This document provides a summary of a report that assesses methods for modeling future energy demand. It finds that current energy demand projections and scenarios often significantly misjudge actual demand. It advocates for more sophisticated modeling approaches that incorporate behavioral factors and uncertainty. Specifically, it recommends (1) linking energy use to economic and social factors, (2) developing behaviorally realistic models, and (3) using robust decision-making approaches given uncertainties in modeling outcomes. The report also provides an overview of different modeling techniques and their appropriate uses based on the time horizon and degree of expected changes.

1. www.irgc.org
ASSESSMENT OF FUTURE
ENERGY DEMAND
A methodological review providing guidance to developers and users
of energy models and scenarios
June 2015

2. Contents
Assessment of Future Energy Demand 2
Page
Introduction 3
On the need for more sophisticated demand models/scenarios 4
Broad scenario categories and development approaches 7
Energy demand models 8
Different facets of demand-side uncertainty 10
Using models and scenarios for energy planning 12
Conclusions 14
IRGC’s project work on energy transitions 15

3. Introduction
Long-held tenets of energy demand are being rewritten
Paradigm shift from sustained growth in energy consumption between 1800 and 2010 (Figure 1)
to ambitious curtailment of per capita energy demand and halving energy demand growth by
2035 (Figure 2)
Energy demand needs to be decoupled from economic growth,
reshaping ideas about drivers of energy demand during and after current energy transitions.
Assessment of Future Energy Demand 3

4. On the need for more sophisticated models/scenarios (1)
Energy demand projections often go widely astray
There is also substantial evidence that energy scenarios have often seriously misjudged energy
demand in the past. For example, the leftmost plot shows an overall overestimation of primary
energy consumption for the US in 2000 while the rightmost plot shows a systemic
underestimation of final energy demand in Germany in 2013.
The outcomes of energy scenarios should not be interpreted as forecasts and should be used
with the correct understanding of underlying drivers and uncertainty.
Assessment of Future Energy Demand 4
Figure 3: Predicted US energy consumption for 2000.
Source: Greenberger (1983), adapted from Morgan
and Keith (2008)
Figure 4: Final energy demand under different scenarios for
Germany for 2010 and 2013.
Source: DLR Analysis (personal communication)

5. On the need for more sophisticated models/scenarios (2)
Inadequate uncertainty analysis and communication
• Long-term forecasts and projects are often incorrect due to:
– unexpected events, e.g. changing policies and regulations
– underestimation of uncertainties, e.g. faster or slower technological progress and
technological breakthrough, and changes in consumption habits.
• Erroneous forecasts matter because they can have real policy consequences if associated
uncertainties are not communicated to decisions makers, and can generate new risks, e.g.:
– negative cross-market externalities, e.g. the 2007 US Renewable Fuel Standard,
mandating higher biofuel production, was based on forecasts of continuous growth in
gasoline use. It led to a rapid expansion of the production of corn ethanol in the US. But
gasoline use stagnated, with adverse effects on biofuels markets, where growth was
desirable.
– policy failures, e.g. projections that show a decline in energy demand as a result of
energy efficiency measures, can result in an underestimation of the effort required on
the supply-side, such as the investment in renewables, to reduce CO2 emissions. But, the
rebound-effect, as one example, may prove energy-efficiency based projections wrong
and could undermine energy transitions goals.
Assessment of Future Energy Demand 5

6. On the need for more sophisticated models/scenarios (3)
Options for improving energy demand scenarios
New representations of energy demand include
• Linking energy use to economic development to help assess technological pathways, i.e.
useful energy;
• Highlighting the energy content of social consumption and social dimension of energy
transitions, i.e. embodied energy;
• Developing behaviourally-realistic energy demand models, e.g. by:
– Varying parameters, e.g. different discount rates for different households
– Adding more variables, e.g. behavioural drivers, such as option value of retrofitting, not
included in current models
Important considerations
• The importance of improving representations of energy demand must be clear at the outset
to help select the best approach.
• New representations of energy demand often involve making some parameters endogenous
or adding new variables. The trade-offs between complexity and expected impact must be
balanced.
Assessment of Future Energy Demand 6

