Skip to main content
Log in

Life cycle inventories of electricity supply through the lens of data quality: exploring challenges and opportunities

  • LCA FOR ENERGY SYSTEMS AND FOOD PRODUCTS
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

Electricity is one of the main contributors to global environmental impacts, and its role as an energy carrier is expected to grow substantially. Consequently, reliable and accurate inventories of material and energy flows associated with electricity supply are essential in environmental assessments. This article aims to summarize existing challenges and opportunities in the modeling of life cycle inventories (LCIs) of electricity supply from a data quality perspective.

Methods

We systematically review the state-of-the-art in LCI modeling of current and future electricity supply worldwide. The analysis is structured according to the data quality characteristics proposed in ISO 14044: 2006: representativeness, completeness, consistency, reproducibility, uncertainty, data sources, and precision.

Results and discussion

Looking at existing LCIs through the lens of data quality, we observe difficulties in obtaining temporally and technologically representative data, while geographically representative data is still unavailable for some regions. Moreover, meta-analyses encountered issues of reproducibility combined with a lack of consistency across studies, impeding interstudy comparability. Additionally, some flows such as upstream fugitive emissions have been underestimated. The aforementioned issues have a negative impact on the quality of LCIs. Here, we provide recommendations on how several methods such as equilibrium models, regression, or parameterization can be used to improve data quality, underpinned by more powerful data formats. Open-source models, data platforms, as well as a list of key parameters to be reported are suggested to facilitate reproducibility and enhance transparency of electricity LCIs.

Conclusions

There are several methods and resources that can be used to improve LCIs of electricity supply, enabling more ambitious and powerful analyses. Nonetheless, special care should be taken concerning tradeoffs between different data quality aspects. For instance, more complex and accurate models may result in a loss of transparency and reproducibility unless additional reporting efforts are conducted. Other approaches, such as systematic parameterization do not compromise data quality and should be used to improve the consistency and reproducibility of inventories.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. Consequential LCA (CLCA) accounts for changes on flows in response to decisions and attributional LCA (ALCA) quantifies flows within a chosen temporal window (Curran et al. 2005). For further clarifications, see Ekvall and Weidema (2004).

  2. The review uses the following classification of uncertainty types: the inherent uncertainty (basic uncertainty) and uncertainty introduced by the use of imperfect data (i.e., use of data not completely representative for the context of use), here referred as epistemic uncertainty.

  3. Power theft needs to separately accounted in LCIs and can be up to 15 % in several countries. However, many of these countries lack of the means to accurately measure electricity use at end user level. See Smith (2004) for an overview of the issue.

  4. Because ILCD and Ecospold 2 were built around specific databases, they conform to particular needs and some differences preclude seamless transition between formats. For a review on the differences on parameterization and uncertainty modeling in Ecospold 2 and ILCD, see Cooper et al. (2012).

References

  • Amor MB, Billette de Villemeur E, Pellat M, Pineau P-O (2014a) Influence of wind power on hourly electricity prices and GHG (greenhouse gas) emissions: evidence that congestion matters from Ontario zonal data. Energy 66:458–469

    Article  Google Scholar 

  • Amor MB, Gaudreault C, Pineau P-O, Samson R (2014b) Implications of integrating electricity supply dynamics into life cycle assessment: a case study of renewable distributed generation. Renew Energy 69:410–419

    Article  Google Scholar 

  • Arushanyan Y, Ekener-Petersen E, Finnveden G (2014) Lessons learned - review of LCAs for ICT products and services. Comput Ind 65:211–234

    Article  Google Scholar 

  • Arvesen A, Birkeland C, Hertwich EG (2013) The importance of ships and spare parts in LCAs of offshore wind power. Environ Sci Technol 47:2948–2956

    Article  CAS  Google Scholar 

  • Arvesen A, Bright RM, Hertwich EG (2011) Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation. Energy Policy 39:7448–7454

    Article  Google Scholar 

  • Astudillo MF, Treyer K, Bauer C, Amor BM (2015) Exploring challenges and opportunities of life cycle Management in the Electricity Sector. In: Sonnemann G, Margni M (eds) Life cycle Manag, 1st edn. Springer, Heildelberg, pp. 295–306

