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Towards a non-ambiguous view of the amortization period for quantifying direct land-use change in LCA

  • LCA FOR AGRICULTURAL PRACTICES AND BIOBASED INDUSTRIAL PRODUCTS
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The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

To clarify the concept of the amortization period (20-year factor) associated with direct land-use change (dLUC) accounting, discuss its main inconsistencies, and propose improvements. The current practice is to divide (amortize) the estimated emissions associated with dLUC that has occurred over the last 20 years by another 20 years. Both periods are referred ambiguously as “amortization period.” Issues arise when considering them as a single temporal aspect (TA) that cannot fully represent the complexity of diverse research and policy contexts.

Methods

First, a systematic review was conducted to understand the 20-year amortization history and concepts and discuss its inconsistencies. Based on the review results, we propose the adoption of two distinct TAs, decomposed from the “amortization period.” Then, we performed a sensitivity analysis by estimating carbon emissions due to dLUC in six land uses in Brazil: soybean, maize, sugarcane, pasture, planted forest, and mango.

Results and discussion

The literature review shows that several strategies have emerged to reduce or avoid adopting the amortization period. However, most of these proposals are based on complex approaches focusing on alternatives associated with the life cycle impact assessment stage. We found that the commonly adopted amortization period has an ambiguous nature that could be explored at the life cycle inventory analysis stage. Thus, we argue that there are two distinct TAs linked to amortization in dLUC: (i) the inventory period adopted to account for land-use changes; and (ii) the period over which carbon emissions are annualized. These temporal aspects were named here the “LUC-inventory period” (IP) and the “LUC-amortization period” (AP), for clarification purposes. The sensitivity analysis showed that different values of IP and AP drastically change the emissions results due to dLUC in Brazil for different crops and land uses.

Conclusion

We advocate that the “amortization period” should be decomposed into two TAs: “LUC-inventory period” and the “LUC-amortization period.” They affect how the carbon debt incurred by expanding agricultural land is accounted for and amortized over a given period-of-time. Therefore, to ensure regulatory compliance, we argued that these proposed TAs should be explicitly defined, based on three possibilities, depending on the goal and context of LCA studies, such as (i) fixed values set in standards and norms; (ii) IPCC’s 20-year defaults; and (iii) customized IP and AP values based on the study’s specificities.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary materials. Raw data supporting this study’s findings are available from the corresponding authors upon reasonable request.

Abbreviations

AP:

LUC-amortization period

C:

Carbon

CRF:

Cumulative radiative forcing

GHG:

Greenhouse gas

GWP:

Global warming potential

IBGE:

Brazilian Institute of Geography and Statistics

IP:

LUC-inventory period

LCA:

Life cycle assessment

LCI:

Life cycle inventory

LCIA:

Life cycle impact assessment

LUC:

Land-use change

dLUC:

Direct land-use change

iLUC:

Indirect land-use change

NDCs:

Nationally Determined Contributions

SDGs:

Sustainable Development Goals

SR:

Systematic literature review

TA:

Temporal aspect

References

  • Alvarenga RAF, Erb KH, Haberl H et al (2015) Global land use impacts on biomass production—a spatial-differentiated resource-related life cycle impact assessment method. Int J Life Cycle Assess 20:440–450. https://doi.org/10.1007/s11367-014-0843-x

    Article  Google Scholar 

  • Beloin-Saint-Pierre D, Albers A, Hélias A et al (2020) Addressing temporal considerations in life cycle assessment. Sci Total Environ 743:140700

    Article  CAS  Google Scholar 

  • Bhatia P, Cummis C, Draucker L, Rich D, Lahd H, Brown A (2011) Greenhouse gas protocol product life cycle accounting and reporting standard.

