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From rise in B to fall in C? Global environmental impacts of biofuels

Author

Listed:
  • Piroli, Giuseppe
  • Rajcaniova, Miroslava
  • Ciaian, Pavel
  • Kancs, d'Artis
Abstract
This is the first 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 find that in the medium- to long-run biofuels significantly 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 (elasticity 0.57). Our findings 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

  • Piroli, Giuseppe & Rajcaniova, Miroslava & Ciaian, Pavel & Kancs, d'Artis, 2014. "From rise in B to fall in C? Global environmental impacts of biofuels," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182804, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae14:182804
    DOI: 10.22004/ag.econ.182804
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    6. 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.
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    More about this item

    Keywords

    Environmental Economics and Policy;

    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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