Incorporating uncertainties into policymaking process for aiding a balanced regional economic development: a fuzzy investment risk assessment of the Delaware brownfields
Date
2017
Authors
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Publisher
University of Delaware
Abstract
This study develops a new solution to the problem of uncertainties into the
policymaking process. As an applied research, this study introduces a new policy
framework called “Vectorial Policy Process” through which to understand and
incorporate them into it. In light of Fuzzy Set Theory, and with the help of Analytic
Hierarchy Process (AHP) and Empirical Bayesian Kriging (EBK), this study develops
a framework to help explore, recognize, and structure various kinds of uncertainties
that are associated with economic development and policymaking at the regional level.
Selecting the Delaware Brownfields Program (DBP) as a case study, this research
employs an exclusive in-depth, market-driven data analysis, which is dominantly used
by the banking, financial, and insurance industries, to conduct an investment-based
risk assessment of brownfield sites. This helps public funds target those sites in a
positively discriminatory way to achieve a more balanced regional economic
development. ☐ This study develops a composite fuzzy membership function which defines the
transition from Investment Risk Set to Investment Safety Set. All Delaware
brownfield sites are assessed based on their degree of membership to each of the two
fuzzy sets. By employing this framework, policymakers can see how safe or risky is
each site from the investors’ lens, with respect to their surrounding communities. This
incorporates uncertainty into the policymaking process by viewing the brownfield
development inequality problem from the perspectives of investors and the private
sector, rather than that of public entities. This facilitates transferring more ‘unknown-knowns’
to ‘known-knowns’ in shaping brownfield policies. Through a data-driven
approach, this study recognizes and classifies 62 different sources of uncertainty that
may be considered as deterrents to new investment in communities affected by the
presence of brownfields. By employing the AHP method as a fuzzy membership
function, these uncertainty sources (risk factors) are structured in a risk hierarchy and
grouped into five main categories, as follows: (1) Socioeconomic Risk; (2)
Demographic Risk; (3) Infrastructure Risk; (4) Spatial (Proximity) Risk; and (5)
Financial Demand Risk. More importantly, this research employs EBK to estimate the
spatial variability of these factors. This is a procedure for quantifying proximity risk
when data becomes available from the area of interest. In EBK, interpolation is carried
out by means of a Bayesian form of Kriging through Geographical Information
Systems (GIS). ☐ The structure presented in this study is completely flexible, and may be
modified and adapted to fit policymakers’ needs in the future. The research outcome is
an effective policy support system for aiding in policymaking under uncertainty,
which can be utilized by decision-makers under the regional scale. ☐ Key Words: Uncertainty, Policymaking Process, Investment Risk Assessment,
Vectorial Policy Process (VPP), Analytical Hierarchy Process (AHP), Empirical
Bayesian Kriging (EBK), Brownfield, Regional Economic Development
Description
Keywords
Social sciences, Applied sciences, Health and environmental sciences, Analytical hierarchy process, Empirical bayesian kriging, Investment risk assessment, Policymaking process, Uncertainty, Vectorial policy process