OPTIMAL EXPANSION TIMING DECISIONS IN MULTI-STAGE PPP PROJECTS INVOLVING DEDICATED ASSET AND GOVERNMENT SUBSIDIES

被引:4
|
作者
Li, Jinghuan [1 ,2 ]
Li, Yu [2 ]
Zhang, Shuhua [2 ]
机构
[1] Tianjin Univ Commerce, Dept Math, Tianjin 300134, Peoples R China
[2] Tianjin Univ Finance & Econ, Coordinated Innovat Ctr Computable Modeling Manag, Tianjin 300222, Peoples R China
基金
中国国家自然科学基金;
关键词
PPP projects; multi-stage investment; expansion timing; dedicated asset; government subsidy; optimal multiple stopping time; Monte Carlo simulation; MONTE-CARLO METHODS; AMERICAN OPTIONS; INVESTMENT; VALUATION; GUARANTEES; SIMULATION; CHOICE;
D O I
10.3934/jimo.2019043
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The topic of investment timing in multi-stage public-private partnership (PPP) projects has not been received much attention so far. This study investigates optimal expansion timing decisions in multi-stage PPP projects under an uncertain demand and where the first-stage greenfield project involving a dedicated asset is immediately implemented as the PPP contract is closed, whereas the timing of the later expansion is flexibly decided according to the demand. In this setting, the optimal multiple stopping timing theory is adopted to model the expansion framework. Furthermore, we integrate a government subsidy, including an investment subsidy and revenue subsidy, into the expansion timing decisions. Through a hypothetical three-stage investment plan for a sanitary sewerage project, the optimal expansion strategies and the value of the multi-stage project before and after the subsidy are provided using a least squares Monte Carlo simulation. Also, the influences of a dedicated asset on the expansion strategies and project value are illustrated. In addition, we compare the incremental value before and after the subsidy and earlier expansion derived from two types of subsidies. The comparisons show that there is more incremental value for the revenue subsidy, and that the investment subsidy brings an earlier expansion.
引用
收藏
页码:2065 / 2086
页数:22
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