Gridlock in compromise, or is multi-objective optimisation possible in renewable energy planning? A stakeholder analysis using scenario-MCDA

ABSTRACT

The energy and climate crises are driving renewable energy, but it is currently facing obstacles in leading countries. Balancing environmental, social and economic interests has become complex at the regional level due to spatial trade-offs in a contested space. To investigate stakeholder willingness to compromise on a joint ranking on wind and solar energy sites, multi-criteria decision analysis (MCDA) planning support was explored. Using a two-part stakeholder survey, four groups were identified: ‘advocates’ who were satisfied with the site ranking (66%), ‘realists’ who were willing to compromise despite previous disagreement (13%), ‘dissenters’ not accepting (35%), and ‘dogmatists’ not engaging. Planning decisions and stakeholder engagement are underpinned by distinct attitudes towards the role of (democratic) planning and sustainable development. The use of trade-off analysis can ensure transparency and trace back stakeholder interests in making planning decisions. However, decision quality factors also need to be considered to ensure a thorough planning reflection.

KEYWORDS:

• Multi-objective optimisation
• wind energy
• solar energy
• stakeholder analysis
• scenario-MCDA
• trade-offs

Acknowledgements

I am grateful to all participants in the survey. A special thanks to Johann Köppel (TU Berlin) for the profound feedback on the manuscript. Thanks to Anna van den Boom for the illustrations. Thanks to the anonymous reviewers for the comments on the manuscript. This work was supported by the scholarship programme of the German Federal Foundation for the Environment (Deutsche Bundesstiftung Umwelt, DBU).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 §1 (2) Onshore Wind Energy Act (WindBG).

2 §26 (3) Federal Nature Conservation Act (BNatSchG).

3 Using linear regression, each criterion level is weighted with a criterion score between 0 and 100, which corresponds to the degree of fulfillment of the criterion relative to the level. This value is multiplied by the relative importance (weight) resulting from the stakeholder decisions to obtain the preference value (%) for a criterion. Scenarios are based on different combinations of criteria and levels. Each scenario is weighted to produce a ranking. This is done by multiplying the preference value (weight) of each criterion in a scenario by a criterion score, which indicates the degree to which the scenario meets the criterion. These weighted scores per criterion are then summed to obtain a total score for each rankable alternative. For detailed mathematical calculations see Hansen and Ombler (2008).

4 A transitive relation is a binary relation in mathematics when in a set B the element a is related to the element c, if a is related to b and b is related to c, for a, b, c in B, (Cuemath 2023).

5 §26 (1) and (3) Federal Nature Conservation Law (BnatSchG).

6 §27 (1) Federal Nature Conservation Law (BnatSchG).

7 §37 (1) Renewable Energy Sources Act (EEG).

Additional information

Funding

I acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin.