Stakeholder Perspectives on the Value of Marine Spatial Planning Towards Advancing Offshore Wind in the U.S.

The United States contributes only 0.2% of the 18,814?MW of global installed offshore wind capacity. Lack of development has been attributed in part to a cumbersome regulatory process that includes the evaluation of environmental impacts. Assessments are based on biological, social, and technical data that are often incomplete. Marine spatial planning (MSP) may help fill data gaps. We conducted semi-structured interviews with key informants to understand (1) whether a lack of biological data impedes offshore wind environmental assessments, (2) whether MSP could mitigate these impediments, and (3) whether MSP could advance offshore wind development in the U.S. in other ways. Most informants stated that a lack of biological data in offshore wind environmental assessments was problematic due to incomplete data, uncertainty of data, and mismatched scales. Data issues may be mitigated by creation of data products and increased communication, outcomes of MSP that may benefit the regulatory process by increasing data availability, resolving conflicts among users, and providing a common operating picture. Challenges remain in integrating MSP into the processes of siting and permitting offshore wind, but it provides a strategic framework for the systematic identification, collection, collation, analyses, application, and management of data in the offshore wind environmental regulatory process.     

Sample size and grouping in the estimation of disaggregate models—A simple case

Using a very simple form of disaggregate model for household car ownership, it appears that two widely held beliefs about disaggregate modelling — that analysis should always be carried out on individual households, and that sample sizes of 500 to 1000 are generally sufficient - are not necessarily valid. Though the results may not be generalizable to the full class of problems to which disaggregate analysis addresses itself, it does seem that more attention needs to be given to the questions of sample size and grouping.In addition, the standard test of goodness of fit (the so-called rho-squared test) is shown to be extremely weak. A far stronger and to some extent complementary, test is to compare the log-likelihood value given by the model with that on the basis of the full or saturated model — a test which has recently been clearly presented by a number of writers in the statistical literature. When using dummy variables, it is important that pair-wise tests on coefficients relating to various levels of the same attribute should be carried out, as well as the standard test assessing difference from zero.These points are illustrated by a number of simple examples.The abstract of this paper appears on p. 369.     

New appraisal values of travel time saving and reliability in Great Britain

Transportation - This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium...