Mexico’s National Baseline Scenario:
A Comparison Exercise in Collaboration
with Denmark
A collaborative project between
the Danish Energy Agency, SEMARNAT and INECC (both Mexico)
on comparing national emission baselines in Mexico
June 2013
Table of Contents
Background ........................................................................................................................................................ 3
Baseline Comparison Results............................................................................................................................. 4
Introduction ................................................................................................................................................... 4
Macro-economics .......................................................................................................................................... 5
Population ................................................................................................................................................. 5
Gross Domestic Product ............................................................................................................................ 5
‘Balance’ + Macro ...................................................................................................................................... 7
Value Added and Activity Variables............................................................................................................... 8
Industrial Value Added .............................................................................................................................. 8
Steel ........................................................................................................................................................... 9
Housing .................................................................................................................................................... 10
Transportation ......................................................................................................................................... 10
‘Balance’ + Macro + Activity .................................................................................................................... 12
Trends, Efficiencies, and Prices ................................................................................................................... 14
Consumption Trends ............................................................................................................................... 14
Electricity Generation Technology Efficiencies ....................................................................................... 15
National Fuel Prices ................................................................................................................................. 16
Proposed Baseline in POLES ........................................................................................................................ 18
Sectoral View of the proposed baseline in POLES ................................................................................... 19
Conclusions .................................................................................................................................................. 21
Appendix: Description of POLES ...................................................................................................................... 23
Background
Recently, the Danish Energy Agency, the Organisation for Economic Co-operation and Development and the
UNEP Risø Centre published the report “National Greenhouse Gas Emissions Baseline Scenarios: Learning
from Experiences in Developing Countries”. The report includes written contributions by experts in ten
developing countries (Brazil, China, Ethiopia, India, Indonesia, Kenya, Mexico, South Africa, Thailand and
Vietnam) and argues that transparency is a key element of good practice in national baseline scenario
setting. Both from a national and international perspective transparency and clarity on calculation methods
for baseline scenarios are important to ensure credibility about national climate change mitigation
planning. Since some parties to the UNFCCC (including Mexico) have pledges quantified emissions
reductions and actions for 2020 (and beyond) relative to their baseline scenario, understanding these
pledges and actions is essential for assessing the likelihood of achieving the agreed goal of limiting global
warming to 2°C. The report argues that transparency on calculation methods, assumptions and sensitivity
analyses is key to reach this understanding, and this argument was also emphasized in Mexico’s chapter in
the report.
Mexico has been a first mover on many international issues not least on climate change actions. In June
2012 the General Law of Climate Change was signed by President Calderon making Mexico one of the few
developing countries to have a domestic law addressing climate change including specific emission targets
relative to a baseline scenario. This law is the foundation on which the forthcoming National Climate
Change Strategy (henceforth called the strategy) will be built. The Ministry of Environment and Natural
Resources (SEMARNAT) is responsible for writing the strategy and it is to be published in June 2013. This
strategy will be the guiding instrument for climate change policies, and will define criteria for prioritizing
mitigation and adaptation actions.
A key element of the strategy is a revision of the national baseline scenario from which the emissions
reduction targets will be measured. The National Institute of Ecology (INECC) is responsible for the revision
of the baseline scenario. It has been important to both SEMARNAT and INECC that the baseline scenario is
robust and conducted in a transparent manner to gain credibility both nationally and internationally.
Building on previous good partnership in writing the report mentioned above, SEMARNAT, INECC and the
Low Carbon Transition Unit (LCTU) at the Danish Energy Agency agreed to conduct a baseline comparison
exercise of the preliminary revised Mexican national baseline scenario before its completion. Specifically,
the assumptions and data from the revised baseline scenario in Mexico will be incorporated into another
modeling framework, whereby differences between resulting baseline scenarios can be explained and
investigated.The purpose is to assess the uncertainty of assumptions and gaining a sense of the robustness
of the baseline scenario. Further, the project is to ensure a transparent process thereby gaining credibility
nationally and internationally by publishing results and lessons learned. The current report presents results
and findings from the comparison exercise.
By engaging in this project, Mexico (as a country with emissions reduction targets relative to a baseline
scenario) now becomes a first mover on transparency in national baseline scenario setting.
Baseline Comparison Results
Introduction
The revised baseline scenario in Mexico is done in the modeling framework Long-range Energy Alternative
Planning System (LEAP) which provides an accounting framework for creating baseline scenarios. This
modeling framework is widely used among developing countries because of its ease-of-use and low data
requirements.1 The baseline comparison will be conducted with the Prospective Outlook on Long-term
Energy Systems model (POLES) run by the consulting company Enerdata. POLES is a global simulation model
with endogenous fossil fuel prices, and can therefore take expected price effects into account when
forecasting emissions.2
Enerdata used the POLES model to help analyse the differences which exist between Enerdata’s reference
scenario (‘Balance’) and the most updated baseline produced by INECC using the LEAP model (‘BAU
Revisada Mas’)3. To arrive at a valid comparison, it was agreed that only those energy and non-energy uses
which are included in both models would be compared (Figure 1).
Figure 1: Baseline emissions from the LEAP (’BAU Revisada Mas’) and POLES-ENERDATA (‘Balance’) models
Until 2020, the two models produce relatively similar results overall. That is, the emissions from primary
sectors (power production, oil and gas production, and fugitive emissions), final demand sectors
(transportation, industry, residential, services, and agriculture), and other non-energy are similar broadly
even if the splits among sub-categories are different. After 2020, ‘Revisada Mas’ forecasts steadily growing
emissions, while the ‘Balance’ scenario has emissions in Mexico levelling-off. Understanding the differences
1
As documented in the baseline publication mentioned above Indonesia, Thailand and Vietnam also uses LEAP for
developing baseline scenarios.
