Land finance and urban Sprawl: Evidence from prefecture-level cities in China

Yu, Binbin; Zhou, Xinru

doi:10.1016/j.habitatint.2024.103074

Habitat International

2024

DOI: 10.1016/j.habitatint.2024.103074

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Land finance and urban Sprawl: Evidence from prefecture-level cities in China

Binbin Yu,

Xinru Zhou

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Cited by 25 publications

References 67 publications

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Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

Naqvi,

Tariq,

Shahzad

et al. 2024

Hydrological Processes

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Anthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Potohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 . The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners.

show abstract

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

Naqvi,

Tariq,

Shahzad

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

show abstract

The impact of China’s dual land supply strategy on urban economic growth: a sectoral analysis

Pu,

Li,

Lian

et al. 2024

Environ Dev Sustain

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Low-carbon innovation effect of heterogeneous environmental regulation under the spatial spillover perspective

Liu,

Fan

2024

Environ Earth Sci

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Land finance and urban Sprawl: Evidence from prefecture-level cities in China

Cited by 25 publications

References 67 publications

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

The impact of China’s dual land supply strategy on urban economic growth: a sectoral analysis

Low-carbon innovation effect of heterogeneous environmental regulation under the spatial spillover perspective

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