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Data from: Rethinking sustainability in seafood: synergies and trade-offs between fisheries and climate change

Citation

McKuin, Brandi; Watson, Jordan; Stohs, Stephen; Campbell, J. Elliott (2021), Data from: Rethinking sustainability in seafood: synergies and trade-offs between fisheries and climate change, Dryad, Dataset, https://doi.org/10.6071/M3768B

Abstract

In the manuscript entitled, "Rethinking sustainability in seafood: synergies and trade-offs between fisheries and climate change", we quantified the climate forcing per unit fish protein associated with several different U.S. tuna fishing fleets, among the most important capture fisheries by both volume and value. The fishing fleets include the U.S. purse seine that operates in the Western Central Pacific Fisheries Commission convention area, the U.S. North Pacific albacore surface gear (which includes both troll and pole-and-line), the Hawaii longline, the American Samoa longline, the Hawaii troll fleet, and the American Samoa troll fleet. Here, we provide the additional supporting information (Appendices A and B) and the materials we used to make the figures. In Appendix A, we include catch statistics tables. In Appendix B, we include total fuel-cycle climate forcing tables and figures. Our climate forcing estimates include crude oil extraction, and crude oil refining phases of the fuel cycle over a span of 20 years (1996-2015). Our analysis includes multiple marine fuels (distillates and heavy fuel oil), fishing territories, engine types, and time horizons (20-y and 100-y). In addition, we provide the materials used to make the figures in the main manuscript and the supplementary material.

Methods

Appendix A: Catch statistics

We obtained catch statistics for each fleet operating within their respective domains (Tables A1-A6) and for fleet fishing activity within the U.S. EEZ (Tables A6 and A7) and fleet fishing activity outside the U.S. EEZ (Tables A6 and A8) (WPRFMC, 2012; WPRFMC, 2015; PFMC, 2017; NOAA Fisheries, 2017; WPRFMC, 2017).

Appendix B: Total fuel-cycle emissions model

We estimated the relative contributions to each climate forcing constituent (e.g. CO2, CH4, N2O, NOx, SOx, black carbon, and organic carbon) for the crude oil refinery process. We used PRELIM (Abella and Bergerson, 2012) to simulate the fuel product densities, lower heating values, and GHGs for our analysis (Tables B1-B7). In our simulations, we selected 62 different oil field assays, two different refinery types (hydro-cracking and coking), and a variety of refinery configurations for each product slate (Table B8). To reflect differences depending on the conversion configuration, we used a mix of refinery processes and fuel blends to achieve the desired fuel quality (sulfur levels) for each marine fuel by weighting the refinery simulation outputs (refinery GHG emissions, lower heating values, and the fuel densities) by the frequency of occurrences of a particular oil field assay in the refinery emissions analysis (Table B9). We used the mean values and 95% confidence intervals for the crude oil refinery GHG emission factors, fuel product lower heating values, fuel product densities (Table S8), and GWPs (Table S9) as inputs to Equation S5. Results by fuel type and sulfur level are presented in Figure B1.

We estimated the relative contributions to each climate forcing constituent for the crude oil extraction phase. We matched the crude extraction emissions to the corresponding oil field assays used in our refinery emissions analysis. As inputs to Equation S5, we used the crude oil extraction emissions data from the literature and technical reports (Table B10) (COWI et al., 2014; Brandt et al., 2015; Duffy, 2015; Gordon et al., 2015; Tormodsgard, 2015) and allocated the crude oil GHG emissions by climate forcing constituent using the pump-to-well crude oil emission factor provided in the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model (Wang, 2011). Results by fuel type and sulfur level are presented in Figure B2.

We estimated the relative contributions to each climate forcing constituent of the vessel-exhaust phase. To estimate the long-lived climate forcing vessel-exhaust emissions of the U.S. tuna fleet we used emission factors and GWPs from technical reports and the literature. We calculated the aerosol short-lived climate forcing pollutant emissions from plume sampling studies for medium-speed diesel (MSD) and high-speed engines (HSD), respectively (Lack et al., 2008; Petzold et al., 2011; Buffaloe et al., 2014; Cappa et al., 2014). Results by fuel type and sulfur level are presented in Figure B3.

Our total fuel-cycle climate forcing calculation is the sum of climate forcing from the oil refinery, oil extraction, and vessel-exhaust phases of the fuel cycle. We calculated the total fuel-cycle climate forcing overtime (1996-2015) by marine fuel type (distillates and heavy fuel oil), fishing territory (U.S. exclusive economic zone and high seas), and engine type (MSD and HSD) on a 20-y and 100-y time horizon. To construct historical (1996-2015) fuel sulfur levels for each fishing territory, we used data found in technical reports, the literature, and statistics from the U.S. Energy Information Administration (Tables B11 and B12). We used weighting factors to estimate the mean net (sum of all constituents) total fuel-cycle climate forcing associated with ships burning HFO and distillates in the U.S. EEZ and the high seas (Table B13-B16). Results by fuel type and sulfur level are presented in Figure B4.

