IAMC-Documentation - User contributions [en]

Appendices - IFs

2025-08-08T21:45:28Z

Barry Hughes: Created text for the page

You can find much more information on IFs at the website of the Frederick S. Pardee Institute for International Futures. You can find that site by searching for "Pardee IFs".

Data - IFs

2025-08-08T21:36:26Z

Barry Hughes: Created text for the page

The interface that accesses the IFs model also accesses an extensive database in support of it and available for longitudinal and cross-sectional analysis with that interface. There are more than 8000 data series from a very wide range of sources in that database. The interface also facilitates the creation of scenarios and the display and export of values for the 188 countries through the model's flexible temporal horizon.

Mathematical model description - IFs

2025-08-08T21:30:18Z

Barry Hughes: Created text for the page

The wiki of IFs includes full mathematical documentation. You can find it by searching on Pardee IFs.

Other materials - IFs

2025-08-08T21:22:17Z

Barry Hughes: Created text for the page

The economic model represents non-energy and non-agricultural materials as one of 6 sectors for supply, demand, and trade in monetary terms. But there is no physical representation of such materials.{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=IFs
|DocumentationCategory=Other materials
}}

Other sustainability dimensions - IFs

2025-08-08T21:18:24Z

Barry Hughes: Created text for the page

As the Sustainable Development Goals (SDGs) make clear and the SHAPE project with contributions from many IAMs has reinforced, there are many sustainability dimensions that are not captured with environmental representations but rather those capturing human development and domestic and international sociopolitical support systems. IFs represents several of these.

== Education ==
The model forecasts gender- and country-specific access, participation and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to project educational attainment for the entire population at any point in time within the analysis horizon.

From simple accounting of the grade progressions to complex budget balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO's ISCED classification) about national systems of education around the world. One difference between other attempts at projecting educational participation and attainment and that of IFs is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs.

The figure displays the major variables and components that directly determine education demand, supply, and flows in the IFs system. We emphasize again the inter-connectedness of the components and their relationship to the broader human development system. For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model. In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility. Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.
[[File:Education model in IFs.png|thumb|Education model in IFs]]

== Governance ==
Governance is the two-way interaction between government and the broader socio-political or, even more broadly, socio-cultural system. The conceptual foundation for the representation of governance in IFs owes much to an analysis of the evolution of governance in countries around the world over several centuries. That analysis (see Chapter 1 of the Strengthening Governance Globally volume by Hughes et al. 2014) identified three dimensions of governance: security, capacity, and inclusion. It traced them over time and noted their largely sequential unfolding for currently developed countries and their currently more nearly simultaneous progression in many lower-income countries.

The three dimensions interact closely and bi-directionally with each other. They also interact bi-directionally with broader human development systems. The level of well-being, often captured quantitatively by GDP per capita or the more inclusive human development index, may be especially important, but is hardly alone in helping drive forward advance in governance; for instance, the age structures of populations and economic structures also interact with governance patterns.

The conceptualization of governance further divides each of the three primary dimensions into two sub-dimensions partly based on the desire to quantify them historically and to facilitate scenario analysis. For security those are the probability of intrastate conflict and the general level of country performance and risk. The two sub-dimensions of capacity are the ability to raise revenue and the effective use of it and the other tools of government—that is, the competence or quality of governance. IFs uses corruption (that is, control of it) as a proxy for such competence. The first sub-dimension of inclusion is the level of formal democratization, typically assessed in terms of competitive elections. More broadly democratization involves inclusion of population groupings across lines such as ethnicity, religion, sex, and age; IFs uses gender equity as a proxy for the second dimension.

== Health ==
Health analysis systems typically can help us either (1) to understand better where broad, long-term patterns of human development appear to be taking us with respect to global health, or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health.

Broad structural models with deep distal drivers such as technological and income advance (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating those distal development drivers to longer-term pattern change in health. Distal driver formulations tend to produce forecasts of constantly decreasing death rates. Yet we know, for instance, that some more proximate factors driving mortality, such as smoking, obesity, and road traffic accidents, tend to increase in developing societies with income and education, before at least smoking and road traffic deaths (and perhaps also obesity) typically decline. The inclusion of such proximate drivers thus opens the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures. A hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.

The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.

== Infrastructure ==
The dominant relations in the Infrastructure model are those that determine the expected levels of infrastructure stocks and access, spending on infrastructure, and the impacts of infrastructure on health and productivity. The expected levels of infrastructure stocks and access are influenced by socio-economic factors related to population, economic activity, governance, and educational attainment—and, of course, spending on construction and maintenance. In almost every case there are also path dependencies that supplement the basic relationships, reflecting the considerable inertia in infrastructure development.

Spending on infrastructure is divided into private and public spending, with the latter further divided in IFs into ‘core’ and ‘other’ infrastructure. ‘Core’ infrastructure refers to those types of infrastructure that are explicitly represented in the model; ‘other’ infrastructure refers to those types of infrastructure that are not explicitly represented in the model. In IFs, public spending on core infrastructure is driven by the required spending to meet the expected levels of infrastructure stocks and access, constrained by total government consumption and the demands for government consumption in other categories (e.g. education and health). Public spending on other infrastructure is driven by average GDP per capita, total government consumption, and the demands on government consumption from other categories. Deficits and surpluses of government funds will affect the actual levels of funds allocated for both core and other infrastructure. The public spending on core infrastructure leverages a proportionate amount of private spending on core infrastructure, with the amount leveraged depending upon historical relationships found in the literature, which normally reflect the variation in public and private returns between particular types of infrastructure. Finally, in recognition of the incremental approaches that public budgeting decisions usually follow, IFs avoids unusually sharp increases in public spending on infrastructure by smoothing it over time.

Infrastructure development directly affects multifactor productivity, with this effect being treated separately for non-ICT and ICT related infrastructure. The use of solid fuels in the home and access to improved water and sanitation directly affect human health through their effects on the mortality and morbidity rates of specific diseases—diarrheal diseases, acute respiratory infections, and respiratory diseases.

== Interstate Politics ==
Threat of conflict among states is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and their level of democratization substantially affect levels of conflict threat.

Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change. IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and of “0” remove the term from power calculation.

Democratization is computed in the domestic socio-political model.

== Socio-political ==
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, and socio-political institutions/process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process

The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers. Literacy rate is an endogenous function of education spending, which the user can influence. Building on such variables, IFs provides aggregated indicators of the physical quality of life and the human development index.

The model computes value or cultural change on three dimensions identified by the global World Values Survey project: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors.

With respect to socio-political institutions/process, the model endogenously projects freedom, democracy, autocracy, economic freedom, and the status of women. All can be shifted by the user via multipliers.

The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, infrastructure, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending.

File:Education model in IFs.png

2025-08-08T21:17:17Z

Barry Hughes:

Education model in IFs

Other sustainability dimensions - IFs

2025-08-08T21:16:00Z

Barry Hughes: Created text for the page

As the Sustainable Development Goals (SDGs) make clear and the SHAPE project with contributions from many IAMs has reinforced, there are many sustainability dimensions that are not captured with environmental representations but rather those capturing human development and domestic and international sociopolitical support systems. IFs represents several of these.

== Education ==
The model forecasts gender- and country-specific access, participation and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to project educational attainment for the entire population at any point in time within the analysis horizon.

From simple accounting of the grade progressions to complex budget balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO's ISCED classification) about national systems of education around the world. One difference between other attempts at projecting educational participation and attainment and that of IFs is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs.

Figure 1 displays the major variables and components that directly determine education demand, supply, and flows in the IFs system. We emphasize again the inter-connectedness of the components and their relationship to the broader human development system. For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model. In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility. Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.

Figure 1: Education model in IFs

== Governance ==
Governance is the two-way interaction between government and the broader socio-political or, even more broadly, socio-cultural system. The conceptual foundation for the representation of governance in IFs owes much to an analysis of the evolution of governance in countries around the world over several centuries. That analysis (see Chapter 1 of the Strengthening Governance Globally volume by Hughes et al. 2014) identified three dimensions of governance: security, capacity, and inclusion. It traced them over time and noted their largely sequential unfolding for currently developed countries and their currently more nearly simultaneous progression in many lower-income countries.

