Modelling system documentation and - Universiteit Twenteowsgip.itc.utwente.nl/projects/complex/images/... · and identify economic behaviour of agents, faced by technological and

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Deliverable Number: D5.6 & D5.7

Modelling system documentation and final report on results

and policy briefing

Date

20 September 2016

Version Number:

Report Number:

Main Authors:

02

D5.6 and D5.7

Tatyana Bulavskaya (TNO)

Hettie Boonman (TNO)

Jinxue Hu (TNO)

Saeed Moghayer (TNO)

Leila Niamir (UT)

Tatiana Filatova (UT)

Kishore Dhavala (BC3)

Iñaki Arto (BC3)

DIFFUSION LEVEL – PU

PU PUBLIC

RIP RESTRICTED INTERNAL AND PARTNERS

RI RESTRICTED INTERNAL

CO CONFIDENTIAL

Author e-mail and contact details: [email protected]

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Report for COMPLEX

“Knowledge Based Climate Mitigation Systems for a Low Carbon Economy”

Seventh Framework Programme, Theme [env.2012.6.1-2]

Grant agreement 308601

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Contents

History table .................................................................................................................................. 1

Definitions and acronyms .............................................................................................................. 2

1. Introduction ........................................................................................................................... 6

2. Integrated system of models ................................................................................................. 8

2.1 Global change energy technology assessment sub-model: GCAM ..................... 8

2.1.1 Energy Module ..................................................................................... 10

2.1.2 Agriculture and land use module ......................................................... 11

2.1.3 Climate module .................................................................................... 11

2.2 Global Socio-economic projection and policy impact analysis sub-model: EXIOMOD ........................................................................................................................... 12

2.2.1 Introduction ......................................................................................... 12

2.2.2 A modular approach ............................................................................. 13

2.2.3 Economic and environmental data ...................................................... 14

2.2.4 The computable General Equilibrium model ....................................... 15

2.3 Behavioural change and energy market sub-model: NIROO ............................. 18

2.3.1 Empirical data on energy related decisions and potential changes .... 19

2.3.2 Agent-based energy market ................................................................. 23

2.4 Integrated system of models ............................................................................. 24

2.4.1 GCAM-CGE integration ......................................................................... 25

2.4.2 ABM-CGE integration ........................................................................... 26

3. Policy simulation results ...................................................................................................... 29

3.1 Policy options and scenarios ............................................................................. 29

3.1.1 Reference scenario ............................................................................... 29

3.1.2 Scenario framework ............................................................................. 31

3.2 Simulation set-up ............................................................................................... 33

3.2.1 GCAM ................................................................................................... 33

3.2.2 EXIOMOD.............................................................................................. 34

3.2.3 NIROO ................................................................................................... 35

3.3 Simulation results .............................................................................................. 35

3.3.1 Energy and emissions (GCAM results) ................................................. 35

3.3.2 Economic development (EXIOMOD results) ........................................ 41

4. Behavioural change and effectiveness of the policy scenarios ........................................... 49

4.1 Reference policy scenario .................................................................................. 50

4.2 Green policy scenario ........................................................................................ 52

4.3 Green or Grey policy scenario? ......................................................................... 53

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5. Sensitivity analysis ............................................................................................................... 56

6. Conclusion and further steps ............................................................................................... 60

7. References ........................................................................................................................... 62

8. Appendix ................................................................................................................................ 1

8.1 Overview GCAM input variables for reference scenario in EXIOMOD. ............... 1

8.2 Overview of GCAM input variables for scenarios in EXIOMOD ........................... 1

8.3 Equations relative competitiveness..................................................................... 3

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Secondary title 1 Date

History table

Version Report

D5.6

Date send Receiver Send by Comments

Version 1.0 31th August 2016 Nick Winder Saeed Moghayer Final draft for

comments

Version 2.0 20th September Nick Winder Saeed Moghayer Revised and

finalized by TNO

and UT team

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Definitions and acronyms

ABM: Agent Based Model. A model composed of agents that interact within an environment. Agents

simultaneously make decisions in order to simulate and forecast complex behaviour in society.

BAU: Business As Usual scenario. Scenario that assumes no significant change in behaviour,

technology, economics or politics. Normal circumstances can be expected to continue unchanged

(definition Oxford Reference).

BC: Black Carbon.

CCS: Carbon Capture Storage. Capture and underground storage of CO2 emissions, preventing the

carbon dioxide to enter the atmosphere.

CES-function: Constant Elasticity of Substitution function, as part of a CGEM. When, for instance,

production technology is modelled as a nested CES function, the nesting structure allows for

different substitution possibilities between different groups of inputs (e.g. capital and labour). (For

more information, see GCEM.)

CGEM: Computational General Equilibrium Model. GCE models are simulations that look at the

economy as a complete system of inter-dependent components (industries, households, investors,

governments, importers and exporters). The model includes behavioural functions which describe

and identify economic behaviour of agents, faced by technological and institutional constraints.

Compensating Variation (CV): compensating variation is the additional amount of money a

consumer would need to reach its initial utility after a (policy) shock changed the economic situation

of an agent. Compensating variation is a way to illustrate the impact of a policy shock on consumers

welfare.

ES: Elasticity of substitution in a CES or LES-CES function. Refers to level of substitution between

capital and labour in the production technology, and level of substitution between products for

households. (For more information, see CES-function and LES-CES function.)

EU-27: The 27 countries of the European Union, before the entry of Croatia in July 2013. That is,

Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany,

Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal,

Romania, Slovakia, Slovenia, Spain, Sweden and United Kingdom.

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EXIOBASE: Database used in the current version of EXIOMOD. It is a Multi-Regional Environmentally

Extended Supply and Use (SU)/ Input-output (IO) database. This database has been developed by

harmonizing and increasing the sectorial disaggregation of national SU and IO tables for a large

number of countries, estimating emissions and resource extractions by industry, harmonizing trade

flows between countries per type of commodities. Moreover, it includes a physical (in addition to the

monetary) representation for each material and resource use per sector and country. (For more

information, see IO-model and SUT.)

EXIOMOD: EXtented Input-Output MODel. “Extended” refers to the fact that EXIOMOD can extend

the standard Input-Output analysis in two main directions: (1) to CGEM analysis (2) to specific topics

such as environmental impacts, energy or transports. (For more information see IO-model and

GCEM.)

FEC: Final energy consumption. Final energy consumption covers all energy supplied to the final

consumer for all energy uses.

GCAM: Global Change Assessment Model. The model combines representation of different modules

such as economy, energy system, agriculture & land use, terrestrial and ocean carbon cycles. The

model is considered as a bottom up integrated assessment model and it is used to explore the

various climate change mitigation policies such as carbon taxes, carbon trading, and deployment of

energy technologies.

GDP: Gross Domestic Product. Gross domestic product is an aggregate measure of production equal

to the sum of the gross values added of all resident institutional units engaged in production (plus

any taxes, and minus any subsidies, on products not included in the value of their outputs). The sum

of the final uses of goods and services (all uses except intermediate consumption) measured in

purchasers' prices, less the value of imports of goods and services, or the sum of primary incomes

distributed by resident producer units (definition OECD website).

GIS: Geographic Information System. Information system with which information about geographical

objects are saved, analysed, integrated and presented.

HBS: Household Budget Survey. HBSs are national surveys mainly focusing on consumption

expenditure. They are conducted in all EU Member States and their primary aim (especially at

national level) is to calculate weights for the Consumer Price Index (definition Eurostat).

IAM: Integrated Assessment model. Model that integrates knowledge between different disciplines,

and aims to generate useful information for policy making.

IO-model: Input-Output model. A model which analyses effects of interdependencies between

sectors in the economy. In general it analyses the impact of a shock in one sector on other sectors in

the economy. An input-output model is based on an input-output table, columns indicate the sectors

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demand for products produced by the sectors indicated in the rows. That is, columns give the sectors

input and rows the sectors output.

IPCC: Intergovernmental Panel on Climate Change.

ISM: Integrated System of Models.

LCE: Low-carbon energy. Comes from technologies which produce power with lower CO2 emissions.

It includes power generation from for example wind, solar, hydro and nuclear technologies.

LES-CES function: Linear Expenditure System-Constant Elasticity of Substitution as part of a GCEM.

Allows households to differentiate between necessity and luxury products. This function defines a

subsistence level for each good consumed which lead to an elasticity between consumption and

revenue lower than one. For instance for food we have a high subsistence level, whereas for other

products consumption is more sensitive to the level of income.

MAGICC: Model of the Assessment of Greenhouse gas Induced Climate Change.

NIROO: The agent based model called Nonlinearities In Residential lOw-carbOn economy transition.

NUTS: Nomenclature of territorial units for statistics.

OC: Organic Carbon.

PEC: Primary Energy Consumption. Primary energy consumption refers to the direct use at the

source, or supply to users without transformation, of crude energy, that is, energy that has not been

subjected to any conversion or transformation process (definition OECD).

Rebound effect: Concept used in energy-economy, referring to an energy efficiency improvement

that results in a smaller decrease in fuel consumption as might be expected from the efficiency.

Rebound effects arise for instance when consumers spend the saved money on energy-consuming

products.

Relative competitiveness: a relative competitiveness indicator is developed to highlight the impact

of the policy scenarios on the competitive position of a region. It answers the question: are the

product prices in a certain region competitive with the product prices of imported products from

foreign competitors?

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RoW: Rest of the world countries. This does not include Australia, New Zealand, Canada, China,

Iceland, Switzerland, Norway, EU-27, Japan, Mexico, Russia, South Korea, United States. (For more

information, see EU-27.)

RCP: Representative Concentration Pathway. RCPs are four greenhouse gases concentration

trajectories. Each of the RCPs specifies radiative forcing and the associated concentrations of the

atmospheric constituents involved over the period 1850–2100.

SSP: Shared Socio-Economic Pathways. SSP1-SSP5 give five plausible alternative trends in the

evolution of society and natural systems over the 21th century at the level of the world and large

world regions. In this study, the SSP2 scenario “Middle of the Road” is applied as the reference

scenario. This means that all scenario results will be shown in comparison with this reference

scenario.

SUT: Supply and Use Tables. Supply and use tables are in the form of matrices that record how

supplies of different kinds of goods and services originate from domestic industries and imports and

how those supplies are allocated between various intermediate or final uses, including exports

(definition OECD website).

TFP: Total factor productivity. The efficiency with which firms turn inputs (e.g. capital and labour)

into outputs.

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1. Introduction

Integrated climate-energy-economy models are a fundamental tool to assess the socio-economic and

environmental impact of climate scenarios and mitigation policies. Currently, there is a great variety

of models developed under different approaches, operating at different scales, that are being used

to assess different questions related to climate mitigation. All of these approaches have their pros

and cons and, in recent years, there is an increasing interest on integrating different models in order

to get benefit of their respective advantages and to overcome their limitations. In COMPLEX we have

developed an Integrated System of Models (ISM) combining the strengths of various models by

utilizing the state-of-the-art in climate, economics, energy technology, and individual behavioural

change literature as well as in modelling techniques including computational, integrated and

participatory modelling.

COMPLEX project had an ambition to set significant steps in the development of a structural ‘Coupled

Modelling Approach’. This can overcome the limitations of using a single modelling approach for all

research questions, regardless their scale, detail or the reason for which these models have been

developed. To broaden the scope across multiple geographic and temporal scales, and to apply the

understanding of the coupled system, we have created a flexible framework to couple existing, more

detailed subsystem models. We selected a combination of dedicated models for economic

assessment, i.e. EXIOMOD, detailed energy system model, i.e. GCAM, and detailed energy market

agent base model, i.e. NIROO. We have used new research on the Model Coupling Toolkit (Belete

and Voinov, 2015) to establish a system for model coupling. Our framework consists of a

combination of procedures, data aggregation and exchange platforms, interfaces and central

controller platforms. The resulting ISM is especially useful to answer specific questions that require

subsystem integration.

The COMPLEX ISM provides an example of how a much broader range of very strong groups

specialized in sub-systems or on assessments at specific scales can, together, make essential

contributions to major assessments. The model coupling approach is a small attempt to open up the

large-scale integrated assessment world to new participants. The larger participation of groups with

more varied backgrounds will foster innovation and quality of major assessments. The undertaken

hybrid modelling methods provide powerful tools for exploring socio-economic, environmental and

energy-related pathways from 2010-2050. In this report we illustrate this method by linking three

models to explore the socio-economic, environmental and energy implications of different mitigation

pathways at different scales.

Climate and energy technology scenarios are modelled in the Global Change Assessment Model

(GCAM). This Integrated Assessment Model (IAM) choses input parameters such that emissions

reduce enough over the years for staying below 2°C increase in global average temperature by 2100.

The model is driven by the selection of energy carriers in low carbon scenarios depending on the cost

per technology. GCAM also fully models the Intended Nationally Determined Contributions (INDCs)

under the United Nations Framework Convention on Climate Change (UNFCCC).

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In most of the existing climate-energy-economy models, representation of economic sectors with a

significant potential for mitigation are not sufficiently developed. The integrated economic-

environmental database used for the implementation of current Integrated Assessment and Climate-

Economy-Environment models must include more details with regard to the representation of

economic sectors. The Computable General Equilibrium (CGE) model EXtended Input-Output MODel

(EXIOMOD) provides a good basis to answer this, as it is based on the EXIOBASE database with up to

200 products. The model takes into account price, substitution and rebound effects throughout the

whole economy.

