Anthropogenic activities influence the loss of mangroves, increase natural phenomena such as hurricanes, tropical storms, and El Niño, and consequently increase concentrations of greenhouse gases such as CO₂, promoting climate change. There are strategies to reduce emissions from Deforestation and Forest Degradation, the Sustainable Development Goals, and the General Law on Climate Change to counteract these conditions. Therefore, this research aims to generate an integral simulation model of sustainable mangrove development scenarios for 2030 and 2050 through mitigation strategies, using geospatial techniques, multi-criteria evaluation, and generating a future surface demand model. The Marismas Nacionales study area is a mangrove ecosystem and an important carbon sink. The simulation model determined that the mangrove area in 2030 will be 77,555 hectares, with an estimated absorption of 358.95 Gg CO₂ e (equivalent). By 2050 there will be 86,476 ha, absorbing 400.24 Gg CO₂ e. This increase will be in disturbed mangrove areas and other wetlands. The sustainable simulation model and the surface demand model can be applied in any study area to increase, protect, and conserve mangroves to benefit the social, economic, and environmental sectors.

Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns.