Snapshot of - IFs

Archive of IFs, version: 7.36

Reference card - IFs

The reference card is a clearly defined description of model features. The numerous options have been organized into a limited amount of default and model specific (non default) options. In addition some features are described by a short clarifying text.

Legend:

• not implemented
• implemented
• implemented (not default option)

About

Name and version

IFs International Futures 7.36

Model link

https://pardee.du.edu/access-ifs

Institution

Frederick S. Pardee Center for International Futures, University of Denver (Pardee Center), Colorado, USA, https://pardee.du.edu/.

Documentation

IFs documentation consists of a referencecard and detailed model documentation

Process state

under review

Model scope and methods

Model documentation: Model scope and methods - IFs

Model type

• Integrated assessment model
• Energy system model
• CGE
• CBA-integrated assessment model

Geographical scope

• Global
• Regional

Solution concept

• Partial equilibrium (price elastic demand)
• Partial equilibrium (fixed demand)
• General equilibrium (closed economy)

Solution horizon

• Recursive dynamic (myopic)
• Intertemporal optimization (foresight)

Solution method

• Simulation
• Optimization
• Dynamic recursive with annual time steps through 2100.

Anticipation

Myopic

Temporal dimension

Base year:2015, time steps:1 year time steps, horizon: 2100

Spatial dimension

Number of regions:186

1. Afghanistan
2. Albania
3. Algeria
4. Angola
5. Argentina
6. Armenia
7. Australia
8. Austria
9. Azerbaijan
10. Bahamas
11. Bahrain
12. Bangladesh
13. Barbados
14. Belarus
15. Belgium
16. Belize
17. Benin
18. Bhutan
19. Bolivia
20. Bosnia
21. Botswana
22. Brazil
23. Brunei
24. Bulgaria
25. Burkina Faso
26. Burundi
27. Cambodia
28. Cameroon
29. Canada
30. Cape Verde
31. Central African Republic
32. Chad
33. Chile
34. China
35. Colombia
36. Comoros
37. Congo, Democratic Republic of
38. Congo, Republic of
39. Costa Rica
40. Cote d'Ivoire
41. Croatia
42. Cuba
43. Cyprus
44. Czech Republic
45. Denmark
46. Djibouti
47. Dominican Republic
48. Ecuador
49. Egypt
50. El Salvador
51. Equatorial Guinea
52. Eritrea
53. Estonia
54. Ethiopia
55. Fiji
56. Finland
57. France
58. Gabon
59. Gambia
60. Georgia
61. Germany
62. Ghana
63. Greece
64. Grenada
65. Guatemala
66. Guinea
67. Guinea Bissau
68. Guyana
69. Haiti
70. Honduras
71. Hong Kong
72. Hungary
73. Iceland
74. India
75. Indonesia
76. Iran
77. Iraq
78. Ireland
79. Israel
80. Italy
81. Jamaica
82. Japan
83. Jordan
84. Kazakhstan
85. Kenya
86. North Korea
87. South Korea
88. Kosovo
89. Kuwait
90. Kyrgyzstan
91. Laos
92. Latvia
93. Lebanon
94. Lesotho
95. Liberia
96. Libya
97. Lithuania
98. Luxembourg
99. Macedonia
100. Madagascar
101. Malawi
102. Malaysia
103. Maldives
104. Mali
105. Malta
106. Mauritania
107. Mauritius
108. Mexico
109. Micronesia
110. Moldova
111. Mongolia
112. Montenegro
113. Morocco
114. Mozambique
115. Myanmar
116. Namibia
117. Nepal
118. Netherlands
119. New Zealand
120. Nicaragua
121. Niger
122. Nigeria
123. Norway
124. Oman
125. Pakistan
126. Palestine
127. Panama
128. Papua New Guinea
129. Paraguay
130. Peru
131. Philippines
132. Poland
133. Portugal
134. Puerto Rico
135. Qatar
136. Romania
137. Russia
138. Rwanda
139. Samoa
140. Sao Tome and Principe
141. Saudi Arabia
142. Senegal
143. Serbia
144. Seychelles
145. Sierra Leone
146. Singapore
147. Slovak Republic
148. Slovenia
149. Solomon Islands
150. Somalia
151. South Africa
152. Spain
153. Sri Lanka
154. St. Lucia
155. St. Vincent and the Grenadines
156. Sudan
157. South Sudan
158. Suriname
159. Swaziland
160. Sweden
161. Switzerland
162. Syria
163. Taiwan
164. Tajikistan
165. Tanzania
166. Thailand
167. Timor-Leste
168. Togo
169. Tonga
170. Trinidad
171. Tunisia
172. Turkey
173. Turkmenistan
174. UAE
175. Uganda
176. Ukraine
177. United Kingdom
178. Uruguay
179. USA
180. Uzbekistan
181. Vanuatu
182. Venezuela
183. Vietnam
184. Yemen
185. Zambia
186. Zimbabwe