As highlighted by Stern (2007) the economic analysis should examine the possibility of non-marginal

change as climate change is likely to cause such abrupt structural change in the ecological-economic

system. The Agent Based Model (ABM) called Nonlinearities In Residential lOw-carbOn economy

transition (NIROO) is a computerized simulation of a number of decision-makers (agents) and

institutions and add such nonlinearities to the ISM. It is a fairly new approach to modelling systems

and the model studies non-marginal shifts in energy markets emerging from the bottom up (i.e.

behavioural changes among households, diffusion of low-carbon energy sources). NIROO specifically

focuses on households’ energy use and potential behavioral change (Niamir and Filatova 2016).

We will use the IAM model GCAM to analyse the environmental, energy and part of the economic

implications at the global level. Part of outputs of this simulation, such as the energy mix, the price of

electricity, or the price of food will be passed to EXIOMOD which will be used to assess the socio-

economic implications in terms of country and sectoral competitiveness, product prices, households’

welfare, household consumption by income group. Finally, part of the results of EXIOMOD, such as

the household electricity consumption, gross income and savings, will be used by agent based model

NIROO to analyses the implications in terms of change in the household preferences for green

electricity.

We will illustrate how the ISM works by assessing the implications of two different mitigation

pathways. In this case study, we assume that the different regions of the world commit to specific

emissions reduction targets for the year 2050, and assuming to alternative futures in terms of

technological progress in terms of the costs of mitigation technologies (high vs. low). Usually climate

change related scenarios presented against business as usual (BAU) or reference scenario. But in real

time, a wide range of factors can influence the economic growth projections. O’Neill (2012) discussed

a wide range of global representative scenarios, called Shared Socioeconomic Pathways (SSP) that

take possible future socio-economic developments into account and explores the economic and

environmental consequences. The SSPs combines the climate forcing and socio-economic conditions

on the other hand (Chateau and Lapillonne, 2012). In this study we use SSP2 as the reference

scenario. SSP2 (middle of the road scenario) is a baseline scenario which assumes that countries do

not implement any emission reduction policies.

The outcomes of our models show that the climate mitigation pathways result in a much lower share

of fossil fuels in the primary energy mix. Which, in turn, results in an increase in food and electricity

prices. At the same time, wealth of households are affected by the climate mitigation policies,

especially the poorest households, who spend a relatively large share of their money on necessity

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goods. Outcomes of the agent based model show that also behaviour of households is affected. We

find that households are less willing to make energy-related investments under the climate

mitigation scenarios.

2. Integrated system of models

In this section we present the Integrated System of Models (ISM) of the COMPLEX project. First we

describe the three models composing the ISM: Integrated Assessment Model GCAM (Section 2.1),

Computational General Equilibrium model EXIOMOD (Section 2.2) and Agent Based Model NIROO

(Section 2.3). Finally, in Section 2.4, we show the integration of the three models.

2.1 Global change energy technology assessment sub-model: GCAM

The Global Change Assessment Model (GCAM) is a climate integrated assessment model developed

by the Joint Global Change Research Institute (Pacific Northwest National Laboratory), University of

Maryland (USA). The model combines representation of different modules such as economy, energy

system, agriculture & land use, terrestrial and ocean carbon cycles (see Figure 2-1). The model is

considered as a bottom up integrated assessment model and it is used to explore the various climate

change mitigation policies such as carbon taxes, carbon trading, and deployment of energy

technologies (Capellán-Pérez et al. 2014). The model has been extensively used to explore different

issues related to energy, climate and environment challenges (Calvin et al. 2009).

Figure 2-1: Elements of the GCAM integrated assessment modeling framework (Wise et al. , 2009)

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GCAM is a dynamic recursive partial-equilibrium economic model with technology-rich

representations of the economy, energy sector, land use linked to a climate model1. GCAM operates

in 5 year time intervals from 1990 to 2100 and it is designed to examine long-term changes in energy,

agriculture/land-use, and climate system. The current version of the model covers 32 geopolitical

regions of the world (see Table 2-1). The model balances the supply and demand of all the markets

and provides market clearing prices for primary and final energy sources, agriculture and land use

products (Calvin et al. 2009). With the energy, agriculture and land-use, the model integrates with

unmanaged ecosystem services and terrestrial carbon cycle. The integration of terrestrial carbon

cycle to economy-energy-agriculture-land use would allow the model to compute the emissions of

the greenhouse gases and other gases influencing global radiation budget2.

Table 2-1: GCAM regions, inputs and main outputs

1 http://www.globalchange.umd.edu/archived-models/gcam/

2 The gases covered be GCAM are: CO2, CH4, N2O, NOx, VOCs, CO, SO2 ,BC, OC, aerosols, HFCs, PFCs, and SF6 .

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Regions Inputs Outputs

USA

Africa regions: Eastern, Western, Northern

and Southern

Australia-New Zealand , Brazil, Canada,

Central America and Caribbean, Central

Asia, China, EU-12, EU-15, Europe: Eastern

and Non-EU , EFTA (Europe free trade:

Iceland, Norway and Switzerland), India,

Indonesia, Japan, Mexico, Middle East,

Pakistan, Russia , South Africa, South

America regions: Northern & Southern,

South Asia, South Korea, Southeast Asia,

Taiwan, Argentina, Colombia and Global

Population

GDP

Policy and

technological

parameters

Primary Energy

Final energy supply and demand

Land Use

Agricultural Production

Emissions

Climate indicators (CO2

concentrations, climate forcing,

RCP forcing, temperature)

Policy costs (by region)

Prices for all markets.

GCAM is linear in structure: the exogenous socioeconomic parameters (population, labour force and

labour productivity) drive the energy, agriculture, land use and climate modules, and there is no

feedback from climate impacts to damages (economic, ecosystem disturbance, agricultural

productivity, etc.).

The model includes a default scenario for the future evolution of the key socio-economic drivers

(population, labour force participation, and labour productivity), which can be changed by the user.

Once the exogenous socio-economic parameters are provided and the policy target is set. These

policies can be in the form of taxes, subsidies, carbon permits or technology targets. The model

provides policy costs (mitigations costs) by region which are derived from marginal abetment cost

curves. Then the model simulates “service” demands (e.g. transportation), the demands for food and

energy, emissions, climate indicators (CO2 concentration, radiative forcing and temperature), policy

costs, land use, prices for different markets, etc. GCAM is a community model and it has been under

constant evolution for over 30 years. The model has been contributing in predicting and estimating

climate related policies and its outputs have been used in all the Assessment Reports of the IPCC.

2.1.1 Energy Module

GCAM considers two types of resources, exhaustible and renewable. Exhaustible resources include

fossil fuels and uranium; the extraction of these resources are depend on the availability, technology

and extraction cost. Since these resources are exhaustible, the marginal cost of extraction is

expected to rise in absence of technological advancement (Calvin et al. 2009; Capellán-Pérez et al.

2014). The availability of exhaustible resources are determined by the supply cost curves, for fossils

the cost curves are determined by the hydrocarbon resource assessment (Rogner, 1997) and for

uranium is based on Schneider and Sailor (2008). For a detailed discussion of the resource basis of

GCAM (see Capellán-Pérez et al., 2016). Renewable resources include wind, geothermal energy,

municipal and industrial waste (for waste-to-energy), photovoltaic and rooftop solar equipment

(Capellán-Pérez et al., 2014). The availability of other renewable resources such as

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bioenergy/biomass depends on the land use for the bioenergy crops and their production (Calvin et

al., 2009).

The energy system module includes the supply of primary and final energy resources and the

demand by sectors (building, industry and transportation). It also provides the energy transformation

to final fuels and energy services such as passenger kilometres in transport, building space etc.

Primary energy includes fuels such as liquids, gases, coal, uranium, hydro, solar and wind energies.

The primary energy resources are transformed into end-use-energy forms such as refined liquids and

gases, coal, hydrogen, electricity etc. These end-use-energy forms are delivered to building, industry,

and transport sectors (Calvin et al., 2009).

2.1.2 Agriculture and land use module

The agriculture, land use and land cover together with terrestrial carbon cycle provides the supply

(production) and demand for the agriculture crops and their corresponding prices. The model data

for the agriculture and land use parts comprises into 238 agro-ecological zones. In the model, the

land is optimally allocated based on the productivity, demand for crop and its price, rent, non-land

costs such as labour, capital, fertilizer etc. The model assumes that productivity crop changes over

time and it is exogenously determined (Calvin et al., 2009, Wise et al., 2009, Capellán-Pérez et al.,

2014).

GCAM includes several different commercial and non-commercial land uses including ten crop

categories, six animal categories, three bioenergy categories, forests, pasture, grassland, shrubs,

desert, tundra, and urban land. All agricultural crops, other land products, and animal products are

globally traded within GCAM. The three categories of bioenergy include traditional bioenergy,

bioenergy from waste products, and purpose-grown bioenergy. Traditional bioenergy comprises

straw, dung, fuel wood, and other energy forms that are utilized in an unrefined state in the

traditional sector of an economy. Traditional bioenergy use, although significant in developing

regions, is a relatively small component of global energy and, as regional incomes increase over the

century, it becomes less economically competitive. Bioenergy from waste products is a by-product of

another activity. The amount of potential waste that is converted to bioenergy is based on the price

of bioenergy. However, the bioenergy price does not affect production of the crop from which the

waste is derived. Purpose-grown third-generation bioenergy refers to crops whose primary purpose

is the provision of energy. The amount produced in this category depends on profitability with

respect to other land-use options (González-Eguino et al., 2016).

2.1.3 Climate module

GCAM takes inputs from MAGICC 5.3 (Model of the Assessment of Greenhouse gas Induced Climate

Change). MAGICC consists of the entire chain from emissions to concentrations to global radiative

forcing to global changes in temperature and sea-level. The first set of models within MAGICC 5.3

converts emissions to concentrations, and covers a wide range of greenhouse and pollutant gases.

With the help of MAGICC 5.3, emissions of greenhouse gases, aerosols, and short-lived species are

determined endogenously in GCAM, in other words, the emissions are linked to underlying human

activities. Emissions mitigation for CO2 is treated explicitly and endogenously for fossil fuel, industrial

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and land-use emissions in the GCAM. Emissions of non-CO2 greenhouse gases, aerosols and short-

lived species are also endogenously determined.

Global radiative forcing from well-mixed greenhouse gases is determined from concentration values

using simple relationships drawn from the literature. Forcing from carbon dioxide is proportional to

the natural logarithm of carbon dioxide concentrations, forcing from methane and nitrous oxide is

proportional to the square root of concentrations (with an interaction term). Forcing from

fluorinated gases is linear in concentration. Forcing from tropospheric ozone is estimated using non-

linear relationships between emissions of methane and reactive gases NOx, CO, and VOCs.

Direct and indirect forcing from aerosols is included. Direct forcing from sulphur dioxide, black

carbon, and organic carbon are taken to be proportional to SO2, black carbon (BC), and organic

carbon (OC) emissions, respectively. The GCAM version of MAGICC has been updated to include a

direct representation of BC and OC emissions provided by GCAM. In the distribution version of

MAGICC, BC and OC forcing is, in contrast, inferred from proxy measures such as land-use change

and SO2 or CO emissions. Indirect cloud forcing in MAGICC is taken to be proportional to the natural

logarithm of sulphur dioxide emissions.

MAGICC has been shown to be able to emulate the global-mean results from most complex general

circulation models (Raper, S. C. B. et al., 1996). To produce the climate scenarios, GCAM allows the

user to change specified parameters, including climate sensitivity, carbon-cycle, and aerosol forcing

strength (Smith et al., 2006).

2.2 Global Socio-economic projection and policy impact analysis sub-

model: EXIOMOD

The description of EXIOMOD below corresponds to the description in Bulavskaya, Hu, Moghayer, &

Reynès (2016).

2.2.1 Introduction

EXIOMOD is an economic model able to measure the environmental impact of economic activities3.

As a multisector model, it accounts for the economic dependency between sectors. It is also a global

and multi-country model with consistent bilateral trade flows between countries at the commodity

level. Based on national account data, it can provide compressive scenarios regarding the evolution

of key economic variables such as GDP, value-added, turn-over, (intermediary and final)

consumption, investment, employment, trade (exports and imports), public spending or taxes.

Thanks to its environmental extensions, it makes the link between the economic activities of various

3 For a full description and examples of applications of EXIOMOD see Bulavskaya, Hu, Moghayer, & Reynès

(2016).

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agents (sectors, consumers) and the use of a large number of resources (energy, mineral, biomass,

land, water) and negative externalities (greenhouse gases, wastes).

Compared to other existing multi-country economic models EXIOMOD has several important

features:

• Based on a flexible modular structure, EXIOMOD can run (and compare) several standard

economic modelling approaches. Whereas Input-Output (IO) analysis concentrates on the

interdependence between economic sectors, general equilibrium analysis takes also into

accounts price effects. Separating IO from general equilibrium effects simplifies the analysis

of the results which overcome certain criticisms formulated to Computational General

Equilibrium Models (CGEM) (see below).

• The modular approach also allows for customizing the model setup by switching on or off

specific blocks in order to adjust the level of model complexity and detail to the question

under study.

