Spatial-temporal variation and nonlinear prediction of environmental footprints and comprehensive environmental pressure in urban agglomerations

Chen, Yizhong; Qiao, Youfeng; Yan, Pengdong; Lu, Hongxi; Yang, Lingzhi; Xia, Jun

doi:10.1016/j.jclepro.2022.131556

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…The trade-offs between ecological resource utilization and ecological compensation from ecological footprint measurements must be assessed to modify the relationship between anthropogenic disturbance and ES protection. The environmental footprint can be used to analyze the spatiotemporal distribution characteristics and spatiotemporal heterogeneity of human disturbances [103] and to monitor, evaluate, and predict the impact of disturbances on ESs from multiple dimensions. The application of the multidimensional ecological footprint model and human footprint index at spatiotemporal scales provides a theoretical basis for effective disturbance policies [104], which is an implementation path for sustainable development in mining areas.…”

Section: Recommendations For How To Cope With the Twofold Pressure Of...mentioning

confidence: 99%

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

Liu

et al. 2022

Sustainability

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Human activities pose significant impacts on ecosystem services (ESs) in mining areas, which will continually increase over time and space. However, the mechanism of ES change on spatiotemporal scales post-disturbance remains unclear, especially in the context of global climate change. Here, we conducted a global literature review on the impact of two of the most frequent disturbance factors (mining and restoration) on 27 different ESs, intending to synthesize the impacts of human disturbance on ESs in mining areas via a meta-analysis, and analyze the spatiotemporal variability of ESs after disturbance. We screened 3204 disturbance studies published on the Web of Science between 1950 and 2020 and reviewed 340 in detail. The results of independence test showed that human disturbance had a significant impact on ESs in the mining areas (p < 0.001). The impacts (positive and/or negative) caused by mining and restoration differed considerably among ESs (even on the same ESs). Additionally, spatiotemporal scales of human disturbance were significantly related to spatiotemporal scales of ES change (p < 0.001). We found that the positive and negative impacts of disturbances on ESs may be interconversion under specific spatiotemporal conditions. This seems to be associated with spatiotemporal variability, such as the temporal lag, spatial spillover, and cumulative spatiotemporal effects. Climate changes can lead to further spatiotemporal variability, which highlights the importance of understanding the changes in ESs post-disturbance on spatiotemporal scales. Our research presents recommendations for coping with the twofold pressure of climate change and spatiotemporal variability, to understand how ESs respond to human disturbance at spatiotemporal scales in the future, and manage disturbances to promote sustainable development in mining areas.

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Section: Recommendations For How To Cope With the Twofold Pressure Of...mentioning

confidence: 99%

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

Liu

et al. 2022

Sustainability

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“…本文数据主要源于 2001-2016 年《中国能源统计年鉴》《湖南省经济年鉴》《江西省经济年鉴》《湖北省经济年鉴》《江西省统计年鉴》和《湖南省统计年鉴》等资料。参考相关文献 [27] [29,30] 对于中国各省市的计算结果。此外，成渝城市群和长三角城市群足迹广度与足迹深度数据源自前期研究成果 [31] ，国外发达地区的人均生态足迹数据源于 Global Footprint Network [32] 。 2022 年 10 月陈义忠等：长江中游城市群生态足迹指标与社会经济发展的适配性 http://www.resci.cn…”

Section: 数据来源unclassified

Coordination between ecological footprint index and socioeconomic development of the urban agglomeration in the middle reaches of the Yangtze River Basin

