August 28, 2023
Source: NOAA
How to evaluate its impact to the emission reduction?
Source: Secretory Jennifer Granholm
Source: Kimberly A Nicholas
Data-driven, evidence-based, energy and climate policy
Using analytic tool to answer questions such as:
George Box:
All models are wrong, but some are useful.
Albert Einstein:
Everything should be made as simple as possible, but not simpler.
Models help us think
Risk: System structural change could break our core data and assumptions that are based on historical experience. Example: shale gas revolution.
Uncertainty: The exact timing and character of pivotal events and technology changes is unpredictable. Example: renewables cost changes, fussion.
Read more from Jon Koomey
Source: EIA
Energy security: Ensure reliable energy supply
Energy equity: Provide universal access to reliable, affordable, and abundant energy
Environmental Sustainability: Avoid environmental harm or climate impact
Those goals sometimes conflict with each other, and decisions has to make trade-offs between them
Source: World Energy Council
Economy: Decent living? Growth? Degrowth?
Environment: Emissions, ecosystems constraints/goals
Energy: Work within constraints
Source: ICMA
Source: National Academies
Source: UN
Source: IEA (2021)
Source: IRENA (2021)
Bill Hogan:
It is not the individual results of a model that are so important; it is the improved user appreciation of the policy problem that is the greatest contribution of modeling.
Huntington, Weyant, Sweeney “Modeling for insights, not numbers”:
The primary goal of policy modeling should be the insights quantitative models can provide, not the precise-looking projections –i.e. numbers – they can produce for any given scenario.