Streamlining scenario analysis and optimization of key choices in value chains using a modular LCA approach
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The environmental performance of products or services is often a result of a number of key decisions that shape their life cycles (e.g., techology choices). This paper introduces a modular LCA approach that is capable of reducing the effort involved in performing scenario analyses and optimization when several key choices along a product’s value chain lead to many alternative life cycles.
The main idea is that the value chain of a product can be divided into interconnected but exchangeable modules, which together represent a full life cycle. A module is comprised of unit processes from the practitioner’s LCI database. The inputs, outputs, and system boundaries of each module can be tailored to the context of the studied system. Alternatives arise whenever multiple modules produce substitutable products. Unlike in conventional LCI databases, no copies are necessary to represent the same process with different inputs. A module-product matrix is used to store this information. It can be used as a basis for an automated scenario analysis of all alternatives or as an input to an optimization model.
Results and discussion
Our approach is illustrated in two case studies: (1) Passenger car fuel choices are modeled by 15 modules representing 33 alternative value chains for diesel, petrol, natural gas and electric cars. The automated comparison of LCA results indicates that electric mobility is often the preferable option from a climate perspective, but impacts depend strongly on the electricity source. (2) A dynamic optimization model including stocks is built from eight modules to analyze the optimal use of wood for material and energy applications. Results indicate that although direct substitution benefits are higher for energy applications, cascading use of wood can maximize environmental performance over the entire life cycle.
The modular LCA approach permits an efficient modeling and comparison of alternative product life cycles, enabling practitioners to focus on key decisions. It can be applied to exploit a potential that is hidden in LCI databases, which is that they contain many specific inventories but not all useful combinations in the context of scenario analyses. The user-defined level of abstraction that is introduced through modules can be helpful in the communication of LCA results. The modular approach also facilitates the integration of LCA and optimization as well as other industrial ecology methods. An open source software is provided to enable others to apply and further develop our implementation of a modular LCA approach.
KeywordsLife cycle assessment (LCA) Life cycle inventory (LCI) Linear programming Optimization Scenario modeling Transport Wood
Although LCA practitioners often face situations similar to this example, it is usually quite cumbersome to model all the alternatives present in such systems using standard LCA software. A main reason for this is that the mathematical structure that is generally used to represent the supply chains described in LCI databases is not well designed for extensive scenario analyses. It relies on a process-process linking, as each process input along the supply chain of a product must come from a clearly defined upstream process in order to perform LCA calculations (Heijungs and Suh 2002; Suh and Huppes 2005). This mathematical structure (often referred to as the technology matrix) requires process copies to represent the same process with inputs from different upstream suppliers, as shown in Fig. 1b. In situations where choices between substitutable products accumulate over several steps along a value chain, the number of processes that are required to represent this grows exponentially. This means that to describe all 144 alternatives in Fig. 1a, a total of 212 processes would be necessary (Fig. 1b).1
Instead of modeling all alternatives within an LCI database, it is, in this case, probably more efficient to first calculate the LCA results of the individual life cycle stages, and then the corresponding sums for each alternative value chain. Such modular LCA approaches have been applied previously, e.g., to the modeling of food supply chains (Jungbluth et al. 2000) and in the context of type III environmental labeling (ISO 2006a, b) and environmental product declarations (EPD) (Buxmann et al. 2009; Rebitzer 2005). Modules describe gate-to-gate processes or life cycle stages, which can be modeled by unit processes. However, their system boundaries are usually larger than that of a single unit process due to energy generation, production of ancillaries, as well as recycling and waste-management processes that are linked to a certain life cycle stage (Rebitzer 2005). Strategic choices regarding the system boundaries of the modules can thereby lead to a simplified life cycle representation reflecting the decision factors (key choices) that are relevant to a given actor, e.g., a company or a policy maker (Buxmann et al. 2009). However, the possibilities for modeling alternative value chains based on combinations of interchangeable modules are currently rather limited in existing LCA software. Therefore, practitioners regularly model such systems manually (i.e., copying and reconnecting inventories) or switch to other modeling environments, if the number of alternatives is larger. Both approaches are associated with considerable extra work, which highlights the need for more streamlined scenario assessments tools.
