Dynamic LCA: Framework and sensitivity analysis Sensitivity analysis of temporal parameters in a dynamic LCA framework

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Life cycle assessment for sustainability assessment

Life Cycle Assessment (LCA) is a method that calculates potential impacts associated with products, processes and services over their entire life cycle (i.e. cradle-to-grave).
ISO standards 14040-14044 specify the guide for conducting a LCA study (ISO, 2006a; ISO, 2006b). The use of this method is encouraged by many governments. European Union, Australia, Canada, Korea, Japan and the USA use LCA as main element in environmental policy (Guinee et al., 2010).
“The purpose of LCA is to compile and evaluate the environmental consequences of different options for fulfilling a certain function.” (Guinée et al., 2002).
The first LCA study is attributed to Harry E Teastley Jr in 1969 in which, in his work to Coca-Cola, he performed an environmental assessment for the comparison between a plastic and glass bottle, from the raw material to the end-of-life (recycling or incineration). Only the summary of this study was published in “Science Magasine” and no complete report was released (Hunt and Franklin, 1996). LCA has only been widely used since the beginning of 1990s. At this period, Heijungs et al. (1992) published a first “schema” in which the possible steps of an environmental analysis are described. Then, the Society of Environmental Toxicology and Chemistry (SETAC) released the guide “Guidelines for Life-cycle Assessment: A code of practice” in 1993 which aimed to present the general principles and framework for a LCA study. Finally, the International Organization for Standardization (ISO) constituted an international standard for LCA. The principles and framework for a LCA study are described in ISO 14040 and the requirements and guidelines are presented in ISO 14044 (ISO 2006a, 2006b).
ISO 14040-14044 (2016a; 2016b) divides a LCA study in four steps (Figure II.3): the definition of the goal and scope, the construction of the Life Cycle Inventory (LCI) based on mass and energy balances over the whole system life cycle, the Life Cycle Impact Assessment (LCIA) based on various impact calculation models, and the interpretation step (ISO, 2006a; ISO, 2006b).

Goal and scope definition

In the goal and scope definition step of LCA, the general information of the project is described. The problematic, objectives and scope of the study are defined. It is in this step where the functional unit (FU) and reference flows of the LCA study are defined. The FU describes the quantity of the production system being investigated. Functional unit is the specification in LCA that allows the products or services to be compared and analysed (Rebitzer et al. 2004). In theory, modelling a LCA study should comprise all processes in the life cycle of a service or product. However, it is impractical to consider all the processes involved in one production system. For this reason, the boundaries and limits of the studied system have also to be defined.

Life Cycle Inventory

The Life Cycle Inventory (LCI) step consists in the quantitative description of the production system. In this step the system boundaries, the flow diagrams, the data collection and allocation are established. The inputs from and outputs to environment which are correlated to a functional unit are accounted (Guinée et al., 2002). This is the most laborious step: all the flows in the studied system are cautiously accounted, i.e. hundreds to thousands of services, products, environmental interventions.
Currently, sequential method and matrix are the two broad techniques for the computational resolution of LCA inventory (Heijungs and Suh 2002). The calculation in the sequential approach starts with the functional unit processes and goes linearly through the supply chain for the required processes and products. The matrix approach considers the LCA as a system of linear equations which can be solved by linear algebra (Heijungs 1994, Heijungs and Suh 2002).
Considering the matrix resolution of LCI, the studied system can be represented by a system of linear equations (Equation II.1): = + Equation II.1.
The technological matrix A represents the exchange between processes. It corresponds to a matrix with zeros on its diagonal. The values ai,j in the matrix represent the quantity of process i (represented in the row) necessary for the production of a unit of output of process j (represented in the column) (Suh and Huppes, 2005). Finally, the vector s can be calculated by: = ( − )−1 Equation II.2 where I denotes the identity matrix.

Dynamic Life Cycle Inventory

Temporal information in the LCI step have been considered as being part of the uncertainty and using probabilistic scenario analysis (see e.g. Huijbregts 1998; Huijbregts et al. 2001). The issue with this approach is that the temporal variability is aggregated with other sources of uncertainty. Some studies considered the distribution of emissions over time (Collinge et al. 2013; Kendall et al. 2009; Pehnt 2006; Stasinopoulos et al. 2012; Zhai and Williams 2010) in limited parts of the production system, mostly for the foreground process. Considering time dimension in every level of the production chain would be an almost impossible task without a clearly defined methodology and dedicated computational tools.
Collet et al. (2014) proposed to consider the time dimension only for selected economic/environmental flows based on their influence on the impact values. Time scales were qualitatively attributed to impact categories and inventory flows. The authors defined a perturbation factor based on the sensitivity analysis of impact results on the variation of economic/environmental flows. The perturbation factor was used to identify the combination process-economic flow-impact for deserving a dynamic study. However, a dynamic approach was proposed for neither LCI nor LCIA.
Beloin-Saint-Pierre et al. (2014) developed an approach called Enhanced Structure Path Analysis, in which environmental interventions (elementary flows, i.e. emissions and natural resources consumed) are distributed over time by considering the convolution product between temporal distributions related to the products demand and temporal distributions related to elementary flows. This method has the merit to be the first attempt of modelling a dynamic LCI; however it still lacks important parameters for describing the process network and a full and complete relationship with an LCA database.
In the global context of DyPLCA project, Tiruta-Barna et al. (2016) have provided a dynamic method for LCI, dealing with the complex supply chain and processes involved in the life cycle system, and linking the method to traditional LCA tools and databases. At full-term of development the method will enable easier implementation of temporal characteristics by LCA practitioners.

Dynamic Life Cycle Impact Assessment

In the field of LCIA, climate change is the most studied impact category for the consideration of the time dimension. The first attempt to alleviate the inconsistency of a fixed time horizon was the proposition of CF for 20, 100 and 500 years – this is what actually LCA databases offer yet.
Cherubini et al. (2011) applied a dynamic method which considers a dynamic carbon removal by biomass. Their method is based on radiative forcing parameter. They combined a function of carbon dioxide decay in atmosphere and the carbon uptake given by a rate of biomass growth. However, dynamic results for midpoint or endpoint climate change impact are not given as the calculated results are integrated in a single unit-based index. Also, only carbon dioxide was assessed and other substances that contribute to climate change were neglected.
Levasseur et al. (2010) and Kendall (2012) studied time dependency in climate change impact by calculating temporal characterization factors (CF) based on radiative forcing indicator. These CF were calculated for 1 year interval and were applied to a case study with a simple dynamic emission. This method still conserves the main principle of conventional LCA, i.e. the CF utilization and the time horizon concept. Nonetheless, thousands of CF values must be calculated for few substances and a limited time horizon, which makes the method heavy to apply.
In case of toxicity impact category, CFs for 20, 100 and 500 years were calculated to be in accordance with the time horizons used for GWP, as it was considered that they provided a useful interval for policy decisions. Those CFs were calculated with USES-LCA model for CML LCIA method (Huijbregts et al., 2000a, 2000b, 2001).
Similarly to the method of Levasseur for climate change, Lebailly et al. (2014) proposed to calculate CFs for substances for every 1 year passed after the initial emission. In this aim, the authors used the USEtox® model for a pulse, unit load of substance by solving the fate model for these particular conditions. Then they applied the method to a case study – the use of zinc as fertilizer in agriculture. Moreover, the case study did not address the behaviour of non-persistent substances (organics) and did not implement complex, temporal LCIs, as actually occur in real LCA case studies. Here again, as in case of climate change, the main principles and, at the same time limitations, of conventional LCA are present, i.e. preservation of the ‘characterization factor’ concept, a particular calculation condition (pulse emission) and time horizon.

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Temporal LCI model and DyPLCA tool

An extended description of the temporal LCI model is presented in Tiruta-Barna et al. (2016); only the main principles are recalled below.
LCA conventional method and databases provide a technological (A) matrix and an environmental intervention (B) matrix. After setting a functional unit, the scaling (S) vector is calculated (as shown in Introduction chapter). Technological matrix provides the network of the process system as the non-diagonal values indicate the quantitative relationships between processes.
The temporal LCI model is based on the conventional inventory (matrixes A and B) and on a few temporal parameters and functions. Process dynamics is represented by the functions alpha and beta and the parameters r, T, t0, all defined in Table II.1 and Figure II.1. Supply function is defined through and parameters.

Database adaptation for chemical and bio-processes

The ISIC groups which were designated to LISBP laboratory were mainly related to chemical industries and waste treatment processes. In order to characterize the temporal parameters of these processes, ecoinvent was analysed as well the reports available for its datasets. When no data was retrieved in the ecoinvent documents, external resources were analysed. Even though a division was done to diminish the quantity of datasets analysed by each working group, the amount of data to characterize was still important. Consequently, arbitrary datasets were chosen in order to identify the temporal parameters of the different ISIC groups. The same values of temporal parameters were then attributed to an entire ISIC group once the analysis of a great number of datasets indicated that they applied for most of processes. Some processes were cited as exceptions as their temporal parameters do not correspond to the ones of the ISIC group and they will be described in dedicated sections. Finally, even though efforts to meticulously analyse the database were done, some processes which were attributed the general ISIC group temporal parameters may be part of exceptions and vice-versa.
In the following, only one category of reference products is presented. For sake of simplicity, all other categories investigated are presented in annex (SI I). The same work methodology was applied.