  • Brandão M, Levasseur A, Kirschbaum MUF et al (2013) Key issues and options in accounting for carbon sequestration and temporary storage in life cycle assessment and carbon footprinting. Int J Life Cycle Assess 18:230–240. https://doi.org/10.1007/s11367-012-0451-6

    Article  CAS  Google Scholar 

  • Brazil (2017) Lei no 13576. Dispõe sobre a Política Nacional de Biocombustíveis (RenovaBio) e dá outras providências [in Portuguese]. Brazil

  • BSI (2011) PAS 2050:2011 Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. Br Stand Inst 45

  • BSI (2012) PAS 2050–1:2012. Assessment of life cycle greenhouse gas emissions from horticultural products. Br Stand Inst 46

  • Castanheira ÉG, Freire F (2013) Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems. J Clean Prod 54:49–60. https://doi.org/10.1016/j.jclepro.2013.05.026

    Article  CAS  Google Scholar 

  • Cederberg C, Persson UM, Neovius K et al (2011) Including Carbon Emissions from Deforestation in the Carbon Footprint of Brazilian Beef. Environ Sci Technol 45:57. https://doi.org/10.1021/es103240z

    Article  CAS  Google Scholar 

  • Curtright AE, Johnson DR, Willis HH, Skone T (2012) Scenario uncertainties in estimating direct land-use change emissions in biomass-to-energy life cycle assessment. Biomass Bioenerg 47:240–249. https://doi.org/10.1016/j.biombioe.2012.09.037

    Article  CAS  Google Scholar 

  • Dalchiavon FC, Lorenzon LA, de Perina R, A, et al (2019) Economic Opportunity for Investment in Soybean and Sunflower Crop System in Mato Grosso, Brazil. J Exp Agric Int 29:1–12. https://doi.org/10.9734/jeai/2019/45695

    Article  Google Scholar 

  • Daystar J, Venditti R, Kelley SS (2017) Dynamic greenhouse gas accounting for cellulosic biofuels: implications of time based methodology decisions. Int J Life Cycle Assess 22:812–826. https://doi.org/10.1007/s11367-016-1184-8

    Article  CAS  Google Scholar 

  • de Jong S, Staples M, Grobler C et al (2019) Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time. GCB Bioenergy 11:427–443. https://doi.org/10.1111/gcbb.12573

    Article  CAS  Google Scholar 

  • De Rosa M (2018) Land use and land-use changes in life cycle assessment: green modelling or black boxing? Ecol Econ 144:73–81. https://doi.org/10.1016/j.ecolecon.2017.07.017

    Article  Google Scholar 

  • De Rosa M, Pizzol M, Schmidt J (2018) How methodological choices affect LCA climate impact results: the case of structural timber. Int J Life Cycle Assess 23:147–158. https://doi.org/10.1007/s11367-017-1312-0

    Article  CAS  Google Scholar 

  • Donke ACG, Novaes RML, Pazianotto RAA et al (2020) Integrating regionalized Brazilian land use change datasets into the ecoinvent database: new data, premises and uncertainties have large effects in the results. Int J Life Cycle Assess 25:1027–1042. https://doi.org/10.1007/s11367-020-01763-3

    Article  CAS  Google Scholar 

  • Dupoux M (2019) The land use change time-accounting failure. Ecol Econ 164. https://doi.org/10.1016/j.ecolecon.2019.05.017

  • EMBRAPA (2019) BRLUC - method for estimating land use change and CO2 emissions associated to agriculture in Brazil

  • Escobar N, Tizado EJ, zu Ermgassen EKHJ, et al (2020) Spatially-explicit footprints of agricultural commodities: mapping carbon emissions embodied in Brazil’s soy exports. Glob Environ Chang 62:102067. https://doi.org/10.1016/j.gloenvcha.2020.102067

    Article  Google Scholar 

  • Europe Comission (2018) Product Environmental Footprint Category Rules Guidance, version 6.3

  • European Commision (2019) COM(2019) 142 final: report on the status of production expansion of relevant food and feed crops worldwide. Brussels

  • European Commission (2009a) Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/ 30/EC. Off. J. Eur. Union 140. European Commission

  • European Commission (2009b) Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources

  • FAO (2019) The Director-General’s Medium Term Plan 2018–21 (reviewed) and Programme of Work and Budget 2020–21 C. 21:1–133

  • Friedlingstein P, Jones MW, O’Sullivan M, Andrew RM, Bakker DC, Hauck J, Zeng J (2022) Global Carbon Budget 2021. Earth Syst Sci Data 14(4):1917–2005. https://doi.org/10.5194/essd-14-1917-2022

    Article  Google Scholar 

  • Guinée J (2002) Handbook on Life Cycle Assessment. Kluwer Academic Publishers, USA

    Google Scholar 

  • Hörtenhuber S, Piringer G, Zollitsch W et al (2014) Land use and land use change in agricultural life cycle assessments and carbon footprints - The case for regionally specific land use change versus other methods. J Clean Prod 73:31–39. https://doi.org/10.1016/j.jclepro.2013.12.027

    Article  Google Scholar 

  • ICAO (2019a) Sustainability Criteria for CORSIA Eligible Fuels

  • ICAO (2019b) CORSIA Methodology for Calculating Actual Life Cycle Emissions Values

  • ILCD (2010) General guide for Life Cycle Assessment - Detailed guidance. Luxembourg

  • INPE (2021) Portal Terra Brasilis. http://terrabrasilis.dpi.inpe.br/en/home-page/. Accessed 15 Jun 2021

  • IPCC (2013) Climate change 2013: the physical science basis.

  • IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

  • IPCC (1996a) Chapter 5 land use change & forestry. In: 1996a IPCC Guidelines for National Greenhouse Gas Inventories. pp 1–74

  • IPCC (2006a) Volume 4: Agriculture, forestry and other land use. Chapter 8: Settlements. In: IPCC guidelines for national greenhouse gas inventories. pp 1–29

  • IPCC (1996b) Chapter 5 - Land use change & forestry. In: 1996b IPCC Guidelines for National Greenhouse Gas Inventories. pp 1–74

  • IPCC (2007) The Physical Science Basis. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. New York

  • IPCC (2006b) Volume 4: Agriculture, forestry and other land use. In: Hayama K (ed) 2006b IPCC Guidelines for national greenhouse gas inventories. Japan

  • ISO (2018) ISO 14067:2018 - Greenhouse gases — carbon footprint of products — requirements and guidelines for quantification. https://www.iso.org/standard/71206.html. Accessed 17 Apr 2020

  • Jones MB, Albanito F (2020) Can biomass supply meet the demands of bioenergy with carbon capture and storage (BECCS)? Glob Chang Biol 26:5358–5364. https://doi.org/10.1111/gcb.15296

    Article  Google Scholar 

  • Khanna M, Crago CL (2012) Measuring indirect land use change with biofuels: implications for policy. Annu Rev Resour Econ 4:161–184. https://doi.org/10.1146/annurev-resource-110811-114523

    Article  Google Scholar 

  • Kløverpris JH, Mueller S (2013) Baseline time accounting: considering global land use dynamics when estimating the climate impact of indirect land use change caused by biofuels. Int J Life Cycle Assess 18:319–330. https://doi.org/10.1007/s11367-012-0488-6

    Article  CAS  Google Scholar 

  • Levasseur A, Cavalett O, Fuglestvedt JS et al (2016) Enhancing life cycle impact assessment from climate science : review of recent findings and recommendations for application to LCA. Ecol Indic 71:163–174. https://doi.org/10.1016/j.ecolind.2016.06.049

    Article  Google Scholar 

  • Levasseur A, Lesage P, Margni M et al (2010) Considering time in LCA: dynamic LCA and its application to global warming impact assessments. Environ Sci Technol 44:3169–3174. https://doi.org/10.1021/es9030003

    Article  CAS  Google Scholar 

  • Li W, Ciais P, Peng S et al (2017) Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14:5053–5067. https://doi.org/10.5194/bg-14-5053-2017

