Literature DB >> 20141104

Direct and indirect water withdrawals for U.S. industrial sectors.

By Michael Blackhurst1, Chris Hendrickson, Jordi Sels i Vidal.   

Abstract

Effective water management is critical for social welfare and ecosystem health. Nevertheless, information necessary to meaningfully assess sustainable water use is incomplete. In particular, little information is available on supply chain or indirect water use for the production of goods and services in the United States. We estimate a vector of water withdrawals for all 428 sectors in the 2002 U.S. economic input-output table. The vector was applied using economic input-output life cycle assessment (EIO-LCA) methods to estimate direct and indirect water withdrawals for each sector's production, both in terms of total withdrawals and per dollar of output. Agriculture and power generation account for an overwhelming majority of direct water withdrawals (90%). A majority of water use (60%) is indirect ("embodied" or "virtual" water) with 96% of the sectors using more water indirectly in their supply chains than directly. The food and beverage industry accounts for 30% of indirect withdrawals. These results can be useful for environmental life cycle assessment of U.S. production and other studies, especially to avoid truncation errors due to boundary setting associated with process based life cycle impact assessments. However, we conclude that better information on water use would be helpful for effective water management.

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Year:  2010        PMID: 20141104     DOI: 10.1021/es903147k

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  USEEIO: a New and Transparent United States Environmentally-Extended Input-Output Model.

Authors:  Yi Yang; Wesley W Ingwersen; Troy R Hawkins; Michael Srocka; David E Meyer
Journal:  J Clean Prod       Date:  2017-08       Impact factor: 9.297

2.  USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0.

Authors:  Wesley W Ingwersen; Mo Li; Ben Young; Jorge Vendries; Catherine Birney
Journal:  Sci Data       Date:  2022-05-03       Impact factor: 8.501

  2 in total

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