2
For more information on POLES see the appendix.
3
Throughout this document, “‘Revisada Mas’” and “LEAP” are used interchangeably to refer to the baseline produced
by INECC in 2013 using the LEAP model.
between individual sectors and the models’ behaviour between 2020 and 2030 forms the main focus points
for this section.
Macro-economics
Different drivers of emissions were compared in a top-down hierarchy beginning with those large-scale
macro-economic assumptions which have strong influences over total energy demand in POLES-Enerdata
(i.e. population and GDP).
Population
‘Revisada Mas’ uses national estimates for population growth to 2030. ‘Balance’ uses the UN World
Population medium fertility scenario, which is more optimistic than the Mexican estimates (approximately
8% higher in 2030). The net effect of adopting the ‘Revisada Mas’ population estimates into ‘Balance’ is a
slight reduction in emissions (Figure 2; shown in blue). The effect is quite small since POLES-Enerdata only
directly uses population related to number of dwellings and vehicle kilometres & new sales (but which are
also dependent on historical activity levels).
Figure 2: Population assumptions and emissions differences between scenarios
Gross Domestic Product
GDP was the other major macro-economic assumption explored during the baseline comparison. Historical
GDP levels in POLES-Enerdata were taken from the World Bank, and are in line with the figures INECC
provided for national GDP levels. INECC also provided their default assumption of 3.6% economic growth
between 2010 and 2030 (2009 is the last year of data in ‘Revisada Mas’). An alternate assumption of 3.2%
growth was also modelled to observe the difference this assumption has on total and sectoral emissions.
POLES-Enerdata uses GDP growth rates from the IMF between 2013 and 2017, and forecasts from CEPII4 for
the period 2018 to 2030 (2012 is the last year of GDP data in ‘Balance’) (Figure 3).
4
A French research centre in international economics producing studies, databases and analyses on the world
economy and its evolution. Using a Computable General Equilibrium model they forecast long-term GDP growth for
several countries in the world.
While the ‘Revisada Mas’ growth rate is higher than that in ‘Balance’ after 2012, absolute levels of GDP are
not greater until after 2025 due to the much higher growth rate in 2010 in ‘Balance’ (historical in POLESEnerdata vs. forecast in LEAP). POLES often operates on variation of variables rather than absolute levels
(i.e. growth rates usually influence model changes more strongly), therefore the higher growth rates of
‘Revisada Mas’ after 2015 lead to higher emissions when incorporated into ‘Balance’ even though the
absolute GDP of ‘Balance’ is greater than ‘Revisada Mas’ over most of the forecast period. GDP is an
important assumption given the strong sensitivity of emissions, however since the differences in
assumptions between the two models are quite small, the difference in emissions is also fairly small.
Figure 3: GDP assumptions and emissions differences between scenarios
The alternative GDP growth assumption proposed by INECC of 3.2% has a modest influence on total
emissions because of the relative small difference between 3.6% and 3.2% (Figure 4). Lowering the growth
rate by 0.4 percentage points per year will only lower GDP by approximately 8% in 2030, but this translates
into a 5% decrease of total emissions in 2030. This indicates an elasticity of approximately 0.6, i.e.
increasing GDP by 1% creates an increase of 0.6% in total emissions. This picture is confirmed by the impact
on total emissions in 2015 and 2020 as well (Figure 4). This close relation between GDP and total emissions
highlight the importance of the growth assumption in GDP and emphasizes GDP growth as a key emission
driver.
Further, the assumption difference has large relative effect in sectors driven directly by GDP (in POLESEnerdata) and their main fuel providing sectors: residential and electricity generation; transport and oil
production; industry and electricity and fossil fuels.
Figure 4: Emissions differences between POLES-Enerdata scenarios using alternative GDP growth assumptions
‘Balance’ + Macro
The net effect of including the INECC assumptions for both population and GDP growth (3.6%) is almost no
change from the ‘Balance’ scenario (Figure 5). The next stage in the baseline comparison used this version of
the ‘Balance’ scenario including ‘Revisada Mas’ large-scale macro-economic assumptions as a base (labelled
‘Balance + Macro’). The emissions differences attributed to the effects of population and GDP would be
shown in grey in following figures (as is the case for following stages); however the combined effect here is
basically undetectable.
Figure 5: Combined effect of population and GDP assumptions
Value Added and Activity Variables
The next stage in the baseline comparison was to evaluate the effects of changing assumptions in ‘Balance’
for the finer-scale macro-economic details (value added in industry) and activity variables (steel production,
number of dwellings, and transportation parameters). Each of these factors was changed individually and
then the combined effect was modelled.
Industrial Value Added
While there are relatively small differences in value added for industry between the baselines, POLESEnerdata forecasts a faster switch from industry and manufacturing to services. This means a greater
portion of economic activity is produced from less emissions intensive sectors. This effect reflects the
overall speed that the economy shifts away from traditional manufacturing and heavier industries towards
a more services based economy, which within the timeframe of this baseline comparison to 2030 can
represent a moderate difference between the scenarios.
Figure 6: Value added assumptions and emissions differences between scenarios
Steel
There is good agreement on the level of historical steel production; however the forecasts to 2030 are
dramatically different. The ‘Revisada Mas’ baseline foresees steel production climbing at a steady rate, far
above historical levels (approximately three times average between 2000 and 2010). The ‘Balance’ baseline
forecasts a slowly declining steel production, as well as consumption, but which stays at more or less the
average seen over the past five years (Figure 7). POLES-Enerdata explicitly models the consumption and
production of steel, as opposed to other industrial sectors where only value added drives consumption of
fuels and emissions. This aspect of POLES-Enerdata is partly due to historical development of the model,
but makes sense given that steel can be a highly emissions intensive product and centres of production can
move globally given differences in production costs. Locations for other emissions intensive industrial
products like cement and glass are less elastic and tend to be located closer to consumption centres.
