Workshop on the
Criteria to establish projections
scenarios
Selected items from
EU workshop on projections of
November 2011
Mario Contaldi, TASK-GHG
Ankara , 15-17 March 2016
EU Reporting requirements
• Development of draft GHG projection
guidelines
• Parameters, assumptions indicators etc.
QA/QC procedures
• The general ideas behind the tiered guidance.
• The 1st order draft guidance per sector (to be
discussed again in sectoral workshops)
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Outline - 1
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Background
Modeling projections
Variables, Parameters and Indicators
Tiers
QA/QC issues:
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Outline- 2
• Further improved quality of data related to:
– the completeness in terms of gases and sources
– the comparability in terms of assumptions
– the consistency related to
• the impacts of policies and measures
• historic GHG inventories and verified emissions from EU ETS
• the accuracy and quality of methodologies, data
and assumptions used for the projections.
• the transparency of data and information on
methodologies and assumptions
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What consider in projections
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Modelling projections
an economy module, estimating
• projected activity data for all years in the
projection (economic scenarios).
a policy module, providing the
• information on what policies and measures are
assumed to be in place in the years of the
projection (policy scenarios)
a technology module, performing
• the actual emission calculations, based on the
projected activity rates for one or more economic
scenario translating the assumed policies in into
emission factors (technology scenarios)
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Variables, parameters and indicators
Model variables
are values that may change within the scope of the
application of a model in a given projection. There are
input variables: the entities on which the value of the
output variable in the model depends
output variables: the entity that is to reflect the result of
the model and communicate it to the model user;
Model parameters
define the behavior of a relation (mathematical
function) between the input variables and the output
variables. They are basically constants, although in
some applications a parameter might be given a
different value for different times
Projection
indicators:
(Manipulations of) output variables that are used to
communicate the results of the projections exercise to
the out side world
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Variables- technology module
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From historic emissions to projected emissions
• We aim at full consistency of the projections
with the latest historic inventory:
– Activity data
• Activity projections are needed in the same
detail
• Emission factors need to be developed in the
same detail
• Both should show an explainable
development from the historic values:
– Historic - Projection time series consistency!
• .
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Reference models in EU framework
• Where you have country specific projection models available, use
these!
• Projections of energy consumption / supply can be obtained from the
following models:
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–
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PRIMES http://www.e3mlab.ntua.gr/
POLES http://www.enerdata.fr/enerdatauk/tools/Model_POLES.html
WEO
TIMES http://www.etsap.org/Tools.asp
• Agricultural forecasts can be obtained from
– The CAPRI model http://www.ilr1.unibonn.de/agpo/rsrch/capri/caprifp4_e.htm
– The Food and Agriculture Organisation http://www.fao.org/
– The European Fertilizer Manufacturer Association (www.efma.org) and,
– The International Fertilizer Industry Association www.fertlizer.org
• Transport projections can be obtained from:
– The TREMOVE model www.tremove.org/.
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PRIMES Energy system model
• Energy system model by country of Europe, including all sectors and markets and
interconnections among countries for electricity and gas
• Modular system with individual sub-models for demand sectors including
industrial processes and energy supply sectors including detailed electricity, CHP,
gas, and RES models
• Core model integrates two detailed sub-models: PRIMES-TREMOVE transport
model and PRIMES biomass model
• Formulation of actors’ behaviours in each sector influenced by market prices,
technology dynamics and policies.
• The PRIMES integrating module simultaneously clears all markets for energy and
allowances by projecting explicitly all prices. The prices influence demand and
supply behaviours.
• The model can handle market imperfections, price and non price barriers
• A rich set of policy instruments is included in the model
• The formulations allow mainly medium and long term projections, but not short
term forecasting.
• The PRIMES model suite projects dynamically to the future detailed energy
balances, investments, costs, prices, CO2 emissions per country, as well as gas
and electricity flows between countries
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Times Energy system model
• The JRC-EU-TIMES model is a linear optimisation bottom-up
technology model generated with the TIMES model generator
from ETSAP1 of the International Energy Agency
• The equilibrium is driven by the maximization (via linear
programming) of the discounted present value of total surplus,
representing the sum of surplus of producers and consumers,
which acts as a proxy for welfare in each region of the model .
• The model is supported by a detailed database, with the
following main exogenous inputs:
– (1) end-use energy services and materials demand, such as
residential lighting, demand for machine drive or steel;
– (2) characteristics of the existing and future energy related
technologies, such as efficiency, stock, availability, investment costs,
operation and maintenance costs, and discount rate;
– (3) present and future sources of primary energy supply and their
potentials; and
– (4) policy constraints and assumptions.
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CAPRI model
• Economic model with full coverage of agriculture and the
Common Agricultural Policy and related environmental
policies.
• Two major components are a set of regional programming
models (approx. 280 regions, 60 activities) that iterate with a
global, spatial multi-commodity model for about 50 products
and 77 world regions in 40 trade blocks
Purpose for Reference scenario quantification:
• Provide agricultural outlook for Reference scenario, in
particular on livestock and fertiliser use
• Cross check with GLOBIOM on overlapping variables, in
particular in crop sector
• Provide the impacts on the agricultural sector from changed
biofuel demand
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TREMOVE transport model
• New development of econometric transport activity projections for the
Reference scenario
• Detailed sectoral model which projects the fuel consumption, CO2
emissions, pollutant emissions, the stock of vehicles and provides detailed
transport sector costs as well the external costs (congestion, accidents,
noise, air pollution)
• The model includes features aiming at simulating consumer behaviour and
capturing the generalised price of transportation influencing decision
making
• TREMOVE features also a spatial infrastructure tool linking in an integrated
manner the density of infrastructure of refuelling/recharging stations in
different areas (urban/inter-urban/short/long distance) with the different
consumer travelling habits (represented through stylised histograms of
trips)
Purpose for Reference scenario quantification:
• Provide detailed projections for the evolution of the entire transport
sector in terms of transport activity, energy consumption, emissions, fleet
development, new technologies and alternative fuels
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Improved Tier methodology
• Tier 1
• Fall back option;
• Simple method with default parameters or centrally
modelled data sets
• Cannot be used for “important” sectors
• Tier 2
– Same method with country specific parameters and/or
higher stratification
• Tier 3
– Anything more complex than this
– Decision tree
• Guides user step by step through the procedure
• Supports methodology choice (Tier 1, 2 or 3)
• Proposes data to be looked for.
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Components of QA/QC for emissions Projections
• QA/QC plan with objectives: goals for the
quality of the projection output
• QA/QC activities: specific tests to determine if
the quality objectives are met. Roles to be
assigned to independent reviewers
• QA/QC implementation: record of QA/QC
activities undertaken and whether objectives
were met
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Roles and responsabilities for QA/QC
• QA/QC Manager: maintains the QA/QC plan, sets
quality objectives and defines, co-ordinates
QA/QC activities and undertakes cross cutting
QA/QC activities.
• Sectoral Experts: Perform sector specific QA/QC
activities and report to the QA/QC Manager.
Sector Experts should also collaborate with Data
suppliers and other key stakeholders to review
estimates and perform QA/QC on supplied
material.
• External Review Teams: Provide expert/peer
review of projections for specific sectors and
report to the QA/QC Manager.
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