IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v69y2017icp207-217.html
   My bibliography  Save this article

Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea

Author

Listed:
  • Lee, Chul-Yong
  • Huh, Sung-Yoon

Abstract

This paper introduces the forecasting model for a new and renewable energy supply utilized in the Fourth Basic Plan for New and Renewable Energy of South Korea in 2014 and presents the estimated results. The Korean government formulated a plan for raising the new and renewable energy deployment rate to 11% by 2035, and this paper presents the development of the corresponding plan. The proposed model essentially uses a bottom-up method to reflect the characteristics of each renewable source. In addition, a competitive diffusion model, a logistic growth model, a linear regression model, and data from government planning and companies’ planned projects are used. The forecasts are classified and presented by renewable source and output type (i.e., electricity, heat, and transportation fuels). The results show that Korean new and renewable energy production will reach about 37 million tonnes of oil equivalent by 2035. In addition, the renewable electricity sector has become mainstream since the 2012 implementation of Renewable Portfolio Standard policy, and is expected to account for 60% of total new and renewable energy supply in 2035. Furthermore, wind, solar photovoltaic, and bioenergy are projected to replace current waste-oriented sources.

Suggested Citation

  • Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 207-217.
    • Handle: RePEc:eee:rensus:v:69:y:2017:i:c:p:207-217
    DOI: 10.1016/j.rser.2016.11.173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116309248
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2016.11.173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Xing-Ping & Cheng, Xiao-Mei, 2009. "Energy consumption, carbon emissions, and economic growth in China," Ecological Economics, Elsevier, vol. 68(10), pages 2706-2712, August.
    2. Kira R. Fabrizio, 2013. "The Effect of Regulatory Uncertainty on Investment: Evidence from Renewable Energy Generation," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 29(4), pages 765-798, August.
    3. Uyterlinde, Martine A. & Junginger, Martin & de Vries, Hage J. & Faaij, Andre P.C. & Turkenburg, Wim C., 2007. "Implications of technological learning on the prospects for renewable energy technologies in Europe," Energy Policy, Elsevier, vol. 35(8), pages 4072-4087, August.
    4. Hua, Jian & Wu, Yi-Hsuan & Jin, Pang-Fu, 2008. "Prospects for renewable energy for seaborne transportation—Taiwan example," Renewable Energy, Elsevier, vol. 33(5), pages 1056-1063.
    5. Bhattacharya, S.C. & Jana, Chinmoy, 2009. "Renewable energy in India: Historical developments and prospects," Energy, Elsevier, vol. 34(8), pages 981-991.
    6. Harijan, Khanji & Uqaili, Mohammad A. & Memon, Mujeebuddin & Mirza, Umar K., 2011. "Forecasting the diffusion of wind power in Pakistan," Energy, Elsevier, vol. 36(10), pages 6068-6073.
    7. McFarland, J. R. & Reilly, J. M. & Herzog, H. J., 2004. "Representing energy technologies in top-down economic models using bottom-up information," Energy Economics, Elsevier, vol. 26(4), pages 685-707, July.
    8. Barry L. Bayus, 1993. "High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable," Management Science, INFORMS, vol. 39(11), pages 1319-1333, November.
    9. Nic Rivers & Mark Jaccard, 2005. "Combining Top-Down and Bottom-Up Approaches to Energy-Economy Modeling Using Discrete Choice Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 83-106.
    10. Kazem, Hussein A. & Chaichan, Miqdam T., 2012. "Status and future prospects of renewable energy in Iraq," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6007-6012.
    11. Huh, Sung-Yoon & Lee, Jongsu & Shin, Jungwoo, 2015. "The economic value of South Korea׳s renewable energy policies (RPS, RFS, and RHO): A contingent valuation study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 64-72.
    12. Frei, Christoph W. & Haldi, Pierre-Andre & Sarlos, Gerard, 2003. "Dynamic formulation of a top-down and bottom-up merging energy policy model," Energy Policy, Elsevier, vol. 31(10), pages 1017-1031, August.
    13. Kumbaroglu, Gürkan & Madlener, Reinhard & Demirel, Mustafa, 2008. "A real options evaluation model for the diffusion prospects of new renewable power generation technologies," Energy Economics, Elsevier, vol. 30(4), pages 1882-1908, July.
    14. Gurung, Anup & Kumar Ghimeray, Amal & Hassan, Sedky H.A., 2012. "The prospects of renewable energy technologies for rural electrification: A review from Nepal," Energy Policy, Elsevier, vol. 40(C), pages 374-380.
    15. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    16. Resch, Gustav & Held, Anne & Faber, Thomas & Panzer, Christian & Toro, Felipe & Haas, Reinhard, 2008. "Potentials and prospects for renewable energies at global scale," Energy Policy, Elsevier, vol. 36(11), pages 4048-4056, November.
    17. Islam, Mazharul & Fartaj, Amir & Ting, David S. -K., 2004. "Current utilization and future prospects of emerging renewable energy applications in Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(6), pages 493-519, December.
    18. Meijer, Ineke S.M. & Hekkert, Marko P. & Koppenjan, Joop F.M., 2007. "The influence of perceived uncertainty on entrepreneurial action in emerging renewable energy technology; biomass gasification projects in the Netherlands," Energy Policy, Elsevier, vol. 35(11), pages 5836-5854, November.
    19. Christiansen, Atle Christer, 2002. "New renewable energy developments and the climate change issue: a case study of Norwegian politics," Energy Policy, Elsevier, vol. 30(3), pages 235-243, February.
