A spatial machine learning approach to valuing development and greenness in well-being", a paper on research conducted by aiESG President Managi and Chief Data Scientist Li Chao, has been published. A spatial machine learning approach to valuing development and greenness in well-being" has been published.

This study analyzed the complex, nonlinear relationship between the subjective well-being (SWB) of Japanese people and their level of development as indicated by green space (NDVI) and nighttime light (NTL). On average, the results showed that an increase in NDVI was positively associated with SWB, while an increase in NTL, indicative of excessive development, was negatively associated, especially in urban areas.
In addition, this study converts the contribution to well-being of a 1 percentage point increase in NDVI and the negative impact of an increase in NTL intensity into monetary values.
In conclusion, it is advocated that a balanced strategy, based on the local environment and level of development, rather than a one-size-fits-all policy, is needed to achieve a sustainable society.

The paper is available through Science Direct, provided by Elsevier.
https://www.sciencedirect.com/science/article/abs/pii/S2210670725008777

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