The three dimensions interact closely and bi-directionally with each other. They also interact bi-directionally with broader human development systems. The level of well-being, often captured quantitatively by GDP per capita or the more inclusive human development index, may be especially important, but is hardly alone in helping drive forward advance in governance; for instance, the age structures of populations and economic structures also interact with governance patterns.

The conceptualization of governance further divides each of the three primary dimensions into two sub-dimensions partly based on the desire to quantify them historically and to facilitate scenario analysis. For security those are the probability of intrastate conflict and the general level of country performance and risk. The two sub-dimensions of capacity are the ability to raise revenue and the effective use of it and the other tools of government—that is, the competence or quality of governance. IFs uses corruption (that is, control of it) as a proxy for such competence. The first sub-dimension of inclusion is the level of formal democratization, typically assessed in terms of competitive elections. More broadly democratization involves inclusion of population groupings across lines such as ethnicity, religion, sex, and age; IFs uses gender equity as a proxy for the second dimension.

== Health ==
Health analysis systems typically can help us either (1) to understand better where broad, long-term patterns of human development appear to be taking us with respect to global health, or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health.

Broad structural models with deep distal drivers such as technological and income advance (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating those distal development drivers to longer-term pattern change in health. Distal driver formulations tend to produce forecasts of constantly decreasing death rates. Yet we know, for instance, that some more proximate factors driving mortality, such as smoking, obesity, and road traffic accidents, tend to increase in developing societies with income and education, before at least smoking and road traffic deaths (and perhaps also obesity) typically decline. The inclusion of such proximate drivers thus opens the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures. A hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.

The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.

== Infrastructure ==
The dominant relations in the Infrastructure model are those that determine the expected levels of infrastructure stocks and access, spending on infrastructure, and the impacts of infrastructure on health and productivity. The expected levels of infrastructure stocks and access are influenced by socio-economic factors related to population, economic activity, governance, and educational attainment—and, of course, spending on construction and maintenance. In almost every case there are also path dependencies that supplement the basic relationships, reflecting the considerable inertia in infrastructure development.

Spending on infrastructure is divided into private and public spending, with the latter further divided in IFs into ‘core’ and ‘other’ infrastructure. ‘Core’ infrastructure refers to those types of infrastructure that are explicitly represented in the model; ‘other’ infrastructure refers to those types of infrastructure that are not explicitly represented in the model. In IFs, public spending on core infrastructure is driven by the required spending to meet the expected levels of infrastructure stocks and access, constrained by total government consumption and the demands for government consumption in other categories (e.g. education and health). Public spending on other infrastructure is driven by average GDP per capita, total government consumption, and the demands on government consumption from other categories. Deficits and surpluses of government funds will affect the actual levels of funds allocated for both core and other infrastructure. The public spending on core infrastructure leverages a proportionate amount of private spending on core infrastructure, with the amount leveraged depending upon historical relationships found in the literature, which normally reflect the variation in public and private returns between particular types of infrastructure. Finally, in recognition of the incremental approaches that public budgeting decisions usually follow, IFs avoids unusually sharp increases in public spending on infrastructure by smoothing it over time.

Infrastructure development directly affects multifactor productivity, with this effect being treated separately for non-ICT and ICT related infrastructure. The use of solid fuels in the home and access to improved water and sanitation directly affect human health through their effects on the mortality and morbidity rates of specific diseases—diarrheal diseases, acute respiratory infections, and respiratory diseases.

== Interstate Politics ==
Threat of conflict among states is a function of many determinants. For instance, contiguity or physical proximity creates contact and therefore the potential for both threat and peaceful interaction. Cultural similarities and differences affect threat levels. Yet certain factors are more subject to rapid change over time than are contiguity or culture. Among factors that change, the relative power of states and their level of democratization substantially affect levels of conflict threat.

Power is a function of population, GDP, technology, and conventional and nuclear military expenditures, in an aggregation with weights that the user can change. IFs computes a power indicator that shows each actor’s portion of global power. It does so by weighting each actor’s share of global GDP (at exchange rates or purchasing power parity), population, a measure of technological sophistication (with GDP per capita as a proxy), government size, military spending, conventional power, and nuclear power. Weights of one "1" add the term to the power calculation, and of “0” remove the term from power calculation.

Democratization is computed in the domestic socio-political model.

== Socio-political ==
Social and political change occurs on three dimensions (social characteristics or individual life conditions, values, and socio-political institutions/process). Although GDP per capita is strongly correlated with all dimensions of change, it might be more appropriate to conceptualize a syndrome or complex of developmental change than to portray an economically-driven process

The model computes some key social characteristics/life conditions, including life expectancy and fertility rates in the demographic model, but the user can affect them via multipliers. Literacy rate is an endogenous function of education spending, which the user can influence. Building on such variables, IFs provides aggregated indicators of the physical quality of life and the human development index.

The model computes value or cultural change on three dimensions identified by the global World Values Survey project: traditional versus secular-rational, survival versus self-expression, and modernism versus postmodernism, which the user can affect via additive factors.

With respect to socio-political institutions/process, the model endogenously projects freedom, democracy, autocracy, economic freedom, and the status of women. All can be shifted by the user via multipliers.

The larger socio-political model provides representation and control over government spending on education, health, the military, R&D, infrastructure, foreign aid, and a residual category. Military spending is linked to interstate politics, both as a driver of threat and as a result of action-and-reaction based arms spending.

Air pollution and health - IFs

2025-08-08T21:04:33Z

Barry Hughes: Created text for the page

Health analysis systems typically can help us either (1) to understand better where broad, long-term patterns of human development appear to be taking us with respect to global health, or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health.

Broad structural models with deep distal drivers such as technological and income advance (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating those distal development drivers to longer-term pattern change in health. Distal driver formulations tend to produce forecasts of constantly decreasing death rates. Yet we know, for instance, that some more proximate factors driving mortality, such as smoking, obesity, and road traffic accidents, tend to increase in developing societies with income and education, before at least smoking and road traffic deaths (and perhaps also obesity) typically decline. The inclusion of such proximate drivers thus opens the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures. A hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.

The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.

Climate damages, temperature changes - IFs

2025-08-08T20:54:46Z

Barry Hughes: Created text for the page

Building upon global temperature change, the grid analysis of the MAGICC/SCENGEN system (version 5.3), which draws upon a great many Atmospheric-Ocean General Circulation Models (AOGSMs), was used to build computational formulations for country-specific values of temperature and precipitation change relative to the average during the 1980-1999 period. For the agriculture model those changes in average country temperatures and precipitation in turn affect country-specific agricultural yields (considering also possible carbon fertilization impact). Drawing upon the Nordhaus quadratic formulation, the model also computes a climate impact variable (CLIMECONIMP) that represents the country-specific, economy-wide impact of temperature change on GDP.

Modelling of climate indicators - IFs

2025-08-08T20:53:19Z

Barry Hughes: Created text for the page

IFs represents the flow of carbon emissions into and among the atmosphere, oceans, and forests/soil sinks. The atmospheric stock is represented in parts per million of carbon dioxide. In each year the environment model computes the resultant average degrees Celsius of global warming from 1990 using a logarithmic formulation (with a changeable scaling parameter '''''envclimsens)''''' that compares that year’s atmospheric concentration with the level in 1990.

Building upon global temperature change, the grid analysis of the MAGICC/SCENGEN system (version 5.3), which draws upon a great many Atmospheric-Ocean General Circulation Models (AOGSMs), was used to build computational formulations for country-specific values of temperature and precipitation change relative to the average during the 1980-1999 period. For the agriculture model those changes in average country temperatures and precipitation in turn affect country-specific agricultural yields (considering also possible carbon fertilization impact).

GHGs - IFs

2025-08-08T20:49:49Z

Barry Hughes: Created text for the page

Fossil fuel consumption generates carbon dioxide emissions with coefficients specific to coal, oil, and natural gas use. Net deforestation generates additional carbon emissions or removal. See sections on the energy and agriculture models for discussion of formulations and of control parameters.

