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Assessment of supply chain energy efficiency potentials: A U.S. case study
This paper summarizes a modeling framework that characterizes the key underlying technologies and processes that contribute to the supply chain energy use and greenhouse gas (GHG) emissions of a variety of goods and services purchased by U.S. consumers. The framework couples an input-output supply chain modeling approach with “bottom-up” fuel end use models for individual IO sectors. This fuel end use modeling detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the supply chain energy and GHG “footprints” of goods and services. To illustrate the policy-relevance of this approach, a case study was conducted to estimate achievable household GHG footprint reductions associated with the adoption of best practice energy-efficient supply chain technologies.
- University of North Texas United States
- Utrecht University Netherlands
- Lawrence Berkeley National Laboratory United States
- University of North Texas United States
- Lawrence Berkeley National Laboratory United States
Simulation Life-Cycle Assessment, Energy Efficiency, Goods And Services, Availability, 32, Supply Chain Modeling, Greenhouse Gases, Households, 29, Life-Cycle Assessment, Input-Output Analysis
Simulation Life-Cycle Assessment, Energy Efficiency, Goods And Services, Availability, 32, Supply Chain Modeling, Greenhouse Gases, Households, 29, Life-Cycle Assessment, Input-Output Analysis
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
