An application of statistical and data mining methods to study the waste charging scheme in Hong Kong

Waste charging policy is one of the tools used by many countries to solve waste management problems. Before a policy can be fully implemented, it is important to study residents' willingness to pay (WTP) for waste disposal and estimate its effectiveness. This study aims to use data mining models to predict the maximum WTP amount and ratio estimation method to estimate the effectiveness of the proposed waste charging policy in Hong Kong. The results show that the average value of the predicted maximum willingness to pay (WTP) amount varies between HK$36.75 and HK$39.99 based on the data mining models. According to the decision tree models, the predicted maximum WTP amount of the respondents in the training dataset varies between HK$11.3 and HK$94.6. At least 8% of the residents may not afford to pay for waste disposal and need help. At least 5% of the respondents may well afford to pay and may not be motivated by the waste charging policy to reduce waste. It is plausible that over 53% of the respondents may accept the waste charging policy. Assuming the residents will reduce waste to keep the waste disposal expenditure within their maximum WTP amount, the percentage of waste reduction is estimated to be around 12.56-28.12% under the price level of HK$0.11 per liter. The findings may be helpful to the related parties to design and implement the waste charging policy.

Automated summary

The study used data mining models to predict residents' maximum willingness to pay and employed ratio estimation method to estimate the effectiveness of waste charging policy in Hong Kong. The results suggest that majority of respondents may accept the policy, but some residents may struggle to pay or not be influenced by the policy.