• EXIOMOD can have the properties of the two main types of CGEM. Walrasian CGEMs assume

perfect prices flexibility whereas neo-Keynesian CGEMs assume market imperfections (e.g.

involuntary unemployment) due to slow adjustment for prices and production factor and

consumption. This difference may lead to major differences in the results.

• EXIOMOD use the EXIOBASE database that covers a high level of detail on economic sectors

as well as environmental extensions on emissions, resources, water and land use.

With these features, EXIOMOD is particularly well suited to evaluate the impact of policies related to

climate change, energy and resource efficiency at the macroeconomic, sector and household levels:

Environmental extensions allows for measuring the impact of economic activities on the use

of a large variety of resources and other environmental indicators.

The international trade flows allows for analysing the impact of national consumption

pattern on the economy and on the resource use in other countries. This feature is

particularly convenient to confront production based and consumption based indicators of

resource footprint per country.

The modular approach allows for separating direct and indirect effects, and in particular

rebound effects.

2.2.2 A modular approach

EXIOMOD’s name stands for EXtended Input-Output MODel. “Extended” refers to the fact that

EXIOMOD can extend the standard Input-Output (IO) analysis in two main directions: (1) to

Computational General Equilibrium Model (CGEM) analysis, and (2) to specific topics such as

environmental impacts, energy, or transport. EXIOMOD is based on a modular approach specifically

designed to conduct both IO analysis and CGEM simulation. With this modular approach and

depending on the subject under investigation, the modeller can easily change the regional and

sectorial segmentation as well as the level of complexity regarding the specification of the model by

switching on or off specific blocks.

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For this study the CGE model will be applied using the Walrasian closure. A CGE model is required in

order to model the price effects that play an important role (e.g. energy prices). As said before, the

Walrasian closure is appropriate as it assumes that prices are perfectly flexible which is the case

when using long term scenarios until 2050/2100. More detail is added to the model on households in

order to investigate energy poverty in more detail. More information about the modelling

assumptions and underlying data is described in the next sections.

2.2.3 Economic and environmental data

The current version of EXIOMOD uses the detailed Multi-regional Environmentally Extended Supply

and Use (SU) / Input Output (IO) database EXIOBASE (www.exiobase.eu, Tukker et al., 2009). This

database has been developed by harmonizing and increasing the sectorial disaggregation of national

SU and IO tables for a large number of countries, estimating emissions and resource extractions by

industry, harmonizing trade flows between countries per type of commodities. Moreover, it includes

a physical (in addition to the monetary) representation for each material and resource use per sector

and country.

The EXIOBASE database has one of the most detailed products and environmental extensions that

are currently available from input-output tables. The database covers 49 regions (44 countries

representing around 90% of the world GDP and five rest of the world regions), 200 products and

various environmental indicators. For this study we use 24 sectors as listed in Figure 2-2.

http://www.exiobase.eu/

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Table 2-2: Sector classification in EXIOMOD for this study

Economic sectors

Agriculture, forestry and fishing

Mining fossil fuels

Mining of metal ores and non-metallic minerals

Manufacturing of food, beverage and tobacco products

Manufacturing of textile, wood and printed products

Manufacturing of coke and refined petroleum products

Manufacturing of chemicals and chemical products

Manufacturing of rubber and plastic products

Manufacturing of non-metallic mineral products

Manufacturing of basic metals and metal products

Manufacturing of electronic computer, optical and electrical equipment

Manufacturing of machinery and equipment nec and other manufacturing

Electricity production from fossil fuels

Electricity production from nuclear

Electricity production from renewable sources

Transmission and distribution services for electricity

Gas and gas distribution services, steam and hot water supply services

Collected and purified water, distribution services of water

Construction

Trade, accommodation and food service activities

Railway transportation services

Other land transportation services

Other transportation services

Other services business and non-commercial services

2.2.4 The computable General Equilibrium model

The notion of “general equilibrium” relates to a state where supply is equal to demand in all markets.

In Walrasian models, the equilibrium force is the price system. Perfect flexibility of prices and

quantities (production factors, consumption, etc.) ensures the instantaneous equilibrium between

supply and demand. When an exogenous shock decreases the supply of a commodity, its price tends

to go up, thereby stimulating additional supply and depressing demand, until supply and demand are

equal again. Arrow & Debreu (1954) demonstrate the conditions under which such an equilibrium

exists4. This equilibrium mechanism does not only operate on the product markets. Depending on

the closures retained (e.g. Shoven & Whalley, 1994), it may also apply on the production factors

markets (labour, capital), on the saving market (savings equal investments) and on the foreign

4 They demonstrate that the Walrasian equilibrium is a Nash (1950, 1953) equilibrium if agents are perfectly rational, if they do not commit anticipation errors, if the production functions do not show increasing returns to scale, and if the utility functions satisfy the standard properties of continuity, non-saturation and strict convexity of their isoquants. Additional more technical properties are also required (see e.g. Hahn, 1982).

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exchange markets (imports equal exports). Walrasian type of CGEM are static: after a shock a new

equilibrium (system of prices and quantities) is found within the period of simulation5.

A CGEM takes into account the interaction and feedbacks between supply and demand as

schematized in Figure 2-2. Demand (consumption, investment, exports) defines supply (domestic

production and imports). Supply defines in return demand through the incomes generated by the

production factors (labor, capital, energy, material, land, etc.). To ensure the equilibrium between

supply and demand, an assumption regarding the “closure” of the system has to be done. Existing

CGEMs generally choose between two main closures. The Walrasian closure assumes that perfect

price flexibility insure the instantaneous equilibrium between supply and demand. On the contrary,

the Keynesian closure assumes that demand defines supply whereas price and quantities are rigid

and adjust slowly to the optimum. Depending on the application, EXIOMOD can be run with different

closures.

Figure 2-2: Architecture of a CGEM

Producers

The production technology is modeled as a nested Constant Elasticity of Substitution (CES) function.

The nesting structure allows for introducing different substitution possibilities between different

groups of inputs. Figure 2-3 illustrates the nesting structure as setup in this version of the model. At

the first level, we assume that material (non-energy intermediaries), land and water are perfectly

complementary to the aggregate capital, labor, energy, that is the Elasticity of Substitution (ES) is

equal to zero. At the second level, energy can be substituted to the aggregate input capital-labor

with an ES equal to 0.4. At the third level, the ES between labor and capital is equal to one (Cobb-

Douglas function) and the ES between energy types is equal to 0.5.

5 Some CGE models introduce a recursive dynamic where the past savings define next year capital stocks. This type of dynamics is not included in the base version of EXIOMOD, but it can be added for a specific project. EXIOMOD instead uses a more advanced Keynesian version of dynamic closure.

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Figure 2-3: Production structure in EXIOMOD

Households

In this study, households are split into five income quintiles and the household’s utility is specified as

a LES-CES function (Linear Expenditure System - Constant Elasticity of Substitution) allowing to

differentiate between necessity and luxury products. This function defines a subsistence level for

each good consumed which lead to an elasticity between consumption and revenue lower than one.

For instance for food we have a high subsistence level, whereas for other products consumption is

more sensitive to the level of income. We assume that the subsistence levels for consumption of

products grows at the same rate as population. The subsistence level for energy products is divided

by the improvement in energy efficiency. The subsistence levels are based on the GTAP values as

used in the study by Lejour et al. (2006). Including all households expenditures, the subsistence level

of consumption corresponds to 33 percent of the base year consumption, but this level jumps to 80

percent for agricultural products. Above this minimum level of consumption, substitution between

good is possible depending on the price, with an ES equal to one.

Trade

The trade structure is schematized in Figure 2-4. Per type of use (e.g. final, intermediate

consumption), a good can either be imported or produced domestically. For simplicity, we assume

that the ES is equal to five for each use except for the following commodities: energy, water,

construction (ES = 0.5). This means that energy, water and construction are less flexible for changing

trade partners compared to the other products. In a second step, all imported products per use are

aggregated to calculate the total level of imports. In a third level, imports can be supplied by

different countries. We assume a CES function characterized by possibilities of substitutions between

regions of origin (with ES = 5). The ES value might seem somewhat high, however it is within the

range discussed in the literature (e.g., McDaniel & Balistreri, 2003). Moreover, the high value reflects

the observations in the literature that the long-term value of the parameter is relatively high,

meaning that trade partners are more flexible in the long-term.

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Figure 2-4: Trade structure in EXIOMOD

Environment

EXIOMOD relates the resource use to the economic activity in several ways. CO2 emissions are

directly related to the level of consumption of the energy commodities responsible for the emission.

Water consumption of economic activities is related to the level of production. For households, it is

related to the water consumption (purchased from the water sector). Materials (such as metal, non-

metallic minerals, etc.) are related to the production of the mining sector responsible of the

extraction.

2.3 Behavioural change and energy market sub-model: NIROO

Residential sector contributes 24% of GHG emissions in Europe and, thus, potentially serves as an

important target group in any transition path to a low carbon economy. Household’s behavioral

change and its impact on GHG emissions is widely discussed in academic and practitioners literature.

McKinsey (2009) claimed that the behavioral change alone can contribute from 4% of overall CO2

emissions. However, the report indicates that quantifying the aggregate impact of behavioral

changes in energy consumption among households is a very challenging task. (Rohde et al 2012)

reviews the ways through which households may change their energy consumption. In general, the

overview of the actions a household may pursue to change their energy footprint discussed in the

literature can be divided in three groups: (1) invest in an energy saving equipment, e.g. purchase of

an A+++ equipment or insulating a house, (2) change in energy consumption habits e.g. switching off

the lights or putting down the heating, and (3) switching to another energy source e.g. from grey to

green electricity. In summary, human behavioral change significantly influences energy use in the

residential sector. It is also the main driver of the rebound effect. Although many researchers discuss

the demand side potential to stimulate a transition to low-carbon economy, it seems to be quite

challenging to really activate this demand potential in practice.

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In order to trace aggregated impacts of households behavioral changes and their potential

contribution to a transition to low-carbon energy, we designed an agent-based model (Niamir and

Filatova 2016). Agent-based modelling is a simulation approach, which models a large number of

actors – households in our case – making decisions and interacting according to prescribed rules

(Farmer and Foley, 2009). The model is well-grounded in social science theories on behavioral change

and is supported by empirical data from a survey eliciting the process of energy-related decision-

making on the households side. An agent-based energy market model is designed and implemented

to study Nonlinearities in Residential lOw-carbOn economy transition (NIROO) . NIROO specifically

focuses on households’ energy use and potential behavioral change and aims to study demand-side

activation and potential non-marginal changes in energy markets. NIROO disaggregates the

residential energy (electricity and heating) demand side to trace cumulative impacts of behavioral

change among heterogeneous households over time and space. The energy supply side is presented

in a rather simplistic way as a more comprehensive modelling of energy production and potential

technological change is envisioned in other models (EXIOMOD and GCAM) of the integrated

modeling suite. Yet, we include the supply side endogenously in NIROO in order to model market

clearing procedure in order to get better understanding of the cumulative influence of households

behavioral changes on whole energy market (Niamir and Filatova 2015b; Niamir and Filatova 2016).

Each time step the cumulative residential demand is estimated based on the energy-related

decisions household agents take

2.3.1 Empirical data on energy related decisions and potential changes

While traditional economic modelling is based on the neoclassical economic theory, which advocates

the use of a rational representative agent with perfect information, there is a large body of literature

doubting these basic assumptions. Niamir and Filatova (2016) presents a discussion along the lines of

this debate with respect to energy behavior and potential changes that are often affected by various

behavioral factors There are many barriers and drivers which could trigger household to make a

decision and/or change their behavior. There are many empirical studies in psychology and

behavioral economics showing that consumer choices and actions often deviate from these

assumptions of rationality, and there are certain persistence biases in human decision making, which

lead them to have different behavior (Frederiks, et al. 2015; Kahneman, 2003; Pollitt and

Shaorshadze, 2013; Stern 1992; Wilson and Dowlatabadi, 2007).

Therefore, we did an extensive literature review and dug into the psychology theories

(environmental psychology specifically) to identify theoretical basis for these barriers and drivers as

well as reporting any empirical evidence. We present the summary of this literature review in Table

2-3. The majority of these factors we tried to measure by means of the survey developed within the

COMPLEX project, and are gradually integrated in the NIROO model.

