Title: Optimising the strategic management of wide-area environmental cleanup campaigns

Authors: Brett D. Steele

Addresses: Washington, DC, USA

Abstract: This paper presents a fundamental analysis of a wide-area environmental cleanup campaign's net-present value or net-utility gain. It is designed to support the site-specific optimisation strategies of US Department of Homeland Security. The analysis is subject to the highly imperfect market condition of 'full competition'. This is where the executive managers can control: 1) the production rate and cleanliness performance of the cleanup process; 2) the supply-side investments in the cleanup-production system to lower (or shift to the right) the variable cleanup-cost function; 3) the demand-side investments in knowledge creation and dissemination or public communication that increase (or shift to the right) the contaminated property's value or cleanup-benefit function for society. The net-utility gain function that is derived under these conditions is then analysed quantitatively to determine optimum values of production rate and cleanliness level, as well as the optimum levels of investments in containment or decontamination, cleanup-production system, and public communication. The net-utility gain function also used to analyse qualitatively six widely contrasting historical case studies. It therefore represents a basic strategic theory for guiding the management of environmental cleanup campaigns.

Keywords: cost-benefit analysis; optimisation; wide-area environmental cleanup; remediation; strategic management; investment; cleanup-production systems; public communication; decontamination; net utility gain; net present value; NPV; imperfect competition; environmental cleanup campaigns; USA; United States; knowledge creation; production rate; cleanliness level; containment.

DOI: 10.1504/IJEWM.2016.080262

International Journal of Environment and Waste Management, 2016 Vol.18 No.1, pp.68 - 99

Received: 29 Oct 2015
Accepted: 25 Jun 2016

Published online: 09 Nov 2016 *

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