Multi-stakeholder Preference Analysis in Ex-ante Evaluation of Policy Options - Use Case: Ultra Low Emission Vehicles in UK

被引:3
|
作者
Talantsev, Anton [1 ]
Ibrahim, Osama [1 ]
Larsson, Aron [1 ,2 ]
机构
[1] Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden
[2] Mid Sweden Univ, Dept Informat & Commun Syst, Sundsvall, Sweden
来源
关键词
Public policy analysis; Multi-criteria decision analysis; Stakeholders; Preference elicitation; Decision support tools; Ultra low emission vehicles; CRITERIA DECISION-MAKING; FRAMEWORK;
D O I
10.1007/978-3-319-45074-2_14
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
While the simulation-based impact assessment of public policy proposals allows policy makers to identify the feasible policy options and verify their economic, social and environmental impacts, it does not provide the explicit evaluation of policy options. Multi-criteria decision analysis (MCDA) techniques can support an in-depth performance evaluation of policy options taking into account the preferences of decision makers and stakeholders. These preferences reflect acceptable trade-offs of performance among objectives. This study reviews multi-attribute decision-making (MADM) technique and presents a common policy appraisal format using main evaluation criteria linked to a set of measurable, context dependent attributes. We argue for a rank-based approach for eliciting preferences, select a novel method for attribute weight elicitation, and show how it can be integrated within a public policy multi-criteria evaluation framework. A use case for policymaking, 'Ultra-Low Emission Vehicles (ULEV) Uptake in UK', is used for demonstration of the proposed approach for policy decision analysis. This approach seeks to couple systems modelling and simulation of policy scenarios with MCDA, stakeholder analysis and preference elicitation. The outputs can further provide analytical insights in controversy/acceptability of policy options, and consequently guide further policy formulation and the design of better options.
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页码:176 / 188
页数:13
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