FUND
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FUND is an integrated assessment model of anthropogenic climate change. It links exogenous scenarios with simple models. The exogenous scenarios concern the rate of economic growth, the share of agriculture in Gross Regional Product, the population growth, autonomous energy efficiency improvements, the rate of decarbonization of energy use, decarbonization of the energy use, and methane and nitrous oxide emissions. These exogenous scenarios are linked with simple models of population, technology, economics, emissions, atmospheric chemistry, climate, sea level, and impacts.
The climate change module describes how greenhouse gas emissions as an output from the socio-economic change module lead to changes in the atmospheric concentration and to climate change. The FUND framework also attempts to assess the impact of climate change. [1]
Result
Regional Scope:
The current model version distinguishes 16 major regions of the world: the United States of America, Canada, Western Europe, Japan and South Korea, Australia and New Zealand, Central and Eastern Europe, the former Soviet Union, the Middle East, Central America, South America, South Asia, Southeast Asia, China, North Africa, Sub-Saharan Africa, and Small Island States. Older model versions had only 9 regions. For selected applications, a 207 country version is also available.[1]
Standard Model Specification:
FUND combines exogenous scenarios with perturbations. The exogenous scenarios concern the rate of economic growth, the share of agriculture in Gross Regional Product, the population growth, autonomous energy efficiency improvements, the rate of decarbonization of energy use, decarbinization of the energy use, and methane and nitrous oxide emissions. These exogenous scenarios are not predictions but assumptions from which the analysis commences.[1]
Depending on the model version FUND comprises a number of endogenous parts:
- The physical change module describes how greenhouse gas emissions (which are modeled as an output from the socio-economic change module) lead to changes in the atmospheric composition. Greenhouse gases are taken up in the atmosphere, and then slowly depleted. Atmospheric change in turn leads to climatic change, here represented by the global mean temperature. Changes in winter precipitation, storm activity, tropical cyclone wheather, and sea level rise are assumed to be driven by the global mean temperature.
- The climate impact module considers: agriculture, forestry, sea level rise, cardiovascular and respiratory disorders related to cold and heat stress, malaria, dengue fever, schistosomiasis, diarrhoea, energy consumption, water resources, and unmanaged ecosystems.
Costs of emission reduction are weighted against the avoided damage of climate. The criterion is the net present value of average utility, a mixture of per capita income and intangible damages of climate change and air pollution and by the costs of emission reduction.
People can die prematurely due to temperature stress or vector-borne diseases, or they can migrate because of sea level rise. Like all impacts of climate change, these effects are monetized. The value of a statistical life is set to be 200 times the annual per capita income. The value of emigration is set to be 3 times the per capita income the value of immigration is 40 per cent of the per capita income in the host region. Losses of dryland and wetlands due to sea level rise are modeled explicitly. The monetary value of a loss of one square kilometer of dryland was on average $4 million in OECD countries in 1990. Dryland value is assumed to be proportional to GDP per square kilometer. Wetland losses are valued at $2 million per square kilometer on average in the OECD in 1990. The wetland value is assumed to have logistic relation to per capita income. Coastal protection is based on cost-benefit analysis, including the value of additional wetland lost due to the construction of dikes and subsequent coastal squeeze.
Other impact categories, such as agriculture, forestry, energy, water, and ecosystems, are directly expressed in monetary values without an intermediate layer of impacts measured in their `natural' units.
Climate change related damages can be attributed to either the rate of change (benchmarked at 0.04°C/yr) or the level of change (benchmarked at 1.0°C). Damages from the rate of temperature change slowly fade, reflecting adaptation.
Impacts of climate change on energy consumption, agriculture, and cardiovascular and respiratory diseases explicitly recognize that there is a climatic optimum which is determined by a variety of factors, including plant physiology and the behavior of farmers. Impacts are positive or negative depending on whether the actual climate conditions are moving closer to or away from that optimum climate. Impacts are larger if the initial climate conditions are further away from the optimum climate. The optimum climate is of importance with regard to the potential impacts. The actual impacts lag behind the potential impacts, depending on the speed of adaptation. The impacts of not being fully adapted to new climate conditions are always negative.
The impacts of climate change on coastal zones, forestry, unmanaged ecosystems, water resources, malaria, dengue fever, and schistosomiasis are modeled as simple power functions.[1]
Required technical infrastructure:
PC and TurboPascal 7.0 for DOS
Structure of Input Data:
Model Calibration:
IMAGE 100-year database (Betjes and Goldewijk 1994) for data 1950 - 1990
Exogenous Scenarios:
Population, income, emissions scenario: FUND Scenario; IPCC IS92a,d,f (Houghton et al., 1995), SRES A1B, A2, B1, B2 (Nakicenovic and Swart, 2000)
Urban Population, age structure, economic structure: Internal
Numerous estimations on the monetization of different impacts are also incorporated.
Base year data:
- The period 1950 - 1990 is used for the calibration of the model.
Calibration:
- The period 1950 - 1990 is used for the calibration of the model. Together with the scenario IPCC IS92e and the assumption that carbon dioxide emissions are zero as of 2200.
Model Extensions:
Versions 1.4 - 2.8[1]
See also
Links to other Models, Projects, Networks:
FUND has been used in many projects such as:
- ExternE, Methodex, GreenSense
- Atlantis
- NEMESIS/ETC
- ORFOIS
- ECO-BICE
- Integration
- Inasud
- Climate Fund[1]
References
Link, P.M. and R.S.J. Tol (2004), `Possible Economic Impacts of a Shutdown of the Thermohaline Circulation: An Application of FUND', Portuguese Economic Journal, 3, 99-114. (Q25)
Tol, R.S.J. (1999a): The Marginal Costs of Greenhouse Gas Emissions, Energy Journal, 20 (1): 61-81.
Tol, R.S.J. (1999b): Time Discounting and Optimal Control of Climate Change - an Application of FUND, Climatic Change, 41 (3-4): 351-362.
Tol, R.S.J. (1999c): Kyoto, Efficiency, and Cost-Effectiveness: Applications of FUND, Energy Journal, Special Issue on the Costs of the Kyoto Protocol: A Multi-Model Evaluation, 130-156.
Tol, R.S.J. (1999d): Safe Policies in an Uncertain Climate: an Application of FUND, Global Environmental Change, 9: 221-232.
Tol, R.S.J. (1999e): Spatial and Temporal Efficiency in Climate Change: Applications of FUND, Environmental and Resource Economics, 14 (1):33-49.
Tol, R.S.J. (2001), `Equitable Cost-Benefit Analysis of Climate Change', Ecological Economics, 36 (1), 71-85. (D63, Q25, Q28)
Tol, R.S.J. (2001), `Climate Coalitions in an Integrated Assessment Model', Computational Economics, 18, 159-172. (C72, Q25)
Tol, R.S.J. (2002), `Welfare Specification and Optimal Control of Climate Change: An Application of FUND', Energy Economics, 24 (4), 367-376. (D63, Q25, Q28)
Tol, R.S.J. (2003), `Is the Uncertainty about Climate Change Too Large for Expected Cost-Benefit Analysis?', Climatic Change, 56 (3), 265-289. (D81, Q25)
Tol, R.S.J. and H. Dowlatabadi (2001), `Vector-borne Diseases, Climate Change, and Economic Growth', Integrated Assessment, 2, 173-181. (I12, Q25)
Tol, R.S.J., R.J. Heintz and P.E.M. Lammers (2003), `Methane Emission Reduction: An Application of FUND', Climatic Change, 57 (1-2), 71-98. (Q25, Q28)