The forest policy research with quantitative approaches is limited especially in the research fields of policy transfer and science-policy interface at the local government level. As a context of the citizen science and the science-policy interface, the attitudes of the Japanese local governments vary from municipality to municipality. For example, certain local governments proactively introduce participation of citizen in forest policy making and its implementation, while such attitudes or policies are absent for others. Where comes such differences amongst municipalities? This study conducts empirical analysis at local policy level, which has been largely overseen. In concrete terms, trends of adoption of local ordinances of forest planning in Japanese prefectures and participatory monitoring activities in forest lands are reviewed and analyzed. This study examines the relationships between political factors, social economic factors, and policy diffusion. We have looked at the various factors including local demographics, size of administrative areas, government structure, percentage of forest lands and net forestry production in local government to examine the differences. The results reveal the significant impacts of behavior of neighboring local governments. In other words, if a neighboring body acts, others will follow. The perceptions of the governors who are not from conservative political party seems to be a factor to motivate local governments to introduce the local ordinances of forest planning which encourage citizen to participate forest managements and forest policy making processes. Furthermore, participatory monitoring is a useful tool in citizen science and the number of biodiversity monitoring activities is increasing in Japan as well as other Asian countries such as Korea. The local ordinances of forest planning can influence the monitoring activities. The focus of recent monitoring activities based on citizen science in Japan is changing from “scientists use citizens as data collectors” to “citizens as scientists” under the recent development of data science. Alternatively, sharing, collecting and analyzing data in effective manner with participations remains as future challenges for data science.