Probability discounting of environmental gains: do we multiply or add up?

Abstract

Individuals often discount and transform risky environmental outcomes into psychological certainty equivalences (CEs) during environmental decision-making. Their preference for different alternatives is influenced by the discounting degree. Our first experiment undertook a preliminary inspection of the probability discounting of environmental gains through a matching method; whereas in Experiment 2, the discounting rule of environmental gain was compared with monetary gain by a new line-projective method, which asked participants to project their subjective value evaluation explicitly on a line of specific length. The environmental gain and its mental value-equivalent monetary gain were analogical with respect to discounting degree and process model fitting. The probability discounting of both gains was better described by an additive-utility model than the normative exponential and hyperbolic models, which means that individuals were likely to discount the risky outcome by simply adding the disutility of uncertainty to the gain’s nominal utility, rather than by multiplying the initial value by a discounting factor. It may be of great value in shedding light on the lay public evaluation of risky environmental interventions and boosting pro-environmental policy support.

Keywords:

• Probability discounting
• environmental decision-making
• hyperbolic model
• additive-utility model

Disclosure statement

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Notes

1 We based our sample size on the power calculation (through GPower3.1, Faul et al. 2007) which assumed that we wanted a statistical power of 0.95 and an effect size of 0.8. The analysis suggested a required sample size of 33 participants for the goodness-of-fit test, 24 participants for the one-sample Wilcoxon signed-rank test and we recruited a slightly larger sample based on it.

2 The power calculation with a statistical power of 0.95 and an effect size of 0.8 suggested a required sample size of 37 participants for each group in the within-between design and we recruited a slightly larger sample based on it.

1 HYPERLINK "file:///\\\\chenas03.cadmus.com\\SmartEdit\\XML_Signal_to_CCE_High_Speed_WF\\ESA\\IN\\INPROCESS\\22" \o "6 = Ref Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. [CrossRef][10.3758/BF03193146]"

Additional information

Funding

This research was funded by National Natural Science Foundation of China (71271189), which is acquired by Guibing He.