Cost‐benefit analyses of life‐saving public programs typically focus on the number of expected deaths avoided (statistical lives saved) as the metric for evaluating benefits. Although this measure of population risk is clearly important, it ignores the distribution of underlying individual risks. A similar number of lives can be saved by protecting relatively large populations with relatively low baseline risk as can be saved by protecting smaller populations faced with higher baseline risks. Should the value of saving a statistical life be sensitive to the baseline levels of risk to exposed individuals? This paper addresses this issue by focusing specifically on individuals' altruistic values with regard to life‐saving programs. Using results from a survey, this study finds that when individuals are asked to state their preference for equally costly life‐saving programs that will only affect others' level of risk, they prefer those that save more lives. More importantly, however, controlling for the number of lives saved, they also prefer programs that affect smaller populations facing higher levels of baseline risk. Furthermore, the results suggest that each order‐of‐magnitude increase in the level of baseline risk to others approximately doubles the altruistic value component of a statistical life saved.
Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program's (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States' largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for similar to 200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework's current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies. ; United States Environmental Protection Agency (USEPA) Chesapeake Bay Program Office [CB96350501, CB96325901, CB96365601, CB96351401] ; Published version ; This material is based upon work funded wholly or in part by the United States Environmental Protection Agency (USEPA) Chesapeake Bay Program Office, including direct salary support for multiple partners within the USEPA-administered Chesapeake Bay Program (including co-authors Shenk and Linker as well as numerous assisting support staff) , assistance agreements CB96350501 to Chesapeake Research Consortium (CRC) , Inc. (co-authors Kaufman, Ball, Bosch, Ellis, Hobbs, Van Houtven, and McGarity) , CB96325901 and CB96365601 to the Uni-versity of Maryland Center for Environmental Science (co-author Asplen) , and CB96351401 to Pennsylvania State University (co-author Bhatt) . The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.; The authors thank Rich Batiuk and the modeling team at the Ches-apeake Bay Program Office (Cuiyin Wu, Andrew Sommerlot, Richard Tian, Isabella Bertani) for constructive input throughout the project. The authors thank and acknowledge Stuart Schwartz of the University of Maryland, Baltimore County, who was an original member of CRC's Advisory and Support Committee and provided important early contributions of both conceptual and practical nature. Many thanks to John Massey for technical support with cloud resources and batch processing and Megan McNeilly for User Experience improvements to the online decision-support tool. Further thanks to Brian Burch, Megan Thynge, Tim Paris, Martin Koslof, and the entire CBPO software development and information technology team.
