3 Alternative Forecasts
3.1 The Use of
Models to Estimate Energy Efficiency Potential
A variety of energy-economy models can be used
to estimate how changes in the energy efficiency, fuel type
or emission controls of technologies could lead to different
levels of GHG emissions. Of these, those with detailed technological
representation are most applicable to modelling the case studies
in this NRTEE research agenda. Typically, in an energy efficiency
analysis, technologies (boilers, light bulbs, electric motors)
that provide the same energy service (heating, lighting, industrial
motive force) are generally assumed to be perfect substitutes
except for differences in their financial costs and their emissions
of GHGs and other pollutants. When their financial costs (capital
and operating) in different time periods are converted into
present value using a social discount rate, many current and
emerging technologies available for reducing GHG emissions appear
to be profitable or just slightly more expensive relative to
existing equipment. These analyses often show that substantial
GHG emission reduction can be profitable or low-cost were these
low-emission technologies to increase from their small market
share to achieve market dominance. 16
Nevertheless, these types of analyses overlook
the complexities of adopting energy-efficient technologies by
focusing on a single, ex ante (anticipated) estimate of financial
cost. 17An
assessment of an alternative scenario that examines the adoption
of energy efficiency by industry needs to explicitly acknowledge
the “efficiency gap” issues highlighted in section
2.2. An energy-economy model that is behaviourally explicit
will provide a more realistic estimate of decarbonization potential.
A model also needs to be technologically explicit. In industry
this means that the unique technologies, processes and technological
interactions of that sector’s diverse sub-sectors should
be adequately represented. It is also important that a model
be integrated between supply and demand sectors because price
feedbacks matter in terms of adjustments caused by technical
change in one sector.
3.2 Development
of Alternative Scenarios
These concerns have guided the development of
the CIMS model. This model was used to develop the baseline
forecasts in the Baseline Study, and is used in the Economic
Study to develop the alternative scenarios.
CIMS was described in some detail in the Baseline
Study (section 3.2). We focus here on the methodology for developing
the alternative scenarios.
Methodology
The CIMS model allows the analyst to explore an
“achievable” potential, rather than that which may
be only technically feasible. Energy efficiency actions (as
represented by technologies that produce less carbon emissions)
are adopted in the model according to the technology competition
step outlined in Step 3 of the CIMS simulation (section 3.2,
Baseline Study). This competition seeks to represent firm purchasing
decisions based not only on minimization of annualized life
cycle costs, but also on performance preferences, cost hetereogeneity,
option value and failure risks.
Simulating a carbon emission shadow price in the
industrial sector sub-models in CIMS can indicate the emission
reduction potential from energy efficiency actions. This methodology
is based on the principle that the goal of decarbonization would
drive the formulation of an alternative GHG scenario (as simulated
by a shadow price for carbon), which would indicate what role
energy efficiency investments could play in decarbonization
amongst other options – fuel switching, reducing fugitive
emissions, reducing process emissions, and CO2 capture
and storage. Carbon abatement actions occur up to the specified
marginal abatement cost for carbon.
Because CIMS describes energy services in flow
models which show the sequence of activities required to generate
particular products or services (see section 3.1, Baseline Study),
efficiency actions can be modelled in an integrated way. This
approach is important because as the literature on energy efficiency
has consistently shown, a focus on individual energy efficiency
actions in isolation will produce different estimates of efficiency
potential and cost than will an integrated systems approach.
Energy efficiency actions are often interrelated and only a
systems approach can explore this interplay18.
For this study, two alternative forecasts, low
carbon I and low carbon II, are produced by simulating two different
shadow prices over a 25-year simulation period (2005-2030).
In addition to applying this shadow price to the industry sector
sub-models, we also apply the price to the electricity sector
so that a carbon price can be reflected in the electricity price
used to evaluate technology investment decisions in the industry
sub-sector models.19
In both cases investment patterns and energy flows change from
their baseline evolution to produce a forecast with lower carbon
emissions. We model a price of $15/tonne CO2e in
low carbon I, and $30/tonne CO2e in low carbon II
to influence a shift in investment patterns in CIMS, which reflects
relatively modest “achievable potential” that could
be influenced by EFR policy.
