ArticleScientific reports2026
Policy-driven carbon sink enhancement in dryland cities: a case study of Urumqi, a core city on China's New Silk Road.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigates the spatiotemporal evolution of carbon sources/sinks in arid urban ecosystems through Net Ecosystem Productivity (NEP) analysis, aiming to reconcile urban expansion with ecological conservation in Urumqi, a core city on China’s Silk Road Economic Belt. Combining multi-source remote sensing data (MODIS, Landsat, NPP-VIIRS) with socioeconomic datasets, we developed a hybrid framework integrating CASA model and soil respiration algorithms to quantify NEP dynamics in Urumqi City (2005–2020). Machine learning approaches (Random Forest) and spatial econometric models were applied to identify dominant drivers, with particular focus on policy-induced land use/cover change (LUCC). Three key findings emerge: (1) NEP exhibited 15-year cumulative growth (+ 27.3%), with carbon sink hotspots concentrating in ecologically restored southern suburbs (910.14 g C m⁻² yr⁻¹), contrasting with carbon source clusters in northern industrial zones (-19.68 g C m⁻² yr⁻¹); (2) Following the 2010 ecological redline policies, localized improvements in carbon sequestration capacity were detected (average enhancement approximately 18.9% in high-response zones), although the overall NEP trend remained statistically stable across most of the study area (non-significant in > 95% of pixels). These results indicate that policy-induced LUCC facilitated spatially concentrated carbon sink strengthening rather than a citywide enhancement. (3) Random Forest modeling revealed LUCC as the predominant driver (18.36% importance), outweighing climate factors (precipitation: 12.7%, temperature: 9.4%) and socioeconomic parameters (NSL < 5%). Our findings challenge the urbanization-carbon loss paradigm by demonstrating targeted land use optimization as an effective policy instrument for dryland cities. The machine learning-enhanced framework provides transferable methodology for SDG 11 (Sustainable Cities) monitoring in arid regions.
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