IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1366-d1049176.html
   My bibliography  Save this article

The Role of Biogas Potential in Building the Energy Independence of the Three Seas Initiative Countries

Author

Listed:
  • Grzegorz Ślusarz

    (Institute of Economics and Finance, University of Rzeszów, M. Cwiklińska Street 2, 35-601 Rzeszów, Poland)

  • Dariusz Twaróg

    (Department of Physics and Medical Engineering, Rzeszów University of Technology, Powstańców Warszawy Street 12, 35-959 Rzeszów, Poland
    Statistical Office in Rzeszów, Jana III Sobieskiego Street 10, 35-001 Rzeszów, Poland)

  • Barbara Gołębiewska

    (Institute of Economics and Finance, Warsaw University of Life Sciences-SGGW, Nowoursynowska Street 166, 02-787 Warsaw, Poland)

  • Marek Cierpiał-Wolan

    (Institute of Economics and Finance, University of Rzeszów, M. Cwiklińska Street 2, 35-601 Rzeszów, Poland)

  • Jarosław Gołębiewski

    (Institute of Economics and Finance, Warsaw University of Life Sciences-SGGW, Nowoursynowska Street 166, 02-787 Warsaw, Poland)

  • Philipp Plutecki

    (Statistical Office in Rzeszów, Jana III Sobieskiego Street 10, 35-001 Rzeszów, Poland)

Abstract

Increasing biogas production in the Three Seas Initiative countries (3SI) is a good way to reduce greenhouse gas emissions and to increase energy self-sufficiency by replacing some of the fossil energy sources. An assessment of the biogas production potential carried out for the 3SI at the NUTS 1 and NUTS 2 level shows that the potential of this energy carrier was stable for the period (from 2010–2021). The results showed that it can cover from approximately 10% (Hungary, Slovakia) to more than 34% (Estonia, Slovenia) of natural gas consumption; moreover, there is strong variation in the value of potential at the regional level (NUTS 2) in most of the countries studied. The biogas production forecast was carried out with the ARIMA model using four regressors, which are GDP, biogas potential utilisation, natural gas consumption and investments in RES (renewable energy sources) infrastructure, including changes in the EU energy policy after 24 February 2022. In the most promising scenario (four regressors), the results obtained for the period from 2022–2030 predict a rapid increase in biogas production in the 3SI countries, from 32.4 ± 11.3% for the Czech Republic to 138.7 ± 27.5% for Estonia (relative to 2021). However, in the case of six countries (Bulgaria, Lithuania, Hungary, Austria, Poland and Romania) the utilisation of 50% of the potential will most likely occur in the fifth decade of the 21st century. The above results differ significantly for those obtained for three regressors, where the highest rise is predicted for Bulgaria at 33.5 ± 16.1% and the lowest for Slovenia, at only 2.8 ± 14.4% (relative to 2021).

Suggested Citation

  • Grzegorz Ślusarz & Dariusz Twaróg & Barbara Gołębiewska & Marek Cierpiał-Wolan & Jarosław Gołębiewski & Philipp Plutecki, 2023. "The Role of Biogas Potential in Building the Energy Independence of the Three Seas Initiative Countries," Energies, MDPI, vol. 16(3), pages 1-23, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1366-:d:1049176
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1366/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/3/1366/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michał Bernard Pietrzak & Magdalena Olczyk & Marta Ewa Kuc-Czarnecka, 2022. "Assessment of the Feasibility of Energy Transformation Processes in European Union Member States," Energies, MDPI, vol. 15(2), pages 1-23, January.
    2. Katarzyna Ignatowicz & Gabriel Filipczak & Barbara Dybek & Grzegorz Wałowski, 2023. "Biogas Production Depending on the Substrate Used: A Review and Evaluation Study—European Examples," Energies, MDPI, vol. 16(2), pages 1-17, January.
    3. Jan Martin Zepter & Jan Engelhardt & Tatiana Gabderakhmanova & Mattia Marinelli, 2021. "Empirical Validation of a Biogas Plant Simulation Model and Analysis of Biogas Upgrading Potentials," Energies, MDPI, vol. 14(9), pages 1-19, April.
    4. Tomislav Gelo & Nika Šimurina & Jurica Šimurina, 2021. "The Economic Impact of Investment in Renewables in Croatia by 2030," Energies, MDPI, vol. 14(24), pages 1-10, December.
    5. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    6. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    7. Mohsen Salimi & Majid Amidpour, 2022. "The Impact of Energy Transition on the Geopolitical Importance of Oil-Exporting Countries," World, MDPI, vol. 3(3), pages 1-12, August.
    8. Elie, Luc & Granier, Caroline & Rigot, Sandra, 2021. "The different types of renewable energy finance: A Bibliometric analysis," Energy Economics, Elsevier, vol. 93(C).
    9. Tomaž Levstek & Črtomir Rozman, 2022. "A Model for Finding a Suitable Location for a Micro Biogas Plant Using Gis Tools," Energies, MDPI, vol. 15(20), pages 1-21, October.
    10. Zell-Ziegler, Carina & Thema, Johannes & Best, Benjamin & Wiese, Frauke & Lage, Jonas & Schmidt, Annika & Toulouse, Edouard & Stagl, Sigrid, 2021. "Enough? The role of sufficiency in European energy and climate plans," Energy Policy, Elsevier, vol. 157(C).
    11. Ştefan Dragoş Cîrstea & Claudia Steluţa Martiş & Andreea Cîrstea & Anca Constantinescu-Dobra & Melinda Timea Fülöp, 2018. "Current Situation and Future Perspectives of the Romanian Renewable Energy," Energies, MDPI, vol. 11(12), pages 1-22, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Beata Piotrowska & Daniel Słyś, 2023. "Analysis of the Life Cycle Cost of a Heat Recovery System from Greywater Using a Vertical “Tube-in-Tube” Heat Exchanger: Case Study of Poland," Resources, MDPI, vol. 12(9), pages 1-17, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniya Tlegenova, 2015. "Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan," Papers 1508.07534, arXiv.org.
    2. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    3. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    4. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    5. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    6. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    7. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAEPR Working Papers 2024-001 Classification-1, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    8. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    9. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    10. Armenia Androniceanu & Irina Georgescu & Ionuț Nica & Nora Chiriță, 2023. "A Comprehensive Analysis of Renewable Energy Based on Integrating Economic Cybernetics and the Autoregressive Distributed Lag Model—The Case of Romania," Energies, MDPI, vol. 16(16), pages 1-28, August.
    11. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    12. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    13. Sergey Zhironkin & Fares Abu-Abed & Elena Dotsenko, 2023. "The Development of Renewable Energy in Mineral Resource Clusters—The Case of the Siberian Federal District," Energies, MDPI, vol. 16(9), pages 1-28, April.
    14. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.
    15. Florin-Constantin Mihai & Ionut Minea, 2021. "Sustainable Alternative Routes versus Linear Economy and Resources Degradation in Eastern Romania," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    16. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.
    17. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    18. Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
    19. Ahmet Goncu & Mehmet Oguz Karahan & Tolga Umut Kuzubas, 2019. "Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 33(1), pages 1-22.
    20. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1366-:d:1049176. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.