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

#jcm.mod.socio		§This package contains regional socioeconomic modules - demography and economy, which drive energy and emissions.    

It is in transition, containing both new bottom up @demog @migration and @economy modules, and old @popgdp scenario-data based curves. 

See @JCMbotupscen regarding this transition.

Detailed socioeconomic projections may also be relevant for  estimates of  climaetchange impacts. 

There is also a (very) old (experimental) module for exploring assumptions within Climate  Abatement and Impacts @costs analyses.

#popgdp		§%%Note: the functions of this module are graudally being replaced by @demog and @economy modules, the documentation below is out of date.) %%

This module manages curve sets of population and GDP for the region sets chosen by the parameter @socregions (in @jcm.mod.obj).  

Historical national data from @histsocdata is combined into larger regions, whilst future projections from @sresdata are downscaled to nations and recombined, by the @interpolator tool.

These projections are completed by the module @sresext. 

This module also contains the method for estimating @future_ppp ;;

Eventually we anticipated to include a simple dynamic economy model to replace interpolation of data from SRES, to adapt more flexibly to new scenarios. See also  @peoplefuture

----
The socioeconomic data helps to reveal some of the driving forces behind the @aboutsres.

Population and gdp data is also used in formulae for distributing regional CO2 emissions (see @shares), in determining costs and welfare (see @costs), and in some estimations of impacts (@jcm.mod.regimp)

€€adju You can use @createvariant to combine this socioeconomic data with other quantities, for example to plot emissions, costs, impacts or responsibility per capita or per-dollar (GDP).

 €€pan For each quantity a @curveset is now provided to show all nations (instead of  @regionsets)

#economy		§This module calculates the economic development of each country / region, based on input from the @demog module, and national varying factors such as capital stock, savings ratio and TFP, which gradually converge in the future.  

It was developed recently (2014-16), as part of the process to develop @jcmbotupscen. To complete that process, this module will have to be futher adapted, including sectoral information relevant to energy demand, landuse change etc. Therefore this intermediate module is only briefly documented. 

On the long timescales of JCM climate projections (typically 1750-2300) accumulated capital stock (infrastructure) and demographic factors such as the @dependency_ratio are the key to economic growth.  The structure is not designed to capture short-timescale economic fluctuations. 

Various historical (and near-term future) datasets are sourced from the IMF World Economic Outlook and Penn World Tables (see   @histsocdata). 

Capital stock is divided into @capital:_infrastructure (buildings, lifetime), @capital:_machines, and @capital:_hitech, with long, medium and short lifetimes respectively. This helps to distinguish stages of development in different countries. 
There is also a human capital stock which depends on cumulative education. 
 These factors combine to influence the @total_factor_productivity. This tends towards a plateau as countries become more developed. The TFP, together with the @workage_pop determine the economic output. 

For the historical period, the TFP may be fixed to force the model to fit the data. 

The @savings_ratio determines how much of this output is consumed or reinvested into capital. In the future, this may converge  towards an inverse  relationship to the @dependency_ratio. Thus the fastest economic growth is achieved by countries with a "demographic dividend" (with neither many young children nor many elderly pensioners). Recently this applied to east Asia but as this region ages, future rapid development may shift towards south Asia and later Africa.  This anticipated economic shift is fundamental to understanding, inter-alia, climate @equity . 

There are evident issues with some parameter combinations - to be fixed. 

The results may be compared with scenario projections (see @popgdp and modules in @jcm.mod.scen package).

#jcmbotupscen		§For many years JCM focused on top-down @stabilisation scenarios, defined to meet climate goals, and therefore relied on external scenarios such as those from @SRES and @SSP to drive the baseline socioeconomic data and regional emissions. 

However, to explore what people can practically change, in their families and homes, economy and energy systems, it is necessary to generate bottom-up socioeconomic scenarios within JCM. 

The basic cause-effect chain flows from @demog  changes (including @migration),  through activity in the @economy and @jcm.mod.energy systems. Reverse feedbacks are also possible, such as from @economy and eventually climate influencing @demog.  

There are many adjustable parameters, to enable exploration of diverse development pathways and potential policy levers. 

Eventually proabilistic distributions of scenarios may be generated. 

This module may be helpful to help explore assumptions behind published scenarios (and possibly contribute to their database). 
 
This part of JCM is still under development - in particular @jcm.mod.energy package is not yet complete, and @futureluc also needs redevelopment.
Meanwhile @SSP scenarios are used to interpolate over gaps.

Most of the relevant modules can be found in the @jcm.mod.socio package.

#demog		§This module was  developed 2014-16, as part of the process to generate
@JCMbotupscen

As typical for demographic modules (but unlike the rest of JCM) it works in 5-yr timesteps. Population information is stored for 5-yr age groups, for each sex, for all countries separately.

