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TNO report

Topical paper 3: Potential approaches for modelling

resource efficiency related to buildings and

infrastructure. Reflections on a hybrid set-up.

Date 25 February 2012 (Draft 2.0)

Author(s) Olga Ivanova (TNO) and Frédéric Reynès (TNO)

Copy no. 1

No. of copies 1

Number of pages 17

Number of appendices 0

Customer EC, DG ENV

Project name ENV.F.l/ETU/2011/0044 "Assessment of Scenarios and Options

towards a Resource Efficient Europe”

Project number TNO project 054.01783

This is the final version of the paper. It does not necessarily represent the views of

the Commission.

All rights reserved.

No part of this publication may be reproduced and/or published by print, photoprint,

microfilm or any other means without the previous written consent of TNO.

In case this report was drafted on instructions, the rights and obligations of contracting

parties are subject to either the General Terms and Conditions for commissions to TNO, or

the relevant agreement concluded between the contracting parties. Submitting the report for

inspection to parties who have a direct interest is permitted.

© 2012 TNO

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Contents

1 Introduction ............................................................................................................ 14

2 General description of the approach ................................................................... 15 2.1 Scope of the study ................................................................................................... 15 2.2 Hybrid modelling: bottom-up information into a macroeconomic model ................. 16

3 Baseline scenario .................................................................................................. 18 3.2 Data for the baseline scenario ................................................................................. 19 3.2.1 Historical data .......................................................................................................... 20 3.2.2 Baseline assumptions for EU countries ................................................................... 20 3.2.3 Baseline assumptions for the rest of the world ........................................................ 26 3.2.4 Qualitative baseline assumptions ............................................................................ 27 3.3 Data for baseline and alternative improvement option scenarios ........................... 27

4 Incorporating bottom-up improvement options into the macroeconomic

framework ............................................................................................................... 30 4.1 Modelling physical flow and stocks for material, buildings and infrastructures ....... 30 4.2 Linkage with the macroeconomic model ................................................................. 32

5 References ............................................................................................................. 34

6 Annex I: Description of EXIOMOD model ........................................................... 35 6.1 Model overview ........................................................................................................ 35 6.2 Geographical coverage of EXIOMOD ..................................................................... 35 6.3 Unique database of EXIOMOD: EXIOPOL and CREEA projects ........................... 36 6.4 Integrated impact assessment of policy measures ................................................. 37 6.5 General framework of the model ............................................................................. 37 6.6 Main structure of EXIOMOD .................................................................................... 38 6.7 Households and labor market .................................................................................. 39 6.8 Production sectors and trade ................................................................................... 39 6.9 Market equilibrium and investments ........................................................................ 40 6.10 Federal government ................................................................................................ 41 6.11 Environmental effects and welfare function ............................................................. 41 6.12 Dynamic features ..................................................................................................... 42 6.13 Endogenous technological progress and growth .................................................... 42 6.14 Treatment of resources and environmental effects ................................................. 42 6.15 Integration of physical and monetary data .............................................................. 42 6.16 Uncertainty and non-rational behavior..................................................................... 43 6.17 Econometric nature of the model ............................................................................. 43 6.18 Main dimensions of the model: sectors and commodities, factors of production,

types of emissions, energy use, physical inputs, land and water use ..................... 43

7 Annex 2: Attendance list of the Stakeholder meeting on "Scenarios towards a

Resource Efficient Europe", 12 September 2012, DG ENV, Brussels .............. 56

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Backgrounds of the project: Assessment of Scenarios and Options towards a Resource Efficient Europe

The Europe 2020 Strategy, endorsed by the European Council in June 2010, establishes

resource efficiency as one of its fundamental flagship initiatives for ensuring the smart,

sustainable and inclusive growth of Europe. In support of the Flagship, the Commission has

placed a contract with TNO, CML, PE and AAU/SEC for a project with the following aims. It

should identify inefficient use of resources across different sectors and policy area’s at

meso- and macro level and then quantitatively asses potentials and socio-economic and

environmental effects of efficiency improvements, both from singular as system wide

changes, up to 2050. The Built environment is focus area. The core methodology is a hybrid

modelling approach: identifying improvement options, their costs and improvement potential

at micro/meso level, and to feed them into a macro-model (EXIOMOD) to assess economy-

wide impacts of improvement scenarios. Stakeholder engagement via workshops is an

important part of the project. The project started in January 2012 and will end in December

2013.

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Non-technical summary:

0.1. Introduction

This “Assessment of Scenarios and Options towards a Resource Efficient Europe”

attempts to model the impact of resource improvement option on European

economies at the country level. It concentrates on the Built environment. The

geographic scope is Europe whereas the time horizon is 2030. The research makes

a distinction between residential buildings, utility buildings, and infrastructure. The

research investigates the possibility of resource efficiency improvement for the

construction phase in a broad sense (new construction, refurbishment and

demolition including recycling) and for the use phase (maintenance and

exploitation) thus covering the whole life-cycle of buildings and infrastructure.

This topical paper describes the key elements of the modelling exercise to be

implemented in the project: main assumptions underlying the baseline scenario

(section 3) and the proposed hybrid modelling approach (section 4) that incorporate

bottom-up improvement options into a macro-economic framework. It points the

main limits of existing approach to measure the impact of resource efficiency and

shows how the proposed approach attempts to overcome these limits.

This paper describes in essence the hybrid economic / Life cycle assessment

modelling approach. Since it is quite technical in nature, the present non-technical

summary provides a short description of the main ideas and intuitions behind the

proposed modeling exercise that is schematized in Figure 0.1.

0.2. What is the EXIOMOD model?

The modeling exercise will be conducted with the EXIOMOD model developed at

TNO. EXIOMOD is a large scale and highly detailed world model built on the

detailed Input-output database EXIOBASE. It is a macro-economic ‘computable

general equilibrium’ (CGE) model that divides the global economy in 43 countries

and a Rest of World, and 129 industry sectors per country. The model includes 5

types of households, a representation of 29 types GHG and non-GHG emissions,

different types of waste, land use and use of material resources (80 types).

Moreover, it includes a physical (in addition to the monetary) representation for

each material and resource use per sector and country. The model is presently

calibrated n the data for 2007. For this study we will recalibrate the model using the

available macro-economic data from national accounts for 2012. The model is

dynamic and will use the period 2013-2030 as the time horizon for its calculations..

Computable General Equilibrium (CGE) models (and in particular EXIOMOD) are

the class of simulation tools that use large datasets of real economic data in

combination with complex computational algorithms in order to assess how the

economy reacts to changes in governmental policy, technology, availability of

resources and other external macro-economic factors. EXIOMOD model consists of

(1) the system of non-linear equations, which describes the behavior of various

economic actors and (2) very detailed database of economic, trade, environmental

and physical data. The core part of the model database is the Social Accounting

Matrix, which represents in a consistent way all annual economic transactions.

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A CGE model accounts for the interaction/feedbacks (a) between price and

demand/supply quantities and (b) between economic agents at the macro and

sectorial level. Therefore, it gives the economic relations between all industry

sectors via their intermediate use. For example, it shows how much of different

materials, products and services are used by the construction sector depending on

the assumptions about its production technology. In case the efficiency of material

use in the construction sector is improving over time, the model will calculate

(1) direct effects: change in material use per unit of output of the sector and

(2) indirect or rebound effects: the change in price of construction services as

well as the change in the total output of the construction sector over time. The later

outcome is translated in EXIOMOD into the change in physical materials use and

extraction as well as changes in generated emissions and waste.

Figure 0.1 The circular economic flows in EXIOMOD

The table below presents the main elements of the EXIOMOD model and their

corresponding dimensions and main outputs. The detailed description of the model

and its dimensions is given in the Annex I (detailed dimensions are provided in

Section 6.18).

Factors of production

markets

Firms

Product markets

Households

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Table 0.1 Overview of main elements of EXIOMOD model per country (43 countries

and Rest of the World

N Element of

EXIOMOD

Dimension Main outputs

1 Households Five income quintiles Consumption of goods and

services, expenditures,

incomes and savings

2 Firms Grouped into 129 types

of sectors

Outputs, value added, use of

factors of production and

intermediate inputs,

investments and capital stock

3 Governments Federal governments Governmental revenues and

expenditures by type including

main taxes and subsidies,

social transfers to households,

unemployment benefits

4 Markets for

factors of

production

Three education levels,

171 types of natural

resources including

land, water, materials,

biomass and energy

Wages, unemployment levels,

natural resource rents, return

to capital, supply of and

demand for factors of

production

5 Markets for

goods and

services

129 types of goods and

services

Prices of goods and services,

supply of and demand for

goods and services

6 International

trade

43 countries and Rest

of the World, 129 types

of goods and services

Trade flows of goods and

services between the

countries, use of international

transport services

7 Savings and

investments

National investment

bank

Total savings, depreciation,

new investments and change

in sector-specific capital stock

8 Use of materials 80 types of physical

materials

Use of materials by each of

129 production sectors and

their extraction

9 Generation of

emissions

29 types of GHG and

non-GHG emissions

Emissions associated with

energy use, emissions

associated with households’

consumption and emissions

associated with general

production process

EXIOMOD calculates what economic, social and environmental changes will

happen as a consequence of resource-efficiency measures in each of the

countries (see Figure 0.2 for the structure of regional dimension of EXIOMOD).

Increase in the efficiency of resource use in the construction sector will lead to less

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demand for resources and reduce their prices, hence making construction services

cheaper. Reduction in the costs of construction will in its turn result in lower prices

of the industries, which use construction services as major part of their intermediate

inputs and leave households with the higher disposable consumption budget.

Increase in the disposable consumption budget of the households will allow them to

buy more goods and services which will give boost to the economic activity and

increase outputs of various industries. The later (rebound) effects will lead to

increase in the use of resources and higher emission levels. It could be the case

that this rebound effect significantly diminishes the initial positive (in terms of less

resources used by the construction sector) effect of the resource-efficiency

measures.

Figure 0.2 Regional dimension of EXIOMOD1

0.3. What are the general limitations (assumptions) of EXIOMOD?

CGE models have some limitations which are closely related to their main

assumptions regarding the functioning of the economy. For instance, they generally

assume free competition or have a limited representation of the financial and

banking system. They assume also that the perfect flexibility of price and quantity

adjusts supply and demand every period, whereas in really, price and quantity

adjust slowly. Therefore, they are more long term models and say little about the

short/medium term adjustment. This limitation is acceptable here because resource

efficiency is a long term problematic.

Another limitation is that results rely on the calibration of certain parameters that are

difficult to estimate empirically. This is particularly true for substitution mechanisms

between production factors (capital, labour), between types of energy that depend

on the value of the price elasticity of substitution. Ideally these price elasticities are

econometrically estimated (i.e. via a database that has time-series from which

relations between prices and demand can be derived). Such estimates often are not

available for all sectors, products and countries or only at an aggregated level.

Usually then ‘similar country’ assumptions are used or the aggregated data are

assumed to be valid for the more disaggregated products.

1 Adapted from presentation of Jacoby et. al. on EPPA model

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0.4. What are the limitations of EXIOMOD for measuring the impacts of

resource efficiency investments?

The present study investigates the wider-economic impacts of resource efficiency

improvement during for the construction phase in a broad sense (new construction,

refurbishment and demolition including recycling) and for the use phase

(maintenance and exploitation) thus covering the whole life-cycle of buildings and

infrastructure. EXIOMOD has a number of important limitations related to the

analysis in the present study.

1. Consumption of households is directly related to their income. With

the income increase in the long run, there is a high risk that they generate

unrealistic wealth effects such as households who would have 3 houses per

person. The models does not include saturation point in the consumption of

the households.

2. There is no explicit representation of the building stock. The model

includes the use of housing by the households as a combination of

construction and maintenance services with energy use. It does not include

the representation housing stock as consisting of old houses with different

energy use characteristics.

3. Emissions are linked to the energy use and production in a linear way.

Emissions in the model are calculated as energy use and/or output and use

multiplied by the unit emissions coefficients, which assume that the level of

marginal emissions does not vary with the output or consumption levels.

4. Production technology has limited amount of details. Technologies of

the sectors are represented at the level of detail of 129 types of goods and

services which are too aggregate for the representation of some

technological improvements.

These limitations call for developing a hybrid modelling approach presented in this

Topical paper.

0.5. Purpose/objective: What is the modeling framework supposed to do and

how?

For the purpose of the present study, the modelling framework should measure the

effect of resource-efficiency improvements on the economy at the macro and

sectorial level both in monetary and physical units. The framework should be able to

evaluate both direct and indirect effects of resource efficiency improvements over

time. Besides the economic effects the modelling framework should be able to

quantify social and environmental effects of resource efficiency changes.