Figures for the main manuscript and the supplementary materials

For Figure 1 in the main manuscript, we included the QGIS file code and shapefiles used to make the maps. The land polygons for Figure 1 are available at the following link: http://openstreetmapdata.com/data/land-polygons. For Figures 3-8, we included the Rstudio files and supporting data frames. We also included the spreadsheet we used to make figures S1 and S2.

References

Abella, JP and Bergerson, JA. 2012. Model to Investigate Energy and Greenhouse Gas Emissions Implications of Refining Petroleum: Impacts of Crude Quality and Refinery Configuration. Environ Sci Technol 46(24): 13037-13047 DOI: 10.1021/es3018682.

Brandt, AR, Sun, Y and Vafi, K. 2015. Uncertainty in Regional-Average Petroleum GHG Intensities: Countering Information Gaps with Targeted Data Gathering. Environ Sci Technol 49(1): 679-686 DOI: 10.1021/es505376t.

Buffaloe, GM, Lack, DA, Williams, EJ, Coffman, D, Hayden, KL, et al. 2014. Black carbon emissions from in-use ships: a California regional assessment. Atmos Chem Phys 14(4): 1881-1896 DOI: 10.5194/acp-14-1881-2014.

Cappa, CD, Williams, EJ, Lack, DA, Buffaloe, GM, Coffman, D, et al. 2014. A case study into the measurement of ship emissions from plume intercepts of the NOAA ship Miller Freeman. Atmos Chem Phys 14(3): 1337-1352 DOI: 10.5194/acp-14-1337-2014.

COWI, E3-M Lab and Exergia. 2014. Study on actual GHG data for diesel, petrol, kerosene and natural gas. Brussels: European Commission, DG Ener.

Duffy, J. 2015. Staff Report: Calculating Carbon Intensity Values of Crude Oil Supplied to California Refineries. Sacramento, CA, USA: California Environmental Protection Agency Air Resources Board.

Gordon, D, Brandt, A, Bergerson, J and Koomey, J. 2015. Know Your Oil: Creating a Global Oil-Climate Index. Carnegie Endowment for International Peace. Available at: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:policyfile&rft_dat=xri:policyfile:article:00174824.

Lack, D, Lerner, B, Granier, C, Baynard, T, Lovejoy, E, et al. 2008. Light absorbing carbon emissions from commercial shipping. Geophys Res Lett 35(13) DOI: 10.1029/2008GL033906.

NOAA Fisheries. 2017. The Western Pacific Fisheries Information Network: Catch, Effort and CPUE by Selected Gears [database]. Honolulu, Hawaii, USA: Pacific Island Fisheries Center, NOAA Fisheries. Available at: https://www.pifsc.noaa.gov/wpacfin/Catch-and-Effort-Data.php.

Petzold, A, Lauer, P, Fritsche, U, Hasselbach, J, Lichtenstern, M, et al. 2011. Operation of Marine Diesel Engines on Biogenic Fuels: Modification of Emissions and Resulting Climate Effects. Environ Sci Technol 45(24): 10394-10400 DOI: 10.1021/es2021439.

PFMC. 2017. Catch of Albacore by Canadian and U.S. Albacore Troll and Pole-and-Line Vessels in the North Pacific Ocean. Portland, Oregon, USA: Pacific Fishery Management Council. Available at https://www.pcouncil.org/wp-content/uploads/2019/05/US_CAN_DWG_Table1__90417l.htm.

Tormodsgard, Y. 2015. Facts 2014: The Norwegian Petroleum Sector. Norway: Norwegian Ministry of Petroleum and Energy.

Wang, M. 2011. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model Software: GREET 1. Argonne, Illinois, US: Argonne National Laboratories. Available at: https://greet.es.anl.gov/index.php.

WPRFMC. 2017. Stock Assessment and Fishery Evaluation (SAFE) Report Pacific Island Pelagic Fisheries 2015. Honolulu, HI, USA: Western Pacific Regional Fishery Management Council. Available at: http://www.wpcouncil.org/wp-content/uploads/2015/04/2017-01-31_Final-2015-SAFE-Report.pdf.

WPRFMC. 2015. Pelagic Fish of Western Pacific Region 2013 Annual Report. Honolulu, HI, USA: Western Pacific Regional Fishery Management Council. Available at: http://www.wpcouncil.org/wp-content/uploads/2013/03/2013-Pelagics-Annual-Report_Final.pdf.

WPRFMC. 2012. Pelagic Fish of Western Pacific Region 2010 Annual Report. Honolulu, HI, USA: Western Pacific Regional Fishery Management Council. Available at: http://www.wpcouncil.org/wp-content/uploads/2019/08/2010-Pelagic-Annual-report-Final.pdf.

Usage Notes

The attached appendices and supporting data for the figures in the main manuscript and the supplementary material are provided to ensure the reproducibility of our study. There are no missing values in the input files. Please see embedded comments in the code provided with the Rstudio files.