Monetizing Carbon Emissions
Advanced Strategies for Optimizing Carbon Economic Value Using Machine Learning and Geospatial Analysis
DOI:
https://doi.org/10.33116/ije.v9i1.296Keywords:
carbon economic potential, geospatial analysis, machine learning, clustering analysis, povertyAbstract
The transition to cleaner energy sources requires appropriate financial policies and regulations. One mechanism that supports this transition is carbon pricing, which encourages emissions reduction and creates economic opportunities through the carbon market. With its vast tropical forests, peatlands, and mangroves, Indonesia has significant potential for terrestrial carbon storage. However, using carbon revenue as a financial instrument to support the energy transition remains underexplored. Therefore, a quantitative analysis is needed to assess the potential carbon revenue under various pricing scenarios and its impact on clean energy investments and regional development. This study aims to (i) measure the potential economic value of carbon in East Java with greater precision and spatial detail using geospatial approaches and remote sensing technology, (ii) model predictions of carbon economic value for the forthcoming years by leveraging machine learning algorithms, aiming to obtain accurate, data-driven projections adaptable to land cover changes and policy shifts, and (iii) examine the relationship between carbon economic potential and social welfare such as poverty. The methods used in this research include remote sensing analysis to calculate Net Primary Productivity (NPP); machine learning techniques, such as LSTM and Neural Network, to forecast Carbon Economic Value (CEV) for future years; and clustering analysis to categorize regions based on socioeconomic conditions and CEV levels. From the results of this study, we found that the East Java government can utilize the economic value of carbon to reduce poverty from 9.79 percent to 5.75 percent. In addition, three regional clusters allow for the formulation more targeted policies for each regional group.
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Copyright (c) 2026 Zenda O. Briantiko, Wahidya Nurkarim, Eko P. Wahyuddin, Muhammad Zulkarnain
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