On the Acceptance and Sustainability of Renewable Energy Projects—A Systems Thinking Perspective

• Main Authors: Ana María González, Harrison Sandoval, Pilar Acosta, Felipe Henao
• Format: Article
• Language: English
• Published: MDPI AG 2016-11-01
• Series: Sustainability
• Subjects: renewable energy systems; sustainable livelihoods; community acceptance; causal-loop diagrams
• Online Access: http://www.mdpi.com/2071-1050/8/11/1171

Rapid population growth and increasing concern related to improving the living standards in impoverished communities without damaging the natural environment have drawn attention to the adoption of renewable energy systems (RES) around the world. Despite this global trend, the implementation of these projects has not succeeded completely in rural poor communities due to several factors, including social barriers faced at the time of their execution. These social barriers lead to poor acceptance of the projects and their consequent abandonment. Acceptance is a social construct that is influenced by several factors that need to be understood to achieve successful and sustainable results in the future. In this paper, we develop a conceptual model, based on principles of sustainability and systems thinking, to understand the interrelationships among the main factors that have been reported in the literature as key to determining the sustainability and community acceptance of RES projects. To do so, we review the existing literature on sustainability and social acceptance of RES and then construct a causal-loop diagram of their driving factors. While doing so, we also view the problem through the lens of the sustainable livelihoods framework, aiming to maintain the perspective of rural communities and observing the impacts of RES on their contextual reality. The resulting model helps to understand the multiple interactions that RES projects have with rural communities as well as identify potential intervention points for future projects. We end the paper with a discussion of the implications of the model and how can it be used to inform future rural energy decision making.