Outline and Description of E3ME-Asia model

Presented by

Soocheol Lee (Meijo University, Japan)

Academia SinicaOctober 6, 2017

1.Introduction to E3 modelling

“Good modelling involves the combination of best econometric practice, coherent economic theory and knowledge of data. Inevitably conflicts can arise between these three factors, and the modeller will need to exercise judgement in the weight that is attached to the econometric evidence, its consistency with theory and the requirement to model properly the time series observations”

Sean Holly (2013)

1.1 Examples of models

General

Non-Equilibrium

Partial

Equilibrium

GEM-E3*WorldScan*

E3ME*GINFORS*

PRIMES (energy)TIMES (energy)

ASTRA (transport)POLES (energy)

* These models cover the World

1.2 Equilibrium and Non-Equilibrium

• There are several different ways of characterising this distinction:• supply or demand driven

• long or short term

• theoretical or empirical

•Not all are completely accurate, especially now as the two approaches are coming closer together

1.3 Equilibrium and Non-Equilibrium (cont.)

• In the 1960s and 1970s the macroeconometric approach dominated

• Holly (2013) provides reasons for a ‘backlash’ against the approach:• neglect of monetary influence

• the Lucas Critique and rational expectations

• lack of micro-foundations

• inadequate treatment of error processes

• failure to use insights from optimal control theory

• time-inconsistency of discretionary policy

• The focus of research moved to CGE approaches

1.4 Long and Short-Term Analysis?• In conceptual terms the distinction is linked to the ‘neo-

classical synthesis’:• aggregate demand may be a short-run constraint

• but long-term growth rates determined by supply-side factors

• Attempts have been made to create CGE models suitable for short-term assessment (DSGE)• these are now widely used in central banks

• Modern econometrics largely supports long-run analysis

• CGE models, difficult to catch low carbon technology innovation and its effective demand to influence good impact on the economy

1.5 In a real-world policy simulation

Macro Econometric model results

CGE model resultsIncreasing levels of ambition for energy efficiency in the EU

2015 report, European Commission, DG ENER:http://ec.europa.eu/energy/sites/ener/files/documents/CE_EE_Jobs_main%2018Nov2015.pdf

NB both the E3ME and GEM-E3 models have been developed substantially since 2015

GEM-E3 E3ME

• perfect competition

• constant returns to scale

• equilibrium solution

• full employment

• projection based on one year's data

• guess-estimated parameters

• varying competition over sectors

• varying returns to scale

• product supply-demand balance

• unemployment

• projection based on 40+years' data used for econometric estimation

1.6 comparing GEM-E3(CGE) and E3ME(Macro Econometirc)

1.7 Model Linkages

Cambridge Econometrics and SERI (2011), for DG Environment

2.E3ME Description

12

2.1 E3ME Model(Energy-Environment-Economy Macro Econometric Model)

Macro-Econometric Modeldeveloped by Cambridge EconometricsReinforced Asian sector by collaboration work withREEPS(Research group of East Asian Environmental Policy Studies)in Japan as E3ME-Asia

・Demand driven・No equilibrium, Non optimization

・Four East Asian regions are in the model・Based on data from past 40 years and OECD/IEA forecast

1975

1978 Spin-off from the University of Cambridge Department of Applied Economics • Origins in the ‘Growth

Project’ led by Nobel Laureate Prof Sir Richard Stone

• pioneered the development of detailed sectoral modelling in an input-output framework MDM1980

1985

1990

1995

2000

2005

2010

2003 CE 25th anniversary. Terry Barker sets up charitable trust fund to ensure CE's independence and principles. New Thinking in Economics:‘to advance education in the field of economics for the benefit of the public’

1993 Start of part-EC funded research project to build E3ME

Today 40 staff based in Cambridge. International reputation in the application of whole-economy, sectoral modelling, particularly the global E3ME model

Early 1990s Expansion of scope of CE’s work to • European Regional• UK Energy,

Environment and Economy service

• More tailored project work

Company grows from 10 to 25 staff.

The move to more consultancy projects Product and model developmentsEarly stage of the company

1980s CE’s work focuses mainly on annual subscription services• UK Industrial service• UK Regional serviceand selected projects, notably the EC-funded HERMES project (in effect, a predecessor for E3ME)

2.2 History of E3METhe E3ME model and its predecessors have been around since 1970s and it is one of the most well-known econometric models in Europe and recently globally.

