Industry-Cost-Curve Approach for Modeling the Environmental Impact

Policy Analysis pubs.acs.org/est

Industry-Cost-Curve Approach for Modeling the Environmental Impact of Introducing New Technologies in Life Cycle Assessment Arne Kaẗ elhön,† Niklas von der Assen,† Sangwon Suh,‡ Johannes Jung,† and André Bardow*,† †

Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstrasse 8, 52062, Aachen, Germany Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, United States

‡

S Supporting Information *

ABSTRACT: The environmental costs and benefits of introducing a new technology depend not only on the technology itself, but also on the responses of the market where substitution or displacement of competing technologies may occur. An internationally accepted method taking both technological and market-mediated effects into account, however, is still lacking in life cycle assessment (LCA). For the introduction of a new technology, we here present a new approach for modeling the environmental impacts within the framework of LCA. Our approach is motivated by consequential life cycle assessment (CLCA) and aims to contribute to the discussion on how to operationalize consequential thinking in LCA practice. In our approach, we focus on new technologies producing homogeneous products such as chemicals or raw materials. We employ the industry cost-curve (ICC) for modeling market-mediated effects. Thereby, we can determine substitution effects at a level of granularity sufficient to distinguish between competing technologies. In our approach, a new technology alters the ICC potentially replacing the highest-cost producer(s). The technologies that remain competitive after the new technology’s introduction determine the new environmental impact profile of the product. We apply our approach in a case study on a new technology for chlor-alkali electrolysis to be introduced in Germany.

■

INTRODUCTION The development and introduction of environmentally benign technologies is an essential element of global strategies to mitigate climate change and other environmental impacts.1,2 Measuring the environmental consequences of introducing a new technology, however, is challenging. The environmental costs and benefits of introducing a new technology depend not only on the technology itself, but also on the responses of the market where substitution or displacement of competing technologies may occur.3−9 An internationally accepted method that takes both technological and market-mediated effects into account, however, is still lacking in life cycle assessment (LCA). So-called Consequential Life Cycle Assessment (CLCA) aims at measuring the effect of a certain policy or technology focusing on both technological and market-mediated effects.10−14,6 Although CLCA is well-defined at a conceptual level, an international consensus has yet to be reached with regard to operational elements including the system boundary of consequences and modeling choices.14,15 In CLCA, market-mediated effects have been modeled using partial equilibrium (PE) modeling,16−19 computational general equilibrium (CGE) modeling,20,21 and energy system analysis (ESA).22−24 PE models focus on a subset of a national economy, while CGE models in principle cover all economic sectors involved. Ekvall and Andrae,16 for example, used PE models of lead and scrap lead markets to quantify the © XXXX American Chemical Society

environmental impacts of banning lead in solder pastes. Kløverpris et al.21,25 developed a framework to estimate land use changes due to changes in crop consumption based on a CGE model covering 57 economic sectors in 87 regions. ESA is used to jointly model technological and market-mediated effects in energy systems, for example, national and international electricity supply systems. Mathiesen et al.23 and Lund et al.22 suggested using ESA for modeling the effects of marginal electricity supplies. Eriksson et al.24 used ESA to compare the environmental impacts of district heating based on waste incineration with combustion of biomass or natural gas. The applicability of the market models currently used in CLCA, however, is limited for the assessment of a new technology’s introduction. Elasticity functions in PE and CGE models are based on econometric analyses of existing markets, and are usually unavailable at the level of granularity necessary to distinguish competing technologies.26,13 Furthermore, the underlying assumptions of PE and CGE models such as fixed elasticity functions, simultaneous optimization of economic agents, perfect information, and markets in equilibrium stages may not be justified in reality.26 Received: March 27, 2014 Revised: June 3, 2015 Accepted: June 10, 2015

A

DOI: 10.1021/es5056512 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Policy Analysis

Environmental Science & Technology Besides, quantifying the environmental implications of introducing a new technology requires reliable estimates for the potential market penetration of the new technology. Such estimates, however, may contain large uncertainties. Fukushima and Kuo27 and Kuo and Fukushima28 therefore estimated the environmental impacts of introducing new energy technologies for several levels of market penetration. Chen et al.29,30 presented a graphical approach to assess the environmental impacts of future products as a function of the potential market volume. These authors determined a theoretically possible range of environmental impacts based on environmentally bestand worst-case scenarios for technologies used to supply the market. By this means, the effect of new technologies can be assessed without market modeling. However, despite the advantage of lower data requirements, the resulting range may in some cases be too wide to support decision-making. In this work, we propose a new approach to model the environmental impacts of introducing a new technology under the framework of life cycle assessment (LCA). Our approach is motivated by CLCA, and aims to contribute to the operationalization of consequential thinking in LCA practice. However, the approach can also be used for scenario modeling in attributional LCA. Our approach focuses on new technologies producing homogeneous products, that is, products where consumers perceive no differences between the products from different suppliers.31 Typical examples are chemicals and raw materials. The environmental assessment of technologies producing homogeneous products is relevant, because such processes are often energy- and emissionintensive.32 Our approach employs the industry cost-curve (ICC)33,34 for modeling market-mediated effects. For markets for homogeneous products, the ICC allows us to model the substitution of competing technologies at a level of granularity sufficient to distinguish them. Since projections for the sales volume of a new technology typically contain large uncertainties, all results of our approach are determined as a function of the sales volume. The following section describes the scope of the approach and the procedure employed. In the subsequent section, we demonstrate the practical applicability in a case study on a new technology for chlor-alkali electrolysis to be introduced in Germany. Finally, we present the case study results and discuss the approach.

