Abstract
Finding an optimal match between installation sites and renewable energy (RE) facilities while ensuring that private initiatives meet local socio-environmental needs is a significant albeit complicated task. Different sites may need diverse considerations, such as landscape conservation, while information on the true local preferences and costs of RE facilities is unknown to the planner, causing information asymmetry and inefficiency. This study explores how a matching model can be utilised for empirically planning RE siting using an illustrative case study. It employs the so-called ‘college admission problem’ of the matching model. The matching algorithm enables the matching of sites and RE specifications, reflecting the true preferences of local people regarding facility siting. The matching result would ensure the most desirable choice for local people, as adopting the ‘student-optimal matching’ algorithm generates desirable matching patterns for the locals among the stable matching patterns.
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