The Collaborative Policy Modeling Paradox: Perceptions of water quality modeling in the Chesapeake Bay Watershed
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Keywords

collaborative modelling
model governance
water quality

How to Cite

Bitterman, P., & Webster, D. G. (2024). The Collaborative Policy Modeling Paradox: Perceptions of water quality modeling in the Chesapeake Bay Watershed. Socio-Environmental Systems Modelling, 6, 18677. https://doi.org/10.18174/sesmo.18677

Abstract

The Chesapeake Assessment Scenario Tool (CAST) serves multiple key functions in meeting nutrient reduction targets across the Chesapeake Bay Watershed (CBW) and is embedded in the water quality governance system. To investigate contested perspectives regarding the model, we interviewed 59 stakeholders engaged in model governance across the CBW. We recorded statements regarding the accuracy, legitimacy, and credibility of the model, influences on its use, and on challenges and opportunities. We found skepticism regarding the legitimacy of CAST, including suggestions its role facilitates a “paper process” of policy design and that past experience has greater influence on policy decisions than model predictions. However, despite its perceived shortcomings, CAST has been central in helping stakeholders in prioritizing mitigative activities. With respect to credibility, most respondents believe the model underestimates the effects of nutrient-reduction practices, thereby underestimating progress toward TMDL-related goals. Respondents also identified opportunities for model improvement, emphasizing co-benefits of conservation practices over and above nutrient reduction. Overall, our analysis demonstrates a Collaborative Policy Modeling Paradox: collaborative model development is necessary for effective policy modeling, but the political processes of collaborative model development can negatively impact perceptions of salience, credibility, and legitimacy. Although it is important to recognize this paradox, as it is linked to dissatisfaction with the models, our findings also point to areas where improvement has occurred and to future opportunities for development.

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References

Allison, S. T., & Messick, D. M. (1985). The group attribution error. Journal of Experimental Social Psychology, 21(6), 563–579. https://doi.org/10.1016/0022-1031(85)90025-3

ATLAS.ti Scientific Software Development GmbH. (2023). Atlas.ti (version 23.2.1) [Computer software]. https://atlasti.com/

Barton, C. M., Lee, A., Janssen, M. A., Van Der Leeuw, S., Tucker, G. E., Porter, C., Greenberg, J., Swantek, L., Frank, K., Chen, M., & Jagers, H. R. A. (2022). How to make models more useful. Proceedings of the National Academy of Sciences, 119(35), e2202112119. https://doi.org/10.1073/pnas.2202112119

Batiuk, R. A., Breitburg, D. L., Diaz, R. J., Cronin, T. M., Secor, D. H., & Thursby, G. (2009). Derivation of habitat-specific dissolved oxygen criteria for Chesapeake Bay and its tidal tributaries. Journal of Experimental Marine Biology and Ecology, 381, S204–S215. https://doi.org/10.1016/j.jembe.2009.07.023

Baumgartner, F. R. (1998). Basic interests: The importance of groups in politics and in political science (F. R. Baumgartner & B. L. Leeth, Eds.). Princeton University Press.

Bitterman, P., & Bennett, D. A. (2018). Leveraging Coupled Agent-Based Models to Explore the Resilience of Tightly-Coupled Land Use Systems. In L. Perez, E.-K. Kim, & R. Sengupta (Eds.), Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 17–30). Springer International Publishing. https://doi.org/10.1007/978-3-319-65993-0_2

Bitterman, P., & Koliba, C. J. (2020). Modeling Alternative Collaborative Governance Network Designs: An Agent-Based Model of Water Governance in the Lake Champlain Basin, Vermont. Journal of Public Administration Research and Theory, 30(4), 636–655.

