An ordinal classification of brownfield remediation projects in China for the allocation of government funding. (September 2018) - Han, Qingye; Zhu, Yuming; Ke, Ginger Y.; Hipel, Keith W.
Highlights: The study facilitates the funding allocation of brownfield remediation projects. The key beneficiary is the provincial government in China. A multi-criteria system is developed to help decision making. An ordinal classification is generated through a Dominance-based Rough Set Approach. The decision process can be updated and reapplied at each planning horizon. Abstract: Given the increasing number of brownfields in China, the Chinese government is now facing a huge financing gap in brownfield remediation projects. Accordingly, the authors present a framework that can assist a provincial government in classifying its brownfields so that the funding can be appropriately allocated. More specifically, the framework starts from constructing a criteria system based on the existing literature and expert opinions. Then, in consonance with the criteria system, a sample set of brownfields is measured by experts and then trained using a Dominance-Based Rough Set Approach to derive judging rules. After the validation, those rules are employed to generate an ordinal classification of the complete set of brownfields. The resulting ordinal classification provides the government with a sequence of brownfields from the highest priority to the lowest, which can support the government in budgeting and planning. Furthermore, as the number and conditions of the brownfields change over time, the criteria system and corresponding categories can be updated and reapplied at the beginning of each fiscal year (or each planning horizon) to facilitate the government's decision making. The proposed decision process can also be applied to assist the investment decisions of any brownfield stakeholders in other counties with limited funding based on their specific preference.