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Table 2-3: Overview of behavioral change barriers and drivers in the housing sector

In parallel, while reviewing agent-based literature on energy applications, we found that many of the

demand-side behavioral energy models employ one of the three behavioral theories rooted in

psychology and sociology. Namely, Theory of Planned Behavior (TPB), Protection Motivation Theory

(PMT), and Norm Activation Theory (NAT). Therefore, we designed a conceptual framework

grounded in TPB and NAT theories and integrating different stages of decision-making leading to a

potential energy-related behavioral change based on the eight main categories (Table 2-3). This

Barriers and drivers categories Examples Last related factsheets

1. Demographical factors - Gender - Age - Education - Economic /energy decisions - Size of household

Ameli and Brandt (2015) Bamberg, et al. (2015) Faber, et al. (2012) Mills and Schleich (2012) Michelsen and Madlener (2012) Bamberg, et al. (2007)

2. Psychological - Environmental concerns - Emotions - Interest in energy-related topics - Personal attitudes - Personal beliefs

Ameli and Brandt (2015) Bamberg, et al. (2015) Faber, et al. (2012) Mills and Schleich (2012)

3. Knowledge-base - Environmental information - Energy-related information - Overestimate of own energy consumption / saving - PV installation information - Switching supplier information

Ameli and Brandt (2015) Broberg and Kazukauskas (2014) Faber, et al. (2012)

4. Economical - Economical concerns - Household’s income - Household’s saving - Investments

Ameli and Brandt (2015) Bamberg, et al. (2015) Faber, et al. (2012) Mills and Schleich (2012)

5. Social - Social norms - Social network - Social activities

Ameli and Brandt (2015) Bamberg, et al. (2015) Faber, et al. (2012)

6. Cultural - Comfort is a priority - Social competition - Social image - Daily patterns

Broberg and Kazukauskas (2014) Faber, et al. (2012) Mills and Schleich (2012)

7. Institutional - Electricity price - Heating costs - Subsidies on installing PVs - Taxes - Energy provider services

Ameli and Brandt (2015) Bamberg, et al. (2015) Faber, et al. (2012) Bamberg, et al. (2007)

8. Structural and physical - Possibility to adjust temperature - Possibility to install PVs - Possibility to improve insulation

Ameli and Brandt (2015) Bamberg, et al. (2015) Broberg and Kazukauskas (2014) Faber, et al. (2012) Michelsen and Madlener (2012) Bamberg, et al. (2007)

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framework forms the core behind a large-scale survey and is at the core of our NIROO simulation

model.

The survey was designed to elicit the factors and stages of a decision-making process with respect to

the three types of energy-related actions households typically make: (1) invest in an energy saving

equipment, (2) energy conservation due to a change in energy consumption habits, and (3) switching

to another energy source. Our survey model assumes of three main steps to reach to one of these

actions: knowledge activation, motivation, and consideration. On each step, several psychological

factors (e.g. awareness, personal norms, feeling guilt), economical (e.g. income), socio-demographic

(e.g. educational level, age), social (e.g. subjective and social norms), and structural and physical (e.g.

energy label and ownership of dwelling) drivers and barriers are considered and calculated based on

TPB and NAT theories (Niamir and Filatova, 2016).

Based on the academic literature and EU reports, we categorized the energy-related behavioral

change of households in three main actions: Investments, energy conservation, and switching. Table

2-4 shows these behavioral changes and the latest publications in this domain. A household can

make a large investment e.g. install solar panel, roof/floor insulation, or small investments e.g.

buying efficient appliances (A++ washing machine or bulbs). At the same time, they could save the

energy use simply by changing their daily patterns and habits. Finally, they can switch to another

supplier that provides green electricity.

Table 2-4: Overview of energy-related behaviors in the housing sector

Energy-related behavioral changes

Examples Last related factsheets

1. Investment - Installing PVs

- Installing Thermal PVs

- Roof/floor insulation

- installing efficient appliances

Ameli and Brandt (2015)

Rai and Robinson (2015)

Robinson and Rai (2015)

Tran (2012)

Chappin and Dijkema (2007)

2. Energy conservation - Turn off extra devices

- Simply use less electricity

- Run just full load washing machines

Amouroux, et al. (2013)

Faber, et al. (2012)

Mills and Schleich (2012)

3. Switching supplier - Switch conventional to green supplier

- Switch to greener supplier

Tran (2012)

The survey, which was carried out in the Netherlands and Spain, collects extensive data on the details

about these three types of actions. In parallel, in our NIROO model, households make their energy

decisions based on psychological factors (e.g. awareness, personal norms, social norms) and their

socio-demographic characteristics (e.g. income, saving). As in the survey, we categorized their

decisions in three main groups: energy-related investments, energy conservation choices, and

switching to a low carbon energy source. The three groups of actions can be further specified to be

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operationalized in the model code. For instance, energy-related investment may include investments

in new technologies (e.g. installing solar panel), installing efficient appliances (e.g. A++ washing

machine), or house insulation (e.g. double or triple glazing). The survey data is being intensively

integrated with the NIROO agent-based model (Niamir and Filatova 2016). Namely, we use the

empirical distribution to find out the level of individual and cumulative household’s knowledge about

climate change, energy, and environment. Therefore, the questions are designed to elicit: (a) a

respondent’s knowledge about environment, climate change, and energy sources, and (b) how much a person

is aware of environmental and climate change issues, and (c) how much he/she believes that his/her energy

decisions contribute to environmental issues and specifically to climate change. There are measured in

comparable ways with Likert scales: 1= “Not at all serious” and 7= “Extremely serious” , 1= “Strongly disagree”

and 7= “Strongly agree”, 1= “Definitely not true” and 7= “Definitely true”. There is some overlap between

questions: the environmental and climate change knowledge is measured with 8 items (e.g. climate change is

caused by a hole in the earth’s atmosphere), Climate change, Economy and Environmental issues (CEE)

awareness is measured with 11 items (e.g. how serious are the environmental issues facing the world: air

pollution, climate change,…), and Energy Decisions (ED) awareness with 6 items (e.g. I am feeling good when I

am using eco-friendly products) (Niamir and Filatova 2016).

In following you can see percentage of the cumulative individual’s knowledge based on 7 income

groups in Navarre region.

Figure XX: Households awareness level

Based on our survey we also found out the still more than 85% of households in Navarre region using

grey electricity, which it means that their electricity source is from fossil fuels. Figure XX shows the

LCE/FF users in 7 income groups. Households in 5th income group (50001 – 70000 Euro per year) are

greater LCE user and installed more solar panels and thermals. In comparing figure XX and XX, we can

see number of lce user increased with the level of awareness.

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Figure XX: Green or grey user?

Detailed statistical analysis of the survey data will continue beyond the COMPLEX lifetime and will be

further used in the PhD research project of Ms. Niamir.

2.3.2 Agent-based energy market

Agent-based modelling gives us a unique opportunity to quantify the aggregated consequences of

individual decisions of heterogeneous agents (households) that are boundedly rational and engage in

interactions. NIROO is designed and implemented in NetLogo 5.2 platform using the GIS extension

(QGIS, version 2.16.1). We used open source applications, such as PostgreSQL and R (version 3.2.3),

for the spatio-temporal and statistical analyses.

NIROO simulates a society of heterogeneous households with different preferences, awareness of

climate change, and socio-economic characteristics, which lead to various energy consumption

choices. Firstly, households have awareness about the state of climate and environmental

preferences (β), which could potentially be heterogeneous and change over time. Secondly, if they

are aware enough, household’s attitudes, beliefs, personal norm and social norms will be check to

calculate their motivation (γ). Thirdly, if they have high motivation level and feel responsibility, they

consider the economical, structural and physical, institutional factors. Their intention (θ) is measured

based on consideration factors. All these main variables awareness (β), motivation (γ), and intention

(θ) are calculated and normalized based on the survey data – mainly based on psychological factors –

which are described in the section 2.3.2.

Fourthly, households utilities (U) that they expect to receive are calculated based on given energy

prices – low-carbon sources (LCE) and fossil fuels sources (FF)- and under budget constraints. As in

the traditional economic model we start with formalizing households’ choice based on utility that it

receives from consuming energy (E) and a composite good (Z) between which its budget is shared

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(α). We further extent it by including the influence of awareness (β), motivation (γ) and consideration

(θ) (Equation 2-1)

𝑼 = 𝒁𝜶 ⋅ 𝑬𝟏−𝜶 ⋅ 𝑨𝒘𝜷 ⋅ 𝑴𝒐𝒕𝜸 ⋅ 𝑪𝒐𝒏𝜽 (Eq. 2-1)

Fifthly, households make their energy decision based on their utilities either using low-carbon energy

sources (LCE) or fossil fuel (FF) based energy (Ulce and Uff), the current energy source statues (LCE

user/ FF user). Eventually we would like to consider the following actions (as coded in the survey):

green investment, green energy conservation, switching to a greener provider (more share of LCE),

grey investment, grey energy conservation and switching to LCE provider.

Sixthly, currently the supply side is presented by heterogeneous energy providers, which may deliver

either electricity based on LCE or FF. The information on the various energy providers will come from

EXIOMOD. Therefore, we simulate simplified providers with different shares of LCE and FF electricity

production. In retail electricity market, providers decide on which type of energy to deliver in order

to optimize their profits. Profit is calculated based on total revenue and total cost. We considered

cumulative price growth, market price of electricity (P), and electricity production (Q) to estimate the

total revenue of provider (Equation 2-2).

𝑷𝒓𝒐𝒇𝒊𝒕 = (𝑪𝑷𝑮 ⋅ 𝑷 ⋅ 𝑸) − 𝑪𝑶𝑺𝑻 (Eq. 2-2)

Seventhly, new energy prices (P*lce/P*

ff) and market shares of LCE and FF electricity are an emergent

outcome of our model. Yet, a household needs to form price expectations regarding energy prices to

be able to take any of the 3 groups of energy-related decisions. Followed by a review of existing price

clearing mechanisms used in agent-based economic models, we chose “gradual price adjustment”

approach for price determination for our model (Niamir and Filatova, 2015b). Niamir and Filatova

(2015b) discussed four market clearing approaches. Although “gradual price adjustment” approach

could have many similarities with other approaches, namely with the discrete-time version of the

standard Walrasian price adjustment mechanism. There are also some differences. In our approach

households are satisfied by maximizing their utility expectations given price expectations based on

previous periods prices. Moreover, due to the absence of optimization on the individual level, there

might be no stability in this market, because our households behavioral change and decisions

modelled based on the cognitive process which are motivated by psychological theories (Niamir and

Filatova 2016). Lastly, at the last stage utilities of households and profits of providers are updated

based on the new prices (P*lce/P*

ff). Then the model goes to the next time step.

2.4 Integrated system of models

COMPLEX integrates the models described in the previous three subsections, i.e. GCAM, EXIOMOD

and NIROO. Linkages are achieved via data exchanges. Figure 2-5 visualizes the input/ output flow of

variables that are exchanged between the models.

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Figure 2-5: Integrated system of ABM, CGE, and GCAM model

2.4.1 GCAM-CGE integration

GCAM is a dynamic-recursive model with technology-rich representations of the economy, energy

sector, land use and water linked to a climate model of intermediate complexity. The model takes

population and labor productivity (i.e. GDP) as exogenous inputs. Although there is no feedback

between the GDP and other climate variables such as temperature and climate mitigation, the model

can be used to explore climate change mitigation policies including carbon taxes, carbon trading,

regulations and accelerated deployment of energy technology. The model assumes that regional

population and labor productivity drive the energy and land-use systems employing numerous

technology options to produce, transform, and provide energy services as well as to produce

agriculture and forest products, and to determine land use and land cover. The model can be used to

test various policy measures and energy adaptation technologies on energy supply technologies and

greenhouse gas emissions.

On the other hand EXIOMOD is a CGE model. EXIOMOD models the economic behaviour of the

economic agents, such as the household sector, government, trade and industry. It takes into

account mechanisms on for instance labour productivity, investments and environment. The

behavior is mainly driven by price changes and the model solves for a Walrasian market equilibrium.

EXIOMOD models the economic effects in all regions of the world and for all different agents and

sectors.

The linkage between the models takes advantage of the two main characteristics of both models: the

detailed description of the energy and land use modules. These are connected to the climate module

COMPLEX


of GCAM and the comprehensive economic module (including the economic impacts of climate

change) of EXIOMOD. The practical integration of both models is implemented in a sequential way.

First GCAM produces price trajectories for electricity, refined liquids and food, and energy mix for all

scenarios. Energy intensity trajectories are produced for a reference scenario only. This information

will be introduced to EXIOMOD, which will produce figures for electricity demand by households,

relative competitiveness by country, compensating variation for households which are divided in five

income groups and relative price levels for several products. A more detailed explanation of the

uptake of GCAM parameters in EXIOMOD is given in Section 3.3.2.

2.4.2 ABM-CGE integration

As mentioned in section 2.3, NIROO model (agent-based energy market model) is an individual piece

of software with the ultimate goal of linking it with the EXIOMOD CGE within the ISM, mainly to

complement the supply side of energy market, get information on the non-residential energy

demand as well as to trace the cross-sectoral impacts of the cumulative changes in energy markets

driven by individual behavioral changes. The integration of an ABM and a CGE in the energy domain

is quite innovative. CGE model simulates the connections across economic sectors as an annual

equilibrium on many markets within an economy, while the ABM will zoom into only the energy

market (Figure 2-6). On the demand side our ABM will disaggregated only residential sector demand

taking the energy demand of all other sectors from CGE (updated annually) (Filatova et al., 2014).

Figure 2-6: ABM-CGE integration conceptual model

When modeling changes in individual energy demands in between annual equilibria of the CGE we

would like to explicitly trace changes in preferences and energy consumptions choices driven by

individual assessments, pro-environmental attitudes and social interactions (norms). This will result

in the new budget shares a household spends on (i) energy vs other goods, and (ii) LCE vs. fossil fuel

energy sources relevant in the CGE estimates. On the supply side we will differentiate between

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energy production based on fossil fuels and low-carbon energy sources taking the aggregate supply

equations structurally similar to the ones in the CGE (Niamir and Filatova 2015a).

