Mapping resource conflicts with probabilistic network models. (15th December 2016)

• Record Type: Journal Article
• Authors: Musella, Flaminia; Bramati, Maria Caterina; Alleva, Giorgio
• Is Part Of: Journal of cleaner production. Volume 139(2016:Dec. 15)
• Page Start: 1463
• Page End: 1477
• Publication Date: 2016-12-15
• Subjects: Resource conflict -- Outcome prediction -- Bayesian networks; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.09.025
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Resource conflict management is an increasingly interesting topic for both scientific advancement and policy analysis. This study provides decision makers with a data-driven tool as a valid support to institutional action in planning and implementing policies. The probabilistic network model here proposed is built using (1) historical data and (2) a general taxonomy on world resource conflicts. Predicting the most likely conflict outcome is a prerequisite for carrying out some scenario building, which is also illustrated in the paper. However, the strength of our study lies not only in the prediction model, which takes into account specific political, socio-economic and geographical features of the areas involved in resource conflicts, but also in the statistical approach proposed herein for assessing the impact of various policy decisions on conflict dynamics and outcome. The empirical analysis shows that economic and social factors play a central role not only as triggers of claims on the use of natural resources, but also on the salience and persistence of the conflict. Moreover, from scenario simulations it appears that bilateral negotiations most likely drive conflicts in coastal areas (biodiversity protection etc.) to end with an agreement between the parties. Not only the importance of economic interests on coastlines is much higher than in other areas, but also issues on the preservation of natural site and biodiversity are more likely to insist on littorals than in a general framework. Highlights: Resource conflicts management is an interesting topic above all for policy analyzers. The paper discusses a probabilistic statistical model based on Bayesian networks. The model provides decision makers with a tool for mapping and predicting conflicts.