Time discounting type

• Discount rate exogenous
• Discount rate endogenous

Policies

• Emission tax
• Emission pricing
• Cap and trade
• Fuel taxes
• Fuel subsidies
• Feed-in-tariff
• Portfolio standard
• Capacity targets
• Emission standards
• Energy efficiency standards
• Agricultural producer subsidies
• Agricultural consumer subsidies
• Land protection
• Pricing carbon stocks

Socio-economic drivers

Model documentation: Socio-economic drivers - IFs

Population

• Yes (exogenous)
• Yes (endogenous)

Population age structure

• Yes (exogenous)
• Yes (endogenous)

Education level

• Yes (exogenous)
• Yes (endogenous)

Urbanization rate

• Yes (exogenous)
• Yes (endogenous)

GDP

• Yes (exogenous)
• Yes (endogenous)

Income distribution

• Yes (exogenous)
• Yes (endogenous)

Employment rate

• Yes (exogenous)
• Yes (endogenous)

Labor productivity

• Yes (exogenous)
• Yes (endogenous)

Total factor productivity

• Yes (exogenous)
• Yes (endogenous)

Autonomous energy efficiency improvements

• Yes (exogenous)
• Yes (endogenous)

Macro-economy

Model documentation: Macro-economy - IFs

Economic sector

Industry

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Energy

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Transportation

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Residential and commercial

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Agriculture

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Forestry

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Other economic sector

• Materials
• Manufactures
• ICT tech

Macro-economy

Trade

• Coal
• Oil
• Gas
• Uranium
• Electricity
• Bioenergy crops
• Food crops
• Capital
• Emissions permits
• Non-energy goods
• Energy
• Agriculture
• Materials
• Manufactures
• Services
• ICT tech

Note: Energy trade is represented in a 'pooled' approach - imports and exports are aggregated across energy types.

Cost measures

• GDP loss
• Welfare loss
• Consumption loss
• Area under MAC
• Energy system cost mark-up

Categorization by group

• Income
• Urban - rural
• Technology adoption
• Age
• Gender
• Education level
• Household size

Institutional and political factors

• Early retirement of capital allowed
• Interest rates differentiated by country/region
• Regional risk factors included
• Technology costs differentiated by country/region
• Technological change differentiated by country/region
• Behavioural change differentiated by country/region
• Constraints on cross country financial transfers

Resource use

Coal

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Conventional Oil

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Unconventional Oil

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Conventional Gas

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Unconventional Gas

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Uranium

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Bioenergy

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Water

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Raw Materials

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Land

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Technological change

Energy conversion technologies

• No technological change
• Exogenous technological change
• Endogenous technological change

Energy End-use

• No technological change
• Exogenous technological change
• Endogenous technological change

Material Use

• No technological change
• Exogenous technological change
• Endogenous technological change

Agriculture (tc)

• No technological change
• Exogenous technological change
• Endogenous technological change

Energy

Model documentation: Energy - IFs

Energy technology substitution

Energy technology choice

• No discrete technology choices
• Logit choice model
• Production function
• Linear choice (lowest cost)
• Lowest cost with adjustment penalties

Energy technology substitutability

• Mostly high substitutability
• Mostly low substitutability
• Mixed high and low substitutability

Energy technology deployment

• Expansion and decline constraints
• System integration constraints

Energy

Electricity technologies

• Coal w/o CCS
• Coal w/ CCS
• Gas w/o CCS
• Gas w/ CCS
• Oil w/o CCS
• Oil w/ CCS
• Bioenergy w/o CCS
• Bioenergy w/ CCS
• Geothermal power
• Nuclear power
• Solar power
• Solar power-central PV
• Solar power-distributed PV
• Solar power-CSP
• Wind power
• Wind power-onshore
• Wind power-offshore
• Hydroelectric power
• Ocean power

Note: Electricity is aggregated for all fuel types in the model.