IFs does not represent other greenhouse gas emissions, including methane. Nor does it represent technologically based carbon dioxide removal options. The model exogenously represents net carbon dioxide removals from the atmosphere by ocean and land sinks; with its endogenous computation of net carbon emissions from fossil fuels and net deforestation it thereby traces resultant level of atmospheric carbon across the forecast horizon.

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=GHGs
}}

Carbon dioxide removal (CDR) options - IFs

2025-08-08T20:47:36Z

Barry Hughes: Added basic text to the page

Not represented in IFs, other than ocean and biomass absorption.{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Carbon dioxide removal (CDR) options
}}

Pollutants and non-GHG forcing agents - IFs

2025-08-08T20:46:09Z

Barry Hughes: Created text for the page

With respect specifically to pollutants in addition to smoking incidence, IFs represents two: urban air pollution (particles) and indoor air pollution from cooking. This representation is tied into the larger health model.

Health analysis systems typically can help us either (1) to understand better where broad, long-term patterns of human development appear to be taking us with respect to global health, or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health.

Broad structural models with deep distal drivers such as technological and income advance (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating those distal development drivers to longer-term pattern change in health. Distal driver formulations tend to produce forecasts of constantly decreasing death rates. Yet we know, for instance, that some more proximate factors driving mortality, such as smoking, obesity, and road traffic accidents, tend to increase in developing societies with income and education, before at least smoking and road traffic deaths (and perhaps also obesity) typically decline. The inclusion of such proximate drivers thus opens the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures. A hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.

The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Pollutants and non-GHG forcing agents
}}

Technological change in land-use - IFs

2025-08-08T20:37:31Z

Barry Hughes: Created text for the page

Not represented in IFs.

Agricultural demand - IFs

2025-08-08T20:36:48Z

Barry Hughes: Created text for the page

On the demand side, crops, meat, and fish are used for direct human consumption (FDEM), animal feed (FEDEM), industrial uses, e.g. biofuels (INDEM), and food processing and manufacturing (FMDEM). The model also accounts for lost production (such as spoilage in the fields, the first stages of the food supply chain), distribution and transformation losses, and consumption losses, which are responsive to average income (and which the user can control in scenarios). Total demand (AGDEM) is the sum of these five use categories. With respect to food demand, it is driven by demand on a daily per capita basis for calories (CLPC) and protein (PROTEINPC) for each food category, namely crops, meat, and fish.

Other land-use - IFs

2025-08-08T20:29:20Z

Barry Hughes: Created text for the page

Bioenergy land-use - IFs

2025-08-08T20:27:19Z

Barry Hughes: Created text for the page

Not represented in IFs.

Land-use change - IFs

2025-08-08T20:26:21Z

Barry Hughes: Created text for the page

In the IFs system, the agriculture model dynamically divides land use into 5 categories, initialized by data from the United Nations Food and Agriculture Organization: crop land, grazing land, forest land, ’other’ land, and urban or built-up land. Three major dynamics drive change over time. First, changes in urban land are driven by changes in average income and population, and they are drawn proportionately from all other land types. Second, investment in cropland development is the primary driver of changes in cropland, with net shifts being offset by changes in forest and "other" land proportionately to the relative size of those two categories. Third, changes in grazing land are a function of average income’s effect on meat demand, with net shifts again being offset by changes in forest and "other" land. In addition, the user can specify conservation policies that influence the amount of forest land, with any specified adjustments coming from crop and grazing land. Beyond the implications of land-use modeling for agricultural production (crops and grazing animals), change in the forest category of land use has implications for carbon emissions and/or sequestration. See the sections on Emissions and Climate.

Forestry - IFs

2025-08-08T20:22:59Z

Barry Hughes: Created text for the page

Forest area in IFs interacts with other land use, see the land-use dynamics explanation.

Technological change - IFs

2025-08-08T20:18:13Z

Barry Hughes: Created text for the page

The general equilibrium economic model uses a Cobb-Douglas production function with a multifactor productivity (MFP) or total-factor productivity (TFP) term. The basic value of the technology term for each country/geopolitical region (r) is a sum of a global productivity growth rate driven by the economically advanced or leading country/region (by default the United States), 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 (MFPHC), social (MFPSC), physical (MFPPC), and knowledge (MFPKN) capital categories that are computed using variables from other models of the hard-linked IFs system. 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. The production function thus constitutes an important linkage across the models in the IFs system.

Partial equilibrium models for agriculture and energy are fully integrated with the general equilibrium model. Productivity for those sectors is determined in those models using logics that represent knowledge advance, physical constraints, economic drivers, and model-used parametric assumptions.

Trade - IFs

2025-08-08T20:06:14Z

Barry Hughes: Created text for the page

The economic representation in IFs consists of a six-sector general equilibrium economic model bi-directionally integrated with extended and physically based partial equilibrium models of agriculture and energy. Trade is represented in monetary terms for all six sectors and also in physical terms for agriculture and energy. Trade in the default versions of IFs is represented with a pooled approach, balanced globally but not indicating specific trading partnerships. With a switch the model can be moved to a dyadic/bilateral approach that does determine flows between specific country partners.

Monetary instruments - IFs

2025-08-08T19:58:14Z

Barry Hughes: Added basic text to the page

The IFs economic model calculates economic variables at market exchange rates and purchasing power parity. It represents those across all years in constant dollars. There is no representation of money supply or inflation.

Capital and labour markets - IFs

2025-08-08T19:55:18Z

Barry Hughes: I have added new text to this topic

Over the long run, investment and capital stocks in IFs are driving variables in an important positive (not equilibrating) 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; (although capital cohorts are not tracked). Endogenously determined investment can be influenced exogenously by a multiplier and the lifetime of capital can be changed in scenarios around technological advance. Similarly, government social investment can increase productivity, production and inventories in another positive feedback loop involving the productivity representation explained above and shown with equations. The partial equilibrium agriculture and energy models compute investment demand which is reconciled with demands for the other economic sectors in the general equilibrium model. The investment from the reconciliation is used in the partial equilibrium models to augment capital stocks (net of depreciation).

The labor force is computed based on the age-sex structure of the population model and the participation rate. The demand for labor by sector is computed in the economic model. The labor sub-model of the economic model reconciles labor supply and demand.

Production system and representation of economic sectors - IFs

2025-08-08T19:45:02Z

Barry Hughes: I have filled out text for this page

The economic model produces GDP at market exchange rates and at purchasing power parity. It also produces supply, demand, and trade in each of six economic sectors: agriculture, primary energy, raw materials, manufactures, services, and information/communications technology. It further provides a calculation of income distribution in the form of a Gini index, thereby facilitating also its calculation of poverty rates at multiple per-capita income (or household consumption) levels.

The model draws upon data from many sources including the World Bank and IMF (and the OECD for higher-income countries). It also uses ongoing updates from the Global Trade and Analysis Project (GTAP); GTAP 11 data were being used as of this writing and IFs version 8.35, but new releases lead to regular IFs updates. The IFs pre-processor (integrated software that reads raw data and prepares the initial base year for all models) collapses the large number of GTAP sectors into the six IFs sectors and theoretically could collapse them into a different aggregated subset.

Two features of the economic model are especially notable. First, it is embedded in a full social accounting matrix (SAM) structure. It assures consistency of financial flows among firms, households, and government and determines transfer payments and direct expenditures in categories including health, education, and infrastructure, thereby affecting dynamics of those models. Second, the production function that calculates value added and sums that across sectors for GDP, fundamentally important to long-term dynamics, includes endogenous representation of multifactor productivity, driving it by changes in human, social, and physical capital from other models in the IFs system, as well as knowledge advance. Both the SAM (as simplified in Figure 5) and the production function are therefore pivot points for the interaction of dynamics across the range of models in the integrated IFs system and for policy analysis.

On the demand side, the six sectors of IFs, the division of households into skilled and unskilled (with differential use of income for consumption versus savings and consumption patterns across sectors that vary with income), and an extensive representation of government revenues and expenditures are all part of that SAM elaboration. For instance, the government finance elaboration represents revenues from domestic taxes, tariffs, and foreign assistance when received and represents expenditures as transfer payments (which affect poverty levels) and direct expenditures in military, health, education, research and development, infrastructure types represented in the IFs infrastructure model, other infrastructure, and other spending (including administration) categories. Model dynamics limit growth in government debt.