Figure 2-7: Integrated system of ABM, CGE, and GCAM models conceptual model

Figure 2-7 illustrates the exchange variables and input/output of ABM with CGE and GCAM and their

integration conceptual model. Majority of ABM initialization is covered by the empirical data coming

from our survey and parameterizing households preferences, awareness and other steps potentially

leading to a change in residential energy consumption. ABM gets households income, saving and

energy consumption, energy production, and LCE share of production from CGE and cost of energy

production from GCAM at initialization. They update them each time step (annually). In Figure 2-8

the schematic representation of the time steps of the integrated ABM-CGE-GCAM system of models

is illustrated. Both ABM and CGE run annually, while GCAM runs each 5 years. The exchange of data

is happening through software wrappers, which were specifically developed in COMPLEX for this type

of model integration (Belete and Voinov, 2015). The wrappers are delivered as a web services and in

principle can be used by the wider research community (Belete et al, Under review).

COMPLEX


Figure 2-8: Integrated system of ABM, CGE and GCAM models time steps

The lowest scale of operation of the CGE model is NUTS1 (country level), while the highest scale of

the ABM is NUTS2 (region level). The lowest scale of ABM is a household on the demand side and

energy-producing firms on the supply side. Therefore, the ABM outputs to CGE are scaled up to NUTS

1 (country scale). We envision doing that by means of endowing households agents in the ABM with

the key attributes of households groups following the structure of the EU Household Budget Survey

(HBS). Thus, changes in behavior with respect to energy consumption in the ABM can be scaled up to

bigger groups of households in other NUTS2 regions in CGE, attributes of which are also harmonized

with the EU HBS.

We used empirical data for parameterizing, calibrating and optimizing NIROO. As it mentioned in

section 2.3.1 we designed and run the detailed household survey. There are two case studies,

Navarre region in Spain and Overijssel in the Netherlands.

COMPLEX


3. Policy simulation results

3.1 Policy options and scenarios

This section presents the results of a set of simulations exploring the implications for climate

mitigation of different pathways of technological progress (high versus low technology costs). These

two scenarios will be compared to a reference scenario.

3.1.1 Reference scenario

Projecting the future is not an easy task, especially for climate change, where very long-term

projections are needed due to the planet’s high thermal inertia. In order to deal the unavoidable

uncertainties of climate change, one has to assume different scenarios/story lines, these scenarios

would be able to offer different approaches for dealing with climate uncertainties.

Usually climate change related scenarios are presented against business as usual (BAU) or reference

scenario. There are a wide range of factors that can influence the economic growth projections.

O’Neill et al. (2012) have discussed a wide range of global representative scenarios, called Shared

Socioeconomic Pathways (SSP) that takes into account five possible future socio-economic

developments and explores the economic and environmental consequences. The SSPs combines the

climate forcing and socio-economic conditions (Chateau and Lapillonne, 2012).

Based on the challenges to mitigation and adaptation, five SSP scenarios were developed, each one

provides a storyline of the main characteristics of the future development paths. Figure 3-1 provides

the schematic representation of the five SSP scenarios. The Y-axis of the figure represents the

challenges for the mitigation while the X-axis represents the challenges for the adaptation. SSP1

scenario is the one which has lower mitigation and adaptation challenges, followed by SSP2 scenario,

which is called the “Middle of the Road” scenario. In the SSP3 scenarios, the challenges for the

mitigation and adaptation would be maximum.

In this study, the SSP2 scenario “Middle of the Road” is applied as the reference scenario. This means

that all scenario results will be shown in comparison with this reference scenario. The SSP2 scenario

comes most close to a baseline scenario because of its business as usual trends, although SSP2 was

not designed by the Working Group as a baseline scenario (O’Neill et al., 2015). In this scenario

current trends continue in terms of income convergence. The emissions follow a business as usual

trend. Therefore there are significant challenges for both mitigation and adaptation but neither is

particularly severe.

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Figure 3-1: Five shared socio-economic pathways representing different combinations of challenges to

mitigation and adaptation

Source: O’Neill et al. (2015)

The available data for each SSP are GDP and population until 2050/2100 at country level. Hence

these two indicators will be used as the economic drivers in our reference scenario. GDP projections

are reported in terms of purchasing power parity (PPP), whereas in this study values are taken in

form of market exchange rate (MER). The GDP in PPP have been converted to GDP in MER as per UN

(1998) methodology.

For the period 2010-2050, the global GDP is expected to grow at 2.54% annually. African region is

expected to grow at 5.9% annually. During this period, other world regions (China, India, Indonesia,

Central America, Central Asia, South Asia, South America etc.) are expected to grow above the global

average. The GDP in EU-27 is expected to grow at 1.56% per annum.

GCAM specific assumptions in the reference scenario

More specific assumptions of the SSP scenarios are reported in the Table 3-1. In GCAM assumptions

related to Total Factor Productivity (TFP) drivers are differentiated between income country groups,

namely low-income (LI) countries, Middle-Income (MI) countries, and High-Income (HI) countries.

TFP growth accounts for the effect of international spill-overs and competitive policies. Openness

measures the cooperation among the countries. As mentioned earlier, SSP2 is considered as the

middle of the road scenario, all the key drivers in this scenario are assumed to have a medium

growth.

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Table 3-1: SSP Scenario-specific assumptions for key growth drivers SSP1 SSP2 SSP3 SSP4 SSP5

TFP – related drivers

TFP frontier growth Medium high Medium Low Medium High

Convergence speed High Medium Low LI :Medium low

MI: Medium

HI: Medium

Very high

Openness Medium Medium Low LI :Low, MI: Medium

HI: Medium

High

Natural resource – related drivers

Resources Conv: Medium

Unconv: Low

Medium Conv: Medium

Unconv: High

Low Oil :Low

Gas: High

Fossil-Fuel prices Low Medium High Oil :High, Gas: Medium High

Demographic drivers

Population growth Low – medium

depending on

country

Medium Low – high

depending on

country

Low – high depending

on country

Low – high

depending

on country

Education High Medium Low Very low – medium High

Note: Conv. : conventional, Unconv : unconventional (shale oil/gas or tar oil), HI: high income, MI: medium income, LI: low

income. This table is taken from Chateau (2012).

EXIOMOD specific assumptions in the reference scenario

As discussed, GDP and population values are based on the SSP2 scenario. Total factor productivity is

used from the Econmap projections (Fouré, Bénassy-Quéré, & Fontagné, 2012) because it is provided

at country level and per year until 2050. Energy related assumptions are aligned with the GCAM

model. We use the energy prices, energy mix and energy and carbon intensities produced by the

GCAM model in our reference scenario as shown in Appendix 8.1Error! Reference source not found..

3.1.2 Scenario framework

In the recent Paris Climate Agreement, COP21, world countries have agreed to limit global

temperature below 2ºC by the end of this century. Some countries went further and extended their

support to limit the temperature rise to 1.5ºC above pre-industrial levels (IEA, 2015). These targets

can only be achieved by cutting the global greenhouse-gases (GHGs) emissions substantially in the

coming few decades. According to the 5th Assessment Report of the IPCC, the carbon budget for the

period 2011-20150 to maintain the temperature increase below 1.5ºC and 2ºC by the end of the

century is 800 and 1300 GtCO2 respectively (50% of the simulations). In order to be consistent with

this carbon budget, countries have to start cutting their emissions urgently. Some of the developed

countries have already submitted their emission reduction targets to UNFCCC, as shown in the table

below.

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Table 3-2: Emission targets submitted by Intended Nationally Determined Contributions (INDCs) to the UNFCCC by different countries/regions

Country/Region INDC

Australia Base year 2000: 25% reduction by 2020

New Zealand Base year 2005: 30% reduction by 2030

Canada Base year 2005: 17% reduction by 2020; 30% by 2030.

China Peaks CO2 emissions in year 2030

European Free Trade Association : Iceland,

Switzerland and Norway

Base year 1990: 30% reduction by 2020; 35-40% by 2025; 50% by 2030

EU-27 Base year 1990: 20% reduction by 2020; 40% by 2030; 80% by 2050

Japan Base year 1990: 25% reduction by 2020.

Mexico Base year 2000: 50% by 2050

Russia Base year 1990: 15-25% by 2020 ; 70-75% by 2030

South Korea Base year Business As Usual: 37% by 2030

USA Base year 2005: 17% reduction by 2020; 42% by 2030; and 83% by 2050

Note: We have taken the information of the countries that have submitted their targets before August 2015. In recent

COP21, several other countries have submitted the targets, which we didn’t consider in this document.

In order to test the suitability of the COMPLEX ISM to analyse mitigation pathways, we have

examined three different scenarios, the reference scenario, as discussed in section 3.1.1, and two

alternative policy scenarios. The policy scenarios assume that all countries in the world are

committed to reduce their emission by 2050 to levels that are compatible with the 1.5°C and 2°C

pathways. We assume that by 2050, global cumulative from the year 2010 are below 1000 GtCO2,

which is equivalent to 75% of the carbon budget to maintain the temperature increase below 2ºC by

the end of the century.6 In order to limit the global emissions to this level, developed countries

would increase the ambitiousness of their nationally determined contributions and by 2050 these

countries would linearly reduce their emissions to zero. This trend will determine the carbon budget

of developed countries. The remaining emissions up to 1000 GtCO2 will be the carbon budget of

developing countries, which will be shared among the developing countries on a per capita basis.

This burden sharing scenario is combined with two alternative scenarios on the evolution of the costs

of nuclear, carbon capture and storage (CCS) and renewables. The resulting three scenarios can be

summarised as follows:

“Reference scenario”: This scenario is in line with the SSP2 story line. It is assumed that

countries do not commit any emission targets and do not implement any mitigation policies.

Scenario “High technology cost”: developed countries will have zero emissions by 2050 and

the remianing of the carbon budget up to 1000 GtCO2 is allocated to developing conutries on

6 According to the 5th Assessment Report of the IPCC, the carbon budget to maintain the temperature increase

below 2ºC by the end of the century is 1300 GtCO2 (50% of the simulations). Thus, the carbon budget of our scenarios would leave 300 GtCO2 of reaming emissions for the future. In the case of the 1.5ºC target (with overshooting) the carbon budget would be around 800 GtCO2, thus our scenarios would require 200GtCO2 of negative emissions in the future, which might be difficult.

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a per capita basis. On the technology side, de cost of nuclear and CCS would be higher than

in the reference scenario and the cost of renewable would be the same as in the reference.

Scenario “Low technology cost”: the mitigation pathwas of the different coutrnies is similar

to the previous scenario but in this scenario the cost of nuclear and renewables would be

low, and the cost of CCS would be the same as in the reference scenario.

3.2 Simulation set-up

3.2.1 GCAM

As described in the earlier section, under reference scenario, countries are not bound to any

emission policy. Accordingly, in this simulation the model is run without any emission constraints. In

the policy scenarios, we have assumed that from 2020 onwards, all the countries start limiting their

aggregate cumulative emissions of the period 2010-2050 to 1000 GtCO2. Some of the developed

countries have already agreed to cut their emissions under the Paris Agreement by 2050 (see Table

3-2). In order to stimulate further mitigation in developing countries, developed countries would

commit to reduce their emissions to zero by 2050. The remaining carbon budget would be allocated

to all the developing countries based on their per capita emissions. Table 3-3 shows the annual

emissions by 2050, the change between 2010 and 2050, and the cumulative emissions for the period

2010-2050 in the different regions resulting from this burden sharing regime.

Table 3-3: Policy scenario: annual emissions by 2050 and cumulative emissions for the period 2010-2050

Annual

emissions

2050

(MtCO2)

Change

2010-2050

(%)

Cumulative

emissions

2010-2050

(GtCO2)

Africa (Eastern) 1037 1707% 23

Africa (Northern) 465 0% 22

Africa (Southern) 548 918% 12

Africa (Western) 1757 1370% 32

Argentina 97 -45% 5

Australia, New Zealand 0 -100% 11

Brazil 453 7% 23

Canada 0 -100% 13

Central America and Caribbean 210 5% 10

Central Asia 193 -59% 11

China 2463 -72% 168

Colombia 125 70% 5

EU-12 24 -97% 17

EU-15 -21 -101% 78

Europe (Eastern) 92 -75% 7

Europe (Non-EU) 226 -45% 13

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European Free Trade Association 0 -100% 2

India 3341 82% 141

Indonesia 556 26% 27

Japan 0 -100% 27

Mexico 296 -36% 15

Middle East 757 -58% 37

Pakistan 558 249% 14

Russia 0 -100% 42

South Africa 123 -73% 7

South America (Northern) 85 -61% 5

South America (Southern) 203 4% 10

South Asia 668 636% 19

South Korea 0 -100% 15

Southeast Asia 958 -8% 49

Taiwan 50 -82% 3

USA 0 -100% 133

Total 15265 -53% 1000

Further, the implications of this mitigation pathway are explored under two different futures in

terms of technological progress: “High technology cost” and “Low technology cost”. For the cost of

nuclear and renewable energy sources, we have followed two different documents: “Projected cost

of generation electricity” (OECD, 2015) and “Economics of nuclear power” (World Nuclear

Association, 2016). Both documents provide estimations of the future evolution of the capital

overnight cost of the nuclear and renewable energy technologies. Based on these reports we have

provided different the capital overnight costs to the model. For CCS, we have used two different

settings for the cost of CCS of the GCAM model: the default setting and the high-cost setting (see

Table 3-4).