In order to overcome the existing limitations of EXIOMOD (top-down approach)

mentioned above we couple it with the following two bottom-up modelling

approaches:

1. Life Cycle Assessment (LCA) adds necessary details to the production

technologies

2. Material Flow Analysis (MFA) allows to trace over time the changes in

material flows and stocks, thus capturing the physical side of production

and consumption

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The combination of CGE modelling with LCA and MFA constitutes the hybrid

approach to the impact assessment in the present study.

Life-cycle analysis (LCA) is a method in which the energy and raw material

consumption, different types of emissions and other important factors related to a

specific product are being measured, analyzed and summoned over the products

entire life cycle from an environmental point of view. Life-Cycle Analysis attempts to

measure the “cradle to grave” impact on the ecosystem. LCAs started in the early

1970s, initially to investigate the energy requirements for different processes.

Emissions and raw materials were added later. LCAs are considered to be the most

comprehensive approach to assessing environmental impact. The main limitation

of LCA is that is provides only partial view of the production process by informing its

links with the rest of the economy.

Figure 0.3 Example of LCA data for coffee machine2

Material Flow Analysis (MFA). Whereas LCA studies the life cycle of a given

product, MFA concentrates on the life cycle of raw materials, i.e. extraction,

production and manufacture, uses and waste. A material flow analysis is a

systematic reconstruction of the way a chemical element, a compound or a material

takes through the natural and/or economic cycle. A material flow analysis is

generally based on the principle of physical balance. MFA is an important tool to

assess the physical consequences of human activities and needs in the field of

Industrial Ecology, where it is used on different spatial and temporal scales.

Examples are accounting of material flows within certain industries and connected

ecosystems, determination of indicators of material use by different societies, and

development of strategies for improving the material flow systems in form of

material flow management. The main limitation of MFA is that is looks in isolation

2 Adapted from Georgia TEC presentation.

assembly

poly-aluminium

extrusion

+ transport

disposal inmunicipalwaste

electricity

disposal of

in org. was te

use

paper

duction filter pro-

sheet s teel

stampingforming

glas

forming

filters + coffee

coffee

roasting

packaging

water

injectionmoulding

bean styrene

7.3 kg 1 kg 0.1 kg 0.3 kg 0.4 kg

375 kWh

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on one material or process without providing a global view of the resoure effficiency

problematic.

Figure 0.4 Material Flow Analysis in case of car repair shop3

The bottom-up and top-down are two complementary modelling approaches that

both have their advantages and drawbacks. The bottom-up approach models

view the functioning of the economy “from the detailed to the aggregate

level”. It has the advantage of realism and of a high level of detail, but it generally

neglects indirect economic effects since prices are considered as exogenous. For

instance, in our case, one expects that resource efficiency will have an effect on

prices and on the consumer wealth which in return will affect resource efficiency

itself. By modeling “from the aggregate to the detailed level”, the top-down

has the advantages to accounts for interactions/feedbacks between price and

quantity and between economic actors. For instance, it can account for rebound

effects (that is when a lower energy bill means an extra revenue which in return

may lead to more energy consumption), or for carbon leakage (that is when a policy

reduces carbon emission in one country but increase emissions in another country

through the displacement of the production processes). The main drawbacks of the

top-down approach compared to the bottom-up one is the lack of details and an

unrealistic representation of certain economic behaviors such as energy

consumption (which is related to the revenue instead of the use).

3 Adapted from UNIDO presentation

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Figure 0.5 Coupling of EXIOMOD with LCA data for improvement options

The coupling of top-down (EXIOMOD model) and bottom-up (LCA of improvement

options) approaches requires one to have a quantified LCA data on identified

detailed improvement options that will feed the macro-level model. LCA includes the

data on the investment costs of efficiency improvement, the time necessary for their

implementation, the use of materials for the realization of each improvement option,

efficiency gains, estimated service life times, generated waste, etc. LCA works at

the very detailed level of analysis (see for example Figure 0.3) and requires some

aggregation to be use together with the EXIOMOD model which operates at

significantly more aggregate level of details (129 sectors and commodities). Figure

0.5 represents the schematic process of coupling between LCA outcomes and

EXIOMOD model.

Figure 0.6 Integrating MFA within the EXIOMOD model

LCA of identified improvement measures

• Complete the choice of improvement options/packages

•Prepare LCA data for each of the options

•The data includes investment costs, changes in resource use and waste generation

Aggregating LCA data to the level of details of EXIOMOD

• Map the detailed types of resources used in the LCA to the ProdCom classification

•Aggregate the LCA data using ProdCom to the level of details used in EXIOMOD that is 129 types of commodities

Using aggregated LCA data to change EXIOMOD technical coefficients

•Recalculate the technical Input coefficients of EXIOMOD on the basis of aggregated LCA data from the previous step

•Use the changes in the technical coefficients to simulate the direct and indirect effects of improvement options

Outputs of EXIOMOD simulations

Calculating physical indicators of MFA on

the basis of EXIOMOD outputs

Linking simulation outputs with the

historical MFA of main resources

•Outputs of goods and services

•Households' consumption

•Intermediate use of products

•Extraction of natural resources and materials

•Use of natural resources and materials for production and consumption

•Calculation of changes in available stocks of natural resources and materials

•Calcuation of contribution to the total waste stock taking into account changes in recycling

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The link of EXIOMOD model with the MFA for main types of materials and

resources will be implemented as a post-processing of the model simulations

results. The overall architecture of the linkage is represented on Figure 0.6 where

the economic outputs of EXIOMOD in terms of production outputs, households’

consumption and intermediate use of resources and materials are translated into

physical units on the basis on the EXIOMOD physical data (see for more details

Annex I). Once the data on output/extraction and resource intermediate and final

use have been translated into physical units, it can be used as an integral part of

the prospective MFA analysis related to the main materials used by the construction

sector. The MFA analysis will be completed to 2030 using the outcomes of the

EXIOMOD. This will allow us to trace how the extraction, recycling and waste flows

of materials have been affected by the resource efficiency measures

0.6. Input: what data is needed to run EXIOMOD?

The main general inputs of EXIOMOD include:

1. The EXIOBASE database is part of EXIOMOD and represent the

aforementioned model (economic relations between sectors, and countries)

2. Data on main behavioral parameters of the model including substitution

elasticities, price elasticities and Armington elasticities

3. Model baseline data for the period 2007-2030 that includes data on

population growth rates, GDP growth rates, productivity changes by sector,

changes in the production technologies of sectors over time, effects of the

implemented policies.

Inputs required for the simulation of the identified packages of improvement

measures in the present study include:

1. Data on the building stock for Europe differentiated by each member state

that includes information on building by their type and age category

2. Results of LCA for each of the identified improvement options including

data on use/waste of materials at construction, use and demolition stages,

investment costs split between labor and other costs, such as capital costs

and taxes.

3. Results of the historical MFA analysis for the main materials used by the

construction sector including the data on extraction, use, waste and

recycling.

4. The list of policy measures associated with the identified packages of

improvements in combination with estimates of their

administrative/implementation costs. The later estimates will be provided on

the basis of review of available literature and studies.

0.7. Output: what kind of results EXIOMOD will provide?

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In essence the model hence must calculate such potential direct and indirect

consequences of resource-efficiency packages compared to a baseline

scenario. It concerns the Europe-wide consequences, hence beyond just the

building and construction sector. Main outputs of the model simulations for this

particular study are summarized in the table below and include also the results of

post-processing in case of MFA part:

Table 0.2 Main outputs of EXIOMOD by country (each of EU member states

separately)

• Level of output, resource efficiency and overall productivty of 129 types of sectors

•Competitiveness indicator for 129 types of sectors

• Imports and expors by 129 types of commodities

• Savings and sectoral investments

•Households' income and consumption by five income classes

•Governmental revenews and expenditures

Economic effects

•Unemployment and wages by three levels of education

• Jobs created/lost by 129 types of sectors and three education levels

•Gini coefficient for income inequality

Social effects

•GHG and non-GHG emissions by 29 types

• Land use and water use

•Materials extraction, use, waste and recycling by 80 types of materials

•Energy supply and use by 18 types

Environmental effects

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1 Introduction

This topical paper describes the key elements of the modelling exercise to be

implemented in the project and serves as an input to the first workshop with EC

staff in M6 and first stakeholder conference in M9. Section 2 provides a general

description of the intended approach that presents the proposed hybrid modelling

approach and defines the scope of the study. In relation with Sub-task 3.4, Section

3 details the main assumptions underlying the baseline scenario. In relation with

Task 2, Section 4 deals with methodological aspects in more details by describing

how the bottom-up improvement options to be developed by PE (in Subtask 3.2.)

will be incorporated into a macro-economic framework and combined to scenarios,

and how these scenarios will be run.

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2 General description of the approach

2.1 Scope of the study

In consultation with the EC, this “Assessment of Scenarios and Options towards a

Resource Efficient Europe” concentrates on the Built environment. The

geographic scope is Europe whereas the time horizon is 2030. The research makes

a distinction between residential buildings, utility buildings, and infrastructure.

Amongst utility buildings, the study focuses on 89% of the stock: wholesale & retail

(28%), offices (23%), educational buildings (17%), hotel & restaurant (11%),

hospital (7%), sport facilities (4%). Industrial buildings because of their specific

nature are not within the scope of the present study. Concerning infrastructures, the

study focuses on road construction. Tunnels and bridges which are only a small

part of road construction, as well as communications and public supply are not

considered.

The research investigates the possibility of resource efficiency improvement for the

construction phase in a broad sense (new construction, refurbishment and

demolition including recycling), the use phase (maintenance and operation), thus

covering the whole life-cycle of the buildings and infrastructure. The study

concentrates on energy use related to the characteristics of the buildings and their

possible improvements. Electrical and electronic appliances, but also other

equipment used in buildings such as flooring and furniture are left out of the scope

of the study. Moreover, it should be noted that the use of infrastructure (such as

water treatment, energy generation and distribution, etc.) is included within the

assessment of buildings. For utility buildings, water use and construction materials

are not considered because (a) water use is not a significant aspect of consumption

for these types of buildings and (b) their construction is far more heterogeneous

than residential construction and it is therefore much harder to provide consistent

construction improvement potentials for these types of buildings.

Figure 2.1 Life cycle of buildings and infrastructure

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2.2 Hybrid modelling: bottom-up information into a macroeconomic model

The development of the assessment framework (Task 2) consists of a hybrid

modelling that integrates the bottom-up assessment of improvement options in

building and infrastructure (Subtask 3.2) into the macroeconomic Computable

General Equilibrium (CGE) model EXIOMOD. The proposed hybrid modelling is

schematized in Figure 1. It combines three modelling approaches by integrating

elements of Life Cycle Assessment (LCA) and Material and substance Flow

Analysis (MFA) approaches into the EXIOMOD CGE model. It also combines two

databases: the EXIOBASE and the IMPRO database.

LCA provides useful information on the different stages of the life-cycle for buildings

and infrastructures, in particular on the amount of raw materials that is needed for

construction phase in a broad sense (including demolition and recycling) and use

phase. It also gives information about the destruction phase (waste and recycling

possibilities). LCA models for buildings and infrastructures can generate detailed

information (micro/meso level) on energy use, product use, cost estimates and

waste generated from construction, repair, restoration and demolition in the building

and infrastructure sectors on a per country basis. This set of information, which

differs for each scenario (baseline and efficiency improvement options), can be

integrated into the CGE model by adjusting private and public demand and their

associated cost for the construction and use phases. It can thus be used to

evaluate the macroeconomic and sectorial impact of alternative scenarios (for more

details see Section 4) and gives information on, among others, resource use and

labour inputs by industry or by consumption category. The high level of sectorial

detail in EXIOMOD allows for concentrating the attention on key and potentially

promising economic activities such as green technologies.

Whereas LCA studies the life cycle of a given product (here buildings or

infrastructures), MFA concentrates on the life cycle of a given raw material, i.e.

extraction, production and manufacture, uses and waste. Incorporating this

approach within a CGE model provides additional useful indicators related to the

development of resource stocks over time. A dynamic stock modelling of a given

resource is particularly important for understanding the environmental impact of the

construction sector because of time lags of several decades between construction

and demolition. Dynamic stock modelling can provide useful insights both from the

perspective of resource use in this sector as well as from the perspective of

estimating future waste flows and emissions and possibilities for recycling and

urban mining. Using this dynamic MFA in combination with the input output data

allows for constructing Environmentally weighted Material Consumption (EMC) and

Raw Material Equivalents indicators.

