Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

Ghadirian, Omid; Lotfi, Ali; Moradi, Hossein; Shetab Boushehri, Seyed Nader; Yousefpour, Rasoul

doi:10.1016/j.ecoinf.2023.102129

Cited by 9 publications

(2 citation statements)

References 63 publications

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“…Among these, local regional policies play a particularly crucial role in shaping the speed and pattern of urban spatial expansion. Although the CLUE-S model considers key influencing factors such as elevation, slope, aspect, GDP, population, and transportation, it has limitations in the selection of influencing factors due to the lack of consideration for natural conditions like climate, precipitation, and soil [67,68].…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Zhang,

Liu,

Sheng

et al. 2024

Sustainability

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Over recent decades, the hilly and gully regions of the northern Loess Plateau in Shaanxi province have grappled with severe soil erosion and a precarious ecological milieu. Shaped by urbanization policies, this locale has encountered a gamut of issues, including an imbalance in human–environment dynamics and the degradation of ecological integrity. Consequently, the comprehension of how urban expansion impacts the optimization of regional landscape configurations, the alignment of human–environment interactions in the Loess Plateau’s hilly and gully domains, and the mitigation of urban ecological challenges assumes paramount importance. Leveraging data from land use remote sensing monitoring, alongside inputs from natural geography and socio-economic spheres, and employing methodologies such as landscape pattern indices, we conduct an exhaustive analysis of Zichang City’s urban fabric from 1980 to 2020. Furthermore, employing the CLUE-S model, we undertake multifaceted scenario simulations to forecast urban expansion in Zichang City through to 2035. Our findings delineate two distinct phases in Zichang City’s urban expansion trajectory over the past four decades. From 1980 to 2000, urban construction land in Zichang City experienced a phase of methodical and steady growth, augmenting by 64.98 hectares, alongside a marginal decrease in the landscape shape index (LSI) by 0.02 and a commensurate increase in the aggregation index (AI) by 1.17. Conversely, from 2000 to 2020, urban construction land in Zichang City witnessed an epoch of rapid and haphazard expansion, doubling in expanse, marked by a notable escalation in LSI (2.45) and a corresponding descent in the AI (2.85). The precision of CLUE-S model simulations for Zichang City’s land use alterations registers at 0.88, fulfilling the exigent demand for further urban expansion and land use change prognostication. Under the aegis of the natural development scenario, the augmentation of urban construction land in Zichang City primarily encroaches upon grassland, farmland, and woodland, effectuating an increase of 159.81 hectares. Conversely, under the ambit of urbanization development, urban construction land contends predominantly with farmland, grassland, and woodland, heralding an augmentation of 520.42 hectares. Lastly, under the mantle of ecological protection, urban construction land expansion predominantly encroaches upon grassland, farmland, and woodland, resulting in an augmentation of 4.27 hectares. Through a nuanced analysis of the spatiotemporal evolution of urban expansion and scenario-based simulations, this study endeavors to furnish multi-faceted, scenario-driven, and policy-centric insights for regional planning, urban spatial delineation, and regional ecological safeguarding.

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Section: Comparison With Other Studiesmentioning

confidence: 99%

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Zhang,

Liu,

Sheng

et al. 2024

Sustainability

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“…The model utilized a gray backpropagation neural network and related carbon emission accounting models, along with four different 2030 scenario simulations [13]. When simulating construction land expansion, the commonly used methods include the Markov chain [14], system dynamics (SD) [15], and elemental dynamics cellular automata (CA) [16] (including CLUE-s (conversion of land use and its effects at small regional extent), SLUETH (slope, land use, excluded, urban, transportation, hillshade), and FLUS (future land use simulation model)), among others. The PLUS (patch generating land use simulation) model integrates the land expansion analysis strategy (LEAS) and cellular automata model (CA-based multiple random seeds, CARS), which can effectively simulate the process of the complex evolution of multiple land types and solve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Li,

Liu,

et al. 2023

Land

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With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

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Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin

Loukika

Reddy

Buri

et al. 2023

Ecological Informatics

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Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

Cited by 9 publications

References 63 publications

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin

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