Article Sidebar

Submitted
October 22, 2025
Accepted
March 10, 2026
Published
April 1, 2026
Download
PDF
Statistic
Read Counter : 109 Download : 63

Downloads

Download data is not yet available.

Main Article Content

Abstract

Degraded peatlands in South Sumatra experience drainage driven subsidence, recurrent fires, and seasonal flooding, yet they also have reliable long term solar resources, making them strong candidates for agrivoltaics that avoids conversion of intact peat. This study aimed to map and quantify agrivoltaic land suitability on degraded peatlands using an integrated GIS and multi-criteria decision analysis workflow. Eight criteria were prepared on a 30 m UTM Zone 48S grid and normalized to a 0 to 1 benefit scale: FRP weighted fire kernel density, peat depth class as a geotechnical proxy, flood hazard index, slope, distance to roads, aspect, topographic position index, and long term global horizontal irradiance. Weights were derived with the Analytic Hierarchy Process (CR= 0.00244) and combined using Weighted Linear Combination with protected areas applied as hard constraints. Across the eligible degraded peat domain (124,007.76 ha), 53.76% (66,665.25 ha) was very suitable and 24.89% (30,867.84 ha) was moderately suitable, while 19.68% (24,408.99 ha) and 1.67% (2,065.68 ha) were unsuitable and very unsuitable. Overall, 78.65% (97,533.09 ha) of eligible land was suitable or very suitable, indicating a substantial opportunity for policy-focused agrivoltaic screening on degraded peatlands while maintaining environmental safeguards.

Keywords

agrivoltaic analytic hierarchy process fire radiative power geographic information system global horizontal irradiance