Table 3-4: Technological progress scenarios: Capital overnight cost of nuclear and renewables technologies

(US$1990/kW) and storage capacity and cost of Carbon Capture Storage (US$1990/tC)

Nuclear Renewables Carbon Capture Storage

Year Low High Low High Capacity Low High

2010 1704 1704 1696 1696 7911 0.27 0.80

2020 1662 2104 1572 1617 158218 13.26 39.77

2030 1585 2055 1408 1496 949308 26.51 79.54

2040 1518 2011 1313 1413 466743 198.84 596.53

2050 1460 1973 1258 1354 466743 198.84 596.53

3.2.2 EXIOMOD

Each policy scenario is defined by four variables that EXIOMOD reads in from GCAM. That is, energy

mix by technologies, and prices of electricity, liquids and food. Where GCAM data is provided for

COMPLEX


every five years, EXIOMOD requires input data for every year, yearly data following the trajectories

from GCAM are interpolated using a spline function. This means that in EXIOMOD the scenarios are

defined by different levels of energy mixes and energy prices coming from GCAM. The energy mix

and prices are described in more detail in Appendix 8.2.

3.2.3 NIROO

The agent based model defines its scenarios slightly different, i.e. a green and a grey scenario. The

grey scenario refers to the reference scenario, and the green to the High technology cost scenario.

This ABM receives input variables from both EXIOMOD and GCAM. That is, EXIOMOD provides gross

income by income quantiles, household savings by income quantile, household electricity

consumption by income quantile, production of the electricity sector, and the share of LCE and fossil

fuels in energy production. GCAM provides the the cost of LCE and fossil fuel energy production to

the agent based model. Inputs from the integrated models partly defines the outputs for the ‘green’

and ‘grey’ scenario.

3.3 Simulation results

3.3.1 Energy and emissions (GCAM results)

Emissions

Table 3-5 shows the emissions of the different regions in the reference and policy scenarios. In the

reference scenario the global cumulative CO2 emissions for the period 2010-2050 would be 2000

GtCO2. About 26% of these emissions would be released to the atmosphere by China, 14% by the

USA, and 10% by the EU-27. The cumulative emissions in the policy scenarios (i.e. high technology

cost and low technology cost scenarios) would be 50% lower compared to the reference scenario.

The share of the USA and the EU-27 in the total cumulative emissions would be similar to the

reference scenario (9% and 13% respectively), although in absolute terms their cumulative emissions

would be significantly (50% in both cases). China would reduce emissions by 62% in the period 2010-

2050, and its cumulative emission would be 70% lower than in the reference.

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Table 3-5: Emissions in the different scenarios

EU27 USA China

CO2

pc

CO2

2050

Cum.

CO2

CO2

pc

CO2

2050

Cum.

CO2

CO2

pc

CO2

2050

Cum.

CO2

Reference 9.8 5.2

(21%)

210 18.9 7.6

(29%)

302 11.3 14.3

(121%)

561

High Technology

cost

0.0 0

(-100%)

94 0.0 0

(-100%)

132 1.9 2.5

(-62%)

124

Low technology

cost

0.0 0 (-100%) 94 0.0 0 (-

100%)

132 1.9 2.5

(-62%)

124

Other developed

countries

Rest of World Global

CO2

pc

CO2

2050

Cum.

CO2

CO2

pc

CO2

2050

Cum.

CO2

CO2

pc

CO2

2050

Cum.

CO2

Reference 12.5 6.8

(36%)

273 4.5 28.9

(278%)

807 6.9 62.9

(114%)

2154

High Technology

cost

0.0 0

(-100%)

167 1.9 12.5

(63%)

483 1.7 15.3

(-48%)

1000

Low technology

cost

0.0 0

(-100%)

167 1.9 12.1

(58%)

483 1.6 14.9

(-49%)

1000

In order to limit emissions world countries would have to substantially reduce emissions, which

would require a vast mitigation effort. In the high technology cost scenario the associated cumulative

mitigation cost would be US$66.1 trillion for the period 2020-2050 (2.33% of global GDP). The cost

for the EU-27 and the US would be US$7.7 trillion (1.2% of its GDP) and US$11 trillion (1.9% of its

GDP) respectively, representing 27% of the global cumulative cost (see Table 3-6). The mitigation

cost in China would be close US$21 trillion (3.7% of its GDP), and the cumulative cost of other

developed countries would account for US$17 trillion in this period. The mitigation cost in the low

technology cost would be US$59 trillion (2.09% of global GDP), which is 11% lower than that of high

technology scenario. In this scenario every region would benefit from the lower costs.

Table 3-6: Mitigation costs in the policy scenarios

Low Technology Cost High Technology Cost

Cost in 2050 Cum. Cost

2020-2050

Cost in 2050 Cum. Cost

2020-2050

EU-27 438.2 6245 (1.05%) 516.3 7262 (1.22%)

USA 640.5 10147 (1.69%) 710.5 11109 (1.85%)

China 718.3 18933 (3.34%) 811.0 20761 (3.67%)

Other developed

countries

542.8 8424 (1.58%) 610.5 9402 (1.76%)

Rest of world 941.2 15559 (2.88%) 1083.1 17488 (3.24%)

Global 3281.0 59404 (2.09%) 3731.4 66126 (2.33%)

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Cost in billion US$2014, and Cumulative cost is for the period 2020-2050. The figures in the brackets report the percentage

of the GDP.

Primary energy consumption

Figure 3-2 shows the evolution of the global consumption of primary energy (PEC) in the period

2010-2050. In general, we can observe that the PEC is lower in the scenarios in which the climate

policy is implemented. In the reference scenario, the PEC is expected to increase at an annual growth

rate of 1.6% per year, while in the policy scenarios the PEC would grow at 1.3% per year.

Figure 3-2: Global primary energy consumption 2005-2050

Table 3-7 provides information on the energy efficiency growth rated for the period 2010-2050.

Climate policy will also affect the efficiency of the energy system. On the one hand, the energy

efficiency measured as the ratio between GDP and final energy consumption (FEC) is observed to

increase close to 1.6% per year globally and 1.5% within the EU-27. On the other hand, the

transformation efficiency, measured as FEC divided by PEC, would decrease at the global and EU-27

level. The main driver of this reduction in the transformation efficiency is the substitution of oil by

biomass in refineries and the higher level of electrification. In both cases we case again we can

observe that the level of efficiency is higher when the technology costs increase.

Table 3-7: Energy efficiency growth rate for the period 2005-2050 all scenarios (source: own elaboration)

Global EU27

GDP/FEC FEC/PEC GDP/FEC FEC/PEC

Reference 1.11% -0.29% 1.10% -0.31%

High technology cost 1.59% -0.26% 1.57% -0.35%

Low technology cost 1.55% -0.25% 1.52% -0.35%

Figure 3-3 depicts the global fuel mix of the scenarios for the year 2050 and for 2010. In 2050, the

energy mix of the reference scenario is quite similar to that of the base year, and fossil fuels would

be the main sources of energy (80%). In the two policy scenarios the share of fossil fuels is reduced to

100

120

140

160

180

200

220

2010 2015 2020 2025 2030 2035 2040 2045 2050

20

10

=1

00

year

Reference scenario

High technological cost

Low technological cost

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58%, while the renewables increase to 38% and 36% in the high and low technological cost

respectively. This difference is compensate by a higher share of nuclear in the low costs scenario.

Figure 3-3: Global primary energy consumption by fuel

Due to zero net emissions by 2050 in EU 27 and US, the share of fossils would be lower in the policy

scenarios compare to the reference scenario (Figure 3-4). Renewable energy technologies occupies

half of the total primary energy consumption. In the high cost scenario, by year 2050, renewable

energy technologies occupy more than half of the total primary energy consumption. In the low

technology cost, the cost of the nuclear, CCS and renewables are expected be lower, especially in the

case of nuclear and CCS which increase their share to the PEC in relation to the high cost scenario.

In China and India, the consumption of fossil fuels would be less than the reference scenario, but in

these two countries, a large part of primary energy would still come from fossil fuels. Irrespective of

the policy scenario (high cost or low cost), the consumption of fossil would be same. The share of

renewables and nuclear would be higher in the policy scenarios, with a higher share of renewables in

the high cost scenario.

Figure 3-4: Primary energy consumption by fuel in different regions

85% 83%

58% 58%

2% 2%

4% 6%

13% 15%

38% 36%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2050 2050 2050

BaseYear Reference scenario High technologicalcost

Low technologicalcost

Renewable

Nuclear

Fossil

85% 79%

38% 39%

5% 5%

9% 12%

10% 16%

53% 49%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050

BaseYear Reference Hightechnology

cost

LowTechnology

cost

EU-27

88% 91%

56% 55%

0% 2%

7% 11%

11% 8%

37% 34%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

China

COMPLEX


Figure 3-5 depicts the fuel mix for electricity generation. In reference scenario, around 70% of

electricity generation comes from fossil fuels (mainly coal and natural gas). With stringent climate

policies, the share on fossil fuels in the electricity mix would be lower and nuclear and renewable

energies will play a crucial role. In the high technology cost scenario, the penetration of nuclear and

fossils would be lower than in the low cost scenario (the opposite for renewables).

Figure 3-5: Global energy (electricity) mix in different scenarios

The introduction of the mitigation policy would generate a drastic change in the electricity mix of

world’s major economies (see Figure 3-6). In the reference scenario, in 2050 around 47% of EU-27

electricity generation would come from fossil fuels, followed by renewables (30%) and nuclear (23%).

Conversely, in the policy scenarios, fossils consumption would drop to 15% by year 2050 and the

share of nuclear and renewables increase significantly, with a higher penetration of renewables in

the high cost scenario (51% versus 41%). Similarly, the fossil fuel consumption US and China would

be much lower in policy scenarios.

92% 87%

46% 45%

3% 3%

5% 8%

5% 10%

48% 47%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

United States

73% 85%

74% 75%

0% 1%

2% 3% 27%

15% 24% 22%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

India

Fossil Nuclear Renewable

66% 67%

36% 34%

13% 10%

20% 29%

21% 23%

43% 37%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050

BaseYear Reference scenario High technologicalcost

Low technologicalcost

Renewable

Nuclear

Fossil

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In the high cost scenario, around 37% of US electricity generation would be produced from

renewable sources, followed by fossils (35%) and nuclear (28%). In the low cost scenario, nuclear

plays crucial role in meeting the electricity mix, in this scenario the nuclear share would be 39% of

the total electricity generation, followed by renewables (31%) and fossils (30%). In both policy

scenarios Chinese electricity generation largely rely on renewable and nuclear, being the share of

nuclear is higher in the low cost scenario compare to renewable. In case of India, the share of fossils

would be lower than in the reference, but more than half of the electricity would still come from

fossil fuels. Unlike EU, US and China the role of nuclear energy would be lower in India.

Figure 3-6: Energy (electricity) mix in different regions

Land-use

At the global level, the land-use pattern would not be very different between the three scenarios

(see Figure 3-7). However, the land used for growing biomass for energy purposes is expected to

increase in the policy scenarios with respect to the reference scenario, at the expense of a reduction

in grass, pastures and shrubs. By the year 2050, around 2.8 million km2 would be used for growing

biomass for energy purposes, representing 5% of the global land use.

48% 47%

15% 14%

28% 23%

34% 45%

25% 30% 51%

41%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

EU-27

80% 72%

21% 18%

2% 9%

32% 45%

18% 19%

47% 37%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

China

68% 69%

35% 30%

19% 14%

28% 39%

13% 16% 37% 31%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

United States

83% 83%

50% 51%

3% 4%

6% 11%

14% 14%

44% 38%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050


cost

LowTechnology

cost

India

Fossil Nuclear Renewable

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Figure 3-7: Global distribution of land use change

EU-27 would see important changes in its land-use pattern (see Figure 3-8). The ambitious emission

reduction targets would boost the use of biofuels. In order to meet this demand, the share of land in

the EU-27 used to produce biomass for energy purposes would increase to 21%, while crop land

would be reduced to 15% (compared to 27% in 2010), the share of forest area would decrease by 5

percentage points, and the share of grass, pastures and shrubs by 2 percentage points. These

changes would lead to an increase in the EU-27’s imports of food from other regions.

Figure 3-8: Distributional land use change in EU-27

3.3.2 Economic development (EXIOMOD results)

GCAM model produces the effects of each policy scenario in the energy system. The projected

changes in electricity technology mix, which also lead to the changes in electricity, agricultural and

liquid fuel price, imply changes in the economy beyond the energy markets. In this section we use

the results projected GCAM model as inputs of EXIOMOD model and demonstrate how the changes

0% 2% 5% 5% 11% 12% 12% 12%

38% 38% 37% 37%

3% 2% 1% 1%

48% 47% 45% 45%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050

BaseYear Referencescenario

Hightechnological

cost

Lowtechnological

cost

grass, pastures, shrubs

other arable

forest

crops

biomass

0% 9%

21% 21% 27% 21%

15% 15%

48% 47% 42% 42%

0% 0% 0% 0% 25% 23% 22% 22%

0%10%20%30%40%50%60%70%80%90%

100%

2010 2050 2050 2050

BaseYear Referencescenario

Hightechnological

cost

Lowtechnological

cost

grass_pastures_shrubs

other arable

forest

crops

biomass

COMPLEX


in energy system cascade further in the economy, affecting prices of other products, relative

competitiveness of the regions and welfare of the consumers.