A different, but complementary, approach is to treat the underdetermined system described in Fig. 1a as an optimization problem with the objective to minimize its environmental impacts. A benefit of using optimization techniques is the possibility of considering additional constraints, such as limited raw material supplies or production capacities. LCA and linear programming have been combined since the 1990s (Azapagic and Clift 1998) for applications ranging from process design (Gassner and Maréchal 2009; Guillén-Gosálbez et al. 2007) to regional resource management (Saner et al. 2014; Vadenbo et al. 2014) and the optimization of large-scale systems (You et al. 2012). A potential advantage of using optimization approaches is also that solutions have been proposed regarding typical LCA problems, e.g., multiple objectives (Azapagic and Clift 1999; Guillén-Gosálbez 2011; Tan et al. 2008) and uncertainties (Guillén-Gosálbez and Grossmann 2010; Tan 2008), leading possibly to more robust results than standard LCA.
In this paper, we present a tool to model and analyze scenarios for key choices along product value chains based on a modular LCA approach (case study transportation). Further, building upon work by Saner et al. (2014), we show how modules can be used as a direct input to an optimization problem (case study wood). In addition, a tool that enables the creation and linking of modules as well as automated scenario analyses is provided as free open source software (Steubing 2014).
2.1 General approach
As described in the introduction, the fundamental idea is to use interconnected, but exchangeable, modules to model the life cycle of products. Modules can be understood as user-defined life cycle stages with product inputs and outputs. Several modules can be linked based on their inputs and outputs to complete value chains. Alternative value chains arise whenever several modules produce the same, substitutable product. Each module is described by unit processes from an LCI database. These unit processes can be both processes modeled by the practitioner as well as processes that come with background LCI databases, such as ecoinvent (Ecoinvent 2015). As unit processes usually have inputs from other unit processes, the supply chain of a module can be as complex as the supply chain of any other unit process in an LCI database. However, user-defined cutoffs may need to be introduced to specify the upstream system boundaries of modules in order to avoid overlaps and double counting. When modules are linked, several partial value chains are combined to represent the full life cycle of a product.
The linking of modules is based on product inputs and outputs. This can be described by a (possibly non-square) matrix, which we call module-product matrix (see Fig. 2, step 2). It is different from the (square) technology matrix of LCI databases, which describes the linking of processes, in the sense that it truly distinguishes between modules (processes) and products and allows the same, substitutable, product to be the input or output of several modules. The drawback and reason why such matrices are not used in LCI databases is that they describe possible alternatives instead of predetermined value chains, which prohibits traditional approaches to solve the inventory problem and perform LCA calculations (Heijungs and Suh 2002; Suh and Huppes 2005).
Figure 2 (step 2) shows all the alternatives when connecting the modules. It includes two technology choices, with two alternatives each, resulting in a total of four different value chains for transport: the choice of natural gas from region 1 or region 2, and the choice of a combustion engine car versus an electric vehicle. The corresponding module-product matrix is non-square, as it includes alternative suppliers for the substitutable products natural gas and transport.
In order to model and compare alternative value chains, the main work of the practitioner consists of defining suitable modules and their inputs and outputs (step 1), which depends on the study context. Steps (2) and (3a), the linking of modules and LCA calculations, can be performed by software for all alternative value chains (provided that each module produces one output only, see 0). In addition, the system of linked modules can be directly integrated into an optimization model step (3b) (see 0). All steps in Fig. 2, except for 3b, are supported in our open source modeling environment (Steubing 2014).
2.2 The link between modules and the LCI database
Part of the supply chain of the module “Transport, natural gas car” (see Fig. 2 based on the technology matrix of ecoinvent 2.2. The demand vector f 1 leads to the scaling vector s 1 . A cutoff for the input of natural gas can be introduced by subtracting the demand of natural gas from the demand vector f 2 , leading to s 2
2.3 Cutoff implementation
To cutoff the production of natural gas and make the module represent a life cycle stage (as opposed to a full life cycle), the amount of natural gas that is required for one person-kilometer of transport is subtracted from the demand vector f 2 as in Table 1. As a result, the scaling factor of the process “natural gas, high pressure, at consumer [CH]” becomes zero, which corresponds to a cutoff. In order to assure that the necessary input of natural gas is delivered by another module, it is stored in the module-product matrix (see Fig. 2).
2.4 LCA of a module
2.5 Comparing alternative value chains
The module-product matrix in Fig. 2 describes alternative value chains whenever it contains several modules that produce substitutable products. While this is an efficient representation of alternatives, it does not allow regular LCA calculations directly, as suppliers cannot be uniquely identified (e.g., in Fig. 2, it would be unclear whether natural gas would be delivered from region 1 or region 2). Therefore, an intermediate step is required: For each alternative value chain contained in the module-product matrix, a smaller, square matrix needs to be constructed that uniquely links modules (as in conventional LCI databases). Based on this, LCA results can be calculated and compared for each alternative value chain.
