Green premium modeling based on total cost ownership analysis: From the Chinese Electric Vehicle sales forecasting perspective

Abstract

In the field of energy economics, the concept of “green premium” refers to the cost difference between zero-emission or emission-reducing technologies and emission-emitting technologies. This cost difference plays a crucial role in the adoption and commercialization of renewable energy technologies, which is essential for addressing climate change. However, the green premium, as an abstract concept, is often used qualitatively, and research on quantifying the green premium is very limited. This has significant adverse effects on both the formulation of business strategies for companies and the planning of government policies. The Total Cost of Ownership (TCO) method is a widely used cost analysis approach in businesses and organizations. It can quantitatively assess and understand the overall costs associated with specific products, assets, or services, thereby assisting decision-makers in making informed choices and managing resources. Therefore, this study introduces, for the first time, a TCO-based green premium modeling model to address the challenge of quantifying the green premium. Considering that China’s Electric Vehicles (EVs) industry has successfully overcome the green premium barrier and entered the commercialization phase, it is an appropriate domain for studying the green premium. To further demonstrate the effectiveness and scalability of the modeling, this paper enhances the classic Bass model for sales forecasting of electric vehicles. It innovatively incorporates the influence of the green premium in the generalized Bass model. Additionally, the study uses primary data from industry research to construct a multi-factor green premium model for EVs based on TCO analysis. It also develops a generalized Bass diffusion model that considers the green premium and selects time-series data on EV diffusion in the Chinese market from 2010 to 2021. A genetic algorithm is employed to fit the model parameters, and different scenarios are considered to forecast EV market penetration over the next decade. Experimental results validate that incorporating the green premium in the generalized Bass model leads to better forecasting performance compared to methods that do not consider the premium which proved these related green premium modeling approaches exhibit good accuracy and scalability.