On the supply side the model user can use the interface to fully override the growth of GDP with values that have been operationalized by other models in their representations of the Shared Socio-economic Pathways (SSPs) and put into the IFs database, just as the user can similarly override population growth patterns and many other SSP variables in IFs. In default mode, however, the Cobb-Douglas production function determines value added, summed across sectors to compute GDP. Two principal production factors are capital and labor. Capital stock is changed over time with depreciation and investment, the latter responsive to inventory stocks as elaborated below; investment rates by sector are flexible and existing capital stocks are fixed by sector (a putty-clay representation). 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, recognizing initial unemployment rates but moving those to standard (scenario-affected) targets over the long run. Immediate energy shortages/shocks can also affect value added. In addition, capacity utilization (CAPUT) of capital and labor is responsive over time to inventories.

The "disembodied" (or Solow residual) technological factor in the production function is often called multifactor productivity (MFP) or total-factor productivity (TFP). In IFs the variable name is TEFF. The basic value of the technology term for each country/geopolitical region (r) is a sum of a global productivity growth rate driven by the economically advanced or leading country/region (by default the United States), 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 (MFPHC), social (MFPSC), physical (MFPPC), and knowledge (MFPKN) capital categories that are computed using variables from other models of the hard-linked IFs system. 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. The production function thus constitutes an important linkage across the models in the IFs system.

Input-output matrices, which are tied to GTAP data but change endogenously with 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 to meet final demand, both domestic and in other countries via trade.

File:The basic logic of the IFs social accounting matrix.png

2025-08-08T19:42:08Z

Barry Hughes:

It represents the social accounting matrix structure of IFs

Production system and representation of economic sectors - IFs

2025-08-08T18:06:45Z

Barry Hughes: First pass at adding text to this page

The IFs system represents for 188 countries six economic sectors: agriculture, energy, raw materials, manufactures, services, and ICT. Initialization data are from the World Development Indicators, the IMF, and GTAP.

On the supply side the model user can use the interface to fully override the growth of GDP with values that have been operationalized by other models in their representations of the Shared Socio-economic Pathways (SSPs) and put into the IFs database, just as the user can similarly override population growth patterns and many other SSP variables in IFs. In default mode, however, the Cobb-Douglas production function determines value added, summed across sectors to compute GDP. Two principal production factors are capital and labor. Capital stock is changed over time with depreciation and investment, the latter responsive to inventory stocks as elaborated below; investment rates by sector are flexible and existing capital stocks are fixed by sector (a putty-clay representation). 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, recognizing initial unemployment rates but moving those to standard (scenario-affected) targets over the long run. Immediate energy shortages/shocks can also affect value added. In addition, capacity utilization (CAPUT) of capital and labor is responsive over time to inventories.

The "disembodied" (or Solow residual) technological factor in the production function is often called multifactor productivity (MFP) or total-factor productivity (TFP). In IFs the variable name is TEFF. The basic value of the technology term for each country/geopolitical region (r) is a sum of a global productivity growth rate driven by the economically advanced or leading country/region (by default the United States), 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 (MFPHC), social (MFPSC), physical (MFPPC), and knowledge (MFPKN) capital categories that are computed using variables from other models of the hard-linked IFs system. 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. The production function thus constitutes an important linkage across the models in the IFs system.

Input-output matrices, which are tied to GTAP data but change endogenously with 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 to meet final demand, both domestic and in other countries via trade.

Production system and representation of economic sectors - IFs

2025-08-08T18:00:33Z

Barry Hughes: Test for adding text to empty page

Test adding text here

Model Documentation - IFs

2025-08-08T17:52:03Z

Barry Hughes: Test edit #2

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model Documentation
}}

== Introduction ==

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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]] for detailed model-by-model documentation. There is an interactive wiki at <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via Pardee’s website it is also possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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.

[[File:BasicModelsoftheIFsSystem.png|The basic models of the IFs system]]

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 Millennium 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 [[/ifsnetworkdiagram.du.edu/|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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]]. A direct link to the IFs wiki is <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via the Pardee Institute’s website it is possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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).

== Table of contents ==

=== [[Model scope and methods - IFs|Model scope and methods]] ===

* '''[[Model concept, solver and details - IFs|Model concept, solver and details]]'''
* '''[[Temporal dimension - IFs|Temporal dimension]]'''
* '''[[Spatial dimension - IFs|Spatial dimension]]'''
* '''[[Policy - IFs|Policy]]'''

=== [[Socio-economic drivers - IFs|Socio-economic drivers]] ===

* '''[[Population - IFs|Population]]'''
* '''[[Economic activity - IFs|Economic activity]]'''

=== [[Macro-economy - IFs|Macro-economy]] ===

* '''[[Production system and representation of economic sectors - IFs|Production system and representation of economic sectors]]'''
* '''[[Capital and labour markets - IFs|Capital and labour markets]]'''
* '''[[Monetary instruments - IFs|Monetary instruments]]'''
* '''[[Trade - IFs|Trade]]'''
* '''[[Technological change - IFs|Technological change]]'''

=== [[Energy - IFs|Energy]] ===

* '''[[Energy resource endowments - IFs|Energy resource endowments]]'''
** [[Fossil energy resources - IFs|Fossil energy resources]]
** [[Uranium and other fissile resources - IFs|Uranium and other fissile resources]]
** [[Bioenergy - IFs|Bioenergy]]
** [[Non-biomass renewables - IFs|Non-biomass renewables]]

* '''[[Energy conversion - IFs|Energy conversion]]'''
** [[Electricity - IFs|Electricity]]
** [[Heat - IFs|Heat]]
** [[Gaseous fuels - IFs|Gaseous fuels]]
** [[Liquid fuels - IFs|Liquid fuels]]
** [[Solid fuels - IFs|Solid fuels]]
** [[Grid, pipelines and other infrastructure - IFs|Grid, pipelines and other infrastructure]]
** [[Energy end-use - IFs|Energy end-use]]
** [[Transport - IFs|Transport]]
** [[Residential and commercial sectors - IFs|Residential and commercial sectors]]
** [[Industrial sector - IFs|Industrial sector]]
** [[Other end-use - IFs|Other end-use]]

* '''[[Energy demand - IFs|Energy demand]]'''
* '''[[Technological change in energy - IFs|Technological change in energy]]'''

=== [[Land-use - IFs|Land-use]] ===

* '''[[Agriculture - IFs|Agriculture]]'''
* '''[[Forestry - IFs|Forestry]]'''
* '''[[Land-use change - IFs|Land-use change]]'''
* '''[[Bioenergy land-use - IFs|Bioenergy land-use]]'''
* '''[[Other land-use - IFs|Other land-use]]'''
* '''[[Agricultural demand - IFs|Agricultural demand]]'''
* '''[[Technological change in land-use - IFs|Technological change in land-use]]'''
=== [[Emissions - IFs|Emissions]] ===
* '''[[GHGs - IFs|GHGs]]'''
* '''[[Pollutants and non-GHG forcing agents - IFs|Pollutants and non-GHG forcing agents]]'''
* '''[[Carbon dioxide removal (CDR) options - IFs|Carbon dioxide removal (CDR) options]]'''

=== [[Climate - IFs|Climate]] ===
* '''[[Modelling of climate indicators - IFs|Modelling of climate indicators ]]'''
* '''[[Climate damages, temperature changes - IFs|Climate damages, temperature changes ]]'''
=== [[Non-climate sustainability dimension - IFs|Non-climate sustainability dimension]] ===

* '''[[Air pollution and health - IFs|Air pollution and health]]'''
* '''[[Water - IFs|Water]]'''
* '''[[Other materials - IFs|Other materials]]'''
* '''[[Other sustainability dimensions - IFs|Other sustainability dimensions]]'''

=== [[Appendices - IFs|Appendices]] ===
* '''[[Mathematical model description - IFs|Mathematical model description]]'''
* '''[[Data - IFs|Data]]'''

=== [[References - IFs|References]] ===

Model Documentation - IFs

2025-08-08T17:46:52Z

Barry Hughes: Testing edit process only, temporary change

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model Documentation
}}

== Introduction ==

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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]] for detailed model-by-model documentation. There is an interactive wiki at <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via Pardee’s website it is also possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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.

