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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.
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 2
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 3
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 4
2
Chart 2: Transition and Physical Risk
Source: Moody’s Analytics
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
Source: Moody’s Analytics
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.
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 5
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
Source: Moody’s Analytics
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 6
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)
Sources: Network for Greening the Financial System, Moody’s Analytics
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)
Sources: Network for Greening the Financial System, Moody’s Analytics
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.
Source: Moody’s Analytics
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 7
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
Source: Moody’s Analytics
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
Source: Moody’s Analytics
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 8
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
Sources: Network for Greening the Financial System, Moody’s Analytics
14Climate Risk Macroeconomic Forecasting 14
Chart 14: U.K. Effective Domestic Price: Coal
1,000
10,000
100,000
15 25F 35F 45F 55F 65F 75F 85F 95F
NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)
1/100 £ per #, log scale, NSA
Sources: U.K. Department for Business, Energy & Industrial Strategy, Moody’s Analytics
12Climate Risk Macroeconomic Forecasting 12
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
NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)
£ per metric ton, NSA
Sources: Network for Greening the Financial System, Moody’s Analytics
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
Source: Moody’s Analytics
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 9
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.
15Climate Risk Macroeconomic Forecasting 15
Chart 15: U.S. Effective Domestic Price: NG
1
10
100
1,000
10 20F 30F 40F 50F 60F 70F 80F 90F 100F
NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)
$ per mmBTU, log scale, SA
Sources: Network for Greening the Financial System, Moody’s Analytics
16Climate Risk Macroeconomic Forecasting 16
Chart 16: U.K. Energy Consumption: Coal
1
10
100
1,000
15 25F 35F 45F 55F 65F 75F 85F 95F
NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)
BTU, tril, log scale, SAAR
Sources: U.S. Energy Information Administration, Moody’s Analytics
17Climate Risk Macroeconomic Forecasting 17
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
NGFS Current (Hot House World)NGFS Delay (Disorderly)NGFS Immediate (Orderly)
Ths, short tons, SAAR
Sources: Network for Greening the Financial System, Moody’s Analytics
18Climate Risk Macroeconomic Forecasting 18
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
Sources: Network for Greening the Financial System, Moody’s Analytics
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 10
20Climate Risk Macroeconomic Forecasting 20
Chart 20: U.K. GVA Svcs Industry Scenario
-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
Sources: Network for Greening the Financial System, Moody’s Analytics
19Climate Risk Macroeconomic Forecasting 19
Chart 19: U.S. Real GDP Scen Comparison
-4
-3
-2
-1
0
1
10Q2 20Q2 30Q2 40Q2 50Q2 60Q2 70Q2 80Q2 90Q2 00Q2
NGFS Delay (Disorderly)NGFS Immediate (Orderly)
% deviation from NGFS current
Sources: Network for Greening the Financial System, Moody’s Analytics
21Climate Risk Macroeconomic Forecasting 21
Chart 21: U.S. GPO Mining Industry Scenario
-80
-60
-40
-20
0
20
10 20F 30F 40F 50F 60F 70F 80F 90F 100F
NGFS Delay (Disorderly)NGFS Immediate (Orderly)
% deviation from NGFS current
Sources: Network for Greening the Financial System, Moody’s Analytics
22Climate Risk Macroeconomic Forecasting 22
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
NGFS Delay (Disorderly)NGFS Immediate (Orderly)
% deviation from NGFS current
Sources: Network for Greening the Financial System, Moody’s Analytics
23Climate Risk Macroeconomic Forecasting 23
Chart 23: U.S. Employment Mining Industry
-40
-30
-20
-10
0
10
10Q4 20Q4 30Q4 40Q4 50Q4 60Q4 70Q4 80Q4 90Q4 00Q4
NGFS Delay (Disorderly)NGFS Immediate (Orderly)
% deviation from NGFS current
Sources: Network for Greening the Financial System, Moody’s Analytics
24Climate Risk & ESG
Chart 24: Climate Risk SensitivitiesClimate change and credit risk
Sources: Network for Greening the Financial System, Moody’s Analytics
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 11
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.
