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Evaluating consumer product life cycle sustainability with integrated metrics: A Paper Towel Case Study Wesley W. Ingwersen, Manuel Ceja, Annie Weisbrod, Heriberto Cabezas, Bayou Demeke, Tarsha Eason, Raymond L. Smith, Debalina Sengupta, Ed Zanoli, Maria Gausman, Seung-Jin Lee, Xin Ma, Bernard Weber, Mauricio Alvarez, Jane Crum Bare, John Abraham, Gerardo J. Ruiz-Mercado, and Michael Albert Gonzalez Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b03743 • Publication Date (Web): 22 Feb 2016 Downloaded from http://pubs.acs.org on February 26, 2016

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Industrial & Engineering Chemistry Research

Evaluating consumer product life cycle sustainability with integrated metrics: A Paper Towel Case Study Wesley W. Ingwersen1, Manuel Ceja2, Anne V. Weisbrod2, Heriberto Cabezas1,*, Bayou Demeke1, Tarsha Eason1, Raymond L. Smith1, Debalina Sengupta3, Ed Zanoli2, Maria Gausman2, Seung-Jin Lee4, Xin (Cissy) Ma1, Bernard Weber2, Mauricio Alvarez2, Jane C. Bare1, John Abraham1, Gerardo J. Ruiz-Mercado1, Michael A. Gonzalez1 1

2

3

4

US Environmental Protection Agency, National Risk Management Laboratory, 26 W Martin Luther King Drive, Cincinnati, OH 45238, USA Global Product Stewardship and Product Supply, The Procter & Gamble Company, Cincinnati, OH 45268, USA Artie McFerrin Department of Chemical Engineering, Texas A & M University, College Station, TX 77843, USA Department of Earth and Resource Science, University of Michigan-Flint, Flint, MI 48502, USA

* Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-513-569-7350.

Abstract: Integrated sustainability metrics provide an enriched set of information to inform decision making. However, such approaches are rarely used to assess product supply chains. In this work, four integrated metrics – presented in terms of land, resources, value added, and stability – are applied in a life cycle context, along with industrial process systems analysis and life cycle assessment, to evaluate Bounty® paper towels from two manufacturing lines. The results show that the paper towels from the more state-of-the-art manufacturing line and newer facility are marginally more sustainable by the majority of environmental measures. Drivers of impacts from land use, resource use, and externality costs in the product life cycle are largely in the supply chains for raw materials and energy. The integrated metrics point to greenhouse gases, criteria air pollutants, land used for pulpwood, and fossil fuel use as important emissions and resources to manage for improving the sustainability of paper towel production. The metrics employed help to reconcile contrasting results from the life cycle impact assessment, such as water and energy use impacts, and provide a reduced set of practical, yet comprehensive information to inform product-related decision making. Keywords: product sustainability; sustainability metrics; sustainability indicators; life cycle assessment; ecological footprint; emergy; Fisher information; green net value added; externality costs; consumer products

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2 1. Introduction There is a growing interest among major manufacturers to integrate more sustainable practices into their operations1. Recently, this interest has grown to encompass the management of supply chains through the product end of life, including operations not within a manufacturer’s control2. Manufacturers are taking a wide variety of approaches that vary in the depth and level of quantification of sustainability. Such approaches are collectively known as sustainable manufacturing3, which relates to the collection and application of product data and their value chains. While sustainable manufacturing provides information for quantifying sustainability4, the lack of available and standardized methodologies for full sustainability assessment impacts the effectiveness of the evaluation tools used by manufacturers, leading to results that vary in credibility and robustness5. Many available tools and methods, such as reporting frameworks6 and scorecards7, are qualitative or semi-quantitative in nature. Accordingly, arriving at accurate and credible quantitative results is challenging due to the lack of fundamental science to support rigorous methods and tools. This paper presents, and describes a case study of, integrated sustainability metrics that incorporate science-based measures of performance into the evaluation of product systems. One of the key benefits of quantitative sustainability indicators is the flexibility in being able to use them within a process8, organizational, or product scope9. For example, they may be used for a single stage assessment (e.g. impacts related to manufacturing), or to track a single environmental aspect of concern (e.g., carbon footprint) at the product scale. When this scope is expanded to include all stages of a product life cycle, then life cycle assessment (LCA) is often used as the evaluation tool10. Typically, the output of a LCA study results in a set of individual impact indicators that are used to identify potentially important environmental impacts associated with the product over its life cycle. These results may be coupled with complimentary economic and social assessment approaches11,12 resulting in a plethora of individual indicators that are comprehensive, but challenging to interpret5, and not always clearly related to sustainability principles or product decisions that must be made. This article builds upon a companion article13, which introduced the concept of incorporating ‘integrated metrics’ for more thorough evaluation of a product’s life cycle. These metrics integrate multiple environmental and/or economic aspects through the quantification of a sustainability principle, such as living with energetic and land resource limitations. In this paper, Bounty® paper towel product systems from two North American manufacturing facilities are evaluated using a process analysis and LCA, coupled with integrated metrics that include additional aspects for land use, resource use, economics, and system stability. We hypothesize that the use of integrated metrics, alongside process analysis and LCA, provides a more objective, thorough sustainability evaluation than calculating individual impact indicators and having to weigh them using subjective values. The hybrid approach explored here is hypothesized to be more objective and scientifically-defensible than aggregated single metrics, and more instructive for understanding how different facility and life cycle characteristics affect sustainability of a product system. Our purpose with this paper is to demonstrate the breadth of information captured by integrated metrics in relationship to indicators commonly used in LCA, and show how the integrated metrics may yield more practical information to affect product decisions.


