D

omes

tic P

rodu

ct [G

DP]

)

FGG

TAX

R_I

_IG

BR (E

ffect

ive

tax

rate

[Int

er-

med

iate

term

])

FSPI

_IG

BR (S

tock

Mar

ket:

FTSE

-100

Inde

x)

FGG

EXP_

RES

ID_I

GBR

(Gov

ernm

ent e

xpen

-di

ture

resid

ual [

Inte

rmed

iate

term

])

3. E

xcha

nge

rate

FTFX

IUSA

_IG

BRN

omin

al b

ilat-

eral

exc

hang

e ra

teSt

ocha

stic

USD

per

GBP

, NSA

FTFX

TW

$_I_

IGBR

(Effe

ctiv

e Ex

chan

ge R

ate

- Rea

l Bro

ad In

dex

[Int

erm

edia

te te

rm])

All c

ount

ries i

n th

e gl

obal

mod

el

FTW

DBR

D_U

S (W

eigh

ted

Aver

age

Exch

ange

Va

lue

of U

.S. D

olla

r: Br

oad

Inde

x - N

omin

al)

FTFX

IUSA

Q_I

EUZ

N (N

omin

al B

ilate

ral

Exch

ange

Rat

e)

4. G

VA o

f ser

vice

s

indu

stry

FGD

PSER

V$_

IGBR

Gro

ss v

alue

ad

ded

[GVA

] -

Serv

ice-

prov

id-

ing

indu

strie

s

Stoc

hasti

cBi

l. C

h. 2

018

GBP

, SA

AR

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)G

VA a

nd e

mpl

oym

ent i

n al

l oth

er se

ctor

s

FGD

PTO

T$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - T

otal

)

FGD

P$_I

GBR

(Nat

iona

l Acc

ount

s: Re

al G

ross

D

omes

tic P

rodu

ct [G

DP]

)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


9 - E

mpl

oym

ent

1. A

rts;

ente

rtai

nmen

t

a

nd re

crea

tion

[I

nter

med

iate

term

]FE

AE_I

_IG

BR

Empl

oym

ent

- Art

s; en

ter-

tain

men

t an

d re

crea

tion

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEAE

_I_I

GBR

(Em

ploy

men

t - A

rts;

ente

rtai

n-m

ent a

nd re

crea

tion

[Int

erm

edia

te te

rm])

FEAE

_IG

BR (E

mpl

oym

ent -

Art

s; en

tert

ainm

ent a

nd re

crea

tion)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEAE

_I_I

GBR

(Em

ploy

men

t - A

rts;

ente

rtai

nmen

t and

recr

eatio

n [I

nter

med

iate

term

])

FGD

PAE$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - A

rts;

ente

rtai

nmen

t and

recr

eatio

n)FE

SERV

_I_I

GBR

(Em

ploy

men

t - S

ervi

ces p

rodu

cing

indu

strie

s [I

nter

med

iate

term

])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

2. A

gric

ultu

re; f

ores

try

a

nd fi

shin

g [I

nter

me

d

iate

term

]FE

AF_I

_IG

BR

Empl

oym

ent

- Agr

icul

ture

; fo

restr

y an

d fis

hing

[Int

er-

med

iate

term

]

Stoc

hasti

cM

il. #

, SA

FEAF

_I_I

GBR

(Em

ploy

men

t - A

gric

ultu

re;

fore

stry

and

fishi

ng [I

nter

med

iate

term

])FE

AF_I

GBR

(Em

ploy

men

t - A

gric

ultu

re; f

ores

try

and

fishi

ng)

FEG

OO

D_I

GBR

(Em

ploy

men

t - G

oods

pr

oduc

ing

indu

strie

s)FE

AF_I

_IG

BR (E

mpl

oym

ent -

Agr

icul

ture

; for

estr

y an

d fis

hing

[I

nter

med

iate

term

])

FGD

PAF$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - A

gric

ultu

re; f

ores

try

and

fishi

ng)

FEG

OO

D_I

_IG

BR (E

mpl

oym

ent -

Goo

ds p

rodu

cing

indu

strie

s [I

nter

med

iate

term

])

FGD

PGO

OD

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Goo

ds p

rodu

cing

indu

strie

s)

3. A

dmin

istra

tive

and

su

ppor

t ser

vice

act

iviti

es

[Int

erm

edia

te te

rm]

FEAS

_I_I

GBR

Empl

oym

ent -

Ad

min

istra

tive

and

supp

ort

serv

ice

activ

i-tie

s [In

term

e-di

ate

term

]

