Multi-Scenario Land Use/Cover Change and Its Impact on Carbon Storage Based on the Coupled GMOP-PLUS-InVEST Model in the Hexi Corridor, China

Sustainability, Journal Year: 2024, Volume and Issue: 16(4), P. 1402 - 1402

Published: Feb. 7, 2024

Understanding the relationship between land use and carbon storage is vital for achieving sustainable development goals. However, our understanding of how develops under policy planning still incomplete. In this study, a comprehensive framework that integrates Gray Multi-objective Optimization Programming (GMOP), Patch-generating Land Use Simulation (PLUS) model, Integrated Valuation Ecosystem Services Trade-offs (InVEST) models introduced to evaluate dynamics ecosystem services. Two scenarios have been established estimate Cover Change (LUCC) patterns in Hexi Corridor by 2035: business-as-usual (BAU) scenario, developed based on historical trends, ecological conservation scenario (ECS), optimized with multiple objectives. The results show following: (1) From 2000 2020, predominant type was unutilized land, LUCC mainly involving transformation grass land. (2) Carbon increased approximately 9.05 × 106 t from 2020 due LUCC, characterized higher levels south lower north. (3) areas arable are expected continue increasing until 2035, while extent projected decrease. ECS poised create balance protection economic development. (4) By both BAU an increase Corridor, result most significant gains. These research findings provide valuable insights administrators researchers, guiding more rational restoration policies achieve peaking neutrality.

Language: Английский

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Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 533 - 533

Published: Feb. 22, 2025

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions soil. It also an important attribute reflecting quality black In this study, machine learning algorithms support vector (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting (GBM), generalized linear model (GLM) were used to study accurate prediction SOM in Tieling County, City, Liaoning Province, China. models trained by using 1554 surface samples 19 auxiliary variables. Recursive feature elimination was as a selection method identify effective results showed that Normalized Difference Vegetation Index (NDVI) elevation key Based on 10-fold cross-validation, RF had highest accuracy. terms accuracy, coefficient determination 0.77, root mean square error 2.85. average 20.15 g/kg. spatial distribution shows higher concentrated east west, while lower found middle. cultivated land than land.

Language: Английский

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Do Land Resources and Green Energy Induce Agriculture and Water Production in China: Sustainability‐Based Evidence From Historical Data of China

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

ABSTRACT Rapid industrialization and globalization have led to significant concerns regarding the improper utilization of land resources. This is particularly troubling for agriculture water resources from a sustainability perspective. The growing population boosts food, agriculture, demand. Therefore, scholars policymakers are consistently investigating factors affecting production attain sustainable development. present research aims investigate nexus between green energy in China. In doing so, we employ extended data China 1973 2022. uses several diagnostic measures concludes mixed integration order variables cointegration's validity. autoregressive distributed lag (ARDL) model found that total resources, per capita income, drivers short‐ long run. However, foreign direct investment sole barrier country's during both periods. long‐run results further checked via robustness tests, including quantile ARDL, cointegration regressions (fully modified ordinary least square, dynamic canonical regression), structural break square. Besides, causal inferences also variables. study recommends development projects targeting efficient use might ensure availability.

Language: Английский

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Genotypic richness affects inorganic N uptake and N form preference of a clonal plant via altering soil N pools

Biology and Fertility of Soils, Journal Year: 2024, Volume and Issue: 60(6), P. 863 - 873

Published: June 24, 2024

Language: Английский

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