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

The necessity for extensive historical data, variables, and weight determination still presents challenges and complexity, notwithstanding the growth in research on socio-ecological vulnerability to climate change. In order to fill in these gaps, this study used China's Fujian Province as a case study to propose a unique strategic approach for studying socio-ecological vulnerability to climate change from 2000 to 2020 by utilizing remote sensing and the framework of the Intergovernmental Panel on Climate Change. In a GIS scenario, this method employs a comprehensive framework with a wide variety of indicators and a data-driven ranking algorithm.

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau.

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.

The Transformation of 40-Year Coastal Wetland Policies in China: Network Analysis and Text Analysis.

The notable improvement of China's wetland management policies over the last four decades prompted this study's goal to quantitatively analyze the transformation of China's coastal wetland policies from 1979 to 2022 by applying an institutional network analysis and policy text analysis. The results of the institutional network analysis revealed an administrative management transformation from a multidepartmental mode to an integrated management framework. Furthermore, the policy text analysis results revealed a change in policy priorities (from exploitation to protection) and management targets (from a single environmental element to a comprehensive ecosystem and further to collaborative governance).

Grid computing method for atmospheric environmental capacity coupled with ventilation coefficient using CALPUFF simulation and GIS spatial analysis technology.

Atmospheric environmental issues have evolved from point source pollution to regional pollution, leading to controlling specific air pollutant emissions. A-value method has been found suitable for estimating large-scale atmospheric environmental capacity rather than small-scale, resulting in the inaccuracy of developing air pollution control strategy. This study proposed a grid computing method based on the CALPUFF modelling system and GIS spatial analysis tool.

Research on the optimization of air quality monitoring station layout based on spatial grid statistical analysis method.

In recent years, with the significant increase in urban development, it has become necessary to optimize the current air monitoring stations to reflect the quality of air in the environment. Highlighting the spatial representation of some air monitoring stations using Beijing's regional air monitoring station data from 2012 to 2014, the monthly mean particulate matter concentration (PM10) in the region was calculated and through the IDW interpolation method and spatial grid statistical method using GIS, the spatial distribution of PM10 concentration in the whole region was deduced. The spatial distribution variation of districts in Beijing using the gridding model was performed, and through the 3-year spatial analysis, PM10 concentration data including the variation and spatial overlay (1.