Predictive modeling of regional carbon storage dynamics in response to land use/land cover changes: An InVEST-based analysis

Assessment of carbon stock (CS) in various land use/land cover (LULC) types is essential for environmental policies focused on reducing CO2 emissions and mitigating climate change. This study utilized the CA-Markov model to simulate future LULC scenarios and the InVEST model to evaluate CS changes in Pakistan from 2001 to 2030. The study employed two decades of yearly composite land cover data from MODIS, achieving high accuracy with a kappa value of 0.856. The results indicate that an increase of 38.1 x 103 km2 in cultivated land could lead to an increment of 13.5 Tg in Pakistan's total CS. In comparison, an increase in forest area can be the reason for raising above-ground carbon (AGC) by 16.8 Tg. These findings enhance the understanding of longterm LULC and CS changes in Pakistan. The study provides valuable insights for governments to refine land use strategies, adjust carbon emission reduction policies, and design better regulations based on the study's findings. Key recommendations include promoting vertical urban development to preserve carbon sequestration areas, implementing strict agricultural zoning laws, expanding afforestation initiatives like the Billion Tree Tsunami and Green Pakistan, and establishing a national LULC and CS monitoring program. Integrating data from various sources will create a comprehensive database to inform policy decisions and land management practices, contributing to global climate change mitigation efforts.