The impact of information on behavior under an ambient‐based policy for regulating nonpoint source pollution

Abstract

Different levels of water quality information lead to different behaviorEnhanced water quality information improves social efficiency Abstract: Stemming from Segerson [1988], literature on nonpoint source pollution shows that ambient-based regulatory policies can induce polluters in a common watershed to comply with an exogenously determined emissions standard. This study uses laboratory economic experiments in a spatially heterogeneous setting to test the effectiveness of an ambient tax/subsidy policy in a setting with realistic in-stream nutrient transport dynamics when varying levels of ambient information about the pollution is available to the agents and the regulator. We find that increasing the frequency of ambient monitoring improves the spatial allocation of emissions reductions. In particular with more frequent monitoring, the ambient-based policy induces firms further from the monitoring point to reduce emissions significantly more than downstream firms. Overall, the results suggest that enhanced information, especially enhanced temporal resolution, leads to efficiency gains.Index terms: 6309 1880 1846 0478

Behavioral and Experimental Agri-Environmental Research: Methodological Challenges, Literature Gaps, and Recommendations

“…18 Duflo, Kremer, and Robsinson (2011) analyze farmer investments in fertilizer and is, therefore, not categorized as an Ag-E paper. 19 A large portion of this literature tests the outcomes of various tax and subsidy mechanisms to reduce nonpoint source (NPS) pollution (Alpízar et al 2004;Poe et al 2004;Spraggon 2004Spraggon , 2013Cochard et al 2005;Vossler et al 2006;Suter et al 2008;Spraggon and Oxoby 2010;Cason 2010;Cason and Gangadharan 2013;Suter and Vossler 2014;Miao et al 2016;Palm-Forster et al 2017, improve extraction of ground water for irrigation (Gardner, Moore, and Walker 1997;Suter et al 2012Suter et al , 2018Li et al 2014;Liu et al 2014), and incentivize land conservation and ecosystem service provision (Parkhurst et al 2002;Cason and Gangadharan 2004;Shogren 2007, 2008;Arnold, Duke, and Messer 2013;Banerjee et al 2014Banerjee et al , 2015Banerjee et al , 2017Fooks et al 2015Fooks et al , 2016Messer et al 2017;Duke et al 2017;Banerjee 2018).…”

Addressing Social Dilemmas With Mascots, Information, and Graphics

“…This model, which is commonly used by the U.S. EPA, allows for complexities in how pollution mixes and flows downstream. A detailed description of the model is provided by Miao et al (, 3297–98). Participants in the experiment were aware that their actions affected the ambient pollution level in complex ways, but they were not given specific details about the model to maintain their focus on the nudges provided, such as information and mascots (i.e., our primary research emphasis)…”

“…Our novel experimental design allows us to analyze changes in polluting behavior in response to public information, feedback from mascots, and data visualizations. Previous laboratory experiments have been widely used to test NPS programs and policies when real-world experimentation is not feasible or when preliminary data must be collected prior to testing interventions in the field Gangadharan 2004, 2013;Cochard, Willinger, and Xepapadeas 2005;Miao et al 2016;Palm-Forster, Suter, and Messer 2019;Poe et al 2004;Spraggon 2002Spraggon , 2004Spraggon , 2013Suter et al 2008Suter et al , 2010Vossler et al 2006;Vossler, Suter, and Poe 2013). This experiment reflects a traditional NPS pollution environment in which participants act as individual firms located within a watershed.…”

Simulating Heterogeneous Farmer Behaviors under Different Policy Schemes: Integrating Economic Experiments and Agent-Based Modeling

“…A few studies have attempted to integrate economic experiments and ABM in NPS pollution management context. Zia et al (2016, in review) constructed agent-based models using an economic experiment documented in Miao et al (2016). The agents were categorized to pursue different behavioral strategies under alternate policy and sensor information regimes, and the agents' type categories were predicted by a multi-level multinomial logistic regression model built from experimental data.…”