Optimal allocation for land in an arid inland basin in northwest China: framework and application based on CLUE-S and MCR models

Xie, Binbin; Wei, Wei; Liu, Congying; Zhou, Junju; Zhou, Liang

doi:10.1007/s11356-023-29071-x

Cited by 2 publications

(1 citation statement)

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“…to optimize the land structure [29,30]. The ant colony algorithm [24], meta-cellular automata model [31], genetic algorithm [32], CLUE-S [33] (conversion of land use and its effects at small region extent) model, and other methods have been more frequently used in land spatial optimization studies [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial Optimization of Land Use Allocation Based on the Trade-off of Carbon Mitigation and Economic Benefits: A Study in Tianshan North Slope Urban Agglomeration

Lee,

Yin,

Zhu

2024

Land

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The rational allocation of land use space is crucial to carbon emissions reductions and economic development. However, previous studies have either examined inter-objective trade-offs or intra-objective trade-offs within a single objective and lacked multilevel and comprehensive studies. Therefore, this paper integrates inter- and intra-objective carbon mitigation and economic efficiency trade-offs to comprehensively study the interaction between land pattern demand and space due to policies. The research methods were mainly multi-objective planning, a gray model, and patch-generating land use simulation model, and the study area was the less-developed urban agglomeration—the Tianshan north slope urban agglomeration. The results of the study show that the total change area of the study area from 2000 to 2020 was 5767.94 km2, the grassland area was transferred out the most, 3582.59 km2, accounting for 62.11%, and the cultivated land area was transferred in the most, 3741.01 km2. Compared with 2020, the simulated land use pattern obtained for 2030 has significantly changed. In addition, the total economic benefits and total carbon emissions under the economic and low-carbon objectives changed in the opposite direction. The four landscape patterns under the three scenarios of economic and low-carbon objectives changed in the same direction, and the degree of landscape fragmentation, agglomeration, and regularity under the low-carbon objective was better than that under the economic objective. The study results are essential references for future land resource management, carbon mitigation, and sustainable development of urban agglomerations.

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