It is used for official analysis for the European Commission (including the recent Clean Energy Package and Climate and Energy Framework 2030).

・E3ME (world) models are:computer-based econometric modelsdeveloped by Cambridge University and Cambridge Econometrics…covering economy, energy and emissions……with detailed sectors (linked by IO tables)……and endogenous technological progress

・They can be used for forecasting but are typically used for policy analysis

… sometimes at national level, usually … international

2.3 E3ME Basic Model Structures:

●

●

2.4 The E3ME model: Dimensions

• Econometric model• cover world 59 regions, including explicit representation of all G20 countries

and all EU Member States. The model has recently been expanded to cover many East Asia and South East Asia regions explicitly including Japan, China, Korea, Taiwan and Indonesia. Other ASEAN countries are grouped together.

• based on the system of national accounts• includes intermediate and all components of final demand• detailed treatment of the labor market• 22 stochastic equation sets, also covering energy and prices

• large sectoral disaggregation: 42 industries, 28 consumption categories• 12 different fuel types, and 19 separate fuel user groups • 14 atmospheric emissions • long and short-term specification• annual solutions to 2050

• For more details see www.e3memodel.com

2.5 E3ME Energy-Environment-Economy interactions

FTT:Transportation

FTT:Power

FTT:Industry

2.6 E3ME Engineering-Energy-Environment-Economy interactions

2.7 Method of analysis :The E3ME-FTT integrated assessment model

• Economic'policies'

• Demographic'

change'

• Global'oil'price'• Energy'policies'

• Environmental'policies'

• Resource'efficiency'policies'

Emissions'trading'scheme'

Environmental'taxes'

Energy'use'

Energy'use,'prices'&'taxes'

Economic'ac=vity'&'general'prices'

e.g.'industrial'emissions'of'SF6'

Technology*

Energy'use'

Energy'prices''&'costs'

investment'

Funding'R&D'

Pollu=on'abatement'equipment'

Material'use,''IO,'material'tax'

Economic'ac=vity'&'general'prices'

Energy'demand'

Emissions*

Units:'tonnes'of'carbon/CO2''

Energy*

Unit:'Tonnes'of'oil'equivalent'

Materials''Unit:'tonnes'

Economy*

Unit:'Euros'

2.8 Treatment of Technology

• It is now generally accepted that technology should be endogenous in modelling

• There are two ways of doing it• explicitly (bottom-up, partial approaches)

• implicitly (top-down, general approaches)

• There will always be a limitation that it is not possible to predict all future technologies

2.9 Top-down and Bottom-up Modelling

• These terms mean different things in different areas of modelling

• In energy modelling:• bottom-up models are specified by technology, they

typically come from an engineering background

• top-down models are based on economic relationships

• As it is not possible to define all technologies explicitly, bottom-up models are partial

2.9.1Comparison of top-down and bottom-up modelling methodology

Bottom-up Top-down

Classifications employed Engineering-based Economics-based

Treatment of capital Precise description of capital equipment

Homogenous and abstract concept

Motive force Discount rate employed by agents

Income and priceelasticities

Perception of market Market imperfection and barriers

Perfect market

Potential efficiency improvements

Usually high : costless improvement

Usually low: constraint on economy

22

E3ME

FTT・Fuel consumption・Investment in

new power source・Electricity price

・Electricity demand・Fuel demand

・CO2 emission・Investment spill over・GDP, Employment

2.10 FTT: Power Link between E3ME and FTT

Post-Keynesian,

Post-Schumpeterian

(evolutionary)

2.11 FTT Power and E3ME Model

2.12 Bottom-up Schumpeterian meeting Top-down Post-Keynesian: the E3ME model

E3ME

25

2.13 FTT: Power (1)Probabilistic decision makingbetween two technologies

Probabilistic Choice Matrix:

J.-F. Mercure, Energy Policy 48, 799-811 (2012)

[𝐹𝑖𝑗]

26

※Δx=xi-xj

0 0

Choice probability represents diversity of investors

J.-F. Mercure, Energy Policy 48, 799-811 (2012)