The integration of elements of LCA and MFA into a CGE model framework can give

precise insights here because EXIOMOD relies on a highly detailed worldwide

database: EXIOBASE. The EXIOBASE has been created during the EXIOPOL (A

New Environmental Accounting Framework Using Externality Data and Input-Output

Tools for Policy Analysis) European research project (www.feem-

project.net/exiopol/; Tukker et al., 2009). It consists of a Multi-Regional

Environmentally Extended Supply and Use Table (MR EE SUT) for the whole world.

The EXIOPOL database (EXIOBASE) has a unique detail and covers 30 emissions,

around 140 resource extractions, use and supply of energy products, and nitrogen

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and phosphorous application in agriculture given specifically for 130 sectors and

products by 43 countries making up 95% of global GDP, plus a Rest of World.

Besides EXIOBASE, we will also make use of the EU buildings stock database from

IMPRO Building study, based on a unique inventory of building and infrastructure

stock from all over Europe. This study defines building models that are the most

“representative” buildings for the EU-25, analyses the life cycle impacts of the

different building models and identifies the environmental hotspots. It also identifies

the improvement options and analyses their environmental effects and their costs.

This dataset can thus be used in order to calculate the overall changes in resource

consumption of buildings and infrastructure given a set of particular scenarios of

their improvements over time. A simplified version of this dataset will be

incorporated into EXIOMOD model in order to facilitate the link between the detailed

changes in the housing and infrastructure stock and their more aggregate

economic, social and environmental impacts. The database will be simplified for the

use in EXIOMOD to consist of not more than ten types of buildings. The

classification will be based on IMPRO Building study and reflect the possibilities for

resource efficiency improvements of the buildings.

Figure 2.2. Schematic representation of the hybrid assessment framework

Buildings

stock database

Reduced version

of the database

MFA

analysis

tool

EXIOBASE

database

EXIOMOD

macro-

economic

model

LCA for

buildings and

infrastructure

Existing

LCA

databases

Resources

stock

model Impacts of various

resource efficiency

options on:

- Environment

- Economy

- Society

- Competitivenes

s

Physical indicators

related to resource

efficiency options

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3 Baseline scenario

In relation with Subtask 3.4, this Section presents the key assumption and sources

relative to the baseline and alternatives scenarios. In addition to external

assumptions, a big part of our hypothesis built on the information on improving

resource-efficiency as gathered in Subtasks 3.1-3.3:

Insights in historical efficiency improvements and ‘hot spots’ of apparent low

resource-efficiency

Insights in bottom-up determined options for improving resource-efficiency in

the focal areas buildings, infrastructure, etc.

Insights from the initial assessment of effectiveness of policy instruments and

their mixes.

3.1 Approach used to build the scenarios

In order to develop a baseline and a set of alternative scenario, we have used a

combination of participatory (qualitative) and model-based (quantitative

approaches). Qualitative inputs are appropriate for the analysis of complex

situations that can be characterized by high-level of uncertainty. One example is

future options about human values and behaviour. Quantitative inputs to scenarios

usually explore and develop resource efficiency, energy use, technology, macro-

economic, land-use and emissions generation forecasts. The combination of

quantitative and qualitative inputs makes developed scenarios more consistent,

robust and reliable. In order to use qualitative and quantitative parts of scenario in

combinations, qualitative descriptions has to be whenever possible translated into

quantitative inputs into the model-bases assessments.

Within this task, we will use a series of workshops with the relevant groups of

stakeholders as an instrument for collecting ideas for the scenarios. Activities of

these workshops will be designed in such a way as to support gradual process of

scenario-building.

The quantitative part of the scenarios consists of a temporal sequence or a time

chain which covers the period 2012-2030, whereas the qualitative part consists of a

number of snap shots into the future which include the following set of years: 2020,

2035 and 2050. Following the recommendation of DG Environment formulated in

the “ENV Comments on the Inception Report” (30 March 2012), we will focus

“especially at the 2020 - 2030 period in a realistic manner, rather than spending an

unreasonable amount of time to agree on the 2050 assumptions”.

Setting the time-horizon for quantitative impact assessment to the period 2013-2030

enable us to use reliable estimates for the baseline scenario but results in some

restrictions during the impact assessment related to the following packages of

measures proposed in TP4 linked to demolition phase:

Design for deconstruction

Increase recycling of waste at end of life

The effects of these measures are only visible at very long term given that the

average life-time of building is between 30 and 50 years. This means that the

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chosen time horizon for economic modelling with EXIOMOD does not allow

capturing full effects of these measures on the stock of new buildings. If we would

want to capture these effects the horizon for quantitative modelling would need to

be extended until at least 2065 which would create quite unreliable estimates. The

effect of the above-mentioned measures related to the stock of already existing

buildings and infrastructure will to the large extend be captured in the quantitative

impact assessment.

The qualitative part of baseline scenario will consist of the assumptions about future

technological trends and changes in consumption patterns in the areas relevant to

the present study for the following points in time: 2020, 2035 and 2050, including

Scenario element Example Translation into

quantitative

scenario

(i) energy and material

use for production

of construction

materials

Change in the type of

materials used for

construction of buildings,

change in the recycling

rate and possibility

Change in

technological input

parameters of

construction sector

(ii) labor efficiency

during the

construction phase

Gradual improvement of

labor efficiency with 0.5%

per year

Change in labor

productivity

parameter of

construction sector

(iii) attitudes of

consumers towards

warmth in the

house

Gradual increase in the

indoors temperature over

the last decades

continues

Increase in

preferences of

consumers for heat

(iv) family composition Higher share of single

people and households

consisting of old people

Change in the

preferences of

consumers for

housing and heat

(v) types of houses Increase in the share of

high-rise buildings, certain

share of zero-emissions

buildings

Change in

households’ energy

use per unit of

income and the

associated

emission

coefficients

They will be based on the views of experts and stakeholders and translated where

possible into the changes in the technological and behavioral parameters of the

model for the period 2013-2030 and provide the basis for the quantitative impact

assessment of packages of improvement options. For the period after 2030 we will

provide the discussion of possible further long-term effects of the chosen packages

of improvement measures for the two time points 2035 and 2050. The qualitative

description of their long-term effects will be based on baseline assumptions

presented above in combination with the insights from the quantitative impact

assessment for the period 2013-2030. The effects of the packages of improvement

measures will continue into the long-term future (until 2050) along the lines

determined by the quantitative analysis with EXIOMOD. However their identified

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effects will be either strengthened or weakened by the long-term trends related to

the build environment and construction sector.

In order to integrate quantitative and qualitative parts of the scenarios, we have

used the Story-And-Simulation approach. Schematic representation of the general

approach is presented in Figure 2.1.

Figure 2.1. Schematic representation of Story-And-Simulation approach adopted from Alcamo (2008)

The Story-And-Simulation approach includes the steps essential for the

development of scenarios including (1) the establishment of scenario team and

panel; (3) construction of the story lines that are quantified and revised in (4-6)

using an iterative procedure and (10) publication and distribution. More specifically,

in the present case, we have defined in consultation with DG Environment the set of

assumptions that could be included in the baseline scenario paying special

attention in formulating scenarios that are coherent with other relevant modeling

publications of the EC (such as “Energy Roadmap 2050”). Senarios regarding

improvement options have been preliminary defined by PE International and

discussed in detail during the Stakeholder meeting on "Scenarios towards a

Resource Efficient Europe" (see the attendance list in Appendix 2). Based on the

feed-backs received, a final list has been published and distributed (see Topical

papers 2 to 4).

3.2 Data for the baseline scenario

To evaluate the impact of improvement option in the building sector, we first have to

derive the assumptions relative to the baseline scenario (that is the scenario without

the improvement option). For this study, only one baseline scenario is defined. It

integrates the general assumptions below regarding the general evolution of the

economy (demography, technical progress, energy mix, etc.). For the building

sector, only trends that reflect the autonomous trend and adopted policies are

integrated in the baseline scenario.

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It is important to mention that the assumptions below are made for each individual

member state, since EXIOMOD covers all 27 Member States individually. This

allows for showing the potential heterogeneity between European countries.

3.2.1 Historical data

The EXIOMOD model is presently calibrated for the year 2007. The database of the

model includes besides economic data also data on extraction and material use,

water and land use by type as well as the data on all GHG and GHG emissions

(see Annex I for more details). The database of EXIOMOD model has been

constructed as a part of FP6 EXIOPOL project, full documentation is available from

the project website http://www.feem-project.net/exiopol/.

For the sake of precision, we will recalibrate the model on the year 2012 by using

available historical physical, macro-economic, sectorial and demographic data. We

use the following sources:

Table 3.1 Correspondence between historical data sources and different elements

of EXIOMOD database to be used to updating the detailed data to 2012

EuroStat: Supply and Use Tables for

2009, EU countries

Economic production and consumption

structure (including households and

government), investments, exports,

imports, taxes, subsidies

EuroStat: National accounts for 2012,

EU countries

Totals for production and consumption,

savings, trade balance

EuroStat: Energy data, 2011, EU

countries

Energy supply and use

EuroStat: Material Flow Accounts, 2011 Material use, extraction and waste

OECD.Stat: National accounts for 2012,

non-EU countries

Totals for production and consumption,

savings, trade balance

OECD.Stat: Energy data, 2011, non-EU

countries

Energy supply and use

OECD.stat: green growth indicators,

2011, non-EU countries

Material use, extraction and waste

FAOstat: agricultural production, 2011,

all countries

Agricultural production, monetary and

physical

UN Comtrade/BACI International trade, monetary and

physical

We will start with collecting the detailed data for the latest year for which it is

available. For example SUTs are only available for 2009 as the latest year. In order

to upscale the detailed data to the year 2012, we will use the growth rates related to

production, consumption, savings, investments, exports and imports based on

available national accounts data.

These data will be used to recalibrate the model on the year 2012 by making

extrapolation for the microeconomic missing data. For each country, a standard

rebalancing procedure will be applied to guaranty the accountancy coherence of the

Social Accounting Matrix (SAM). The first simulation period will therefore be 2013.

3.2.2 Baseline assumptions for EU countries

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The baseline scenario is developed separately for each individual EU country

and follows the reference scenario of “Impact assessment of Energy Roadmap

2050”. This study draws its main assumptions upon a number of publicly available

documents prepared for/by European Commission. Table below summaries the

main elements of the baseline scenario of EXIOMOD, their respective coverage and

sources:

Table 3.1 Overview of the baseline scenario assumptions

Scenario element Geographical and

sectoral coverage

Source of data

Population

projections

Country level European Population

Projections, base year

2008 from Eurostat

Economic growth:

including GDP per

capita and

productivity

Country level 2009 Ageing Report

prepared by European

Commission (baseline

scenario)

Development of

sectoral value added

Country and sectoral

level (NACE)

“EU Energy Trends to

2030” report

Development of the

energy mix

EU level “Impact assessment of

Energy Roadmap 2050”

report

Policy assumptions EU level “Impact assessment of

Energy Roadmap 2050”

report

Development of

sectoral productivity

including labor and

Multi Factor

productivity

Country and sectoral

level (NACE)

“Sectoral Growth Drivers

and Competitiveness in

European Union”

3.2.2.1 Macroeconomic and demographic assumptions

The population projections until 2050 draws on the EUROPOP2008 convergence

scenario (EUROpean POPulation Projections, base year 2008) from Eurostat4.

Assumptions relative to participation rates in the labour market, GDP per capita

growth and labour productivity follow the “baseline” scenario of the 2009 Ageing

Report (European Economy, April 2009)5.

Figure 3.1 Projection of the total population (percentage and absolute change in the

period 2008-2060)

4 See http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_

code=KS-SF-10-001. Data available on http://epp.eurostat.ec.europa.eu/portal/page/portal/

population/data/database. 5 http://ec.europa.eu/economy_finance/publications/publication_summary14911_en.htm

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Figure 3.2 Labor force projections (as % change of population of the age between

15 and 64)

Since the later report uses also as demographic assumptions the EUROPOP2008

population projections, we have the same hypothesis concerning the long term

GDP growth per country. Consistent with the intermediate scenario 2 “sluggish

recovery” presented in the Europe 2020 strategy, we assume that the recent

economic crisis has long lasting effects leading to a permanent loss in GDP. We

assume that the short term GDP growth will gradually converge to its long term

trend in 2015.

Figure 3.3 Projections of the GDP per capita growth and decomposition of the

growth rate between explanatory factors

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3.2.2.2 Energy import prices

As reported in Figure 3.4, international fuel prices are projected to grow over the

projection period with oil prices reaching 88$’08/bbl in 2020, 106$’08/bbl in 2030

and 127 $08/barrel in 2050. With 2% inflation (ECB target), this corresponds to

some 300 $ in 2050 in nominal terms. Gas prices follow a trajectory similar to oil

prices reaching 62$’08/boe in 2020, 77$’08/boe in 2030 and 98 $(08)/boe in 2050

while coal prices increase progressive from 23$’08/boe to around 33$’08/boe after

2030.

