Are Shocks to the Grazing Land Footprint Permanent or Transitory? Evidence from a Machine Learning-Based Unit Root Test

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dc.authorid0009-0009-4549-1679
dc.authorid0000-0001-5738-690X
dc.contributor.authorYilanci, Veli
dc.contributor.authorOzgur, Onder
dc.contributor.authorSaritas, Merve Mert
dc.date.accessioned2026-02-03T11:59:49Z
dc.date.available2026-02-03T11:59:49Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractUnderstanding the dynamic behavior of the grazing land footprint (GLF) is critical for sustainable land management. This study examines the GLF in 92 countries to determine if the series is stationary, a statistical property indicating that shocks have transitory effects, or non-stationary, which implies that shocks have permanent, cumulative impacts (a phenomenon known as persistence). We employ a novel machine learning framework that uses an XGBoost algorithm to synthesize information from multiple conventional tests and time-series characteristics, enhancing analytical robustness. The results reveal significant cross-country heterogeneity. The GLF exhibits stationary behavior in a subset of nations, including China, India, and Norway, suggesting that their ecosystems can absorb shocks. However, for most countries, the GLF is non-stationary, indicating that ecological disruptions have lasting and cumulative impacts. These findings underscore that a one-size-fits-all policy approach is inadequate. Nations with a stationary GLF may find short-term interventions effective, whereas those with non-stationary series require profound structural reforms to mitigate long-term degradation. This highlights the critical role of advanced methodologies in shaping evidence-based environmental policy.
dc.identifier.doi10.3390/su17146312
dc.identifier.issn2071-1050
dc.identifier.issue14
dc.identifier.scopus2-s2.0-105011843052
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su17146312
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34433
dc.identifier.volume17
dc.identifier.wosWOS:001553372200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectgrazing land footprint
dc.subjectunit root tests
dc.subjectmachine learning
dc.subjectshock persistence
dc.titleAre Shocks to the Grazing Land Footprint Permanent or Transitory? Evidence from a Machine Learning-Based Unit Root Test
dc.typeArticle

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