    20. Kazem, Hussein A., 2011. "Renewable energy in Oman: Status and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3465-3469.
    21. Park, Sang Yong & Yun, Bo-Yeong & Yun, Chang Yeol & Lee, Duk Hee & Choi, Dong Gu, 2016. "An analysis of the optimum renewable energy portfolio using the bottom–up model: Focusing on the electricity generation sector in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 319-329.
    22. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    23. Huacuz, Jorge M., 2005. "The road to green power in Mexico--reflections on the prospects for the large-scale and sustainable implementation of renewable energy," Energy Policy, Elsevier, vol. 33(16), pages 2087-2099, November.
    24. Klinge Jacobsen, Henrik, 1998. "Integrating the bottom-up and top-down approach to energy-economy modelling: the case of Denmark," Energy Economics, Elsevier, vol. 20(4), pages 443-461, September.
    25. Llera, E. & Scarpellini, S. & Aranda, A. & Zabalza, I., 2013. "Forecasting job creation from renewable energy deployment through a value-chain approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 262-271.
    26. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    27. Gross, Robert, 2004. "Technologies and innovation for system change in the UK: status, prospects and system requirements of some leading renewable energy options," Energy Policy, Elsevier, vol. 32(17), pages 1905-1919, November.
    28. Bohringer, Christoph & Rutherford, Thomas F., 2008. "Combining bottom-up and top-down," Energy Economics, Elsevier, vol. 30(2), pages 574-596, March.
    29. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    30. Böhringer, Christoph & Rutherford, Thomos F., 2009. "Integrated assessment of energy policies: Decomposing top-down and bottom-up," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1648-1661, September.
    31. Park, JaeHyun & Hong, TaeHoon, 2013. "Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 543-551.
    32. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    33. Aguilar, Francisco X. & Cai, Zhen, 2010. "Exploratory analysis of prospects for renewable energy private investment in the U.S," Energy Economics, Elsevier, vol. 32(6), pages 1245-1252, November.
    34. Barradale, Merrill Jones, 2010. "Impact of public policy uncertainty on renewable energy investment: Wind power and the production tax credit," Energy Policy, Elsevier, vol. 38(12), pages 7698-7709, December.
    35. Ashraf Chaudhry, M. & Raza, R. & Hayat, S.A., 2009. "Renewable energy technologies in Pakistan: Prospects and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1657-1662, August.
    36. K Hossain, A & Badr, O, 2007. "Prospects of renewable energy utilisation for electricity generation in Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(8), pages 1617-1649, October.
    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. Puppala, Harish & K Jha, Shibani & Singh, Ajit Pratap & Madurai Elavarasan, Rajvikram & Elia Campana, Pietro, 2022. "Identification and analysis of barriers for harnessing geothermal energy in India," Renewable Energy, Elsevier, vol. 186(C), pages 327-340.
    2. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    4. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    5. Gal Hochman & Chrysostomos Tabakis, 2020. "Biofuels and Their Potential in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-17, September.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    8. Mohammed H. Alsharif & Jeong Kim & Jin Hong Kim, 2018. "Opportunities and Challenges of Solar and Wind Energy in South Korea: A Review," Sustainability, MDPI, vol. 10(6), pages 1-23, June.
    9. Razmjoo, Armin & Mirjalili, Seyedali & Aliehyaei, Mehdi & Østergaard, Poul Alberg & Ahmadi, Abolfazl & Majidi Nezhad, Meysam, 2022. "Development of smart energy systems for communities: technologies, policies and applications," Energy, Elsevier, vol. 248(C).
    10. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "A Study on the Limitations of South Korea’s National Roadmap for Greenhouse Gas Reduction by 2030 and Suggestions for Improvement," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    11. Sung, Bongsuk, 2019. "Do government subsidies promote firm-level innovation? Evidence from the Korean renewable energy technology industry," Energy Policy, Elsevier, vol. 132(C), pages 1333-1344.
    12. Park, Minsun & Barrett, Mark & Gallo Cassarino, Tiziano, 2019. "Assessment of future renewable energy scenarios in South Korea based on costs, emissions and weather-driven hourly simulation," Renewable Energy, Elsevier, vol. 143(C), pages 1388-1396.
    13. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    14. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    15. Londoño-Pulgarin, Diana & Cardona-Montoya, Giovanny & Restrepo, Juan C. & Muñoz-Leiva, Francisco, 2021. "Fossil or bioenergy? Global fuel market trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    16. Gi-Young Chae & Seung-Hyun An & Chul-Yong Lee, 2021. "Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    17. da Silva, Felipe L.C. & Cyrino Oliveira, Fernando L. & Souza, Reinaldo C., 2019. "A bottom-up bayesian extension for long term electricity consumption forecasting," Energy, Elsevier, vol. 167(C), pages 198-210.
    18. Kumru Türköz & Utku Utkulu, 2021. "Türkiye’de Sektör ve Kaynak Bazlı Enerji Kullanımları Yakınsıyor mu? Panel TAR ve Çoklu Kırılmalı Birim Kök Bulguları," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 6(1), pages 254-274.
    19. Min-Kyu Lee & Ju-Hee Kim & Seung-Hoon Yoo, 2018. "Public Willingness to Pay for Increasing Photovoltaic Power Generation: The Case of Korea," Sustainability, MDPI, vol. 10(4), pages 1-11, April.

