August 22, 2022
How to evaluate its impact to the emission reduction?
Life cycle analysis
All models are wrong, but some are useful.
Everything should be made as simple as possible, but not simpler.
We also call it energy modeling, using computing models to simulate, design, and operate energy systems. Answering questions such as:
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.
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
Economy: Decent living? Growth? Degrowth?
Environment: Emissions, ecosystems constraints/goals
Energy: Work within constraints
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.