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From a rise in B to a fall in C? SVAR analysis of environmental impact of biofuels

Author

Listed:
  • Pavel Ciaian
  • d'Artis Kancs
  • Giuseppe Pirolix
  • Miroslava Rajcaniova

Abstract

This is the rst paper that econometrically estimates the impact of rising Bioenergy production on global CO2 emissions. We apply a structural vector autoregression (SVAR) approach to time series from 1961 to 2009 with annual observation for the world biofuel production and global CO2 emissions. We nd that in the medium- to long-run biofuels signicantly reduce global CO2 emissions: the CO2 emission elasticities with respect to biofuels range between -0.57 and -0.80. In the short-run, however, biofuels may increase CO2 emissions temporarily. Our ndings complement those of life-cycle assessment and simulation models. However, by employing a more holistic approach and obtaining more robust estimates of environmental impact of biofuels, our results are particularly valuable for policy makers.

Suggested Citation

  • Pavel Ciaian & d'Artis Kancs & Giuseppe Pirolix & Miroslava Rajcaniova, 2015. "From a rise in B to a fall in C? SVAR analysis of environmental impact of biofuels," Working Papers of LICOS - Centre for Institutions and Economic Performance 516223, KU Leuven, Faculty of Economics and Business (FEB), LICOS - Centre for Institutions and Economic Performance.
    • Handle: RePEc:ete:licosp:516223
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    2. Gohin, Alexandre, 2016. "Understanding the revised CARB estimates of the land use changes and greenhouse gas emissions induced by biofuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 402-412.
    3. Li, Hui & Mu, Wenyu & Huang, Weiwei & Torvanger, Asbjørn & Chen, Tianqi, 2025. "Bioenergy utilization and greenhouse gas emission reduction: A global impact assessment," Renewable Energy, Elsevier, vol. 250(C).
    4. Ondřej Filip & Karel Janda & Ladislav Krištoufek, 2018. "Ceny biopaliv a souvisejících komodit: analýza s použitím metod minimální kostry grafu a hierarchických stromů [Prices of Biofuels and Related Commodities: an Analysis Using Methods of Minimum Spanning Tree and Hierarchical Tree]," Politická ekonomie, Prague University of Economics and Business, vol. 2018(2), pages 218-239.
    5. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit & Kuşkaya, Sevda, 2017. "Can biomass energy be an efficient policy tool for sustainable development?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 830-845.
    6. Lee, Jaeseok & Yue, Chengyan, 2017. "Impacts of the US dollar (USD) exchange rate on economic growth and the environment in the United States," Energy Economics, Elsevier, vol. 64(C), pages 170-176.
    7. Zeng, Shihong & Nan, Xin & Liu, Chao & Chen, Jiuying, 2017. "The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices," Energy Policy, Elsevier, vol. 106(C), pages 111-121.
    8. Ko, Chun-Han & Chaiprapat, Sumate & Kim, Lee-Hyung & Hadi, Pejman & Hsu, Shu-Chien & Leu, Shao-Yuan, 2017. "Carbon sequestration potential via energy harvesting from agricultural biomass residues in Mekong River basin, Southeast Asia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1051-1062.
    9. Rahman, Farahiyah Abdul & Aziz, Md Maniruzzaman A. & Saidur, R. & Bakar, Wan Azelee Wan Abu & Hainin, M.R & Putrajaya, Ramadhansyah & Hassan, Norhidayah Abdul, 2017. "Pollution to solution: Capture and sequestration of carbon dioxide (CO2) and its utilization as a renewable energy source for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 112-126.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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