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3 2. Methods This study involved numerous steps to arrive at a comprehensive sustainability evaluation of a consumer product, including the identification of an appropriate set of indicators and metrics, further development of methodologies and life cycle tools, selection of a case study, data collection, and modeling. These steps are described below. 2.1.

Identification of Metrics & Indicators

Based on expertise in the production systems and sustainability indicators, an interdisciplinary team of environmental scientists, engineers, economists, and business managers from the US Environmental Protection Agency (USEPA) and Procter & Gamble (P&G) developed a list of environmental, economic and social indicators and related tools for a comprehensive evaluation of consumer product manufacturing systems and their supply chains. The indicators selected were of varying scope with some covering the manufacturing line or the larger facility, others including the business line and the remaining capturing supplier specific information. However, it was agreed that the scope of the most critical indicators needed to reflect what in LCA is commonly referred to as “cradle-to-grave”, or a full product system. The team also agreed to account for the potential to broaden the scope depending on the indicators of interest and allow for exploration of possible linkages to the communities surrounding the manufacturing facility. For these indicators, typical environmental and cost indicators from life cycle impact assessment (LCIA) (including the USEPA TRACI 2.1 impact assessment methodology14 and selected indicators from other LCIA methodologies) and life cycle cost (LCC) approaches15 were adopted. Some of the metrics identified for sustainability assessment (i.e. emergy, ecological footprint, full cost accounting, and Fisher Information) had not yet been fully developed, or never applied, in the product systems context. Thus, existing methodologies for these sustainability metrics were expanded or modified to permit their application to a complex product system. 2.2.

Development of Methodologies and Tools

Applying LCA and integrated metrics to consumer product system evaluation required advances in methodologies for more accurate product-specific accounting. Consumer product manufacturing facilities often produce a multitude of products, typically in a series of production lines, each operating at different efficiencies and often with specific emission controls. Furthermore, there are general utilities and other services provided for the facility as a whole that need to be allocated to products for unit process modeling. Data to model specific line combinations for products are often either unavailable (because data are only available at a facility level) or propriety because they potentially reveal trade secrets. Accordingly, allocation of at least some of the inputs and outputs to model line production is required. However, using general allocation for the entire facility does not permit differentiation of products from specific line combinations. For this reason, a standardized procedure called Industrial Process System Analysis (IPSA) was developed to facilitate the assignment of inputs and outputs to products in complex, multi-product and multi-line facilities. IPSA sorts all facility inputs and outputs into one of three categories (process, ancillary or non-process) and uses a series of steps for estimating the quantities of these inputs and outputs associated with a specific product. More ACS Paragon Plus Environment


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4 background information and the details of the IPSA methodology has been described in Sengupta et al.16. In line with the companion paper to this article13, the ‘integrated’ metrics selected for this case study are ecological footprint (EF), a measure of use of biologically productive land; emergy (EME), a measure of total environmental support for resources and ecosystem services being used by the product system; Green Net Value Added (GNVA), a measure of economic value added minus the externality cost of the product system, and Fisher information (FI), a statistical measure of the order and stability of a system. Each metric is associated with one or more primary indicators that are used to evaluate relative movement toward or away from sustainability17. Additional information on the calculation of each metric is provided in the Supporting Information. Table 1 indicates the more sustainable direction for each of the indicators. For EF, EME, GNVA – Externality Cost, and FI-SD (standard deviation in FI), a decreased score indicates a more sustainable option; conversely for GNVA-Total and FI-Mean (average FI value over time), an increased score represents a more sustainable option. Note that in some of the presentation and discussion of results from the case study, references to these specific metrics have been simplified to facilitate ease of communication. Hence, EF is referred to as “land”, EME as “resources”, GNVA either as “value added or externality cost” (depending on whether the focus of the result is the value added or just the externality cost), and Fisher as “stability”.


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Integrated Metric

Ecological Footprint1

Emergy

Green Net Value Added

FI – Mean


Table 1. Direction toward sustainability for the integrated metrics and associate indicators. Abbr. Brief Description Indicator Sustainable Interpretation of direction (what is direction more sustainable) EF

EME

GNVA

FI

Total land use to support supply chain and sequester carbon Direct and indirect resource requirement accounted for as all available energy to supporting ecosystems Total value added of an activity minus externality costs

Total & subcomponent totals Total & subcomponent totals % Renewable Externality Cost Total & subcomponent totals Mean Standard deviation

↓

Less use of productive land

↓

Less use of resources

↑

Higher percentage of renewable resources Less human and environmental damage More value added

↓ ↑

A statistical measure of ↑ More orderly system the dynamic order of a ↓ More stable system system based on information theory 1 – Except for EF – Carbon Sequestration, for which an increase indicates the more sustainable direction.



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While LCA has already been applied to product systems and LCA tools are capable of impact indicator calculations for these systems, a more flexible, extensible LCA tool was needed in this case. In particular, the tool needed to: a) allow for the creation of inventory data with new elementary flow names necessary for characterizing the new metrics for the LCA models, and b) afford the ability to import and use customized LCIA methods for quantification of EME, EF, GNVA and their various components. Additionally, considering the need to develop publically-available tools to support sustainable supply chains design that would incorporate life cycle calculations, it was important to select a platform that could be further modified for incorporation of additional data types and calculation procedures in the future. Therefore, USEPA sponsored additions to the openLCA platform (an open-source LCA software) such as more convenient and flexible modes of data import/export, new result analysis features, and export of product system and result matrices. The improvements developed with this project are now incorporated into the publically available openLCA software (since version 1.3)18. 2.3.

Product System Selection and Data Collection

The Bounty® regular roll paper towel production system was selected as the subject of the first case study for application of the IPSA, LCA and integrated metric approaches. In order to test and apply the described sustainability approaches, the team needed to select processes that represented two alternative manufacturing systems for Bounty regular and encompassed aspects for exploring potential differences in sustainability performance. Bounty manufacturing systems include a paper facility with production lines consisting of a one papermaking and one paper converting line (a line pair). Since, the paper facilities that produce Bounty regular also produce other paper products, it was important to not only compare Bounty from two facilities, but also to compare two distinctive line pairs by collecting data that covered periods in which they were only used for producing Bounty regular and not other products. Furthermore, the paper facilities themselves represent different vintages, locations, and capacities which could affect sustainability performance. Two line pairs, each from a different North American facility (referred to henceforth as Lines A & B from Facilities A & B, respectively), were thus chosen based on their operation for producing Bounty regular over the same time period, differences in papermaking and converting technologies, and differences in facility characteristics. Table 2 summarizes some of the key differences between the selected lines.


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2 Table 2. Characteristics of Bounty® Lines Selected for Case Study Aspect Line A Line B Most Representative Newest Platform Line Technology Primary Fuel & Natural gas, Biomass, Grid Natural gas, Grid Electricity Electricity Energy Sources US South US West Facility Location Comparative Facility Large Small Size More than 30 years old Less than 10 years old Facility Age BACT Combustion, Separators & Low-NOx Combustion, Separators Primary Emission Scrubbers, Wet ESP, Bag & Drum & Scrubbers, Drum Filtration Control Technology Filtration BACT = Best Available Control Technology; ESP = Electro-Static Precipitator Data were first gathered directly from the two paper facilities, then from primary suppliers (e.g., pulp) for the upstream processes in the life cycle. Standardized templates for data entry were prepared and shared with facility managers, and data was validated following collection by internal experts. Data collected from the facilities and primary suppliers included all materials and energy inputs to the facilities, production emissions to air and water, and waste generation over the selected time period. Data from each facility were collected and averaged over a one year period from August 2010 to July 2011. Both physical quantities and costs were collected for the lines. Similar data requests were also sent to suppliers of primary chemicals unique to papermaking and converting and to primary suppliers of pulp. P&G provided data on distribution modes and average distances for their products to retailers, and a standard USEPA municipal solid waste management model, the Waste Reduction Model (WARM)19, was used for modeling end-of-life. The facility and other life cycle data were used for calculation of all LCIA indicators and the integrated metrics. More details on these additional upstream and general background data collected for the LCA are described in a separate article20. For the Fisher information calculations, a time series of data for the facility and the community was collected and is described in the Supporting Information. 2.4. Modeling All original life cycle inventory data and LCIA methods were input and processed through a standardized USEPA LCA Microsoft Excel LCI data template, and exported in Ecospold 1 format for import into the LCA software, openLCA 1.421. Modules for the product systems were built in openLCA for the full life cycle and also independently for the designated life cycle stages. Characterization factors for the various components of EF, EME, and GNVA, where imported as LCIA methods into openLCA, and result calculations were run in openLCA and exported into Excel files. The IPSA and the steps for the GNVA calculations not related to the emissions inventories were modeled in Microsoft Excel. Matlab (Release 2013b) code was developed for the Fisher information calculations.