Stoc

hasti

cM

il. #

, SA

FEAS

_I_I

GBR

(Em

ploy

men

t - A

dmin

istra

tive

and

supp

ort s

ervi

ce a

ctiv

ities

[Int

erm

edia

te

term

])

FEAS

_IG

BR (E

mpl

oym

ent -

Adm

inist

rativ

e an

d su

ppor

t ser

vice

act

ivi-

ties)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEAS

_I_I

GBR

(Em

ploy

men

t - A

dmin

istra

tive a

nd su

ppor

t ser

vice

activ

ities

[In

term

edia

te te

rm])

FGD

PAS$

_IG

BR (G

ross

Valu

e Add

ed [G

VA] -

Ad

min

istra

tive a

nd su

ppor

t ser

vice a

ctivi

ties)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

4. C

onstr

uctio

n

[Int

erm

edia

te te

rm]

FEC

N_I

_IG

BR

Empl

oym

ent -

C

onstr

uctio

n [I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FEC

N_I

_IG

BR (E

mpl

oym

ent -

Con

struc

tion

[Int

erm

edia

te te

rm])

FEC

N_I

GBR

(Em

ploy

men

t - C

onstr

uctio

n)

FEG

OO

D_I

GBR

(Em

ploy

men

t - G

oods

pr

oduc

ing

indu

strie

s)FE

CN

_I_I

GBR

(Em

ploy

men

t - C

onstr

uctio

n [I

nter

med

iate

term

])

FGD

PCN

$_IG

BR (G

ross

Val

ue A

dded

[GVA

] - C

onstr

uctio

n)FE

GO

OD

_I_I

GBR

(Em

ploy

men

t - G

oods

pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PGO

OD

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Goo

ds p

rodu

cing

indu

strie

s)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


5. E

duca

tion

[I

nter

med

iate

term

]FE

ED_I

_IG

BR

Empl

oym

ent

- Edu

catio

n [I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FEED

_I_I

GBR

(Em

ploy

men

t - E

duca

tion

[Int

erm

edia

te te

rm])

FEED

_IG

BR (E

mpl

oym

ent -

Edu

catio

n)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEED

_I_I

GBR

(Em

ploy

men

t - E

duca

tion

[Int

erm

edia

te te

rm])

FGD

PED

$_IG

BR (G

ross

Val

ue A

dded

[GVA

] - E

duca

tion)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies [

Inte

r-m

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

6. E

lect

ricity

; gas

; ste

am

a

nd ai

r con

ditio

ning

sup

ply [

Inte

rmed

iate

t

erm

]

FEEG

_I_I

GBR

Empl

oym

ent

- Ele

ctric

ity;

gas;

steam

and

ai

r con

ditio

n-in

g su

pply

[I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FEEG

_I_I

GBR

(Em

ploy

men

t - E

lectri

city

; gas

; ste

am an

d ai

r con

ditio

ning

supp

ly [I

nter

med

iate

te

rm])

FEEG

_IG

BR (E

mpl

oym

ent -

Ele

ctric

ity; g

as; s

team

and

air

cond

ition

ing

supp

ly)

FEG

OO

D_I

GBR

(Em

ploy

men

t - G

oods

pr

oduc

ing

indu

strie

s)FE

EG_I

_IG

BR (E

mpl

oym

ent -

Elec

trici

ty; g

as; s

team

and

air c

ondi

tioni

ng

supp

ly [I

nter

med

iate

term

])

FGD

PEG

$_IG

BR (G

ross

Val

ue A

dded

[GVA

] - E

lect

ricity

; gas

; ste

am a

nd a

ir co

nditi

onin

g su

pply

)

FEG

OO

D_I

_IG

BR (E

mpl

oym

ent -

Goo

ds p

rodu

cing

indu

strie

s [I

nter

med

iate

term

])

FGD

PGO

OD

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Goo

ds p

rodu

cing

indu

strie

s)

7. A

ccom

mod

atio

n an

d

foo

d se

rvic

e ac

tiviti

es

[

Inte

rmed

iate

term

]FE

FA_I

_IG

BR

Empl

oym

ent

- Acc

omm

o-da

tion

and

food

serv

ice

activ

ities

[I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FEFA

_I_I

GBR

(Em

ploy

men

t - A

ccom

mod

a-tio

n an

d fo

od se

rvic

e ac

tiviti

es [I

nter

med

iate

te

rm])

