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Analysis of Trade-offs Between First-generation Biofuels and Food Production for Japan Using CGE Modelling

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Abstract

Depending on the technology used to produce liquid biofuels, they can be classified into: first-, second-, and third-generation biofuels. The first generation uses as feedstock edible biomass such as corn and sugar cane, the second generation uses lignocellulosic biomass such as forest residues, and the third generation uses algae. The technologies employed to produce first-generation liquid biofuels are more developed and mature than those employed by the second- and third-generation. However, the fact that they use edible crops as feedstock leads to a debate about the negative impacts that this can cause in an economy, particularly over the prices of the food sector. This work joins this debate by analyzing the economic impacts that introducing ethanol produced from rice can cause in the Japanese economy. A comparative-static, multi-sector, Computable General Equilibrium (CGE) model is used to execute the analysis. The model simulates a neoclassical economy where producers maximize profits subject to technological constraints, and households maximize utility subject to budget constraints. It incorporates intermediate demands, has a government and an investment agent, and includes the rest of the world interacting with the economy through exports, imports, and transfers. Different case study scenarios have been set based on two criteria: the bioethanol production process and the gasoline-ethanol mix ratio. Among the bioethanol production processes, an only ethanol production system, and an ethanol and power production system are considered. For the gasoline-ethanol mix ratio the model includes a biofuel penetration rate parameter that represents how much of the gross output of the gasoline sector is replaced by the new biofuel. We run various simulations for different values of this biofuel penetration rate parameter (in a range from 5% to 85%) with the intention to cover all the common ethanol fuel mixtures standards. The relevance of the study is that understanding the consequences of introducing biofuels into an economy is crucial to be able to design a supportive policy that helps to enhance the positive effects and decrease the negative ones. This work offers that understanding by analyzing and measuring the impact of allocating part of the domestic rice production to produce ethanol. Changes in prices and gross outputs for the different sectors in the economy for the different scenarios are some of the results. Additionally, how the Gross Domestic Product (GDP), international trade (exports and imports), and emissions of CO2 behave before the introduction of bioethanol is inquired. Our main results are as follows. (1) The food sector is not affected significantly by the introduction of ethanol; instead, the sectors related to food production that suffer more negative impacts are the other crops farming and vegetable oils sectors. (2) The economic sectors more sensitive in price to the introduction of bioethanol are (in descending order): other crops farming, petroleum refinery products, and paddy rice. (3) The trend for the real GDP is to decrease as more bioethanol is introduced into the economy, and the net trade flows keep almost constant. (4) First-generation bioethanol is not a cost-effective measure for CO2 emissions reductions.

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References

  • Ajanovic A. (2011). Biofuels versus food production: Does biofuels production increase food prices? Energy 36: 2070–2076.

    Article  Google Scholar 

  • Burfisher, M. (2011). Introduction to Computable General Equilibrium Models. Cambridge University Press, New York, pp.3.

    Book  Google Scholar 

  • Chitake, Y. (2010). Production of bioethanol taking rice as feedstock. [Online; accessed 28-August-2015]. URL: http://www.sce-net.jp/enrgypdf/biorice.pdf

    Google Scholar 

  • Demirer R., Kutan A.M. and F. Shen. (2012). The effect of ethanol listing on corn prices: Evidence from spot and futures markets. Energy Economics 34: 1400–1406.

    Article  Google Scholar 

  • GAMS Development Corporation. (2015). MPSGE Models in GAMS. [Online; accessed 7-April-2015]. URL: http://www.gams.com/solvers/mpsge/

    Google Scholar 

  • Hansen A., Kyritsis D. and C. Lee. (2010). Characteristics of Biofuels and Renewable Fuel Standards. (in: Vertès A., Qureshi N., Blaschek H. and H. Yukawa-ed., Biomass to Biofuels: Strategies for Global Industries), West Sussex, UK, pp. 3–26.

    Google Scholar 

  • Hosoe N., Gasawa K. and H. Hashimoto. (2010). Textbook of Computable General Equilibrium Modelling. Palgrave Macmillan, New York, pp. 41–60.

    Google Scholar 

  • International Energy Agency. (2013). Bionergy. [Online; accessed 4-September-2015]. URL: https://www.iea.org/topics/renewables/subtopics/bioenergy/

    Google Scholar 

  • Japanese Ministry of Economy, Trade and Industry [METI]. (2014). The 2011 Updated Input-Output Table — Basic sector. [Online; accessed 25-June-2015]. URL: http://www.meti.go.jp/english/statistics/tyo/entyoio/index.html

    Google Scholar 

  • Japanese Ministry of the Environment. (2012). Details on the Carbon Tax (Tax for Climate Change Mitigation). [Online; accessed 5-December-2015]. URL: https://www.env.go.jp/en/policy/tax/env-tax/20121001a_dct.pdf

    Google Scholar 

  • Klass, D. (2004). Biomass for Renewable Energy and Fuels. (in: C. Cleveland-ed., Encyclopedia of Energy), Massachusetts, USA, pp. 193–212.

    Chapter  Google Scholar 

  • Kretschmer B., Peterson S. and A. Ignaciuk. (2008). Integrating Biofuels into the DART model. Kiel Institute for the World Economy, Kiel, pp. 9.