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 12
App
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x Ta
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aria
bles
Cap
turin
g th
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ysic
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mpa
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umpt
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nditu
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MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 13
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EBO
IU_U
S (F
utur
es P
rice:
Bren
t cru
de o
il 1-
mon
th fo
rwar
d [fo
b])
FCPW
TI_
US
(Fut
ures
Pric
e: N
YMEX
Lig
ht S
weet
Cru
de O
il - C
ontra
ct 1
)
FPC
POIL
$Q_I
WR
LD (R
eal G
loba
l Pric
e of
Oil)
2 - C
O2
taxe
s
1. D
ivid
end
dum
my
DU
M_C
ARBO
N-
DIV
_IG
BR
Dum
my
varia
ble f
or
U.K
. car
bon
divi
dend
: 1=
divi
dend
; 0=
no d
ivid
end
Exog
enou
sBo
olea
n, N
SAN
A
FGG
EXP_
I_IG
BR (G
over
nmen
t Fin
ance
: Exp
endi
ture
- To
tal
[Int
erm
edia
te te
rm])
FYPD
_IG
BR (H
ouse
hold
Disp
osab
le In
com
e - G
ross
)
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 14
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 15
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 16
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 17
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 18
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 19
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 20
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$_I
GBR
(Nat
iona
l Acc
ount
s: Re
al G
ross
D
omes
tic P
rodu
ct [G
DP]
)
App
endi
x Ta
ble:
Sel
ecte
d U
.K. V
aria
bles
Cap
turin
g th
e Ph
ysic
al a
nd T
rans
itio
n Ri
sk (C
ont.
)
Des
crip
tion
Mne
mon
icSe
ries
de
scri
ptio
nSt
ate
Uni
tsVa
riab
le d
epen
ds o
nD
epen
d on
var
iabl
e
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 21
9 - E
mpl
oym
ent
1. A
rts;
ente
rtai
nmen
t
a
nd re
crea
tion
[I
nter
med
iate
term
]FE
AE_I
_IG
BR
Empl
oym
ent
- Art
s; en
ter-
tain
men
t an
d re
crea
tion
[Int
erm
edia
te
term
]
Stoc
hasti
cM
il. #
, SA
FEAE
_I_I
GBR
(Em
ploy
men
t - A
rts;
ente
rtai
n-m
ent a
nd re
crea
tion
[Int
erm
edia
te te
rm])
FEAE
_IG
BR (E
mpl
oym
ent -
Art
s; en
tert
ainm
ent a
nd re
crea
tion)
FESE
RV_I
GBR
(Em
ploy
men
t - S
ervi
ces
prod
ucin
g in
dustr
ies)
FEAE
_I_I
GBR
(Em
ploy
men
t - A
rts;
ente
rtai
nmen
t and
recr
eatio
n [I
nter
med
iate
term
])
FGD
PAE$
_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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 22
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 23
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 24
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
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 25
17. O
ther
serv
ice
a
ctiv
ities
[Int
erm
edia
te te
rm]
FESO
_I_I
GBR
Empl
oym
ent
- Oth
er se
rvic
e ac
tiviti
es
[Int
erm
edia
te
term
]
Stoc
hasti
cM
il. #
, SA
FESO
_I_I
GBR
(Em
ploy
men
t - O
ther
serv
ice
activ
ities
[Int
erm
edia
te te
rm])
FESO
_IG
BR (E
mpl
oym
ent -
Oth
er se
rvic
e ac
tiviti
es)
FESE
RV_I
GBR
(Em
ploy
men
t - S
ervi
ces
prod
ucin
g in
dustr
