11 research outputs found

    Ancillary human health benefits of improved air quality resulting from climate change mitigation

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    <p>Abstract</p> <p>Background</p> <p>Greenhouse gas (GHG) mitigation policies can provide ancillary benefits in terms of short-term improvements in air quality and associated health benefits. Several studies have analyzed the ancillary impacts of GHG policies for a variety of locations, pollutants, and policies. In this paper we review the existing evidence on ancillary health benefits relating to air pollution from various GHG strategies and provide a framework for such analysis.</p> <p>Methods</p> <p>We evaluate techniques used in different stages of such research for estimation of: (1) changes in air pollutant concentrations; (2) avoided adverse health endpoints; and (3) economic valuation of health consequences. The limitations and merits of various methods are examined. Finally, we conclude with recommendations for ancillary benefits analysis and related research gaps in the relevant disciplines.</p> <p>Results</p> <p>We found that to date most assessments have focused their analysis more heavily on one aspect of the framework (e.g., economic analysis). While a wide range of methods was applied to various policies and regions, results from multiple studies provide strong evidence that the short-term public health and economic benefits of ancillary benefits related to GHG mitigation strategies are substantial. Further, results of these analyses are likely to be underestimates because there are a number of important unquantified health and economic endpoints.</p> <p>Conclusion</p> <p>Remaining challenges include integrating the understanding of the relative toxicity of particulate matter by components or sources, developing better estimates of public health and environmental impacts on selected sub-populations, and devising new methods for evaluating heretofore unquantified and non-monetized benefits.</p

    SEER: A Delphic approach applied to information processing

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    Probabilistic Exposure Analysis for Chemical Risk Characterization

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    EVS Trend File 1981-2017 – Sensitive Dataset

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    The European Values Study is a large-scale, cross-national and longitudinal survey research program on how Europeans think about family, work, religion, politics, and society. Repeated every nine years in an increasing number of countries, the survey provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe. The EVS Trend File 1981-2017 is constructed from the five EVS waves and covers almost 40 years. In altogether 160 surveys, more than 224.000 respondents from 48 countries/regions were interviewed. It is based on the updated data of the EVS Longitudinal Data File 1981-2008 (v.3.1.0) and the current EVS 2017 Integrated Dataset (v.5.0.0). For the EVS Trend File, a Restricted-Use File (ZA7504) is available in addition to the (factually anonymised) Scientific-Use File (ZA7503). The EVS Trend File – Sensitive Dataset (ZA7504) is provided as an add-on file. In addition to a small set of admin and protocol variables needed to merge with the SUF data, the Sensitive Dataset contains the following variables that could not be included in the scientific-use file due to their sensitive nature: W005_3 Job profession/industry (3-digit ISCO88) - spouse/partner EVS 2008W005_3_01 Job profession/industry (3-digit ISCO08) - spouse/partner EVS 2017W005_4 Job profession/industry (4-digit ISCO88) - spouse/partner EVS 2008X035_3 Job profession/industry (3-digit ISCO88) – respondent EVS 1999, EVS 2008 X035_3_01 Job profession/industry (3-digit ISCO08) - respondent EVS 2017X035_4 Job profession/industry (4-digit ISCO88) – respondent EVS 1999, EVS 2008 x048c_n3 Region where the interview was conducted (NUTS-3): NUTS version 2006 EVS 2008X048J_N3 Region where the interview was conducted (NUTS-3): NUTS version 2016 EVS 2017X049 Size of town (8 categories) EVS 2008, EVS 2017 Detailed information on the anonymization process in the EVS Trend File is provided in the EVS Trend File Variable Report
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