Figure 7: Steel production assumptions and emissions differences between scenarios
Given that POLES-Enerdata models steel demand through an elasticity to GDP per capita and the price of
fuel inputs to the production process, it is possible that steel production could continue to grow despite
rising costs to the industry from increased fuel prices (e.g. through beneficial policy measures). However,
the ‘Revisada Mas’ forecast of steel production after 2010 does not appear to be in line with past levels of
growth in the industry and should be reviewed for consistency with the scenario.
Coupled with the diverging assumptions on steel production, there are also different assumptions between
‘Revisada Mas’ and ‘Balance’ on the fuels being used to produce the steel. ‘Revisada Mas’ uses mostly gas
and coking coal, with some electricity, for the production process and maintains this fuel mix to 2030.
‘Balance’ forecasts that the necessary fuel will decrease and shift to entirely electric processes by 2030.
When the ‘Revisada Mas’ assumption on the level of steel production is adopted in ‘Balance’, the fuel mix
shifts to be based heavily on gas and coal, as well as electricity and biomass. This leads to the large
difference in emissions between the two scenarios.
Housing
The estimated number of dwellings in Mexico is larger in the INECC data between 2000 and 2010 than the
data used by POLES-Enerdata for ‘Balance’, especially in the earlier years.5 Both scenarios maintain a
relatively constant level of growth in the number of dwellings, but that growth rate is higher in ‘Revisada
Mas’. This results in a difference of approximately 10% by 2030. However, given the relatively low
emissions intensity of the residential sector compared to other more energy intensive sectors, the resulting
difference in emissions from including the INECC dwelling assumptions is quite small (Figure 8).
Figure 8: Housing assumptions and emissions differences between scenarios
Transportation
INECC provided the projections of various types of vehicles including personal cars and trucks, as well as
heavy trucks. Since the historical estimates before 2010 for these vehicles were unavailable at the time of
the baseline comparison, a linear trend was used to project estimates back to 2000. There is a large
5
POLES uses the number of persons per dwelling from the Instituto Nacional de Estadística y Geografía, but since
population sources are different between POLES and LEAP, the number of dwellings calculated is also different.
difference in the estimates for the number of personal cars, especially between 2005 and 2010 (Figure 9).
POLES-Enerdata uses the North American Transportation Statistics Database (http://nats.sct.gob.mx/) for
its transportation statistics on Mexico. The main transportation assumptions, number of vehicles and
distance travelled, have important differences between ‘Revisada Mas’ and ‘Balance’. The evolution of the
car park in both scenarios evolves with very similar growth rates, but differences in historical data create
the large difference in number of vehicles after 2010. The number of trucks is closer in agreement between
the scenarios, but suffers from the same gap in historical data. The average number of kilometres travelled
is quite similar for cars, while ‘Balance’ forecasts a strong decrease in the kilometres travelled by trucks
whereas ‘Revisada Mas’ maintains the level recently observed constant throughout the forecast.
These differences in car park and kilometres travelled lead to strong differences in average consumption
per kilometre when combined with the fuel consumption data and forecasts. The fuel consumption data
used in each scenario is relatively similar. Given that the consumption is similar, the differences in activity
lead to a much higher consumption per kilometre for both cars and trucks in ‘Revisada Mas’. When the
INECC activity assumptions are included in ‘Balance’, there is a relatively large increase in emissions that
can be attributed to transport.
Because of the large differences in historical estimates and the importance of the transport sector for GHG
emissions, the assumptions in both models could be reviewed to try to arrive at a consistent set of data.
Figure 9: Transportation assumptions and emissions differences between scenarios
Oil & Gas
There was a large discrepancy between the consumption included in LEAP and POLES-Enerdata for the
upstream oil and gas sector. During the baseline comparison it was difficult to obtain detailed data to help
reconcile the existing differences: upstream oil and gas development is only described in a limited fashion
in POLES-Enerdata and INECC used forecasts from SENER for inputs to LEAP, so access to timely
explanations was somewhat difficult. In view of the importance of this sector in Mexico’s economy and the
consideration that Mexican officials would have a better description of consumption and emissions, it was
decided to use the historical data from INECC in the POLES-Enerdata baseline exercise.
Figure 10: Oil & Gas sector consumption data and emissions differences between scenarios
‘Balance’ + Macro + Activity
The combined effect of including all of the preceding INECC assumptions in POLES-Enerdata leads to a
baseline with emissions that are much closer to ‘Revisada Mas’ (Figure 11). This scenario includes both INECC
estimates for historical data as well as assumptions for the future evolution of those parameters. This
method removes some of the feedback mechanisms included in the POLES-Enerdata model (e.g. gasoline
price on the number of kilometres driven), however a large amount of flexibility remains. For instance,
while the structure of industrial value added is fixed according to the INECC assumption, the fuels used to
produce that value added are free to evolve with fuel prices and demand.
The next phase in the baseline comparison was to move beyond data assumptions and determine if
changes to some modelling assumptions could explain the remaining gap between ‘Revisada Mas’ and
’Balance + Macro + Activity’
Figure 11: Emissions differences between scenarios using macro-economic and activity assumptions
Trends, Efficiencies, and Prices
Certain parameters in the POLES-Enerdata model can be adjusted to better reflect a constant behaviour
relative to that observed today. This appears to be implicitly included in the LEAP model for most
parameters in that no explicit change in consumption behaviour is included other than that embedded in
the ratio of consumption to activity variables. To try to reflect a constant behaviour towards energy
consumption, several types of model parameters were adjusted. These included consumption trends not
linked to price effects, efficiencies of electricity generation technologies, and national fuel prices
experienced inside Mexico.
