Overcoming Uncertainty in Nonpoint Source Water Pollution Governance through Collaborative, Flexible Programs
*Names in bold indicate Presenter
This paper examines how collaboratively designed, flexible plans and policies address transboundary environmental problems in the face of significant technical and regulatory uncertainties. Nonpoint source (NPS) water pollution is produced by numerous environmental land use and development actions, spans administrative boundaries, and is not amenable to regulatory monitoring and enforcement actions. Policymakers thus often seek to manage NPS pollution through non-regulatory programs that involve all relevant actors, encompass numerous significant drivers, and are flexible so as to be robust to future technology and governance changes. One prominent example is the Coastal Nonpoint Pollution Control Program (CNPCP), which provides funds to states for the development and implementation of comprehensive NPS control and mitigation plans in coastal regions. CNPCP plans must have sufficient geographic scope, address all significant drivers, and meet standards of public participation and administrative coordination. While inclusiveness and comprehensiveness are often held to produce plans and policies that are of higher quality and that better reflect local conditions, in practice these same factors can result in products that are overly complex, vague, and difficult to apply to specific circumstances. This paper tests how plan complexity, administrative coordination required for each program, and other key attributes affect pollution outcomes.
The data I use for this study begin with coastal ecological assessment observations in 34 coastal states and territories from the EPA’s series of National Coastal Assessment surveys spanning from 1990 to 2014. I then code for when state’s CNPCP plan was conditionally accepted and formally accepted by the EPA, plan and program attributes such as organizational involvement, public participation, geographic scope, enforcement mechanisms, and management areas that each plan focuses on. I also collect and control for key covariates such as longitudinal satellite-generated land use data, economic indicators and major industries, nonprofit advocacy efforts, and state political climate.
I model these data using Bayesian multilevel longitudinal regression models with water quality and habitat indicators (including chemical samples, benthic community surveys, trawl samples) as the dependent variables to assess the temporal relationship between plan adoption--and attributes--and ecological conditions. The multilevel approach models site and time specific observations as nested within a state management program. The advantage of a multilevel model is that variation can be modeled as a function of covariates at the observation level (e.g., spatial location, time, water depth) and state level (to examine how program features affect predicted impact). Further, Bayesian formulation accounts for measurement errors and sparseness associated with environmental monitoring data by examining the probability of parameter estimates given observed data.
Implications for Research and Policy
The extant literature lacks evidence regarding the efficacy of comprehensive, flexible governance strategies intended to address highly uncertain, transboundary environmental problems such as nonpoint source water pollution. This analysis provides empirical evidence of the environmental outcomes associated with such efforts. Further, by comparing and contrasting different programmatic features and strategies, this analysis directly informs policymakers about how to design and implement these interventions.