Manufacture of basic chemicals

Ecoinvent database presents 1224 activities which are included in the ISIC group “Manufacture of basic chemicals”. The activity “Chemical plant, organics” is the infrastructure process which is present in most of the datasets in this ISIC group. For this reason, the representative period (T) parameter is based in the infrastructure lifetime of “Chemical plant, organics” activity. The value presented in this dataset is 50 years and the same value is also applied for land use (Althaus et al, 2007).
The value for the parameter delta was chosen as the “consensus” of different datasets. “Methanol from natural gas” and “Methyl chloride / Tetrachloroethylene, at regional storage” are some of the processes which showed a value of 2 months for storage of products before shipping (Althaus et al, 2007). For solid chemicals, the storage time is also considered 2 months as described in the section “Storage building, chemicals, solid” of Althaus et al, (2007). Therefore, the value of 2 months is then considered as delta for the ISIC group “Manufacture of basic chemicals”.
The production time in a chemical process can last between hours to days because it depends on the time required for the downstream, the upstream, the time of reaction, etc. For this ISIC group, the general value assumed for the parameter r was 1 day as chemicals processes do not usually last long.
The function alpha was considered as constant value of 0.02 kg.year-1 (i.e. 1 kg/50 years) As the emissions from a process are linked to the process functioning, the beta function was also considered as a constant: it was calculated by the product between the same constant value and the value of b element in the intervention matrix B for the respective substance. The constant value means that the production and the associated emissions are steady during the production period r.

Table of contents :

Chapter I. General introduction: Life cycle assessment and sustainable development
I.1. Sustainable development: finding the balance towards the Earth
I.1.1. Environmental sustainability
I.2. Life cycle assessment for sustainability assessment
I.2.1. Goal and scope definition
I.2.2. Life Cycle Inventory
I.2.3. Life Cycle Impact Assessment
I.2.4. Interpretation step
I.2.5. Some limitations of the LCA method
I.3. Temporal consideration in LCA
I.3.1. Dynamic Life Cycle Inventory
I.3.2. Dynamic Life Cycle Impact Assessment
I.4. Research objectives
I.4.1. Thesis outline
Chapter II. Dynamic Life Cycle Inventory: temporal database for dynamic processes 
II.1. Introduction
II.2. Method
II.2.1. Temporal LCI model and DyPLCA tool
II.2.2. Development of a temporal database
II.3. Results and discussions
II.3.1. Database adaptation for chemical and bio-processes
II.3.2. Exceptions in DyPLCA model
II.4. Conclusions
Chapter III. A dynamic approach for Climate Change impact
III.1. Introduction
III.2. Method
III.2.1. Modelling of GHG behaviour and effects
III.2.2. Conventional assessment and metrics
III.2.3. Time consideration in climate change impact
III.3. Results and discussion
III.4. Method conclusion
Case study: Environmental assessment of bioenergy production from microalgae based systems
III.5. Introduction
III.6. Methodology
III.6.1. Bioenergy production systems
III.6.2. Energy balance and analysis
III.6.3. Life Cycle Assessment
III.6.4. Dynamic LCA – climate change
III.6.5. Process description
III.7. Results and discussion
III.7.1. Energy balance and analysis
III.7.2. Life Cycle Assessment
III.7.3. Dynamic LCA – climate change
III.8. Case study conclusion
Chapter IV. A dynamic approach for Toxicity impact categories Operational integration of time dependent toxicity impact category in dynamic LCA
IV.1. Introduction
IV.2. Method
IV.2.1. Toxicity Impact Assessment – USEtox® method
IV.2.2. Time consideration in toxicity impact assessment
IV.2.3. Integration in a Dynamic LCA framework – DyPLCA
IV.3. Results and Discussion
IV.3.1. Discussion of the dynamic toxicity approach
IV.3.2. Testbed case results
IV.4. Conclusions
Chapter V. Dynamic LCA: Framework and sensitivity analysis Sensitivity analysis of temporal parameters in a dynamic LCA framework
V.1. Introduction
V.2. Methods
V.2.1. Dynamic LCA framework
V.3. Case study
V.3.1. Sensitivity analysis
V.4. Results and discussion
V.4.1. Influence of the dynamic LCI profile and the time span of the impact calculation
V.4.2. Influence of the time step of the dynamic impact model resolution
V.5. Conclusion
Case study: French electricity mix for the period between 2010 and 2070
V.6. Introduction
V.7. Case study: French electricity mix for a sustainable future
V.7.1. Goal and scope definition
V.7.2. LCI
V.7.3. Dynamic LCI
V.7.4. LCIA
V.8. Results and discussions
V.8.1. Inventory
V.8.2. Climate change assessment
V.8.3. Toxicity assessment
V.9. Case study conclusions
Chapter VI. Conclusions and perspectives
References

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