    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. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    2. Park, Sang Yong & Yun, Bo-Yeong & Yun, Chang Yeol & Lee, Duk Hee & Choi, Dong Gu, 2016. "An analysis of the optimum renewable energy portfolio using the bottom–up model: Focusing on the electricity generation sector in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 319-329.
    3. Gi-Young Chae & Seung-Hyun An & Chul-Yong Lee, 2021. "Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    4. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    5. Halkos, George, 2014. "The Economics of Climate Change Policy: Critical review and future policy directions," MPRA Paper 56841, University Library of Munich, Germany.
    6. Chris Bataille, Mark Jaccard, John Nyboer and Nic Rivers, 2006. "Towards General Equilibrium in a Technology-Rich Model with Empirically Estimated Behavioral Parameters," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 93-112.
    7. Mukisa, Nicholas & Zamora, Ramon & Lie, Tek Tjing, 2021. "Diffusion forecast for grid-tied rooftop solar photovoltaic technology under store-on grid scheme model in Sub-Saharan Africa: Government role assessment," Renewable Energy, Elsevier, vol. 180(C), pages 516-535.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Theodoridou, Ifigeneia & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "A feasibility evaluation tool for sustainable cities – A case study for Greece," Energy Policy, Elsevier, vol. 44(C), pages 207-216.
    10. Olegs Krasnopjorovs & Daniels Jukna & Konstantins Kovalovs, 2022. "On the Use of General Equilibrium Model to Assess the Impact of Climate Policy in Latvia," Post-Print hal-03861139, HAL.
    11. Andersen, Kristoffer S. & Termansen, Lars B. & Gargiulo, Maurizio & Ó Gallachóirc, Brian P., 2019. "Bridging the gap using energy services: Demonstrating a novel framework for soft linking top-down and bottom-up models," Energy, Elsevier, vol. 169(C), pages 277-293.
    12. Fortes, Patrícia & Pereira, Rui & Pereira, Alfredo & Seixas, Júlia, 2014. "Integrated technological-economic modeling platform for energy and climate policy analysis," Energy, Elsevier, vol. 73(C), pages 716-730.
    13. Xu, Mei & Xie, Pu & Xie, Bai-Chen, 2020. "Study of China's optimal solar photovoltaic power development path to 2050," Resources Policy, Elsevier, vol. 65(C).
    14. Shin, Jungwoo & Lee, Chul-Yong & Kim, Hongbum, 2016. "Technology and demand forecasting for carbon capture and storage technology in South Korea," Energy Policy, Elsevier, vol. 98(C), pages 1-11.
    15. Lee, Hwarang & Kang, Sung Won & Koo, Yoonmo, 2020. "A hybrid energy system model to evaluate the impact of climate policy on the manufacturing sector: Adoption of energy-efficient technologies and rebound effects," Energy, Elsevier, vol. 212(C).
    16. Xavier Labandeira, Pedro Linares and Miguel Rodriguez, 2009. "An Integrated Approach to Simulate the impacts of Carbon Emissions Trading Schemes," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    17. Rivers, Nic & Jaccard, Mark, 2006. "Useful models for simulating policies to induce technological change," Energy Policy, Elsevier, vol. 34(15), pages 2038-2047, October.
    18. Xu, Jiuping & Li, Li & Zheng, Bobo, 2016. "Wind energy generation technological paradigm diffusion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 436-449.
    19. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    20. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).

    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:eee:rensus:v:69:y:2017:i:c:p:207-217. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.