No abstract

There is increasing evidence that respondents to choice experiment surveys do not consider all attributes presented in the choice sets. Not accounting for this 'attribute non-attendance' leads to biased parameter estimates, and hence biased estimates of willingness to pay. Various methods exist to account for non-attendance in the analysis of choice data, with limited agreement as to which method is 'best'. This paper compares modelling approaches that can account for non-attendance, based on stated and inferred attribute non-attendance. Respondents' stated non-attendance is incorporated in the specification of multinomial and mixed logit models. Inference of non-attendance is based on equality constrained latent class models. Results show that model fit is significantly improved when attribute non-attendance is taken into account, and that welfare estimates are lower when incorporating non-attendance. The inference based on equality constrained latent class models provides the best model fit. There is little concordance between stated and inferred non-attendance, suggesting that respondents may not answer attendance statements truthfully.

Interdisciplinary research is often essential to develop the integrated systems understanding needed to manage complex environmental issues that are faced by decision-makers worldwide. The scientific, institutional and funding challenges to interdisciplinary research have been the subject of considerable discussion. Funders remain willing to support such research and to evaluate its impact. In this paper, we develop and apply a set of review concepts to systematically evaluate a large interdisciplinary research project. The project was conducted at a national research organisation that seeks to facilitate interdisciplinary integration. We categorise evaluation concepts as process-and outcome-related and propose five practical management interventions to bridge the concepts to improve interdisciplinary integration. These management interventions are: agree on a conceptual model, incorporate independent review, support synthesisers, foster intra-project communication, and build-in organisational learning. We end with reflections on lessons for the structure of research organisations and of the research team to develop effective interdisciplinary research as well as providing a set of recommendations for interdisciplinary research funders.

JEL classification: C53 D62 Q40 Q48 Q51Keywords: Meta-regression Renewable energy Green electricity Valuation Willingness to pay a b s t r a c t Using renewable energy for domestic consumption has been identified as a key strategy by the Intergovernmental Panel on Climate Change to reduce greenhouse gas emissions. Critical to the success of this strategy is to know whether consumers are willing to pay to increase the proportion of electricity generated from renewable energy in their electricity portfolio. There are a number of studies in the literature that report a wide range of willingness to pay estimates. In this study, we used a meta-regression analysis to determine how much of the variation in willingness to pay reflects true differences across the population and how much is due to study design, such as survey design and administration, and model specification. The results showed that factors that influence willingness to pay, such as renewable energy type, consumers' socio-economic profile and consumers' energy consumption patterns, explain less variation in willingness to pay estimates than the characteristics of the study design itself. Because of this effect, we recommend that policy makers exercise caution when interpreting and using willingness to pay results from primary studies. Our meta-regression analysis further shows that consumers have significantly higher (C. Ma). http://dx.doi.org/10.1016/j.reseneeco.2015.07.003 0928-7655/© 2015 Elsevier B.V. All rights reserved. 94 C. Ma et al. / Resource and Energy Economics 42 (2015) 93-109willingness to pay for electricity generated from solar, wind or generic renewable energy source (i.e. not a specific source) than hydro power or biomass.