Figure 1. Graphical illustration of effects from introducing a new technology: technological and market-mediated effects are distinguished by their location in the horizontal dimension and organized according to their stage in the life cycle. The arrows indicate causal relationships originating from the introduction of the new technology (filled box). For homogeneous products with constant market demand, relevant effects are indicated by the shaded area.

Technological and market-mediated effects are distinguished by their location in the horizontal dimension, whereas the vertical axis organizes them according to their stage in the life cycle. Technological effects include additional raw material demand as well as the production, use, and disposal of the product. These effects occur within the life cycle of the product of the new technology itself. Market-mediated effects take place through raw material markets, sales markets, complementary product markets, and waste management markets. In our approach, we assume constant market demand in sales markets. This assumption implies that the same amount of the respective homogeneous product will be used, recycled and disposed after the introduction of the new technology. Thus, there are no downstream effects from the use-phase onward. Consequently, the system boundaries of our approach only include effects highlighted by the shaded area in Figure 1. We believe that assuming constant market demand allows us to capture the main effects of introducing a new technology while significantly reducing the amount of data required, and thus improving the practical applicability of our approach. The effect of this assumption is further discussed in the Discussion section, and quantified for the case study in the Supporting Information. Industry-Cost-Curve Based Environmental Assessment Approach. Based on the definition of the system boundaries, our approach proceeds in four steps: In step 1, we apply the industry cost curve to determine for all possible sales volumes, which competing technologies will be substituted by the new technology. In step 2, we investigate the environmental impacts associated with the production of one product unit by the new technology and each competing technology. In step 3, we calculate the environmental impacts of introducing the new technology as a function of its sales volume. Finally, in step 4, we complement the results from step 3 with the environmental best- and worst case scenarios of substitution in sales markets. In the following, all steps are explained in detail.

■

MATERIALS AND METHODS Scope. Our approach aims at determining the environmental impacts of introducing a new technology producing a homogeneous product. Therefore, the system boundaries of our approach should in principle include all activities that are affected by the introduction of the new technology, and that trigger environmental impacts.35 An initial change in the system affects other activities through chains of causal relationships.36 These causal relationships can be either physical or nonphysical relationships. Physical relationships are based on flows of energy and matter, while nonphysical relationships exist through behavioral and market effects. By tracing all chains of causal relationships originating from the initial change in the system, changes in affected activities can be determined. In this work, changes due to purely physical relationships are denoted technological effects; other changes are referred to as marketmediated effects. Behavioral effects are not considered. Figure 1 shows technological and market-mediated effects associated with the introduction of a new technology. B


Policy Analysis

Environmental Science & Technology

To apply the industry cost curve in our approach, we first need to identify the affected sales market. Guidelines for market delimitation are provided by Weidema et al.37 Subsequently, we identify the technologies used to supply this market. Here, the term “technology” describes the entire production chain, that is, two identical production plants with different supply structures are considered as two different technologies. This strict definition of the term “technology” allows the consideration of heterogeneity in both costs and environmental efficiencies among suppliers using the same production technique. Finally, we determine the production capacities and marginal production costs of all competing technologies. Information on production costs, capacities, and technologies used may be obtained from sector analyses, company reports, and industrial associations.38 Based on these data, the industry cost curve provides the relationship between substituted technologies and the sales volume of the new technology. Step 2: Environmental Impact Intensities. As a next step, we determine the environmental impact intensities of the new technology and each competing technology using LCA. The environmental impact intensity Ii is here defined as upstream environmental impact of producing one product unit with technology i. The environmental impact intensities are determined for all environmental impact categories of interest. For each impact category, the following steps 3 and 4 are carried out separately. Step 3: Environmental Impacts of Introducing the New Technology. In this step, we determine the environmental impacts of introducing the new technology as a function of its sales volume Y*. For each environmental impact category of interest, we first determine the marginal environmental impact e(Y*). Subsequently, we integrate e(Y*) to obtain the net environmental impact E(Y*). The net environmental impact is the cumulated environmental impact in the respective impact category caused by the introduction of the new technology. The resulting curves for both functions e(Y*) and E(Y*) are exemplified in Figure 3 (full lines). The market situation in Figure 3 relates to the hypothetical market introduced in Figure 2. At small sales volumes of the new technology, product units of technology T1 are substituted, since T1 has the highest marginal production costs among the competing technologies (cf. Figure 2). The marginal environmental impact of the substitution of technology T1 by the new technology T* is given by the difference in emission intensities of T* and T1. Consequently, for 0 ≤ Y* ≤ Y1, the marginal environmental impact e(Y*) is constant: e(Y*) = I* − I1. At the end of this range, all capacities of T1 are substituted and the technology with the second highest marginal production cost (T2) will be substituted next. For this substitution, we again obtain a constant section, that is, e(Y*) = I* − I2. We continue this sequence until the sales volume Y* equals the sum of all Yi. By this means, we obtain the following relation:

Step 1: Substitution Effects in Sales Markets. In step 1, we aim at determining which competing technologies will be substituted by the new technology at a given sales volume. Since we are focusing on technologies producing homogeneous products, we can employ a market model that is specifically designed for homogeneous products: the industry cost curve.33,34 In this model, suppliers producing the same homogeneous product are assumed to achieve the same sales price for their product. Individual suppliers can only remain in the market if their marginal production costs are below the sales price. Consequently, in equilibrium, the sales price equals the marginal production costs of the least cost-efficient supplier that is still needed to satisfy the overall market demand. Figure 2a illustrates the industry cost curve for a hypothetical market supplied by four technologies T1−T4. The production