Boesch, D. F. (2002). Challenges and opportunities for science in reducing nutrient over-enrichment of coastal ecosystems. Estuaries, 25(4), 886–900. https://doi.org/10.1007/BF02804914

Boesch, D. F., Brinsfield, R. B., & Magnien, R. E. (2001). Chesapeake Bay Eutrophication: Scientific Understanding, Ecosystem Restoration, and Challenges for Agriculture. Journal of Environmental Quality, 30(2), 303–320. https://doi.org/10.2134/jeq2001.302303x

Bolsen, T., & Druckman, J. N. (2015). Counteracting the Politicization of Science: Counteracting the Politicization of Science. Journal of Communication, 65(5), 745–769. https://doi.org/10.1111/jcom.12171

Bryan, B. A., Raymond, C. M., Crossman, N. D., & King, D. (2010). Comparing Spatially Explicit Ecological and Social Values for Natural Areas to Identify Effective Conservation Strategies: Spatial Comparison of Social and Ecological Values. Conservation Biology, 25(1), 172–181. https://doi.org/10.1111/j.1523-1739.2010.01560.x

Cash, D. W., Clark, W. C., Alcock, F., Dickson, N., Eckley, N., & Jäger, J. (2002). Salience, Credibility, Legitimacy and Boundaries: Linking Research, Assessment and Decision Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.372280

Cash, D. W., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N., Guston, D. H., Jäger, J., & Mitchell, R. B. (2003). Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences, 100(14), 8086–8091. https://doi.org/10.1073/pnas.1231332100

CBP. (2023). CAST - About CAST. Chesapeake Bay Program. https://cast.chesapeakebay.net/About

Chang, S. Y., Zhang, Q., Byrnes, D. K., Basu, N. B., & Van Meter, K. J. (2021). Chesapeake legacies: The importance of legacy nitrogen to improving Chesapeake Bay water quality. Environmental Research Letters, 16(8), 085002. https://doi.org/10.1088/1748-9326/ac0d7b

Chesapeake Bay Program. (2020). Chesapeake Assessment and Scenario Tool (CAST) (Version 2019) [Computer software]. cast.chesapeakebay.net

Chesapeake Bay Program. (2021). Water Quality Goal Implementation Team Governance Protocols. https://d18lev1ok5leia.cloudfront.net/chesapeakebay/documents/WQGIT-Governance-Protocols_-Final-Version-06.23.2021.pdf

Chesapeake Bay Program. (2023a). Our History. Chesapeake Bay Program. https://www.chesapeakebay.net/who/bay-program-history

Chesapeake Bay Program. (2023b). What Is A Watershed? https://www.chesapeakebay.net/discover/watershed

Cumming, G. S., Cumming, D. H. M., & Redman, C. L. (2006). Scale Mismatches in Social-Ecological Systems: Causes, Consequences, and Solutions. Ecology and Society, 11(1). https://doi.org/10.5751/ES-01569-110114

Deitrick, A. R., Torhan, S. A., & Grady, C. A. (2021). Investigating the Influence of Ethical and Epistemic Values on Decisions in the Watershed Modeling Process. Water Resources Research, 57(12), e2021WR030481. https://doi.org/10.1029/2021WR030481

Diaz, R. J., & Rosenberg, R. (2008). Spreading Dead Zones and Consequences for Marine Ecosystems. Science, 321(5891), 926–929. https://doi.org/10.1126/science.1156401

Dinar, S. (2006). Assessing side-payment and cost-sharing patterns in international water agreements: The geographic and economic connection. Political Geography, 25(4), 412–437. https://doi.org/10.1016/j.polgeo.2006.03.007

Dryzek, J. S. (2002). The politics of the earth: Environmental discourses. Oxford university press.

Exchange Network. (2023). National Environmental Information Exchange Network. http://exchangenetwork.net

Falconi, S. M., & Palmer, R. N. (2017). An interdisciplinary framework for participatory modeling design and evaluation—What makes models effective participatory decision tools? Water Resources Research, 53(2), 1625–1645. https://doi.org/10.1002/2016WR019373

Goelz, T., Hartley, T., Scheld, A., & Carboni, I. (2020). The Development of Attitudes Toward Scientific Models During a Participatory Modeling Process – The Impact of Participation and Social Network Structure. Frontiers in Marine Science, 7, 644. https://doi.org/10.3389/fmars.2020.00644

Gray, S., Chan, A., Clark, D., & Jordan, R. (2012). Modeling the integration of stakeholder knowledge in social–ecological decision-making: Benefits and limitations to knowledge diversity. Ecological Modelling, 229, 88–96. https://doi.org/10.1016/j.ecolmodel.2011.09.011

Guston, D. H. (2001). Boundary Organizations in Environmental Policy and Science: An Introduction. Science, Technology, & Human Values, 26(4), 399–408. https://doi.org/10.1177/016224390102600401

