This is part of the labels / documentation for <a href='http://jcm.chooseclimate.org'>Java Climate Model</a><hr/>

#fuels		§The resource availability and prices of fuels are  fundamental to an investment-driven energy module.
 
Currently, this is a simple module holding parameters which scale the prices of fuel used in the @electricity module. 

It could be made more sophisticated. 
However these prices are notoriously hard to predict,  fluctuating wildly on the timescale of  climate / devleopment models.

#electricity		§This module calculates the sources of electricity generated in each country.  It was developed mainly during a project in IVIG, UFRJ Rio (Brazil), 2011.

For each country the model knows the existing power generation capacity - initial data is from IEA.

Investment in new capacity depends on the 	anticipated economic benefits, depending on the supply and demand costs at the time of investment.  

This method assumes that policymakers are short-sighted (unlike inter-temporal optimisation models which assume perfect foresight). As being simpler to model (especially when there are feedbacks with other modules) this is also in practice quite realistic.  
    
The costs are influenced, evidently, by factors such as @demand_elasticity, @discount_rates and @carbon_price (parameters in @energeneral) and costs of @fuels. 

You can experiment to see how these change the allocation, for example, lower discount rates or higher carbon prices favour renewables.
 
The capacity of each type is gradually decreased by @depreciation (adjustable) and @lifetime. Longer lifetimes also favour renewables.   

The capacity is increased whenever the discounted future  costs suggests that a new source would be cheaper than an existing one.
There are limits  on how much new capacity of any type may be introduced in any one year (adjustable parameters - eg 20%/yr) - as it takes time to develop know-how for new capacity.  

If the supply capacity exceeds demand (this is more likely in developed countries than developing), electricity sources with cheaper marginal costs are used first. There is no restriction on "premature retirement of capital stock. This can be changed with the @algorithm parameter.

Currently electricity demand is based on scaling exogenous scenarios, it is intended to make this endogenous as the other energy sectors and the @economy module are developed.

#energy_general		¨[oldkey=energy_all]		§This module holds 'global' parameters which affect all energy sectors. 

These include a presumed global @carbon_price and a @demand elasticity factor. 

Here you can also choose which IEA scenario is used as a data source, and which IEA region is used for plots in this package.  

%%  (these are temporarily  independent of socioeconomic scenarios and regions used elsewhere in JCM - to be fixed). %%

If @regional_discount_rates is selected (as by default), the discount rate varies by country and over time, dependent on the economic growth rates. 

If this is deselected, one global rate can be applied - this is less realistic but convenient for experimenting to understand its impact.

#industry		§This is a placeholder module to be developed.

Note that in standard IPCC usage, "Industry" includes construction of buildings and infrastructure (whose related emissions are a large fraction of the total in some rapidly developing countries).

One issue for joining up JCM  is that well-known economic datasets have different sectoral definitions to those reported by IPCC and typical energy model analyses.

#buildings		§This is a placeholder module to be developed.
 
Note that in standard IPCC usage, "Buildings" means in-use consumption of energy for heating etc., not their construction which is included in Industry.

#transport		§This is a placeholder for later development

This module poses particular challenges, because the author considers it essential, for the long timescale projections of JCM, to include the potential for modal shift between different modes of transport (eg air to rail, driving to walking), including related infrastructure investment. 

This is not considered well (or at all) by many other transport energy models, which have focused mainly on fuel or vehicle substitution (mainly for roads). 
 
If there is a large shift to electrified transport, this could increase the demand feeding into the @electricity module.  

See also @aviashipemit

#energeneral		¨[oldkey=en_general][oldkey=engeneral]		§This module holds 'global' parameters which affect all energy sectors. 

These include a presumed global @carbon_price and a @demand elasticity factor. 

Although in reality we are far from having one global carbon price applied across all countries, this is a standard assumption in many energy models, as it provides a single lever to experiment with policy impacts. 
 
Here you can also choose which IEA scenario is used as a data source, and which IEA region is used for plots in this package.  

%%  (these are temporarily  independent of socioeconomic scenarios and regions used elsewhere in JCM - to be integrated later). %%

If @regional_discount_rates is selected (as by default), the discount rate varies by country and over time, dependent on the economic growth rates. 

If this is deselected, one global rate can be applied - this is less realistic but convenient for experimenting to understand its impact.

#enerdata		¨[oldkey=en_indata]		§This module contains data from IEA that is used as input to some other modules in this package. 

Plots are provided to compare regions, scenarios, sectors, or energy sources. 

%%Note TPED = Total Primary Energy Demand %%