Although the energy price and demand feedback
functions are included in the simulation, we were requested
not to incorporate the macro-economic feedback function in CIMS.
This was done to maintain consistency with the other two decarbonization
case studies. The NRTEE may use the outputs from the case studies
as inputs to a macro-economic model at a later stage in its
research program. This often creates methodological inconsistencies
because of differences in macro-models and a technology-rich
model, such as CIMS. An alternative would be to simulate this
and other decarbonization actions/policies with CIMS.20
This project considers a longer timeline than
is typically conducted in most GHG emission analysis (which
has been focused on the Kyoto target of six to eight years).
Emerging technologies have a greater ability to gain market
acceptance in a 25-year time frame. In order to capture the
long-term promotion of these technologies through R&D and
commercialization support, we adjust the “intangible costs”
in the model in the alternative scenarios to reflect a more
targeted commercialization effort. These adjustments were made
to the following technologies:
Table 3-1: Emerging Technologies
| Sector |
Technology |
| Aluminum |
Inert anodes / Wetted cathodes |
| Chemicals |
New catalysts |
| Iron & steel |
Thin and strip slab casting |
| Iron & steel |
Direct-reduced iron |
| Industrial minerals |
Fluidized bed kilns |
| Pulp and paper |
High-intensity drying |
| Pulp and paper |
Black liquor gasification |
| Metals |
Hydrometallurgy (nickel) |
3.3
Results / Discussion – Alternative Scenarios
Table
3-2 summarizes the results of the low carbon I and low carbon
II scenarios relative to the scenario presented in the Baseline
Study. The low carbon I and II scenarios result in GHG reductions
of 46 Mt CO2e and 58 Mt CO2e by 2030.
Though the shadow price doubles between the two scenarios (from
a $15/t CO2e to a $30/t CO2e price), only
26% more reductions result from an increase in price. This non-linear
relationship between the shadow price and emission reductions
reflects the relative cost of actions that underly the results.
21
Direct
emissions make up most of these emission reductions, though
the response of indirect emissions to the imposition of a shadow
price is stronger than the response of direct emissions (indirect
emissions decline by 53-62% in 2030, while direct emissions
only decline by 5-7%). Actions behind this strong indirect response
include the greater adoption of cogeneration systems and actions
that improve the overall efficiency of auxiliary motor systems.
Results
for individual sub-sectors are shown in Tables 3-3 to 3-13.
Only total emissions are shown (sum of direct and indirect).
For each sector we show the relative trends in direct GHG intensities
(t CO2e/GJ) and energy intensities (GJ/physical production)
in each simulation. These indicators suggest the relative role
of energy efficiency compared to fuel switching in the results.
However, for some sectors these are not clearcut. For instance,
changes in the energy intensity indicator also represent saved
natural gas from leak programs (natural gas extraction sector),
and changes in the GHG intensity indicator represent changes
to process emissions (metal smelting, chemical products, iron
and steel) and fugitive emissions (upstream oil and gas sectors,
coal mining). In the chemical products and the pulp and paper
sectors, total emissions decline in the low carbon I and II
scenarios despite increasing energy consumption. This is due
to the increased adoption of cogeneration, which results in
increases in total energy, which offset by indirect emissions
savings associated with cogenerated electricity.
Energy
efficiency actions figure among a variety of different types
of actions in the GHG reductions in each sub-sector. The upstream
oil and gas sector, which is responsible for significant emission
reductions in each time period, makes many reductions through
actions that curtail fugitive emissions.22 The metal smelting
and refining sector, petroleum refining, and iron and steel
sub-sectors contribute the most emission reductions due to improved
energy efficiency in the alternative scenario simulations.
The
decarbonization potential described in the alternative scenarios
are likely conservative based on the following.