2.5.1 Determining all alternatives
As shown in Fig. 2, the module-product matrix has a graph representation that distinguishes two types of nodes: modules and products. A recursive depth-first graph traversal algorithm is used to determine all possible value chain combinations contained in a module-product matrix. Its general logic is as follows: starting at the demanded product, the algorithm goes back through the value chains defined in the graph. When a product has multiple upstream producers, each of them will be considered in an alternative value chain. In contrast, when a module has several product inputs, all of them must be delivered simultaneously. The algorithm and a figure describing this logic are provided in the Electronic Supplementary Material.
2.5.2 LCA calculations
Module-products matrix for the transport by natural gas car
There is also a faster way to perform LCA calculations for many alternatives: It consists of first calculating the LCA results for each module and then summing these up based on the scaling factors provided in s′. In this case, the number of required LCA calculations scales with the number of modules instead of the number of alternative value chains.
2.6 Multifunctional modules
Some processes produce several products, such as refineries or combined heat and power plants. The integration of multifunctional processes in LCI databases may result in non-square, overdetermined technology matrices, for which traditional methods fail to solve the inventory problem (Heijungs and Suh 2002). This problem is conventionally solved by system expansion or allocation (ISO 2006a, b). Both approaches lead to square technology matrices where each coproduct can be demanded independently. While these approaches could also be applied to multifunctional modules and the module-product matrix, it may, in some cases, be preferable to model multifunctional processes as they are in reality, i.e., considering their entire impacts, as well as the ratios of coproducts. For example, when designing chemical plants or energy systems, it is important to consider the integration of coproducts to avoid suboptimal outcomes.
2.7 Using linked modules in optimization problems
Equation (6), where B and Q are matrices for environmental interventions and their characterization, respectively, formulates the goal of the optimization—to minimize environmental impacts—and thereby provides a metric for choosing between alternatives. Eq. (7) determines the constraints of the system. It differs from matrix-based LCA by requiring the system’s output to be greater or equal than the final product demand f. This means that the solution may include product surpluses in cases where multioutput activities do not generate outputs in exactly the necessary ratios to satisfy the final demand. The decision variable in this context is the scaling vector s, which represents the use of technologies. An algorithm, such as the simplex algorithm, is usually applied to solve the optimization problem and identify the set of technologies that satisfy the product demand with minimal environmental impacts.
While this is the basic optimization model for modules, case-specific constraints may need to be added. An application of this model with additional constraints to a case study of the optimal use of wood is described in 3.2.
3 Application in case studies
3.1 Comparison of alternative scenarios for passenger car transport
It can be observed that electric cars tend to perform better in this illustrative case study than cars with combustion engines. While the smaller electric city car performs better than the larger electric car, another important influence is the source of electricity, which can also lead to higher GHG emissions than combustion engines. In the case of combustion engines, diesel cars outperform petrol and natural gas cars. For the natural gas car, the source of natural gas plays an important role, due to differences in transportation distance and methane leakage.
Combustion engine-driven cars emit GHG emissions mainly at the transportation stage, while the main source of GHGs for electric cars is at the electricity generation stage. The observed GHG emissions at the transportation stage for electric cars arise from the fact that this stage also includes the road infrastructure and maintenance. While this could easily have been excluded or modeled as a “road” input, we leave it here at this to remind the reader that the definition of what a module comprises and how its products are called is case-specific and up to the practitioner.
3.2 Optimal use of wood in a cascading system
3.2.1 Model description
In order to illustrate how modules (including multioutput ones) can be used within an optimization problem, we devised a linear programming case study around the use of forest wood for material and energy applications. The central question raised is whether it is environmentally beneficial to use wood in cascades, i.e., first for a material applications and then for energy. As the case study is of illustrative nature, we limit the discussion to GHG emissions, although other impact categories could be assessed with the same model.
We assume that the demand from this system is 2 buildings and 20 TJ heat, which represent, respectively, material and energy applications of wood. A supply constraint applies for the harvest of forest wood, which is assumed to be limited to 1000 m3 (as round wood and residual wood are coproduced at a ratio of 0.65 to 0.35, respectively, their supplies are limited to 650 and 350 m3). Due to this constraint, wood alone is insufficient to meet the product demand. To provide conventional alternatives, the processes “concrete building” and “fuel oil heating” are included.
