Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm

Area of Science:

• Environmental Science
• Climate Change Research
• Socio-Ecological Systems Analysis

Background:

• Assessing socio-ecological vulnerability to climate change faces challenges with data and weighting.
• Existing research necessitates improved methodologies for comprehensive vulnerability assessment.

Purpose of the Study:

• To propose a novel, flexible, and data-driven strategic approach for studying socio-ecological vulnerability to climate change.
• To analyze socio-ecological vulnerability in China's Fujian Province from 2000 to 2020 using remote sensing and the IPCC framework.

Main Methods:

• Utilized remote sensing and the Intergovernmental Panel on Climate Change (IPCC) framework within a Geographic Information System (GIS) environment.
• Employed a comprehensive indicator framework and a data-driven ranking algorithm for vulnerability assessment.
• Conducted spatial autocorrelation analysis to identify key influencing factors and their spatial relationships.

Main Results:

• Revealed moderate coastal socio-ecological vulnerability with significant spatial heterogeneity.
• Identified an expansion of highly vulnerable zones by 6.04% over two decades, with substantial increases in Fuzhou and Ningde.
• Determined that vegetation changes, precipitation, Gross Domestic Product (GDP), and land use (LULC) were principal drivers of vulnerability, with precipitation showing a strong correlation.

Conclusions:

• The developed methodology offers a flexible, self-sufficient, and adaptable model for socio-ecological vulnerability assessment.
• The findings provide actionable insights for decision-makers and stakeholders to develop strategies for enhancing coastal system resilience.
• Highlighted the critical role of precipitation in exacerbating socio-ecological vulnerability in the study region.