Enhancing life cycle impact assessment from climate science: Review of recent findings and recommendations for application to LCA

Levasseur, Annie; Cavalett, Otávio; Fuglestvedt, Jan S.; Gasser, Thomas; Johansson, Daniel; Jørgensen, Susanne Vedel; Raugei, Marco; Reisinger, Andy; Schivley, Greg; Strømman, Anders Hammer; Tanaka, Katsumasa; Cherubini, Francesco

doi:10.1016/j.ecolind.2016.06.049

Cited by 118 publications

(144 citation statements)

References 88 publications

(100 reference statements)

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“…They are used in areas such as life cycle assessment (e.g. Levasseur et al, 2016), ecosystem service study (e.g. Neubauer and Megonigal, 2015) and integrated assessment modelling (e.g.…”

Section: Introductionmentioning

confidence: 99%

Accounting for the climate–carbon feedback in emission metrics

et al. 2017

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Abstract. Most emission metrics have previously been inconsistently estimated by including the climatecarbon feedback for the reference gas (i.e. CO 2 ) but not the other species (e.g. CH 4 ). In the fifth assessment report of the IPCC, a first attempt was made to consistently account for the climate-carbon feedback in emission metrics. This attempt was based on only one study, and therefore the IPCC concluded that more research was needed. Here, we carry out this research. First, using the simple Earth system model OSCAR v2.2, we establish a new impulse response function for the climate-carbon feedback. Second, we use this impulse response function to provide new estimates for the two most common metrics: global warming potential (GWP) and global temperature-change potential (GTP). We find that, when the climate-carbon feedback is correctly accounted for, the emission metrics of non-CO 2 species increase, but in most cases not as much as initially indicated by IPCC. We also find that, when the feedback is removed for both the reference and studied species, these relative metric values only have modest changes compared to when the feedback is included (absolute metrics change more markedly). Including or excluding the climate-carbon feedback ultimately depends on the user's goal, but consistency should be ensured in either case.

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“…They are used in areas such as life cycle assessment (e.g. Levasseur et al, 2016), ecosystem service study (e.g. Neubauer and Megonigal, 2015) and integrated assessment modelling (e.g.…”

Section: Introductionmentioning

confidence: 99%

Accounting for the climate–carbon feedback in emission metrics

et al. 2017

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“…These methodological choices and assumptions have led to a wide divergence among published studies assessing the effectiveness of bioenergy for climate change mitigation (Brandão & Cowie, ; Zanchi, Pena, & Bird, ). One aspect that has received only scant attention in the past is the sensitivity of climate impact results to the impact assessment method applied (Breton et al, ; Helin et al, ; Levasseur et al, ; Plattner, Stocker, Midgley, & Tignor, ; Røyne et al, ), and the present paper focuses on that aspect.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the climate change effects of bioenergy systems: Comparison of 15 impact assessment methods

et al. 2019

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Ongoing concern over climate change has led to interest in replacing fossil energy with bioenergy. There are different approaches to quantitatively estimate the climate change effects of bioenergy systems. In the present work, we have focused on a range of published impact assessment methods that vary due to conceptual differences in the treatment of biogenic carbon fluxes, the type of climate change impacts they address and differences in time horizon and time preference. Specifically, this paper reviews fifteen different methods and applies these to three hypothetical bioenergy case studies: (a) woody biomass grown on previously forested land; (b) woody biomass grown on previous pasture land; and (b) annual energy crop grown on previously cropped land. Our analysis shows that the choice of method can have an important influence on the quantification of climate change effects of bioenergy, particularly when a mature forest is converted to bioenergy use as it involves a substantial reduction in biomass carbon stocks. Results are more uniform in other case studies. In general, results are more sensitive to specific impact assessment methods when they involve both emissions and removals at different points in time, such as for forest bioenergy, but have a much smaller influence on agricultural bioenergy systems grown on land previously used for pasture or annual cropping. The development of effective policies for climate change mitigation through renewable energy use requires consistent and accurate approaches to identification of bioenergy systems that can result in climate change mitigation. The use of different methods for the same purpose: estimating the climate change effects of bioenergy systems, can lead to confusing and contradictory conclusions. A full interpretation of the results generated with different methods must be based on an understanding that the different methods focus on different aspects of climate change and represent different time preferences.

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“…Emission fluxes from the latter two effects are generally time-dependent, particularly when land clearing is involved (instigating land use change (LUC) emissions) and/or long-rotation feedstocks are used (e.g., forestry biomass; Cherubini, Bright, & Strømman, 2012;Cherubini, Peters, Berntsen, Strømman, & Hertwich, 2011;O'Hare et al, 2009;Porsö, Hammar, Nilsson, & Hansson, 2017;Zetterberg & Chen, 2015). However, both methods neglect the fact that the climate impact of GHGs increases with the atmospheric residence time and may therefore lead to incomplete conclusions about (relative) system performance and the timing of climate mitigation benefits (Cherubini et al, 2013;Daystar et al, 2017;Kendall et al, 2009;Levasseur et al, 2016Levasseur et al, , 2010O'Hare et al, 2009). The conventional GHG-LCA approach often employs linear amortization of carbon stock changes, measured relative to the initial carbon stocks, over an arbitrary production period ).…”

Section: Introductionmentioning

confidence: 99%

“…The climate impact can be quantified according to different impact categories along the cause-effect chain (i.e., GHG emissions, radiative forcing, temperature change, and climate damages) at or over different analytical time horizons for instantaneous and cumulative metrics, respectively (Cherubini et al, 2013Levasseur et al, 2016). The climate impact can be quantified according to different impact categories along the cause-effect chain (i.e., GHG emissions, radiative forcing, temperature change, and climate damages) at or over different analytical time horizons for instantaneous and cumulative metrics, respectively (Cherubini et al, 2013Levasseur et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time

et al. 2018

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The climate impact of bioenergy is commonly quantified in terms of CO2 equivalents, using a fixed 100‐year global warming potential as an equivalency metric. This method has been criticized for the inability to appropriately address emissions timing and the focus on a single impact metric, which may lead to inaccurate or incomplete quantification of the climate impact of bioenergy production. In this study, we introduce Dynamic Relative Climate Impact (DRCI) curves, a novel approach to visualize and quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category (e.g., emissions, radiative forcing, and temperature change), equivalency metric, and analytical time horizon. The DRCI curves constructed for fourteen bioenergy systems illustrate how value judgments affect the merit order of bioenergy systems, because they alter the importance of one‐time (associated with land use change emissions) versus sustained (associated with carbon debt or foregone sequestration) emission fluxes and short‐ versus long‐lived climate forcers. Best practices for bioenergy production (irrespective of value judgments) include high feedstock yields, high conversion efficiencies, and the application of carbon capture and storage. Furthermore, this study provides examples of production contexts in which the risk of land use change emissions, carbon debt, or foregone sequestration can be mitigated. For example, the risk of indirect land use change emissions can be mitigated by accompanying bioenergy production with increasing agricultural yields. Moreover, production contexts in which the counterfactual scenario yields immediate or additional climate impacts can provide significant climate benefits. This paper is accompanied by an Excel‐based calculation tool to reproduce the calculation steps outlined in this paper and construct DRCI curves for bioenergy systems of choice.

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Enhancing life cycle impact assessment from climate science: Review of recent findings and recommendations for application to LCA

Abstract: International audienc

Cited by 118 publications

References 88 publications

Accounting for the climate–carbon feedback in emission metrics

Accounting for the climate–carbon feedback in emission metrics

Quantifying the climate change effects of bioenergy systems: Comparison of 15 impact assessment methods

Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time

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