Although this module calculates  by country, plots are shown for regional aggregates, depending on the chosen @regset. 

Most of the data is derived from the UNPD World Population Prospects 2012

@migration is considered in a separate module, with feedbacks from this one. A parameter is provided to use this, switch off migration, or use the WPP data.

Migration data has to be allocated to age groups - this is done with a assumed age and sex distribution (peaking for 20-35-yr old males, the youngest and oldest people move less). 

There are also feedbacks with the @economy module,  as fertility is assumed to fall with higher per capita GDP - this assumption  may be adjusted with the parameter. 

You can also explore simply scaling the fertility higher or lower - a typical way to generate a range of scenarios.

Plots are provided for various subsets of the population.

You can also view a population pyramid for all regions for any specific year. Changing this year provides an animation of how the global population distribution changes over time.

#migration		§This new module (2016)  contains options and data regarding international migration, and feeds into the @demog module. 

Standard population projections (such as those from UNDP or comntained within @SSP scenarios) typically make rather simple assumptions about future migration, such as starting from current flows and tapering down to zero in the longer term. 
 
This avoids controversial policy assumptions, but is not realistic, particularly in a world with significant climate change which could make some densely populated areas (such as tropical river deltas) uninhabitable,  while others (at higher latitude or altitude) may become more habitable. 

On the timescale of JCM (eg 1750-2300) migration has had a huge impact in the past (think how different the emissions from America would be without any migration since 1750!) and could do so in the future.

So the eventual aim of developing this module in JCM is to include feedbacks from  climate impacts, to explore large scale migration as adaptation to climate change. 
 
 It is also useful simply for exploring current migration flows, about which there is much public misunderstanding.   

This module has a 5-yr timestep.
The flows data are derived from tables of G.Abel (2013, 2014).

Currently  parameters are provided only to explore  simple future assumptions (similar to other scenarios). Even these can be useful for testing the sensitivity of demographics, economy and emissions to changing future migration.

#migration_flows		§This curveset shows, for a single country selected by @Country_for_migration_plot, the migration flows to/from all other countries.

#net_migration		§This curveset shows the net migration (positive = influx), for all regions selected by @regset. 

To see a similar plot for individual countries, look in the @migration module. 

%% (note the totals are not the same as this plot  excludes migration within a region).%%

#net_mign_by_country		§This curveset shows the net migration (positive = influx) for individual countries

To see a similar plot for regions, look in the @demog module.

#wpp_med_pop		§Curves from the UNPD World Population Prospects (2012) medium variant, for comparison with JCM calculations

#energy_pop		§Population weighted by age group, for an better estimate of the energy demand. The youngest and oldest people use less energy than those in the middle.

Relative weights are (for five year groups) : 
1,2,3,4, 5,5,6,6, 7,7,8,8, 7,6,5,5, 4,4,3,3,3 
%%(note this may change, please check code)%%

#total_fertility_rate		§The number of children per woman, over her lifetime. When this is lower than ~2.1, the population will eventually decline. 

The TFR varies widely between countries, but until recently has been falling in most regions.  
For the near-term, TFR is taken from WPP data, while in the long term it is related to economic growth. For developing countries it is generally assumed that TFR drops as people get richer, but this may not necessarily be true for developed countries - factors such as education, urbanisation and living space, as well as cultural preferences are also critical.

#workage_pop		§The working age population is a driving factor for economic growth

#dependency_ratio		§The Dependency Ratio is the ratio of young+old people, to working-age people. This is strongly anti-correlated with economic growth - as can be seen by comparing regions over time in the @economy module.

#over-60_survival_rate		§For  "greying" populations this rate gives a better indicator for comparing countries than the total mortality (which depends on the fraction of young people). It makes a lot of difference to long term population projections.

#pyramid		§A classic population pyramid, showing the distribution by age group, for female (above) and male (below), for for all regions, for a  specified year.

The pyramid is on its side for convenience, to be drawn similarly to other stacked plots in JCM. Age is on the x-axis, the area gives the total population.

Try adjusting this @year_for_pyramid to animate how the global population distribution changes (and ages) over time, or changes (in future) with varying assumptions.

#normalise_pyramid		§Option to normalise the population pyramid, so the total for each region is the same, for comparing relative age distributions in different regions. 
%%With this option, view the pyramid un-stacked. %%

#fertility		§Choice of options for future fertility: 
based on UNPD WPP data, converging gradually to a function of  GDP/capita, or instantly applying this function (for experimentation).

#source_migration		§For future migration - choice of using the input from the  @migration module, using data from UNPD WPP scenarios, or  testing what would happen with zero migration.

#fertility_factor		§A parameter to scale the @total_fertility_rate for all countries and all future years.

Although this produces unrealistic changes, it is useful for experimentation, to test the sensitivity or reproduce assumptions underlying ranges of published scenarios.