assessment of future energy demand, overview presentation

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ASSESSMENT OF FUTURE ENERGY DEMAND A methodological review providing guidance to developers and users of energy models and scenarios

June 2015

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

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.


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.


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)

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.


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.


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

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


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)

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.


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.


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.


Using models and scenarios for energy planning Importance of time horizons


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

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


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


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.


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