    Chapter  Google Scholar 

  • Barros N, Cole JJ, Tranvik LJ, et al. (2011) Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat Geosci 4:593–596

    Article  CAS  Google Scholar 

  • Bouman EA, Ramirez A, Hertwich EG (2015) Multiregional environmental comparison of fossil fuel power generation—assessment of the contribution of fugitive emissions from conventional and unconventional fossil resources. Int J Greenh Gas Control 33:1–9

    Article  CAS  Google Scholar 

  • Breuer C, Seeger N, Moser A (2013) Determination of alternative bidding areas based on a full nodal pricing approach. IEEE Power Energy Soc Gen Meet. doi:10.1109/PESMG.2013.6672466

    Google Scholar 

  • Burkhardt JJ, Heath G, Cohen E (2012) Life cycle greenhouse gas emissions of trough and tower concentrating solar power electricity generation. J Ind Ecol 16:S93–S109

    Article  CAS  Google Scholar 

  • Caduff M, Huijbregts MAJ, Althaus HJ, et al. (2012) Wind power electricity: the bigger the turbine, the greener the electricity? Environ Sci Technol 46:4725–4733

    Article  CAS  Google Scholar 

  • Ciroth A, Muller S, Weidema B, Lesage P (2013) Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int J Life Cycle Assess. doi:10.1007/s11367-013-0670-5

    Google Scholar 

  • Collinge WO, Landis AE, Jones AK, et al. (2013) Dynamic life cycle assessment: framework and application to an institutional building. Int J Life Cycle Assess 18:538–552

    Article  CAS  Google Scholar 

  • Cooper JS, Noon M, Kahn E (2012) Parameterization in life cycle assessment inventory data: review of current use and the representation of uncertainty. Int J Life Cycle Assess 17:689–695

    Article  Google Scholar 

  • Curran MA (2006) Report on activity of task force 1: data registry - global life cycle inventory data resources. Int J Life Cycle Assess 11:284–289

    Article  Google Scholar 

  • Curran MA, Mann M, Norris G (2005) The international workshop on electricity data for life cycle inventories. J Clean Prod 13:853–862

    Article  Google Scholar 

  • Dandres T, Gaudreault C, Tirado-Seco P, Samson R (2011) Assessing non-marginal variations with consequential LCA: application to European energy sector. Renew Sust Energ Rev 15:3121–3132

    Article  Google Scholar 

  • Deane JP, Drayton G, Ó Gallachóir BP (2014) The impact of sub-hourly modelling in power systems with significant levels of renewable generation. Appl Energy 113:152–158

    Article  Google Scholar 

  • Earles JM, Halog A, Ince P, Skog K (2013) Integrated economic equilibrium and life cycle assessment modeling for policy-based consequential LCA. J Ind Ecol 17:375–384

    Article  Google Scholar 

  • Ecoinvent (2015) New data in ecoinvent 3.2. http://www.ecoinvent.org/database/ecoinvent-32/new-data-in-ecoinvent-32/new-data-in-ecoinvent-32.html. Accessed 13 Feb 2016

  • Ekvall T, Weidema BP (2004) System boundaries and input data in consequential life cycle inventory analysis. Int J Life Cycle Assess 9:161–171

    Article  Google Scholar 

  • ENTSO-E (2016) Transparency platform. https://transparency.entsoe.eu/. Accessed 23 Feb 2016

  • EPA (2015) Avoided emissions and generation tool. http://www3.epa.gov/avert/. Accessed 16 Feb 2016

  • Espinosa N, Hösel M, Jørgensen M, Krebs FC (2014) Large scale deployment of polymer solar cells on land, on sea and in the air. Energy Environ Sci 7:855

    Article  CAS  Google Scholar 

  • Frischknecht R (2004) Transparency in LCA-a heretical request? Int J Life Cycle Assess 9:211–213

