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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
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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 [email protected]
Agnes Schuurmans Rockwool Int. [email protected]
Annick Carpentier EUROMETAUX [email protected]
Antonio Paparella European
Commission
Arjan de Koning CML [email protected]
Aurelio Braconi EUROFER [email protected]
Benjamin Denis ETUC [email protected]
Bernard Lanfranchi Veolia Environment [email protected]
Bert Lieverse VMRG / FAECF [email protected]
Birgit Horvath Federal Environment
Ministry of Austria
Bruno Ziegler EDF Research
Division
Celine Carré St. GobainIsover [email protected]
Christian Leroy European Aluminium
Association
Cuno van Geet NLAgency [email protected]
David McKinnon Copenhagen
Resource Institute
ETC/SCP
Edmar Meuwsissen EUMEPS [email protected]
Ester van der Voet CML [email protected]
Evert Schut Ministry I&M,
Netherlands
Frans Vollenbroek European
Commission
Fred Seifert Forbo Flooring [email protected]
Frédéric Reynès TNO [email protected]
James Drinkwater World Green
Building Council
Jan Urlings International
Synergies
Jane Anderson PE International [email protected]
Jane Thornback Construction
Products Association
Johannis Kreissig PE International [email protected]
Josefina Lindblom European
Commission
K. Philips Broadview Holding [email protected]
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Name Organisation E-mail
Kris Broos Flemish Institute on
Technical Research
(VITO)
Lisa Wastiels Belgian Buildings
Research Institute
Martin Erlandsson IVL Swedish
Environmental
Research Institute
Nina Eisenmenger SEC [email protected]
Oscar Nieto CEPMC [email protected]
Peter van der Mars Royal Metaalunie [email protected]
R. Franklin Dept. for Business
Innovation and Skills
Ruud Baartmans TNO [email protected]
Shpresa Kotaji PU Europe [email protected]
Stephen White European
Commission
Sylvia Maurer BEUC [email protected]
Wim Debacker Flemish Institute on
Technical Research
(VITO)
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.