Consumption Trends
POLES-Enerdata includes autonomous trends in some sectors that attempt to reflect past behaviours that
cannot be explicitly linked to price effects or other major drivers like GDP. These trends are relatively minor
compared to the calibrated price effects and are only meant to capture persistent long-term trends. One
example of this type of trend is the shift in services to consuming more electricity over time as more and
more electronics are added in the workplace. This trend is occurring despite the price of electricity that has
risen over time in Mexico. More than just an inelastic demand of electricity, an increase in electricity use
has been observed. A small positive trend attempts to capture this phenomenon. For the baseline
comparison, we set of these trends in Mexico to zero. This effectively removes any autonomous trends and
leaves only price or activity effects to influence energy consumption. The effect from this modelling change
is quite small given that the autonomous trends are not designed to have strong effects in POLES-Enerdata
(Figure 12). The overall impact is a small decrease in the emissions from ‘Balance + Macro + Activity’, which
appears to mostly be related to the demand for electricity in tertiary sectors and the associated production
in the power generation sector.
Figure 12: Emissions differences between scenarios adding the 'no trends' assumption
Electricity Generation Technology Efficiencies
New installations of existing electricity generation technologies are assumed in POLES-Enerdata to become
more efficient over time. This effect is different from the average efficiency of the generating stock
becoming more or less efficient as capacities of different technologies are retired or added. Improvements
in the efficiency of new electricity generation technologies are assumed to occur as a technology is
developed and operated over time. For example, a new installation of a combined cycle gas power plant is
assumed to be somewhat more efficient if installed five years from now because there has been five more
years of experience with the technology.
Like the autonomous consumption trends, this effect is relatively small compared to the change in average
generating stock efficiency, which is mostly driven by fuel prices. The resulting change in emissions from
assuming a fixed efficiency for each electricity generating technology is a small increase in emissions for the
‘Balance + Macro + Activity’ scenario (Figure 13).
Figure 13: Emissions differences between scenarios adding the 'frozen efficiency' assumption
National Fuel Prices
A fundamental aspect of the POLES-Enerdata model is the feedback between energy prices and supply and
demand. This feature of the model allows for a realistic link between consumption and energy choices as
endogenous fuel prices change. Prices are simulated for international energy markets, import prices to
national markets, and final user prices including taxes and subsidies.
The LEAP model does not explicitly include energy prices, either endogenously or exogenously. Given that
the trends and forecasts for energy consumption used in the ‘Revisada Mas’ baseline were generally based
on data and behaviour observed in the recent past, and that there is no price feedback on demand in LEAP,
one could argue that ‘Revisada Mas’ is implicitly assuming frozen fuel prices. To replicate this behaviour in
POLES-Enerdata we have kept final user energy prices constant inside of Mexico. International energy
prices, and national prices in other countries, were allowed to change dynamically with the scenario. This
method is equivalent to fluctuating subsidies or taxes applied to the import price to maintain constant user
prices. Fuel prices for industry, electricity generation, tertiary sectors, and transportation were fixed at
their 2009 values (last year of data in ‘Revisada Mas’).
The net effect of freezing prices at these levels is a strong increase in GHG emissions from several sectors:
gasoline and diesel consumption in transport, more oil and gas versus renewables in electricity generation,
and increased auto-consumption in the oil and gas sector. The change in emissions is very large since fossil
fuel prices have more than doubled between 2009 and 2030 in the ‘Balance’ scenario (Figure 15). By 2030,
this effect is roughly equivalent to providing oil subsidies in the transport sector 4-5 times greater than
those applied today in Mexico. Note that it is coincidental that total GHG emissions from the ‘Balance +
Macro + Activity + Frozen Price’” are approximately equal to ‘Revisada Mas’; the GHG emissions from the
electricity generation and transport sectors are quite different, but in opposing directions.
Figure 14: Emissions differences between scenarios adding the 'frozen national fuel prices' assumption
Energy prices play a pivotal role in the POLES-Enerdata model and feedbacks between scenario
assumptions and international fossil fuel prices can be quite strong, therefore consideration should be
given when making assumptions regarding prices, or lack thereof. Between the scenario ‘Balance’ (our
reference scenario), the scenario ‘Emergence’ (our scenario with stronger climate policies, countries meet
their Copenhagen pledges), and the scenario ‘Renaissance’ (our scenario with easier access to fossil fuel
resources, especially non-conventional types), by 2030 there are differences of $50/bbl for the
international oil price and $3/MMBtu for the North American gas market price. These prices changes in
turn have strong impacts on energy consumption and the types of fuels used. Energy price behaviour was
the strongest factor evaluated during the baseline comparison process (Figure 22).
Figure 15: Variability of international fuel prices
Proposed Baseline in POLES-Enerdata
For the purpose of using POLES-Enerdata to analyze emissions reductions potentials in the Mexican energy
system a baseline based on Enerdata’s ‘Balance’ scenario, and incorporating INECC’s assumptions for
population, GDP, value added, activity variables (e.g. steel production, number of dwellings, transport, oil &
gas development) is proposed as the final result from the baseline comparison (Figure 16). This baseline
directly includes INECC’s own data assumptions and maintains the well tested modelling functionality of
POLES-Enerdata. Excluding autonomous consumption trends and freezing power technology efficiencies
have only minor effects on GHG emissions and we prefer to keep these parameters in the model since they
help maintain realistic behaviour.