    Article  Google Scholar 

  • Frischknecht R, Stucki M (2010) Scope-dependent modelling of electricity supply in life cycle assessments. Int J Life Cycle Assess 15:806–816

    Article  CAS  Google Scholar 

  • Gibon T, Wood R, Arvesen A et al (2015) A methodology for integrated, multiregional life cycle assessment scenarios under large-scale technological change. Environ Sci Technol 49:11218–11226

  • Guan D, Liu Z, Geng Y, et al. (2012) The gigatonne gap in China’s carbon dioxide inventories. Nat Clim Chang 2:672–675

    Article  CAS  Google Scholar 

  • Heath GA, Mann MK (2012) Background and reflections on the life cycle assessment harmonization project. J Ind Ecol 16:8–11

    Article  Google Scholar 

  • Heath GA, O’Donoughue P, Arent DJ, Bazilian M (2014) Harmonization of initial estimates of shale gas life cycle greenhouse gas emissions for electric power generation. Proc Natl Acad Sci U S A 111:E3167–E3176

    Article  CAS  Google Scholar 

  • Heck T, Bauer C, Dones R (2009) Technical paper n ° 4 . 1 - RS Ia “Development of parameterisation methods to derive transferable life cycle inventories”

  • Henriksson PJG, Zhang W, Guinée JB (2014) Updated unit process data for coal based energy in China including parameters for overall dispersions. Int J Life Cycle Assess 10:185–195

    Google Scholar 

  • Hertwich EG (2013) Addressing biogenic greenhouse gas emissions from hydropower in LCA. Environ Sci Technol 47:9604–9611

    Article  CAS  Google Scholar 

  • Hertwich EG, Gibon T, Bouman EA, et al. (2015) Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc Natl Acad Sci 112:6277–6288

    Article  CAS  Google Scholar 

  • Hsu DD, O’Donoughue P, Fthenakis V, et al. (2012) Life cycle greenhouse gas emissions of crystalline silicon photovoltaic electricity generation: systematic review and harmonization. J Ind Ecol. doi:10.1111/j.1530-9290.2011.00439.x

    Google Scholar 

  • Hunter K, Sreepathi S, DeCarolis JF (2013) Modeling for insight using tools for energy model optimization and analysis (Temoa). Energy Econ 40:339–349

    Article  Google Scholar 

  • IEA (2013) Transition to Sustainable Buildings: strategies and opportunities to 2050. IEA Publications, Paris

  • IEA (2014a) Energy Technology Perspectives 2014 Harnessing electricity’s potential. IEA Publications, Paris

  • IEA (2014b) Africa Energy Outlook. IEA Publications, Paris

  • IEA (2015) CO2 emissions from fuel combustion. IEA Publications, Paris

  • Igos E, Rugani B, Rege S, et al. (2015) Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios. Appl Energy 145:234–245

    Article  Google Scholar 

  • ISO (2006) ISO 14044 Environmental management—life cycle assessment—requirements and guidelines

  • Itten R, Frischknecht R, Stucki M (2014) Life Cycle Inventories of Electricity Mixes and Grid version 1.3

  • Joint Research Centre (2015) EPLCA - European reference life-cycle database. http://eplca.jrc.ec.europa.eu/ELCD3/index.xhtml. Accessed 20 Jul 2015

  • Kahrl F, Wang X (2014) Integrating renewables into power systems in China: a technical primer—power system operations. Regulatory Assistance Project, Bejing, China

  • Kannan R, Turton H (2013) A long-term electricity dispatch model with the TIMES framework. Environ Model Assess 18:325–343

    Article  Google Scholar 

  • Kintisch E (2014) Hunting a climate fugitive. Science 344:1472–1473

    Article  Google Scholar 

  • Laurent A, Espinosa N (2015) Environmental impacts of electricity generation at global, regional and national scales in 1980-2011: what can we learn for future energy planning? Energy Environ Sci 8:689–701

    Article  Google Scholar 

  • Lise W, Linderhof V, Kuik O, et al. (2006) A game theoretic model of the northwestern European electricity market-market power and the environment. Energ Policy 34:2123–2136

