Sustainable energy governance in South Tyrol (Italy): A probabilistic bipartite network model

• Published in: Journal of cleaner production Vol. 221; pp. 854 - 862
• Main Authors: Balest, J., Secco, L., Pisani, E., Caimo, A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2019
• Subjects: Bayesian analysis; Bipartite network models; Energy transition; Local authorities; Statistical network analysis; Sustainability; Bayesian analysis; Bipartite network models; Energy transition; Sustainability; Statistical network analysis; Local authorities
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2019.02.191

At the national scale, almost all of the European countries have already achieved energy transition targets, while at the regional and local scales, there is still some potential to further push sustainable energy transitions. Regions and localities have the support of political, social, and economic actors who make decisions for meeting existing social, environmental and economic needs recognizing local specificities. These actors compose the sustainable energy governance that is fundamental to effectively plan and manage energy resources. In collaborative relationships, these actors share, save, and protect several kinds of resources, thereby making energy transitions deeper and more effective. This research aimed to analyse a part of the sustainable energy governance composed of formal relationships between municipalities and public utilities and to investigate the opportunities to further spread sustainable energy development within a region. In the case study from South Tyrol, Italy, the network structures and dynamics of this part of the actual energy governance were investigated through a social network analysis and Bayesian exponential random graph models. The findings confirmed that almost all of the collaborations are based on spatial closeness relations and that the current network structures do not permit a further spread of the sustainable energy governance. The methodological approach can be replicated in other case studies and the findings are relevant to support energy planning choices at regional and local scales.