This paper presents Twin City 2.0, a comprehensive framework that integrates explainable artificial intelligence (XAI) with multiscale digital twin technologies to address the systemic challenges of contemporary urban governance. The methodology follows a clear three-step sequence: a systematic literature review, comparative case studies (Virtual Singapore, CityGML Helsinki, Decidim Barcelona), and pedagogical experiments conducted across several European universities. Findings show that coupling XAI with participatory digital twin platforms significantly improves urban diagnostics, transparency, and citizen involvement, thereby strengthening decision-making processes at multiple scales. These results highlight the strategic role of universities as intermediaries supporting ecological and democratic transitions. The study ultimately proposes a systemic governance framework in which open standards, explainable algorithms, and participatory mechanisms converge to produce more intelligible, inclusive, and resilient urban futures.