[[File:BasicModelsoftheIFsSystem.png|The basic models of the IFs system]]

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 [[/ifsnetworkdiagram.du.edu/|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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]]. A direct link to the IFs wiki is <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via the Pardee Institute’s website it is possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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). Test edit

== Table of contents ==

=== [[Model scope and methods - IFs|Model scope and methods]] ===

* '''[[Model concept, solver and details - IFs|Model concept, solver and details]]'''
* '''[[Temporal dimension - IFs|Temporal dimension]]'''
* '''[[Spatial dimension - IFs|Spatial dimension]]'''
* '''[[Policy - IFs|Policy]]'''

=== [[Socio-economic drivers - IFs|Socio-economic drivers]] ===

* '''[[Population - IFs|Population]]'''
* '''[[Economic activity - IFs|Economic activity]]'''

=== [[Macro-economy - IFs|Macro-economy]] ===

* '''[[Production system and representation of economic sectors - IFs|Production system and representation of economic sectors]]'''
* '''[[Capital and labour markets - IFs|Capital and labour markets]]'''
* '''[[Monetary instruments - IFs|Monetary instruments]]'''
* '''[[Trade - IFs|Trade]]'''
* '''[[Technological change - IFs|Technological change]]'''

=== [[Energy - IFs|Energy]] ===

* '''[[Energy resource endowments - IFs|Energy resource endowments]]'''
** [[Fossil energy resources - IFs|Fossil energy resources]]
** [[Uranium and other fissile resources - IFs|Uranium and other fissile resources]]
** [[Bioenergy - IFs|Bioenergy]]
** [[Non-biomass renewables - IFs|Non-biomass renewables]]

* '''[[Energy conversion - IFs|Energy conversion]]'''
** [[Electricity - IFs|Electricity]]
** [[Heat - IFs|Heat]]
** [[Gaseous fuels - IFs|Gaseous fuels]]
** [[Liquid fuels - IFs|Liquid fuels]]
** [[Solid fuels - IFs|Solid fuels]]
** [[Grid, pipelines and other infrastructure - IFs|Grid, pipelines and other infrastructure]]
** [[Energy end-use - IFs|Energy end-use]]
** [[Transport - IFs|Transport]]
** [[Residential and commercial sectors - IFs|Residential and commercial sectors]]
** [[Industrial sector - IFs|Industrial sector]]
** [[Other end-use - IFs|Other end-use]]

* '''[[Energy demand - IFs|Energy demand]]'''
* '''[[Technological change in energy - IFs|Technological change in energy]]'''

=== [[Land-use - IFs|Land-use]] ===

* '''[[Agriculture - IFs|Agriculture]]'''
* '''[[Forestry - IFs|Forestry]]'''
* '''[[Land-use change - IFs|Land-use change]]'''
* '''[[Bioenergy land-use - IFs|Bioenergy land-use]]'''
* '''[[Other land-use - IFs|Other land-use]]'''
* '''[[Agricultural demand - IFs|Agricultural demand]]'''
* '''[[Technological change in land-use - IFs|Technological change in land-use]]'''
=== [[Emissions - IFs|Emissions]] ===
* '''[[GHGs - IFs|GHGs]]'''
* '''[[Pollutants and non-GHG forcing agents - IFs|Pollutants and non-GHG forcing agents]]'''
* '''[[Carbon dioxide removal (CDR) options - IFs|Carbon dioxide removal (CDR) options]]'''

=== [[Climate - IFs|Climate]] ===
* '''[[Modelling of climate indicators - IFs|Modelling of climate indicators ]]'''
* '''[[Climate damages, temperature changes - IFs|Climate damages, temperature changes ]]'''
=== [[Non-climate sustainability dimension - IFs|Non-climate sustainability dimension]] ===

* '''[[Air pollution and health - IFs|Air pollution and health]]'''
* '''[[Water - IFs|Water]]'''
* '''[[Other materials - IFs|Other materials]]'''
* '''[[Other sustainability dimensions - IFs|Other sustainability dimensions]]'''

=== [[Appendices - IFs|Appendices]] ===
* '''[[Mathematical model description - IFs|Mathematical model description]]'''
* '''[[Data - IFs|Data]]'''

=== [[References - IFs|References]] ===

Model scope and methods - IFs

2024-12-28T23:06:48Z

Barry Hughes:

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model scope and methods
}}
International Futures (IFs) is an interactive tool for the exploration of the long-term future of closely interacting and dynamically hard-linked issue clusters, namely human development, social change, and environmental sustainability. Among other applications, because it includes a dense pattern of linkages across its models, IFs facilitates scenario analysis across a wide range of the Sustainable Development Goals. The subcategories below in this section are those of the IAMC Wiki, maintained here to simplify updates of that over time.

__FORCETOC__

Socio-economic drivers - IFs

2024-12-28T23:03:12Z

Barry Hughes: showing?

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=IFs
|DocumentationCategory=Socio-economic drivers
}}
In forecasting long-term change, it is useful to distinguish distal and proximate drivers. The former consist of driving variables that help account for long-term structural change. They may operate at some causal distance from the variable we are forecasting, but we know them to be deeply structurally related to that variable, often via multiple paths. On the other hand, proximate variables are those that create shorter-term variation, often in part as intermediate variables between the deeper or distal drivers and the target variable, but often also as levers that policy or other short-term factors may influence somewhat independently of the deeper drivers. Key formulations across the models of IFs generally involve a combination of distal and proximate drivers.

The common approach across other IAMs represented in the IAMC Wiki is to use this section to discuss the representation of population and economic activity (exogenously or less often in IAMs endogenously) as drivers for the biophysical systems that that the IAMs typically model elaborately (e.g. energy). However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that also require some elaboration. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems, directly or via the economy. In keeping with the structure of the IAM Wiki, we will save discussion of the energy, agriculture, and environment models for other top-level topics and similarly elaborate the economic model in its own topic (labeled Macro-economy). But we will also provide topics in this documentation with summary information on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables. Because as of this writing the IAMC Wiki does not allow the addition of topics to its general organizational heading scheme, this latter set of sub-topics can be found clustered on that Wiki under the general heading of Non-climate sustainability dimension and its sub-heading of Other materials.

__FORCETOC__

Bioenergy - IFs

2024-12-28T22:25:09Z

Barry Hughes: Bioenergy resource removal

Economic activity - IFs

2024-12-28T22:10:04Z

Barry Hughes: Economic actilvity

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=IFs
|DocumentationCategory=Economic activity
}}
The economic model (elaborated below in the Macro-economy topic) produces GDP at market exchange rates and at purchasing power parity. It also produces value added, consumption, and trade in each of six economic sectors: agriculture, primary energy, raw materials, manufactures, services, and information/communications technology. It further provides a calculation of income distribution in the form of a Gini index, thereby facilitating also its calculation of poverty rates at multiple per-capita income (or household consumption) levels.

Two features of the economic model are especially notable. First, it is embedded in a full social accounting matrix (SAM) structure, thereby assuring consistency of financial flows among firms, households, and government. Second, the production function, fundamentally important to long-term dynamics, includes endogenous representation of multifactor productivity, driving it by changes in human, social, and physical capital from other models in the IFs system, as well as knowledge advance. Both the SAM and the production function are therefore pivot points for the interaction of dynamics across the range of models in the integrated IFs system and for policy analysis.

On the forward linkage side, GDP per capita is one of the key distal or deep developmental drivers that affect many other variables across the models of the IFs system. Most often, GDP per capita at purchasing power parity (PPP) is the variable used, but the model also computes it at market exchange rates. See documentation of other IFs models for details of its impact.

Population - IFs

2024-12-28T22:08:20Z

Barry Hughes: Population

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=IFs
|DocumentationCategory=Population
}}
The dominant population equation is a simple addition of births (BIRTHS) at the bottom of the single-year cohort distribution, subtraction of deaths (DEATHS) from each population cohort, and advance of people to the next cohort over time. Although the output from the IFs interface displays population aggregated into standard 5-year age categories, the model maintains and uses single-year categories internally to be consistent with its one-year time steps, thereby avoiding numerical diffusion of aging via premature movement of people aging into any 5-year category (in the bottom cohort year) on up the next higher 5-year category in the following year.