2.14 FTT: Power (2)Choice probability distributiondepends on cost distribution

Modelling technology substitution

Coal

CCGT

Hydro

Wind

PV

BIGCC…

t = 1

…

J.-F. Mercure, Energy Policy 48, 799-811 (2012)

Coal

CCGT

Hydro

Wind

PV

BIGCC…

t = 3

Substitutions

Fijéë ùû

Coal

CCGT

Hydro

Wind

PV

BIGCC…t = 2

Substitutions

Fijéë ùû

28

Coal

CCGT

Hydro

Wind

PV

BIGCC…

Coal

CCGT

Hydro

Wind

PV

BIGCC…t

Coal

CCGT

Hydro

Wind

PV

BIGCC…

Substitutions

=j

åD

tJ.-F. Mercure, Energy Policy 48, 799-811 (2012)

[𝐹𝑖𝑗]

2.15 FTT: Power (3)Aggregate technology substitution

29

tFAFASSSj

jijiijijjii

Si : Share of technology i

Aij : Life time of technology i and lead time of technology j

(matrix of substitution frequencies)

Fij : Probability that technology i would be chosen between i and j

(probabilistic choice matrix)

t : time

J.-F. Mercure, Energy Policy 48, 799-811 (2012)

2.16 FTT: Power (4)Technology shares determined byAggregating every choices

30

Mercure & Salas, Energy 46, 322-336 (2012)

・Cost increases as the technology diffusessince less resource being available

・Learning effect from cumulative capacity

2.17 FTT: Power (5)Cost-Supply Curve= Natural resource availability

2.18 Cost-supply curves for renewables

2.19 Cost-quantity curves for fossil & nuclear fuels

2.20 E3ME-FTT Transport

car salesdistance driven

incomecar price fuel price

FTT-Transport

car demand by technology

average car price

road transport fuel demand

road transport CO2

- fuel tax- carbon tax

E3ME consumption equation

FTT transport policies*

IO feedback

2.21 Diffusion: technology choices

t

The uptake of new technology:

Innovation – Selection – Diffusion

J.-F. Mercure, Energy Policy 48, 799-811 (2012)

t t + Δt

https://www.toyota.co.jp/jpn/sustainability/environment/challenge2050 /トヨタ環境2050

15.$UK Prices'of'cars'(USD/km)Std'of'price'' fuel'cost'(USD)std'fuel'cost O&M'costs'(USD)O&M'costs'(std)'Discount'ratelifetime energy'use'(MJ/km)Distance'travelled'per'year'occupancy'rate'Capacity'factor'C'(initial) CO2EmissionsSeats/Veh Learning'exponent

Petrol$Econ 18683.00 4396.00 0.095 0.013 0.038 0.020 15% 12 1.583 12104 0.375 4539.0 1.00744 109.732966 4 R0.0144996Petrol$Mid 32185.00 8164.00 0.123 0.014 0.051 0.030 15% 12 2.064 12104 0.375 4539.0 0.72093 143.044759 4 R0.0144996

Petrol$Lux 99538.00 49455.00 0.202 0.057 0.064 0.030 15% 12 3.379 12104 0.375 4539.0 R0.216744 234.16231 4 R0.0144996

Advance$Petrol$Econ 18683.00 4396.00 0.076 0.013 0.038 0.020 15% 12 1.425 12104 0.375 4539.0 1.00744 98.759669 4 R0.0740006

Advance$Petrol$Mid 32185.00 8164.00 0.110 0.014 0.051 0.030 15% 12 1.858 12104 0.375 4539.0 0.72093 128.740283 4 R0.0740006

Advance$Petrol$Lux 99538.00 49455.00 0.161 0.057 0.064 0.030 15% 12 3.041 12104 0.375 4539.0 0.216744 210.746079 4 R0.0740006

Diesel$Econ 22608.00 3297.00 0.069 0.010 0.038 0.020 15% 12 1.351 12104 0.375 4539.0 0.3 100.104906 4 R0.0144996

Diesel$Mid$ 33755.00 7065.00 0.077 0.015 0.051 0.030 15% 12 1.581 12104 0.375 4539.0 0 117.12274 4 R0.0144996

Diesel$Large 54793.00 14601.00 0.118 0.025 0.064 0.040 15% 12 2.104 12104 0.375 4539.0 R0.441858 155.926763 4 R0.0144996