Hydrogen production

• Coal to hydrogen w/o CCS
• Coal to hydrogen w/ CCS
• Natural gas to hydrogen w/o CCS
• Natural gas to hydrogen w/ CCS
• Oil to hydrogen w/o CCS
• Oil to hydrogen w/ CCS
• Biomass to hydrogen w/o CCS
• Biomass to hydrogen w/ CCS
• Nuclear thermochemical hydrogen
• Solar thermochemical hydrogen
• Electrolysis

Refined liquids

• Coal to liquids w/o CCS
• Coal to liquids w/ CCS
• Gas to liquids w/o CCS
• Gas to liquids w/ CCS
• Bioliquids w/o CCS
• Bioliquids w/ CCS
• Oil refining

Refined gases

• Coal to gas w/o CCS
• Coal to gas w/ CCS
• Oil to gas w/o CCS
• Oil to gas w/ CCS
• Biomass to gas w/o CCS
• Biomass to gas w/ CCS

Heat generation

• Coal heat
• Natural gas heat
• Oil heat
• Biomass heat
• Geothermal heat
• Solarthermal heat
• CHP (coupled heat and power)

Grid Infra Structure

Electricity

• Yes (aggregate)
• Yes (spatially explicit)

Gas

• Yes (aggregate)
• Yes (spatially explicit)

Heat

• Yes (aggregate)
• Yes (spatially explicit)

CO2

• Yes (aggregate)
• Yes (spatially explicit)

Hydrogen

• Yes (aggregate)
• Yes (spatially explicit)

Energy end-use technologies

Passenger transportation

• Passenger trains
• Buses
• Light Duty Vehicles (LDVs)
• Electric LDVs
• Hydrogen LDVs
• Hybrid LDVs
• Gasoline LDVs
• Diesel LDVs
• Passenger aircrafts

Freight transportation

• Freight trains
• Heavy duty vehicles
• Freight aircrafts
• Freight ships

Industry

• Steel production
• Aluminium production
• Cement production
• Petrochemical production
• Paper production
• Plastics production
• Pulp production

Residential and commercial

• Space heating
• Space cooling
• Cooking
• Refrigeration
• Washing
• Lighting

Land-use

Model documentation: Land-use - IFs

Land cover

• Cropland
• Cropland irrigated
• Cropland food crops
• Cropland feed crops
• Cropland energy crops
• Forest
• Managed forest
• Natural forest
• Pasture
• Shrubland
• Built-up area

Agriculture and forestry demands

• Agriculture food
• Agriculture food crops
• Agriculture food livestock
• Agriculture feed
• Agriculture feed crops
• Agriculture feed livestock
• Agriculture non-food
• Agriculture non-food crops
• Agriculture non-food livestock
• Agriculture bioenergy
• Agriculture residues
• Forest industrial roundwood
• Forest fuelwood
• Forest residues

Agricultural commodities

• Wheat
• Rice
• Other coarse grains
• Oilseeds
• Sugar crops
• Ruminant meat
• Non-ruminant meat and eggs
• Dairy products

Emission, climate and impacts

Model documentation: Emissions - IFs, Climate - IFs, Non-climate sustainability dimension - IFs

Greenhouse gases

• CO2 fossil fuels
• CO2 cement
• CO2 land use
• CH4 energy
• CH4 land use
• CH4 other
• N2O energy
• N2O land use
• N2O other
• CFCs
• HFCs
• SF6
• PFCs

Pollutants

• CO energy
• CO land use
• CO other
• NOx energy
• NOx land use
• NOx other
• VOC energy
• VOC land use
• VOC other
• SO2 energy
• SO2 land use
• SO2 other
• BC energy
• BC land use
• BC other
• OC energy
• OC land use
• OC other
• NH3 energy
• NH3 land use
• NH3 other

