Study on carbon sink of cropland and influencing factors: A multiscale analysis based on geographical weighted regression model

Song, Shixiong; Kong, Mingli; Su, Mingjian; Ma, Yongxi

doi:10.1016/j.jclepro.2024.141455

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.141455

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Study on carbon sink of cropland and influencing factors: A multiscale analysis based on geographical weighted regression model

Shixiong Song,

Mingli Kong,

Mingjian Su

et al.

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2024

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Cited by 7 publications

References 64 publications

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A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model

Song,

Tan,

Zhang

et al. 2024

Discov Sustain

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It is of great practical significance to explore the relationship between urbanization and CO2 emissions for the low-carbon development of cities. However, the multiscale assessment of spatial relationship between population, land and economic urbanization and CO2 emissions is lacked. In this study, we first adopted the spatial statistical methods to evaluate the spatial pattern of China’s CO2 emissions in 2019. Then, we spatially quantified China’s urbanization of land, population and economy based on statistical data. Finally, we used the geo-weighted regression model to explore the spatial relationship between urbanization and CO2 emissions at the national-economic zone-province scale. The results displayed that there is obvious spatial heterogeneity in the relationship between China’s urbanization and CO2 emissions. The significant positive correlation between urbanization and CO2 emissions were mainly located in the northeastern, eastern and southwestern regions, consistent with the characteristics of the Heihe–Tengchong Line. The uneven development of land, population and economic urbanization would lead to more CO2 emissions. We suggest that China should attend the balanced development of urban land, population and economy, and avoid the additional carbon emissions caused by incongruence, to further the development of low-carbon cities.

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A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model

Song,

Tan,

Zhang

et al. 2024

Discov Sustain

View full text Add to dashboard Cite

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Exploring the Construction and Practice of Intelligent Teaching Environment for Civic and Political Education in Colleges and Universities

Chen,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, the basic framework of AI education is firstly constructed by combining educational AI technology, and then the technical architecture, including intelligent classroom, online learning platform, and interactive learning tools, is built based on the content of intelligent teaching environment of Civics education in colleges and universities. Then, we explored the students’ Civics learning under the intelligent teaching environment, analyzed the correlation between the Civics teaching effect and intelligent teaching, and investigated the influence of the intelligent teaching environment on the Civics teaching effect through a stepwise regression model. Intelligent teaching correlates with students’ learning interest, theoretical knowledge, professional ability, and learning attitude at the 5% level, with correlation coefficients of 0.798, 0.656, 0.712, and 0.553. In this study, theoretical references are provided for the integration of intelligent technology into Civics teaching.

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Carbon Balance Zoning and Spatially Synergistic Carbon Reduction Pathways—A Case Study in the Yangtze River Delta in China

Guo,

Sun

2024

Land

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The concept of major function-oriented zones is highly compatible with the idea of spatially synergistic carbon reduction. In this study, 2005–2020 is taken as the research period, and 305 counties in the Yangtze River Delta (YRD) region are taken as the research unit. The S0M-K-means clustering model and GeoDetector are adopted on the basis of carbon emission/absorption accounting to analyse the spatial and temporal variations in the carbon balance in the YRD region. Furthermore, carbon balance zoning and influencing factors are analysed. Then, a regional spatially synergistic carbon reduction pathway is proposed. The results show that carbon absorption in the YRD region struggles to offset carbon emissions; the regional carbon imbalance is gradually becoming worse; and each county’s carbon emission/absorption shows a significant spatial imbalance. Optimised development zones and key development zones are high-value agglomerations of carbon emissions, while the main sources of carbon sinks in the YRD region are the key ecological functional zones. The YRD region has 87 high carbon control zones, 167 carbon emission optimisation zones, and 51 carbon sink functional zones, which are further subdivided into 9 types of carbon balance zones in accordance with the major function-oriented zones (MFOZs). Based on the driving factors of carbon balance changes in the YRD region, this study proposes differentiated spatially synergistic carbon reduction paths for each zone in accordance with the carbon balance zones. As the Yangtze River Delta is an essential engine for China’s economic development, the study of its carbon balance is highly relevant in formulating differentiated low-carbon development pathways for each functional zone and promoting regional spatially synergistic carbon reduction to realise the target of “dual-carbon” development.

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Study on carbon sink of cropland and influencing factors: A multiscale analysis based on geographical weighted regression model

Cited by 7 publications

References 64 publications

A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model

A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model

Exploring the Construction and Practice of Intelligent Teaching Environment for Civic and Political Education in Colleges and Universities

Carbon Balance Zoning and Spatially Synergistic Carbon Reduction Pathways—A Case Study in the Yangtze River Delta in China

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