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Figure 3.4: Reference scenario fossil fuel price assumptions

Source: EU Energy Roadmap 2050

3.2.2.3 Sector-specific trends and assumptions

The study on EU Energy trends until 2030 includes country specific data tables that

include baseline assumption of the study in terms of the development of levels and

structure of value added of different economic sectors. An example of the data

available from the study is presented in the figure below. This rich country and

sector-specific data will be used as an input to baseline of the present study.

Figure 3.5: Key demographic and economic assumptions of EU Energy trends until

2030 study for Belgium

The 2009 Aging Report provides the data on the development of labor and Total

Factor Productivity at the country level without split between different sectors of the

economy. In order to be able to translate the country-level values of this study to the

sectoral level using proportionality assumption we will make use of the study on

“Sectoral Growth Drivers and Competitiveness in European Union”. This study

provides the analysis of the historical development of labor and Total Factor

Productivity of various economic sectors of the European countries. Figure below

gives an example of the sector-specific data available from the study.

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Figure 3.6 Sector-specific developments of labor and Total Factor Productivity

The EU Energy Roadmap 2050 impact assessment is based on a set of

assumptions relative to the trends of technical efficiency parameters and energy

costs. These assumptions reflect expectations regarding technical improvements

but also a large lists of adopted European policy measures: regulatory measures

relative to energy efficiency, energy markets, transport, European financial support

in large infrastructures, national measures (renewable and nuclear), or internal

market.

Figure 3.7 Development of the shares of different types of fuels in total primary

energy

The study provides information on the change over time in the composition of the

fuel mix at the European level and the projections of demand for heat and steam

from the tertiary, residential and industry sectors that will be used as a part of the

baseline for the present study. In order to be able to apply the EU level outcomes of

the study for specific countries and sectors we will use the information on the

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present energy mix and present levels of steam and heat demand from the

database of the EXIOMOD model. In order to calculate the country specific

development of energy mix over time, we will multiply its present energy mix with

percentage changes over time provided on Figure 3.7. The same technique will be

applied for the calculation of the trajectory for the development of demand for steam

and heat over time based on the data on Figure 3.8.

Figure 3.8 Projections of demand for heat and steam by economic sector

3.2.3 Baseline assumptions for the rest of the world

Consumption and production of natural resources cannot be considered for EU

alone in isolation from the rest of the world. This means that EXIOMOD baseline

scenario should also cover non-EU countries. EXIOMOD model includes detailed

representation of 43 main countries of the world and covers about 90% of the total

world GDP. For the construction of the baseline scenario for non-EU countries we

will use the IMF Economic Outlook for the medium term projection (until 2016) and

the CEPII study of long-term growth of various non-EU countries6. IMF Economic

outlook provides information about demographic development of the countries,

governmental deficit and debt, savings and investments as well as its average GDP

growth. CEPII study on long-term growth is one of the rare existing study which

calculates the growth prospects of various countries until the year 2050 using

advanced econometric techniques. The results of this study include information

about future developments of demography, savings/savings rate and investments,

energy efficiency and Total Factor Productivity (TFP) as well as the developments

of country-level GDP. Environmental and energy assumptions will be taken from the

OECD Environmental Outlook 2050 and the IEA World Energy Outlook 2011.

6 http://www.cepii.fr/anglaisgraph/workpap/summaries/2006/wp06-16.htm

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3.2.4 Qualitative baseline assumptions

Besides quantitative baseline assumptions until 2030, we will also provide a

qualitative view of the possible development of the baseline scenario for the longer

2050 horizon. Comprehensive scenario requires elaborated description of future

developments of different parts of the EU economy and society. Making quantitative

long-term forecasts requires the availability of reliable and complete historical and

prospective time-series data and can thus generally be developed only for certain

parts of the economy (certain economic agents and certain variables). Complex

issues, such as gradual changes in behaviour of households due to certain cultural

changes, are quite difficult to capture within formal quantitative forecasts and they

should be addressed from a qualitative perspective using participatory approaches.

The baseline scenario of EXIOMOD will incorporate information relative to historical

resource efficiency trends from Subtask 3.1 into the model. The way these trends

are going to develop until 2050, that is whether they will be maintained,

accentuated or on the contrary inflected, is crucial to here to understand the

resource efficiency problematic and will be coherent with the qualitative part of the

scenarios.

3.3 Data for baseline and alternative improvement option scenarios

Data for alternative improvement option scenarios will be defined in Subtask 3.2.

This subtask will also clearly define the baseline assumptions, i.e. the trajectories of

buildings characteristics under the business-as-usual scenario.

The data relative to the baseline and alternative improvement option scenarios are

the data related to the resource use (including materials, energy, water and labor)

during the construction phase in a broad sense (new construction, refurbishment

and demolition including recycling) and for the use phase (maintenance and

exploitation) thus covering the whole life-cycle of buildings and infrastructure.

PE International is currently working on LCA analysis for different improvement

options covering the different stages of life-cycle of the buildings and infrastructure

and it is not possible to provide detailed information at this stage about which data

will be delivered exactly. Below we provide an example of LCA data which is

available from the finalized IMPRO-Building Study.

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Figure 3.9 Example of the life-cycle assessment data from IMPRO-Building Study

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4 Incorporating bottom-up improvement options into the macroeconomic framework

The improvement options identified need quantified information to feed the macro-

level model. This information includes investment costs, time for implementation of

the improvement, use of materials, efficiency gains, estimated service life times,

and amount of waste that is generated (where relevant). We now describe more

concretely the way the identified bottom-up improvements options will be

incorporated in EXIOMOD. After describing the approach to be used for modelling

physical flow and stocks, we present the methodology used for linking it with the

macroeconomic model.

4.1 Modelling physical flow and stocks for material, buildings and infrastructures

To measure properly the impact of improvement options on the economy, the

current version of EXIOMOD will be expended with a country-specific physical

module that represents stocks and flows in physical units. Firstly, it is important to

have a complete representation of the stock of buildings and infrastructure in

physical units, i.e. the number of square meters for buildings, of kilometres for

roads. We will follow the methodology of IMPRO build study for classification of

buildings and group the detailed house types into a maximum of five to ten large

groups. The stock of houses in each of the groups will be associated with the

certain material use requirements for reconstruction and/or construction and certain

level of energy use for heating and warm water.

Each resource efficiency scenario will thus define the evolution over time of each

stock of buildings and infrastructures and their associated characteristics. The

characteristics and the quantity of each shock will then define the physical flows for

each type of materials both in the construction and user phases. For instance, each

type of building will require a certain level of consumption of energy and water

during the user phase per m2. Depending on the scenario, the type and quantity of

material used for the construction phase will also differ. The LCA IMPRO build

study based on building and infrastructure models will provide information on the

characteristics of buildings and infrastructures in terms of resource use for the

construction and use phases. This information will be used to calibrate the different

resource intensity parameters of EXIOMOD such as the annual quantity of energy

or water consumed or the quantity of resources used for reconstruction and/or

construction (per m2 of each type of buildings).

LCA data from IMPRO-building study includes data on resource and product use at

very detailed level that does not coincide with the relatively-low level of details of

the EXIOMOD model (includes only 129 types of commodities and materials). This

is especially true for the data related to the use of different types of construction

materials. This means that is in order to be used as input to simulations LCA results

should be aggregated to the level of details of EXIOMOD. In order to do that we will

use the data and classification of ProdCom database of EuroStat. This data

includes information about production and sales of about 4000 types of goods and

materials, hence it could be relatively easy linked with the detailed data of LCA

studies. By using ProdCom data as a bridge between very detailed LCA data and

relatively aggregated structure of the EXIOMOD model we will be able to create a

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proper linkage between the bottom-up and top-down parts of our hybrid modelling

framework. Please notice that a number main materials used by the construction

sector such as different types of ferrous and non-ferrous metals, stone, sand and

clay, glass and bricks are explicitly represented in EXIOMOD and can be directly

linked to the results of LCA analysis, which means that the limitation of the

proposed approach (use of ProdCom for aggregation) for the analysis are not very

significant.

Secondly, a proper analysis of the resource efficiency requires a global picture by

tracking the used quantities of each of the resources over time. Looking at the

annual flow alone is insufficient for full analysis, one needs to analyze the changes

in the cumulated flow of each resource resulting from the implementation of the

improvement options. In order to do that we will use the dynamic MFA that will be

based on post-processing the simulation results of the EXIOMOD model. Dynamic

MFA will be applied until 2030 which coincides with the horizon for the quantitative

results of the present study. Dynamic stock modelling provides information not only

about the future development resources stocks but also about future waste flows

and emissions associated with the accumulated stocks of waste.

MFA covering the period until 2030 will be based on the simulation results of

EXIOMOD and include the following steps:

1. EXIOMOD calculates for each year of the simulation period 2013-2030 the

extraction, use of materials by each of the economic sectors, use of

materials for creation of capital stock as well as consumers’ and

governments’ consumption

2. Historical MFA provides us with data on available and accumulated stocks

resources and waste for the year 2012. These data can be used as a

starting point for the calculation of respective stocks (of materials and

waste) for the period 2013-2030 based on results of EXIOMOD.

3. For the period 2013-2030 we will calculate changes in the amounts of

materials that enter national economies, accumulate in capital stock, and

exit to the environment during extraction, manufacturing, use,

recycling/reuse, disposal.

4. The final step is to estimate the remaining available stocks of materials and

the accumulated stocks of waste that give raise to emissions.

Figure below presents the schematic view of different material flows that will be

calculated for the present study. The calculations will be done outside of EXIOMOD

model code and on the basis of its simulation results. In order to perform dynamic

MFA we will create an additional program in VBA or GAMS that will allow us to

automatically create the range of MFA indicators on the basis of EXIOMOD outputs.

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Figure 4.1 Dynamic Material Flow Accounting7

4.2 Linkage with the macroeconomic model

Once the stock of buildings and the related physical flows have been defined, one

needs to relate these flows with the macroeconomic general equilibrium model

expressed in monetary terms. It is important to mention that the consistency of such

a hybrid modelling relies on the fact that the representation of the consumer is not

based on the standard nested utility approach used in most CGE models. The

standard approach assumes that expenditures in each commodity evolve (more or

less) proportionally to the revenue of households. This representation may give

inaccurate projections concerning the use of resources (materials, energy and

water) since their consumption is not related to the service they provide to

households.

Taking inspiration from the approach borrowed from typical bottom-up engineering

models and advocated in hybrid modelling (Laitner and Hanson, 2006), our hybrid

modeling assumes that housing expenditures, which includes all expenditures

related to the construction and user phases, are a sort of priority expenditures.

Households first meet their housing spending requirements. Then the rest of their

7 Source: World Resources Institute 2004

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revenue (minus savings) is allocated between the other goods. This conception is

consistent with the fact that shelter is generally considered as one of the main

human "basic needs". Because there are heterogeneity between consumers, the

number of m2 per households and the energy consumption per m

2 increases with

the revenue of households. But in both cases, saturation levels avoid unrealistic

rebound or wealth effects8 by imposing ceilings on the number of m

2 per

households and on the energy consumption per m2.

With this setting, the economic impact of resource efficiency improvement can be

consistently measured by EXIOMOD. Each scenario (baseline and alternative)

relative to the development of buildings and infrastructures defines the demand for

each type of resource in physical units in the broad construction phase and

indirectly in the user phase. Then, the link between the physical and monetary

demands for the consumers, government and firms has to be made. Knowing the

cost of construction and of energy and water per physical unit, this link is

straightforward to make in EXIOMOD and the impact of resource efficiency

improvement options on final demand and other national account indicators

(production, GDP, etc.) can be evaluated.

Logically, expenditures related to residential buildings will be supported by

households. Expenditures related to infrastructures will be supported by the

government. Therefore, the final expenditures equation of the government will be

modified accordingly. Depending on their use, the charges related to utility buildings

will be supported by the governments (educational buildings, hospital) or by the

economic sectors (wholesale & retail, hotel & restaurant). Depending on the user or

the owner, offices and sport facilities will be affected proportionally between the

public and the private sectors. For both sectors, the national account capital stock

data used in EXIOMOD will have to be separated between real estates (buildings

and infrastructures) and productive capital (machinery and apparatus) to be in full

coherence with the physical representation of buildings and infrastructures stock.

8 The rebound effect (or Jevons paradox) refers to the positive response of the demand for a

resource induced by the introduction of new technologies that increases resource efficiency. This

increase in demand tends to offsets some or all of the expected reductions in resource

consumption from resource efficiency improvements. The wealth effect simply refers to the fact

that the consumption of most commodities tends to increase with the level of revenue. However,

this relation is not necessarily linear depending if a given commodity is more a “necessary” or

more a “luxury” good or/and because of saturation levels.