7. Broad scenario categories and development approaches
Assessment of Energy Demand 7
Forecast-based scenarios Exploratory Scenarios Normative scenarios
Concerns What will happen? What can happen? How to reach a certain goal?
Timeframe Often short Often long Typically very long-term oriented
Methods • Intuitive/expert qualitative
forecasts
• Simple trend extrapolation
• Complex multivariate
econometric forecasting
based on best-fit criterion
• What-if forecasts: dynamic
causal forecasting
• Participatory and expert
scenario-making
• Qualitative scenarios combined
with appropriate energy model
• Robust decision-making
approach recommended for
making strategic plans
• Starting point: visions of desired
future goals, needs, desires, etc.
• Backcasting combined with
either exploratory or forecast-
based scenarios
• Strategic plans developed for
different temporal milestones
Example EIA World Energy Outlook World Energy Council Energy
Scenarios
Greenpeace Energy Revolution
Scenarios

8. Modelling energy demand (1)
Mainstream approaches
Bottom-up Model
• Techno-economic partial-equilibrium models
• Often based on least-cost optimisation
• Used, e.g. for assessing energy efficiency
measures as in MARKAL models
Ideal Model
• Full-fledged flexible model
• Incorporates all relevant features from the
three dimensions
• Involves both computational and
informational costs
• Most models are hybrids of top-down and
bottom-up models, e.g. MARKAL-MACRO
• Macroeconomic models using general-
equilibrium approach, econometric analysis
or input-output models
• Based on realistic assumptions about the
microeconomic behaviour of agents
• Particularly suited for evaluating policy
reforms
• E.g.: NEMESIS, DICE
Top-down Model
Assessment of Energy Demand 8
Models vary across three dimensions: technological, macroeconomic and microeconomic.
Environment is often modelled as a fourth dimension.
Figure 5: Hybrid modelling of
energy-environment policies –
reconciling bottom-up and
top-down.
Source: Adapted from Hourcade
et al. (2006)

9. Modelling energy demand (2)
Improving behavioural foundations of energy models
• Recent work by the EIA and IIASA indicates that there is a need for models to reflect
behavioural realism, since conventional top-down and bottom-up models do not adequately
capture complex and technological dynamics of end-sectors in terms of end-use behaviour.
• There are a few behavioural models that relax the rationality assumptions and attempt to
augment models to represent different forms of market barriers and failures, e.g. by:
– Using discount rates to capture time preference, option value and risk premium
– Varying market heterogeneity parameters to account for different preferences across
consumers and businesses
Important considerations
• Behavioural factors to be added must be prioritized based on the significance of impact on
energy demand, policy implications (including links to policy levers), strength of evidence and
ease of implementation.
• The current approach to behaviourally augment energy models is to use parameterizations
based on expert elicitation. This approach may generate biases and rigorous choice of
parameters is needed.
Assessment of Energy Demand 9

10. Different facets of demand-side uncertainty (1)
Modelling features laden with uncertainty and some suggested solutions
1. Expectations, usually modelled as perfect or myopic foresight
One alternative is to use systems dynamics to model expectations as in TIMER model
2. Human behaviour and social preferences are context-dependent
In context of energy transitions, it is important to account, e.g. for NIMBY and BANANA
attitudes
3. Time discounting and time preference
It is important, amongst others, to assess sensitivity of outcomes to different discount rates
4. Welfare assessment often assumes existence of a representative agent
To avoid associated scaling biases, it is important to use appropriate segmentation
5. Structural uncertainty, across time and across scale
Models can be refined, e.g. by incorporating adjustment lags and spatial heterogeneities
6. Energy demand elasticities are typically assumed to be static
Account for temporal variations in price, income and cross elasticities of demand
Further details available in the main text.
Assessment of Energy Demand 10