Figure 2. (a) Industry cost curve of a market supplied by four technologies T1−T4. (b) Industry cost curve of the same market after the introduction of the new technology T*. The vertical dashed lines represent the overall market demands. The horizontal dashed lines indicate the sales prices.

capacities of individual technologies are arranged according to their marginal production costs, beginning with the lowest. The vertical dashed line represents the overall market demand. The resulting sales price is indicated by the horizontal dashed line. Figure 2b shows the effect of a new technology T* entering the market. Due to the additional production capacities of T*, there will be an excess in production, resulting in price competition among suppliers. The price competition will be lost by suppliers using the least cost-efficient technology, here technology T1. The market reaches a new equilibrium in which the sales price equals the marginal production costs of T2. If the sales volume of T* further increases, other technologies will be substituted in descending order of their marginal production costs.

e(Y *) = I * − Ik k

for ∑ Yi − 1 ≤ Y * ≤ i=1

k

∑ Yi ; i=1

Y0 ≡ 0 k ∈ [1, n]

(1)

Here, e(Y*) represents the marginal environmental impact of introducing the new technology as a function of its sales volume Y*. The environmental impact intensities and the production capacities of the n competing technologies are C


Policy Analysis


possible range of environmental impacts (shaded area in Figure 3b). Figure 3b shows that the environmental impacts of introducing a new technology may strongly depend on its sales volume. The graphical representation of our approach allows the determination of the environmentally optimum sales volume (here: Ynew = ∑Yi) and local minima of environmental impacts. The comparison between the LCA results based on market modeling and the theoretically possible range of environmental impacts allows the assessment of the environmental efficiency of the substitution mechanism as further discussed in the Discussion section.

■

CASE STUDY FOR CHLOR-ALKALI ELECTROLYSIS To demonstrate the practical applicability of our approach, we apply it to a new technology for chlor-alkali electrolysis entering the German market. The new technology uses oxygendepolarized cathodes (ODC) to jointly produce gaseous chlorine and caustic soda from brine and oxygen according to the following reaction: 2NaCl + H 2O + 0, 5O2 → Cl 2 + 2NaOHaq

The key benefit of the so-called ODC technology lies in a 30% reduced electricity demand for electrolysis compared to the current state-of-the-art membrane technology.39 Jung et al.40,41 compare the environmental impacts of ODC technology with the current state-of-the-art technology using attributional LCA. Their results reveal a lower global warming impact for ODC technology. The environmental impacts of a potential market introduction, however, have not been investigated by previous studies. In this section, this investigation is performed based on the LCA approach proposed in the previous chapter. Since the primary purpose of the case study is to demonstrate the workflow of our approach, we focus only on the global warming impact category. Other environmental impact categories can be assessed in the same way. Step 1: Substitution Effects in Sales Markets Due to the Introduction of ODC Technology. The introduction of the new ODC technology will affect the chlorine market and the caustic soda market. In Germany, both chlorine and caustic soda are almost exclusively produced via chlor-alkali electrolysis.42 There are 21 chlor-alkali electrolysis plants using three different electrolysis technologies: mercury technology, diaphragm technology, and membrane technology.42 All three technologies produce chlorine, caustic soda, and hydrogen from brine according to the so-called chlor-alkali reaction:

Figure 3. Exemplary graphical representation of the LCA results. Figure 3a shows the marginal environmental impact e of introducing a new technology. Figure 3b illustrates the net environmental impact E. The full lines represent the results based on the substitution order determined through the industry cost curve. The shaded area in Figure 3b represents the theoretically possible range of environmental impacts.

indicated by Ii and Yi, respectively. The indices i ϵ [1,n] reflect the order of marginal production costs of competing technologies starting from the highest (order of substitution). Based on the function e(Y*) for the marginal environmental impact, the net environmental impact E over the sales volume Y* is calculated by E(Y *) =

∫

(2)

2NaCl + 2H 2O → Cl 2 + 2NaOHaq + H 2

(3)

Thus, the chlorine and caustic soda markets are interdependent: the same processes supply both markets, and the sales volumes correspond to the stoichiometric relationship specified in eqs 2 and 3.43 Furthermore, due to the toxic properties of chlorine, its transport is kept to a minimum.44 Competition between different suppliers is therefore limited geographically: In 2012, the cumulated German imports and exports of chlorine45 represented less than 0.5% of the produced quantities.42 In this case study, we therefore assume that the introduction of ODC technology in Germany will only affect national producers. Existing production plants can be further classified in terms of hydrogen use, which significantly affects the environmental

e(Y *)dY *·

Step 4: Best and Worst Case Scenarios. In the last step, we contrast the results from step 3 with the environmental bestand worst case scenario for substitution in sales markets. The determination of the environmental impacts in the best- and worst case scenario is carried out as in step 3, but with different orders of substitution: In the best case scenario, competing technologies are substituted in the order of their environmental impacts starting from the environmentally least favorable technology. In the worst case scenario, competing technologies are substituted in the opposite order. The resulting curves for both scenarios represent the boundaries of the theoretically D


Policy Analysis


Table 1. Chlor-Alkali Technologies in Germany: Capacities, Sum of Relevant Cost Factors, Substitution Order Rank, and Global Warming Impact Intensities H2 use

capacity Yi/ktchlorinea

sum of cost factors/€/tchlorine

substitution order rank

Mercury

reactant fuel none

374 299 75

241 265 306

6 3 1

global warming impact intensity /tCO2‑eq/tchlorine 3.37 3.45 3.67

Diaphragm

reactant fuel none

570 456 114

220 244 285

7 5 2

2.92 3.00 3.22

Membrane

reactant fuel none

1505 1204 301

184 208 249

9 8 4

2.62 2.70 2.93

electrolysis technology

ODC

1.69

impacts, as discussed in detail by Jung et al.41 The hydrogen produced according to reaction 3 is either used as reactant, as fuel, or is flared without further utilization. Worldwide, 50% of the hydrogen produced via chlor-alkali electrolysis is used as reactant, 40% as fuel, and 10% is flared.39 In this work, we assume the same use pattern for the hydrogen produced. Hence, we differentiate between nine different technologies: three electrolysis technologies each with three hydrogen utilization options. These technologies as well as their corresponding production capacities in Germany are summarized in the first three columns of Table 1. The order in which competing technologies will be substituted by the ODC technology follows from the marginal production costs of the competing technologies. These costs can be determined through the analysis of cost factors. Here, only those cost factors that differ among technologies are relevant. Differences in marginal production costs of chlorine and caustic soda result from the consumption of electricity and steam, as well as from the revenue generated by the sale of the byproduct hydrogen. In contrast, expenses for salt, water, precipitants, anode reactivation, personnel, taxes, insurance, repairs, and maintenance are approximately the same for all technologies.46 The revenue from hydrogen depends on its further utilization. The highest revenue can be achieved by selling the hydrogen for use as reactant. We assume that the sales price then equals the marginal production costs of the most common hydrogen production process in Germany, that is, methane steam reforming.47 Lower revenue is assumed if the hydrogen is used as fuel. Here, the sales price is assumed to equal the price of an equivalent amount of natural gas based on the lower heating value. No revenue is generated from flaring the hydrogen. The sum of relevant cost factors equals the sum of electricity and steam costs minus the revenue from hydrogen. A detailed calculation of the cost factors of each competing technology is provided in the Supporting Information. The results are summarized in Table 1, as well as the resulting order of substitution according to the industrycost-curve. Step 2: Environmental Impact Intensities of ChlorAlkali Electrolysis Technologies. As a next step, we determine the environmental impact intensities of ODC technology and all competing technologies for the global warming impact category using LCA. The functional unit is one product unit, that is, 1 ton of chlorine and 1.13 tons of caustic soda. The system boundaries for the ODC technology include

the electrolysis process itself, a caustic soda concentration process, and processes for the supply of inputs of the electrolysis and the concentration process. The concentration process takes place after the electrolysis process and is required to increase the concentration of the caustic soda from 32 mass percent to the industrial standard concentration of 50 mass percent. The cumulated in- and outputs of the electrolysis process and the concentration process are summarized in Table 2. Negative values indicate inputs, while positive values indicate Table 2. Unit Process Data for ODC-, Membrane-, Diaphragm-, and Mercury Process Including Caustic Soda Concentration to the Industrial Standard Concentration of 50 Mass Percent (for ODC, Membrane, and Diaphragm)a flow electricity steam O2 H2O NaCl Cl2 (NaOH)aq H2 a

unit kWh kWh t t t t t t

ODC −1558 −180 −0.25 −0.254 −1.75 1 1.128

membrane −2790 −180

diaphragm −2970 −610

mercury −3560

−0.508 −1.75 1 1.128 0.028

−0.508 −1.75 1 1.128 0.028

−0.508 −1.75 1 1.128 0.028

Data from refs 41 and 49.

outputs. Processes for the supply of all inputs except NaCl are included in the system boundaries. The NaCl supply is not considered for ODC technology and all competing technologies, because all technologies require the same amount of NaCl. Thus, the substitution of one product unit produced by an existing technology by one product unit produced by ODC technology does not affect the overall NaCl supply. The system boundaries for membrane- and diaphragm technology also include processes for electrolysis, caustic soda concentration, and the relevant inputs (Table 2). In the concentration processes for membrane- and diaphragm technology, the initial concentrations are 32 and 12 mass percent, respectively. The system boundaries for the mercury technology do not include a concentration process since the electrolysis process directly produces caustic soda with a concentration of 50 mass percent. Consequently, the production chain only involves the electrolysis process itself and processes for the inputs specified in Table 2. Auxiliary processes such as raw material preparation and product E


Policy Analysis


global warming impact of introducing ODC technology in Germany. Positive values indicate an impact increase, while negative values indicate an impact reduction. The horizontal axis shows the sales volume of ODC technology. The full line represents the LCA results based on the industry cost curve. The shaded area illustrates the theoretically possible range of environmental impacts in the global warming impact category. The lower- and upper boundary of the shaded area represent the global warming impacts in the best- and worst case scenario of substitution in sales markets.

treatment, as well as the manufacturing, installation, and deconstruction of plants are neglected for all technologies. Due to technical similarities, these auxiliary processes are expected to be similar for all chlorine production technologies. At the same time, the contribution of auxiliary processes to the global warming impact of chlorine production is very low (about 0.1% for electrolyzer construction and recycling), as shown by the detailed analysis presented by Jung et al.41 For the electricity supply, we assume a marginal energy technology, that is, electricity generation from hard coal. Hard coal power plants are chosen, because these power plants are the base load power plants with the lowest marginal production costs among those with currently free capacities in Germany.48 Consequently, hard coal power plants represent the marginal technology in the ICC (cf. step 2 of the Materials and Methods section), and are therefore most likely to be affected by variations in energy demand. Further processes are included to account for the avoided environmental burden due to the production of the byproduct hydrogen. The choice of these so-called avoided burden processes is again based on the marginal production technology and depends on the hydrogen utilization pathways: we assume that hydrogen used as reactant avoids the production of hydrogen by methane steam gas reforming. Methane steam gas reforming is chosen, because more than 80% of the hydrogen produced as primary product (not as by product) is produced using this process.47 Hydrogen used for thermal energy generation is assumed to avoid the supply of natural gas for thermal energy generation. No avoided burden process is included for hydrogen being flared. The environmental impact intensities in the global warming impact category are calculated using 100-year global warming potentials (GWP100), and summarized in the last column of Table 1. More details on the LCA calculations are provided in the Supporting Information. Steps 3 and 4: Environmental Impacts of Introducing ODC Technology in Germany. The case study results are illustrated in Figure 4. The vertical axis represents the net

■

RESULTS The expected global warming impacts determined from our approach are shown in Figure 4 for the case study on the potential market introduction of ODC technology in Germany. Our study indicates that the introduction of ODC technology in Germany will lead to a reduction in global warming impact at all stages of market penetration (full line in Figure 4). The actual impact reduction depends on the sales volume of ODC technology: the higher the sales volume of this new technology, the higher the impact reduction is. From a climate-protection point of view, increasing the sales volume of ODC technology is therefore advisable at all stages of market penetration. The marginal impact reduction, however, decreases with an increasing sales volume: at high sales volumes, it is about half as high as at low sales volumes. Thus, increasing production results in much higher global warming impact reductions at low sales volumes than at higher sales volumes. The comparison between the results based on market modeling (full line) and the environmental best case scenario of substitution (lower boundary of the shaded area) allows the assessment of the environmental eff iciency of the substitution mechanism in sales markets. A substitution mechanism is environmentally efficient if competing technologies are substituted in the environmentally most favorable order, that is, the order of their environmental impacts beginning with the highest. In case of an environmentally efficient substitution mechanism, the LCA results based on market modeling equal the results in the environmental best-case scenario of substitution. In Figure 4, both curves exhibit very similar shapes. Hence, the market mechanism in the sales markets of the chlor-alkali industry is close to being environmentally efficient in the global warming impact category. This is due to the fact that the energy demand is both the most significant cost factor and the largest contributor to global warming for all chlor-alkali electrolysis technologies. The case study demonstrates that substitution effects in sales markets may have a high impact on the LCA results. These substitution effects largely depend on the future sales volume of the new technology, which is typically difficult to predict. Therefore, normalized indicators (for example environmental impact per product unit) cannot provide a comprehensive picture of the potential environmental impacts if the sales volume is uncertain and large-scale market effects are involved. Rather than assuming a certain sales volume, the proposed approach determines the environmental impacts as a function of the sales volume. Hence, it is particularly suitable to assess new technologies featuring an uncertain sales volume. The shaded area in Figure 3b and 4 represents best- and worst case scenarios for substitution effects in sales markets. This shaded area is inspired by the graphical representation of CLCA of future technologies proposed by Chen et al.29 However, methodological differences between the two

Figure 4. Results of the case study: annual global warming impact of ODC technology introduction in Germany (E; y-axis) depending on the annual sales volume of ODC technology (Y*; x-axis). The full line displays the results based on market modeling. The shaded area represents the theoretically possible range of global warming impacts. F


Policy Analysis

Environmental Science & Technology approaches exist: Chen et al.29 determine environmentally bestand worst case scenarios for the technology mix used to produce a product as a function of the overall market volume. In contrast, in our approach, the overall market volume is assumed to be constant, but the sales volume of the new technology is varied to determine the theoretically possible range of environmental impacts. The lower- and upper boundaries of this range represent the environmental impacts in the best- and worst case scenarios for substitution in sales markets, respectively. It is important to note that the results of our case study are sensitive to choices of marginal technologies and avoided burden processes, which are subject to uncertainties. For marginal energy technologies, for example, Mathiesen et al.23 identified considerable inaccuracies in past CLCA studies potentially leading to false conclusions. It is therefore recommended to assess the influence of the choice of marginal technologies and avoided burden processes on the LCA results. Such sensitivity analysis, however, has not been conducted in this work, since the primary aim of the case study is to demonstrate the practical application of our ICC based approach.

interdependence between price and demand is not considered in the ICC approach. If the price elasticity of demand is known, however, the ICC approach can be designed to reflect possible changes in demand due to price change. For example, the demand at time t + 1 can be determined in response to the price in time t using the price elasticity of demand. In section 5 of the Supporting Information, the effect of considering the price elasticity of demand in the ICC approach is quantified for the case study. Furthermore, it is important to note that our approach models so-called short-term effects,36 that is, effects within existing production capacities. Effects on future investment decisions (long-term effects36) are not considered. Modeling the short-term effects of a large-scale technology introduction is only relevant if (1) the duration of the short-term effects is long enough to cause substantial environmental impacts, and (2) the new technology can reach a significant market share within short time. For the case of introducing a new technology producing a homogeneous product, the duration of short-term effects can be long, because homogeneous products are often produced in large-scale production plants with long plant lifetimes. Chlor-alkali electrolysis plants, for example, have plant lifetimes of up to 60 years.44 During this period of time, the technical parameters of the production plant such as input/ output ratios are largely determined by the plant design. Thus, the short-term effects of substituting such a production plant may be long-lasting. In addition, a new technology can reach a significant market share within short time if it is implemented in a large-scale production plant. For example, the capacity of the biggest German chlor-alkali electrolysis plant is sufficient to satisfy about 23% of the German chlorine demand (in 2012).42 Furthermore, the capacities of new chemical plants are often bigger than those of older plants to exploit economies of scale.50,51 Thus, the implementation of a new technology in a new production plant can substitute more than one older plants. For this case, our approach can illustrate how the impact reductions depend on the new technology’s sales volume. However, if the conditions of long-lasting short-term effects and a fast market entry are not satisfied, our approach provides a too simplistic picture. In addition, the ICC assumes perfect competition in sales markets. In reality, however, market imperfections exist, for example, through imperfect information. For example, buyers may not know all suppliers and their prices. As a result, they do not necessarily make optimal decisions to minimize their procurement costs as assumed by the ICC. Market imperfections and suboptimal decision-making are not accounted for in the present approach. Furthermore, even though heterogeneity among production plants using the same production technique can in principle be considered in our approach (see step 2 of the Materials and Methods section), such a detailed assessment requires specific data that may be difficult to obtain. In this work, we propose a new approach for the environmental assessment of introducing a new technology. Our approach adopts new ways in (1) modeling marketmediated effects in LCA based on the ICC and (2) determining environmental impacts of introducing a new technology as a function of its sales volume. The advantage of our approach lies in its ability to model substitution effects in sales market at a level of granularity suitable for distinguishing between competing technologies producing the same homogeneous product, and its applicability to new technologies with an