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5 References

Laitner, J. A. and Hanson D. A. 2006. “Modeling Detailed Energy-Efficiency

Technologies and Technology Policies within a CGE Framework.” The

Energy Journal, Special Issue on Hybrid Modelling: New Answers to Old

Challenges. Pages 139-158.

Tukker A., Poliakov E., Heijungs R., Hawkins T., Neuwahl F., Rueda-Cantuche J.

M., Giljum, S., Moll S., Oosterhaven J., Bouwmeester M., Towards a global

multi-regional environmentally extended input–output database, Ecological

Economics, Volume 68, Issue 7, 15 May 2009, Pages 1928-1937.

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6 Annex I: Description of EXIOMOD model

6.1 Model overview

EXIOMOD combines the main structure of traditional CGE analysis with the

innovative elements of semi-endogenous growth and adaptive expectations under

the framework of Dynamic General Equilibrium. All main behavioral parameters of

the model have been estimated econometrically based on the available data.

The model incorporates the representation of 43 main countries of the world. It

includes an individual representation of all EU27 countries and candidate member

states. It also includes the largest emitters such as US, Japan, Russia, Brazil, India

and China. The EXIOMOD model is a dynamic, recursive over time, model,

involving dynamics of capital accumulation and technology progress, stock and flow

relationships and adaptive expectations.

EXIOMOD combines economic, environmental and social domains in an efficient

and flexible way:

1. Social effects: includes the representation of three education levels, ten occupation types and households grouped into five income classes. One can trace the effects of specific policy on income redistribution and unemployment.

2. Economic effects: the model captures both direct and indirect (wide-economic and rebound) effects of policy measures. EXIOMOD allows for calculation of detailed sectoral impacts at the level of 129 economic sectors.

3. Environmental effects: the model includes representation of 28 types GHG and non-GHG emissions, different types of waste, land use (15 types) and use of material resources (171 types).

6.2 Geographical coverage of EXIOMOD

The model incorporates the representation of 43 main countries of the world. It

includes an individual representation of all EU27 countries and candidate member

states. It also includes the largest emitters such as US, Japan, Russia, Brazil, India

and China. Countries which are not represented separately in EXIOMOD are

grouped together into the rest of the world “country” with its separate technology,

production, consumption and trade.

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Table 6.1 Country list

Countries represented in EXIOMOD

EU27 (each country separately)

United States

Japan

China

Canada

South Korea

Brazil

India

Mexico

Russia

Australia

Switzerland

Norway

Turkey

Taiwan

Indonesia

South Africa

Rest of the world

6.3 Unique database of EXIOMOD: EXIOPOL and CREEA projects

The project EXIOPOL (A New Environmental Accounting Framework Using

Externality Data and Input-Output Tools for Policy Analysis) had as a key goal to

produce a Multi-Regional Environmentally Extended Supply and Use Table (MR EE

SUT) for the whole world. The EXIOPOL database (EXIOBASE) has a unique detail

and covers 30 emissions, around resource extractions, given specifically for 130

sectors and products by 43 countries making up 95% of global GDP, plus a Rest of

World. A follow-up project of 3.5 Mio Euro under the EU’s FP7 program, called

Compiling and Refining Environmental and Economic Accounts (CREEA), will

expand this database with improved extensions for water, land use and other

resources, but above all to create an additional layer with physical information in the

(economic) SUT in the EXIOPOL database (in short: EXIOBASE). For the first time

this will produce a global, integrated Multi Regional Environmentally Extended

Economic and Physical Supply and Use Table (MR EE E&PSUT).

In EXIOPOL project, the following steps were taken

1. Harmonizing and detailing SUT

a. Gathering SUT from the EU27 via Eurostat, and other SUT and IOT from

16 other countries (covering in total 95% of the global GDP). Gap filling of

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missing European SUT via ‘same country assumption’. Converting IOT into

SUT by assuming a diagonal Supply table.

b. Constructing Use tables in basic prices via reversed engineering

c. Harmonizing and detailing SUT with auxiliary data from FAO and a

European AgriSAMS for agriculture, the EIA database for energy carriers

and electricity, various resource databases for resources, etc.

2. Harmonizing and estimating extensions

a. Allocating available resource extraction data (e.g. FAOSTAT, Aquastat)

to industry sectors

b. Allocating the International Energy Agency database for 60 energy

carriers to sectors of use. Estimating emissions on the basis of energy and

other activity data and TNOs TEAM model

3. Linking the country SUT via trade

a. Splitting of Import Use tables and allocating imports to countries of

exports using UN COMTRADE trade shares

b. Confronting the resulting implicit exports with exports in the SUT,

adjusting differences and rebalancing via RUGs GRAS procedure

6.4 Integrated impact assessment of policy measures

Sustainability is a complex issue which develops along social, economic and

environmental domains. Modern impact assessment tool should be capable of

assessing the impact of a particular policy measure or a combination of policy

measure on all three dimensions of sustainability. EXIOMOD combines those three

domains in an efficient and flexible way:

1. Social effects: includes the representation of three education levels and households grouped into five income classes. One can trace the effects of specific policy on income redistribution and allocation of negative impacts of local pollutants between various income groups. Effect of employment and unemployment by three education types and ten occupations can be evaluated.

2. Economic effects: the model captures both direct and indirect (wide-economic and rebound) effects of policy measures. It assesses policy impacts on GDP, consumption, production, investment etc. EXIOMOD allows for calculation of detailed sectoral impacts at the level of 129 economic sectors.

3. Environmental effects: the model includes representation of all GHG and non-GHG emissions, different types of waste, land use and use of material resources.

EXIOMOD permits two-way linkages between social, economic and environmental pillars of sustainability by allowing these three dimensions to interact and influence each other.

6.5 General framework of the model

Traditional computable general equilibrium (CGE) models as well as macro-models

have ignored uncertainty, possibility to go beyond the rational behavior of

households and proper treatment of expectations. Most of them also treat

technological progress as exogenous to the model which makes it difficult to use

such models for long-term policy analysis. EXIOMOD combines the main structure

of traditional CGE analysis with the innovative elements of adaptive expectations

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and semi-endogenous growth under the framework of Dynamic General

Equilibrium. All main behavioral equations of the model have been estimated

econometrically based on the available time-series data.

The use of CGE as a main structure of EXIOMOD allows for:

Capturing intra-regional and inter-regional effects

Full representation of inter-sectoral spillovers

Efficient incorporation of all main resource constraints

Proper treatment of unemployment and under-utilization of capital stock

By combining various methodological approaches EXIOMOD framework allows for:

Dynamic analysis with endogenous investment decisions and development of capital stock, human capital and RTD stock

Addressing uncertainty and provide confidence interval for policy affects by means by formal sensitivity analysis

Incorporation of uncertainty and irrationality into the behavior of economic agents via adaptive expectations

Semi-endogenous technological progress

6.6 Main structure of EXIOMOD

Computable General Equilibrium (CGE) framework is the basis of EXIOMOD. This

framework takes as a basis the notion of the Walrasian equilibrium. Walrasian

equilibrium is one of the foundations of the modern micro economics theory.

CGE models are a class of economic models that use actual economic data to

estimate how an economy might react to changes in policy, technology or other

external factors. A model consists of (a) equations describing model variables and

(b) a database (usually very detailed) consistent with the model equations.

The model equations tend to be neo-classical in spirit, assuming cost-minimizing

behavior by producers, average-cost pricing, and household demands based on

optimizing behavior. A CGE model database consists of tables of transaction values

and elasticities: dimensionless parameters that capture behavioral response. The

database is presented as a social accounting matrix (SAM). It covers the whole

economy of a country, and distinguishes a number of sectors, commodities, primary

factors and types of households.

CGE models utilize the notion of the aggregate economic agent. They represent the

behavior of the whole population group or of the whole industrial sector as the

behavior of one single aggregate agent. It is further assumed that the behavior of

each such aggregate agent is driven by certain optimization criteria such as

maximization of utility or minimization of costs.

The EXIOMOD model includes the representation of the micro-economic behavior

of the following economic agents: several types of households differentiated by 5

income quintiles, production sectors differentiated by 129 classification categories

developed in EXIOPOL project; investment agent; federal government and external

trade sector.

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6.7 Households and labor market

Each household group in the EXIOMOD model consists of the individuals

differentiated by three types of education levels and ten types of professions. The

composition of households is based on the extensive socio-economic dataset.

Behavior of the households is based on the utility-maximization principle.

Household’s utility is associated with the level and structure of its consumption.

Each household spends its consumption budget on services and goods in order to

maximize its satisfaction from the chosen consumption bundle.

Households have substitution possibilities between different consumption

commodities. They can substitute consumption of transport for the consumption of

other goods and services. They are also able to substitute between their

consumption of electricity and other energy. The inclusion of substitution

possibilities is important for a realistic representation of the consumption decisions

of the households and better assessment of the welfare and economic effects of

transport and energy policies. Households in the EXIOMOD model receive their

income in the form of wages, capital rent, unemployment benefits and other

transfers from the federal government.

The level of the unemployment benefits, received by the household, depends upon

the level of unemployment associated with the particular education level and

occupation type of the individuals within the household. The unemployment in the

EXIOMOD is modeled according to the search-and-matching approach, which

explains the existence of frictional unemployment in the country. The main idea

behind this approach is that there exists a mismatch between the available

vacancies and the unemployed labor. It takes firms and individuals some time to

find the right vacancy/employee, which results in the frictional unemployment. The

level of this type of unemployment varies between the education levels and

occupation types.

The levels of the wages earned in different sectors of the economy by individuals

with different education levels and occupation types are determined by the national-

level bargaining process between the sector-specific trade union and the firms

within this sector. Firms share partially their profits with their employees by paying

them wages, which are higher then their marginal product of labor.

6.8 Production sectors and trade

Behavior of the sectors is based on the minimization of the production costs for a

given output level under the sector’s technological constraint. Production costs of

each sector in the EXIOMOD model include labor costs by type of labor, capital

costs and the costs of intermediate inputs. The sector’s technological constraint

describes the production technology of each sector. It provides information on how

many of different units of labor, capital and of the 129 commodities and services,

traded in the economy, are necessary for the production of one unit of the

composite sectoral output.

In accordance with their production technology, sectors have substitution

possibilities between different intermediate inputs and production factors. They can

substitute between the use of different education types and between different

occupations within each education type. They are also able to substitute between

their consumption of electricity and other energy types such as gas, coal, oil and

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refined oil. Existence of the technological substitution possibilities is an important

feature of the production process and cannot be neglected while modeling sectoral

production.

Each sector in the economy may produce more than one type of commodity and the

combination of these different commodities corresponds to the sectoral composite

output. Production output of each sector can be either delivered to the domestic

market or exported. Each sector determines the shares of its outputs, sold

domestically and exported, based on the profit maximization principle. It takes into

account the relative prices of the same type of commodities in its own country and

abroad.

An Armington assumption on international trade is adopted in the model. According

to this assumption the commodities produced by the domestic sectors for the

consumption inside the country and for the consumption outside of it have different

specifications.

Figure 6.1 Production structure of sectors in EXIOMOD

6.9 Market equilibrium and investments

The equilibrium prices of all commodities and capital are defined by the market

equilibrium conditions. Under the market equilibrium the sum of demands for a

particular commodity is equal to the sum of its supplies. Due to the existence of

unemployment and wage bargaining on the labor market, it is in disequilibrium. The

level of the wages is determined by the bargaining process between the trade

unions and firms. It depends positively upon the probability to find a new job and the

firms’ profits.

The model incorporates the representation of investment and savings decisions of

the economic agents. Savings in the economy are made by households,

government and the rest of the world. The total savings accumulated at each period

Land types Resources types

Materials and services types

Electricity types

Fuels types

Output

Other inputs

Materials/Services

ElectricNonelectric

Capital/Labor/Energy

Capital/Labor

Low-skilled labor

Non-coalCoal

Gas Fuels

Land/Resources

Energy

Capital/Medium-& High-skilled labor

Capital Labor

Medium-skilled labor High-skilled labor

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of time are invested into accumulation of the sector-specific physical capital, which

is not mobile between the sectors. The stock of this capital at each period of time is

equal to the last period stock minus depreciation plus the new capital accumulated

during the previous period of time.

The total investment into the sector-specific capital stock is spent on buying

different types of capital goods such as machinery, equipment and buildings. The

concrete mixture of different capital goods used for physical investments is

determined by the maximization of the utility of the investment agent. This is an

artificial national economic agent responsible for buying capital goods for physical

investments in all the domestic sectors.