Price levels

Price indices, as taken from GCAM, show that, in case the carbon budget for the period 2010-2050 is

limited to 1000 GtCO2, electricity, crops and liquid fuels prices will be higher in the future compared

to “no policy” scenario (reference scenario). It is also interesting to investigate the effect of policy

scenarios on the prices of other products. EXIOMOD model keeps track of inter-sectoral and inter-

regional linkages in the global economy and therefore can show the spill-over price effects. Figure

3-9 shows the development of relative prices for a number of products in the EU. The prices of

individual products are corrected for overall inflation and are expressed as relative prices compared

to the consumer price index. This means that prices are shown as indices with respect to 2010 and

the reference scenario. Also due to the fact that these are relative prices, it is logical that some

products become relatively more expensive (price index higher than 1) and some products become

relatively less expensive (price index lower than 1).

In all the graphs we don’t observe large difference between the policy scenarios, but it is visible that

high technology costs lead to wider variation of prices, both upwards and downwards. The increase

of processed food prices by around 17% is explained by higher price of crops (60%), which is one the

main inputs into food processing industry. Production of chemicals requires large electricity inputs

and the increase of electricity prices results in almost 2% increase in the prices of chemical products.

The land transport is affected both by higher price of liquid fuel. The relatively modest effect of 0.5%

is explained by decrease of relative prices of other commodities, such as services by 2.5%, as

displayed in the last graph.

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Figure 3-9: Relative basic price trajectory of processed food products, chemical, land transport and service sector for EU-27. Price indices are with respect to 2010 and the reference scenario.

Relative competitiveness

A relative competitiveness indicator is developed to highlight the impact of the policy scenarios on

the competitive position of a region. It answers the question: are the product prices in a certain

region competitive with the product prices of imported products from foreign competitors?

When competitiveness indicator is smaller than one, the price of competitors is higher than the basic

price in its own country. This is the case for all developing regions. For competitiveness indicator

higher than one, the prices of other regions in the world become relatively smaller over the years.

This implies a worsened competitive position. Which will be the case for developed regions like EU-

27, United States or Canada.

Figure 3-10 shows that China has a worse relatively competitive position under the reference

scenario compared to the policy scenarios with climate mitigation options. While this is the other

way around for India. It should be taken into account that the relative competitiveness indicator is a

relative parameter. That is, where China’s position might be better under the climate mitigation

scenarios, other regions should have a worse relative competitive position under the climate

mitigation policies. For China, it is moreover prices of textile, wood and printed products that are less

0.9

0.95

1

1.05

1.1

1.15

1.2

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COMPLEX


competitive with the rest of the world under climate mitigation scenarios. While for those products,

under those scenarios, the relative competitive position of the EU-27 improved. EU-27 is expected to

observe a worsened competitive position under the climate mitigation scenarios for coke and refined

petroleum products, and the USA for agricultural products. While the relative position of some other

products improved in those countries, these mentioned products dominate its relative competitive

position.

Figure 3-10: Relative competitiveness indicator for EU-27, United States, China and India.

Compensating variation

Compensating variation is the additional amount of money a consumer would need to reach its initial

utility after a (policy) shock changed the economic situation of an agent. Compensating variation is a

way to illustrate the impact of a policy shock on consumers welfare.

Figure 3-11 illustrates the compensating variation for EU-27 by income group, where Q1 represents

the poorest 20% of the population, and Q5 the richest 20% of the population. A negative number

means that a consumer should receive money in order to obtain the same level of utility as before

the policy change. All income levels are corrected for overall inflation, thus a person in income group

Q1, should in 2050 be compensated for the climate mitigation option with approximately €150 (in

2007 euros). Consumers should be mostly compensated for the increased food, refined liquids and

1

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India


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electricity prices. These prices levels are higher under the high technological cost scenario than under

the low technological cost scenario. Therefore consumers in all quintiles need a larger compensation

for the climate policy measures in this scenario.

Figure 3-11: Compensating variation by capita for EU-27

Obviously, consumers in the fifth quintile spend more money on electricity, refined liquids and food,

because they have bigger houses with more electric equipment, more expensive food and bigger

cars. Thus, in absolute numbers, they also need a bigger compensation in income when facing higher

price. However, in relative numbers, consumers with a smaller income (income quantile Q1) are

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COMPLEX


forced to spend a larger portion of their total income on goods like electricity and food. Then, the

budget left, after subtracting the absolute minimum consumption expenditures, will be much smaller

for those consumers. Figure 3-12 shows the share of compensated income over the available budget

that consumers can spend on goods of choice. This can be up to 27% for consumers in the first

quantile, where it is at most 16% for consumers in the richest quantile.

Figure 3-12: Share of compensating variation over available budget under the reference scenario for EU-27.

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COMPLEX


Household consumption of electricity

Because prices of electricity increase, consumers spend less on electricity under the two climate

mitigation policies. Consumers have a minimal required expenditure of energy and other products.

The remaining of its budget can be spend by choice of the consumer. When the price of the

electricity increases, consumers are tempted to spend their money on other products.

Figure 3-13 Spending on household electricity in EU-27

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COMPLEX


The modelling results indicate that reaching the climate targets by 2100 has an effect on the

economy beyond the energy market. The repercussions throughout the economy can be measured

at the sectoral, social and spatial level. At the sectoral level, we observe that energy intensive sectors

become more expensive while other sectors such as services become cheaper. Also we see a small

shift in relative competitiveness from more developed regions such as US to lower developed regions

such as India. At the social level, the results indicate that lower income groups are more affected

compared to higher income groups.

COMPLEX


4. Behavioural change and effectiveness of the policy scenarios

As described in 2.3, the NIROO model (agent-based energy market model) is designed and

implemented to investigate the contribution of changes in the residential sector of energy markets

towards low-carbon economy transition. NIROO focuses on household’s decisions regarding

electricity and heating use, and the impact of potential behavioral changes. The latter are

categorized in three main types of energy-related behavior: 1) investments, 2) energy conservation,

and 3) switching between energy types and suppliers. In this report we present the results of NIROO

simulating and tracking the cumulative impact of behavioral changes among 3500 households in the

Navarre region in Spain over 43 years (2007-2050). The model can be extended in scale and is generic

enough to be applied to other cases, subject to data availability.

The reference scenario can be considered as the conventional (grey) scenario and the scenario ‘High

Technological Cost’ as the green policy scenario. In what follows we discuss the cumulative regional

impacts driven by household’s behavioral changes under these two policy scenarios.

Figure 4-1: Households behavioral change, GIS visualization

Figure 4-1 shows the GIS visualization of a representative run indicating the households behavioural

change in Navarre region, Spain after eight years of simulations. The orange indicates investments,

dark purple is Low-Carbon Energy (LCE) user conserving energy, light purple Fossil Fuel (FF) user

conserving energy, grey FF user switching to green, and green LCE user switching to greener.

COMPLEX


4.1 Reference policy scenario

We simulate and track aggregated impacts of individual behavioural changes among households

under the reference scenario over 43 years. All the results below are based on 100 Monte Carlo runs

of the NIROO model to evaluate the robustness of the estimates.

Based on the simulation results grounded in empirical data from our survey and the macro-scenarios

coming from GCAM-EXIOMOD projections, we find that households are more likely to invest in

energy efficiency upgrades (insulation) and the installation of a PV rather than implement energy

conservation actions (e.g. changing their daily life patterns in order to save energy) or switching to

green or greener supplier. Over the simulation horizon the maximum of households who would like

to invest, conserve, and switch a supplier are 1781, 875, and 172 respectively. Figure 4-2 illustrates

the paths along which these three main households behavioural changes evolve overs 43 years.

Figure 4-2: Household’s actions by the three types of behavioral change under the reference (grey) scenario

Figure 4-3 shows the overall number of households who undertake behavioral changes based under

the reference scenario, 2007-2050 across all three actions. The black curves show the average trends

COMPLEX


across 100 Monte Carlo runs of NIROO. The colourful dots shows the different runs. In addition, the

results show that number of actions (overall household’s behaviour change) under reference

scenario is increased 44 percent (from 15.65% to 59.60%) during 43 years.

Figure 4-3: Households total actions based on the reference (grey) scenario

Figure 4-4 shows the fossil fuels vs. low carbon consumption share over the 43 years……

Figure 4-4: LCE vs. FF consumption share based on the reference (grey) scenario

COMPLEX


4.2 Green policy scenario

For the green policy scenario we simulated and tracked cumulative behavioural changes of

households over 43 years across 100 Monte Carlo runs of the NIROO model. Based on green policy

scenario we found that the number of household who did an action is increased form 16.98% to

59.89% during 43 years. Figure 4-4 shows an overview of households behavioural changes based on

the green policy scenario.

Figure 4-4: Households total actions across three types of energy-related actions under the High

Technological Cost (green) scenario

The total results are quite similar to the reference policy scenario. Also, similar to the reference

scenario, green policy scenario shows that household prefer to invest in energy investment rather

than pursue energy conservation or switch from supplier. Over 43 years the maximum of households

would like to invest, conserve, and switch the supplier are 1754, 889, and 178 respectively. Although,

we can see that the intention to pursue energy conservation and to switch from supplier increased a

bit (1.6% and 3.5% correspondingly). At the same time, the number of households deciding to make

a major energy-related investment decreased by 2%.

Figure 4-5: Household’s actions by the three types of behavioral change based on the High Technological Cost

(green) scenario

COMPLEX


4.3 Green or Grey policy scenario?

To get a better overview of these selected policy scenarios, we compared the patterns of households

behavioural changes in both case over 43 years. As Figure 4-6 illustrates, households have more

inclination to invest and change their own energy consumption habits and less desire to switch to

another (greener) energy producer under the green policy scenario.

Figure 4-6: Reference (grey) and High Technological Cost (green) scenario

COMPLEX


Figure 4-7 shows the percentage of household who changed their behaviour under both scenarios. In

early years, households changed their behaviour more under the green policy scenario rather than

the grey policy scenario, e.g. in 2008, 17% and 15.65% respectively. As this trend continues, gradually

the differences between the green and the grey policy scenarios is increasing till 2020. In 2020

household changed their behaviour 2.76% more under the green scenario than the grey scenario.

From 2021 this trend has decreased and stabilized gradually, resulting just 0.30% difference in 2049.

Figure 4-7: A closer look at the green and grey scenarios impact on household’s actions, 2007-2050

Figure 4-8 shows impacts of these two scenarios on the three type of household’s behaviours.

Although there is stronger tendency towards energy investment and conservation under the green

policy scenario, there are significantly fewer households switching an electricity supplier. This could

mean that under the green policy scenario 1) households may be willing to produce their own

electricity by investing in solar panels. 2) households may be willing to conserve energy by investing

in energy efficient equipment, e.g. A++ refrigerator 3) households may willing to conserve energy by

changing their daily energy use patterns.

COMPLEX


Figure 4-8: Reference (grey) and High Technological Cost (green) scenario

COMPLEX


5. Sensitivity analysis

The simulation results of EXIOMOD, presented in Section 3.3.2, are driven by the input variables from

the GCAM model. The question remains: how sensitive are the EXIOMOD results with respect to

different input? EXIOMOD will be rerun using the “Low technology cost” scenario.

In order to understand the sensitivity of EXIOMOD with respect to external price changes, for each

price trajectory under the Low technology cost scenario two additional price trajectories are

introduced. GCAM model provides three price trajectories to EXIOMOD under the Low technology

cost scenario, i.e. electricity, food and refined liquids. For this sensitivity analysis, we look at the

isolated effect of a single price trajectory. This is unlike the full Low technology cost scenario where

food, electricity and refined liquid price trajectories are introduced simultaneously.

In the end there will be nine extra runs. For all nine runs, the energy mix assumption of the Low

technology cost scenario is adopted and in addition one price trajectory is introduced. This price

trajectory could be ‘Medium electricity price’, where medium refers to the exact same price

trajectory in the Low technology cost scenario. ‘High electricity price’ refers to an electricity price

50% higher than the one in the Low technology cost scenario, ‘Low electricity price’ refers to 50%

lower. The high, medium and low price scenarios for food and refined liquids are defined in a similar

way.

COMPLEX


Figure 5-1: Effect of different price trajectories on Compensating Variation

Looking at the isolated effect of the individual price trajectories on the compensating variation, there

are a couple of interesting results.

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High electricity price Medium electricity price Low electricity price

High food price Medium food price Low food price

High refined liquid prices Medium refined liquid prices Low refined liquid prices

COMPLEX


We find that price of electricity has a lower impact on compensating variation than refined

liquids and food prices. This has two reasons. First, the GCAM output price of electricity for

the Low technology cost scenario increases less steep than food prices. These are taken as

the medium trajectories, where the low and high version only deviate from these by 50%. A

lower increase in price obviously has a smaller impact on a consumers utility. Second, when

the price trajectory of electricity would be as steep as the trajectory of food, then electricity

price changes still affect the utility of a consumer less than changes in food prices.

Remember that compensating variation is the the additional amount of money a consumer

would need to reach its initial utility after a (policy) shock. When its utility is less affected by

a price change, it also requires less money to compensate the consumer.

As expected, for low income groups (Q1), food has the biggest impact on a consumers utility

level. Lower incomes have only a small budget, and a large share will be needed for

grosseries. Consumers with a high income (Q5) are still greatly affected by changes in food

prices, however they need an even bigger compensation for increased prices of refined

liquids (gas and oil).