    Article  Google Scholar 

  • Lizin S, Van Passel S, De Schepper E, et al. (2013) Life cycle analyses of organic photovoltaics: a review. Energy Environ Sci 6:3136

    Article  CAS  Google Scholar 

  • Loulou R, Remme U, Kanudia A, et al (2005) Documentation for the TIMES Model Part I

  • Marvuglia A, Benetto E, Rege S, Jury C (2013) Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: critical review and proposed framework for biogas production. Renew Sust Energ Rev 25:768–781

    Article  CAS  Google Scholar 

  • Mathiesen BV, Münster M, Fruergaard T (2009) Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments. J Clean Prod 17:1331–1338

    Article  Google Scholar 

  • Meinshausen I, Müller-Beilschmidt P, Viere T (2014) The EcoSpold 2 format-why a new format? Int J Life Cycle Assess. doi:10.1007/s11367-014-0789-z

    Google Scholar 

  • Menten FM, Tchung-ming S, Lorne D, Bouvart F (2015) Lessons from the use of a long-term energy model for consequential life cycle assessment : the BTL case. Renew Sust Energ Rev 43:942–960

    Article  Google Scholar 

  • Moreau V, Bage G, Marcotte D, Samson R (2012) Estimating material and energy flows in life cycle inventory with statistical models. J Ind Ecol 16:399–406

    Article  Google Scholar 

  • Nature (2016) Scientific data. Sci data. http://www.nature.com/sdata/. Accessed 17 Feb 2016

  • NEEDS (2009) New energy externalities development for sustainability. Research stream 1a, FP6-SUSTDEV. http://www.needs-project.org/needswebdb/index.php. Accessed 23 Feb 2016

  • O’Donoughue PR, Heath GA, Dolan SL, Vorum M (2014) Life cycle greenhouse gas emissions of electricity generated from conventionally produced natural gas. J Ind Ecol 18:125–144

    Article  Google Scholar 

  • Padey P, Blanc I, Le Boulch D, Xiusheng Z (2012) A simplified life cycle approach for assessing greenhouse gas emissions of wind electricity. J Ind Ecol 16(suppl s1):S28–S38

    Article  CAS  Google Scholar 

  • Pauliuk S, Majeau-Bettez G, Mutel CL, et al. (2015) Lifting industrial ecology modeling to a new level of quality and transparency: a call for more transparent publications and a collaborative open source software framework. J Ind Ecol 19:937–949

    Article  Google Scholar 

  • Pehnt M, Oeser M, Swider DJ (2008) Consequential environmental system analysis of expected offshore wind electricity production in Germany. Energy 33:747–759

    Article  Google Scholar 

  • Pfenninger S, Hawkes A, Keirstead J (2014) Energy systems modeling for twenty-first century energy challenges. Renew Sust Energ Rev 33:74–86

    Article  Google Scholar 

  • Pineau P-O, Rasata H, Zaccour G (2011) Impact of some parameters on investments in oligopolistic electricity markets. Eur J Oper Res 213:180–195

    Article  Google Scholar 

  • Portugal-Pereira J, Koberle A, Lucena A, Szklo A, Schaeffer R (2016) Overlooked impacts of power generation: the life cycle side of the story. Energy. doi:10.1016/j.energy.2016.03.062

    Google Scholar 

  • Price L, Kendall A (2012) Wind power as a case study: improving life cycle assessment reporting to better enable meta-analyses. J Ind Ecol 16(supple s1):S22–S27

    Article  Google Scholar 

  • Raichur V, Callaway DS, Skerlos SJ (2015) Estimating emissions from electricity generation using electricity dispatch models: the importance of system operating constraints. J Ind Ecol 20(1):42–53

    Article  Google Scholar 

  • Rinne S, Syri S (2013) Heat pumps versus combined heat and power production as CO2 reduction measures in Finland. Energy 57:308–318

    Article  Google Scholar 

  • Smith TB (2004) Electricity theft: a comparative analysis. Energ Policy 32:2067–2076