6.10 Federal government

The EXIOMOD model incorporates the representation of the federal government.

The governmental sector collects taxes, pays subsidies and makes transfers to

households, production sectors and to the rest of the world. The federal government

consumes a number of commodities, where the optimal governmental demand is

determined according to the maximization of the governmental consumption utility

function. The model incorporates the governmental budget constraint. According to

this constraint the total governmental tax revenues are spend on subsidies,

transfers, governmental savings and consumption.

Finally, the model includes the trade balance constraint, according to which the

value of the country’s exports plus the governmental transfers to the rest of the

world are equal to the value of the country’s imports.

6.11 Environmental effects and welfare function

All production and consumption activities in the EXIOMOD model are associated

with emissions and environmental damage. This is in particular true for the

transportation. The model incorporates the representation of all major greenhouse

gas and non-greenhouse gas emissions. Emissions in the EXIOMOD model are

associated either with the use of different energy types by firms and households or

with the overall level of the firms’ outputs.

Environmental quality is one of the main factors of the households’ utility function.

Changes in the levels of emissions have a direct impact upon the utilities of the

households. Different income classes in the model are influenced differently by the

changes in emission levels of various pollutants. Local pollutants have more impact

upon the poor household groups, who live closer to the industrial sites and areas

with dense traffic. The evaluation of emissions by each household group depends

upon its willingness-to-pay. It is assumed that the willingness-to-pay is closely

correlated with the income of the household. Rich households put a higher value to

the emissions then the poor ones. The willingness-to-pay of the households is

determined endogenously in the EXIOMOD model and influences their respective

welfare function. The welfare of each household type (population group) in the

EXIOMOD model is calculated as the equivalent variation measure and depends

upon consumption of commodities and the level of emissions.

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6.12 Dynamic features

The EXIOMOD model is a dynamic, recursive over time, model, involving dynamics

of capital accumulation and technology progress, stock and flow relationships and

adaptive expectations. A recursive dynamic structure composed of a sequence of

several temporary equilibriums. The first equilibrium in the sequence is given by the

benchmark year. In each time period, the model is solved for an equilibrium given

the exogenous conditions assumed for that particular period. The equilibriums are

connected to each other through capital accumulation. Thus, the endogenous

determination of investment behavior is essential for the dynamic part of the model.

Investment and capital accumulation in year t depend on expected rates of return

for year t+1, which are determined by actual returns on capital in year t.

6.13 Endogenous technological progress and growth

The general structure of the EXIOMOD extends to include endogenous growth

elements such as technological progress and human capital accumulation.

Specifically, the specification of endogenous growth in the model is based on

models of economic growth and catch-up that are widely used in the literature on a

leader-follower context of economic development. In this framework, productivity

growth is generated through own innovations, knowledge spillovers and technology

adoption (catching-up).

The greater this distance and the higher the absorptive capacity, the greater is the

potential for growth through technology transfer. The basic framework results in

short-run growth rates being endogenous and long-run relative productivity levels

being endogenous (but constant), implying that long-run growth rates converge.

These properties imply that we can classify the growth equation as a semi-

endogenous growth model. Productivity relative to the frontier is endogenous. Still,

the model remains realistic in that it maintains the long-run stability properties of

neo-classical growth theory.

6.14 Treatment of resources and environmental effects

EXIOMOD incorporates the representation of all major environmental effects related

to production and consumption choices of households and firms. The model

includes all main types of GHG and non-GHG emissions, waste and waste water,

land use changes and deforestation. In case of waste it also incorporates the

modeling of the treatment of waste and recycling by type of waste.

6.15 Integration of physical and monetary data

Integration of physical and monetary data allows one to take proper account on the

physical restrictions on consumption and production activities as well as to provide

a full analysis of sustainability issues. EXIOMOD database includes both monetary

and physical units in a consistent way and allows for their integration in a unified

modeling framework. Physical dimension provides the representation of all main

resource constraints in the global economy.

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6.16 Uncertainty and non-rational behavior

Uncertainty is included in EXIOMOD is addressed in two separate ways. First one is

related to the representation of expectations of consumers and producers in the

model. They are treated using adaptive expectations framework where the

economic agents adjust their behavior according the past realizations of their

expectations. The framework of adaptive expectation is flexible enough to allow for

some non-rational and stochastic elements in it such a hysteric expectations for

example or group-related behavior. This can potentially be useful for modeling of

penetration of new technologies and behavioral changes of consumers over time.

6.17 Econometric nature of the model

All main behavioral equations of the model are estimated econometrically on the

time-series data from EU KLEMS, international trade data and other relevant time-

series data. These behavioral equations include: (1) production functions of groups

of sectors including the substitution possibilities between production inputs; (2)

semi-endogenous growth of total factor productivity; (3) international trade part with

gravity framework and (4) unemployment modeling with logistic wage curve.

6.18 Main dimensions of the model: sectors and commodities, factors of

production, types of emissions, energy use, physical inputs, land and water

use

Table 6.2 Sectors/commodities in EXIOMOD

N Name of production sector Extended

NACE code

1 Cultivation of paddy rice p01.a

2 Cultivation of wheat p01.b

3 Cultivation of cereal grains nec p01.c

4 Cultivation of vegetables, fruit, nuts p01.d

5 Cultivation of oil seeds p01.e

6 Cultivation of sugar cane, sugar beet p01.f

7 Cultivation of plant-based fibers p01.g

8 Cultivation of crops nec p01.h

9 Cattle farming p01.i

10 Pigs farming p01.j

11 Poultry farming p01.k

12 Meat animals nec p01.l

13 Animal products nec p01.m

14 Raw milk p01.n

15 Wool, silk-worm cocoons p01.o

16 Forestry, logging and related service activities (02) p02

17 Fishing, operating of fish hatcheries and fish farms;

service activities incidental to fishing (05)

p05

18 Mining of coal and lignite; extraction of peat (10) p10

19 Extraction of crude petroleum and services related to

crude oil extraction, excluding surveying

p11.a

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20 Extraction of natural gas and services related to

natural gas extraction, excluding surveying

p11.b

21 Extraction, liquefaction, and regasification of other

petroleum and gaseous materials

p11.c

22 Mining of uranium and thorium ores (12) p12

23 Mining of iron ores p13.1

24 Mining of copper ores and concentrates p13.20.11

25 Mining of nickel ores and concentrates p13.20.12

26 Mining of aluminium ores and concentrates p13.20.13

27 Mining of precious metal ores and concentrates p13.20.14

28 Mining of lead, zinc and tin ores and concentrates p13.20.15

29 Mining of other non-ferrous metal ores and

concentrates

p13.20.16

30 Quarrying of stone p14.1

31 Quarrying of sand and clay p14.2

32 Mining of chemical and fertilizer minerals, production

of salt, other mining and quarrying n.e.c.

p14.3

33 Processing of meat cattle p15.a

34 Processing of meat pigs p15.b

35 Processing of meat poultry p15.c

36 Production of meat products nec p15.d

37 Processing vegetable oils and fats p15.e

38 Processing of dairy products p15.f

39 Processed rice p15.g

40 Sugar refining p15.h

41 Processing of Food products nec p15.i

42 Manufacture of beverages p15.j

43 Manufacture of fish products p15.k

44 Manufacture of tobacco products (16) p16

45 Manufacture of textiles (17) p17

46 Manufacture of wearing apparel; dressing and dyeing

of fur (18)

p18

47 Tanning and dressing of leather; manufacture of

luggage, handbags, saddlery, harness and footwear

(19)

p19

48 Manufacture of wood and of products of wood and

cork, except furniture; manufacture of articles of straw

and plaiting materials (20)

p20

49 Manufacture of pulp, paper and paper products (21) p21

50 Publishing, printing and reproduction of recorded

media (22)

p22

51 Manufacture of coke oven products p23.1

52 Manufacture of motor spirit (gasoline) p23.20.a

53 Manufacture of kerosene, including kerosene type jet

fuel

p23.20.b

54 Manufacture of gas oils p23.20.c

55 Manufacture of fuel oils n.e.c. p23.20.d

56 Manufacture of petroleum gases and other gaseous

hydrocarbons, except natural gas

p23.20.e

57 Manufacture of other petroleum products p23.20.f

58 Processing of nuclear fuel p23.3

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59 Manufacture of chemicals and chemical products (24) p24

60 Manufacture of rubber and plastic products (25) p25

61 Manufacture of glass and glass products p26.a

62 Manufacture of ceramic goods p26.b

63 Manufacture of bricks, tiles and construction products,

in baked clay

p26.c

64 Manufacture of cement, lime and plaster p26.d

65 Manufacture of other non-metallic mineral products

n.e.c.

p26.e

66 Manufacture of basic iron and steel and of ferro-alloys

and first products thereof

p27.a

67 Precious metals production p27.41

68 Aluminium production p27.42

69 Lead, zinc and tin production p27.43

70 Copper production p27.44

71 Other non-ferrous metal production p27.45

72 Casting of metals p27.5

73 Manufacture of fabricated metal products, except

machinery and equipment (28)

p28

74 Manufacture of machinery and equipment n.e.c. (29) p29

75 Manufacture of office machinery and computers (30) p30

76 Manufacture of electrical machinery and apparatus

n.e.c. (31)

p31

77 Manufacture of radio, television and communication

equipment and apparatus (32)

p32

78 Manufacture of medical, precision and optical

instruments, watches and clocks (33)

p33

79 Manufacture of motor vehicles, trailers and semi-

trailers (34)

p34

80 Manufacture of other transport equipment (35) p35

81 Manufacture of furniture; manufacturing n.e.c. (36) p36

82 Recycling of metal waste and scrap p37.1

83 Recycling of non-metal waste and scrap p37.2

84 Production of electricity by coal p40.11.a

85 Production of electricity by gas p40.11.b

86 Production of electricity by nuclear p40.11.c

87 Production of electricity by hydro p40.11.d

88 Production of electricity by wind p40.11.e

89 Production of electricity nec, including biomass and

waste

p40.11.f

90 Transmission of electricity p40.12

91 Distribution and trade of electricity p40.13

92 Manufacture of gas; distribution of gaseous fuels

through mains

p40.2

93 Steam and hot water supply p40.3

94 Collection, purification and distribution of water (41) p41

95 Construction (45) p45

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96 Sale, maintenance, repair of motor vehicles, motor

vehicles parts, motorcycles, motor cycles parts and

accessoiries

p50.a

97 Retail sale of automotive fuel p50.b

98 Wholesale trade and commission trade, except of

motor vehicles and motorcycles (51)

p51

99 Retail trade, except of motor vehicles and motorcycles;

repair of personal and household goods (52)

p52

100 Hotels and restaurants (55) p55

101 Transport via railways p60.1

102 Other land transport p60.2

103 Transport via pipelines p60.3

104 Sea and coastal water transport p61.1

105 Inland water transport p61.2

106 Air transport (62) p62

107 Supporting and auxiliary transport activities; activities

of travel agencies (63)

p63

108 Post and telecommunications (64) p64

109 Financial intermediation, except insurance and

pension funding (65)

p65

110 Insurance and pension funding, except compulsory

social security (66)

p66

111 Activities auxiliary to financial intermediation (67) p67

112 Real estate activities (70) p70

113 Renting of machinery and equipment without operator

and of personal and household goods (71)

p71

114 Computer and related activities (72) p72

115 Research and development (73) p73

116 Other business activities (74) p74

117 Public administration and defence; compulsory social

security (75)

p75

118 Education (80) p80

119 Health and social work (85) p85

120 Collection and treatment of sewage p90.01

121 Collection of waste p90.02.a

122 Incineration of waste p90.02.b

123 Landfill of waste p90.02.c

124 Sanitation, remediation and similar activities p90.03

125 Activities of membership organisation n.e.c. (91) p91

126 Recreational, cultural and sporting activities (92) p92

127 Other service activities (93) p93

128 Private households with employed persons (95) p95

129 Extra-territorial organizations and bodies p99

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Table 6.3 Types of physical extractions represented in EXIOMOD including land

use, water use and material use

ExtractionType Id Extraction Type Name

1 Land Use - Arable Land - rice 2 Land Use - Arable Land - wheat 3 Land Use - Arable Land - other cereals 4 Land Use - Arable Land - roots and tubers 5 Land Use - Arable Land - sugar crops 6 Land Use - Arable Land - pulses 7 Land Use - Arable Land - nuts 8 Land Use - Arable Land - oil crops 9 Land Use - Arable Land - vegetables

10 Land Use - Arable Land - fruits 11 Land Use - Arable Land - fibres 12 Land Use - Arable Land - other crops 13 Land Use - Arable Land - fodder crops 14 Land Use - Permanent Pasture 15 Land Use - Forest Area

