This study examines farmers’ intentions towards pro-environmental behavior in a famous tourist village in China called Guanshan, whose ecological environment is polluted. By adopting the empirically validated norm activation model (NAM) of Schwartz and merging it with Vroom’s expectancy theory, the current research aims to develop a refined framework for understanding the formation of and predicting changes in pro-environmental intention. Field surveys were conducted in Guanshan, which resulted in sample data consisting of 275 valid responses collected by the research team. We develop a refined model, including six latent variables and 24 observational items. The structural equation modeling results show that the final model enjoys a better predictive accuracy of pro-environmental intention than does the original NAM. The study also discovers that the motivational force of expectancy theory significantly influences pro-environmental intention, whose motivational force comes from the impact of valence and expectancy. The pro-environmental intentions of farmers are mainly affected by the environmental personal norm and by a certain degree of personal expectancy. The improvement of farmers’ pro-environmental intention needs be promoted in two approaches: the cultivation of personal environmental protection norms and the guidance of producing a desired expectation for pro-environmental intention.

Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

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Since the COVID-19 pandemic, governments have implemented lockdowns and movement restrictions to contain the disease outbreak. Previous studies have reported a significant positive correlation between NO2 and mobility level during the lockdowns in early 2020. Though NO2 level and mobility exhibited similar spatial distribution, our initial exploration indicated that the decreased mobility level did not always result in concurrent decreasing NO2 level during a two-year time period in Southeast Asia with human movement data at a very high spatial resolution (i.e., Facebook origin-destination data). It indicated that factors other than mobility level contributed to NO2 level decline. Our subsequent analysis used a trained Multi-Layer Perceptron model to assess mobility and other contributing factors (e.g., travel modes, temperature, wind speed) and predicted future NO2 levels in Southeast Asia. The model results suggest that, while as expected mobility has a strong impact on NO2 level, a more accurate prediction requires considering different travel modes (i.e., driving and walking). Mobility shows two-sided impacts on NO2 level: mobility above the average level has a high impact on NO2, whereas mobility at a relatively low level shows negligible impact. The results also suggest that spatio-temporal heterogeneity and temperature also have impacts on NO2 and they should be incorporated to facilitate a more comprehensive understanding of the association between NO2 and mobility in the future study.

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