■

DISCUSSION The innovation of the suggested approach lies in the application of the industry cost curve (ICC) for modeling market-mediated effects in LCA. The ICC approach aims in particular at providing a practical tool for CLCA. Compared to PE and CGE models used in CLCA, the ICC has the advantage of a higher level of granularity, suitable to distinguish between competing technologies producing the same product. Therefore, the ICC is capable of modeling substitution effects among competing technologies or even individual production plants producing the same product. These substitution effects have been shown to be of major importance for the environmental assessment of a new technology (see case study). The advantage of the higher level of granularity results from different data requirements: PE and CGE models are based on elasticity functions derived from econometric analyses of (usually) existing markets. The availability of elasticity functions is limited, especially at a high level of granularity.26,14 In contrast, the ICC is based on process and price data down to the level of individual plants, which can be obtained from databases for a large variety of technologies. However, the detailed modeling of the sales market in the ICC approach comes at the cost of a simplified modeling of markets within the supply chain: the effects of raw materials consumption are determined as marginal effects in isolation from each other, as discussed in detail in section 1 of the Supporting Information. Therefore, the interdependence between markets within the supply chain is only partially captured. This contrasts with CGE models better reflecting the interconnections within our global economy by linking all sectors within the economy and translating changes within one sector into changes within other sectors.10 However, in principle, it may also be possible to capture these market interconnections at a high level of granularity using the ICC. For this purpose, the ICC needs to be simultaneously applied to all markets within the supply chain, which seems to be a promising approach. In addition, while PE and CGE models endogenously determine the demand together with the price, in the ICC approach, the demand is exogenously given. Consequently, the G


Policy Analysis


(9) Miller, S. A.; Keoleian, G. A. Framework for analyzing transformative technologies in life cycle assessment. Environ. Sci. Technol. 2015, 150216121136004 DOI: 10.1021/es505217a. (10) Earles, J.; Halog, A. Consequential life cycle assessment: a review. Int. J. Life Cycle Assess 2011, 16 (5), 445−453 DOI: 10.1007/ s11367-011-0275-9. (11) Finnveden, G.; Hauschild, M. Z.; Ekvall, T.; Guinée, J.; Heijungs, R.; Hellweg, S.; Koehler, A.; Pennington, D.; Suh, S. Recent developments in life cycle assessment. J. Environ. Manage. 2009, 91 (1), 1−21 DOI: 10.1016/j.jenvman.2009.06.018. (12) Guinée, J. B.; Heijungs, R.; Huppes, G.; Zamagni, A.; Masoni, P.; Buonamici, R.; Ekvall, T.; Rydberg, T. Life cycle assessment: past, present, and future. Environ. Sci. Technol. 2011, 45 (1), 90−96 DOI: 10.1021/es101316v. (13) Zamagni, A., Buttol, P., Porta, P. L., Buonamici, R., Masoni, P., Guinée, J., Heijungs, R., Ekvall, T., Bersani, R., Bieńkowska, A., Pretato, U. Critical Review of the Current Research Needs and Limitations Related to ISO-LCA practice, Deliverable D7 of work package 5 of the CALCAS project, 2008. (14) Zamagni, A.; Guinée, J.; Heijungs, R.; Masoni, P.; Raggi, A. Lights and shadows in consequential LCA. Int. J. Life Cycle Assess. 2012, 17 (7), 904−918 DOI: 10.1007/s11367-012-0423-x. (15) Baitz, M.; Albrecht, S.; Brauner, E.; Broadbent, C.; Castellan, G.; Conrath, P.; Fava, J.; Finkbeiner, M.; Fischer, M.; Fullana i Palmer, P.; Krinke, S.; Leroy, C.; Loebel, O.; McKeown, P.; Mersiowsky, I.; Möginger, B.; Pfaadt, M.; Rebitzer, G.; Rother, E.; Ruhland, K.; Schanssema, A.; Tikana, L. LCA’s theory and practice: like ebony and ivory living in perfect harmony? Int. J. Life Cycle Assess. 2013, 18 (1), 5−13 DOI: 10.1007/s11367-012-0476-x. (16) Ekvall, T.; Andrae, A. Attributional and Consequential Environmental Assessment of the Shift to Lead-Free Solders. Int. J. Life Cycle Assess. 2006, 11 (5), 344−353 DOI: 10.1065/ lca2005.05.208. (17) Vázquez-Rowe, I.; Rege, S.; Marvuglia, A.; Thénie, J.; Haurie, A.; Benetto, E. Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg. Int. J. Life Cycle Assess. 2013, 18 (8), 1593−1604 DOI: 10.1007/s11367-013-0604-2. (18) Marvuglia, A.; Benetto, E.; Rege, S.; Jury, C. Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: Critical review and proposed framework for biogas production. Renewable Sustainable Energy Rev. 2013, 25, 768−781 DOI: 10.1016/j.rser.2013.04.031. (19) Earles, J. M.; Halog, A.; Ince, P.; Skog, K. Integrated Economic equilibrium and life cycle assessment modeling for policy-based consequential LCA. J. Ind. Ecol. 2012, n/a DOI: 10.1111/j.15309290.2012.00540.x. (20) Dandres, T.; Gaudreault, C.; Tirado-Seco, P.; Samson, R. Assessing non-marginal variations with consequential LCA: Application to European energy sector. Renewable Sustainable Energy Rev. 2011, 15 (6), 3121−3132 DOI: 10.1016/j.rser.2011.04.004. (21) Kløverpris, J.; Wenzel, H.; Nielsen, P. H. Life cycle inventory modelling of land use induced by crop consumption. Int. J. Life Cycle Assess. 2008, 13 (1), 13−21 DOI: 10.1065/lca2007.10.364. (22) Lund, H.; Mathiesen, B. V.; Christensen, P.; Schmidt, J. H. Energy system analysis of marginal electricity supply in consequential LCA. Int. J. Life Cycle Assess. 2010, 15 (3), 260−271 DOI: 10.1007/ s11367-010-0164-7. (23) Mathiesen, B. V.; Münster, M.; Fruergaard, T. Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments. J. Clean. Prod. 2009, 17 (15), 1331−1338 DOI: 10.1016/j.jclepro.2009.04.009. (24) Eriksson, O.; Finnveden, G.; Ekvall, T.; Björklund, A. Life cycle assessment of fuels for district heating: A comparison of waste incineration, biomass- and natural gas combustion. Energy Policy 2007, 35 (2), 1346−1362 DOI: 10.1016/j.enpol.2006.04.005. (25) Kløverpris, J.; Baltzer, K.; Nielsen, P. H. Life cycle inventory modelling of land use induced by crop consumption. Int. J. Life Cycle Assess. 2010, 15 (1), 90−103 DOI: 10.1007/s11367-009-0132-2.

