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Author:

Zheng, Shiyuan (Zheng, Shiyuan.) | Jia, Rongwen (Jia, Rongwen.) | Shang, Wen-Long (Shang, Wen-Long.) | Fu, Xiaowen (Fu, Xiaowen.) | Wang, Kun (Wang, Kun.)

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SSCI EI Scopus SCIE

Abstract:

This paper studies an information provision game between a better-informed government (on the climate change-related disaster) and a transport facility operator. The government can acquire better disaster information and adopt a persuasion strategy to affect the facility operator's adaptation investment. The Bayesian persuasion framework is used to model the government's design on its information signaling mechanism. We also solve the equilibrium outcomes of government subsidy policy and benchmark them with those under the persuasion strategy. Our analytical results suggest that the government's optimal persuasion strategy depends on the common prior belief on the disaster and the expected level of unavoidable loss (NL). When the NL is low (or intermediate, respectively), the persuasion strategy should be full (or partial, respectively) disclosure of the government's information to the facility operator. When the NL is high, the persuasion strategy has no impacts. In terms of social welfare improvement, the persuasion strategy dominates (or is dominated by, respectively) the subsidy policy if the effectiveness of the adaptation investment is low (or high, respectively). When the effectiveness of the adaptation investment is intermediate, the comparison is uncertain. Furthermore, we extend our model to the oligopoly case with multiple competing transport facilities and also allow their adaptation investments to have positive externality. We find that more intense competition among the facility operators leads to more conservative persuasion strategy, and larger spillover effect of the adaptation investment among facilities leads to more radical persuasion strategy.

Keyword:

Adaptation investment Bayesian persuasion Climate change-related disaster Signaling mechanism Transport facility resilience Government subsidy

Author Community:

  • [ 1 ] [Shang, Wen-Long]Beijing Univ Technol, Coll Metropolitan Transport, Beijing Key Lab Traff Engn, Beijing, Peoples R China
  • [ 2 ] [Jia, Rongwen]Univ Int Business & Econ, Sch Int Trade & Econ, Beijing, Peoples R China
  • [ 3 ] [Zheng, Shiyuan]Shanghai Maritime Univ, Sch Transportat, Shanghai, Peoples R China
  • [ 4 ] [Fu, Xiaowen]Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
  • [ 5 ] [Wang, Kun]Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
  • [ 6 ] [Fu, Xiaowen]Univ Sydney Business Sch, Inst Transport & Logist Studies, Sydney, Australia

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Source :

TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE

ISSN: 0965-8564

Year: 2023

Volume: 176

6 . 4 0 0

JCR@2022

ESI Discipline: SOCIAL SCIENCES, GENERAL;

ESI HC Threshold:9

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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