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A linear programming approach towater allocation during a drought

Abstract:
The economic impacts of a drought depend critically on how water is allocated to different users. Choices as to water allocation can often reflect wider economic policy, environmental, and social goals and constraints. This research applies a multi-objective linear programming input-output method to determine a suite of water supply allocations for different economic sectors in a drought. Using the UK as a case study, we develop estimates of the minimum potential economic impact associated with different water allocations under a range of climate and policy scenarios. Estimates of total impact range from -0.16% to -1.48% of total output depending on the drought scenarios tested. The approach offers the flexibility to set different policy objectives in terms of water allocations/ restrictions, employment or a range of other objectives, including constraints to rebalance the economic system. In allowing for the inclusion of other economic, social, and environmental constraints, it provides a framework for policymakers to assess how water allocation decisions interact with other policy goals to determine the economic impacts of a drought. Challenging decisions about how to allocate water during a drought are likely to remain important in the future.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/w10040363

Authors

More by this author
Institution:
University of Oxford
Division:
Social Sciences Division
Department:
Law; Socio-Legal Studies Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Social Sciences Division
Department:
SOGE; Environmental Change Institute
Role:
Author


Publisher:
MDPI
Journal:
Water More from this journal
Volume:
10
Issue:
4
Pages:
363
Publication date:
2018-03-23
Acceptance date:
2018-03-21
DOI:
EISSN:
2073-4441
ISSN:
2073-4441


Keywords:
Pubs id:
pubs:834357
UUID:
uuid:3f346aff-8cbd-409e-87bc-05bb0b26cf3a
Local pid:
pubs:834357
Source identifiers:
834357
Deposit date:
2018-06-20
ARK identifier:

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