CHEN¹,

QIAO²,

HAO³

et al. 2022

资源科学

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Ecological footprint is a key index to quantify the sustainability of regional natural capital utilization. Based on a three-dimensional ecological footprint (EF3D) model, this study evaluated the sustainable development of the urban agglomeration in the middle reaches of the Yangtze River Basin (UAMRYR), and introduced the disequilibrium index and environmental Kuznets curve to analyze the coordination between the ecological footprint index and socioeconomic development. The results show that: (1) Per capita biological capacity of the UAMRYR was decreasing while its per capita EF3D presented an increasing trend with an average annual growth rate of 6.81% , which led to serious ecological deficit. The inflection points of per capita EF3D of the Changsha-Zhuzhou-Xiangtan City Group (CZTCG) and Poyang Lake City Group (PYLCG) appeared in 2007 and 2009, respectively. The capital stock of regional grassland and cultivated land showed an increasing trend, while the capital flow of forestland and water areas could meet the demand of resource consumption. A serious loss of capital stock can be found in the UAMRYR, but the situation was better than that of the Chengdu-Chongqing and Yangtze River Delta City Groups. The sustainable utilization space of natural capitals in the PYLCG was better than that in the Wuhan urban agglomeration (WUG) and CZTCG. (2) There was a regional complementarity between footprint size (EFsize) and footprint depth (EFdepth), and areas with rich resources generally had lower EFsize and larger EFdepth. Cluster analysis showed that Jingmen, Xianning, and Xiangyang had a better pattern of natural capital utilization owing to their large EFsize and small EFdepth, while Wuhan, Ezhou, and Huanggang had a poor ecological sustainability owing to their small EFsize and large EFdepth. (3) The matching degrees of EF3D with water resources and population were poor. In comparison, a high matching degree was observed between EF3D and GDP. When the urbanization rate of the urban agglomeration reached a certain threshold, its relationship with per capita EF3D and EFdepth may change. The correlation between per capita EF3D and urbanization rate of the WUG, CZTCG, and PYLCG showed an inverted U shape. In addition, EFdepth of the PYLCG increased with the increase of urbanization rate, and the correlation between EFdepth and urbanization rate of the WUG and CZTCG accorded with the Kuznets curve. Findings can provide theoretical support for the sustainable use of natural capital and green development in the UAMRYR.

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“…Against the backdrop of high-quality development of urban and rural living environments today, China's urbanization rate has increased from 17.92% to 66.16% [1,2] over the 55 years from 1978 to 2023, with an average annual increase of 0.88%. The urban population has rapidly expanded, with nearly half of the population concentrated in cities, causing the initially developed old urban areas to face various "urban diseases" [3], and residents' physical and mental health issues have gradually emerged from the shadow of urban physical space development.…”

Section: Introductionmentioning

confidence: 99%

“Is What We See Always Real?” A Comparative Study of Two-Dimensional and Three-Dimensional Urban Green Spaces: The Case of Shenzhen’s Central District

Jing,

Li,

Chen

et al. 2024

Forests

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This paper takes the central area of Shenzhen as an example to explore the correlation and differences between 2D and 3D green spaces on urban roads during the summer of 2023. By collecting street view image data and using convolutional neural networks for image semantic segmentation, the Green View Index (GVI) was calculated and combined with the Normalized Difference Vegetation Index (NDVI) for analysis. The results show that the road greening levels in Nanshan District, Futian District, and Luohu District of Shenzhen are relatively high, with GVI exceeding 25%. The Pearson correlation coefficient between the 2D and 3D greening data is 0.5818, indicating a moderate correlation. By analyzing four typical greening scenarios (high NDVI and high GVI, high NDVI and low GVI, low NDVI and high GVI, and low NDVI and low GVI), the study found specific reasons for the differences in green data in different dimensions; the analysis revealed that factors such as building height, density, and elevated transportation facilities significantly affect the accuracy of NDVI in urban spaces. The study suggests that in urban greening assessments, the complementarity and differences between street view data and remote sensing data should be comprehensively considered to improve the accuracy and comprehensiveness of the analysis.

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Spatial-temporal variation and nonlinear prediction of environmental footprints and comprehensive environmental pressure in urban agglomerations

Cited by 10 publications

References 57 publications

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

Coordination between ecological footprint index and socioeconomic development of the urban agglomeration in the middle reaches of the Yangtze River Basin

“Is What We See Always Real?” A Comparative Study of Two-Dimensional and Three-Dimensional Urban Green Spaces: The Case of Shenzhen’s Central District

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