Births are most immediately a function of the total fertility rate (TFR) and numbers of women in their child-bearing years. Over time TFR responds especially to educational attainment of the adult population, infant mortality rates, and contraception usage rates (some attention to differential fertility between urban and rural populations, which are represented in IFs and affect fertility rates around the world, would be a useful addition). The model user has direct control over TFR with a multiplier and, to avoid movement to unreasonably low rates in low fertility countries, with a parameter specifying long-term stabilization level for TFR. As is common in IFs, the TFR function, including control by a minimum TFR value and the possibility of increase to that minimum from below for countries (like Italy and South Korea) that may have base year values below a value specified by users, is not simply an equation, but an equation wrapped in algorithmic logic that attempts to prevent unrealistic behavior in the long run. Long-term modeling requires not only equations, but structural logic elaborated in algorithms.

Deaths are primarily a function of age-sex specific mortality rates computed within the IFs health model where they change over time with adult education, GDP per capita and technology change, as well as with selected mortality-cause-specific proximate drivers (e.g. indoor solid fuel-use and urban air pollution). The model user has direct control over all deaths with a general mortality multiplier or with an alternative, cause-specific mortality multiplier. The population model also computes morbidity. The principal data source for mortality and morbidity by cause, age, and sex is the Global Burden of Disease project.

The model further represents inward and outward migration rates and numbers, as well as foreign-born population totals. Although the principal data source for initialization of all demographic data series are the newest releases by the United Nations Population Division, data on bilateral (or dyadic) migration flows also come from Guy Abel (2019). Given migration-based population stocks abroad, the model also computes remittances, which in the economic model adjust household income in sending and receiving countries, as well as affecting current account balances.

Socio-economic drivers - IFs

2024-12-28T22:07:18Z

Barry Hughes: Socio-economic drivers

Policy - IFs

2024-12-28T22:05:00Z

Barry Hughes: Policy

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Policy
}}
All IFs model parameters and initial conditions are accessible via the interlace and therefore changeable by users. Example areas where policy interventions can be introduced into the model are 

*'''Demographics:''' fertility, mortality (in more detail via the health model), and migration—all of which affect population growth and level.
*'''Economics:''' on the production side are investment, labor force participation, and productivity (via assumptions about systemic advance, convergence rate, and a wide range of specific endogenous drivers); interventions are also possible on savings/investment levels and consumption patterns and on patterns of trade and government revenue and expenditure patterns from multiple sources and to multiple targets. 
*'''Education:''' enrollment, continuation, and completion/transition rates; spending levels.
*'''Health:''' mortality and morbidity rates across 15 different causes.
** government revenue and expenditure patterns from multiple sources and to multiple targets; corruption and democracy levels; status of women; value change.
* '''Socio-Political Change:''' corruption and democracy levels; status of women; value change.
*'''Geopolitics:''' Country and regional power levels.
*'''Food and Agriculture:''' Land use and yield/production levels; trade patterns—all of which affect calorie availability and malnutrition rates.
*'''Energy:''' Resource and production level by energy type including renewable energy share, demand level and supply patterns; technology-related reduction in capital costs of energy production (by type).
*'''Infrastructure:''' investment and access extension by type (road, water, sanitation, electricity, ICT).
*'''Environment:''' atmospheric carbon dioxide levels via interventions in models noted above which generate emissions (e.g. land use including deforestation, the balance between fossil fuel and renewable energy production and use); water demand by final use and water supply by source.

Prepackaged scenario intervention files also allow integrated analysis of scenario sets including the UNEP GEO-4 set, the Shared Socioeconomic Pathway (SSP) set, and a set built with Pardee-UNDP analysis of the possible future impacts of COVID and/or a Big Push toward achievement of the SDGs.

Spatial dimension - IFs

2024-12-28T21:59:01Z

Barry Hughes: Spatial Dimension

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Spatial dimension
}}
'''Countries.''' There are 188 countries/socio-political units in the IFs database and model system as of Version 8.35.

'''Groups.''' The IFs interface can aggregate projections across countries into country groupings using variable-specific rules (including sums and various weighted averages); the interface includes a great many standard country groupings as identified by different international organizations. The user can create additional groupings.

'''Country sub-units.''' The ability to split countries down into smaller geopolitical entities (provinces, districts, states, etc.) is available for all countries and has been developed with data support in various projects for selected countries.

'''G-Lists.''' For ease of result presentation users can define lists of countries, groups, and country sub-units for analysis; many have been pre-defined.

Temporal dimension - IFs

2024-12-28T21:57:55Z

Barry Hughes: Temporal Dimension

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Temporal dimension
}}
'''Future-Oriented Analysis.''' The base year of IFs in version 8.35 at the time of this writing is 2020 and the model runs recursively in annual time steps through alternative horizons up to 2100 with the option of running further for selected analyses. The interface also has a stop and start capability to facilitate scenario analysis. The base year is changed fairly often as new data releases allow.

'''Historical Analysis.''' IFs also includes an extensive and growing historical data base (nearly 7,000 series) starting in 1960, of which more than 10% are used to initialize model variables and the others support model development and use. The database allows initiation of model runs in 1960 and comparison of projections with empirical values.

'''The preprocessor of IFs and Base Year variable initializations.''' In the early days of world model/IAM development, rebasing the models for new first/base years or merely in the face of data updates for a fixed base year was often a very challenging enterprise. Such rebasing almost invariably involves changing some series to address unit and scaling issues, analyzing and making judgments about data for the same variables from different sources, and reconciling multiple series that flesh some part of the model (e.g., within the social accounting matrices of the economy) that are not inherently consistent from the same or multiple sources. Further, in a model like IFs with 188 countries, there are inevitably completely missing data series for some of them and only very old data for others. Complete absence requires hole-filling logic and old data requires algorithmic updating processes.

To greatly facilitate all of this, the IFs system has access within its interface to an integrated data “preprocessor” that automates these processes. In combination with the Pardee Institute’s constantly refined mechanisms and rules for putting large numbers of data series into its associated database (and regularly updating those with new data releases), the use of the preprocessor essentially automates both the updating of data in an ongoing base year and the rebasing of the model to a new base year. The interface of the IFs system (under the Main Menu option in Figure 4 labeled “Extended Features”) has a sub-option named “Rebuild Base” that does all of this in the IFs version on stand-alone computers (not possible for the web-based version). Further, a sub-option labeled “Rebuild Historical Base” allows the user to pick any base year back to l960 and rebase the model’s first year to that using the large historical database (inevitably with more holes or incompatible data in earlier years for the preprocessor to handle). Runs of the model from such earlier years allows direct comparison via the interface of historical data and values from model runs.

Model concept, solver and details - IFs

2024-12-28T21:55:48Z

Barry Hughes: small things

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (nearly 7,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] and in downloadable form at [[/korbel.du.edu/pardee/content/download-ifs|https://korbel.du.edu/pardee/content/download-ifs]]. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.

=== The models of IFs and their interactions ===
Figure 2 shows one representation of the major hard-linked models within the IFs system; the models each have multiple subcomponents that can be aggregated in various ways in such a diagram. Thus alternative portrayals of the total system in such diagrams vary somewhat. [[File:IFsModelQuilt.png|Figure 2: The basic issue clusters and models of the IFs system|left|thumb|731x731px]]
Ongoing development and use of IFs directs much attention to the full range of the Sustainable Development Goals (SDGs). Thus the color-coding of the representation of the IFs system in Figure 2 corresponds very crudely to the subsets that correspond to human development SDGs (green models), to socio-economic and to some degree more instrumental SDGs (blue), and to physical system sustainability SDGs (black), Clearly, however, that coloring scheme is extremely crude because the pursuit of all SDGs is greatly affected by demographic (notably Population) and economic (notably GDP and GDP per capita) variables, as suggested by their size and hence centrality in the network diagram for IFs of Figure 3. Again, the full set of models and interactions facilitate broad analysis of SDG pursuit and other analyses.

The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 
[[File:Picture3.png|left|thumb|509x509px|Figure 3: A network diagram of the IFs system with interacting models and variables]]
The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and also facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, solar, wind, geothermal, and other renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provides information to and constraints upon the physical models.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

Across the broader IFs system and well beyond the GDP and population variables, two aspects are especially important in the interaction of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches in scenario analysis representing pursuit of alternative futures including attention to the SDGs. The second area where models in the IFs system are especially interactive is via the endogenous representation of productivity in the economic model. Multifactor (or total factor) productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries). See the Macro-economy topic.

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions and relationships globally (drawing especially on economic, population, and technology variables), a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.

This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run (Version 8.35) with the system through 2100 requires less than 8 minutes with the “stand-alone” version on a fast laptop or 3 minutes on the project’s own server, where it is available for use by anyone.

Figure 4 shows the main screen for interaction with the system in the stand-alone version. Leaving aside Extended Features (which the IFs Wiki documents in some detail), there are three major functionalities in the main menu, each of which lead into a great many sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs) and scenarios representing policy directions for accelerated pursuit of the Sustainable Development Goals (SDGs). ''Important:'' The user of IFs can run multiple scenarios either in batch mode or individually, saving all of the results from the run (all model parameters and variables) in run files; the Display features then allows extensive comparison of scenario assumptions and results.
[[File:IFsMainMenu.JPG|Figure 4: The main menu of the IFs interface with key functionalities|left|thumb|822x822px]]

Model concept, solver and details - IFs

2024-12-28T21:50:25Z

Barry Hughes: Figure addition and text update

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (nearly 7,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] and in downloadable form at [[/korbel.du.edu/pardee/content/download-ifs|https://korbel.du.edu/pardee/content/download-ifs]]. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.

=== The models of IFs and their interactions ===
Figure 2 shows one representation of the major hard-linked models within the IFs system; the models each have multiple subcomponents that can be aggregated in various ways in such a diagram. Thus alternative portrayals of the total system in such diagrams vary somewhat. [[File:IFsModelQuilt.png|Figure 2: The basic issue clusters and models of the IFs system|left|thumb|731x731px]]
Ongoing development and use of IFs directs much attention to the full range of the Sustainable Development Goals (SDGs). Thus the color-coding of the representation of the IFs system in Figure 2 corresponds very crudely to the subsets that correspond to human development SDGs (green models), to socio-economic and to some degree more instrumental SDGs (blue), and to physical system sustainability SDGs (black), Clearly, however, that coloring scheme is extremely crude because the pursuit of all SDGs is greatly affected by demographic (notably Population) and economic (notably GDP and GDP per capita) variables, as suggested by their size and hence centrality in the network diagram for IFs of Figure 3. Again, the full set of models and interactions facilitate broad analysis of SDG pursuit and other analyses.

The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 
[[File:Picture3.png|left|thumb|509x509px|Figure 3: A network diagram of the IFs system with interacting models and variables]]
The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and also facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, solar, wind, geothermal, and other renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provides information to and constraints upon the physical models.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

Across the broader IFs system and well beyond the GDP and population variables, two aspects are especially important in the interaction of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches in scenario analysis representing pursuit of alternative futures including attention to the SDGs. The second area where models in the IFs system are especially interactive is via the endogenous representation of productivity in the economic model. Multifactor (or total factor) productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries). See the Macro-economy topic.

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions and relationships globally (drawing especially on economic, population, and technology variables), a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.

This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run with the system requires less than 15 minutes on a fast laptop or the project’s own server, where it is available also for use by others.
Figure 2 shows the main screen for interaction with the system. Leaving aside Extended Features (which the IFs Wiki also documents), there are three major functionalities in the main menu, each of which lead into a large number of sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs)
[[File:IFsMainMenu.JPG|Figure 4: The main menu of the IFs interface with key functionalities|left|thumb|822x822px]]

File:Picture3.png

2024-12-28T21:44:42Z

Barry Hughes:

Network diagram showing prominence of POP and GDP

Model concept, solver and details - IFs

2024-12-28T21:41:19Z

Barry Hughes: Textual elaboration

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (nearly 7,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] and in downloadable form at [[/korbel.du.edu/pardee/content/download-ifs|https://korbel.du.edu/pardee/content/download-ifs]]. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.

=== The models of IFs and their interactions ===
Figure 2 shows one representation of the major hard-linked models within the IFs system; the models each have multiple subcomponents that can be aggregated in various ways in such a diagram. Thus alternative portrayals of the total system in such diagrams vary somewhat. [[File:IFsModelQuilt.png|Figure 2: The basic issue clusters and models of the IFs system|left|thumb|731x731px]]
Ongoing development and use of IFs directs much attention to the full range of the Sustainable Development Goals (SDGs). Thus the color-coding of the representation of the IFs system in Figure 2 corresponds very crudely to the subsets that correspond to human development SDGs (green models), to socio-economic and to some degree more instrumental SDGs (blue), and to physical system sustainability SDGs (black), Clearly, however, that coloring scheme is extremely crude because the pursuit of all SDGs is greatly affected by demographic (notably Population) and economic (notably GDP and GDP per capita) variables, as suggested by their size and hence centrality in the network diagram for IFs of Figure 3. Again, the full set of models and interactions facilitate broad analysis of SDG pursuit and other analyses.

The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 

The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and also facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, solar, wind, geothermal, and other renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provides information to and constraints upon the physical models.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

There are also water (not shown separately in Figure 2) and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

Across the broader IFs system and well beyond the GDP and population variables, two aspects are especially important in the interaction of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches in scenario analysis representing pursuit of alternative futures including attention to the SDGs. The second area where models in the IFs system are especially interactive is via the endogenous representation of productivity in the economic model. Multifactor (or total factor) productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries). See the Macro-economy topic.

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions and relationships globally (drawing especially on economic, population, and technology variables), a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.

This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run with the system requires less than 15 minutes on a fast laptop or the project’s own server, where it is available also for use by others.
Figure 2 shows the main screen for interaction with the system. Leaving aside Extended Features (which the IFs Wiki also documents), there are three major functionalities in the main menu, each of which lead into a large number of sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs)
[[File:IFsMainMenu.JPG|Figure 4: The main menu of the IFs interface with key functionalities|left|thumb|822x822px]]

Model concept, solver and details - IFs

2024-12-28T21:33:13Z

Barry Hughes: Figure placement and size

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (nearly 7,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] and in downloadable form at [[/korbel.du.edu/pardee/content/download-ifs|https://korbel.du.edu/pardee/content/download-ifs]]. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.

=== The models of IFs and their interactions ===
Figure 2 shows one representation of the major hard-linked models within the IFs system; the models each have multiple subcomponents that can be aggregated in various ways in such a diagram. Thus alternative portrayals of the total system in such diagrams vary somewhat. [[File:IFsModelQuilt.png|Figure 2: The basic issue clusters and models of the IFs system|left|thumb|799x799px]]
The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 

The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, and renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provide information to and constraints upon the physical models.

There are also water and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences.

Across the broader IFs system, two aspects are especially important in the interaction of all of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches. The second area where systems are highly interactive is via the endogenous representation of productivity in the economic model. Multifactor productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries).

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions globally, a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.
This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run with the system requires less than 15 minutes on a fast laptop or the project’s own server, where it is available also for use by others.
Figure 2 shows the main screen for interaction with the system. Leaving aside Extended Features (which the IFs Wiki also documents), there are three major functionalities in the main menu, each of which lead into a large number of sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs)
[[File:IFsMainMenu.JPG|Figure 4: The main menu of the IFs interface with key functionalities|left|thumb|822x822px]]

File:Picture2.png

2024-12-28T19:03:22Z

Barry Hughes:

Models of IFs

Model concept, solver and details - IFs

2024-12-28T18:56:44Z

Barry Hughes:

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (nearly 7,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] and in downloadable form at [[/korbel.du.edu/pardee/content/download-ifs|https://korbel.du.edu/pardee/content/download-ifs]]. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.

=== The models of IFs and their interactions ===
Figure 2 shows one representation of the major hard-linked models within the IFs system; the models each have multiple subcomponents that can be aggregated in various ways in such a diagram. Thus alternative portrayals of the total system in such diagrams vary somewhat. [[File:IFsModelQuilt.png|frame|right|The basic issue clusters and models of the IFs system]]
The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 

The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, and renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provide information to and constraints upon the physical models.