uncertain future sales volume. Our approach aims at balancing data requirements and accuracy to facilitate its practical applicability. We believe that the approach is especially suited to implement consequential thinking in LCA practice.

■

ASSOCIATED CONTENT

S Supporting Information *

Linear and nonlinear effects of introducing a new technology, chlorine production capacities in Germany, price data used for calculating the marginal production costs, determination of environmental impact intensities, quantification of the effect of assuming constant market demand on the case study results. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/es5056512.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: +49 241 80 95381; fax: +49 241 80 92255; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work has been carried out within the project “CO2 Based ProductsFrom Dream to Reality”. The project is funded by Climate-KIC. A.K. gratefully acknowledges support by the German Academic Exchange Service (DAAD) through its Thematic Network “ACalNet” funded by the German Federal Ministry of Education and Research (BMBF). One of the authors (SS) contribution is based in part upon work supported by the National Science Foundation under Grant Number 1360445. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. We further thank Patrick Eichstädt for his technical support, as well as the anonymous reviewers for their helpful suggestions.

■

REFERENCES

(1) European Commission. http://ec.europa.eu/clima/policies/ lowcarbon/index_en.htm (accessed October 11, 2014). (2) White House. http://www.whitehouse.gov/energy/climatechange#energy-menu (accessed October 19, 2014). (3) Searchinger, T.; Heimlich, R.; Houghton, R. A.; Dong, F.; Elobeid, A.; Fabiosa, J.; Tokgoz, S.; Hayes, D.; Yu, T.-H. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 2008, 319 (5867), 1238−1240 DOI: 10.1126/science.1151861. (4) Bento, A.; Landry, J.; Klotz, R. Are there carbon savings from US biofuel policies? The critical importance of accounting for leakage in land and fuel markets. Energy J. 2013, DOI: 10.2139/ssrn.2219503. (5) Rajagopal, D. Consequential life cycle assessment of policy vulnerability to price effects. J. Ind. Ecol. 2013, n/a DOI: 10.1111/ jiec.12058. (6) Sandén, B. A.; Karlström, M. Positive and negative feedback in consequential life-cycle assessment. J. Clean. Prod. 2007, 15 (15), 1469−1481 DOI: 10.1016/j.jclepro.2006.03.005. (7) Pehnt, M.; Oeser, M.; Swider, D. J. Consequential environmental system analysis of expected offshore wind electricity production in Germany. Energy 2008, 33 (5), 747−759 DOI: 10.1016/j.energy.2008.01.007. (8) Zink, T.; Maker, F.; Geyer, R.; Amirtharajah, R.; Akella, V. Comparative life cycle assessment of smartphone reuse: Repurposing vs. refurbishment. Int. J. Life Cycle Assess 2014, 19 (5), 1099−1109 DOI: 10.1007/s11367-014-0720-7. H


