Promoting Community Renewable Energy in a Corporate Energy World

Small-scale, decentralized and community-owned renewable energy is widely acknowledged to be a desirable feature of low carbon futures, but faces a range of challenges in the context of conventional, centralized energy systems. This paper draws on transition frameworks to investigate why the UK has been an inhospitable context for community-owned renewables and assesses whether anything fundamental is changing in this regard. We give particular attention to whether political devolution, the creation of elected governments for Scotland, Wales and Northern Ireland, has affected the trajectory of community renewables. Our analysis notes that devolution has increased political attention to community renewables, including new policy targets and support schemes. However, these initiatives are arguably less important than the persistence of key features of socio-technical regimes: market support systems for renewable energy and land-use planning arrangements that systemically favour major projects and large corporations, and keep community renewables to the margins. There is scope for rolling out hybrid pathways to community renewables, via joint ownership or through community benefit funds, but this still positions community energy as an adjunct to energy pathways dominated by large, corporate generation facilities

Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer

A benefit function transfer obtains estimates of willingness-to-pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the benefit transfer model. A more expensive alternative to estimate WTP is to analyze only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian model averaging (BMA) techniques can be used to optimally combine information from all models. The Bayesian algorithm searches for the set of sites that can form the basis for estimating a benefit function and reveals whether such information can be transferred to new sites for which only a small data set is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is 'poolable'. © 2008 Elsevier Inc. All rights reserved.

Lean software management case study: Timberline Inc.

This paper is believed to be the first documented account of a full adoption of lean by a software company. Lean techniques were devised by Toyota and other manufacturers over the last 50 years. The techniques are termed lean because they require less resource to produce more product and exceptional quality. Lean ideas have also been successful in service industries and product development. Applying lean to software has been advocated for over 10 years. Timberline, Inc started their lean initiative in Spring 2001 and this paper records their journey, results and lessons learned up to Fall 2003. This case study demonstrates that lean thinking can work successfully for software developers. It also indicates that the extensive lean literature is a valuable source of new ideas for software engineering.