Table 6.4 Types of factor inputs in EXIOMOD

Factor Input Type Code Factor Input Type Name

w02 Other net taxes on production w03.a Compensation of employees; Low-skilled w03.b Compensation of employees; Medium-skilled w03.c Compensation of employees; High-skilled w04.a Operating surplus: Consumption of fixed capital w04.b Operating surplus: Rents on land w04.c Operating surplus: Royalties on resources w04.d Operating surplus: Remaining net operating surplus z01 Compensation of Employees; wages & salaries z02 Comp of Emp; employers social contributions z03 Employed persons z04.a Employment hours: Low-skilled z04.b Employment hours: Medium-skilled z04.c Employment hours: High-skilled z05 Fixed capital formation z06 Fixed capital stock

Table 6.5 Representation of physical inputs and outputs in EXIOMOD including

energy, materials, water and biomass

Physical Type Id

Physical Type Name

1 Gross Energy Use - Anthracite 2 Gross Energy Use - Coking Coal 3 Gross Energy Use - Other Bituminous Coal 4 Gross Energy Use - Sub-Bituminous Coal 5 Gross Energy Use - Lignite/Brown Coal 6 Gross Energy Use - Patent Fuel 7 Gross Energy Use - Coke Oven Coke 8 Gross Energy Use - BKB/Peat Briquettes 9 Gross Energy Use - Coke Oven Gas

10 Gross Energy Use - Blast Furnace Gas 11 Gross Energy Use - Industrial Waste 12 Gross Energy Use - Municipal Waste (Renew) 13 Gross Energy Use - Municipal Waste (Non-Renew) 14 Gross Energy Use - Primary Solid Biomass

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15 Gross Energy Use - Biogas 16 Gross Energy Use - Other Liquid Biofuels 17 Gross Energy Use - Natural Gas 18 Gross Energy Use - Crude Oil 19 Gross Energy Use - Natural Gas Liquids 20 Gross Energy Use - Refinery Feedstocks 21 Gross Energy Use - Additives/Blending Components 22 Gross Energy Use - Refinery Gas 23 Gross Energy Use - Liquefied Petroleum Gases (LPG) 24 Gross Energy Use - Motor Gasoline 25 Gross Energy Use - Gasoline Type Jet Fuel 26 Gross Energy Use - Kerosene Type Jet Fuel 27 Gross Energy Use - Kerosene 28 Gross Energy Use - Gas/Diesel Oil 29 Gross Energy Use - Residual Fuel Oil 30 Gross Energy Use - White Spirit & SBP 31 Gross Energy Use - Lubricants 32 Gross Energy Use - Bitumen 33 Gross Energy Use - Petroleum Coke 34 Gross Energy Use - Non-specified Petroleum Products 35 Gross Energy Use - Hydro 36 Gross Energy Use - Geothermal 37 Gross Energy Use - Solar Photovoltaics 38 Gross Energy Use - Solar Thermal 39 Gross Energy Use - Wind 40 Gross Energy Use - Electricity 41 Gross Energy Use - Heat 42 Gross Energy Use - Aviation Gasoline 43 Gross Energy Use - Naphtha 44 Gross Energy Use - Paraffin Waxes 45 Gross Energy Use - Nuclear 46 Gross Energy Use - Other Hydrocarbons 47 Gross Energy Use - Peat 48 Gross Energy Use - Charcoal 49 Gross Energy Use - Gas Works Gas 50 Gross Energy Use - Oxygen Steel Furnace Gas 51 Gross Energy Use - Ethane 52 Gross Energy Use - Tide, Wave and Ocean 53 Gross Energy Use - Coal Tar 54 Gross Energy Use - Other Sources 55 Gross Energy Use - Gas Coke 56 Gross Energy Use - Biogasoline 57 Gross Energy Supply - Lignite/Brown Coal 58 Gross Energy Supply - Peat 59 Gross Energy Supply - Coke Oven Coke 60 Gross Energy Supply - Coal Tar 61 Gross Energy Supply - Coke Oven Gas 62 Gross Energy Supply - Blast Furnace Gas 63 Gross Energy Supply - Industrial Waste 64 Gross Energy Supply - Municipal Waste (Renew) 65 Gross Energy Supply - Municipal Waste (Non-Renew) 66 Gross Energy Supply - Primary Solid Biomass 67 Gross Energy Supply - Biogas 68 Gross Energy Supply - Other Liquid Biofuels 69 Gross Energy Supply - Natural Gas 70 Gross Energy Supply - Crude Oil 71 Gross Energy Supply - Natural Gas Liquids 72 Gross Energy Supply - Refinery Gas

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73 Gross Energy Supply - Liquefied Petroleum Gases (LPG) 74 Gross Energy Supply - Motor Gasoline 75 Gross Energy Supply - Kerosene Type Jet Fuel 76 Gross Energy Supply - Kerosene 77 Gross Energy Supply - Gas/Diesel Oil 78 Gross Energy Supply - Residual Fuel Oil 79 Gross Energy Supply - Lubricants 80 Gross Energy Supply - Bitumen 81 Gross Energy Supply - Petroleum Coke 82 Gross Energy Supply - Non-specified Petroleum Products 83 Gross Energy Supply - Hydro 84 Gross Energy Supply - Geothermal 85 Gross Energy Supply - Solar Photovoltaics 86 Gross Energy Supply - Solar Thermal 87 Gross Energy Supply - Wind 88 Gross Energy Supply - Electricity 89 Gross Energy Supply - Heat 90 Gross Energy Supply - Dissipative Energy Losses 91 Gross Energy Supply - Sub-Bituminous Coal 92 Gross Energy Supply - Patent Fuel 93 Gross Energy Supply - Naphtha 94 Gross Energy Supply - White Spirit & SBP 95 Gross Energy Supply - Nuclear 96 Gross Energy Supply - Other Bituminous Coal 97 Gross Energy Supply - BKB/Peat Briquettes 98 Gross Energy Supply - Other Hydrocarbons 99 Gross Energy Supply - Charcoal

100 Gross Energy Supply - Coking Coal 101 Gross Energy Supply - Gas Works Gas 102 Gross Energy Supply - Biodiesels 103 Gross Energy Supply - Refinery Feedstocks 104 Gross Energy Supply - Additives/Blending Components 105 Gross Energy Supply - Aviation Gasoline 106 Gross Energy Supply - Paraffin Waxes 107 Gross Energy Supply - Oxygen Steel Furnace Gas 108 Gross Energy Supply - Gasoline Type Jet Fuel 109 Gross Energy Supply - Biogasoline 110 Gross Energy Supply - Tide, Wave and Ocean 111 Gross Energy Supply - Ethane 112 Gross Energy Supply - Other Sources 113 Gross Energy Supply - Gas Coke 114 Gross Energy Supply - Anthracite 115 Net Energy Use - Total 116 Emission-relevant Energy Use - Anthracite 117 Emission-relevant Energy Use - Coking Coal 118 Emission-relevant Energy Use - Other Bituminous Coal 119 Emission-relevant Energy Use - Sub-Bituminous Coal 120 Emission-relevant Energy Use - Lignite/Brown Coal 121 Emission-relevant Energy Use - Patent Fuel 122 Emission-relevant Energy Use - Coke Oven Coke 123 Emission-relevant Energy Use - BKB/Peat Briquettes 124 Emission-relevant Energy Use - Coke Oven Gas 125 Emission-relevant Energy Use - Blast Furnace Gas 126 Emission-relevant Energy Use - Industrial Waste 127 Emission-relevant Energy Use - Municipal Waste (Renew) 128 Emission-relevant Energy Use - Municipal Waste (Non-Renew) 129 Emission-relevant Energy Use - Primary Solid Biomass 130 Emission-relevant Energy Use - Biogas

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131 Emission-relevant Energy Use - Other Liquid Biofuels 132 Emission-relevant Energy Use - Natural Gas 133 Emission-relevant Energy Use - Crude Oil 134 Emission-relevant Energy Use - Natural Gas Liquids 135 Emission-relevant Energy Use - Refinery Feedstocks 136 Emission-relevant Energy Use - Additives/Blending Components 137 Emission-relevant Energy Use - Refinery Gas 138 Emission-relevant Energy Use - Liquefied Petroleum Gases (LPG) 139 Emission-relevant Energy Use - Motor Gasoline 140 Emission-relevant Energy Use - Gasoline Type Jet Fuel 141 Emission-relevant Energy Use - Kerosene Type Jet Fuel 142 Emission-relevant Energy Use - Kerosene 143 Emission-relevant Energy Use - Gas/Diesel Oil 144 Emission-relevant Energy Use - Residual Fuel Oil 145 Emission-relevant Energy Use - Lubricants 146 Emission-relevant Energy Use - Petroleum Coke 147 Emission-relevant Energy Use - Non-specified Petroleum Products 148 Emission-relevant Energy Use - Aviation Gasoline 149 Emission-relevant Energy Use - Other Hydrocarbons 150 Emission-relevant Energy Use - Peat 151 Emission-relevant Energy Use - Charcoal 152 Emission-relevant Energy Use - Gas Works Gas 153 Emission-relevant Energy Use - Naphtha 154 Emission-relevant Energy Use - Oxygen Steel Furnace Gas 155 Emission-relevant Energy Use - Ethane 156 Emission-relevant Energy Use - Bitumen 157 Emission-relevant Energy Use - Coal Tar 158 Emission-relevant Energy Use - Gas Coke 159 Domestic Extraction Used - Biomass - Primary Crops - rice 160 Domestic Extraction Used - Biomass - Primary Crops - wheat 161 Domestic Extraction Used - Biomass - Primary Crops - other

cereals 162 Domestic Extraction Used - Biomass - Primary Crops - roots and

tubers 163 Domestic Extraction Used - Biomass - Primary Crops - sugar

crops 164 Domestic Extraction Used - Biomass - Primary Crops - pulses 165 Domestic Extraction Used - Biomass - Primary Crops - nuts 166 Domestic Extraction Used - Biomass - Primary Crops - oil crops 167 Domestic Extraction Used - Biomass - Primary Crops - vegetables 168 Domestic Extraction Used - Biomass - Primary Crops - fruits 169 Domestic Extraction Used - Biomass - Primary Crops - fibres 170 Domestic Extraction Used - Biomass - Primary Crops - other crops 171 Domestic Extraction Used - Biomass - Crop Residues - straw 172 Domestic Extraction Used - Biomass - Crop Residues - other crop

residues 173 Domestic Extraction Used - Biomass - Fodder Crops - fodder

crops 174 Domestic Extraction Used - Biomass - Fodder Crops - biomass

harvested from grasslands 175 Domestic Extraction Used - Biomass - Grazed Biomass - grazing 176 Domestic Extraction Used - Biomass - Wood - timber 177 Domestic Extraction Used - Biomass - Wood - other extractions 178 Domestic Extraction Used - Biomass - Animals - marine fish 179 Domestic Extraction Used - Biomass - Animals - inland water fish 180 Domestic Extraction Used - Biomass - Animals - other aquatic

animals 181 Domestic Extraction Used - Biomass - Animals - hunting

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182 Domestic Extraction Used - Metal Ores - iron ores 183 Domestic Extraction Used - Metal Ores - bauxite and aluminium

ores 184 Domestic Extraction Used - Metal Ores - copper ores 185 Domestic Extraction Used - Metal Ores - lead ores 186 Domestic Extraction Used - Metal Ores - nickel ores 187 Domestic Extraction Used - Metal Ores - tin ores 188 Domestic Extraction Used - Metal Ores - uranium and thorium

ores 189 Domestic Extraction Used - Metal Ores - zinc ores 190 Domestic Extraction Used - Metal Ores - precious metal ores 191 Domestic Extraction Used - Metal Ores - other metal ores 192 Domestic Extraction Used - Non-Metallic Minerals - chemical and

fertilizer minerals 193 Domestic Extraction Used - Non-Metallic Minerals - clays and

kaolin 194 Domestic Extraction Used - Non-Metallic Minerals - limestone,

gypsum, chalk, dolomite 195 Domestic Extraction Used - Non-Metallic Minerals - salt 196 Domestic Extraction Used - Non-Metallic Minerals - slate 197 Domestic Extraction Used - Non-Metallic Minerals - other industrial

minerals 198 Domestic Extraction Used - Non-Metallic Minerals - building

stones 199 Domestic Extraction Used - Non-Metallic Minerals - gravel and

sand 200 Domestic Extraction Used - Non-Metallic Minerals - other

construction materials 201 Domestic Extraction Used - Fossil Energy Carriers - hard coal 202 Domestic Extraction Used - Fossil Energy Carriers - lignite/brown

