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Introduction

Exploring uncertainty through scenario analysis

The Canada Energy Regulator’s (CER) Canada Energy Future 2023: Energy Supply and Demand Projections to 2050 (EF2023) utilizes scenario analysis to explore potential future outcomes for Canada’s energy system. Scenario analysis is a useful tool for understanding how future trends could evolve given certain conditions, and the key uncertainties that could affect outcomes. In using scenario analysis, it is important to understand the premise of the scenario and its underlying assumptions. EF2023 includes three Appendices that give details about the specific scenarios and assumptions for the modeled scenarios:

Appendix 1: Domestic Climate Policy Assumptions
Appendix 2: Technology Assumptions
Appendix 3: Overview of the Energy Futures Modeling System

Related, detailed data sets are also available:

Access and Explore Energy Future Data

Defining Energy Futures scenarios and assumptions

The EF2023 scenarios start with a central premise for how the future will unfold in both Canada and the world. The Global Net-zero scenario premise is a world where all countries reduce emissions cooperatively to limit warming to 1.5°C; alternatively, the Current Measures scenario is premised on no further climate action beyond those in place today.

A critical step in modeling a scenario is transforming the scenario premise into actual parameters that can be used in the Energy Futures Modeling System. This is done by choosing scenario assumptions consistent with the overall scenario premise. The assumptions are explicit numerical values that populate the energy model. Examples of assumptions include global crude oil prices, carbon prices, technology efficiency improvements, and technology cost reductions. Table I.1 lists key assumptions used in the Energy Futures Modeling System, and how they are developed.

Table I.1: Key assumptions in the Energy Futures Modeling System

Key assumptions in the Energy Futures Modeling System
Assumption Examples Summary
International prices for crude oil (such as BrentDefinition* and West Texas Intermediate (WTI)Definition*) and natural gas (such as Henry HubDefinition*)
  • Brent or WTI crude oil price
  • Henry Hub natural gas price
  • Chosen to reflect global supply and demand dynamics of a scenario. For example, scenarios with stronger climate action globally often show lower oil demand, implying a lower crude oil price.
  • Price assumptions may be chosen based on a survey of external agencies and forecasters that do global modeling (International Energy Agency, Energy Information Administration, private forecasters), or directly align to a global scenario (such as using global crude oil prices from an IEA World Energy Outlook scenario).
Canadian price differentials
  • Reflects Canadian and North American crude oil and natural gas market conditions.
  • Differentials can reflect quality and transportation costs of Canadian commodities compared to a benchmark (i.e., Western Canadian Select (WCS)Definition* oil vs WTI), and/or reflect commodity transportation constraints. For example, the EF2016 “Constrained Pipeline Case” explored the implications of major pipeline projects not being built, which resulted in a widening of Canadian crude oil price differentials.
Major export projects
  • LNG export volumes
  • Hydrogen export volumes
  • Reflects potential Canadian commodity export facilities, which have direct implications for commodity production.
  • In scenarios with greater global demand for natural gas or hydrogen, assumed exports may be higher. Alternatively, scenarios with lower global demand could imply lower exports.
  • Determined by a survey of proposed Canadian projects and initiatives, various projections and forecasts by governments, institutions and/or private forecasters.
Technology cost and performance
  • Electric generation technology cost and performance (on- and off-shore wind, natural gas, natural gas with carbon capture and storageDefinition*, nuclear, hydro).
  • Utility-scale battery storage
  • Vehicle costs (battery electric, plug-in hybrid electric, fuel cell, internal combustion engine, etc.).
  • The Energy Futures Modeling System requires a wide range of technology costs.
  • Cost and performance assumptions may be chosen based on a survey of external agencies and forecasters (International Energy Agency, Energy Information Administration, National Renewable Energy Laboratories private forecasters), or directly align to global scenario assumptions from other agencies.
Canadian climate policies
  • Carbon pricing
  • Efficiency standards
  • Renewable fuel regulations
  • Vehicle sales mandates
  • Includes policies from all levels of government.
  • Determined based on the premise of the scenario. For example, the Current Measures Scenario from EF2023 only includes policies that are law or near-law. Scenarios with additional climate action, such as the EF2023 net-zero scenarios, or EF2020/2021 Evolving Policies Scenario, require additional policy assumptions.
  • These additional assumptions could be based on announced, but not legislated policy increases, or hypothetical strengthening of existing policies (such as carbon price increases beyond 2030).
  • A special case is for scenarios that aim to reach a given target, such as net-zero. In these, increasing the stringency of assumed climate policies is a key way to solve for net-zero by 2050.
Behaviour
  • Driving patterns
  • Business and personal air-travel
  • Industrial material recycling
  • Space heating and/or cooling behaviour
  • Policies and/or market developments could affect how consumers and industry produce and consume energy.
  • Behavioural assumptions refer to additional assumed changes beyond what the Energy Futures Modeling System would project given other assumptions.
  • Examples could include changes in transportation behaviour, recycling, conservation, etc.
  • These assumptions could be chosen in a way that ties to the overall scenario premise, either developed through research and expert engagement, or by aligning to assumptions from other agencies.
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