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Interpreting life cycle assessment f...
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Parsons, Sophie.
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Interpreting life cycle assessment for decision-making on emerging materials.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Interpreting life cycle assessment for decision-making on emerging materials./
作者:
Parsons, Sophie.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:
252 p.
附註:
Source: Dissertation Abstracts International, Volume: 74-12C.
Contained By:
Dissertation Abstracts International74-12C.
標題:
Systems science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10297726
Interpreting life cycle assessment for decision-making on emerging materials.
Parsons, Sophie.
Interpreting life cycle assessment for decision-making on emerging materials.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 252 p.
Source: Dissertation Abstracts International, Volume: 74-12C.
Thesis (Ph.D.)--University of Surrey (United Kingdom), 2016.
Understanding the potential environmental impacts of the materials we use is important. By doing this we can chose whether or not to regulate, reduce or ban substances which potentially present significant risk to human health and ecosystems. But in order to make environmental decisions effectively, we must collect, analyse and communicate information in the right way. Decision-making processes for emerging materials often do not consider the life cycle implications of substitute materials, nor the implications of uncertain data. This Engineering Doctorate (EngD) project explores how information provision about emerging materials can allow effective decision-making on environmental issues. This research examines the use of life cycle assessment (LCA) as a tool for understanding the environmental impact of emerging materials across the life cycle within a product. It reviews the use of life cycle thinking in policy-making to determine key aspects for decision-support; the challenges of applying LCA principles towards emerging materials and key pathways for managing uncertainty. It also evaluates how LCA can be relevant to industry as a mechanism for decision-support on new materials. These aspects are explored through novel LCA case studies. Key contributions to knowledge come from development of strategic pathways for managing uncertainty relating to carbon nanomaterials and the identification of appropriate methods of uncertainty assessment of emerging materials where uncertainty is very high. Novel LCA studies on emerging nanomaterial and solar technology also contribute new understanding on the life cycle aspects of these systems. Case study on the industry use of LCA adds to discussion on organisational environmental footprinting, and suggests new approaches for LCAs use within decision-support. Examination of life cycle thinking within policy highlights the urgent need for policy-makers to better assess the potential for unforeseen consequences as a result of precautionary action. This thesis brings together discussion on the implications of life cycle thinking for policy-makers and industry, with the practical challenges of performing LCA on emerging materials where uncertainty is high and little data is available. Its conclusions accentuate the need for better collaboration with industry both in obtaining life cycle data and incorporating LCA into decision-support, and the important role scenario analysis, expert engagement and risk assessment has in supporting uncertainty management where uncertainties are very high.Subjects--Topical Terms:
3168411
Systems science.
Interpreting life cycle assessment for decision-making on emerging materials.
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Understanding the potential environmental impacts of the materials we use is important. By doing this we can chose whether or not to regulate, reduce or ban substances which potentially present significant risk to human health and ecosystems. But in order to make environmental decisions effectively, we must collect, analyse and communicate information in the right way. Decision-making processes for emerging materials often do not consider the life cycle implications of substitute materials, nor the implications of uncertain data. This Engineering Doctorate (EngD) project explores how information provision about emerging materials can allow effective decision-making on environmental issues. This research examines the use of life cycle assessment (LCA) as a tool for understanding the environmental impact of emerging materials across the life cycle within a product. It reviews the use of life cycle thinking in policy-making to determine key aspects for decision-support; the challenges of applying LCA principles towards emerging materials and key pathways for managing uncertainty. It also evaluates how LCA can be relevant to industry as a mechanism for decision-support on new materials. These aspects are explored through novel LCA case studies. Key contributions to knowledge come from development of strategic pathways for managing uncertainty relating to carbon nanomaterials and the identification of appropriate methods of uncertainty assessment of emerging materials where uncertainty is very high. Novel LCA studies on emerging nanomaterial and solar technology also contribute new understanding on the life cycle aspects of these systems. Case study on the industry use of LCA adds to discussion on organisational environmental footprinting, and suggests new approaches for LCAs use within decision-support. Examination of life cycle thinking within policy highlights the urgent need for policy-makers to better assess the potential for unforeseen consequences as a result of precautionary action. This thesis brings together discussion on the implications of life cycle thinking for policy-makers and industry, with the practical challenges of performing LCA on emerging materials where uncertainty is high and little data is available. Its conclusions accentuate the need for better collaboration with industry both in obtaining life cycle data and incorporating LCA into decision-support, and the important role scenario analysis, expert engagement and risk assessment has in supporting uncertainty management where uncertainties are very high.
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