Lunar New Year (LNY) gifting of new notes in Singapore involves issuing approximately 90 million new notes annually. Most are used once before being returned to the Monetary Authority of Singapore (MAS) where notes exceeding demand are destroyed through incineration, generating substantial carbon emissions.


Behavioural research suggests effectiveness of loss-framed and social influence messages in promoting environmentally sustainable behaviours, though none have focused on LNY gifting. This study examines attitudes towards sustainable LNY gifting and whether loss-framed or social influence messages affect intentions and values driving such behaviours.


A randomised controlled trial (n = 1,853) assigned participants to three conditions: (i) control group (no message), (ii) loss-framed message highlighting the destruction of excess notes by incineration (iii) social influence message emphasising that new notes are not important to most receivers of hongbaos.


Across groups most receivers had no strong preference for new notes. Older individuals preferred new notes while the digitally savvy were more likely to prefer Fit notes (used currency notes that are generally clean and of suitable quality for recirculation) and digital hongbaos (digitally transferred money gifts). PayNow users were less likely to prefer new notes and more inclined towards digital gifting.


Both intervention messages significantly reduced intent to gift new notes, with social influence messaging proving more effective. It reduced preference for new notes among youth receivers and achieved higher knowledge comprehension. However, messages alone may not overcome fundamental demand as around 2 in 5 still intend to use new notes.


Personal values analyses revealed that givers' preference for Fit notes was linked to "respecting the earth" while those preferring new notes valued "influence". Youth receivers who preferred new and Fit notes prioritised different values like "social power", suggesting targeted messages appealing to givers and receivers based on their values may be more effective than a one-size-fits-all approach.