Climate indicators

• Concentration: CO2
• Concentration: CH4
• Concentration: N2O
• Concentration: Kyoto gases
• Radiative forcing: CO2
• Radiative forcing: CH4
• Radiative forcing: N2O
• Radiative forcing: F-gases
• Radiative forcing: Kyoto gases
• Radiative forcing: aerosols
• Radiative forcing: land albedo
• Radiative forcing: AN3A
• Radiative forcing: total
• Temperature change
• Sea level rise
• Ocean acidification
• Precipitation change

Carbon dioxide removal

• Bioenergy with CCS
• Reforestation
• Afforestation
• Soil carbon enhancement
• Direct air capture
• Enhanced weathering

Climate change impacts

• Agriculture
• Energy supply
• Energy demand
• Economic output
• Built capital
• Inequality

Co-Linkages

• Energy security: Fossil fuel imports & exports (region)
• Energy access: Household energy consumption
• Air pollution & health: Source-based aerosol emissions
• Air pollution & health: Health impacts of air Pollution
• Food access
• Water availability
• Biodiversity

Model Documentation - IFs

International Futures (IFs) is a long-term integrated assessment system, which is a collection of multiple hard–linked, heavily interconnected models. Although sometimes referred to as modules, they are large-scale models in and of themselves.  IFs represents 188 countries connected through a variety of flows, facilitates aggregation of them to global regions, and allows subdivision of them into more local socio-political units. It is dynamic recursive with annual time steps to 2100 and beyond (while myopic, many supply-demand equilibrating mechanisms with target specifications direct attention forward). The IFs system has an extensive user-friendly interface and is available for use by others both on-line and in a downloadable version, and it is open source.  

The IFs system is extensively documented elsewhere. See Hughes (2019) for attention to the full system and the Frederick S. Pardee Institute for International Future’s website at https://korbel.du.edu/pardee for detailed model-by-model documentation. There is an interactive wiki at  https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs). Via Pardee’s website it is also possible freely to use IFs on line at https://www.ifs.du.edu/ifs/frm_MainMenu.aspx or to download IFs for use on machines with Windows operating systems from https://korbel.du.edu/pardee/content/download-ifs.

The purpose here is to provide a much shorter summary version of IFs documentation. Major models in IFs (see Figure 1 below) include

• a multistate population model, which represents 22 age sex cohorts to age 100+ and differentiates their educational attainment and cause-specific mortality patterns in the endogenous calculation of age-specific fertility and mortality.
• a multisector general equilibrium economic model, which uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time; it provides labor, investment, and consumption information to partial equilibrium energy and agriculture models as well as GDP and GDP per capita (at market exchange rates and purchasing power parity) to all IFs models. Its structure contains a full social accounting matrix (SAM) representing financial flows among households, firms, and governments.
• an education model that tracks grade-by-grade student progression and aging of adults with variable attainment levels.
• a health model that represents age-sex specific mortality and morbidly by 15 causes of death.
• socio-political representations that include governance capacity and stability, as well as information on social values and cultural change.
• an international politics model that calculates multiple measures of national power plus patterns of interstate relationships, both positive and representing threat and conflict.
• an energy model (which portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable). Physical values from the partial equilibrium model are converted to currency values to replace those in the general equilibrium economic model.
• an agricultural model, which is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes) the model represents crop, gazing, forest, developed and other land. As with energy, physical values converted to monetary values override selected sectoral values in the general equilibrium model..
• an infrastructure model that projects paved roads, access to safe water and sanitation, electricity access, and access to various forms of information and communications technology,
• an environmental model, which allows tracking of remaining resources of fossil fuels, area of forested land, water supply-demand, atmospheric carbon dioxide, and changes in temperature and precipitation.
• an implicit technology model with elements scattered across other models, which allows changes in assumptions about rates of technological advance in health, agriculture, energy, and the broader economy.

The variables shown as linking the models in Figure 1 are only a small subset of those that do so; the sections that explain the models will explain those and other linkage variables.

Figure 1: The basic models of the IFs system and illustrative linkages

Although other issues such as air quality, deforestation and species extinction have been very important, the very rapid development of Integrated Assessment Models (IAMs) during the 1980s, 1990s, and more recently was driven substantially by the recognition of the reality and danger of climate change.  That and a call from the Intergovernmental Panel on Climate Change for a research organization focused on alternative climate futures led in 2007 to the establishment of the IAM Consortium. The Pardee Institute for International Futures is a member of the IAMC and IFs is an IAM.  Its attention to energy, agriculture, and the environment reinforces its IAM character.

IFs differs, however, from most of the models developed and used by institutional members of the IAMC.  Most of those models do not have elaborated demographic and economic treatment but rely instead on alternative scenarios of population and GDP futures generated by models specialized in producing those. The creation of Shared Socioeconomic Pathway scenarios (SSPs) has codified that use for most models. Similarly, attention to education and health is almost non-existent in other IAMs, and the attention in IFs to governance/socio-cultural change and international politics is unique. Across its developmental history, attention to broad sets of issues like those represented by the earlier Millenium Development Goals (MDGs) and the successor Sustainable Development Goals (SDGs) has motivated much of IFs development.

On the other hand, the treatment within IFs of environmental issues is considerably less developed than that of typical IAMs.  That treatment is, however, of importance, and this document will provide summary details on it as well as on the other models in the IFs system.

A very important feature of the IFs system is that it is imbedded in an interactive user interface.  The interface allows access to all the data that underlies model base-year initialization and facilitates estimation of functional forms, to a wide range of display options for examining results within and across model runs, and to a scenario-development interface for changing parameters within functional forms or more directly reshaping the behavior of model formulations via a wide range of multipliers and/or additive factors. The interface facilitates saving, retrieving, and modifying sets of scenario interventions, including direct exogenous specification of 11 or more key variables, including many that have come from quantification by other models focused on the Shared Socioeconomic Pathways (SSPs).  The interface also facilitates saving, retrieving, and modifying resultant run files, as well as comparing runs files in research analyses and across model versions.

At https://ifsnetworkdiagram.du.edu is an interactive diagram that graphically shows the variables and parameters in the IFs models (modules) and allows exploration of causality and directional interconnection. More complete documentation of IFs is available on the Pardee Institute’s website at https://korbel.du.edu/pardee.  A direct link to the IFs wiki is https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs). Via the Pardee Institute’s website it is possible freely to use IFs on line at https://www.ifs.du.edu/ifs/frm_MainMenu.aspx or to download IFs for use on machines with Windows operating systems from https://korbel.du.edu/pardee/content/download-ifs.

The model code (but at this point not the interface code) is open source. For access to text files of the code and appropriate software to change it, contact the Pardee Institute and accept a general public use license that requires sharing code changes with the Institute.  The programing language is vb.NET and the interface is built in asp.NET, which needs to run using Microsoft’s Internet Information Services (IIS).

3) Macro-economy - IFs

The economic model represents supply, demand, and trade in each of six economic sectors: agriculture, primary energy, raw materials, manufactures, services, and information/communications technology. The model draws upon data from many sources including the Global Trade and Analysis Project (GTAP) with 57 sectors as of GTAP 8; the IFs pre-processor (integrated software that reads raw data and prepares the initial base year for all models) collapses those into the six IFs sectors and theoretically could collapse them into a different aggregated subset. On the supply side, a Cobb-Douglas production function determines value added. Thus two principal factors are capital and labor. Labor is responsive not just to population size and structure, but to the labor participation rate, including the changing role of women in the work force. A labor submodel calculates labor demand that is equilibrated over time with supply, but allows unemployment rate changes. Accumulated growth in the level of technology or multifactor productivity (MFP), in a "disembodied" representation (TEFF), modifies these factors. Immediate energy shortages/shocks can also affect value added.

The technological factor in the production function is often called multifactor productivity (MFP). The basic value of MFP is a sum of a global productivity growth rate driven by the economically advanced or leading country/region, a technological convergence factor dependent on GDP per capita, and an exogenous or scenario factor. In addition, however, other factors affect productivity growth over time. These include a wide range of variables across human, social, physical, and knowledge capital categories. For instance, years of adult education attainment and the level of economic freedom, respectively are among the variables that affect change in MFP associated with human and social capital.

Input-output matrices, which are tied to GTAP data but specified dependent on the level of development (GDP per capita), allow the computation from value added of gross production. The calculation of gross production (ZS) in value terms within the economic model is overridden by calculations of physical production converted to value in the agricultural and energy models when respective switches (AGON and ENON) are thrown as in the default of the IFs Base Case scenario. After satisfaction of intersectoral flows, the remainder of gross production is available for meeting final demand, both domestic and in other countries via trade.

On the demand side, most of household income supports consumption, which is directed to sectors via a linear expenditure system. It is the balance of this production for final demand with actual final demand that determines whether inventories grow or decline. Inventories (or stocks) are the key equilibrating variable in four negative or equilibrating feedback loops. As inventories rise, prices fall, increasing final demand (one loop), decreasing production (a second loop), and thereby in total decreasing inventories in the pursuit over time of a target value and equilibrium. Similarly, as inventories rise, capacity utilization falls, decreasing production, and restraining inventories. Finally, relative prices affect the levels of trade among countries.

In the longer run, investment and capital stocks are the key driving variables in an important positive feedback loop. As capital rises, it increases value added and GDP, increasing final demand and further increasing investment. Capital stock is a function of investment and depreciation rates. Endogenously determined investment can be influenced exogenously by a multiplier and the lifetime of capital can be changed. Similarly, government social investment can increase productivity, production and inventories in another positive feedback loop.

4) Energy - IFs

Energy demand in IFs is most immediately a function of GDP and the energy demand per unit of GDP. Energy production is most immediately a function of capital stock in each energy type, the capital/output ratio for that energy type, and a capacity utilization factor.

Energy demand per unit of GDP depends on GDP per capita, energy prices, and an autonomous trend in energy efficiency. The first two of these are computed endogenously, the latter is provided exogenously. The user can control the price elasticity of energy demand and the autonomous trend in efficiency of energy use. The user can also use an energy demand multiplier to directly modify energy demand.

For fossils fuels and hydro, there are upper bounds on production. For fossil fuels, these are based on reserve production ratios, as well as user-specified upper bounds. For hydro, the upper bound relates to hydropower potential. The model user can also control production using an energy demand multiplier to directly modify energy production by energy type.

The capital/output (capital/production) ratios for all fuel types decline over time due to technological improvements at rates determined by two user controllable parameters. For fossil fuels, this is counteracted by a factor that increases the capital/output ratio as the amount of remaining resources decreases. Something similar happens for hydro and other renewables, but here the capital/output ratios increase as production approaches a maximum possible level. The user can further modify the capital/output ratios with the multipliers.

Energy capital, by fuel type, is initialized based on the initial levels of production and capital/output ratios. Energy capital depreciates at a rate determined by the lifetime of energy capital and it grows with investment. Total desired investment in energy capital is influenced by many factors, including existing capital, domestic and global energy demand, the production of other renewables, changes in the global capital/output ratio, world and domestic energy stocks, expected overall profits in the energy sector, and imports. Users can influence this in the aggregate and can also control the effect of expected profits and world energy stocks. Desired investment by energy type increases with individual profit expectations, but also by limits related to reserve production factors (for fossil fuels and hydro), any exogenous restrictions on maximum production (for fossil fuels), ultimate potential (for hydro), and other, unspecified factors (nuclear). Users can influence the effect of profit expectations by fuel type as well as influence the desired investment by energy type in the aggregate. The user can also specify an exogenous growth rate for energy investment by fuel type. The economic model ultimately determines whether all of the investment needs can be met; in case of shortfalls, the investment in each type of energy is reduced proportionately.

IFs separately represents ultimate resources and known reserves, where the latter are the amount of energy resources available to be produced. Resources and reserves, both conventional and unconventional, are set in the pre-processor. The user can modify the default assumptions on ultimate resources, either directly or via the use of multipliers. Reserves decline with production and increase with discoveries. The rate of discovery depends on the ultimate resources remaining, the intensity of current production, world energy prices, and a base rate of discovery. The user can control the effect of world prices on discovery, augment the base rate of discovery, and use a multiplier to affect the rates of discovery. Finally, IFs keeps track of any production not used in the current year, i.e., stocks, and shortages.

