Workbook of atmospheric dispersion estimates

This is the advice of a committee of leading American experts, chaired by Harvey Fineberg from the Harvard School of Public Health. It represents the fifth report in a series, commissioned by the US National Research Council, that considers how society can understand and cope with decisions about risks. The Committee's remit was to provide advice on risk characterisation, defined by the Research Council as the translation of `the information in a risk assessment ... into a form usable by a risk manager, individual decision maker, or the public'. In this book, the Committee has responded to the challenge clearly and authoritatively, beginning with a profound re-definition of `risk characterisation' that forms the basis for all that follows. In the view of the Committee, risk characterisation is a process. It is a process which starts before quantitative analysis of the risks, because it includes defining what risks to assess, and how most appropriately to assess them. It is an iterative process, in which assumptions are challenged and re-worked, and new information may be incorporated. It is a process in which qualitative judgements contribute to the fuller understanding of the problem; where quantitative scientific estimates, although important, contribute only a part. Most fundamentally, it is a process which, in a democratic society, needs to involve all those affected by the perceived problem and consequent decision. In the words of the Committee: `Experience shows that analyses, no matter how thorough, that do not address the decision-relevant questions, use reasonable assumptions, and meaningfully include the key affected parties can result in huge expenses and long delays and jeopardise the quality of understanding and the acceptability of the final decisions.' In other words, until or unless we expand our understanding of risk characterisation to include the process of defining the assessment itself, we are unlikely to make progress in gaining public acceptance for major decisions on health or environmental issues. For those without sufficient time to read the book, the ten-page summary provides a succinct overview of the Committee's advice, complete with bullet points and emboldened key phrases. However, the main body of the book (and particularly Appendix A, which discusses a number of case studies) is well worth scanning for its well-reasoned and well-structured discussion of the issues and the suggested way forward. Nor is the Committee lost in an `academic ivory tower'. It recognises that involvement of all those with vested interests cannot ensure a rapid or consensus solution or preclude some groups `dropping out' and choosing the route of litigation. It also recognises that allowing a `voice' for a wide range of interest groups can be time consuming and difficult to manage. However, the Committee argues: `While we are sensitive to concerns about cost and delay, we note that huge costs and delays have sometimes resulted when a risk situation was inadequately diagnosed, a problem misformulated, key interested and affected parties did not participate, or analysis proceeded unintegrated with deliberation. We believe that following [our] principles can reduce delays and costs as much as or more than it increases them.' So what are the Committee's principles? Getting the science right - any quantitative science that is undertaken must be of the highest standards. Getting the right science - this ensures that all the relevant risks are considered. Getting the right participation - this ensures that all those affected have a `voice' in the process. Getting the participation right - this ensures that the process is responsive to the needs of all the participants. Developing an accurate, balanced and informative synthesis - this should include a balanced understanding of the uncertainties in current knowledge, encompassing ignorance and indeterminacy as well as more quantifiable uncertainties. Again, in the words of the Committee: `These criteria are related. To be decision-relevant, risk characterisation must be accurate, balanced and informative. This requires getting the science right and getting the right science. Participation helps ask the right questions of the science, check the plausibility of assumptions, and ensure that any synthesis is both balanced and informative.' In order to set up an appropriate risk characterisation process, the Committee recommends that those responsible for it should `begin by developing a provisional diagnosis of the decision situation' in order to identify potential participants, allocate resources and structure the process. However, in doing so, they should `treat the diagnosis as tentative and remain open to change, always keeping in mind that their goal is a process that leads to a useful and credible risk characterization'. The Committee also stresses the need for those responsible for the process to `develop the capability to cope with attempts by some interested and affected parties to delay decision, and to develop a range of strategies for reaching closure'. This is likely to require the development of new skills and may require organisational changes `to improve communication across sub-units and to allow for the flexibility and judgement necessary to match the process to decision'. This balanced, reasoned and authoritative book is, in my opinion, a `must for all those involved in informing societal decision on risks.

Understanding Risk - Informing Decisions in a Democratic Society

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Compass and Gyroscope: Integrating Science and Politics for the Environment

