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INDONESIA
Indonesian Journal of Energy
Published by Purnomo Yusgiantoro Center
ISSN : 25491016     EISSN : 2549760X     DOI : -
Core Subject : Science,
Energy
The journal covers research with a strong focus on energy economics, energy analysis, energy modeling, and prediction, integrated energy systems, energy planning, and energy management. The journal also welcomes papers on related topics such as energy conservation, energy efficiency, energy innovation, energy technology, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, energy in buildings, energy finance, energy law and on economic and policy issues, also provided such topics are within the context of the broader multi-disciplinary scope of energy.
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Articles 93 Documents
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AI-Driven Carbon Pricing Optimization: A Geospatial Analysis Framework for Indonesia’s Energy Transition Wijayanto, Arie W.; Putri, Salwa R.; Putra, Yoga C.; Natasya Afira; Anggita, Fauzan F.; Aziz, Jafar H.
Indonesian Journal of Energy Vol. 9 No. 1 (2026): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v9i1.289

Abstract

Indonesia faces a critical climate challenge as the world’s sixth-largest carbon emitter, with coal accounting for more than 60% of its electricity generation. Achieving its ambitious net-zero target by 2060 requires urgent action. While Indonesia has introduced various carbon pricing mechanisms to advance carbon neutrality, these initiatives demand sophisticated optimization across the archipelago’s diverse regions to balance emissions reduction with sustainable development goals. This research presents an innovative artificial intelligence framework that leverages geospatial big data to estimate carbon stock and inform pricing strategies while supporting Indonesia’s transition away from coal dependency. The framework integrates three key components: (1) a remote sensing-based Measurement, Reporting, and Verification (MRV) model that accurately quantifies carbon stocks across varied ecosystems; (2) an automated reporting system powered by generative Artificial Intelligence that enhances transparency and reduces bias in carbon accounting; and (3) a comprehensive analytics dashboard that visualizes dynamic carbon stock data to inform policy decisions. By addressing Indonesia’s geographical complexities through tailored carbon stock estimation policies and optimizing resource allocation across diverse ecological contexts, this framework provides a data-driven foundation for Indonesia to navigate its energy transition and meet its climate commitments through enhanced MRV systems and targeted green financing initiatives.
Ar-Rahnu Energy Cooperative (AREC): A Community-Based Microfinance Model for Green Development in Indigenous Aceh Mustaqilla, Nazhira
Indonesian Journal of Energy Vol. 9 No. 1 (2026): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v9i1.290

Abstract

The tradition of gold dowries in Aceh has resulted in women owning significant amounts of gold, yet these assets often remain unproductive and are merely stored as passive savings. This study explores the Gala or Ar-Rahnu or Jeulamee tradition and proposes a community-based financing model that optimizes unproductive gold as capital for Energy Independent Villages in Aceh. The novelty of this research lies in introducing Jeulamee as an alternative financing mechanism for renewable energy. This approach has not been previously examined while designing a closed-loop ecosystem to ensure sustainable energy production at the community level. Employing a mixed methods approach, including Location Quotient, Dynamic Location Quotient, Shift Share, and Input-Output analysis, the study identifies strategic sectors for sustainable energy development and formulates the Ar-Rahnu Energy Cooperative concept. By integrating Islamic microfinance, indigenous cultural practices, and blockchain-based gold tokenization, this research contributes to the literature on green financing while offering practical implications for women’s economic empowerment, improved access to clean energy, and the advancement of inclusive and sustainable energy transitions in indigenous communities.
Monetizing Carbon Emissions: Advanced Strategies for Optimizing Carbon Economic Value Using Machine Learning and Geospatial Analysis Briantiko, Zenda O.; Nurkarim, Wahidya; Wahyuddin, Eko P.; Zulkarnain, Muhammad
Indonesian Journal of Energy Vol. 9 No. 1 (2026): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v9i1.296

Abstract

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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