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3

3. Results and Discussion Modeling began at the line and facility scale using IPSA, and then expanded to include all stages of the product life cycle in the LCA. The IPSA and LCA results are presented, followed by those for the integrated metrics, and then all results are interpreted together. Results from all analyses are presented as relative, rather than absolute values, in the interest of protecting manufacturer intellectual property. 3.1. IPSA Results The IPSA results for Line B show relative differences from Line A for line inputs that vary from ± 1% to ±100% for an equal unit output of Bounty paper towel (Table 3). Total input of pulp, natural gas, and water are greater for Line B, whereas Line A recycled more waste product (e.g., Broke, an industry term for unusable paper). Biomass is an important fuel source for Line A, but is not used in Line B. The lines use nearly identical grid electricity per unit output paper towel, although the source of that grid electricity differs due to the different power mixes in their locations. Not indicated in Table 3 are the pulp sources and quantities that differ between the two locations. Air emissions and other wastes results reflect significant differences between the facilities (Table 4). Line B produces a larger amount of particulate matter (PM) emissions per unit output, but releases less emissions of the other criteria air pollutants (SO2, NO2, CO, Lead), less CO2 emissions, and less evaporative water loss denoting higher water efficiency. Further, Line B sends no waste to the municipal landfill. The reduced CO2 emissions from Line B can be explained in part by the use of less carbon-intensive fuels, whereas the increased PM emissions are likely related to the differences in emission control technologies (see Table 2) at the two facilities. Table 3. Comparative IPSA results for Bounty® inputs to Lines A & B. Bold text indicates the lesser quantity between the lines for the given input. Pulp

Line A Line B

0.84 1.00

Broke

1.00 0.49

Pulp

Chemicals:

Chemicals:

Natural

and

Process

Process

Gas

Broke

Essentials

Additives

0.89 1.00

0.67 1.00

1.00 0.07

0.96 1.00

Biomass

Water

Electricity

1.00 0.00

0.90 1.00

0.99 1.00

Table 4. Comparative IPSA results for Bounty® emissions and waste streams from Lines A & B. Bold text indicates the lesser quantity between the lines for the given output. PM

PM

10

2.5

SO2

NO2

VOC

CO

Lead

NH3

CO2

POTW

Water

LndFl

loss

Line A 0.61 0.32 1.00 1.00 0.58 1.00 1.00 1.00 1.00 0.83 1.00 Line B 1.00 1.00 0.05 0.21 1.00 0.10 0.01 0.06 0.23 1.00 0.89 POTW = Publically-Owned Treatment Works; LndFl = Waste to municipal landfill. 3.2. LCA Results ACS Paragon Plus Environment

1.00 0.00

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4 The results of the LCA comparing the two facilities are summarized in Table 5, which are derived from the full LCA published in a separate article20. For emission-related impacts, Line B shows lower impacts in all categories except human health-respiratory effects (HH-RESP). For the resource use impacts, Line B shows greater impacts for water and metal depletion but less for energy-related categories including cumulative energy demand, fossil depletion and land use. These results over the full life cycle of the paper towel are different from results specific to the manufacturing phase in Table 5. For example, although Line A exhibits higher direct water loss than Line B, it has lower life cycle water loss at the facility level when the whole life cycle is considered. Such contradictions can be explained by more significant environmental impacts occurring offsite (either in the material or energy supply chains or after the product leaves the manufacturer) than onsite. As Figure S1 shows, more than 50-99% of total impact in all categories occurs indirectly, either upstream or downstream of the two facilities’ operations. Therefore, a facility evaluation alone is not sufficient to capture important sustainability relevant information for the Bounty product system.