FEFA

_IG

BR (E

mpl

oym

ent -

Acc

omm

odat

ion

and

food

serv

ice

activ

ities

)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEFA

_I_I

GBR

(Em

ploy

men

t - A

ccom

mod

atio

n an

d fo

od se

rvic

e ac

tiviti

es [I

nter

med

iate

term

])

FGD

PFA$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - A

ccom

mod

atio

n an

d fo

od se

rvic

e ac

tiviti

es)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

8. F

inan

cial a

nd

in

sura

nce a

ctiv

ities

[Int

erm

edia

te te

rm]

FEFI

_I_I

GBR

Empl

oym

ent

- Fin

anci

al

and

insu

ranc

e ac

tiviti

es

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEFI

_I_I

GBR

(Em

ploy

men

t - F

inan

cial

and

in

sura

nce

activ

ities

[Int

erm

edia

te te

rm])

FEFI

_IG

BR (E

mpl

oym

ent -

Fin

anci

al a

nd in

sura

nce

activ

ities

)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEFI

_I_I

GBR

(Em

ploy

men

t - F

inan

cial

and

insu

ranc

e ac

tiviti

es

[Int

erm

edia

te te

rm])

FGD

PFI$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] -

Fina

ncia

l and

insu

ranc

e ac

tiviti

es)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


9. A

ctiv

ities

of h

ouse

hold

s

as e

mpl

oyer

s

[Int

erm

edia

te te

rm]

FEH

H_I

_IG

BR

Empl

oym

ent

- Act

iviti

es o

f ho

useh

olds

as

em

ploy

ers

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEH

H_I

_IG

BR (E

mpl

oym

ent -

Act

iviti

es o

f ho

useh

olds

as e

mpl

oyer

s [In

term

edia

te te

rm])

FEH

H_I

GBR

(Em

ploy

men

t - A

ctiv

ities

of h

ouse

hold

s as e

mpl

oyer

s)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEH

H_I

_IG

BR (E

mpl

oym

ent -

Act

iviti

es o

f hou

seho

lds a

s em

ploy

ers

[Int

erm

edia

te te

rm])

FGD

PHH

$_IG

BR (G

ross

Val

ue A

dded

[GVA

] - A

ctiv

ities

of h

ouse

hold

s as e

mpl

oyer

s)FE

SERV

_I_I

GBR

(Em

ploy

men

t - S

ervi

ces p

rodu

cing

indu

strie

s [I

nter

med

iate

term

])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

10. H

uman

hea

lth a

nd

so

cial

wor

k ac

tiviti

es

[I

nter

med

iate

term

]FE

HS_

I_IG

BR

Empl

oym

ent -

H

uman

hea

lth

and

soci

al

wor

k ac

tiviti

es

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEH

S_I_

IGBR

(Em

ploy

men

t - H

uman

hea

lth

and

socia

l wor

k ac

tiviti

es [I

nter

med

iate t

erm

])FE

HS_

IGBR

(Em

ploy

men

t - H

uman

hea

lth a

nd so

cial

wor

k ac

tiviti

es)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEH

S_I_

IGBR

(Em

ploy

men

t - H

uman

hea

lth a

nd so

cial

wor

k ac

tiviti

es

[Int

erm

edia

te te

rm])

FGD

PHS$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - H

uman

hea

lth a

nd so

cial

wor

k ac

tiviti

es)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

11. I

nfor

mat

ion

and

c

omm

unic

atio

n

[Int

erm

edia

te te

rm]

FEIC

_I_I

GBR

Empl

oym

ent

- Inf

orm

atio

n an

d co

m-

mun

icat

ion

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEIC

_I_I

GBR

(Em

ploy

men

t - In

form

atio

n an

d co

mm

unic

atio

n [I

nter

med

iate

term

])FE

IC_I

GBR

(Em

ploy

men

t - In

form

atio

n an

d co

mm

unic

atio

n)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEIC

_I_I

GBR

(Em

ploy

men

t - In

form

atio

n an

d co

mm

unic

atio

n [I

nter

med

iate

term

])

FGD

PIC

$_IG

BR (G

ross

Val

ue A

dded

[GVA

] -

Info

rmat

ion

and

com

mun

icat

ion)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

12. M

anuf

actu

ring

[I

nter

med

iate

term

]FE

MF_

I_IG

BR

Empl

oym

ent -

M

anuf

actu

ring

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEM

F_I_

IGBR

(Em

ploy

men

t - M

anuf

actu

r-in

g [I

nter

med

iate

term

])FE

MF_

IGBR

(Em

ploy

men

t - M

anuf

actu

ring)

FEG

OO

D_I

GBR

(Em

ploy

men

t - G

oods

pr

oduc

ing

indu

strie

s)FE

MF_

I_IG

BR (E

mpl

oym

ent -

Man

ufac

turin

g [I

nter

med

iate

term

])

FGD

PMF$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - M

anuf

actu

ring)

FEG

OO

D_I

_IG

BR (E

mpl

oym

ent -

Goo

ds p

rodu

cing

indu

strie

s [I

nter

med

iate

term

])

FGD

PGO

OD

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Goo

ds p

rodu

cing

indu

strie

s)

App

endi

x Ta

ble:

Sel

ecte

d U

.K. V

aria

bles

Cap

turin

g th

e Ph

ysic

al a

nd T

rans

itio

n Ri

sk (C

ont.

)

Des

crip

tion

Mne

mon

icSe

ries

de

scri

ptio

nSt

ate

Uni

tsVa

riab

le d

epen

ds o

nD

epen

d on

var

iabl

e


13. M

inin

g an

d

qua

rryi

ng

[I

nter

med

iate

term

]FE

MQ

_I_I

GBR

Empl

oym

ent

- Min

ing

and

quar

ryin

g [I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FEM

Q_I

_IG

BR (E

mpl

oym

ent -

Min

ing

and

quar

ryin

g [I

nter

med

iate

term

])FE

MQ

_IG

BR (E

mpl

oym

ent -

Min

ing

and

quar

ryin

g)

FEG

OO

D_I

GBR

(Em

ploy

men

t - G

oods

pr

oduc

ing

indu

strie

s)FE

MQ

_I_I

GBR

(Em

ploy

men

t - M

inin

g an

d qu

arry

ing

[Int

erm

edia

te

term

])

FGD

PMQ

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Min

ing

and

quar

ryin

g)FE

GO

OD

_I_I

GBR

(Em

ploy

men

t - G

oods

pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PGO

OD

$_IG

BR (G

ross

Val

ue A

dded

[G

VA] -

Goo

ds p

rodu

cing

indu

strie

s)

14. P

ublic

adm

inist

ratio

n

and

def

ence

;

com

pulso

ry

so

cial

secu

rity

[I

nter

med

iate

term

]

FEPD

_I_I

GBR

Empl

oy-

men

t - P

ublic

ad

min

istra

tion

and

defe

nce;

co

mpu

lsory

so

cial

secu

rity

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEPD

_I_I

GBR

(Em

ploy

men

t - P

ublic

ad

min

istra

tion

and

defe

nce;

com

pulso

ry so

cial

se

curit

y [I

nter

med

iate

term

])

FEPD

_IG

BR (E

mpl

oym

ent -

Pub

lic a

dmin

istra

tion

and

defe

nce;

co

mpu

lsory

soci

al se

curit

y)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEPD

_I_I

GBR

(Em

ploy

men

t - P

ublic

adm

inist

ratio

n an

d de

fenc

e;

com

pulso

ry so

cial

secu

rity

[Int

erm

edia

te te

rm])

FGD

PPD

$_IG

BR (G

ross

Valu

e Add

ed [G

VA] -

Pu

blic

adm

inist

ratio

n an

d de

fenc

e; co

mpu

lsory

so

cial

secu

rity)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

15. P

rofe

ssio

nal;

scie

ntifi

c

and

tech

nica

l act

iviti

es

[

Inte

rmed

iate

term

]FE

PS_I

_IG

BR

Empl

oym

ent

- Pro

fess

iona

l; sc

ient

ific

and

tech

nica

l ac

tiviti

es

[Int

erm

edia

te

term

]

Stoc

hasti

cM

il. #

, SA

FEPS

_I_I

GBR

(Em

ploy

men

t - P

rofe

ssio

nal;

scie

ntifi

c an

d te

chni

cal a

ctiv

ities

[I

nter

med

iate

term

])FE

PS_I

GBR

(Em

ploy

men

t - P

rofe

ssion

al; sc

ientifi

c and

tech

nica

l act

iviti

es)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FEPS

_I_I

GBR

(Em

ploy

men

t - P

rofe

ssio

nal;

scie

ntifi

c an

d te

chni

cal

activ

ities

[Int

erm

edia

te te

rm])