There are also water and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences.

Across the broader IFs system, two aspects are especially important in the interaction of all of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches. The second area where systems are highly interactive is via the endogenous representation of productivity in the economic model. Multifactor productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries).

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions globally, a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.
This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run with the system requires less than 15 minutes on a fast laptop or the project’s own server, where it is available also for use by others.
Figure 2 shows the main screen for interaction with the system. Leaving aside Extended Features (which the IFs Wiki also documents), there are three major functionalities in the main menu, each of which lead into a large number of sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs)
[[File:IFsMainMenu.JPG|frame|right|The main menu of the IFs interface with key functionalities]]

Model scope and methods - IFs

2024-12-28T18:51:15Z

Barry Hughes:

Model concept, solver and details - IFs

2024-12-28T18:45:27Z

Barry Hughes:

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model concept, solver and details
}}
International Futures (IFs) is a large-scale, long-term, integrated global modeling system. The system is dynamic recursive with 1-year time steps, normally through 2100, but with capacity for extended analysis through 2200.

IFs is a tool for thinking about long-term global trends and strategic planning for the future. Among the unique features of the system is the extensive integration of models across human capacity, social, and natural systems. IFs contains highly integrated (hard-linked) models across demographic, economic, education, health, governance, agriculture, energy, infrastructure, water, climate and other subsystems for 188 countries/political units interacting in the global system. The central purpose of IFs is to facilitate exploration of global futures through alternative scenarios.

IFs can help a user 
*Understand the state of major global systems
*Explore long-term trends and consider where they might take us
*Learn about the dynamic interactions across global subsystems
*Clarify long-term organizational goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate how different agent categories (households, firms and governments) can shape the future 

IFs is integrated with a large database (more than 4,000 series) for its countries, most of which include data since 1960. The system is fully imbedded in an interactive interface, and it is both open source and freely available to users both on-line (www.ifs.du.edu) and in downloadable form. The interface facilitates data analysis, projection exploration and comparison, and flexible scenario analysis.
The models of IFs and their interactions. Figure 1 shows the major hard-linked models within the IFs system.
[[File:IFsModelQuilt.png|frame|right|The basic issue clusters and models of the IFs system]]
The multistate demographic model uses a standard cohort-component representation. Fertility and mortality are endogenous in response to other drivers, including adult educational attainment from a full education model and 15 categories of mortality and morbidity from a health model. In the education model students flow by grade through primary, lower secondary upper secondary, and tertiary levels. In the health model, mortality and morbidity are age and sex specific. 

The multisector economic model is general equilibrium, although the model uses changes in inventory stocks and relative prices to drive changes in supply and demand, chasing equilibrium over time rather than solving for it or iterating to it in every time cycle. This is consistent with actual market behavior and facilitates computational efficiency. The partial equilibrium physical agriculture model differentiates crop, meat and fish and generates calorie and protein availability. The partial equilibrium energy model differentiates coal, oil, natural gas, hydroelectric power, nuclear power, and renewable energy, tracking resources bases and production. Key variables from the agriculture and energy models, converted to monetary terms, override those of the same sectors in the economic model, while the integration across sectors in the economic model of variables including household, government and firm demand and savings/investment availability provide information to and constraints upon the physical models.

There are also water and infrastructure models. The water model represents supply from renewable and nonrenewable sources and demand for agricultural, municipal and industrial uses. Agricultural water use is linked to irrigation in the agriculture model and can constrain yields. The infrastructure model represents extent of and access to paved roads, safe water and sanitation, electricity, and information and communication technologies. It requires investment from public and private sectors that compete with demand from the education and health models, as well as other uses.

The physical agriculture and energy models determine land use and generate carbon emissions. A representation based on the MAGICC model, used widely in IAMs. connects carbon emissions to atmospheric levels, global warming, and country-level changes in temperature and precipitation. Those feed forward to the agriculture model with a mixture of potentially positive and negative consequences.

Across the broader IFs system, two aspects are especially important in the interaction of all of the separate models. The first is finance, including governmental revenues and expenditures, but also those of households and firms. The economic system is imbedded within a full social accounting matrix that represents all financial flows within and across these agent categories and assures that representation of those flows is appropriately balanced and constrained—there are thus no free lunches. The second area where systems are highly interactive is via the endogenous representation of productivity in the economic model. Multifactor productivity is responsive to changes in human capital (education and health), social capital (including governance quality), physical capital (including infrastructure development), and knowledge capital (including research and development spending but also the knowledge imbedded in trade among countries).

A broader model of governance represents the state of domestic security or instability, corruption, and inclusion (democracy) and connects to government finance. Socio-political variables also trace change in basic value/cultural structures, and portray various elements of formal and informal socio-economic structures and processes, including income distribution. The IFs system also represents changes in country power positions globally, a foundation of international politics and potentially of capability for collective global action.

Technological change is important across most of the models in IFs. While exogenously represented in many instances, the production functions in the economic, agriculture, and energy models have elements of learning by doing.
This brief summary only touches on the model structure and interconnections; for more extensive coverage see Hughes (2019 forthcoming) and the IFs project Wiki (http://pardee.du.edu/wiki/Main_Page).

'''The interactive interface of IFs and analysis with the system.''' Because IFs is dynamic recursive and computationally efficient, scenario development and exploration with it can be interactive. The completion of a single run with the system requires less than 15 minutes on a fast laptop or the project’s own server, where it is available also for use by others.
Figure 2 shows the main screen for interaction with the system. Leaving aside Extended Features (which the IFs Wiki also documents), there are three major functionalities in the main menu, each of which lead into a large number of sub-options:

#Data analysis. This functionality allows interaction with the more than 4,000 data series packaged with IFs, including display on maps as well as both displays and statistical exploration longitudinally and in cross-sectional analysis.
#Display. This functionality facilitates standard graphical and tabular representations from the Base Case and other scenarios of IFs, using any combination of individual countries or country groupings. At the most detailed level, one form of display (Self-Managed) can call up any variable or parameter in the system. There is also a very extensive set of prepackaged displays described in natural language that contain most of the variables in the model individually or in combination, often in combination with historical series for the same variable(s). In addition, the Display functionality includes a wide selection of specialized displays including age-sex population graphics for population alone or overlaid with educational attainment or mortality rates, analysis of contributions of multiple drivers of economic growth, and both overview and drill down capability on a wide range of indicators across the Sustainable Development Goals.
#Scenario analysis. This functionality provides access to all parameters and initial conditions in IFs. For temporally specific parameter changes, it supports interpolation or even year-by-year specification. Users can save collections of parameter changes into scenario specification files for further future editing and can use the specification files at any time to create scenario run files for analysis and comparison via the display functionality. A large library of saved scenario specification files exists for users, including representation of the Shared Socioeconomic Pathways (SSPs)
[[File:IFsMainMenu.JPG|frame|right|The main menu of the IFs interface with key functionalities]]

Model scope and methods - IFs

2024-12-28T18:43:02Z

Barry Hughes:

Model Documentation - IFs

2024-12-28T18:40:56Z

Barry Hughes: Model documentation into section

{{ModelDocumentationTemplate
|IsDocumentationOf=IFs
|DocumentationCategory=Model Documentation
}}
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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]] for detailed model-by-model documentation. There is an interactive wiki at <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via Pardee’s website it is also possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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.

[[File:BasicModelsoftheIFsSystem.png|The basic models of the IFs system]]

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 [[/ifsnetworkdiagram.du.edu/|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 [[/korbel.du.edu/pardee|https://korbel.du.edu/pardee]]. A direct link to the IFs wiki is <nowiki>https://pardeewiki.du.edu/index.php?title=International_Futures_(IFs)</nowiki>. Via the Pardee Institute’s website it is possible freely to use IFs on line at [[/www.ifs.du.edu/ifs/frm MainMenu.aspx|https://www.ifs.du.edu/ifs/frm_MainMenu.aspx]] or to download IFs for use on machines with Windows operating systems from [[/korbel.du.edu/pardee/content/download-ifs|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).

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2024-12-28T17:56:02Z

Barry Hughes:

Figure 1