Despite the inclusion of frozen national fuel prices bringing the emissions from the POLES-Enerdata
baseline much closer to those produced in ‘Revisada Mas’, this effect is not recommended for the final
baseline to be used in future analyses of Mexico’s energy system using POLES-Enerdata. Endogenous
energy prices is the principal method in the POLES-Enerdata model for transmitting signals to final users
about the carbon content of fuels, resource scarcity, and efficient mitigation options. If this proposed
baseline is used to investigate energy policy changes or mitigation potentials, then the advantage of
endogenous energy prices is highly recommended, even if the result is a baseline that is not as similar to
‘Revisada Mas’.
Figure 16: Emissions differences between LEAP and POLES-Enerdata using proposed set of assumptions
Sectoral View of the proposed baseline in POLES-Enerdata
There is relatively broad agreement of the historical data for industry used in the models, although industry
is one of the most difficult sectors to be certain of same definitions and perimeters. The largest forecast
differences come from production and consumption in heavy industries like steel, cement, and glass. There
appears to be agreement between historical and forecast emissions for non-energy uses, however, given
that non-energy uses are not well detailed in LEAP the comparison remains unclear.
Figure 17: Relative industry sector differences between scenarios
There is relatively little agreement on the emissions and consumption for the oil and gas sector. This could
be due to the broad definition for this sector in POLES-Enerdata (includes oil and gas production, refining,
auto-consumption by industry, and biofuels production), but details regarding the PEMEX baseline used as
the basis for ‘Revisada Mas’ were not available to confirm this at the time of the baseline comparison
study. More would be work needed to reconcile the large differences in the oil and gas sector (details by
fuel for consumption and emissions of non-energy). Consequently, the INECC data for the historical period
were incorporated into the POLES-Enerdata baseline to help bridge the gap between forecasts.
Figure 18: Relative oil and gas sector differences between scenarios
The main differences for consumption of energy and GHG emissions in the electricity generation sector
appear to be due to different perimeters for the sector (i.e. public and private capacities). It is important to
determine if the consumption and emissions included in ‘Revisada Mas’ are for public only, or both public
and private generation. SENER appears to provide two sets of capacity data (one set in line with ‘Revisada
Mas’ and included in the SENER prospectives, and another set from the SENER Sistema de Información
Energética online data portal more in line with the ‘Balance’ estimates). The main differences are between
fossil fuel technologies, which are likely those linked with auto-producers (i.e. not connected to the public
electricity network). The INECC data was included in the POLES-Enerdata baseline to help reconcile these
differences.
Figure 19: Relative electricity sector differences between scenarios
Overall in the baseline, the tertiary sectors only contribute about 5% of total emissions, so impacts from
differences in these emissions are limited. However, there are strong relative differences for emissions in
services and residential. Most of these differences appear to be due to oil and gas consumption, which
could be related to the implicit use of static fossil fuel prices.
Figure 20: Relative tertiary sector differences between scenarios
Transport GHG emissions are highly dependent on oil prices in POLES-Enerdata, which begin to increase
strongly around 2018 in the final baseline scenario. Assumptions for car park and kilometres travelled also
have strong influences on the total emissions, and strong differences exist in the resulting consumption per
kilometre calculated for each model. These assumptions should be reviewed to ensure they are coherent
and consistent with the overall scenario assumptions (see Transportation section). Further investigation is
warranted given that transport contributes a large portion of the total GHG emissions.
Figure 21: Relative transport sector differences between scenarios
Conclusions
Many of the differences in the historical data in this study can be attributed to small variations in
conversion factors, exchange rates, re-publication and conversion of the same data by different providers,
as well as misinterpretations of sector perimeters and years of data. Overall, for most sectors the historical
data used between the models agrees well and a forecast can be generated in POLES-Enerdata that
includes most of the broad features from ‘Revisada Mas’.
The differences in activity assumptions have a very strong effect, especially in later years and specifically
assumptions about steel production and number of cars and distance travelled (Figure 22). While the effect
of freezing prices appears to be one of the only drivers capable of bridging the remaining gap between
emissions calculated in POLES-Enerdata and LEAP, we feel that incorporating some forecast of future prices
is extremely important (whether through endogenous modelling or exogenously when creating
consumption forecasts).
Therefore, we recommend using a final baseline incorporating INECC’s assumptions for population, GDP,
value added, activity variables (e.g. steel production, number of dwellings, transport, oil & gas
development) is proposed as the final result from the baseline comparison to be used for further analysis of
the Mexican energy system. Comparing energy consumption and GHG emissions calculated for ‘Revisada
Mas’ and the proposed POLES-Enerdata baseline provides valuable insights into assumptions implicitly
included in the LEAP model or included assumptions, but which are not explicitly detailed. The detailed
modelling included in POLES-Enerdata provides the opportunity to advance beyond the capabilities of the
LEAP model for evaluating policy options and mitigation potentials.
Figure 22: Relative size of emissions differences due to different assumptions
Appendix: Description of POLES-Enerdata
The POLES-Enerdata energy-economy model
The POLES-Enerdata model provides a complete system for the simulation and economic analysis of the
sectoral impacts of climate change mitigation strategies. The POLES model is not a General Equilibrium
Model, but a dynamic Partial Equilibrium Model, essentially designed for the energy sector but also
including other GHG emitting activities, with the 6 GHG of the “Kyoto basket”. The simulation process is
dynamic, in a year by year recursive approach that allows describing full development pathways from 2005
to 2050.
The use of the POLES-Enerdata model combines a high degree of detail on the key components of the
energy systems and a strong economic consistency, as all changes in these key components are at least
partly determined by relative price changes at sectoral level. Thus each mitigation scenario can be
described as the set of consistent transformations of the initial Reference case that are induced by the
introduction of a carbon constraint or carbon value/penalty.