Domestic energy prices are influenced by world stocks, domestic stocks, and the ratio of capital to production at the global level. The user can control the effect of domestic stocks on prices. Users can also include a “cartel premium” and a carbon tax. More directly users can set domestic energy prices exogenously for just the first year or for multiple future years. The world energy price is calculated as a weighted sum of the domestic prices.

The energy model also provides representation and control over energy trade. The levels of imports and exports depend upon levels of production and demand, as well as past propensities to import and export energy. The user can set maximum limits on of energy imports and energy exports, as well as general limits on trade.

4.1.1) Fossil energy resources - IFs

For fossils fuels and hydro, there are upper bounds on production. For fossil fuels, these are based on reserve production ratios, as well as user-specified upper bounds. For hydro, the upper bound relates to hydropower potential. The model user can also control production using an energy demand multiplier to directly modify energy production by energy type.

4.4) Energy demand - IFs

Energy demand in IFs is most immediately a function of GDP and the energy demand per unit of GDP. Energy demand per unit of GDP depends on GDP per capita, energy prices, and an autonomous trend in energy efficiency. The first two of these are computed endogenously, the latter is provided exogenously. The user can control the price elasticity of energy demand and the autonomous trend in efficiency of energy use. The user can also use an energy demand multiplier to directly modify energy demand.

5) Land-use - IFs

In the IFs model, land is divided into 5 categories: crop land, grazing land, forest land, ’other’ land, and urban or built-up land. First, changes in urban land are driven by changes in average income and population, and draw from all other land types. Second, the investment in cropland development is the primary driver of changes in cropland, with shifts being compensated by changes in forest and "other" land. Third, changes in grazing land are a function of average income (reflecting meat demand), with shifts again being compensated by changes in forest and "other" land. Finally, conservation policies can influence the amount of forest land, with any necessary adjustments coming from crop and grazing land.

5.1) Agriculture - IFs

The agriculture/food model represents crops, meat, and fish production, consumption, and trade in the aggregate, Agricultural production is a function of the availability of resources, e.g. land, livestock, capital, and labor, as well as climate factors and technology. Crop yield is a function of technology, capital, and labor. It is differentiated between irrigated and non-irrigated land. It is posited to have upper limits and to be responsive to stocks/inventories.

The model also accounts for lost production (such as spoilage in the fields or in the first stages of the food supply chain), distribution and transformation losses, and consumption losses, which are responsive to average income. Income levels are the primary drivers of caloric demand, which then determines food demand taking into account country-specific differences than can reflect income and cultural differences. The model distinguishes crop demand for food, animal feed, and industrial usage. Stocks or inventories (or relative prices in different model versions) provide feedback to supply and demand sides of the model.

Stocks and stock changes also play a role in driving the model’s demand for agricultural investment. The actual levels of investment are finalized in the economic model of IFs and subject to constraints there. The investment can be of two types – investment for expanding and maintaining cropland (extensification) and investment for increasing crop yields per unit area (intensification). The expected relative rates of return determine the split.

The final key dynamics addressed in the agriculture model relate to land, livestock, and water. The latter of these is very straightforward, driven only by crop production. Changes in livestock herd size are influenced by changes in the demand for meat, but also by changes in available grazing land, and the ability of countries to import.

6) Emissions - IFs

Fossil fuel consumption generates carbon emissions specific to coal, oil, and natural gas use. Net deforestation generates additional carbon emissions or removal. IFs does not represent other greenhouse gas emissions. The model exogenously represents net removals from the atmosphere by ocean and land sinks and traces resultant level of atmospheric carbon. See topics on the energy and agriculture models for discussion of formulations and of control parameters.

10) References - IFs

Birkmann, Joern, Susan L. Cutter, Dale S. Rothman, Torsten Welle, Matthias Garschagen, Bas van Ruijven, et al. 2013. "Scenarios for Vulnerability: Opportunities and Constraints in the Context of Climate Change and Disaster Risk.” Climatic Change 133(1): 53–68. DOI: 10.1007/s10584-013-0913-2.

Burt, Alison, Barry B. Hughes, and Gary Milante. 2014. “Eradicating Poverty in Fragile States: Prospects of Reaching the ‘High-Hanging’ Fruit by 2030.” WB Policy Research Working Paper no. 7002. World Bank, Washington, DC. http://documents.worldbank.org/curated/en/2014/08/20040315/eradicating-poverty-fragile-states-prospects-reaching-high-hanging-fruit-2030.

