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W. Ingwersen et al. / Journal of Cleaner Production 131 (2016) 509 e 522
Acknowledgments
This manuscript was developed through a Cooperative Research and Development Agreement (CRADA, No. 683-12) between the U.S. Environmental Protection Agency National Risk Management Research Laboratory and The Procter & Gamble Company. The contributions of Drs. Sengupta and Lee were supported by an appointment to the Postdoctoral Research Program at the U.S. EPA, National Risk Management Research Laboratory, administered by the Oak Ridge Institute for Science and Education through an Interagency Agreement between the U.S. Department of Energy and the U.S. EPA.
Appendix A. Supplementary material
Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2016.04.149.
Appendix
Table A1 Electricity power mix for the regions in which the facilities were located in comparison with the US average in 2011.
US-Avg
Albany region (US SE)
Box Elder region (US NW)
45%
52%
Electricity, coal Electricity, oil Electricity, gas
31%
1%
0%
0%
24% 20%
25% 17%
14%
Electricity, nuclear Electricity, hydro Electricity, biomass Electricity, wind
3%
44%
6% 1% 2% 0% 0% 0%
3% 3% 0% 0% 0% 0%
1% 5% 0% 1% 0%
Electricity, solar
Electricity, geothermal Electricity, other fossil fuel
Note. Predominant fuel sources for each region are bolded.
TableA2 Scenarios for sensitivity analysis.
Scenario
Life cycle stage affected
Description of model change
High forest yield (m 3 wood/hec) Low forest yield (m 3 wood/hec)
Forestry stage (for pulp) Forestry stage (for pulp)
Increase yield in forestry process by 25% Reduce yield in forestry process by 25%
High water loss for pulp Low water loss for pulp
Pulp production Pulp production Pulp production
Increase water lost to evaporation during pulp production by 100% Reduce water lost to evaporation during pulp production by 50% Modify the pulp production data to use a national average energy and air emissions data in place of existing supplier data Use the Ecoinvent ‘ electricity, medium voltage, at grid ’ process in place of the region-speci fi c electricity LCI Use a facility-level mass allocation approach in place of the IPSA procedure for determining inputs and output quantities per unit towel Increase distribution distance of paper towel to retailers by 100% Decrease distribution distance of paper towel to retailers by 50% Use ‘ Newspaper ’ as the proxy material for paper towel in the WARM model for estimating disposal stage emissions and energy use
Use national average pulp energy and emissions data National average electricity LCI
Paper towel production
Facility-level mass allocation
Paper towel production
Long distribution distance Short distribution distance
Distribution Distribution
Alternate material selection for WARM
Disposal
TableA3 Data quality scores.
Process
Data quality Indicators Source reliability
Completeness
Temporal correlation
Geographical correlation
Technical correlation
Electricity
1 1 2 2 1 1 2
1 1 1 1 1 2 1
1 1 1 1 1 1 1
1 1 1 1 1 2 2
1 1 1 1 1 4 4
Fuel Pulp
Pulp transport
Other facility purchases
Distribution
Disposal
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