11. Different facets of demand-side uncertainty (2)
Some technical approaches to deal with general uncertainty
1. Cross-impact analysis
Used for instance to generate diverse qualitative scenarios and identify trends
2. Stochastic scenarios
Used, e.g. to assign likelihoods to scenarios beyond worst- and best-case scenarios
3. Real options theory
Relevant, e.g. to assess the diffusion rate of energy efficiency
4. Agent-based approaches
Used, e.g. in PRIMES model
Further details available in the main text.
Assessment of Energy Demand 11

12. Using models and scenarios for energy planning
Importance of time horizons
Assessment of Energy Demand 12
Time
High
Low
Extent of socio-
economic and
technological
change
Short-term,
e.g. 1-5 years
Very long-term,
e.g. 100 years
Medium-term,
e.g. couple of decades
• Quantitative models less
useful
• Build qualitative and
internally consistent
models
• Examples: IAMs, VLEEM
Use either top-down or
bottom models in line with
research and policy
questions:
• Bottom-up models help
assess the role of different
technologies
• Top-down models help
examine economic
restructuring
• Use econometric-based
models and general
equilibrium models when
energy infrastructures are
relatively stable
• Some predictions are
feasible

13. Using models and scenarios for energy planning
Robust decision making and communication
• Use robust decision-making approaches
– Scenarios only offer plausible pictures on how the future may develop
– Uncertainty and biases in energy scenarios and forecasts are likely to persist
– Energy policies should be robust over a wide-range of scenarios
– Energy Models designed to that end include MESSAGE-MACRO
• Best practices for communicating scenario and modelling outcomes to policymakers
– Modellers should select and communicate the most relevant information in the most
useful format to their targeted audience
– Modelling outcomes and policy suggestions should be communicated in everyday-life
terms, e.g. using “gallons per mile” instead of “miles per gallon”
– Highlight time delays for reaping the full impact of policy and potential need for “earlier”
interventions
– Use interactive online energy calculators, e.g. UK 2050 Calculator and the Swiss Energy
Scope to help policy makers and other stakeholders better understand trade-offs
Assessment of Energy Demand 13

14. Conclusion
• Current energy transitions, with its focus on energy efficiency and sufficiency as well as
increasing penetration of renewable resources (including, distributed generation), require a
change in energy consumption paradigms and lifestyles.
• Mainstream energy models and scenarios do not adequately capture these changes.
• IRGC’s work presented here suggests, among others, the following needs:
– Evaluate the relevance of including behavioural drivers of energy demand for different
uses of models and scenarios
– Prioritise the drivers to be included and obtaining information about then in objective
and verifiable ways
– Assess the relationship between short-run and long-run behavioural change, and the
impact of policies and strategies on the emergence of new behavioural patterns
– Identify instances when insights from behavioural sciences can help improve the
effectiveness of or crowd out traditional instruments of energy policymaking
– Improve the coherence between scenarios and energy models to better assess future
energy demand and inform strategic and policy decisions for governing energy
transitions
Assessment of Future Energy Demand 14

15. IRGC’s project work on energy transition
• IRGC began project work on “Energy Transitions: Demand Anticipation and Consumer
Behaviour” in 2014.
• In line with IRGC’s focus on risk governance, it is motivated by the fact that large-scale
transitions or transformation of energy systems that are to take place over the next few
decades will redefine risks and opportunities within the energy and other sectors.
• Ability to anticipate such changes in crucial. But while decision makers rightly use scenarios
to inform their strategies and policies, many scenarios provide a false sense of confidence in
projected or narrated evolutions of energy systems.
• The focus on demand anticipation and consumer behaviour is informed by the realisation
that scenarios have traditionally focused on the supply side of energy systems while
behaviour and end-use demands have received less attention.
Assessment of Future Energy Demand 15

16. www.irgc.org
International Risk Governance Council
Improving the governance of systemic risk
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