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

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.

[1]  Merrill Jones Barradale Impact of public policy uncertainty on renewable energy investment: Wind power and the production tax credit , 2010 .

[2]  M. Guidolin,et al.  Cross-country diffusion of photovoltaic systems: Modelling choices and forecasts for national adoption patterns , 2010 .

[3]  Ignacio Zabalza,et al.  Forecasting job creation from renewable energy deployment through a value-chain approach , 2013 .

[4]  Aie,et al.  World Energy Outlook 2011 , 2001 .

[5]  E. Mansfield TECHNICAL CHANGE AND THE RATE OF IMITATION , 1961 .

[6]  Hussein A. Kazem,et al.  Renewable energy in Oman: Status and future prospects , 2011 .

[7]  Kira R. Fabrizio,et al.  The Effect of Regulatory Uncertainty on Investment: Evidence from Renewable Energy Generation , 2013 .

[8]  John M. Reilly,et al.  Representing energy technologies in top-down economic models using bottom-up information , 2004 .

[9]  R. Haas,et al.  Potentials and prospects for renewable energies at global scale , 2008 .

[10]  Umar K. Mirza,et al.  Forecasting the diffusion of wind power in Pakistan , 2011 .

[11]  Christoph Böhringer,et al.  Integrated assessment of energy policies: Decomposing top-down and bottom-up , 2009 .

[12]  S. C. Bhattacharya,et al.  Renewable energy in India: Historical developments and prospects , 2009 .

[13]  Jian Hua,et al.  Prospects for renewable energy for seaborne transportation—Taiwan example , 2008 .

[14]  E. Kırtay,et al.  Current Status and Future Prospects of Renewable Energy Use in Turkey , 2010 .

[15]  Toshihiko Masui,et al.  Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions , 2016 .

[16]  Ali Azadeh,et al.  Optimum estimation and forecasting of renewable energy consumption by artificial neural networks , 2013 .

[17]  Christoph Böhringer,et al.  The synthesis of bottom-up and top-down in energy policy modeling , 1998 .

[18]  Sung-Yoon Huh,et al.  Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships. , 2014 .

[19]  T. Rutherford,et al.  Combining bottom-up and top-down , 2008 .

[20]  A. C. Christiansen,et al.  New renewable energy developments and the climate change issue: a case study of Norwegian politics , 2002 .

[21]  Abul Kalam Hossain,et al.  Prospects of renewable energy utilisation for electricity generation in Bangladesh , 2007 .

[22]  Mark Jaccard,et al.  Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods , 2005 .

[23]  Marko P. Hekkert,et al.  The influence of perceived uncertainty on entrepreneurial action in emerging renewable energy technology; biomass gasification projects in the Netherlands , 2007 .

[24]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[25]  Robert Gross,et al.  Technologies and innovation for system change in the UK: status, prospects and system requirements of some leading renewable energy options , 2004 .

[26]  H. Jacobsen Integrating the bottom-up and top-down approach to energy–economy modelling: the case of Denmark , 1998 .

[27]  Jungwoo Shin,et al.  The economic value of South Korea׳s renewable energy policies (RPS, RFS, and RHO): A contingent valuation study , 2015 .

[28]  V.V.N. Kishore,et al.  A review of technology diffusion models with special reference to renewable energy technologies , 2010 .

[29]  S. Baldwin Renewable Energy: Progress and Prospects , 2002 .

[30]  Francisco X. Aguilar,et al.  Exploratory analysis of prospects for renewable energy private investment in the U.S. , 2010 .

[31]  Miqdam T. Chaichan,et al.  Status and future prospects of renewable energy in Iraq , 2012 .

[32]  Barry L. Bayus,et al.  High-definition television: assessing demand forecasts for a next generation consumer durable , 1993 .

[33]  F. Bass A new product growth model for consumer durables , 1976 .

[34]  Pierre-André Haldi,et al.  Dynamic formulation of a top-down and bottom-up merging energy policy model , 2003 .

[35]  Tasneem Abbasi,et al.  ‘Renewable’ hydrogen: Prospects and challenges , 2011 .

[36]  Karolin Sjöö The influence of uncertainty on venture capital investments in renewable energy technology : an exploratory study , 2008 .

[37]  Sang Yong Park,et al.  An analysis of the optimum renewable energy portfolio using the bottom–up model: Focusing on the electricity generation sector in South Korea , 2016 .

[38]  Xingping Zhang,et al.  Energy consumption, carbon emissions, and economic growth in China , 2009 .

[39]  Taehoon Hong,et al.  Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model , 2013 .

[40]  Jorge M. Huacuz The road to green power in Mexico--reflections on the prospects for the large-scale and sustainable implementation of renewable energy , 2005 .

[41]  Reinhard Madlener,et al.  A Real Options Evaluation Model for the Diffusion Prospects of New Renewable Power Generation Technologies , 2008 .

[42]  Anup Gurung,et al.  The prospects of renewable energy technologies for rural electrification: A review from Nepal , 2012 .

[43]  Kenneth B. Kahn New Product Forecasting : An Applied Approach , 2014 .

[44]  David S.-K. Ting,et al.  Current utilization and future prospects of emerging renewable energy applications in Canada , 2004 .

[45]  Wim Turkenburg,et al.  Implications of technological learning on the prospects for renewable energy technologies in Europe , 2007 .

[46]  Rizwan Raza,et al.  Renewable energy technologies in Pakistan: Prospects and challenges , 2009 .