The Importance of Revenue Sharing for the Local Economic Impacts of a Renewable Energy Project: A Social Accounting Matrix Approach

As demand for electricity from renewable energy sources grows, there is increasing interest, and public and financial support, for local communities to become involved in the development of renewable energy projects. In the UK, “Community Benefit” payments are the most common financial link between renewable energy projects and local communities. These are “goodwill” payments from the project developer for the community to spend as it wishes. However, if an ownership stake in the renewable energy project were possible, receipts to the local community would potentially be considerably higher. The local economic impacts of these receipts are difficult to quantify using traditional Input-Output techniques, but can be more appropriately handled within a Social Accounting Matrix (SAM) framework where income flows between agents can be traced in detail. We use a SAM for the Shetland Islands to evaluate the potential local economic and employment impact of a large onshore wind energy project proposed for the Islands. Sensitivity analysis is used to show how the local impact varies with: the level of Community Benefit payments; the portion of intermediate inputs being sourced from within the local economy; and the level of any local community ownership of the project. By a substantial margin, local ownership confers the greatest economic impacts for the local community.

Pareto or log-normal? A recursive-truncation approach to the distribution of (all) cities

Traditionally, it is assumed that the population size of cities in a country follows a Pareto distribution. This assumption is typically supported by nding evidence of Zipf's Law. Recent studies question this nding, highlighting that, while the Pareto distribution may t reasonably well when the data is truncated at the upper tail, i.e. for the largest cities of a country, the log-normal distribution may apply when all cities are considered. Moreover, conclusions may be sensitive to the choice of a particular truncation threshold, a yet overlooked issue in the literature. In this paper, then, we reassess the city size distribution in relation to its sensitivity to the choice of truncation point. In particular, we look at US Census data and apply a recursive-truncation approach to estimate Zipf's Law and a non-parametric alternative test where we consider each possible truncation point of the distribution of all cities. Results con rm the sensitivity of results to the truncation point. Moreover, repeating the analysis over simulated data con rms the di culty of distinguishing a Pareto tail from the tail of a log-normal and, in turn, identifying the city size distribution as a false or a weak Pareto law.