Contribution of N 2 O to the greenhouse gas balance of first‐generation biofuels
Open accessAbstract
In this study, we analyze the impact of fertilizer‐ and manure‐induced N 2 O emissions due to energy crop production on the reduction of greenhouse gas (GHG) emissions when conventional transportation fuels are replaced by first‐generation biofuels (also taking account of other GHG emissions during the entire life cycle). We calculate the nitrous oxide (N 2 O) emissions by applying a statistical model that uses spatial data on climate and soil. For the land use that is assumed to be replaced by energy crop production (the ‘reference land‐use system’), we explore a variety of options, the most important of which are cropland for food production, grassland, and natural vegetation. Calculations are also done in the case that emissions due to energy crop production are fully additional and thus no reference is considered. The results are combined with data on other emissions due to biofuels production that are derived from existing studies, resulting in total GHG emission reduction potentials for major biofuels compared with conventional fuels. The results show that N 2 O emissions can have an important impact on the overall GHG balance of biofuels, though there are large uncertainties. The most important ones are those in the statistical model and the GHG emissions not related to land use. Ethanol produced from sugar cane and sugar beet are relatively robust GHG savers: these biofuels change the GHG emissions by −103% to −60% (sugar cane) and −58% to −17% (sugar beet), compared with conventional transportation fuels and depending on the reference land‐use system that is considered. The use of diesel from palm fruit also results in a relatively constant and substantial change of the GHG emissions by −75% to −39%. For corn and wheat ethanol, the figures are −38% to 11% and −107% to 53%, respectively. Rapeseed diesel changes the GHG emissions by −81% to 72% and soybean diesel by −111% to 44%. Optimized crop management, which involves the use of state‐of‐the‐art agricultural technologies combined with an optimized fertilization regime and the use of nitrification inhibitors, can reduce N 2 O emissions substantially and change the GHG emissions by up to −135 percent points (pp) compared with conventional management. However, the uncertainties in the statistical N 2 O emission model and in the data on non‐land‐use GHG emissions due to biofuels production are large; they can change the GHG emission reduction by between −152 and 87 pp.
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The choice of reference scenario is crucial and strongly determines the outcome of a comparison of GHG emissions of energy crops relative to a reference scenario.
“The choice of the reference scenario is crucial and strongly determines the outcome of such a comparison (Smeets et al., 2009).”
From Land‐use change to bioenergy production in <scp>E</scp> urope: implications for the greenhouse gas balance and soil carbon (2012) · cites this paper as Smeets et al., 2009
Cites
Land Clearing and the Biofuel Carbon Debt
Fargione, J., Hill, J., Tilman, D., Polasky, S., & Hawthorne, P. (2008) · Science · doi:10.1126/science.1152747 · Cited by 3,902 · see citing works
Crutzen, P. J., Mosier, A. R., Smith, K. A., & Winiwarter, W. (2007) · doi:10.5194/acpd-7-11191-2007 · Cited by 1,298 · see citing works
LIFE-CYCLE ASSESSMENT OF NET GREENHOUSE-GAS FLUX FOR BIOENERGY CROPPING SYSTEMS
Adler, P. R., Grosso, S. J. D., & Parton, W. J. (2007) · Ecological Applications · doi:10.1890/05-2018 · Cited by 652 · cited 1× · see citing works
“…N2O emissions due to energy crop production are estimated using process-based biogeochemical models, such as the denitrification–decomposition (DNDC) model or DAYCENT (e.g. JRC et al., 2004; Adler et al., 2007) or are derived from field measurements (e.g. Wang et al., 2003; Wu et al., 2006).”
MethodPotential of biomass energy out to 2100, for four IPCC SRES land-use scenarios
Hoogwijk, M., Faaij, A., Eickhout, B., de Vries, B., & Turkenburg, W. (2005) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2005.05.002 · Cited by 444 · see citing works
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Reducing food's environmental impacts through producers and consumers
Poore, J., & Nemecek, T. (2018) · Science · doi:10.1126/science.aaq0216 · Cited by 6,085 · see citing works
Biophysical and economic limits to negative CO2 emissions
Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., Kato, E., Jackson, R. B., Cowie, A., Kriegler, E., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate2870 · Cited by 1,440 · see citing works
Bioenergy and climate change mitigation: an assessment
Creutzig, F., Ravindranath, N. H., Berndes, G., Bolwig, S., Bright, R., Cherubini, F., Chum, H., Corbera, E., Delucchi, M., Faaij, A., et al. (2015) · GCB Bioenergy · doi:10.1111/gcbb.12205 · Cited by 752 · see citing works
Don, A., Osborne, B., Hastings, A., Skiba, U., Carter, M. S., Drewer, J., Flessa, H., Freibauer, A., Hyvönen, N., Jones, M. B., et al. (2012) · GCB Bioenergy · doi:10.1111/j.1757-1707.2011.01116.x · Cited by 363 · see citing works
Gasparatos, A., Stromberg, P., & Takeuchi, K. (2011) · Agriculture, Ecosystems & Environment · doi:10.1016/j.agee.2011.04.020 · Cited by 210 · see citing works
An evaluation of the global potential of bioenergy production on degraded lands
Nijsen, M., Smeets, E., Stehfest, E., & van Vuuren, D. P. (2012) · GCB Bioenergy · doi:10.1111/j.1757-1707.2011.01121.x · Cited by 113 · see citing works
Self-citationHamelin, L., Jørgensen, U., Petersen, B. M., Olesen, J. E., & Wenzel, H. (2012) · GCB Bioenergy · doi:10.1111/j.1757-1707.2012.01174.x · Cited by 77 · see citing works
- Authors
- SMEETS, E. M. W., BOUWMAN, L. F., STEHFEST, E., Van VUUREN, D. P., & POSTHUMA, A.
- Affiliations
- Utrecht University (NL)
- Netherlands Environmental Assessment Agency (NL)
- Corresponding author
- SMEETS, EDWARD M. W.
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2009-01
- Volume
- 15
- Issue
- 1
- Pages
- 1-23
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2147026391
- Cited by
- 181
- References
- 4
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1 · 2023: 7 · 2022: 8 · 2021: 5 · 2020: 5 · 2019: 3 · 2018: 9 · 2017: 8 · 2016: 10 · 2015: 11 · 2014: 14 · 2013: 13 · 2012: 19
- FWCI
- 37.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Global Change Biology (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Exxon Mobil Corporation
- European Commission
- Universiteit Utrecht
- Fertiliser Association of New Zealand
- Planbureau voor de Leefomgeving
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Bioenergy crop production and management
- Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
- Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
- Keywords
- nitrous oxide emissions, greenhouse gas balance, first-generation biofuels, energy crops, ethanol, sugarcane, sugar beet, corn ethanol, nitrification inhibitors, crop management optimization, life cycle assessment, land use
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