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Delphi Technique: Predicting Emerging Opportunities and Challenges in Renewable Energy

Final Remarks on the Delphi Technique

The Delphi technique outlined in this paper provides a valuable means of systematically achieving a convergence of opinion from a panel of reputable subject-area experts. The method provides a robust, iterative, and anonymous group communication approach designed to shed light on alternatives, correlate expert insights on a specific subject or challenge, generate background information for decision-making, and reveal hidden consensus relevant to the forecasting of future events.

In the context of the multimethod evaluation of renewable energy sources explored here, the technique provided an opportunity to incorporate expert knowledge and experiences from global thought leaders and practitioners to enhance evaluative findings. Specifically, the panel perspectives helped (i) establish future scenarios for the rapidly evolving industry, which helped (ii) identify critical barriers that need to be overcome to scale up RE, and (iii) formulate a set of priority reforms countries can undertake to overcome these challenges, against which (iv) the Bank Group’s readiness to help clients was assessed.

Though active steps were taken to offset potential response biases from participants (for example, confirmation, satisficing, bandwagon effects), more could have been done to ensure balanced representation in the panel of experts, particularly with respect to gender.

In addition, it should be noted that findings may be subject to the Von Restorff effect — in appraising challenges or opportunities, respondents may unconsciously bias responses towards more visible, severe, or otherwise distinctive issues. This would be of greatest concern with respect to the actions or solutions proposed in response to challenges. While the predictions generated via the Delphi technique may not always prove to be accurate, the primary challenge in implementing such a process involves the respondents themselves.

The panel’s findings are ultimately a product of the collective knowledge of the assembled experts. As such, they reflect the biases, misperceptions, or knowledge gaps the panelists may possess. Nonetheless, thoughtful question design and the rigorous implementation of the iterated data collection procedure can help minimize the risks posed by these challenges.