FGD

PPS$

_IG

BR (G

ross

Valu

e Add

ed [G

VA] -

Pr

ofes

siona

l; sc

ientifi

c and

tech

nica

l act

iviti

es)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

vice

s pro

duci

ng in

dustr

ies

[Int

erm

edia

te te

rm])

FGD

PSER

V$_

IGBR

(Gro

ss V

alue

Add

ed

[GVA

] - S

ervi

ces p

rodu

cing

indu

strie

s)

16. R

eal e

state

act

iviti

es

[Int

erm

edia

te te

rm]

FER

E_I_

IGBR

Empl

oy-

men

t - R

eal

esta

te a

ctiv

ities

[I

nter

med

iate

te

rm]

Stoc

hasti

cM

il. #

, SA

FER

E_I_

IGBR

(Em

ploy

men

t - R

eal e

state

ac

tiviti

es [I

nter

med

iate

term

])FE

RE_

IGBR

(Em

ploy

men

t - R

eal e

state

act

iviti

es)

FESE

RV_I

GBR

(Em

ploy

men

t - S

ervi

ces

prod

ucin

g in

dustr

ies)

FER

E_I_

IGBR

(Em

ploy

men

t - R

eal e

state

act

iviti

es [I

nter

med

iate

term

])

FGD

PRE$

_IG

BR (G

ross

Val

ue A

dded

[GVA

] - R

eal e

state

act

iviti

es)

FESE

RV_I

_IG

BR (E

mpl

oym

ent -

Ser

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About the Authors

Dr. Juan M. Licari is a managing director at the Moody’s Analytics London office. He is the global head of the Business Analytics team consisting of risk modelers, econ-omists and statisticians in the U.K., the U.S., China, United Arab Emirates, Czech Republic, and Singapore. Dr. Licari’s team provides consulting support to major industry players, builds econometric tools to model credit phenomena, and implements several stress-testing platforms to quantify portfolio risk exposure. His team is an industry leader in developing and implementing credit solutions that explicitly connect credit data to the underlying economic cycle, allowing portfolio managers to plan for alter-native macroeconomic scenarios. He currently leads the overall effort to address climate change risk from the perspective of climate risk economic scenarios, economic losses linked to climate events, the impact of climate change on retail portfolios, and the ESG score predictor for small and medium enterprises. Dr. Licari has extensive hands-on experience as a project lead with respect to development, validation, calibration and monitoring of IRB, IFRS 9 and stress-testing credit risk models especially for U.K. banks and financial institutions, for both retail and corporate portfolios. He is actively involved in communicating the team’s research and methodologies to the market, including senior management and board members. He often speaks at credit events and economic conferences worldwide. Dr. Licari holds a PhD and an MA in economics from the University of Pennsylvania and graduated summa cum laude from the National University of Cordoba in Argentina.

Dr. Petr Zemcik is a senior director at the Moody’s Analytics London office who manages a team of risk modelers and economists in the London and Prague offices. He frequently serves as an engagement lead and a head modeler for projects across several lines of business in the U.K., continental Europe, the Middle East, and Africa to design and validate PD/LGD/EAD credit risk models for IFRS 9, A-IRB, and stress-testing. He supervises quality control, development and validation of macroeconomic country models, credit risk products using proprietary data, satellite market risk models, and other forecasting products. He currently manages the development of climate risk scenarios at Economics and Business Analytics. He previously worked at CERGE-EI, a joint workplace of the Center for Economic Research and Graduate Education of Charles University in Prague and the Economics Institute of the Academy of Sciences of the Czech Republic, and at Southern Illinois University in Carbondale IL. He holds a PhD and an MA in economics from the University of Pittsburgh and an MSc in econometrics and operations research from the University of Economics in Prague.

Chris Lafakis is a director at Moody’s Analytics. He is the lead modeler for the Moody’s Analytics climate risk initiative and is responsible for climate modeling and scenario creation. He also has expertise in macroeconomics, energy economics, model development and model validation. Based in West Chester PA, he also contributes to Economic View. Mr. Lafakis has been quoted by media outlets, including The Wall Street Journal, CNBC, Bloomberg, and National Public Radio and often speaks at economic conferences and events. Mr. Lafakis received his bachelor’s degree in economics from the Georgia Institute of Technology and his master’s degree in economics from the University of Alabama.