    Google Scholar 

  • Kunimitsu, Y. (2015). Regional Impacts of Long-term Climate Change on Rice Production and Agricultural Income: Evidence from Computable General Equilibrium Analysis. Japan Agricultural Research Quarterly 49 (2): 173–185.

    Article  Google Scholar 

  • Lee R.A. and J.M. Lavoie. (2013). From first- to third-generation biofuels: Challenges of producing a commodity from a biomass of increasing complexity. Animal Frontiers 3(2): 6–11.

    Article  Google Scholar 

  • Leontief, W. (1985). The Choice of Technology. Scientific American 252(6): 25–33.

    Article  Google Scholar 

  • Löfgren, H. (2003a). Exercises in General Equilibrium Modeling Using GAMS. International Food Policy Research Institute (IFPRI), pp. 21–33.

    Google Scholar 

  • Löfgren, H. (2003b). Key to Exercises in CGE Modeling Using GAMS. International Food Policy Research Institute (IFPRI), pp. 50–66.

    Google Scholar 

  • Markusen, J. and T. Rutherford. (1995). Small open economy model with a benchmark trade imbalance. General Equilibrium Modeling with MPSGE: Some Examples for Self-Study. [Online; accessed 4-April-2015]. URL: http://www.mpsge.org/markusen/markusen.htm

    Google Scholar 

  • Nigam, P.S. and A. Singh. (2011). Production of liquid biofuels from renewable resources. Progress in Energy and Combustion Science 37: 52–68.

    Article  Google Scholar 

  • Petroleum Association of Japan [PAJ]. (2013). Petroleum Industry in Japan 2013. [Online; accessed 5-December-2015]. URL: http://www.paj.gr.jp/english/data/paj2013.pdf

    Google Scholar 

  • Petroleum Association of Japan [PAJ]. (2015). Supply and Demand of Petroleum Product [xls]. Oil Statistics. [Online; accessed 22-August-2015]. URL: http://www.paj.gr.jp/english/statis/index.html

    Google Scholar 

  • Rajagopal D., Sexton S., Hochman G., Roland-Holst D. and D. Zilberman. (2009). Model estimates food-versus-biofuel trade-off. California Agriculture 63 (4): 199–201.

    Article  Google Scholar 

  • Rutherford, T. (2002). Tourism in a small open economy. GAMS-MPSGE. [Online; accessed 25-May-2015]. URL: http://www.mpsge.org/mainpage/mpsge.htm

    Google Scholar 

  • Rutherford, T. and S. Paltsev. (1999). From an Input-Output Table to a General Equilibrium Model: Assessing the Excess Burden of Indirect Taxes in Russia. Department of Economics, University of Colorado, mimeo.

    Google Scholar 

  • Saga K., Imou K., Yokoyama S. and T. Minowa. (2010). Net energy analysis of bioethanol production system from high-yield rice plant in Japan. Applied Energy 87: 2164–2168.

    Article  Google Scholar 

  • Schmidt, M. (2008). The Sankey Diagram in Energy and Material Flow Management. Journal of Industrial Ecology 12 (2): 173–185.

    Article  Google Scholar 

  • Ministry of Agriculture, Forestry and Fisheries [MAFF]. (2013). FY2012 Annual Report on Food, Agriculture and Rural Areas in Japan. [Online; accessed 15-August-2016]. URL: http://www.maff.go.jp/j/wpaper/w_maff/h24/pdf/e_all.pdf

    Google Scholar 

  • Mizuho Information and Research Institute. (2004). Well-to-Wheel Analysis of Greenhouse Gas Emissions of Automotive Fuels in the Japanese Context. [Online: accessed 8-December-2015]. URL: http://www.mizuho-ir.co.jp/english/knowledge/report/pdf/wtwghg041130.pdf

    Google Scholar 

  • Tyner W.E. (2013). Biofuels and food prices: Separating wheat from chaff. Global Food Security 2: 126–130.

    Article  Google Scholar 

  • Wianwiwat, S. and J. Asafu-Adjaye. (2013). Is there a role for biofuels in promoting energy self sufficiency and security? A CGE analysis of biofuel policy in Thailand. Energy Policy 55: 543–555.

    Article  Google Scholar 

  • Worldwatch Institute. (2006). Biofuels for Transport: Global Potential and Implications for Energy and Agriculture, Worldwatch Institute for the German Ministry of Food, Agriculture and Consumer Protection (BMELV) in coordination with the German Agency for Technical Cooperation (GTZ) and the German Agency of Renewable Resources (FNR), Earthscan, London.

    Google Scholar 

  • Yoda K., Furubayashi T. and T. Nakata. (2013). Design of Automotive Bioethanol Supply Chain Using Mixed Integer Programming. Journal of the Japan Institute of Energy 92: 1173–1186.

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to thank Dr. Yoji Kunimitsu from the Japanese National Institute for Rural Engineering for his expert advice throughout the development of this work. Also, we are grateful to Professor Soo Cheol Lee from Meijo University, and Shin-ichi Hanada from Kanazawa Seiryo University for their valuable and keen comments contributing to the betterment of this work at the 14th International Conference of the Japan Economic Policy Association.

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Correspondence to Héctor Fernando Villatoro Flores.

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Villatoro Flores, H.F., Furubayashi, T. & Nakata, T. Analysis of Trade-offs Between First-generation Biofuels and Food Production for Japan Using CGE Modelling. IJEPS 11, 1–24 (2016). https://doi.org/10.1007/BF03405763

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