{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T16:48:20Z","timestamp":1768236500645,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Program of the National Natural Science Foundation of China","award":["51838003"],"award-info":[{"award-number":["51838003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In the context of the \u201cDual Carbon\u201d goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosystem models to project NPP dynamics, they often face challenges in acquiring high-resolution future vegetation parameters and typically overlook the stability of NPP under changing climates. To address these gaps, this study focuses on Nanjing and develops a long-term, multi-scenario analysis framework based on the Dynamic Land Cover\u2013Climate Model (DLCC\u2013NPP). This framework innovatively integrates the PLUS model with a Random Forest (RF) algorithm. By establishing a direct statistical mapping between macro-climate\/micro-land cover and NPP, the RF model functions as a statistical downscaling tool. This approach bypasses the uncertainty accumulation associated with simulating future vegetation indices, enabling precise spatiotemporal NPP prediction at a 30 m resolution. Using this approach, we systematically analyzed the NPP dynamics from 2004 to 2044 under three SSP scenarios. The results revealed that Nanjing\u2019s NPP exhibited a fluctuating upward trend, with urban forests contributing the highest productivity (mean NPP ~266.15 gC\/m2). Crucially, the volatility analysis highlighted divergent response characteristics: forests demonstrated the highest stability and \u201cbuffering effect,\u201d whereas grasslands and croplands showed high volatility and sensitivity to climate fluctuations. Spatially, a distinct \u201cstable high-NPP core, decreasing periphery\u201d pattern was identified, driven by the interaction of urban expansion and ecological conservation policies. In conclusion, the DLCC\u2013NPP framework effectively overcomes the data scarcity bottleneck in future simulations and characterizes the spatiotemporal heterogeneity of vegetation carbon fixation in urban ecosystems, providing scientific support for optimizing green space patterns and enhancing urban ecological resilience in high-density cities.<\/jats:p>","DOI":"10.3390\/ijgi15010038","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T12:44:44Z","timestamp":1768221884000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatiotemporal Heterogeneity Analysis of Net Primary Productivity in Nanjing\u2019s Urban Green Spaces Based on the DLCC\u2013NPP Model: A Long-Term and Multi-Scenario Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8297-3055","authenticated-orcid":false,"given":"Yuhao","family":"Fang","sequence":"first","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]},{"given":"Yuyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]},{"given":"Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0238-3191","authenticated-orcid":false,"given":"Yilun","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]},{"given":"Yuning","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.scitotenv.2016.03.168","article-title":"Carbon sequestration through urban ecosystem services: A case study from Finland","volume":"563","author":"Kuittinen","year":"2016","journal-title":"Sci. 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