Haas, E. B. (1980). Why Collaborate? Issue-Linkage and International Regimes. World Politics, 32(3), 357–405. https://doi.org/10.2307/2010109

Harstad, B. (2007). Harmonization and Side Payments in Political Cooperation. American Economic Review, 97(3), 871–889. https://doi.org/10.1257/aer.97.3.871

Hegger, D., Lamers, M., Van Zeijl-Rozema, A., & Dieperink, C. (2012). Conceptualising joint knowledge production in regional climate change adaptation projects: Success conditions and levers for action. Environmental Science & Policy, 18, 52–65. https://doi.org/10.1016/j.envsci.2012.01.002

Hood, R. R., Shenk, G. W., Dixon, R. L., Smith, S. M. C., Ball, W. P., Bash, J. O., Batiuk, R., Boomer, K., Brady, D. C., Cerco, C., Claggett, P., de Mutsert, K., Easton, Z. M., Elmore, A. J., Friedrichs, M. A. M., Harris, L. A., Ihde, T. F., Lacher, L., Li, L., … Zhang, Y. J. (2021). The Chesapeake Bay program modeling system: Overview and recommendations for future development. Ecological Modelling, 456, 109635. https://doi.org/10.1016/j.ecolmodel.2021.109635

Hulme, M., & Mahony, M. (2010). Climate change: What do we know about the IPCC? Progress in Physical Geography: Earth and Environment, 34(5), 705–718. https://doi.org/10.1177/0309133310373719

Ivanović, R. F., & Freer, J. E. (2009). Science versus politics: Truth and uncertainty in predictive modelling. Hydrological Processes, 23(17), 2549–2554. https://doi.org/10.1002/hyp.7406

Jasanoff, S. (1998). The fifth branch: Science advisers as policymakers. Harvard University Press.

Jones, C. P. (2000). Levels of racism: A theoretic framework and a gardener’s tale. American Journal of Public Health, 90(8), 1212–1215. https://doi.org/10.2105/AJPH.90.8.1212

Jones, T. M., Felps, W., & Bigley, G. A. (2007). Ethical Theory and Stakeholder-Related Decisions: The Role of Stakeholder Culture. Academy of Management Review, 32(1), 137–155. https://doi.org/10.5465/amr.2007.23463924

Kemp, W., Boynton, W., Adolf, J., Boesch, D., Boicourt, W., Brush, G., Cornwell, J., Fisher, T., Glibert, P., Hagy, J., Harding, L., Houde, E., Kimmel, D., Miller, W., Newell, R., Roman, M., Smith, E., & Stevenson, J. (2005). Eutrophication of Chesapeake Bay: Historical trends and ecological interactions. Marine Ecology Progress Series, 303, 1–29. https://doi.org/10.3354/meps303001

Kolstad, C. D. (2005). Piercing the Veil of Uncertainty in Transboundary Pollution Agreements. Environmental & Resource Economics, 31(1), 21–34. https://doi.org/10.1007/s10640-004-6980-0

Lemos, M. C., & Morehouse, B. J. (2005). The co-production of science and policy in integrated climate assessments. Global Environmental Change, 15(1), 57–68. https://doi.org/10.1016/j.gloenvcha.2004.09.004

Lim, T. C. (2021). Model emulators and complexity management at the environmental science-action interface. Environmental Modelling & Software, 135, 104928. https://doi.org/10.1016/j.envsoft.2020.104928

Lim, T. C., Glynn, P. D., Shenk, G. W., Bitterman, P., Guillaume, J. H. A., Little, J. C., & Webster, D. G. (2023). Recognizing political influences in participatory social-ecological systems modeling. Socio-Environmental Systems Modelling, 5, 18509. https://doi.org/10.18174/sesmo.18509

Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences. Science, 263(5147), 641–646. https://doi.org/10.1126/science.263.5147.641

R Core Team. (2022). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Raymond, C. M., Fazey, I., Reed, M. S., Stringer, L. C., Robinson, G. M., & Evely, A. C. (2010). Integrating local and scientific knowledge for environmental management. Journal of Environmental Management, 91(8), 1766–1777. https://doi.org/10.1016/j.jenvman.2010.03.023

Sabatier, P. A., & Weible, C. A. (2007). The Advocacy Coalition Framework Innovations and Clarifications. In Theories of the Policy Process. Routledge. Second Edition, pp. 189–220.