Advance$Diesel$Econ 22608.00 3297.00 0.084 0.010 0.038 0.000 15% 12 1.216 12104 0.375 4539.0 0.3 90.0944151 4 R0.0740006

Advance$Diesel$Mid$ 33755.00 7065.00 0.095 0.015 0.051 0.000 15% 12 1.423 12104 0.375 4539.0 0 105.410466 4 R0.0740006

Advance$Diesel$Large 54793.00 14601.00 0.095 0.025 0.064 0.000 15% 12 1.894 12104 0.375 4539.0 R0.441858 140.334087 4 R0.0740006

CNG$Econ 21485.45 1000.00 0.048 0.055 0.039 0.020 15% 12 1.508 12104 0.375 4539.0 0.627906 84.5988 4 R0.0144996

CNG$Mid 37012.75 3000.00 0.071 0.069 0.056 0.030 15% 12 2.210 12104 0.375 4539.0 0.321858 123.981 4 R0.0144996

CNG$large 114468.70 5000.00 0.082 0.076 0.066 0.040 15% 12 2.548 12104 0.375 4539.0 R0.78 142.9428 4 R0.0144996

Hybrid$Econ 29202.00 2826.00 0.084 0.003 0.039 0.030 15% 12 1.414 12104 0.375 4539.0 R0.302328 97.9758621 4 R0.1520031

Hybrid$Mid 34540.00 6594.00 0.073 0.009 0.056 0.040 15% 12 1.216 12104 0.375 4539.0 R0.72 84.2592414 4 R0.1520031

Hybrid$Lux$ 78343.00 9263.00 0.124 0.017 0.066 0.040 15% 12 2.078 12104 0.375 4539.0 R0.78093 144.024517 4 R0.1520031EV$Econ 10990.00 157.00 0.000 0.000 0.046 0.040 15% 12 0.210 12104 0.375 4539.0 1.32558 0 4 R0.1520031

EV$Mid 44745.00 1256.00 0.000 0.000 0.065 0.050 15% 12 0.540 12104 0.375 4539.0 0 0 4 R0.1520031

EV$Lux 89961.00 2355.00 0.000 0.000 0.080 0.060 15% 12 0.580 12104 0.375 4539.0 R3 0 4 R0.1520031

2$Wheelers$Motorcycle$Econ$(<500cc)3808.00 1326.00 0.040 0.003 0.030 0.030 15% 7 0.676 6052 0.55 3328.6 2.2693 46.8324621 2 R0.0144996

2$Wheelers$Motorcycle$Lux$(>500cc)14932.00 5760.00 0.127 0.039 0.030 0.030 15% 7 2.124 6052 0.55 3328.6 0.906978 147.159745 2 R0.0144996Adv$Mot 3808.00 1326.00 0.040 0.095 0.030 0.030 15% 7 0.676 6052 0.55 3328.6 2.2693 46.8324621 2 R0.0144996

Adv$Mot$ 14932.00 5760.00 0.095 0.095 0.030 0.030 15% 7 2.124 6052 0.55 3328.6 0.906978 147.159745 2 R0.0144996

53 regions, 25 technologies

2.22 The diffusion of vehicle technology

Mercure & Lam, In preparation (2016)

The effectiveness of policy on consumer choices for private road passenger transport emissions reductions in six major economies 2015https://www.repository.cam.ac.uk/bitstream/handle/1810/247636/WorldCarFleets_ArXiv_v3.pdf?sequence=2

2.23 The effectiveness of policy on consumer choices

2.24 E3ME-FTT Industry

• Similar concept as FTT-Power and FTT-Transport – technology diffusions

• More complex because different industries use different production technologies

Development stage:

• which industries to include?

Possible sectors: chemical, steel, petroleum, pulp and paper

・defining technologies

• defining policy options

• data collection

• link to E3ME – inputs and feedback• link to E3ME raw material demand?