    Article  Google Scholar 

  • Livoreil B, Glanville J, Haddaway NR et al (2017) Systematic searching for environmental evidence using multiple tools and sources. Environ Evid 6:23. https://doi.org/10.1186/s13750-017-0099-6

    Article  Google Scholar 

  • Maciel VG, Zortea RB, Grillo IB et al (2016) Greenhouse gases assessment of soybean cultivation steps in southern Brazil. J Clean Prod 131:747–753. https://doi.org/10.1016/j.jclepro.2016.04.100

    Article  CAS  Google Scholar 

  • MAPA (2021) Plano setorial para adaptação à mudança do clima e baixa emissão de carbono na agropecuária com vistas ao desenvolvimento sustentável (2020–2030) [in Portuguese]. Brasília

  • Martin J, Co-chair JHK, Hare MO (2010) White paper - Time accounting subgroup

  • MCTI (2021) Fourth National Communication of Brazil to the UNFCCC. Brasilia

  • Mike van Paassen, Braconi N, Kuling L, et al (2019) Agri-footprint 5.0. Part 2: description of data

  • Mohan DG, Gopi S, Rajasekar V et al (2019) A multi-data assessment of land use and land cover emissions from Brazil during 2000-2019. Mater Today Proc 27:0–31. https://doi.org/10.1080/14484846.2018.1432089

  • Nation U-U (2015) United nations general assembly - Transforming our world: the 2030 Agenda for Sustainable Development

  • Nemecek T, Bengoa X, Rossi V et al (2019) World Food LCA database: methodological guidelines for the life cycle inventory of agricultural products. Version 3(5):88

    Google Scholar 

  • Nepstad D, McGrath D, Stickler C et al (2014) Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science (80-. ). 344:1118–1123

  • Newell JP, Vos RO (2012) Accounting for forest carbon pool dynamics in product carbon footprints: challenges and opportunities. Environ Impact Assess Rev 37:23–36. https://doi.org/10.1016/j.eiar.2012.03.005

    Article  Google Scholar 

  • Novaes RML, Pazianotto RAA, Brandão M et al (2017) Estimating 20-year land-use change and derived CO2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states. Glob Chang Biol 23:3716–3728. https://doi.org/10.1111/gcb.13708

    Article  Google Scholar 

  • OECD (2017) Medium-Term Prospects for Major Agricultural Commodities 2017–2026 Brazil. www.agri-outlook.org

  • Ogle SM, Kurz WA, Green C et al (2019) Chapter 2: Generic methodologies applicable to multiple land-use categories. 2019 Refinement to 2006 IPCC Guidel Natl Greenh Gas Invent 1–59

  • Persson UM, Henders S, Cederberg C (2014) A method for calculating a land-use change carbon footprint (LUC-CFP) for agricultural commodities - applications to Brazilian beef and soy, Indonesian palm oil. Glob Chang Biol 20:3482–3491. https://doi.org/10.1111/gcb.12635

    Article  Google Scholar 

  • Ponsioen TC, Blonk TJ (2012) Calculating land use change in carbon footprints of agricultural products as an impact of current land use. J Clean Prod 28:120–126. https://doi.org/10.1016/j.jclepro.2011.10.014

    Article  Google Scholar 

  • RED II – Directive (EU) (2018) Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable source.

  • Reddy S, Panichelli L, Waterworth RM, Federici S, Green C, Jonckheere I, Suzuki K (2019) Chapter 3: Consistent representation of land. In: Calvo Buendia E, Tanabe K, Kranjc A, Baasansuren J, Fukuda M, Ngarize S, Osako A, Pyrozhenko Y, Shermanau P, Federici S (eds) pp 3–1

  • Saez de Bikuña K, Hamelin L, Hauschild MZ et al (2018) A comparison of land use change accounting methods: seeking common grounds for key modeling choices in biofuel assessments. J Clean Prod 177:52–61. https://doi.org/10.1016/j.jclepro.2017.12.180

