Crowdsourcing Innovations in Renewable Energy and Collective Intelligence - Labash, Huda; Kareem Abass, Haithem; Abdulkarim Dheeb, Shaimaa; Bodnar, Nataliia; Abdulkhaleq Ali, Ammar (Society of Visual Informatics, 2025)
As the pace of transition to sustainable energy systems is accelerating, there is an urgent need for creative and all-inclusive approaches capable of expediting the development and deployment of technologies. This study presents crowdsourcing and collective intelligence as strategic means to improve efficiency in innovation and stakeholder engagement in the renewable energy context. Using a multi-methods approach in which structured surveys, multi-case analysis, and algorithmic model building are combined, this study assesses the operational and leading indicators of crowdsourcing effectiveness. Data was gathered from 250 respondents and five real-life energy projects and supported by two algorithmic models: the Participation-Weighted Solution Prioritization (PWSP) model and the Dynamic Implementation Success Estimator (DISE). The results show that it is possible to achieve significant reductions in development time and energy consumption while increasing the quality of the solution and the percentage of implementations. Finally, the synthesis of survey and algorithmic results demonstrated a significant alignment between perceived importance and predictive relevance of features, like submission quality and review protocols. While the work identifies clear advantages, it also surfaces ongoing challenges around data availability, algorithm scalability, and inclusivity in terms of who gets to contribute. These findings highlight the role of smart evaluation mechanisms and adaptive platform governance to enhance the contribution of crowdsourcing to energy innovation. The authors argue that, when guided by strong design and ethics, crowdsourcing can be a potent tool for speeding sustainable energy transitions and democratizing innovation.