Article Details

License

Copyright (c) 2026 Muaffan Alfaiz Wisaksono

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite
Wisaksono, M. A. (2026). GIS–MCDA–based land suitability analysis for agrivoltaic development on degraded peatlands in South Sumatra. Jurnal Lahan Suboptimal : Journal of Suboptimal Lands, 15(1), 7–20. https://doi.org/10.36706/jlso.15.1.2026.783
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Al Garni, H. Z., & Awasthi, A. (2017). Solar PV power plant site selection using a GIS–AHP approach with application in Saudi Arabia. Applied Energy, 206, 1225–1240. https://doi.org/10.1016/j.apenergy.2017.10.024
  2. Alfieri, L., Burek, P., Feyen, L., & Forzieri, G. (2015). Global warming increases the frequency of river floods in Europe. Hydrology and Earth System Sciences, 19, 2247–2260. https://doi.org/10.5194/hess-19-2247-2015
  3. Amaducci, S., Yin, X., & Colauzzi, M. (2018). Agrivoltaic systems to optimise land use for electric energy production. Applied Energy, 220, 545–561. https://doi.org/10.1016/j.apenergy.2018.03.081
  4. Badan Nasional Penanggulangan Bencana (BNPB). (n.d.). InaRISK: Indonesia’s Disaster Risk Assessment Portal [Data portal]. Retrieved October 7, 2025, from https://inarisk.bnpb.go.id/
  5. Barron-Gafford, G. A., Minor, R. L., Allen, N. A., Cronin, A. D., Brooks, A. E., & Pavao-Zuckerman, M. A. (2019). Agrivoltaics provide mutual benefits across the food–energy–water nexus in drylands. Nature Sustainability, 2, 848–855. https://doi.org/10.1038/s41893-019-0364-5
  6. Baykal, T. M., et al. (2025). Performance assessment of GIS-based spatial clustering methods in forest fire data. Natural Hazards. https://doi.org/10.1007/s11069-025-07135-0
  7. Çolak, H. E., Memişoğlu, T., & Gerçek, Y. (2020). Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya Province, Turkey. Renewable Energy, 149, 565–576. https://doi.org/10.1016/j.renene.2019.12.078
  8. Columbia Sabin Center. (2022). Solar panels reduce CO₂ emissions more per acre than trees. Retrieved from https://blogs.law.columbia.edu/climatechange/2022/10/25
  9. Deng, H., Li, D., Cai, S., Zhao, F.. (2025). Spatio-temporal dynamics of forest fire occurrence in Yunnan, China from 2001 to 2021 based on MODIS. npj Natural Hazards, 2, Article 52. https://doi.org/10.1038/s44304-025-00102-6
  10. Dinesh, H., & Pearce, J. M. (2016). The potential of agrivoltaic systems. Renewable and Sustainable Energy Reviews, 54, 299–308. https://doi.org/10.1016/j.rser.2015.10.024
  11. Doljak, D., & Stanojević, G. (2017). Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia. Energy, 127, 291–300. https://doi.org/10.1016/j.energy.2017.03.140
  12. Doorga, J. R. S., Rughooputh, S. D. V., & Boojhawon, R. (2019). Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in Mauritius. Renewable Energy, 133, 1201–1219. https://doi.org/10.1016/j.renene.2018.08.105
  13. ESMAP. (2019a). Global Solar Atlas 2.0: Technical report. Washington, DC: World Bank.
  14. ESMAP. (2019b). Validation report for global solar radiation model (Global Solar Atlas 2.0). Washington, DC: World Bank. https://documents.worldbank.org/curated/en/507341592893487792/pdf/Global-Solar-Atlas-2-0-Validation-Report.pdf
  15. Food and Agriculture Organization of the United Nations (FAO). (2021). The state of the world’s land and water resources for food and agriculture: Systems at breaking point (SOLAW 2021) – Synthesis report. Rome: FAO.
  16. Finlayson, R. (2021). Managing peatlands in Indonesia’s South Sumatra for multiple benefits. CIFOR-ICRAF Forests News.
  17. Giamalaki, M., & Tsoutsos, T. (2019). Sustainable siting of solar power installations in the Mediterranean using GIS/AHP. Renewable Energy, 141, 64–75. https://doi.org/10.1016/j.renene.2019.03.100
  18. Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31–41. https://doi.org/10.1016/j.rse.2016.02.054
  19. Goepel, K. D. (2018). Implementation of an online software tool for the Analytic Hierarchy Process (AHP-OS). International Journal of the Analytic Hierarchy Process, 10(3), 469–487. https://doi.org/10.13033/ijahp.v10i3.590
  20. Hernandez, R. R., Easter, S. B., Murphy-Mariscal, M. L., Maestre, F. T., Tavassoli, M., Allen, E. B., Barrows, C. W., Belnap, J., Ochoa-Hueso, R., Ravi, S., & Allen, M. F. (2015). Environmental impacts of utility-scale solar energy. Renewable and Sustainable Energy Reviews, 29, 766–779. https://doi.org/10.1016/j.rser.2013.08.041
  21. Hooijer, A., Page, S., Jauhiainen, J., Lee, W. A., Lu, X. X., Idris, A., & Anshari, G. (2012). Subsidence and carbon loss in drained tropical peatlands. Biogeosciences, 9(3), 1053–1071. https://doi.org/10.5194/bg-9-1053-2012
  22. International Energy Agency (IEA). (2023). World Energy Outlook 2023. Paris: IEA.
  23. International Energy Agency (IEA). (2020). Annual direct CO₂ emissions avoided per 1 GW of installed capacity by technology and displaced fuel. International Energy Agency.
  24. Intergovernmental Panel on Climate Change (IPCC). (2022). AR6 WGIII: Mitigation of Climate Change. Cambridge: Cambridge University Press.
  25. Intergovernmental Panel on Climate Change (IPCC). (2023). AR6 Synthesis Report. Geneva: IPCC.
  26. Irfan, M., Khakim, M. Y. N., Mardiansyah, W., Kurniawati, N., Awaluddin, Sulaiman, A., Iskandar, I., Suwignyo, R. A., Yang, H., & Choi, E. (2025). Peatland hydro-climatological parameters variability in response to 2019–2022 climate anomalies in the OKI Regency. Atmosphere, 16(1), 81. https://doi.org/10.3390/atmos16010081
