The causal relationships between electrification and development of poor, rural communities are complex and contextual. The existing literature focuses mainly on the impact of rural electrification and electricity use on local socio-economic development, while the reverse feedbacks of various social and economic changes on electricity demand and supply have not been fully characterised. Most electricity access impact assessments assume linear, one-way effects and linear growth in electricity demand. However, the projections rarely match the reality, creating challenges for rural utilities. From a modelling perspective, the lack of attention to dynamic complexities of the electricity-development nexus prevents the appropriate modelling of electricity demand over time and, hence, informed planning for and sizing of power plants. With the goal to improve modelling of the electricity-development nexus, we undertake a comprehensive review and extensive analysis of the peer-reviewed literature on electricity access and its impact on rural socio-economic development, and vice versa. We characterise and describe the nexus between electricity access and development through graphical causal diagrams that allow us to capture, visualise and discuss the complexity and feedback loops. Based on this, we suggest guidelines for developing appropriate models able to include and simulate such complexities.Our analysis confirms that electricity use is interconnected through complex causal relations with multiple dimensions of socio-economic development, viz. income generating activities, market production and revenues, household economy, local health and population, education, and habits and social networks. The causal diagrams can be seen as a first step of the conceptualization phase of model building, which aims at describing and understanding the structure of a system. The presence of multiple uncertain parameters and complex diffusion mechanisms that describe the complex system under analysis suggests that systems-dynamic simulations can allow modelling such complex and dynamic relations, as well as dealing with the high uncertainties at stake, especially when coupled with stochastic approaches.

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