2.25 The FTT family of models

Aggregate and shares of

household consumption

Material use

Normal output

Labour market participation

rate

Investment in dwellings

Residual income (non-employment)

Industrialaverage earnings

Hours worked

Industrial employment

Shares of coal, oil, gas

and electricity

Aggregate energy

Bilateral tradeIndustrial

investment

Industrial prices

Export and import prices

Labour market equations

2.26 The stochastic equation sets

Energy equations

Price and wage equations

Trade equations

2.27 Exogenous variables

• Population

• Natural resources (including coal, oil and natural gas, raw commodity price)

• Current and capital spending of government

• Tax rates and allowances

• Exchange rates

• Short- and long-term interest rates

• World prices of traded goods, e.g. crude oil

2.28 The determination of output in E3ME

Input-Output

Table

IndustriesP

roduct

s

Ho

use

ho

ld F

inal

Co

nsu

mp

tio

n

Go

ver

nm

ent

Fin

al

Co

nsu

mp

tio

n

Fix

ed I

nv

estm

ent

Ch

ang

e in

Inv

ento

ries

Ex

po

rts

To

tal

Dem

an

d

Do

mes

tic

Su

pp

ly

Imp

ort

s

Value Added:

wages

profits

deprecation

Taxes and

Subsides

Gross Output

+ + + + +

+

+

=

==

2.29 The core input-output structure Connected by I-O table

2.30 Energy and emission classifications in E3ME Version 6

FU:23 (fuel users)

1. Power Generation

2. Own use

3. Hydrogen production

4. Iron & Steel

5. Non-ferrous Metals

6. Chemicals

7. Mineral Products

8. Ore-extraction

9. Food, Drink & Tob.

10. Tex., Cloth. & Foot.

11. Paper & Printing

12. Engineering etc

13. Other Industry

J:12 (fuels)

1. Hard coal

2. Other coal etc

3. Crude oil etc

4. Heavy fuel oil

5. Middle distillates

6. Other gas

7. Natural gas

8. Electricity

9. Heat

10.Combustible waste

11.Biofuels

12.Hydrogen

EM:14 (air emissions)

1. CO2

2. SO2

3. NOX

4. CO

5. Methane

6. Black smoke

7. VOC

8. Nuclear - air

9. Lead - air

10.CFCs

11.N2O (GHG)

12.HF6 (GHG)

13.PFC (GHG)

14.SF6 (GHG)

14. Construction

15. Rail Transport

16. Road Transport

17. Air Transport

18. Other Transport serv.

19. Households

20. Agriculture, forestry, etc.

21. Fishing

22. Other Final Users

23. Non-energy use

2.31 Energy and emission classifications in E3ME Version 6

Power technology

1 Nuclear

2 Oil

3 Coal

4 Coal + CCS

5 IGCC

6 IGCC + CCS

7 CCGT

8 CCGT + CCS

9 Solid Biomass

10 S Biomass CCS

11 BIGCC

12 BIGCC + CCS

Road transport

technology

1 Petrol Econ

2 Petrol Mid

3 Petrol Lux

4 Adv Petrol Econ

5 Adv Petrol Mid

6 Adv Petrol Lux

7 Diesel Econ

8 Diesel Mid

9 Diesel Lux

10 Adv Diesel

Econ

11 Adv Diesel Mid

12 Adv Diesel Lux

13 Biogas

14 Biogas + CCS

15 Tidal

16 Large Hydro

17 Onshore

18 Offshore

19 Solar PV

20 CSP

21 Geothermal

22 Wave

23 Fuel Cells

24 CHP

13 LPG Econ

14 LPG Mid

15 LPG Lux

16 Hybrid Econ

17 Hybrid Mid

18 Hybrid Lux

19 Electric Econ

20 Electric Mid

21 Electric Lux

22 motorcycles Econ

23 motorcycles Lux

24 Adv motorcycles

Econ

25 Adv motorcycles

Lux

3. How to run the E3ME model (v6 2016)

3.1 Overall operation sequence

Sep up assumption or scenario

Edit ‘Run.cmd’

Type ‘run’ on the ‘Command and Prompt’

Copy and paste the new ‘XX.mre’ file and ‘Dan_ba.mre’

on the ‘Data folder’ in the folder of E3ME_FE

Front-end interface is launched

Analyse the results

Double Click !