  27. Joosten, H., Tanneberger, F., & Moen, A. (Eds.). (2016). Mires and peatlands of Europe: Status, distribution and conservation. Stuttgart: Schweizerbart Science Publishers.
  28. Kiely, L., Spracklen, D. V., Arnold, S. R., Papargyropoulou, E., Conibear, L., Wiedinmyer, C., Knote, C., Adrianto, H. A. (2021). Assessing costs of Indonesian fires and the benefits of restoring peatland. Nature Communications, 12, 7044. https://doi.org/10.1038/s41467-021-27353-x
  29. Kiely, L., Spracklen, D. V., Wiedinmyer, C., Conibear, L., Reddington, C. L., Archer-Nicholls, S., Lowe, D., Arnold, S. R., Knote, C., Khan, M. F., Latif, M. T., Kuwata, M., Budisulistiorini, S. H., & Syaufina, L. (2019). New estimate of particulate emissions from Indonesian peat fires in 2015. Atmospheric Chemistry and Physics, 19, 11105–11121. https://doi.org/10.5194/acp-19-11105-2019
  30. Kırcalı, Ş., & Selim, S. (2021). Site suitability analysis for solar farms using the geographic information system and multi-criteria decision analysis: The case of Antalya, Turkey. Clean Technologies and Environmental Policy, 23(4), 1233–1250. https://doi.org/10.1007/s10098-020-02018-3
  31. Kurniawan, I., Ichwani, R., Aryansyah, Fionasari, R., & Huda, A. (2024). The implementation of export–import (E–I) subsidies regulation on rooftop photovoltaic plant system in Indonesia based on techno-economic point of view: A study case in Ogan Komering Ulu Region, South Sumatera, Indonesia. International Journal of Sustainable Development and Planning, 19(4), 1371–1378. https://doi.org/10.18280/ijsdp.190414
  32. Kocabaldır, C., & Yücel, M. A. (2023). GIS-based multicriteria decision analysis for spatial planning of solar photovoltaic power plants in Çanakkale province, Turkey. Renewable Energy, 212, 455–467. https://doi.org/10.1016/j.renene.2023.05.075
  33. Merrouni, A., Ghazanfari, A., & Amara, M. (2018). Large-scale PV sites selection by combining GIS and the Analytic Hierarchy Process: Case study of Eastern Morocco. Renewable Energy. https://doi.org/10.1016/j.renene.2017.10.044
  34. Mezbahuddin, S., Nikonovas, T., Spessa, A., Grant, R. F., Imron, M. A., Doerr, S. H., Clay, G. D. (2023). Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology. Scientific Reports, 13, Article 619. https://doi.org/10.1038/s41598-022-27075-0
  35. NASA. (n.d.). Fire Information for Resource Management System (FIRMS) [Data set]. NASA Earthdata/LANCE. Retrieved October 7, 2025.
  36. NASA. (n.d.). LANCE | FIRMS. Retrieved October 7, 2025, from https://firms.modaps.eosdis.nasa.gov/
  37. Noorollahi, E., Ghodsipour, S. H., & Yousefi, A. (2016). Land suitability analysis for solar farms exploitation using GIS and fuzzy AHP. Energies, 9(8), 643. https://doi.org/10.3390/en9080643
  38. OpenStreetMap contributors. (2025). OpenStreetMap [Data set]. OpenStreetMap Foundation. Retrieved October 7, 2025, from https://www.openstreetmap.org
  39. Riaz, H., Imran, H., Alam, H., Alam, M. A., & Butt, N. Z. (2021). Crop-specific optimization of bifacial PV arrays for agrivoltaic production. arXiv preprint arXiv:2104.00560. https://doi.org/10.48550/arXiv.2104.00560
  40. Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.
  41. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
  42. UNEP-WCMC, & IUCN. (2023). Protected Planet: The World Database on Protected Areas (WDPA). Cambridge: UNEP-WCMC & IUCN.
  43. U.S. Environmental Protection Agency. (2025a). Greenhouse gas emissions from a typical passenger vehicle. https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle
  44. U.S. Environmental Protection Agency. (2025b). Greenhouse gas equivalencies calculator.
  45. Vernimmen, R., Hooijer, A., Akmalia, R., Giesen, W., Dommain, R., Rompas, A., & Silvius, M. (2020). Mapping deep peat carbon stock from a LiDAR-based DTM and field measurements, with application to eastern Sumatra. Carbon Balance and Management, 15(1), 19. https://doi.org/10.1186/s13021-020-00139-2
  46. WattTime. (2020). Lifecycle and avoided emissions of solar technologies. Retrieved from https://watttime.org
  47. Weselek, A., Ehmann, A., Ziegler, M., Lewandowski, I., & Högy, P. (2021). Agrivoltaic system impacts on microclimate and yield of different crops within an organic crop rotation in a temperate climate. Agronomy for Sustainable Development, 41, 59. https://doi.org/10.1007/s13593-021-00714-y
  48. Wichtmann, W., Schröder, C., & Joosten, H. (Eds.). (2016). Paludiculture – Productive use of wet peatlands: Climate protection – biodiversity – regional economic benefits. Stuttgart: Schweizerbart Science Publishers.
  49. Widmer, J., Christ, B., Grenz, J., & Norgrove, L. (2024). Agrivoltaics, a promising new tool for electricity and food production: A systematic review. Renewable and Sustainable Energy Reviews, 192, Article 114277. https://doi.org/10.1016/j.rser.2023.114277
  50. World Bank Group; Solargis. (2023). World – Global Horizontal Irradiation (GHI) GIS data (Global Solar Atlas) [Data set]. EnergyData.info. Retrieved October 7.
  51. Yousefi, H., Hafeznia, H., & Yousefi-Sahzabi, A. (2018). Spatial Site Selection for Solar Power Plants 2025Using a GIS-Based Boolean-Fuzzy Logic Model: A Case Study of Markazi Province, Iran. Energies, 11(7), 1648. https://doi.org/10.3390/en11071648
  52. Zoghi, M., Ehsani, A. H., Sadat, M., Amiri, M. J., & Karimi, S. (2017). Optimization of solar site selection by fuzzy logic model and weighted linear combination method in arid and semi-arid region: A case study in Isfahan, Iran. Renewable and Sustainable Energy Reviews, 68, 986–996. https://doi.org/10.1016/j.rser.2015.07.014