Ecological Modelling, Год журнала: 2024, Номер 501, С. 110974 - 110974
Опубликована: Дек. 6, 2024
Язык: Английский
Ecological Modelling, Год журнала: 2024, Номер 501, С. 110974 - 110974
Опубликована: Дек. 6, 2024
Язык: Английский
Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 145661 - 145661
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Sustainability, Год журнала: 2024, Номер 16(16), С. 6889 - 6889
Опубликована: Авг. 11, 2024
Arable land green and low-carbon utilization (ALGLU) is an important pathway to safeguard food safety achieve the transformation progress of agriculture, playing a crucial role in promoting agricultural ecological protection economic sustainability. This study takes Yangtze River Delta region (YRD), where rapid urbanization most typical, as area. On basis fully considering carbon sink function arable land, measures level using Super-slack based measure (Super-SBM) model, analyzes its spatial temporal evolution autocorrelation center gravity, standard ellipsoid then impact with help geographic detector geographically weighted regression model. We analyzed multifactor interaction heterogeneity factors geodetector Results: (1) The ALGLU YRD has shown fluctuating upward tendency, increasing from 0.7307 2012 0.8604 2022, growth rate 17.75%. phased changes correspond national development policies stages socio-economic development. (2) There are significant differences YRD, high levels distributed southwest Jiangsu, northern Zhejiang, northwest Anhui, while low YRD. Positive exists transfer trends gravity deviation ellipses essentially align pattern. (3) affected by many factors, intensity effects far exceeding that individual factors. When single-factor effects, precipitation, topography, farmers’ income influencing ALGLU. In scenarios involving multiple-factor interactions, become primary focus effects. Furthermore, driving exhibit heterogeneity, direction extent each factor different cities. can provide valuable insights for future regional sustainable
Язык: Английский
Процитировано
2Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер unknown, С. 101350 - 101350
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
2Forests, Год журнала: 2024, Номер 16(1), С. 17 - 17
Опубликована: Дек. 25, 2024
Urban forests, as vital components of green infrastructure, provide essential ecosystem services (ESs) that support urban sustainability. However, rapid expansion and increased density threaten these creating significant imbalances between the supply demand for services. Understanding characteristics reasonably dividing ecological management zones are crucial promoting sustainable development. This study introduces an innovative zoning framework based on matching degree synergies relationships ESs. Focusing Fuzhou’s fourth ring road area in China, data from 1038 forest sample plots were collected using mobile LIDAR. By integrating i-Tree Eco model Kriging interpolation, we assessed spatial distribution four key ESs—carbon sequestration, avoided runoff, air purification, heat mitigation—and analyzed their supply–demand synergies. Based characteristics, employed unsupervised machine learning classification to identify eight distinct zones, each accompanied by targeted recommendations. Key findings include following: (1) forests Fuzhou exhibit pronounced heterogeneity, with clearly identifiable high-value low-value areas statistical relevance; (2) mitigation, purification all synergistic effects, while carbon sequestration shows trade-offs other three areas, necessitating optimization; (3) identified, unique characteristics. offers precise insights into providing a foundation strategies.
Язык: Английский
Процитировано
2Ecological Modelling, Год журнала: 2024, Номер 501, С. 110974 - 110974
Опубликована: Дек. 6, 2024
Язык: Английский
Процитировано
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