    Article  Google Scholar 

  • Sovacool BK (2008) Valuing the greenhouse gas emissions from nuclear power: a critical survey. Energ Policy 36:2940–2953

    Google Scholar 

  • Steinmann ZJN, Hauck M, Karuppiah R, et al. (2014a) A methodology for separating uncertainty and variability in the life cycle greenhouse gas emissions of coal-fueled power generation in the USA. Int J Life Cycle Assess 19:1146–1155

    Article  CAS  Google Scholar 

  • Steinmann ZJN, Venkatesh A, Hauck M, et al. (2014b) How to address data gaps in life cycle inventories: a case study on estimating CO2 emissions from coal-fired electricity plants on a global scale. Environ Sci Technol 48:5282–5289

    Article  CAS  Google Scholar 

  • Teodoru CR, Bastien J, Bonneville M, et al. (2012) The net carbon footprint of a newly created boreal hydroelectric reservoir. Glob Biogeochem Cycles 26:1–14

    Article  Google Scholar 

  • Treyer K, Bauer C (2013) Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part I: electricity generation. Int J Life Cycle Assess. doi:10.1007/s11367-013-0665-2

    Google Scholar 

  • Treyer K, Bauer C (2014) Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part II: electricity markets. Int J Life Cycle Assess. doi:10.1007/s11367-013-0694-x

    Google Scholar 

  • UNEP (2011) Global Guidance Principles for Life Cycle Assessment Databases, a basis for greener processes and products. Publication of the UNEP/SETAC Life Cycle Initiative, UNEP, Paris

  • Vázquez-Rowe I, Marvuglia A, Rege S, Benetto E (2014) Applying consequential LCA to support energy policy: land use change effects of bioenergy production. Sci Total Environ 472:78–89

    Article  Google Scholar 

  • Volkart K, Bauer C, Boulet C (2013) Life cycle assessment of carbon capture and storage in power generation and industry in Europe. Int J Greenh Gas Control 16:91–106

    Article  CAS  Google Scholar 

  • Warner ES, Heath GA (2012) Life cycle greenhouse gas emissions of nuclear electricity generation: systematic review and harmonization. J Ind Ecol 16:73–92

    Article  Google Scholar 

  • Weber CL, Jiaramillo P, Marriott J, Samaras C (2010) Life cycle assessment and grid electricity: what do we know and what can we know? Environ Sci Technol 44:1895–1901

    Article  CAS  Google Scholar 

  • Weidema BP, Bauer C, Hischier R, et al (2013) Overview and methodology. Data quality guideline for the ecoinvent database version 3. Ecoinvent report 1(v3). St. Gallen

  • Weidema BP, Ekvall T, Heijungs R (2009) Guidelines for application of deepened and broadened LCA. Deliverable D18 of work package 5 on the CALCAS project. ENEA, The Italian National Agency on new Technologies, Energy and the Environment, Rome

  • Whitaker M, Heath GA, O’Donoughue P, Vorum M (2012) Life cycle greenhouse gas emissions of coal-fired electricity generation: systematic review and harmonization. J Ind Ecol 16(Suppl s1):S53–S72

    Article  CAS  Google Scholar 

  • Williams JH, DeBenedictis A, Ghanadan R, et al. (2012) The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity. Science 335:53–59

    Article  CAS  Google Scholar 

  • Zamagni A, Guinée J, Heijungs R, et al. (2012) Lights and shadows in consequential LCA. Int J Life Cycle Assess 17:904–918

    Article  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the financial support of the industrial partners of the Chair in Energy Sector Management (HEC Montreal) and also the NSERC discovery grant. The work was supported by the Swiss Competence Center for Energy Research (SCCER) “Supply of Electricity”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mourad Ben Amor.

Additional information

Responsible editor: Hans-Joerg Althaus

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Astudillo, M.F., Treyer, K., Bauer, C. et al. Life cycle inventories of electricity supply through the lens of data quality: exploring challenges and opportunities. Int J Life Cycle Assess 22, 374–386 (2017). https://doi.org/10.1007/s11367-016-1163-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11367-016-1163-0

Keywords

Navigation