TY - JOUR
T1 - Direct network effects in electric vehicle adoption
AU - Zhang, Xiang
AU - Hu, Xiaoming
AU - Qi, Liang
AU - Jin, Taosheng
N1 - Publisher Copyright:
© 2024
PY - 2024/12
Y1 - 2024/12
N2 - Direct network effects (DNEs) are important market rules that influence the diffusion of electric vehicles (EVs). However, there is little empirical evidence on the role of DNEs in promoting EVs. We aim to reveal how the DNEs of EVs change with external shocks (e.g. the termination of subsidies and the outbreak of COVID-19) and further affect EV adoption. Using monthly panel data on EV adoption from 132 Chinese cities, we find that DNEs are larger during the subsidy period, decrease after the subsidy ends, and are not significantly affected by the pandemic. Cities that previously had local subsidies have larger DNEs, which have a multiplier effect that further reinforces the positive role of subsidies. Furthermore, we find that the removal of subsidies has a larger negative impact on EV adoption than the negative impact of the pandemic. Implications for policy makers and managers are proposed based on our findings.
AB - Direct network effects (DNEs) are important market rules that influence the diffusion of electric vehicles (EVs). However, there is little empirical evidence on the role of DNEs in promoting EVs. We aim to reveal how the DNEs of EVs change with external shocks (e.g. the termination of subsidies and the outbreak of COVID-19) and further affect EV adoption. Using monthly panel data on EV adoption from 132 Chinese cities, we find that DNEs are larger during the subsidy period, decrease after the subsidy ends, and are not significantly affected by the pandemic. Cities that previously had local subsidies have larger DNEs, which have a multiplier effect that further reinforces the positive role of subsidies. Furthermore, we find that the removal of subsidies has a larger negative impact on EV adoption than the negative impact of the pandemic. Implications for policy makers and managers are proposed based on our findings.
KW - Direct network effects
KW - Electric vehicle adoption
KW - Subsidy cessation
KW - The COVID-19 pandemic
UR - http://www.scopus.com/inward/record.url?scp=85204475228&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2024.123770
DO - 10.1016/j.techfore.2024.123770
M3 - Article
AN - SCOPUS:85204475228
SN - 0040-1625
VL - 209
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 123770
ER -