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Updated: Aug 6, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Unraveling land-use carbon-pollution co-evolution: A dynamic coordination framework with causal pathways and spatial
Xue Zhao1, Bilin Shao1, Jia Su1
1School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China.
This study quantifies the co-evolution of land-use carbon emissions and fine particulate matter (PM2.5) pollution in China. It reveals spatial clustering and identifies urban expansion as a key factor, enabling targeted mitigation strategies.
Area of Science:
- Environmental Science
- Earth System Science
- Spatio-temporal Analysis
Background:
- Land-use change drives both carbon emissions and air pollution (PM2.5).
- Understanding their co-evolution is crucial for regional governance.
- Existing research lacks quantification of temporal dynamics and causal links.
Purpose of the Study:
- To develop a framework for diagnosing carbon-pollution synergy.
- To analyze the spat-temporal dynamics and causal pathways of this synergy.
- To inform differentiated regional governance strategies for mitigation.
Main Methods:
- Developed a multi-stage framework for 113 district-county units (2013-2023).
- Constructed a synergistic evolution index (SEI) using dynamic time warping (DTW) and coupling coordination degree (CCD).
- Employed Bayesian network analysis and density-based clustering.
Main Results:
- Identified pronounced spatial polarization and significant clustering in synergy performance.
- Revealed heterogeneous causal pathways, with urban expansion mediating ecological and anthropogenic drivers.
- Delineated four distinct governance zones based on stability and enhancement potential.
Conclusions:
- The study provides a novel framework for assessing carbon-pollution synergy.
- Findings support spatially tailored mitigation strategies for environmental governance.
- Highlights the critical role of urban expansion in the co-evolution of emissions and pollution.
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