List of Figures. Preface. Acknowledgments. Acronyms & Shorthands-Glossary. PART I. UNDERSTAND TECH MINING. Chapter 1. Technological Innovation and the Need for Tech Mining. 1.1 Why Innovation is Significant. 1.2 Innovation Processes. 1.3 Innovation Institutions and Their Interests. 1.4 Innovators and Their Interests. 1.5 Technological Innovation in an Information Age. 1.6 Information About Emerging Technologies. Chapter 1 Take-Home Messages. Chapter Resources. Chapter 2. How Tech Mining Works. 2.1 What is Tech Mining? 2.2 Why Do Tech Mining? 2.3 What Is Tech Mining's Ancestry? 2.4 How to Conduct the Tech Mining Process? 2.5 Who Does Tech Mining? 2.6 Where Is Tech Mining Most Needed? Chapter 2 Take-Home Messages. Chapter Resources. Chapter 3. What Tech Mining Can Do for You. 3.1 Tech Mining Basics. 3.2 Tech Mining Analyses. 3.3 Putting Tech Mining Information to Good Use. 3.4 Managing and Measuring Tech Mining. Chapter 3 Take-Home Messages. Chapter 4. Example Results: Fuel Cells Tech Mining. 4.1 Overview of Fuel Cells. 4.2 Tech Mining Analyses. 4.3 Tech Mining Results. 4.4 Tech Mining Information Processes. 4.5 Tech Mining Information Products. Chapter 4 Take-Home Messages. Chapter Resources. Chapter 5. What to Watch For in Tech Mining. 5.1 Better Basics. 5.2 Research Profiling and Other Perspectives on the Data. 5.3 More Informative Products. 5.4 Knowledge Discovery. 5.5 Knowledge Management. 5.6 New Tech Mining Markets. 5.7 Dangers. Chapter 5 Take-Home Messages. Chapter Resources. PART II. DOING TECH MINING. Chapter 6. Finding the Right Sources. 6.1 R&D Activity. 6.2 R&D Output Databases. 6.3 Determining the Best Sources. 6.4 Arranging Access to Databases. Chapter 6 Take-Home Messages. Chapter Resources. Chapter 7. Forming the Right Query. 7.1 An Iterative Process. 7.2 Queries Based on Substantive Terms. 7.3 Nominal Queries. 7.4 Tactics and Strategies for Query Design. 7.5 Changing the Query. Chapter 7 Take-Home Messages. Chapter 8. Getting the Data. 8.1 Accessing Databases. 8.2 Search and Retrieval from a Database. 8.3 What to Do, and Not to Do. Chapter 8 Take-Home Messages. Chapter 9. Basic Analyses. 9.1 In the Beginning. 9.2 What You Can Do with the Data. 9.3 Relations Among Documents and Terms Occurring in Their Information Fields. 9.4 Relationships. 9.5 Helpful Basic Analyses. Chapter 9 Take-Home Messages. Chapter 10. Advanced Analyses. 10.1 Why Perform Advanced Analyses? 10.2 Data Representation. 10.3 Analytical Families. Chapter 10 Take-Home Messages. Chapter Resources. Chapter 11. Trend Analyses. 11.1 Perspective. 11.2 An Example Time Series Description and Forecast. 11.3 Multiple Forecasts. 11.4 Research Fronts. 11.5 Novelty. Chapter 11 Take-Home Messages. Chapter Resources. Chapter 12. Patent Analyses. 12.1 Why patent Analyses? 12.2 Getting Started. 12.3 The 'What' and 'Why' of patent Analysis. 12.4 Tech Mining Patent Analysis Case Illustration: Fuel Cells. 12.5 Patent Citation Analysis. 12.6 For Whom? 12.7 TRIZ. 12.8 Reflections. Chapter 12 Take-Home Messages. Chapter Resources. Chapter 13. Generating and Presenting Innovation Indicators. 13.1 Expert Opinion in Tech Mining. 13.2 Innovation Indicators. 13.3 Information Representation and Packaging. 13.4 Examples of Putting Tech Mining Information Representation to Use. 13.5 Summing Up. Chapter Resources. Chapter 14. Managing the Tech Mining Process. 14.1 Tough Challenges. 14.2 Tech Mining Communities. 14.3 Process Management. 14.4 Enhancing the Prospects of Tech Mining Utilization. 14.5 Institutionalizing the Tech Mining Function. 14.6 The Learning Curve. Chapter 14 Take-Home Messages. Chapter 15. Measuring Tech Mining Results. 15.1 Why Measure? 15.2 What to Measure. 15.3 How to Measure. 15.4 Enabling Measurement. 15.5 Effective Measurement. 15.6 Using Measurements to Bolster Tech Mining. Chapter 15 Take-Home Messages. Chapter Resources. Chapter 16. Examples Process: Tech Mining on Fuel Cells. 16.1 Introduction. 16.2 First Step: Issue Identification. 16.3 Second Step: Selection of Information Sources. 16.4 Third Step: Search Refinement and Data Retrieval. 16.5 Fourth Step: Data Cleaning. 16.6 Fifth Step: Basic Analyses. 16.7 Sixth Step: Advanced Analyses. 16.8 Seventh Step: Representation. 16.9 Eight Step: Interpretation. 16.10 Ninth Step: Utilization. 16.11 What Can We Learn. Chapter 6 Take-Home Messages. Chapter Resources. Appendix A: Selected Publication and patent Databases. Appendix B: Text Mining Software. Appendix C: What You Can Do Without Tech Mining Software. Appendix D: Statistics and Distributions for Analyzing Text Entities. References. Index.

Tech Mining: Exploiting New Technologies for Competitive Advantage

The role of science in environmental impact assessment: process and procedure versus purpose in the development of theory

: During the 1960s, there was an upsurge of concern among advanced industrialized nations over Unforeseen adverse effects of technological innovation. The concern led to adoption of methods for estimating and forecasting the impacts of technology and development on public health and safety, social and economic stability, and the environment. Among these were technology assessment, risk assessment, cost benefit analysis, and environmental impact assessment. In the United States, the assessment of the effects of public projects or proposals having a major foreseeable impact on the environment became statutory law under the National Environmental Policy Act of 1969. This statute, and the methods it stimulated, has possibly had the greatest international impact of any American legislation.

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John J Fittipaldi

Papers

Environmental Methods Review: Retooling Impact Assessment for the New Century