We also extend the optimization model with time periods and two types of stocks: one for harvestable wood in the forest and one for timber in buildings. The harvestable wood stock increases each period by 1000 m3, and we assume that wood left in the forest can be harvested in later periods without losses. Timber in buildings on the other hand has a discrete lifespan: once the building is demolished, it becomes residual wood. For the sake of simplicity, our model includes two time periods of the length of a building lifespan (e.g., 60 years). After each period, all of the previously stored timber becomes residual wood and is burned to produce energy.
Parameters, variables, and indices used in the optimization model
Total/module-specific environmental impact
Module-product matrix describing the linking of modules
Demand (2 buildings; 20 TJ heat)
Harvestable wood that is added to the harvestable wood stock each period (1000 m3)
Lifetime of a product in a stock (wood in buildings: 1 period)
Scaling factors for modules
Harvestable wood stock
Description (and subsets)
Products (p wood = round wood, residual wood)
Modules (m harvest = wood harvest)
As shown in Fig. 5, the building and heating demand can only partly be satisfied by wood. The remainder is delivered by means of conventional technologies. The model was set up like this to reflect the fact that wood is a scarce resource in many countries. As wood cannot fully supply the product demand, the optimization model uses it for those applications with the highest environmental leverage. As a result, round wood is used entirely for material applications in period 1, whereas it is used entirely for direct energy purposes in period 2. The reason for this behavior is that the substitution of fuel oil for heating yields a higher benefit per unit wood used than the use of wood in buildings. The reason why round wood is used in a material way despite lower direct benefits is the fact that the end-of-life residual wood from buildings can be reused energetically in period 2. Since there are only two periods, round wood in period 2 is directly used for energy.
4.1 Modular LCA approach
4.1.1 Areas of application
In the context of explorative studies, the modular LCA approach offers a powerful tool to model and compare alternative value chains, possibly reducing the necessary time investment (see also 4.1.1.). A key element is the module-product matrix that links modules based on product inputs and outputs. It is a compact representation of alternative value chains, without the need for additional copies of datasets for alternative suppliers as in conventional LCI databases. However, it relies on the assumption that products from alternative suppliers are in fact substitutable. It is the responsibility of the practitioner to check whether this can be justified in a given context. As modules are most likely tailored to a specific context, a drawback is that they may not be reusable in a different context.
Modules can also be used in optimization models. This provides the possibility to add aspects that are not included in a standard LCA, such as multifunctional modules or constraints. Further, the temporal dimension (Beloin-Saint-Pierre et al. 2014; Finnveden et al. 2009; Levasseur et al. 2010) and the change of material stocks are important parameters that are generally not considered in LCA studies (Cherubini et al. 2011; Levasseur et al. 2012; Pauliuk and Müller 2014). The case study illustrates that six equations are sufficient to formulate an optimization problem that extends the LCA model with a simple temporal dimension, stocks, and supply and demand constraints. The use of modules could therefore potentially facilitate the combination of LCA with other industrial ecology methods such as material flow analysis (MFA). Modules could also be used in combination with input-output analysis (IOA) to describe the environmental impacts related to economic sectors with consistent system boundaries (i.e., cutoffs).
4.1.2 Unlocking the hidden potential of LCI databases
While LCI databases contain many specific inventories, they are designed to describe average value chains as opposed to all possible alternatives. For example, ecoinvent version 3 (Ecoinvent 2015) contains different inventories for the generation of electricity, depending on the energy source, but then combines all producers of electricity in a geographical region into a market to represent a production volume weighted average electricity mix (Treyer and Bauer 2014; Weidema et al. 2013). Downstream consumers are by default linked to the market mix, while direct links to specific electricity producers are the exception. Therefore, many alternative value chains—for which inventories exist—are not readily available to the practitioner. The transportation case study is a good example showing how modules can help to unlock the additional potential of inventories that are contained in LCI databases. Naturally, it is the responsibility of the practitioner to combine inventories in a meaningful way. Version 3 of ecoinvent has made an interesting development supporting this by formally distinguishing between processes and products. As a result, the same product (e.g., “electricity, high voltage”) may now be produced by several activities. This facilitates the search for alternative producers of substitutable products and, therefore, the design of modules.
