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Local energy systems (LES) are designed to decarbonize, balance, and coordinate supply, storage and demand resources. Which local conditions enable LES to flourish? Using a unique dataset of 146 LES projects in the UK from 2010 to 2020, we apply econometric methods to identify energy, institutional and socio-economic conditions significantly associated with LES, but not other local energy forms. We show distributed power generation, low-carbon infrastructure firm activity, local government strategy and active energy efficiency markets are enablers of LES involving multiple actors, sectors and skill sets. These conditions describe a clear policy agenda for stimulating and supporting emerging local energy markets. [ABSTRACT FROM AUTHOR]

Natural capital accounting requires exchange rather than welfare values, but lack of data and standards have encouraged practitioners to use different approaches (e.g. simple lookup table vs spatial modeling approaches). In this paper, we demonstrate how choice modeling data can provide simulated exchange values which are more robust than simple (fast track) valuation approaches for natural capital accounting. A survey of East of England residents collected the preferences for saltmarsh management and simulated exchange values, coherently linked with the ecosystem conditions. This approach is more informative for environmental local planning purposes. We claim that expanding the set of tools available for natural capital accounting can enhance management of ecosystem services and policy decision making. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jeem.2006.10.007 Byline: Silvia Ferrini, Riccardo Scarpa Abstract: Good practice in experimental design is essential for choice experiments used in nonmarket valuation. We review the practice of experimental design for choice experiments in environmental economics and we compare it with advances in experimental design. We then evaluate the statistical efficiency of four different designs by means of Monte Carlo experiments. Correct and incorrect specifications are investigated with gradually more precise information on the true parameter values. The data generating process (DGP) is based on estimates from data of a real study. Results indicate that D-efficient designs are promising, especially when based on Bayesian algorithms with informative prior. However, if good quality a priori information is lacking, and if there is strong uncertainty about the real DGP -- conditions which are quite common in environmental valuation -- then practitioners might be better off with shifted designs built from conventional fractional factorial designs for linear models. Author Affiliation: Department of Economics, University of Siena, Siena, Italy Department of Economics, Waikato Management School, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand Article History: Received 6 October 2005