Encouraging Crowd Avoidance Behavior using Dynamic Pricing Framework Towards Preventing the Spread of COVID-19

In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.

Recession and fossil fuel dependence undermine climate policy commitments

Knowledge on the determinants of more or less ambitious climate policies on the country level is still limited, especially with regards to the 2015 Paris Agreement to mitigate global climate change. This is a significant knowledge gap, especially given the review of many contributions to the Paris Agreement due in 2021. I analyse why some countries make insufficient pledges to reduce their greenhouse gas emissions under the Paris Agreement, while other countries pursue more ambitious climate change mitigation goals. Using qualitative comparative analysis (QCA), the study finds that economic recession, dependence on fossil fuels for energy generation, and levels of development are strong predictors of insufficient climate policies. These results are worrisome in the context of the economic recession triggered by the COVID-19 pandemic as well as the continued predominance of fossil fuels in the world’s energy mix. © 2020 The Author(s). Published by IOP Publishing Ltd.