    Article  Google Scholar 

  • Schmidt JH, Weidema BP, Brandão M (2015) A framework for modelling indirect land use changes in Life Cycle Assessment. J Clean Prod 99:230–238. https://doi.org/10.1016/j.jclepro.2015.03.013

    Article  Google Scholar 

  • Searchinger T, Heimlich R, Houghton RA et al (2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science (80)319:1238–1240. https://doi.org/10.1126/science.1151861

  • Shine KP (2009) The global warming potential-the need for an interdisciplinary retrial. Clim Change 96:467–472. https://doi.org/10.1007/s10584-009-9647-6

    Article  Google Scholar 

  • Sparovek G, Antoniazzi LB, Barretto A et al (2016) Sustainable bioproducts in Brazil: disputes and agreements on a common ground agenda for agriculture and nature protection. Biofuels, Bioprod Biorefining 10:204–221. https://doi.org/10.1002/BBB.1636

    Article  CAS  Google Scholar 

  • Tanaka K, Cavalett O, Collins WJ, Cherubini F (2019) Asserting the climate benefits of the coal-to-gas shift across temporal and spatial scales. Nat Clim Chang 9:389–396. https://doi.org/10.1038/s41558-019-0457-1

    Article  Google Scholar 

  • UN UN (2017) The Sustainable Development Goals Report 2017., 2017. New York

  • USEPA (2010) Regulation of fuels and fuel additives: changes to renewable fuel standard program

  • Witcover J, Yeh S, Sperling D (2013) Policy options to address global land use change from biofuels. Elsevier

    Book  Google Scholar 

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Acknowledgements

The authors are grateful to PhD. Professor Marcus Seferin (PUC-RS), PhD. Luis Barioni (Embrapa), and PhD. Rodrigo Alvarenga (Ghent University) for their comments on early versions of the manuscript, as well as to PhD. Bruno Alves (Embrapa) and PhD. Marcelo Moreira (Agroicone) for their comments on the history of amortization period definitions.

Funding

This research was funded by Embrapa project number 30.20.90.004.00.00, which granted research scholarships to V.M and D.G. The project was co-funded with public resources provided by the Brazilian Agricultural Research Corporation (Embrapa), and private resources provided by four industry associations: the Brazilian Feed Industry Association (Sindirações), the Brazilian Animal Protein Association (ABPA), the Brazilian Association of Vegetable Oil Industries (Abiove), and the Brazilian Association of Soybean Producers (Aprosoja).

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Authors and Affiliations

  1. Embrapa Meio Ambiente, Rodovia SP 340, km 127,5, Jaguariúna, SP, Brazil

    Vinícius Gonçalves Maciel, Renan Milagres L. Novaes, Ricardo Antonio Almeida Pazianotto, Danilo Trovo Garofalo & Marília I. S. Folegatti-Matsuura

  2. KTH Royal Institute of Technology, Stockholm, Sweden

    Miguel Brandão

  3. Department of Energy and Process Engineering, Industrial Ecology Programme, Norwegian University of Science and Technology, Trondheim, Norway

    Otávio Cavalett

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  1. Vinícius Gonçalves Maciel
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  2. Renan Milagres L. Novaes
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  3. Miguel Brandão
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  5. Ricardo Antonio Almeida Pazianotto
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Correspondence to Vinícius Gonçalves Maciel or Renan Milagres L. Novaes.

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The authors declare that there was a potential conflict of interest of industry associations, which was addressed through the following measures: open call for scholarship candidates and selection by the Embrapa team; submitting previous hypotheses, methods and results to scrutiny by independent specialized researchers; submitting the manuscript to a peer-review journal, and not involving the associations in the discussion about the results.

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Maciel, V.G., Novaes, R.M.L., Brandão, M. et al. Towards a non-ambiguous view of the amortization period for quantifying direct land-use change in LCA. Int J Life Cycle Assess 27, 1299–1315 (2022). https://doi.org/10.1007/s11367-022-02103-3

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