Paying for green tide management or participating in cleanup activities? Testing and controlling for payment vehicle bias in the valuation of ecological damage caused by green tides

Abstract

Payment vehicle bias is one of the biases in the contingent valuation method (CVM). This research uses the ecological damage assessment of the Ulva prolifera green tides in Jiaozhou Bay, Qingdao, China, as a case study to examine the factors influencing the payment vehicle bias and control for it. The results indicate that nearly 50% of respondents might refuse to pay because monetary or labor payment vehicles do not meet their payment preferences. This situation leads to nonrandom sample selection and payment vehicle bias. To address these issues, this research proposes a novel valuation framework that combines monetary and labor payment vehicles. And the Heckman two-stage model is used to estimate the economic value of labor time and combines sample data from both monetary and labor payment vehicles to control for bias and obtain a more reliable valuation result. The results show that, compared with sole reliance on monetary payment vehicles, the use of dual payment vehicles increases the observable sample size from 222 to 330. Furthermore, respondents’ average WTP for green tide management increases from 101 CNY/person·year to 116 CNY/person·year. Therefore, employing both payment vehicles simultaneously can reveal more respondents’ true preferences, reduce non-randomness in sample selection and mitigate payment vehicle bias. The findings of this study can demonstrate the value of labor payment vehicles in CVM applications and provide a scientific basis for the Qingdao municipal government to establish more effective strategies for green tide management.