As the model identifies 57 regions of the world, with 22 energy demand sectors and more than 40 energy
technologies – now including generic Very Low Energy end-use technologies – the description of climate
policy induced changes can be quite extensive (see below for a brief presentation of key features,
technologies and modelling principles).
As far as induced technological change is concerned, the model provides dynamic cumulative processes
through the incorporation of Two Factor Learning Curves, which combine the impacts of “learning by
doing” and “learning by searching” on the technologies’ improvement dynamics. As price induced diffusion
mechanism (such as feed-in tariffs) can also be included in the simulations, the model allows for a taking
into account of the key drivers to the future development of new energy technologies.
One key aspect of the analysis of energy technology development with the POLES-Enerdata model is indeed
that it relies in all cases on a framework of permanent inter-technology competition, with dynamically
changing attributes for each technology. In parallel, the expected cost and performance data for each key
technology are gathered and examined in the TECHPOL database that is developed at LEPII-EPE for any
modelling and policy-making purpose.
Finally one can emphasise the fact that, although the model does not provide the total indirect macroeconomic costs of mitigation scenarios, it however allows to produce reliable economic assessments that
are principally based on the costs of developing low or zero carbon technologies, thus benefiting from a
strong engineering background.
POLES-Enerdata General information
The POLES-Enerdata model is a world simulation model for the energy sector. It works in a year-by-year
recursive simulation and partial equilibrium framework, with endogenous international energy prices and
lagged adjustments of supply and demand by world region. Developed under different EU research
programmes (JOULE, FP5, FP6), the model is fully operational since 1997. It has been used for policy
analyses by EU-DG Research, DG Environment and DG TREN, as well as by the French Ministry of Ecology
and Ministry of Industry. The model enables to produce:
-
Detailed long term (2050) world energy outlooks with demand, supply and price projections by main
region;
-
CO2 emission Marginal Abatement Cost curves by region and/or sector, and emission trading systems
analyses, under different market configurations and trading rules;
-
Technology improvement scenarios – with exogenous or endogenous technological change – and
analyses of the value of technological progress in the context of CO2 abatement policies.
Beyond the research community, the target users of the model are international organisations and policy
makers and energy analysts in the field of global energy markets and environmental issues.
Key issues addressed
 Long-term (2050) simulation of world energy scenarios / projections and international energy markets.

World energy supply scenarios by main producing country/region with consideration of reserve
development and resource constraints.

Outlook for energy prices at international, national and sectoral level

National / regional energy balances, integrating final energy demand, new and renewable energy
technologies diffusion, electricity, Hydrogen and Carbon Capture and Sequestration systems, fossil fuel
supply.

Impacts of energy prices and tax policies on regional energy systems. National Greenhouse Gas
emissions and abatement strategies.

Costs of international GHG abatement scenarios with different regional targets / endowments and
flexibility systems. Emission Quotas Trading Systems analysis at world or regional level.

Technology diffusion under conditions of sectoral demand and inter-technology competition based on
relative costs and merit orders

Endogenous developments in energy technology, with impacts of public and private investment in R&D
and cumulative experience with “learning by doing”. Induced technological change of climate policies
Model characteristics
The POLES-Enerdata model is a global sectoral model for the world energy system. It has been developed in
the framework of a hierarchical structure of interconnected sub-models at the international, regional,
national level. The dynamics of the model is based on a recursive (year by year) simulation process of
energy demand and supply, with lagged adjustments to prices and a feedback loop through international
energy prices.
Figure 1: The POLES-Enerdata model – global energy system
Figure 2: The POLES-Enerdata model – national balance
(& hydrogen)
Structure of the model
In the current geographic disaggregation of the model, the world is divided into 57 countries or regions,
with a detailed national model for each Member State of the European Union (27), four industrialised
countries (USA, Canada, Japan and Russia) and five major emerging economies (Mexico, Brazil, India, South
Korea and China). The other countries/regions of the world are dealt with in a simplified but consistent
demand model.
Table 1: POLES-Enerdata regional disaggregation
Regions
North America
Europe
Japan – South Pacific
CIS
Latin America
Asia
Africa / Middle East
Countries
USA, Canada
- France, United Kingdom, Italy, Germany,
Austria,
Belgium,
Luxembourg,
Denmark, Finland, Ireland, Netherlands,
Sweden, Spain, Greece, Portugal,
- Hungary, Poland, Czech Republic, Slovak
Republic, Estonia, Latvia, Lithuania,
Slovenia, Malta, Cyprus, Bulgaria,
Romania,
- Iceland, Norway, Switzerland, Turkey,
Croatia, Rest of Europe
Japan, Rest of South Pacific
Russia, Ukraine, Rest of CIS
Mexico, Rest of Central America
Brazil, Rest of South America
India, Rest of South Asia
China, South Korea, Rest South East Asia
- Egypt, North African Oil & Gas
Producers,
North
African
NonProducers,
- South Africa, Rest of Sub-Saharan Africa
- Gulf countries, Rest of Middle East
This allows to identify the key world regions of most energy studies: North America; South America; Former
Soviet Union; North Africa and Middle-East; Africa South of Sahara; South Asia; South East Asia; Continental
Asia; Pacific OECD.
For each region, the model articulates five main modules dealing with :
-
final energy demand by main sector
-
new and renewable energy technologies
-
the Hydrogen and Carbon Capture and Sequestration technologies and infrastructures
-
the conventional energy and electricity transformation system
-
fossil fuel supply
While the simulation of the different energy balances allows for the calculation of import demand / export
capacities by region, horizontal integration is ensured in the energy markets module, the main inputs of
which are import demand and export capacities of the different regions.