Consumers in the first income quantile (Q1) have a lower income than consumers in the last income

quantile (Q5), therefore, only a small portion of their budget can be spend on products of choice. A

large share of their income will be spend on insuperable fixed costs. This share is higher for higher

income groups. Increases in product prices need to be financed via this fraction of income that the

consumer can freely spend. Figure 5-2 gives the share of compensating variation over available

budget for consumers. The shares of Figure 5-2 illustrate that compensating variation for consumers

in the first income group is a large portion of their available budget.

COMPLEX


Figure 5-2: Effect of different price trajectories on the share of compensating variation over available budget

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High food price Medium food price Low food price

High refined liquid prices Medium refined liquid prices Low refined liquid prices

COMPLEX


6. Conclusion and further steps

In this report we have shown how the combination of different modelling approaches can be

consistently combined to provide a better understanding of the environmental, social and economic

implications of mitigation pathways. The modelling exercise benefits from the main features of each

of the models.

The GCAM, a technology-rich bottom-up integrated assessment that integrates different sub-

modules such as economy, energy system, agriculture & land use, terrestrial and ocean carbon. The

model has been extensively used to explore different issues related to energy, climate and

environment challenges. In the context of the COMPLEX integrated system of models, GCAM

provides detailed information on the impact of limiting the emissions for the energy and land use

systems, costs and prices at regional and global level.

EXIOMOD, a global multi-country computable general equilibrium model, enables the assessment of

the socio-economic implications in terms of country and sectoral competitiveness, product prices,

household welfare, household consumption by income group and other economic indicators. It takes

into account the price, substitution and rebound effects throughout the economy and represents a

high level of detail regarding the economic agents.

NIROO, an agent based model designed and implemented to investigate and trace aggregated

impacts of household’s behavioural changes and their potential cumulative contribution to a

transition to low-carbon energy. The model is well-grounded in social science theories on

behavioural change and is supported by empirical data from our survey eliciting the process of

energy-related decision-making on the households’ side. The model is used to analyse the

implications in terms of change in the preferences of households for green electricity

We have illustrated with a case study how the integration of the three models can be implemented

and which are the results that can be expected from the integrated system of models. In the case

study we compare a reference scenario with no climate policy with two alternative policy scenarios

in which the different regions of the world commit to specific emissions reduction targets for the

year 2050, and assuming to alternative futures in terms of technological progress in terms of the

costs of mitigation technologies (high vs. low).

We find that the mitigation effort of the policy scenarios would boost the penetration of low carbon

technologies at the expenses of fossil fuels. By the year 2050, the share of fossil fuels in the primary

energy mix would be below 40% in the EU-27 (compared to 80% in the reference) and below 55% in

China compared to 91% in the reference). The cost of this low-carbon transition would range

between 2-2.3% of the World’s GDP. Furthermore, the reduction on CO2 emissions would contribute

to an increase in electricity and crop prices in all the regions of the world. For instance, in the EU-27

the annual growth rate of the prices of electricity and crop would increase by 0.4% and 1.3%

annually respectively in the low technology cost scenario.

COMPLEX


The increase in the prices of electricity and crops, would translate in higher prices of other products

such as processed food products, chemical products and land transport. Changes in prices would also

affect the competitiveness of the countries but in different ways. Developing regions like China and

India would become more competitive over the years, the opposite would occur to developed

countries such as EU-27 and US. Furthermore, the increase in prices would reduce the wealth of

households, especially of poorest ones which have a relatively larger share of necessity goods

(including food and electricity). The increase in the price of electricity would contribute to reduce the

demand of electricity of households.

The mitigation policy would also impact the behaviour of households. Compared to the reference

scenario, we observe that the intention of households to pursue an energy conservation action or to

switch from supplier increases (1.6% and 3.5% correspondingly). At the same time, the number of

households deciding to make a major energy-related investment would decrease by 2%. We also find

that households in the middle income groups (2nd, 3rd and 4th quintiles) are more willing to change

their behaviour in comparison to other income groups.

COMPLEX


7. References

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renewables: evidence from the OECD survey on household environmental behaviour and attitudes.

Environmental Research Letters 10(4).

Amouroux, E., T. Huraux, F. Sempe, N. Sabouret, and Y Haradji, 2013. Simulating Human Activities to

Investigate Household Energy Consumption. ICAART 2013 2013.

Arrow, K. J., & Debreu, G. 1954. Existence of an equilibrium for a competitive economy.

Econometrica, 22(3), 265–290. http://doi.org/10.2307/1907353

Bamberg, S., M. Hunecke, and A. Blobaum, 2007. Social context, personal norms and the use of

public transportation: Two field studies. Journal of Environmental Psychology 27(3):190-203.

Bamberg, S., J. Rees, and S. Seebauer, 2015. Collective climate action: Determinants of participation

intention in community-based pro-environmental initiatives. Journal of Environmental Psychology

43:155-165.

Belete G.F., A.Voinov, T.Bulavskaya, L. Niamir, K. Dhavala , I. Arto, S. Moghayer, and T. Filatova

(Under review) ‘Web service based approach to linking heterogeneous climate-energy-economy

models for climate change mitigation analysis’

Belete, G.F. and A. Voinov, 2015. Exploring temporal and functional synchronization in integrating

models: A sensitivity analysis. Computers & Geosciences.

Broberg, T., and A. Kazukauskas, 2014. Inefficiencies in Residential use of energy - a critical overview

of literature and energy efficiency policies in the EU. International Review of Environmental and

Resource Economics 8(2):225-279.

Bulavskaya, T., Hu, J., Moghayer, S., & Reynès, F. 2016. EXIOMOD 2.0: EXtended Input-Output

MODel. A full description and applications (TNO Working Paper).

Capellán-Pérez, I., I. Arto, J. Polanco-Martínez, M. González-Eguino, M. Neumann, 2016. Likelihood of

climate change pathways under uncertainty on fossil fuel resource availability. Energy Environ. Sci.,

2016,9, 2482-2496

Capellán-Pérez, I., M. González-Eguino, I. Arto, A. Ansuategi, K. Dhavala, P. Patel and A. Markandya,

(2014). New climate scenario framework implementation in the GCAM integrated assessment model.

Calvin, K., J. Edmonds, B. Bond-Lamberty, L. Clarke, S. Kim, P. Kyle, A. Thomson and M. Wise, 2009.

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8. Appendix

8.1 Overview GCAM input variables for reference scenario in EXIOMOD.

Table 8-1: Overview reference scenario inputs from GCAM into EXIOMOD, EU-27

Indicator Unit EU-27 World

2010 2010-2050 2010 2010-2050

Level Annual

growth

Level Annual

growth

Electricity price (1975$/GJ) 8.24 -0.05% 7.85 -0.02%

Food and liquids price (1975$/Mcal) 0.06 0.22% 0.06 0.27%

Energy intensity households (EJ/bln m2 ) 1.47 -0.21% 22.98 -0.45%

Energy intensity fossil fuel sector (EJ/EJ) 2.33 -0.32% 2.67 -0.56%

Energy intensity other sectors (EJ/mln€) 1.45E-6 -1.11% 2.71E-6 -1.01%

Carbon intensity households (Mt/EJ) 57.66 -0.35% 40.98 0.03%

Carbon intensity land transport (Mt/EJ) 71.45 -0.09% 71.49 -0.11%

Carbon intensity other transport (Mt/EJ) 48.83 -0.32% 49.77 -0.43%

Carbon intensity other sectors (Mt/EJ) 46.59 -0.41% 54.46 -0.17%

Electricity price (1975$/GJ) 8.24 -0.05% 7.85 -0.02%

Improvements in energy intensity for households could be the result of better isolated houses or

technological improvements in electric households equipment. This results in a decrease in electricity

demand for consumers. Energy intensity shares for industries (other industries) are also constructed

from GCAM data, namely, final energy demand in all non-residential sectors in a region, divided by

GDP of the corresponding region. Energy intensity for the fossil fuel sector is expressed differently,

i.e. the electricity fuel consumption level divided by the electricity generation level of fossil fuel. It is

assumed that the energy intensity of the nuclear and renewable energy sector remains constant.

8.2 Overview of GCAM input variables for scenarios in EXIOMOD

Energy mix for electricity production

GCAM provides electricity generation trajectories on technology and regional level. These

technologies are mapped towards three electricity sectors, as defined in EXIOMOD. That is,

renewable, nuclear and fossil fuel electricity sector. The technology shares for electricity production

are subsequently easily obtained.

The EXIOBASE input-output table illustrates that most electricity is produced within these three

electricity sectors. We allow the input output shares of electricity production to change over the

years with the imposed electricity shares of GCAM. Table 8-2 gives an overview of the GCAM energy

mix as input data for EXIOMOD.

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Table 8-2: Overview scenario input on energy mix from GCAM into EXIOMOD

Base year

2010

Reference Low technology

cost

High technology

cost

Level 2010 Level 2050 Level 2050 Level 2050

EU-27 Share renewable

energy

20% 27% 42% 52%

Share fossil fuels 52% 50% 17% 17%

Share nuclear

energy

28% 23% 41% 31%

World Share renewable

energy

19% 21% 35% 40%

Share fossil fuels 68% 70% 40% 42%

Share nuclear

energy

13% 9% 25% 18%

Price indices

GCAM provides actual price levels for electricity, food and refined liquids (e.g. in dollar per GJ for

electricity price). EXIOMOD, however, assumes price indices with 2007 as reference year. For

connecting purposes, price indices for the GCAM data are found. Additionally, GCAM price indices

should be compared with the price index in the reference scenario. EXIOMOD endogenously finds

optimal price levels for all industries. Electricity price indices that are similar to the GCAM indices are

found by choosing correct tax rates for electricity production. An overview of input data from GCAM,

the annual change in electricity and food prices, is given in Table 8-3.

Table 8-3: Overview scenario input on energy prices from GCAM into EXIOMOD

Base year

2010

Reference Low technology

cost

High technology

cost

Level 2010 Annual change

2010-2050

Annual change

2010-2050

Annual change

2010-2050

EU-27 Electricity price

(1975$/GJ)

8.24 -0.05% 0.40% 0.64%

Food price

(1975$/Mcal)

0.06 0.22% 1.34% 1.41%

World Electricity price

(1975$/GJ)

7.85 -0.02% 0.62% 0.85%

Food price

(1975$/Mcal)

0.06 0.27% 1.38% 1.45%

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8.3 Equations relative competitiveness

The relative competitiveness indicator shows the impact of the policy scenarios on the competitive

position of a region, as explained in Section 3.3.2. Equations behind this indicator are given in this

appendix.

The relative competitiveness indicator is specified as follows:

𝑃𝑋𝐼𝑟,𝑦 =∑𝑃𝑟,𝑝𝑟𝑑,𝑦

𝑃𝐶𝑋𝐼𝑟,𝑝𝑟𝑑,𝑦⋅

𝑒𝑥𝑝𝑜𝑟𝑡𝑟,𝑝𝑟𝑑,𝑦∑ 𝑒𝑥𝑝𝑜𝑟𝑡𝑟,𝑝𝑟𝑑𝑑,𝑦𝑝𝑟𝑑𝑑

𝑝𝑟𝑑

where

𝑃𝐶𝑋𝐼𝑟,𝑝𝑟𝑑,𝑦 = ∑𝑡𝑟𝑎𝑑𝑒𝑟,𝑝𝑟𝑑,𝑟𝑟,𝑦

𝑒𝑥𝑝𝑜𝑟𝑡𝑟,𝑝𝑟𝑑,𝑦⋅ 𝑃𝐶𝑋𝐼𝐾𝑟,𝑝𝑟𝑑,𝑟𝑟,𝑦

𝑟𝑟≠𝑟

is the weighted average of export prices of product 𝑝𝑟𝑑 from region 𝑟 to any region in the world.

𝑃𝐶𝑋𝐼𝐾𝑟𝑟,𝑝𝑟𝑑,𝑟,𝑦 = ∑

𝑡𝑟𝑎𝑑𝑒𝑟𝑟𝑟,𝑝𝑟𝑑,𝑟𝑟,𝑦𝑖𝑚𝑝𝑜𝑟𝑡𝑝𝑟𝑑,𝑟𝑟,𝑦

1 −𝑡𝑟𝑎𝑑𝑒𝑟,𝑝𝑟𝑑,𝑟𝑟,𝑦𝑖𝑚𝑝𝑜𝑟𝑡𝑝𝑟𝑑,𝑟𝑟,𝑦

⋅ 𝑃𝑟𝑟𝑟,𝑝𝑟𝑑,𝑦𝑟𝑟𝑟≠𝑟

is the weighted average of prices of all regions in the world to region 𝑟, i.e. an average ‘export price’.

Trade parameter 𝑡𝑟𝑎𝑑𝑒𝑟,𝑝𝑟𝑑,𝑟𝑟,𝑦 gives the trade of product 𝑝𝑟𝑑 between region 𝑟 to region 𝑟𝑟 in year

𝑦. Taking the sum over the import regions 𝑟𝑟 gives 𝑒𝑥𝑝𝑜𝑟𝑡𝑟,𝑝𝑟𝑑,𝑦, and taking the sum over the export

regions 𝑟 give 𝑖𝑚𝑝𝑜𝑟𝑡𝑝𝑟𝑑,𝑟𝑟,𝑦. Basic product prices in region 𝑟 are given by 𝑃𝑟,𝑝𝑟𝑑,𝑦.

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