coal 203 Domestic Extraction Used - Fossil Energy Carriers - crude oil 204 Domestic Extraction Used - Fossil Energy Carriers - natural gas 205 Domestic Extraction Used - Fossil Energy Carriers - natural gas

liquids 206 Domestic Extraction Used - Fossil Energy Carriers - peat for

energy use 207 Unused Domestic Extraction - Biomass - Primary Crops - rice 208 Unused Domestic Extraction - Biomass - Primary Crops - wheat 209 Unused Domestic Extraction - Biomass - Primary Crops - other

cereals 210 Unused Domestic Extraction - Biomass - Primary Crops - roots

and tubers 211 Unused Domestic Extraction - Biomass - Primary Crops - sugar

crops 212 Unused Domestic Extraction - Biomass - Primary Crops - pulses 213 Unused Domestic Extraction - Biomass - Primary Crops - nuts 214 Unused Domestic Extraction - Biomass - Primary Crops - oil crops 215 Unused Domestic Extraction - Biomass - Primary Crops -

vegetables 216 Unused Domestic Extraction - Biomass - Primary Crops - fruits 217 Unused Domestic Extraction - Biomass - Primary Crops - fibres 218 Unused Domestic Extraction - Biomass - Primary Crops - other

crops 219 Unused Domestic Extraction - Biomass - Crop Residues - straw 220 Unused Domestic Extraction - Biomass - Crop Residues - other

crop residues 221 Unused Domestic Extraction - Biomass - Fodder Crops - fodder

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crops 222 Unused Domestic Extraction - Biomass - Fodder Crops - biomass

harvested from grasslands 223 Unused Domestic Extraction - Biomass - Grazed Biomass -

grazing 224 Unused Domestic Extraction - Biomass - Wood - timber 225 Unused Domestic Extraction - Biomass - Wood - other extractions 226 Unused Domestic Extraction - Biomass - Animals - marine fish 227 Unused Domestic Extraction - Biomass - Animals - inland water

fish 228 Unused Domestic Extraction - Biomass - Animals - other aquatic

animals 229 Unused Domestic Extraction - Biomass - Animals - hunting 230 Unused Domestic Extraction - Metal Ores - iron ores 231 Unused Domestic Extraction - Metal Ores - bauxite and aluminium

ores 232 Unused Domestic Extraction - Metal Ores - copper ores 233 Unused Domestic Extraction - Metal Ores - lead ores 234 Unused Domestic Extraction - Metal Ores - nickel ores 235 Unused Domestic Extraction - Metal Ores - tin ores 236 Unused Domestic Extraction - Metal Ores - uranium and thorium

ores 237 Unused Domestic Extraction - Metal Ores - zinc ores 238 Unused Domestic Extraction - Metal Ores - precious metal ores 239 Unused Domestic Extraction - Metal Ores - other metal ores 240 Unused Domestic Extraction - Non-Metallic Minerals - chemical

and fertilizer minerals 241 Unused Domestic Extraction - Non-Metallic Minerals - clays and

kaolin 242 Unused Domestic Extraction - Non-Metallic Minerals - limestone,

gypsum, chalk, dolomite 243 Unused Domestic Extraction - Non-Metallic Minerals - salt 244 Unused Domestic Extraction - Non-Metallic Minerals - slate 245 Unused Domestic Extraction - Non-Metallic Minerals - other

industrial minerals 246 Unused Domestic Extraction - Non-Metallic Minerals - building

stones 247 Unused Domestic Extraction - Non-Metallic Minerals - gravel and

sand 248 Unused Domestic Extraction - Non-Metallic Minerals - other

construction materials 249 Unused Domestic Extraction - Fossil Energy Carriers - hard coal 250 Unused Domestic Extraction - Fossil Energy Carriers -

lignite/brown coal 251 Unused Domestic Extraction - Fossil Energy Carriers - crude oil 252 Unused Domestic Extraction - Fossil Energy Carriers - natural gas 253 Unused Domestic Extraction - Fossil Energy Carriers - natural gas

liquids 254 Unused Domestic Extraction - Fossil Energy Carriers - peat for

energy use 255 Water Consumption Blue - Agriculture - rice 256 Water Consumption Blue - Agriculture - wheat 257 Water Consumption Blue - Agriculture - other cereals 258 Water Consumption Blue - Agriculture - roots and tubers 259 Water Consumption Blue - Agriculture - sugar crops 260 Water Consumption Blue - Agriculture - pulses 261 Water Consumption Blue - Agriculture - nuts 262 Water Consumption Blue - Agriculture - oil crops

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263 Water Consumption Blue - Agriculture - vegetables 264 Water Consumption Blue - Agriculture - fruits 265 Water Consumption Blue - Agriculture - fibres 266 Water Consumption Blue - Agriculture - other crops 267 Water Consumption Blue - Agriculture - fodder crops 268 Water Consumption Green - Agriculture - rice 269 Water Consumption Green - Agriculture - wheat 270 Water Consumption Green - Agriculture - other cereals 271 Water Consumption Green - Agriculture - roots and tubers 272 Water Consumption Green - Agriculture - sugar crops 273 Water Consumption Green - Agriculture - pulses 274 Water Consumption Green - Agriculture - nuts 275 Water Consumption Green - Agriculture - oil crops 276 Water Consumption Green - Agriculture - vegetables 277 Water Consumption Green - Agriculture - fruits 278 Water Consumption Green - Agriculture - fibres 279 Water Consumption Green - Agriculture - other crops 280 Water Consumption Green - Agriculture - fodder crops 281 Water Consumption Total - Livestock - dairy cattle 282 Water Consumption Total - Livestock - nondairy cattle 283 Water Consumption Total - Livestock - pigs 284 Water Consumption Total - Livestock - sheep 285 Water Consumption Total - Livestock - goats 286 Water Consumption Total - Livestock - buffaloes 287 Water Consumption Total - Livestock - camels 288 Water Consumption Total - Livestock - horses 289 Water Consumption Total - Livestock - chicken 290 Water Consumption Total - Livestock - turkeys 291 Water Consumption Total - Livestock - ducks 292 Water Consumption Total - Livestock - geese 293 Water Consumption Total - Manufacturing - food products,

beverages and tobacco 294 Water Consumption Total - Manufacturing - textiles and textile

products 295 Water Consumption Total - Manufacturing - pulp, paper, publishing

and printing 296 Water Consumption Total - Manufacturing - chemicals, man-made

fibres 297 Water Consumption Total - Manufacturing - non-metallic, mineral

products 298 Water Consumption Total - Manufacturing - basic metals and

fabrication of metals 299 Water Consumption Total - Domestic - domestic Water

Consumption Total 300 Water Consumption Total - Electricity - tower 301 Water Consumption Total - Electricity - once-through 302 N loads - Biomass - Primary Crops - Rice 303 N loads - Biomass - Primary Crops - Wheat 304 N loads - Biomass - Primary Crops - Other cereals 305 N loads - Biomass - Primary Crops - Roots and tubers 306 N loads - Biomass - Primary Crops - Sugar crops 307 N loads - Biomass - Primary Crops - Pulses 308 N loads - Biomass - Primary Crops - Nuts 309 N loads - Biomass - Primary Crops - Oil crops 310 N loads - Biomass - Primary Crops - Vegetables 311 N loads - Biomass - Primary Crops - Fruits 312 N loads - Biomass - Primary Crops - Fibres 313 N loads - Biomass - Primary Crops - Other crops

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314 N loads - Biomass - Fodder Crops - Fodder Crops 315 N loads - Biomass - Grazed Biomass - Permanent Pasture 316 P loads - Biomass - Primary Crops - Rice 317 P loads - Biomass - Primary Crops - Wheat 318 P loads - Biomass - Primary Crops - Other cereals 319 P loads - Biomass - Primary Crops - Roots and tubers 320 P loads - Biomass - Primary Crops - Sugar crops 321 P loads - Biomass - Primary Crops - Pulses 322 P loads - Biomass - Primary Crops - Nuts 323 P loads - Biomass - Primary Crops - Oil crops 324 P loads - Biomass - Primary Crops - Vegetables 325 P loads - Biomass - Primary Crops - Fruits 326 P loads - Biomass - Primary Crops - Fibres 327 P loads - Biomass - Primary Crops - Other crops 328 P loads - Biomass - Fodder Crops - Fodder Crops 329 P loads - Biomass - Grazed Biomass - Permanent Pasture

Table 6.6 GHG and non-GHG emissions represented in EXIOMOD

Emission type Discharge

CO2 air

N2O air

CH4 air

HFCs air

PFCs air

SF6 air

NOX air

SOx air

NH3 air

NMVOC air

CO air

CFCs air

HCFCs air

Pb air

Cd air

Hg air

As air

Cr air

Cu air

Ni air

Se air

Zn air

Aldrin air

Chlordane air

Chlordecone air

Dieldrin air

Endrin air

Heptachlor air

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Hexabr.-biph. air

Mirex air

Toxaphene air

HCH air

DDT air

PCB air

dioxin air

PM10 air

BaP air

Benzene air

1,3 Butadiene air

Formaldehyd air

N water

P water

BOD water

N soil

P soil

Cd soil

Cu soil

Zn soil

Pb soil

Hg soil

Cr soil

Ni soil

PM2.5 air

Furans air

Benzo-[a]-pyrene (PAHs) air

PBDEs air

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7 Annex 2: Attendance list of the Stakeholder meeting on "Scenarios towards a Resource Efficient Europe", 12 September 2012, DG ENV, Brussels

Name Organisation E-mail

Adrian Tan BIO IS adrian.tan@biois.com

Agnes Schuurmans Rockwool Int. agnes.schuurmans@rockwool.com

Annick Carpentier EUROMETAUX carpentier@eurometaux.be

Antonio Paparella European

Commission

antonio.paparella@ec.europa.eu

Arjan de Koning CML koning@cml.leidenuniv.nl

Aurelio Braconi EUROFER a.braconi@eurofer.be

Benjamin Denis ETUC bdenis@etuc.org

Bernard Lanfranchi Veolia Environment bernard.lanfranchi@veolia.com

Bert Lieverse VMRG / FAECF b.lieverse@vmrg.nl

Birgit Horvath Federal Environment

Ministry of Austria

birgit.horvath@lebensministerium.at

Bruno Ziegler EDF Research

Division

bruno.ziegler@edf.fr

Celine Carré St. GobainIsover celine.carre@saint-gobain.com

Christian Leroy European Aluminium

Association

leroy@eaa.be

Cuno van Geet NLAgency cuno.vangeet@agentschapnl.nl

David McKinnon Copenhagen

Resource Institute

ETC/SCP

dalmc@etc.min.dk

Edmar Meuwsissen EUMEPS e.meuwissen@eumaps.org

Ester van der Voet CML voet@cml.leidenuniv.nl

Evert Schut Ministry I&M,

Netherlands

evert.schut@rws.nl

Frans Vollenbroek European

Commission

frans.vollenbroek@ec.europa.eu

Fred Seifert Forbo Flooring fred.seifert@forbo.com

Frédéric Reynès TNO frederic.reynes@tno.nl

James Drinkwater World Green

Building Council

drinkwater@worldgbc.org

Jan Urlings International

Synergies

jan.urlings@international-synergies.com

Jane Anderson PE International j.anderson@pe-international.com

Jane Thornback Construction

Products Association

jane.thornback@constructionproducts.org.uk

Johannis Kreissig PE International j.kreissig@pe-international.com

Josefina Lindblom European

Commission

josefina.lindblom@ec.europa.eu

K. Philips Broadview Holding k.philips@broadviewholding.com

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Name Organisation E-mail

Kris Broos Flemish Institute on

Technical Research

(VITO)

kris.broos@vito.be

Lisa Wastiels Belgian Buildings

Research Institute

lisa.wastiels@bbri.be

Martin Erlandsson IVL Swedish

Environmental

Research Institute

martin.erlandsson@ivl.se

Nina Eisenmenger SEC nina.eisenmenger@aau.at

Oscar Nieto CEPMC oscar.nieto@cepmc.org

Peter van der Mars Royal Metaalunie mars@metaalunie.nl

R. Franklin Dept. for Business

Innovation and Skills

robert.franklin@bis.gsi.gov.uk

Ruud Baartmans TNO ruud.baartmans@tno.nl

Shpresa Kotaji PU Europe shpresa_kotaji@huntsman.com

Stephen White European

Commission

stephen.white@ec.europa.eu

Sylvia Maurer BEUC sma@beuc.eu

Wim Debacker Flemish Institute on

Technical Research

(VITO)

wim.debacker@vito.be

Please note that not all participants registered their names and details on the

attendance list above. Participants who did not agree to be on the public

attendance list were not included in the lists sent out to those participants who

requested a list of participants.