Janet Lee is a director with the Economics and Business Analytics unit at Moody’s Analytics. Currently she is working on the climate risk research effort to quantify the economic impact of acute physical risk and developing models to link transition risk to the macroeconomy. Previously, she led custom projects to develop loss-forecasting and stress-testing models for clients, which included large and medium-size commercial banks, credit unions, auto captives, and financial technology lenders. Janet graduated from the University of Chicago and did her PhD studies in economics at the University of Pennsylvania. She is a CFA charter holder and has previously worked at Prudential Financial and the Federal Reserve Bank of Chicago.

About Moody’s Analytics

Moody’s Analytics provides fi nancial intelligence and analytical tools supporting our clients’ growth, effi ciency

and risk management objectives. The combination of our unparalleled expertise in risk, expansive information

resources, and innovative application of technology helps today’s business leaders confi dently navigate an

evolving marketplace. We are recognized for our industry-leading solutions, comprising research, data, software

and professional services, assembled to deliver a seamless customer experience. Thousands of organizations

worldwide have made us their trusted partner because of our uncompromising commitment to quality, client

service, and integrity.

Concise and timely economic research by Moody’s Analytics supports fi rms and policymakers in strategic planning, product and sales forecasting, credit risk and sensitivity management, and investment research. Our economic research publications provide in-depth analysis of the global economy, including the U.S. and all of its state and metropolitan areas, all European countries and their subnational areas, Asia, and the Americas. We track and forecast economic growth and cover specialized topics such as labor markets, housing, consumer spending and credit, output and income, mortgage activity, demographics, central bank behavior, and prices. We also provide real-time monitoring of macroeconomic indicators and analysis on timely topics such as monetary policy and sovereign risk. Our clients include multinational corporations, governments at all levels, central banks, fi nancial regulators, retailers, mutual funds, fi nancial institutions, utilities, residential and commercial real estate fi rms, insurance companies, and professional investors.

Moody’s Analytics added the economic forecasting fi rm Economy.com to its portfolio in 2005. This unit is based in West Chester PA, a suburb of Philadelphia, with offi ces in London, Prague and Sydney. More information is available at www.economy.com.

Moody’s Analytics is a subsidiary of Moody’s Corporation (NYSE: MCO). Further information is available at www.moodysanalytics.com.

DISCLAIMER: Moody’s Analytics, a unit of Moody’s Corporation, provides economic analysis, credit risk data and insight, as well as risk management solutions. Research authored by Moody’s Analytics does not refl ect the opinions of Moody’s Investors Service, the credit rating agency. To avoid confusion, please use the full company name “Moody’s Analytics”, when citing views from Moody’s Analytics.

About Moody’s Corporation

Moody’s Analytics is a subsidiary of Moody’s Corporation (NYSE: MCO). MCO reported revenue of $4.8 billion in 2019, employs more than 11,000 people worldwide and maintains a presence in more than 40 countries. Further information about Moody’s Analytics is available at www.moodysanalytics.com.

© 2021 Moody’s Corporation, Moody’s Investors Service, Inc., Moody’s Analytics, Inc. and/or their licensors and affi liates (collectively, “MOODY’S”). All rights reserved.CREDIT RATINGS ISSUED BY MOODY’S CREDIT RATINGS AFFILIATES ARE THEIR CURRENT OPINIONS OF THE RELATIVE FUTURE CREDIT RISK OF ENTITIES, CREDIT COMMITMENTS, OR DEBT OR DEBT-LIKE SECURITIES, AND MATERIALS, PRODUCTS, SERVICES AND INFORMATION PUBLISHED BY MOODY’S (COLLECTIVELY, “PUBLICATIONS”) MAY INCLUDE SUCH CURRENT OPINIONS. MOODY’S DEFINES CREDIT RISK AS THE RISK THAT AN ENTITY MAY NOT MEET ITS CONTRACTUAL FINANCIAL OBLIGATIONS AS THEY COME DUE AND ANY ESTIMATED FINAN-CIAL LOSS IN THE EVENT OF DEFAULT OR IMPAIRMENT. SEE APPLICABLE MOODY’S RATING SYMBOLS AND DEFINITIONS PUBLICATION FOR INFORMATION ON THE TYPES OF CONTRACTUAL FINANCIAL OBLIGATIONS ADDRESSED BY MOODY’S CREDIT RATINGS. CREDIT RATINGS DO NOT ADDRESS ANY OTHER RISK, INCLUDING BUT NOT LIMITED TO: LIQUIDITY RISK, MARKET VALUE RISK, OR PRICE VOLATILITY. 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