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Incentives for Innovation in Robotics and Automated Construction: Based on a Tripartite Evolutionary Game Analysis

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  • Leng Yi

    (Faculty of Environmental Engineering, University of Kitakyushu, Kitakyushu 808-0135, Japan)

  • Fukuda Hiroatsu

    (Faculty of Environmental Engineering, University of Kitakyushu, Kitakyushu 808-0135, Japan)

Abstract

The current construction industry faces many challenges, such as low productivity, low material application rates, and poor environmental benefits. Robotic and automated construction (RAAC) technologies represent a breakthrough from traditional construction methods and are considered an effective way to improve productivity, ecological efficiency, and sustainability in the construction industry. However, the high cost of research and development and the lack of investment in the construction field have discouraged Chinese construction companies from innovating. In this paper, an evolutionary game model between the government, construction firms, and public universities is constructed around the choice of RAAC innovation, and the evolutionary stabilization strategy (ESS) of the three parties is discussed. Establishing a compound incentive policy of financial and reputational rewards and increasing the share of RAAC scores in academic evaluations can accelerate the realization of ESS among the government, construction companies, and public universities. This study informs policymakers to develop RAAC innovation strategies, and stakeholders such as the government, construction companies, and public universities should consider and jointly promote the development and application of RAAC technologies to improve productivity and environmental sustainability in the construction industry.

Suggested Citation

  • Leng Yi & Fukuda Hiroatsu, 2022. "Incentives for Innovation in Robotics and Automated Construction: Based on a Tripartite Evolutionary Game Analysis," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2475-:d:754925
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    References listed on IDEAS

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    Cited by:

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    2. Feixiao Wang & Yaoqun Xu, 2022. "Evolutionary Game Analysis of the Quality of Agricultural Products in Supply Chain," Agriculture, MDPI, vol. 12(10), pages 1-16, September.

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