The rise of urban populations has rendered cities in both developed and developing countries vulnerable to poor health and diseases that are associated with urban living conditions and environments.

In an era of global warming, long-standing challenges for rural populations, including land inequality, poverty and food insecurity, risk being exacerbated by the effects of climate change.

Existing studies on blockchain within land administration have focused mainly on replacing or complementing the technology for land registration and titling.

To investigate the rural labor transfer effects of China’s Collective Forestland Tenure Reform (CFTR), we employ binary probit models by using survey data of 694 households from China’s northern collective forest areas.

A discussion of the assumptions that underlie efforts to register land enables us to not only evaluate their validity across different contexts, but most importantly, to further understand how the low incidences of land registration might derive from very fundamental sources outside of difference