Dickson, Janet R., Barry B. Hughes, and Mohammod T. Irfan. 2010. Advancing Global Education. Vol. 2 of the Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.

Hillebrand, Evan E., and Stacy Closson. 2015. Energy, Economic Growth, and Geopolitical Futures. Cambridge, MA: MIT Press.

Hughes, Barry B., ed. 2013a. "Development-Oriented Policies and Alternative Human Development Paths: Aggressive but Reasonable Interventions." Prepared by the Frederick S. Pardee Center for International Futures. UNDP Occasional Paper 2013/05. Human Development Report Office, United Nations Development Programme, New York, NY. http://hdr.undp.org/sites/default/files/hdro_1305_pardee.pdf.

Hughes, Barry B. 2016. “International Futures (IFs) and Integrated, Long-Term Forecasting of Global Transformations.” Futures 81(August): 98–118. DOI: 10.1016/j.futures.2015.07.007.

Hughes, Barry B. forthcoming 2019. Exploring and Understanding International Futures: Building a Global Modeling System. Amsterdam: Elsevier.

Hughes, Barry B., David Bohl, Mohammod Irfan, Eli Margolese-Malin, and José R. Solórzano. 2016. “ICT/Cyber Benefits and Costs: Reconciling Competing Perspectives on the Current and Future Balance.” Technological Forecasting and Social Change 115(February): 117–130. DOI: 10.1016/j.techfore.2016.09.027.

Hughes, Barry B., and Evan E. Hillebrand. 2006. Exploring and Shaping International Futures. Boulder, CO: Paradigm.

Hughes, Barry B., Mohammod T. Irfan, Haider Khan, Krishna B. Kumar, Dale S. Rothman, and José R. Solórzano. 2009. Reducing Global Poverty. Vol. 1 of the Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.

Hughes, Barry B., Mohammod T. Irfan, Jonathan D. Moyer, Dale S. Rothman, and José R. Solórzano. 2011. "Forecasting the Impacts of Environmental Constraints on Human Development." Human Development Research Paper 2011/08. United Nations Development Programme, New York, NY. http://hdr.undp.org/sites/default/files/hdrp_2011_08.pdf.

Hughes, Barry B., Mohammod T. Irfan, Jonathan D. Moyer, Dale S. Rothman, and José R. Solórzano. 2012. "Exploring Future Impacts of Environmental Constraints on Human Development." Sustainability 4(5): 958–994. DOI: 10.3390/su4050958.

Hughes, Barry B., and Peter D. Johnston. 2005. “Sustainable Futures: Policies for Global Development.” Futures 37(8): 813–831. DOI: 10.1016/j.futures.2005.01.017.

Hughes, Barry B., Devin K. Joshi, Jonathan D. Moyer, Timothy D. Sisk, and José R. Solórzano. 2014. Strengthening Governance Globally. Vol. 5 of the Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.

Hughes, Barry B., Randall Kuhn, Cecilia M. Peterson, Dale S. Rothman, and José R. Solórzano. 2011a. Improving Global Health. Vol. 3 of the Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.

Hughes, Barry B., Randall Kuhn, Cecilia M. Peterson, Dale S. Rothman, José R. Solórzano, Colin D. Mathers, and Janet R. Dickson. 2011b. "Projections of Global Health Outcomes from 2005 to 2060 Using the International Futures Integrated Forecasting Tool." Bulletin of the World Health Organization 89(7): 478–486. DOI: 10.2471/BLT.10.083766.

Milante, Gary, Barry B. Hughes, and Alison Burt. 2016. “Poverty Eradication in Fragile Places: Prospects for Harvesting the Highest Hanging Fruit by 2030.” Stability: International Journal of Security and Development 5(1): 1–24. DOI: 10.5334/sta.435.

Moyer, Jonathan D., and Barry B. Hughes. 2012. "ICTs: Do They Contribute to Increased Carbon Emissions?" Technological Forecasting and Social Change 79(5): 919–931. DOI: 10.1016/j.techfore.2011.12.005.

Rothman, Dale S., Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, and Jonathan D. Moyer. 2014. Building Global Infrastructure. Vol. 4 of the Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press.