4.1.3 Do practitioners gain time?
LCA studies are usually done under time constraints. Using modules to perform, e.g., scenario analyses, is associated with an up-front time investment. From our experience, much of it is related to developing a better system understanding, which enables the definition of suitable system boundaries to capture the key choices in modules. At the same time, modeling alternatives in conventional LCA software may be equally time-consuming or even limiting, e.g., if 212 processes need to be modeled like in the example of Fig. 1. A further consideration is that error corrections or sensitivity analyses can be realized much quicker in a small number of modules than with many processes. A clear advantage of the modular approach is also that the LCA calculation time scales with the number of modules and not with the number of alternatives (i.e., using the example of the introduction, 14 calculations instead of 144). As LCA is usually an iterative procedure, this can be relevant. Nevertheless, whether practitioners save time by applying the modular approach depends on the complexity of the system, the practitioner’s knowledge and modeling skills, and the LCA software itself.
4.1.4 Software implementation
In order to perform the individual modeling steps, we have developed the Activity Browser (Steubing 2014), which is an open source LCA software with a graphical user interface that builds upon the brightway2 LCA framework (Mutel 2015). The source code and documentation are provided online for the LCA community to apply and further develop the presented approach or to include it in other LCA software.
4.2 Case studies
Two case studies illustrate the application of the module framework for modeling and comparing alternatives as well as for extending an LCA problem to include constraints, a temporal dimension, and stocks. While they are mainly of illustrative nature, they are based on inventories from the ecoinvent database, which are regularly used as a reference within the LCA community and beyond. The comparison of 33 passenger car transportation alternatives showed that electric cars could significantly reduce GHG emissions compared to cars using conventional fuels. However, the size of the car, the battery, and the electricity source have substantial influence on environmental performance and may also make electric cars perform worse than conventional ones.
Regarding the optimal use of wood, we show that using wood in a use cascade, i.e., first for material and then for energy applications, may be advantageous for mitigating climate change. However, the potentially significant time gap between the material and the energy use (as much as a building’s life time) adds ambiguity to this conclusion as it is difficult to predict what will be the alternative (substitution) to wood energy in the future (Gärtner et al. 2013). Further, the assumption that wood substitutes other material or energy sources needs to be carefully examined (Gustavsson and Sathre 2011). These and other factors, such as the relation between product design, recycling efficiencies, and end-of-life energy substitution (Höglmeier et al. 2015), should be further investigated to better understand the conditions for an environmentally beneficial cascading of wood.
A modular LCA approach is presented with the potential to considerably reduce the effort involved in comparing alternative products life cycles, especially in situations, where numerous alternatives along the value chain lead to many possible scenarios. The fundamental idea is to represent the individual life cycle stages and their alternatives through modules and then recombine these into alternative value chains. While modules link to processes in LCI databases, their interdependencies and the way they can be combined are described in a separate module-product matrix. The latter describes the product inputs and outputs of each module, which are customized to a specific context by the practitioner. Alternatives arise whenever several modules produce substitutable products. Unlike in conventional LCI databases, no copies are necessary to represent the same process with different inputs. Further, the modular LCA approach can be used to exploit the hidden potential of LCI databases, which provide many specific inventories, but not all useful combinations in the context of scenario analyses, as illustrated by the transportation case study.
The modular approach can also serve other purposes, such as yielding a simplified the representation of a life cycle for communication purposes. The use of modules to formulate an optimization model enables the consideration of constraints and acts as a potential bridge to other industrial ecology methods, such as MFA and IOA.
The creation of modules and an automated scenario analysis are supported within an open source LCA software. However, the approach is generic and could be implemented in other software as well.
Technically, it is sufficient that LCI databases record the product inputs of processes, instead of specific suppliers (ISO 14048 2002). Therefore, systems as in Fig. 1a can be described in an LCI database. However, an additional step, involving possibly additional information and linking rules, is necessary to link the process inventories to uniquely determined supply chains and perform LCA calculations. Ecoinvent, for example, exploits this as of version 3 to produce different database versions (termed “system models”) for attributional and consequential LCA from a common, underlying LCI database. In practice, LCA software providers and practitioners have mainly worked with these process-linked LCI database versions. Due to their mathematical structure, separate processes are necessary to represent the same process with different product suppliers. In the following, the term “LCI database” is used as a synonym for process-linked versions of LCI databases.
This research was funded within the National Research Programme “Resource Wood” (NRP 66, www.nfp66.ch) by the Swiss National Science Foundation (project no. 136612). We would like to thank the two anonymous reviewers as well as Carl Vadenbo for their valuable input.
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