A Stackelberg game framework for promoting battery-powered construction machinery through government subsidies

Abstract

Diesel-powered construction machinery is a major source of harmful pollutants and carbon emissions. Technological advancements have made battery-powered construction machines (BPCMs) increasingly viable in operations. However, the high purchase prices of BPCMs represent a huge obstacle for contractors to embrace these low-emission alternatives. To facilitate the sustainable development of the construction industry, it is imperative for the government to implement effective subsidy allocation policies to promote the electrification of construction machinery. This paper proposes a Stackelberg game framework for optimal subsidy allocation. In the proposed framework, the government decides the subsidy amount offered to each type of BPCMs to minimize both pollutant emissions and carbon emissions. The contractors can then observe the government’s decision and make their optimal decisions regarding the purchase, operation, and replacement of construction machines accordingly to minimize their total costs. Contractors’ decisions in turn influence the government’s decision. Such an intricate framework has many appealing properties, which are analyzed in depth to provide useful managerial insights. Additionally, the design of effective subsidy policies by the government depends on a precise prediction of the contractors’ demand for construction machinery. To this end, a random forest machine learning model is developed. Real data were collected for model construction and testing. Statistical results and industrial comments show the high quality of our predictions. Overall, this paper is expected to reduce both pollutant and carbon emissions from construction machinery, thereby facilitating the development of green construction.