A Statistical Framework for an Evidence Base to Support Environmental Regulation and Policy

Scott, E. M. , Bowman, A. , Ferguson, C., Lee, D., O’Donnell, D., Villoria, M. F. and Gemmell, J. C. (2010) A Statistical Framework for an Evidence Base to Support Environmental Regulation and Policy. In: 25th International Workshop on Statistical Modelling (IWSM 2010), Glasgow, UK, 5-9 Jul 2010, pp. 25-32.

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Publisher's URL: http://www.statmod.org/workshops_archive_proceedings_2010.htm

Abstract

A recent article in the Economist, Feb 2010, stated “Chief information offi- cers (CIOs) have become somewhat more prominent in the executive suite, and a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.” When we consider the environment, with its associated statements of policy and regulation, there are similarly issues and situations where “the wisdom is not yet extracted” or where we are “data rich and information poor”. There have been many statements of a similar nature to “How much or how little we know about the links between environmental policy measures and their actual impact in the environment”, or “much of the information gathered is of limited use in assessing the impact of environmental measures” (Nigel Haigh, foreword of Environmental Issues, Report 25/EC).

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Scott, Professor E Marian and Bowman, Professor Adrian and Lee, Professor Duncan and Gemmell, Professor James Campbell
Authors: Scott, E. M., Bowman, A., Ferguson, C., Lee, D., O’Donnell, D., Villoria, M. F., and Gemmell, J. C.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Mathematics and Statistics > Statistics

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