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Research on manufacturer's investment strategy and green credit policy for new energy vehicles based on consumers' preferences and technology adoption.

Authors :
Feng, Xiao
Li, Yuyu
Huang, Bo
Source :
Technological Forecasting & Social Change; Jun2023, Vol. 191, pN.PAG-N.PAG, 1p
Publication Year :
2023

Abstract

New energy vehicles (NEVs) are in the "early adopters" stage of their technology adoption life cycle with insufficient R&D funds as the main factor hindering NEV manufacturers from increasing R&D investment. This study examines how the government provides NEV manufacturers with green credit to increase their R&D investment, and consequently, to a large extent, increase the technical level and sales volume of NEVs. This study established a tri-level game model and its numerical analysis among the government, NEV manufacturers, and consumers based on early adopters' preferences and selection behaviors to study the manufacturer's R&D investment strategy and the government's green credit policies. Through theoretical and numerical analyses, it was found that green credit plays two key roles in providing financial support and incentive effects on manufacturers' R&D investment. When the manufacturer's R&D funds are insufficient and the gap is too large, financial support is needed, so the government should provide interest subsidies. When both the government and the manufacturer aim to achieve a certain technical level of NEVs and the government is more ambitious namely, the manufacturer lacks motivation, the government should provide free green credit. • The new energy vehicles (NEVs) industry is now in the "early adopter stage". • Green credit for NEVs is an ideal policy for financial support and R&D incentives. • Green credit can motivate NEVs manufacturers to invest in R&D for "early adopters". • Higher technical level or lower consumer price sensitivity raises the sales of NEVs. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00401625
Volume :
191
Database :
Supplemental Index
Journal :
Technological Forecasting & Social Change
Publication Type :
Academic Journal
Accession number :
163518806
Full Text :
https://doi.org/10.1016/j.techfore.2023.122476