Temporal and Spatial Effect Distribution on Soil Erosion from Nationwide Forest Restoration Policies in China Revealed by Causal Machine Learning

The Bonn Challenge and the UN Decade on Ecosystem Restoration
promote
global forest restoration, while the implementation mechanisms and
their ecological effects remain insufficiently understood. This study
focuses on China’s Natural Forest Protection Program (NFPP)
as a case study to address this gap. The study uses a causal machine
learning approach, i.e., the forest doubly robust learner, to investigate
the individual treatment effect by dividing the NFPP implementation
into three phases (1–5, 6–14, and 15–20 years)
to capture short-term and long-term policy impacts. Provincial-level
panel data (1998–2020), incorporating indicators of the natural
environment, socioeconomic factors, and ecological governance are
used. The results show that the NFPP significantly reduced soil erosion
after 15 years of implementation. The policy’s effectiveness
differed regionally, contingent on nonlinear thresholds that delineate
specific ″efficiency traps″ and ″safe operating
spaces″. Crucially, driving mechanisms underwent a structural
transition, shifting from early anthropogenic disturbance dominance
to mature natural background regulation. Mitigation outcomes were
constrained by stressors such as extreme rainfall and population density.
Notably, excessive afforestation in specific regions failed to yield
benefits, exemplifying the adverse trade-offs of violating ecological
thresholds. These findings underscore the critical need for long-term
commitment and precision governance to ensure sustainable ecological
resilience.