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Analysis of Ecological and Environmental Change Trends and Influencing Factors in the Middle Yangtze River Urban Agglomeration

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DOI: 10.23977/erej.2025.090212 | Downloads: 0 | Views: 79

Author(s)

Yanjun Zhang 1, Yunfei Bao 1, Runping Zhang 1, Peiran Yang 1

Affiliation(s)

1 School of Management, Chongqing University of Technology, Chongqing, 400054, China

Corresponding Author

Yunfei Bao

ABSTRACT

With China's rapid economic development, human activities have encroached upon ecological spaces, subjecting regional ecosystems to increasing pressures. To advance the United Nations Sustainable Development Goals for ecological conservation, it has become imperative to strengthen environmental protection in critical regions. This paper uses the Middle Yangtze River Urban Agglomeration (MYRUA) as a case study. It employs the upgraded Remote Sensing Ecological Index (RSEI-new) to assess the quality of the regional ecological environment quality (EEQ) and analyzes its spatial and temporal trends. To further reveal the key drivers of ecological improvement or degradation, an Optimal Parameter Geographic Detector is utilized to systematically investigate the factors influencing EEQ and their interactions. The findings indicate that the regional ecological environment exhibits a general pattern of "low values in urban areas and high values in forested areas," with low-value zones primarily concentrated in densely urbanized belts along the Yangtze River. Temporally, the ecological environment in the study area shows an overall positive trend, with approximately 54% of the region demonstrating significant improvement. Net primary productivity (NPP) of vegetation emerges as the key single-factor driver of ecological and environmental change, while the interaction effect between NPP and population density is most pronounced (q = 0.413), making it the primary composite driver of ecological and environmental change. Overall, identifying the trends and dominant factors of ecological and environmental change in the MYRUA provides vital statistical insights and scientific support for regional environmental management, ecological restoration, and policy optimization.

KEYWORDS

Ecological Environment, Trend, Optimal Parameter Geographic Detector

CITE THIS PAPER

Yanjun Zhang, Yunfei Bao, Runping Zhang, Peiran Yang, Analysis of Ecological and Environmental Change Trends and Influencing Factors in the Middle Yangtze River Urban Agglomeration. Environment, Resource and Ecology Journal (2025) Vol. 9: 111-118. DOI: http://dx.doi.org/10.23977/erej.2025.090212.

REFERENCES

[1] LEI K, ZHANG H, QIU H, et al. A two-dimensional four-quadrant assessment method to explore the spatiotemporal coupling and coordination relationship of human activities and ecological environment [J]. Journal of Environmental Management, 2024, 370: 122362.
[2] LUO H, XU Y, HAN Q, et al. Remote sensing assessment of ecological quality of Baiyangdian wetland in response to extreme rainfall [J]. Remote Sensing Applications: Society and Environment, 2024, 36: 101284.
[3] SUN B, TANG J, YU D, et al. Ecosystem health assessment: A PSR analysis combining AHP and FCE methods for Jiaozhou Bay, China1 [J]. Ocean & Coastal Management, 2019, 168: 41-50.
[4] LI N, WANG J. Comprehensive Eco-Environment Quality Index Model with Spatiotemporal Characteristics [J]. Sensors, 2022, 22(24): 9635.
[5] XU H Q. A remote sensing urban ecological index and its application [J]. Acta Ecologica Sinica, 2013, 33(24): 7853-7862.
[6] AIZIZI Y, KASIMU A, LIANG H, et al. Evaluation of ecological space and ecological quality changes in urban agglomeration on the northern slope of the Tianshan Mountains [J]. Ecological Indicators, 2023, 146: 109896.
[7] WANG J, MA J L, XIE F F, et al. Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example [J]. Chinese Journal of Applied Ecology, 2020, 31(11): 3795-3804.
[8] AN M, XIE P, HE W, et al. Local and tele-coupling development between carbon emission and ecologic environment quality [J]. Journal of Cleaner Production, 2023, 394: 136409.
[9] SONG Y, WANG J, GE Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data [J]. GIScience & Remote Sensing, 2020, 57: 593 - 610.

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