Policy Analysis

Environmental Science & Technology (26) Suh, S.; Yang, Y. On the uncanny capabilities of consequential LCA. Int. J. Life Cycle Assess. 2014, 19 (6), 1179−1184 DOI: 10.1007/ s11367-014-0739-9. (27) Fukushima, Y.; Kuo, Y. Evaluation of GHG emission reduction potentials of PV system considering power mix shifts. J. Energy Eng. 2008, 134 (2), 58−62 DOI: 10.1061/(ASCE)0733-9402(2008) 134:2(58). (28) Kuo, Y.-M.; Fukushima, Y. Greenhouse gas and air pollutant emission reduction potentials of renewable energyCase studies on photovoltaic and wind power introduction considering interactions among technologies in Taiwan. J. Air Waste. Manage. Assoc. 2009, 59 (3), 360−372 DOI: 10.3155/1047-3289.59.3.360. (29) Chen, I.-C.; Fukushima, Y.; Kikuchi, Y.; Hirao, M. A graphical representation for consequential life cycle assessment of future technologies. Part 1: Methodological framework. Int. J. Life Cycle Assess. 2012, 17 (2), 119−125 DOI: 10.1007/s11367-011-0356-9. (30) Chen, I.-C.; Fukushima, Y.; Kikuchi, Y.; Hirao, M. A graphical representation for consequential life cycle assessment of future technologies. Part 2: two case studies on choice of technologies and evaluation of technology improvements. Int. J. Life Cycle Assess. 2012, 17 (3), 270−276 DOI: 10.1007/s11367-011-0359-6. (31) Khemani, R. S.; Shapiro, D. M. Glossary of Industrial Organisation Economics and Competition Law, Commissioned by the Directorate for Financial, Fiscal and Enterprise Affairs, OECD, 1993 (32) Neelis, M.; Patel, M.; Blok, K.; Haije, W.; Bach, P. Approximation of theoretical energy-saving potentials for the petrochemical industry using energy balances for 68 key processes. Energy 2007, 32 (7), 1104−1123 DOI: 10.1016/j.energy.2006.08.005. (33) Spitz, P. H. The Chemical Industry at the Millenium. Maturity, Restructuring, And Globalization; Chemical Heritage Press: Philadelphia, PA, 2003. (34) Watters, D. C. The industry cost curve as a strategic tool. http://faculty.msb.edu/homak/homahelpsite/webhelp/Content/ McKinsey%20%20The%20Industry%20Cost%20Curve%20as%20a%20Strategic%20 Tool.pdf (accessed 25th February, 2014). (35) Global Guidance Principles for Life Cycle Assessment DatabasesA Basis for Greener Processes and Products; Sonnemann, G., Vigon B., Eds.; 2011. (36) Ekvall, T.; Weidema, B. System boundaries and input data in consequential life cycle inventory analysis. International Journal of Life Cycle Assessment 2004, 9 (3), 161−171. (37) Weidema, B. P., Ekvall, T., Heijungs, R. Guidelines for Application of Deepened and Broadened LCA, Deliverable D18 of work package 5 of the CALCAS Project, 2009. (38) Paul, H.; Wollny, V. Instrumente des strategischen Managements. Grundlagen und Anwendung; Oldenbourg: München, 2011. (in German). (39) Jörissen, J.; Turek, T.; Weber, R. Chlorherstellung mit Sauerstoffverzehrkathoden. Energieeinsparung bei der Elektrolyse (in German). Chem. Unserer Zeit 2011, 45 (3), 172−183 DOI: 10.1002/ ciuz.201100545. (40) Jung, J.; v.d.Assen, N.; Bardow, A. Comparative LCA of multiproduct processes with non-common products: a systematic approach applied to chlorine electrolysis technologies. Int. J. Life Cycle Assess. 2013, 18 (4), 828−839 DOI: 10.1007/s11367-012-0531-7. (41) Jung, J.; Postels, S.; Bardow, A. Cleaner chlorine production using oxygen depolarized cathodes? A life cycle assessment. J. Clean. Prod. 2014, 80, 46−56 DOI: 10.1016/j.jclepro.2014.05.086. (42) Eurochlor. Chlorine Industry Review 2012−2013. http://www. eurochlor.org/download-centre/the-chlorine-industry-review.aspx (accessed March 12, 2014). (43) Baerns, M. Tech. Chem.; Wiley-VCH-Verl: Weinheim, 2006. (in German). (44) Lindley, A. A. An Economic and Environmental Analysis of the Chlor-Alkali Production Process, 1997. (45) United Nations Statistics Division. UN Data. A World of Information. http://data.un.org/Default.aspx (accessed March 14, 2014).

(46) Schmittinger, P.; Florkiewicz, T.; Curlin, L. C.; Lüke, B.; Scannell, R.; Navin, T.; Zelfel, E.; Bartsch, R. Chlorine. Ullmann’s Encyclopedia of Industrial Chemistry; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2000. (47) Baufumé, S.; Grüger, F.; Grube, T.; Krieg, D.; Linssen, J.; Weber, M.; Hake, J.-F.; Stolten, D. GIS-based scenario calculations for a nationwide German hydrogen pipeline infrastructure. Int. J. Hydrogen Energy 2013, 38 (10), 3813−3829 DOI: 10.1016/j.ijhydene.2012.12.147. (48) European Energy Exchange AG. EEX Transparency Platform. http://www.transparency.eex.com/en/ (accessed March 21, 2014). (49) Ecoinvent. http://www.ecoinvent.ch (accessed March 11, 2014). (50) Wittcoff, H. A.; Reuben, B. G.; Plotkin, J. S. Industrial Organic Chemicals, 3rd ed.; Wiley: Hoboken, N.J, 2013. (51) Riegel, E. R.; Kent, J. A. Kent and Riegel’s Handbook of Industrial Chemistry and Biotechnology, 11th ed.; Springer: New York, 2007.

I


Industry-Cost-Curve Approach for Modeling the Environmental Impact

Recommend Documents