Modeling and Forecasting Carbon Sequestration Using Markov Chain and Invest Model: A Case Study of Malakand Division, Pakistan

Abstract

Using Markov Chain analysis and the InVEST model, this study investigates carbon sequestration in the Malakand Division of Pakistan, concentrating on land use changes between 2003 and 2033. In addition to discussing the economic implications of carbon dynamics in the area, the paper emphasizes the critical function that forests play in sequestering carbon and its importance in reducing climate change. The research finds regions with significant potential for storing carbon dioxide as well as those that are degrading by closely examining patterns of land use and cover (LULC). The results show a concerning drop in carbon content, with estimates of the economic impact of carbon loss between 2003 and 2023 being as high as $214.57 million and estimates of further declines through 2033. This demonstrates the critical necessity for efficient carbon sequestration plans to prevent additional losses. The research also takes socioeconomic variables into account, specifically the reliance on forest resources for fuelwood, which aggravates environmental deterioration. This research gives policymakers the information they need to make well-informed decisions on conservation and reforestation projects by supplying spatial data on carbon stocks and potential economic outcomes. In the end, the research advances our knowledge of ecosystem services and highlights the significance of sustainable resource management techniques in the Malakand Division. The projections are further validated by the inclusion of local community opinions, making this research an essential resource for future carbon management initiatives.

Keywords: Carbon sequestration, Biomass Economic evaluation of carbon stock, Ecosystem services, Markov chain model, Carbon pool