Sustainability strategies for carbon sequestration in South Tangerang City using InVEST model to support sustainable urban planning

¹ Graduate Program in Natural Resources and Environmental Management, IPB University, Baranangsiang Campus, Bogor, 16680, Indonesia.
² Sustainable Infrastructure Engineering Division, Faculty of Engineering and Technology, IPB University, Dramaga, Jawa Barat, 16680, Indonesia.
³ Vocational School, IPB University, Cilibende, Bogor, 16680, Indonesia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Rapid urbanisation in South Tangerang City has triggered substantial conversion of green spaces, critically impacting the carbon-sequestration capacity of urban ecosystems. This study integrates remote sensing technology, Cellular Automata-Markov predictive modelling, and the InVEST Carbon Storage model to examine the spatiotemporal dynamics of land cover and estimate carbon stocks over the 2016–2025 period. Random Forest classification achieved an accuracy of 93.61% with a kappa coefficient of 0.874, demonstrating robust temporal consistency across all observation periods. The analysis revealed a decline in carbon stocks of 90,028 tC (9.6%), from 939,287 tC to 849,259 tC, with carbon density decreasing from 64.93 to 58.04 tC/ha. Forest ecosystems accounted for 67.1% of total carbon stocks, despite absolute losses of 106,336 tC. Landscape metrics demonstrated structural improvements, with mean patch size increasing by 50.4% and connectivity enhancing by 14.7%, indicating successful habitat consolidation. Key predictors of carbon sequestration sustainability include mean patch size as the strongest predictor of carbon density (r = 0.82), urban development pressure (r = 0.847), and NDVI-carbon correlation (r = 0.85). A strong positive correlation (r² = 0.87) between forest area and carbon stocks confirms that vegetation conservation policies are essential for maintaining urban carbon sequestration capacity in rapidly urbanising regions.