• Growing greenhouse gas emissions from the transport sector jeopardize the achievement of the EU's emission reduction target under the Kyoto Protocol.
• Impacts on air quality, noise nuisance, and the increasing fragmentation of the EU's territory are equally worrying.
• Transport growth, which remains closely linked to economic growth, and the shift toward roads and aviation are the main drivers behind this development.
• Technology and fuel improvements are only partly effective in reducing impacts.
• They must be complemented with measures to restrain the growth in transport and to redress the modal balance.
With the first two steps complete, defining and selecting indicators becomes a clearer and more focused exercise. When indicators for complex cross-cutting issues (e.g., measuring the positive and negative impacts of biofuels on the environment) are being developed, specific integrated frameworks must be built for assessing the broad, cross-sectoral environmental impacts to ensure that all important factors are taken into account. Indeed, even for less complex issues an explicit framework or model of relevant processes is useful to steer indicator development. The DPSIR framework can be a useful basis for such models.
To be effective, indicators must be selected that come close to answering the policy questions, taking into account the relevant environmental, societal, and economic interactions described in the framework or model for that issue and the relevant policy levers (i.e., the policy measures that could have an effect on the issue). We can improve the indicators by making connections between the type of policy questions and the type of indicators used to provide answers, as defined in the indicator typology. To ensure relevance, it is important not only to consider indicators for which data are currently available but also to identify ideal indicators that may have new requirements.
Because indicators are often constructed using a combination of data sets (e.g., map-based indicators derived from geospatially referenced data made up of multiple data layers combined in complex algorithms), it is necessary to define the algorithm of
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