Date of Award

2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Environmental and Natural Resources Economics

Department

Environmental & Natural Resource Economics

First Advisor

Emi Uchida

Abstract

Nonpoint source pollution is recognized as the primary cause of water pollution in the United States and has many adverse environmental effects in other areas such as Europe and China. In this dissertation research, I examine the role of information in managing nonpoint source pollution through voluntary programs and regulatory policies. Specifically, I look into the effect of informational nudges, information appealing to people to act, and financial incentives to reduce nonpoint source pollution through behavioral changes. Also, I investigate the impact of information on nonpoint source polluters’ behavior under the ambient-based policy when the environmental uncertainty exists at the individual level, and the information about other polluters’ action vary. We use three methods to study the impact of information on nonpoint source polluters' behavior: a randomized field experiment, a controlled laboratory experiment, and one integrated agent-based model. We test the following general hypotheses: (1) Informational nudges affect nonpoint source polluters’ behavior, but the effect is not persistent when we combine informational nudges with financial incentives to affect behavior. (2) Decreased environmental uncertainty leads to more efficient allocation of abatement efforts across nonpoint source polluters and better social efficiency under the ambient-based policy. (3) Under the ambient-based policy, different levels of environmental uncertainty and the ability to obtain information about other polluters’ actions affect nonpoint source polluters’ learning pattern and equilibrium behavior.

I find that informational nudges and financial incentives both work to change behavior, but they may substitute each other, especially when the financial incentive is small. The ambient-based policy is effective when uncertainty levels vary, but eliminating environmental uncertainty leads to less pollution. Different levels of environmental uncertainty and information disclosure induce different learning patterns of nonpoint source polluters. The agent-based model shows that high degrees of uncertainty lead to behavioral differences in the long run given the agents can observe other group members’ behavior.

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