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The Use of Scenarios in International Policy Venues

Scenarios are used across various policy venues. In general, quantitative scenarios are widely adopted in economic policymaking, for example the European Commission and the International Monetary Fund apply model based scenarios for tracking expected budget deficits. They are also commonly used for several aspects of physical planning, for example demographic trends, traffic projections, expected sea level rise, or the land use requirements for biofuels. In environmental policy planning, scenarios for national emissions of greenhouse gasses and air pollutants must be reported to the United Nations periodically. All the above-mentioned scenarios are typically developed by (external) experts, where needed with some input from policymakers (for example, on envisaged policy measures), and are relatively undisputed. The time horizon and the indicators used are generally well defined.

Scenarios are also indispensable for the impact assessment of (large) investment projects. At least a reference scenario (in other words, future without the project) and a scenario including the project are needed. The time horizon and the set of relevant indicators are less well defined, may vary from project to project, and are often subject to public debate (for example, for a shale gas project, an extension of an airport, or a plan to prevent flooding). Meaningful scenarios and indicators are often co-produced by experts and stakeholders.

International Environmental Negotiations: Transboundary Air Pollution

Since 1979, international negotiations to reduce air pollution have resulted in agreements (protocols) with emission reduction obligations for European countries. The scientific community has played a key role in providing measurements, modelling and information on air pollution impacts and the cost-effectiveness of available abatement measures. From the beginning of the 1990s, flat rate reduction targets were replaced by protocols aiming at a cost-effective, effect-oriented approach, meaning that measures should be taken that offer the best protection for health and ecosystems at the lowest costs. This approach causes emission reduction obligation percentages to vary widely among countries. For example, in a less densely populated area, in principle, fewer measures are needed.

Scenario calculations by the International Institute for Applied Systems Analysis (IIASA) using the GAINS model are the basis for political negotiations. GAINS delivers optimization results: given (politically chosen) ambition levels to protect health and ecosystems, the model gives the minimum cost solution for a target year (with a 10-20 year time horizon). Scenario results give insights to policymakers (in other words, negotiators) on the relationship between environmental protection ambitions and the costs for their country. This is effectively a backcasting scenario and addresses the following question: 'what do we need to do today to reach that desired level of protection?'

The scenarios describe the most likely future of emissions and their impacts, and are based on model extrapolations of drivers (for example, population, GDP, energy use, transport, agriculture), emission factors (influenced by abatement measures), dispersion models, dose-response relationships for health and ecosystems, and costs of (additional) abatement measures. Scenario selections are made by the policymakers, namely the leaders of the various national delegations. Differences between scenarios are the result of differences in policy measures (policy variants). In order to increase trust in the GAINS model, much effort has been spent on the review of the quality of all the input data. Country experts check and improve data on emissions, base-year activity and existing policies, the assumptions made for the development of drivers and ecosystem data. Countries are stimulated to deliver their own national projection. The GAINS team at IIASA checks the consistency of the data officially delivered by the countries. Conflicts can be managed by a Task Force on Integrated Assessment Modelling, which oversees the process (Reis et al. 2012).

The use of scenario-derived knowledge in the last thirty years has been highly significant. However, uncertainty management is likely to become steadily more important in the future, as most of the low-cost measures have already been taken and the complexity increases as air pollution and climate change interactions become more important. Uncertainty analysis will also be needed to deal with systematic biases in the scenario approach: potentially optimistic assumptions about the (full) implementation of additional policies, and pessimistic assumptions about (the absence of) emerging new technologies and behavioural change.

 
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