Table 5. Comparative LCA Results for Bounty® from Lines A & B. The grayed categories represent impacts related to emissions; the yellow categories are related to resource use. Bold indicates the lesser impact for the given category. GWP- GWP- HH- EP SFP AP ODP CED MDP FDP WAT LO Net Gross RESP Line A 100% 100% 86% 100% 100% 100% 100% 100% 90% 100% 46% 100 Line B 93% 92% 100% 87% 82% 80% 71% 85% 100% 84% 100% 83% GWP-Net = Global Warming Potential (net); GWP-Gross = Global Warming Potential (gross); HH-RESP = Potential Human Health-Respiratory Effects; EP = Eutrophication Potential; SFP = Smog Form ation Potential; AP = Acidification Potential; ODP = Ozone depletion potential; CED = Energy Demand - CED; MDP = Metal Depletion Potential; FDP = Fossil Depletion Potential; WAT = Water Loss from Evaporation; LO = Land occupation.

3.3.

Integrated Metrics Results

Both the IPSA results for the paper facilities, and the LCA results, enabled the calculation of integrated metrics for the two lines. Results in Table 6 indicate that all the land and resources metrics, estimated by EF and EME, are about the same for Lines A and B. Line B has a similar EF (95% of Line A) despite less carbon sequestration than Line A. Line B has a lower total emergy (86% of Line A) and a higher % renewable emergy (6% vs. 4%). For the value added metrics, estimated by GNVA components, we have assumed the same revenue for products from both lines. Line B has a slightly lower total externality cost (91% of A), due to less human health damages from emissions containing USEPA criteria air pollutants (CAP)22 and less climate change damages. CAP includes inorganics and fine particles that affect human lungs; these can be released by heavy industries (e.g. energy production) and road traffic. Line A, however, has better ACS Paragon Plus Environment


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5 scores for the other 3 GNVA estimates – Realized Cost, Net Value Added, and Green Net Value Added. The FI results for the production system (computed using monthly data from December 2009 to June 2012) indicate greater stability for Line A over the time period (higher mean and lower SD in FI). In part, this result can be qualified by the fact than Line B was in a start-up phase for much of the time period of the study, after which, the index stabilized for both facilities (see SI Figure S2). While, the FI results from data gathered on the communities surrounding the facilities showed that Line A had higher mean and standard deviation in FI, there was a great stability in both communities. To summarize the metric results in non-technical language: Line B provides less added value, is slightly less resource and land-intensive than Line A, and the production systems and surrounding communities for Line B displayed a similar level of stability to Line A once they were in full operation mode. One of the advantages of using the integrated metrics along with life cycle models is the ability to identify sources of less sustainable practices in complex product systems. In addition, these novel metrics represent relatively independent, yet important, system properties like land use, resource consumption, value creation, and stability, in an integrated manner. Figure 1a presents a contribution analysis to three of the integrated metrics (EF, EME, Externality Cost from GNVA) by life cycle stage, including the raw material production (pulp); paper facility emissions, electricity, and the fuels used to make the paper towels; the transport of the pulp (and other inputs) to the paper facility, distribution of the paper towels to retailers; and the end of life of paper towel and production solid wastes disposed of in municipal landfills or incinerated. Each metric is affected differently by each life cycle stage. Pulp, facility electricity, fuels used, and facility emissions appear to be the dominant sources of land impacts, resource use, and externality costs. The transport of raw materials (pulp), distribution and disposal are smaller contributors. The pulp is estimated to be the most significant source of land impacts (EF) for both lines A and B (46%, 52%) and the primary driver of EME as well (44%, 39%). Electricity contributes 12% to 54% of the total for all of the metrics, but more so for Line A. The facility emissions are minor contributors to EF (13%, 9%), but can be more important for externality costs (13%, 21%). Fuels contribute approximately 23% and 16% of the total EME for the respective lines, but not more than 4% to the other metrics. Figure 1b indicates the types of resources, pollutants, or natural processes (carbon sequestration) contributing to each of the metrics from the two lines, and shows that they are driven by very different sources. While EF of Bounty is mostly driven by land occupation (in this case to grow the trees for pulp) and the carbon balance, the EME of Bounty is strongly impacted by the use of fossil fuels, and emission of CAP has the greatest influence on externality costs. Based on EF, EME and externality cost, the results slightly favor Line B, but differences of

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