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Abstract Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types’ impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.
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On Farmland and Floodplains—Modeling Urban Growth Impacts Based on Global Population Scenarios in Pune, India
Emerging megacities in the global south face unprecedented transformation dynamics, manifested in rapid demographic, economic, and physical growth. Anticipating the associated sustainability and resilience challenges requires an understanding of future trajectories. Global change models provide consistent high-level urbanization scenarios. City-scale urban growth models accurately simulate complex physical growth. Modeling approaches linking the global and the local scale, however, are underdeveloped. This work introduces a novel approach to inform a local urban growth model by global Shared Socioeconomic Pathways to produce consistent maps of future urban expansion and population density via cellular automaton and dasymetric mapping. We demonstrate the approach for the case of Pune, India. Three scenarios are explored until 2050: business as usual (BAU), high, and low urbanization. After calibration and validation, the BAU scenario yields a 55% growth in Pune’s population and 90% in built-up extent, entailing significant impacts: Pune’s core city densifies further with up to 60,000 persons/km2, adding pressure to its strained infrastructure. In addition, 66–70% more residents are exposed to flood risk. Half of the urban expansion replaces agriculture, converting 167 km2 of land. The high-urbanization scenario intensifies these impacts. These results illustrate how spatially explicit scenario projections help identify impacts of urbanization and inform long-term planning.
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- Award ID(s):
- 1829999
- PAR ID:
- 10427990
- Date Published:
- Journal Name:
- Land
- Volume:
- 12
- Issue:
- 5
- ISSN:
- 2073-445X
- Page Range / eLocation ID:
- 1051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/land12051051
