Policy - GCAM

Note: The documentation of GCAM is 'under review' and is not yet 'published'!

Model Documentation - GCAM
Model scope and methods - GCAM Model concept, solver and details - GCAM Temporal dimension - GCAM Spatial dimension - GCAM Policy - GCAM Socio-economic drivers - GCAM Population - GCAM Economic activity - GCAM Macro-economy - GCAM Trade - GCAM Energy - GCAM Energy resource endowments - GCAM Fossil energy resources - GCAM Uranium and other fissile resources - GCAM Bioenergy - GCAM Non-biomass renewables - GCAM Energy conversion - GCAM Electricity - GCAM Heat - GCAM Gaseous fuels - GCAM Liquid fuels - GCAM Solid fuels - GCAM Grid, pipelines and other infrastructure - GCAM Energy end-use - GCAM Transport - GCAM Residential and commercial sectors - GCAM Industrial sector - GCAM Other end-use - GCAM Energy demand - GCAM Technological change in energy - GCAM Land-use - GCAM Agriculture - GCAM Forestry - GCAM Land-use change - GCAM Bioenergy land-use - GCAM Agricultural demand - GCAM Technological change in land-use - GCAM Emissions - GCAM GHGs - GCAM Pollutants and non-GHG forcing agents - GCAM Carbon dioxide removal (CDR) options - GCAM Climate - GCAM Modelling of climate indicators - GCAM Non-climate sustainability dimension - GCAM Air pollution and health - GCAM Water - GCAM Appendices - GCAM Mathematical model description - GCAM Data - GCAM References - GCAM
Corresponding documentation
AIM-Hub BLUES C3IAM DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
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Model information
Model link	http://jgcri.github.io/gcam-doc/toc.html https://github.com/JGCRI/gcam-core
Institution	Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri.
Solution concept	General equilibrium (closed economy)GCAM solves all energy, water, and land markets simultaneously
Solution method	Recursive dynamic solution method
Anticipation	GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.

One of GCAM’s uses is to explore the implications of different future policies. There are a number of types of policies that can be easily modeled in GCAM, including policies related to emissions, land-use, and energy production. The most common of these are discussed in the documentation's policy section.

Emissions-Related Policies

There are three main policy approaches that can be applied in GCAM to reduce emissions of CO₂ or other greenhouse gases: carbon or GHG prices, emissions constraints, or climate constraints. In all cases, GCAM implements the policy approach by placing a price on emissions. This price then filters down through all the systems in GCAM and alters production and demand. For example, a price on carbon would put a cost on emitting fossil fuels. This cost would then influence the cost of producing electricity from fossil-fired power plants that emit CO₂, which would then influence their relative cost compared to other electricity generating technologies and increase the price of electricity. The increased price of electricity would then make its way to consumers that use electricity, decreasing its competitiveness relative to other fuels and leading to a decrease in electricity demand. The three policy approaches, carbon or GHG prices, emissions constraints, and climate constraints, are discussed in detail here.

Energy Production Policies

There are times in which users would like to explore the implications of a constraint on production or a minimum production requirement. This capability allows GCAM users to model policies such as renewable portfolio standards and biofuels standards. Across sectors, these constraints must be applied as quantity constraints, but they can be applied as share constraints within individual sectors (e.g., fraction of electricity that comes from solar power). In implementing these policies, this can either be a lower bound or upper bound. The model will solve for the tax (upper bound) or subsidy (lower bound) required to reach the given constraint.

Land-Use Policies

There are a number of ways that policies can be applied directly to influence the land sector in GCAM. These include the following.

• Protected lands
• Valuing carbon in land
• Bioenergy constraints
• Land constraints

See the Land-Use Policies section in the documentation for more details.

Calculating Emissions Policy Costs

The cost of GHG emissions mitigation is a concept that is not uniquely defined. A wide range of measures are used in the literature. These include, the price of carbon (or as appropriate given the policy) needed to achieve a desired emission mitigation goal, reduction in Gross Domestic Product (GDP), consumption loss, deadweight loss, and equivalent variation. Beyond that the concept of net cost, which includes the benefits of emissions mitigation as well as the resource cost of emissions reduction and the social cost of carbon are also encountered. GCAM makes no attempt to calculate the benefits. See Calculating Emissions Policy Costs for details.