Sarewitz, D., & Pielke, R. A. (2007). The neglected heart of science policy: Reconciling supply of and demand for science. Environmental Science & Policy, 10(1), 5–16. https://doi.org/10.1016/j.envsci.2006.10.001

Schlüter, M., Müller, B., & Frank, K. (2019). The potential of models and modeling for social-ecological systems research: The reference frame ModSES. Ecology and Society, 24(1). https://doi.org/10.5751/ES-10716-240131

Shenk, G. W., & Linker, L. C. (2013). Development and Application of the 2010 Chesapeake Bay Watershed Total Maximum Daily Load Model. Journal of The American Water Resources Association, 49(5), 1042–1056. https://doi.org/10.1111/jawr.12109

Tollison, R. D., & Willett, T. D. (1979). An economic theory of mutually advantageous issue linkages in international negotiations. International Organization, 33(4), 425–449. https://doi.org/10.1017/S0020818300032252

Turnhout, E., Hisschemöller, M., & Eijsackers, H. (2007). Ecological indicators: Between the two fires of science and policy. Ecological Indicators, 7(2), 215–228. https://doi.org/10.1016/j.ecolind.2005.12.003

Ulibarri, N. (2018). Collaborative model development increases trust in and use of scientific information in environmental decision-making. Environmental Science & Policy, 82, 136–142. https://doi.org/10.1016/j.envsci.2018.01.022

US Environmental Protection Agency. (2021). Chesapeake Bay Total Maximum Daily Load (TMDL). October 6, 2021. https://www.epa.gov/chesapeake-bay-tmdl

Van Voorn, G. A. K., Verburg, R. W., Kunseler, E.-M., Vader, J., & Janssen, P. H. M. (2016). A checklist for model credibility, salience, and legitimacy to improve information transfer in environmental policy assessments. Environmental Modelling & Software, 83, 224–236. https://doi.org/10.1016/j.envsoft.2016.06.003

Walker, B., Carpenter, S. R., Anderies, J. M., Abel, N., Cumming, G., Janssen, M. A., Lebel, L., Norberg, J., Peterson, G. D., & Pritchard, R. (2002). Resilience Management in Social-ecological Systems: A Working Hypothesis for a Participatory Approach. Conservation Ecology, 6(1). https://doi.org/10.5751/ES-00356-060114

Webster, D. G. (2015). Beyond the Tragedy in Global Fisheries. The MIT Press.

Webster, D. G. (2022). Chesapeake Governance Study: Report of 2021 Decision-Maker Interview Results. https://digitalcommons.dartmouth.edu/facoa/4314/

Webster, D. G., Aytur, S., Axelrod, M., Wilson, R., Hamm, J., Sayed, L., Pearson, A., Torres, P., Akporiaye, A., & Young, O. (2022). Learning from the Past: Pandemics and the Governance Treadmill. Sustainability, 14(6), 3683. https://doi.org/10.3390/su14063683

White, D. D., Wutich, A., Larson, K. L., Gober, P., Lant, T., & Senneville, C. (2010). Credibility, salience, and legitimacy of boundary objects: Water managers’ assessment of a simulation model in an immersive decision theater. Science and Public Policy, 37(3), 219–232. https://doi.org/10.3152/030234210X497726

Whitney, J. C., & Smith, R. A. (1983). Effects of Group Cohesiveness on Attitude Polarization and the Acquisition of Knowledge in a Strategic Planning Context. Journal of Marketing Research, 20(2), 167–176.

Young, O. R., Webster, D. G., Cox, M. E., Raakjær, J., Blaxekjær, L. Ø., Einarsson, N., Virginia, R. A., Acheson, J., Bromley, D., Cardwell, E., Carothers, C., Eythórsson, E., Howarth, R. B., Jentoft, S., McCay, B. J., McCormack, F., Osherenko, G., Pinkerton, E., van Ginkel, R., Wilson, J. A., Rivers, L., III, & Wilson, R. S. (2018). Moving beyond panaceas in fisheries governance. Proceedings of the National Academy of Sciences, 115(37), 9065–9073. https://doi.org/10.1073/pnas.1716545115

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