ies)
FESO
_I_I
GBR
(Em
ploy
men
t - O
ther
serv
ice
activ
ities
[Int
erm
edia
te
term
])
FGD
PSO
$_IG
BR (G
ross
Val
ue A
dded
[GVA
] - O
ther
serv
ice
activ
ities
)FE
SERV
_I_I
GBR
(Em
ploy
men
t - S
ervi
ces p
rodu
cing
indu
strie
s [I
nter
med
iate
term
])
FGD
PSER
V$_
IGBR
(Gro
ss V
alue
Add
ed
[GVA
] - S
ervi
ces p
rodu
cing
indu
strie
s)
18. T
rans
port
atio
n an
d
st
orag
e [I
nter
med
iate
term
]FE
TS_
I_IG
BR
Empl
oym
ent -
Tr
ansp
orta
tion
and
stora
ge
[Int
erm
edia
te
term
]
Stoc
hasti
cM
il. #
, SA
FET
S_I_
IGBR
(Em
ploy
men
t - T
rans
port
atio
n an
d sto
rage
[Int
erm
edia
te te
rm])
FET
S_IG
BR (E
mpl
oym
ent -
Tra
nspo
rtat
ion
and
stora
ge)
FESE
RV_I
GBR
(Em
ploy
men
t - S
ervi
ces
prod
ucin
g in
dustr
ies)
FET
S_I_
IGBR
(Em
ploy
men
t - T
rans
port
atio
n an
d sto
rage
[I
nter
med
iate
term
])
FGD
PTS$
_IG
BR (G
ross
Val
ue A
dded
[GVA
] - T
rans
port
atio
n an
d sto
rage
)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)
19. W
hole
sale
and
reta
il
tr
ade;
repa
ir of
mot
or
v
ehic
les [
Inte
rmed
iate
term
]
FEW
R_I
_IG
BR
Empl
oym
ent
- Who
lesa
le
and
reta
il tr
ade;
repa
ir of
m
otor
veh
icle
s [I
nter
med
iate
te
rm]
Stoc
hasti
cM
il. #
, SA
FEW
R_I
_IG
BR (E
mpl
oym
ent -
Who
lesa
le
and
reta
il tr
ade;
repa
ir of
mot
or v
ehic
les
[Int
erm
edia
te te
rm])
FEW
R_I
GBR
(Em
ploy
men
t - W
hole
sale
and
reta
il tr
ade;
repa
ir of
mot
or
vehi
cles
)
FESE
RV_I
GBR
(Em
ploy
men
t - S
ervi
ces
prod
ucin
g in
dustr
ies)
FEW
R_I
_IG
BR (E
mpl
oym
ent -
Who
lesa
le a
nd re
tail
trad
e; re
pair
of
mot
or v
ehic
les [
Inte
rmed
iate
term
])
FGD
PWR
$_IG
BR (G
ross
Val
ue A
dded
[GVA
] - W
hole
sale
and
reta
il tr
ade;
repa
ir of
mot
or
vehi
cles
)
FESE
RV_I
_IG
BR (E
mpl
oym
ent -
Ser
vice
s pro
duci
ng in
dustr
ies [
Inte
r-m
edia
te te
rm])
FGD
PSER
V$_
IGBR
(Gro
ss V
alue
Add
ed
[GVA
] - S
ervi
ces p
rodu
cing
indu
strie
s)
20. W
ater
supp
ly;
se
wer
age;
was
te
m
anag
emen
t and
tem
edia
tion
[I
nter
med
iate
term
]
FEW
W_I
_IG
BR
Empl
oym
ent -
W
ater
supp
ly;
sew
erag
e;
was
te m
an-
agem
ent a
nd
rem
edia
tion
[Int
erm
edia
te
term
]
Stoc
hasti
cM
il. #
, SA
FEW
W_I
_IG
BR (E
mpl
oym
ent -
Wat
er
supp
ly; s
ewer
age;
was
te m
anag
emen
t and
re
med
iatio
n [I
nter
med
iate
term
])
FEW
W_I
GBR
(Em
ploy
men
t - W
ater
supp
ly; s
ewer
age;
was
te m
anag
e-m
ent a
nd re
med
iatio
n)
FEG
OO
D_I
GBR
(Em
ploy
men
t - G
oods
pr
oduc
ing
indu
strie
s)FE
WW
_I_I
GBR
(Em
ploy
men
t - W
ater
supp
ly; s
ewer
age;
was
te m
anag
e-m
ent a
nd re
med
iatio
n [I
nter
med
iate
term
])
FGD
PWW
$_IG
BR (G
ross
Val
ue A
dded
[G
VA] -
Wat
er su
pply
; sew
erag
e; w
aste
man
-ag
emen
t and
rem
edia
tion)
FEG
OO
D_I
_IG
BR (E
mpl
oym
ent -
Goo
ds p
rodu
cing
indu
strie
s [In
ter-
med
iate
term
])
FGD
PGO
OD
$_IG
BR (G
ross
Val
ue A
dded
[G
VA] -
Goo
ds p
rodu
cing
indu
strie
s)
Sour
ce: M
oody
’s An
alyt
ics
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
MOODY’S ANALYTICS Climate Risk maCRoeConomiC FoReCasting 26
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
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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.
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