A policy prioritization framework using causal layered analysis and MCDM: case study of Iran’s environmental policies

Abstract

Purpose

This paper aims to propose a policy prioritization framework in view of a layered scenario building along with key stakeholder analysis and has been applied in a case study to determine the priority of Iran environmental policies at the horizon of 2030. A creative framework that covers future scenarios and allows for a more accurate and intelligent policy assessment and prioritization.

Design/methodology/approach

The general environmental policies of the Islamic Republic of Iran are evaluated, and observation policies in social area were identified. Causal layered analysis (CLA) is applied for policy prioritization based on layered probable scenarios and key stakeholder role consideration. The Multiple-criteria decision-making (MCDM) is also used for ranking General Environmental Policies by the technique for order of preference by similarity to ideal solution (TOPSIS).

Findings

Four uncertainties were obtained in different layers based on the CLA analysis, resulting in the creation of four main scenario and 16 discrete scenarios. Finally, Iran’s environmental policies were prioritized given the probable scenarios and the centralized policies on the social and political domains. The proposed model will be effective in policy-making in multilateral atmosphere to prioritize policies and alternative macro-strategies.

Originality/value

This paper shows that foresight and especially developed scenarios provide intelligent, efficient and effective planning and policy-making, and in addition to illustrating surrounding changes and probable future imagery, it generates common understanding and inter-subjective knowledge by increasing participation of various officials and stakeholders.

Citation

Fouladgar, M.M., Borumand Kakhki, A., Nasr Esfehani, A. and Sedighi, M. (2022), "A policy prioritization framework using causal layered analysis and MCDM: case study of Iran’s environmental policies", Foresight, Vol. 24 No. 6, pp. 678-693. https://doi.org/10.1108/FS-04-2021-0085