Only one world market is considered for the oil market (the "one great pool" concept), while three regional
markets (America, Europe, Asia) are identified for coal, in order to take into account for different cost,
market and technical structures. Natural gas production and trade flows are modelled on a bilateral trade
basis, thus allowing for the identification of a large number of geographical specificities and the nature of
different export routes.
The comparison of import and export capacities and the changes in the Reserves/Production ratio for each
market determines of the variation of the prices for the subsequent periods.
Final Energy Demand module and Very Low Energy technologies
In the detailed demand model for the main countries or regions, energy consumption is disaggregated into
homogeneous sectors which allows identification of the key energy intensive industries, the main transport
modes and the residential and tertiary activities: Steel industry ; Chemical industry ; Non-metallic mineral
industries ; Other industries ; Road passenger transport ; Road freight transport ; Rail passenger transport ;
Rail freight transport ; Air transport ; Residential sector ; Tertiary sector ; Agriculture.
Table 2: POLES-Enerdata energy demand – final sectors
INDUSTRY
TRANSPORT
RAS
Steel Industry
Chemical industry
(+feedstock)
Non-metallic mineral industry
Other industries (+non energy use)
Road transport
Rail transport
Air transport
Other transports
Residential sector
Service sector
Agriculture
STI
CHI (CHF)
NMM
OIN (ONE)
ROT
RAT
ART
OTT
RES
SER
AGR
Energy consumption is calculated in each sector on the one hand for substitutable fuels and on the other
hand for electricity, while taking into account specific energy consumption (electricity in electrical
processes and coke for the other processes in steel-making, feedstock in the chemical sector, electricity for
heat and for specific uses in the Residential and Tertiary sectors). Each demand equation combines a
revenue or activity variable elasticity, price elasticity, technological trends and, when appropriate,
saturation effects. Particular attention has been paid to the dynamic impacts of price of price effects.
The POLES-Enerdata 6 version of the model represents the development of Very Low Energy/Emission enduse technologies (VLE). These technologies go beyond the concept of energy efficiency to almost zero
energy use and emissions, through new concepts and product designs, and may allow to considerably
improve the energy performance in the two strategic sectors of buildings and road vehicles. In the building
sector two generic VLE buildings are considered with energy consumption being cut by a factor of 2 (Low
Energy Building, new and retrofitting) or 3-4 (Very Low Energy Building, new). In the transport sector, the
competition between six types of vehicles is described, allowing for the potential introduction of Hydrogen
and/or electricity in road transport (while biofuels are mixed, according to relative costs, to conventional
petroleum products).
Vehicle types:
-
Conventional ICE
Hybrid (plug-in)
-
Electric (battery)
Gas Fuel Cell
Hydrogen Fuel Cell
Hydrogen in a conventional ICE
Power production module
The electricity system is dealt with in POLES-Enerdata in a fairly detailed manner, mostly due to the fact
that the electricity system is in any country is not only one of the main energy consuming sector but also
probably the major sector for inter-fuel substitution. It must be added that because of the particularly long
lifetime of equipment, this sector presents a higher price-elasticity in the long-term than in the short-term.
Production needs are derived from the total power demand appearing on the national grid, including net
exports:
Total production needs = final demand + self-consumption + losses + net exports
The production means are split into different categories, based on their distance to the final consumer:
- Distributed and decentralised means
- Centralised means
“Distributed and decentralised” means in POLES-Enerdata are described as competing with electricity from
grid to satisfy electricity final demand. They include PV, CHP, fuel cells and small hydro.
“Centralised” means include all the other technologies, for which there is a full modelling of capacity
development and production based on merit order functions.
In order to take into account capacity constraints, the model simulates the evolution of existing capacities
at each period as a function of equipment development decisions taken in the preceding periods, and thus
of the anticipated demand and costs at the corresponding time.
To simplify, the existing capacities of each type of power plant at time t are equal to the target capacities
calculated in t-10 for t, after the taking into account of decommissioning constraints.
Most power production technologies are considered as “centralised”, including some key renewables. They
obey the same general principles in terms of capacity planning.
The modelling of power production is differentiated for:
- “must-run” technologies: technologies with a with small (or null) variable cost,
- “merit order” technologies: technologies with an important variable production cost.
A number of them are associated to resource and technical potentials possibly limiting their development.
Table 3: Large scale non-renewables technologies
Large Scale Power Generation
Large Hydro**
Nuclear LWR**
New Nuclear Design**
Geothermal**
Super Critical Pulverized Coal*
Integrated Coal Gasification Comb. Cycle*
Coal Conventional Thermal
Lignite Conventional Thermal
Gas Conventional Thermal
Gas Fired Gas Turbines
Gas Turbines Combined Cycle*
Oil Conventional Thermal
Oil Fired Gas Turbines
Must run
Merit order
*These technologies are considered without and with CCS.
** These technologies are associated to a potential.
Table 4: Large scale renewables technologies
Large scale Renewable technologies
Onshore Wind**
Offshore Wind**
Must run
Solar Thermal Power plants**
Biomass Power plants**
Merit order
Biomass Gasification*, **
*These technologies are considered without and with CCS.
** These technologies are associated to a potential.
Hydrogen module
POLES-Enerdata uses a full description of future Hydrogen production, transport and consumption
technologies. While Hydrogen is only an energy carrier, great attention is paid to the description of the
many technological solutions to produce H2, to transport costs in new infrastructures and to the interfaces
of the H2 system with the conventional electricity system.
Ten competing options are identified for the mass production of Hydrogen, relying on fossil fuels (coal or
gas, with or without Carbon Capture and Sequestration) or electrolysis, from network electricity or
dedicated nuclear or renewable electricity. Two end-use markets are considered for Hydrogen: distributed
electricity with cogeneration and Very Low Emission vehicles in road transport with fuel cells (direct
injection in a conventional ICE is also considered).