Wait for finishing solving and generating ‘XX.mre’ file at the ‘Output folder’ in the folder of E3ME

Pre-preparation: model install and software

download

3.2 Necessary model files to install

Install the two folders of E3ME and E3ME_FE in a local drive (C:) in your

computer

3.3 E3ME : storing all necessary model inputs

③

①

②

Folder or files Sub-folder or sub-files Explanations

In

AsnsAssumptions.csv

Assumptions.idiomYou can change the assumption with this file

Scenarios B_ETS.idiom You can change the scenarios with this file

XX.idiom

Enforecast.idiom You can change the period of simulation

DAN1.idiomDANA~E.idiom

Printing files for ‘.mre’ (results in figure)Printing files for ‘.tab’ (results in text)

OutputXX.tab Result in text form

XX.mre Result for graph in the front-end phage

RUN.cmdThis is solution script command file. For a new assumption or scenario, you need to make changes on this file before running the model.

3.4 In , Output, Run files

3.5 File requirements for E3ME

3.6 What are the files?

3.7 How to edit the model input

The model inputs are in text format and can be edited using OxEdit or Notepad.

• 1. Notepad

Find ‘Notepad’ under Accessories folder in the start-up menu.

• 2. OxEdit

Tip

• 3.8 Assumption file: Assumptions.idiom

Year: 2000-2050

Country

Percentage change from previous year

CPRICE_FOOD_FEED Commodity Price: Food/ feed EXRATE_LC_P_EURO Exchange rate

CPRICE_WOOD Commodity Price: Wood SR_INTEREST Interest rate: short run

CPRICE_CONS_MIN Commodity Price: Construction minerals LR_INTEREST Interest rate: long run

CPRICE_IND_MIN Commodity Price: Industrial minerals TOT_GOV_SPENDING Government spending

CPRICE_FER_ORES Commodity Price: Ferrous metals SHAR_DEFENCE Government spending: Defence

CPRICE_NFER_ORES Commodity Price: Non-ferrous metals SHAR_EDUCATION Government spending: Education

CPRICE_COAL Commodity Price: Coal SHAR_HEALTH Government spending: Health

CPRICE_BRENT_OIL Commodity Price: Brent oil TAX_INDIRECT Tax: Indirect

CPRICE_GAS Commodity Price: Gas VAT_RATE Tax: VAT

CPRICE_OTHERS Global Inflation TAX_DIRECT Tax: Direct

BENEFIT_PAYMENT Benefit Payment

SOCSEC_EMPLOYEES Soc sec employees’ contributions

SOCSEC_EMPLOYERS Soc sec employers’ contributions

SPARE1

3.9 Abbreviations

• 3.10 Scenario file: B_ETS.idiom

59: number of country50: Number of yearR: Regions Y1: YearsRTEA: regional energy tax as additional excise duties (euro/toe)

Editing scenario file in text editor (OxEdit)

Open up a solution script .cmd file to set up input files for run

3.11 Command prompt

One default .cmd file: Run.cmd(change name of input files if necessary)

3.12 EnForecast: Run model over forecast period

3.13 Run.cmd

The model solutions script is a ‘.cmd’ batch file.

It is stored in C:E3ME folder. Right click to edit the file.

It contains information such as location of model files inputs, their names and names of output file.

If we want to set up a new scenario different from the default E3ME baseline, then we will need to make changes to the solution script accordingly to refer it to the new set of model inputs.

Command Prompt page

3.14 The model solving…

3.14 .tab: results in text form

• Ex. DanA_ba.tab

3.16 xx.mre: results in graph

• .mre file is a large text file which can be read in the model Front-End

• To open results files in the Frone-End, copy over the large “xx.mre” result files and paste in the ‘Data folder’

• Copy and paste the output of baseline (Dan.ba.mre) for comparison with baseline

• Double click on sever.exe in E3ME_FE folder

• Front-End interface is launched automatically

3.17 Inspecting E3ME results

3.18 … and model outputs

3.19 Inspecting E3ME results

• Inspect one file: variables, regions & sectors

• Comparison between files: scenarios vs baseline

– choose any baseline to compare with

– inspect results by variables, regions and sectors

– level, absolute & percentage differences from baseline

• Option to export to Excel

3.20 E3ME Limitations

• Data– annual model

– quality and availability of data

• Econometric– dealing with structural change

– based on historical relationships (Lucas critique)

• Complexity– complex linkages between different part of models

• Treatment of financial markets

Low-carbon, Sustainable

Future in East Asia:

Improving energy systems,

taxation and policy cooperation

Edited by Soocheol Lee, Hector Pollitt and Seung-

Joon park