How to understand our willingness-to-pay to fight climate change? A choice experiment approach
AbstractWe explore the willingness-to-pay (WTP) to fight climate change in a choice experiment. Since tree planting prevents climate change, subjects are offered to choose between receiving a high amount of money or receiving a lower amount of money plus participating to tree planting action. This allows us to get an individual interval of the WTP to prevent climate change. We also set the experiment to control for framing effects: we measure whether subjects WTP is higher not to prevent a tree planting action (negative framing) than to contribute to it (positive framing). Finally, we measure subjects' individual characteristics like altruism and risk aversion with a questionnaire, to understand the determinants of WTP. The results show that the WTP to prevent climate change is high: subjects are ready to give up half their gains to participate to a tree planting action. Women tend to have a higher WTP. We also find that both altruistic and self-interested motives can explain WTP. Surprisingly, their degree of knowledge of climate change related issues do not influence subjects WTP. Finally, when the choice is negatively phrased, WTP increases: subjects are ready to pay more not to make the number of trees planted decrease than to increase it. This suggests that negative eco-labelling might have a greater impact on consumer preferences than positive labels.
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Bibliographic InfoPaper provided by HAL in its series Working Papers with number hal-00370738.
Date of creation: 25 Mar 2009
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willingness-to-pay; preferences elicitation; carbon-offset schemes; framing effect; climate change.;
This paper has been announced in the following NEP Reports:
- NEP-AGR-2009-08-22 (Agricultural Economics)
- NEP-ALL-2009-08-22 (All new papers)
- NEP-DCM-2009-08-22 (Discrete Choice Models)
- NEP-ENE-2009-08-22 (Energy Economics)
- NEP-ENV-2009-08-22 (Environmental Economics)
- NEP-EXP-2009-08-22 (Experimental Economics)
- NEP-UPT-2009-08-22 (Utility Models & Prospect Theory)
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