Oil and gas production module
Oil and gas production is simulated for each region using a full discovery-process model for the main
producing countries and simplified relations for minor producing countries.
Figure 3: Oil discovery process
For each main producing country the available data cover the estimate of Ultimate Recoverable Resources
for oil and for gas, the cumulative drilling and cumulative production since the beginning of fields
development and the evolution of reserves. Cumulative discoveries are then calculated as the sum of
cumulative production and remaining reserves. For base producers, oil or gas production then depends on
a depletion ratio, applied to the remaining reserves (discoveries - cumulative production) in each period.
International Energy Prices module
In the current version of the model, the basis for international oil price modelling combines a Target
Capacity Utilisation Rate model for the Gulf countries and the global oil Reserve/Production ratio as a longterm explanatory variable. This reflects the fact that most applied analyses of the oil market point to the
fact that, as experienced in the seventies and eighties, the shorter term variations or shocks in the price of
oil can be explained by the development of under- or over- capacity situations in the Gulf region.
Coal and natural gas prices are computed for each one of the three main regional markets with regional
coal and gas trade matrixes and price variations linked respectively to coal production capacities and to the
gas R/P ratio of the key residual producers for each region.
Inputs
The energy balance data for the POLES-Enerdata model are extracted from an international energy
database, which also includes international macro-economic data concerning GDP, the structure of
economic activity, deflators and exchange rates.
Techno-economic data (energy prices, equipment rates, costs of energy technologies, etc.) are gathered
both from international and national statistics.
Regular updates of the database (currently twice a year) are provided by ENERDATA.
Outputs
The core output of the model is the production of regularly updated Energy Outlooks. POLES-Enerdata
provides endogenous international energy prices and all information on energy flows for each country /
region, in a structure similar to that of a standard IEA-type energy balance. A summary balance provides a
synthesis of information on energy consumption and transformation, new energy technologies and
electricity production capacities.
Studies on CO2 abatement policies are currently performed using the model by the systematic introduction
of a “shadow-carbon tax” wherever it is relevant. Multiple simulations of the model then allow analysing
the impacts on emissions by sector and regions, to build the Marginal Abatement Cost curves and to
analyse emission trading issues. A dedicated software, ASPEN (Analyse des Systèmes de Permis d’Emission
Négociables), allows to calculate – on robust micro-economic bases – the MAC, permit price, total cost and
quantities exchanged under different market configurations.
The impact of technological change in the Baseline and in Emission Control Scenarios can be addressed
either with a set of exogenous “Technology Story” alternatives or with a module of R&D driven endogenous
technology improvement, which also includes “learning by doing” or experience effects.
Relevant POLES-Enerdata references
2010-2012: EMF, University of Stanford, USA
Annual Energy Modelling Forum bringing together experts of energy and climate modelling from around
the world; inter-model comparisons and model results validation; use of outputs as inputs for the UN’s IPCC
Assessment Reports. POLES-Enerdata participates in EMF 24, 27 and 28.
http://emf.stanford.edu/
2011-2013: AMPERE, EC DG-Environment
Assessment of Climate Change Mitigation Pathways and Evaluation of the Robustness of Mitigation Cost
Estimates: use of the POLES-Enerdata model in conjunction with other European energy & climate models
to investigate commonalities and divergences between models; development of a climate change module.
http://www.ampere-project.eu/
2010-2012: POLINARES, EC DG-Research
Policy on Natural Resources: the project aims at exploring the conditions for cooperation and conflict on
the access to natural resources. POLES-Enerdata is used to characterise the future economic and political
framework and consequent future energy markets.
http://www.polinares.eu/
2008-2010: SECURE, EC DG-Research
Energy security in Europe under various international and European policy contexts. Strong focus on future
gas markets and gas supply to Europe.
2006-2008: ADAM, EC DG-Research
Climate change Adaptation and Mitigation policies. POLES-Enerdata has been used in the Europe and
Global Mitigation working groups, to investigate the future role of technological options and the effects of
various international policy rules on abatement efforts.
Academic references
The POLES-Enerdata model was the focus of or has been used in several articles and reports that have been
published in peer-reviewed journals or that were public reports of research projects. Some are provided
here.
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European Commission (2007) EUR 22038 - World Energy Technology Outlook - 2050 - WETO H2.
Office for Official Publications of the European Communities, Luxembourg
European Commission (2009) EUR 23768 - Joint Research Centre - Institute for Prospective
Technological Studies - Economic Assessment of Post-2012 Global Climate Policies. Analysis of
Greenhouse Gas Emission Reduction Scenarios with the POLES and GEM-E3 models. Office for
Official Publications of the European Communities, Luxembourg
Hulme, M., Neufeldt, H., Colyer, H. and Angela Ritchie (eds.) (2009) Adaptation and Mitigation
Strategies: Supporting European Climate Policy: The Final Report from the ADAM Project. Revised
June 2009. Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, UK
Kitous, A., Criqui, P., Bellevrat, E., Chateau, B. (2010) Transformation Patterns of the Worldwide
Energy System – Scenarios for the Century with the POLES Model. The Energy Journal, Volume 31
(Special Issue 1: The Economics of Low Stabilization). International Association for Energy
Economics (IAEE), Cleveland, Ohio, USA
Knopf, B., Edenhofer, O. et. Al. (2010) The economics of low stabilisation: implications for
technological change and policy. Chapter 11 in Hulme, M. and Neufeldt, H. (eds.) (2010) Making
climate change work for us: European perspectives on adaptation and mitigation strategies
Cambridge University Press, Cambridge, UK
World Energy Council (2007) Energy Scenario Development Analysis: WEC Policy to 2050. World
Energy Council, London, UK