Environmental Enforcement and Compliance: Lessons from Pollution, Safety, and Tax Settings

James Alm & Jay P. Shimshack

Foundations and Trends in Microeconomics2014https://doi.org/10.1561/0700000048book
ABDC C
Weight
0.34

What the paper says

Environmental monitoring and enforcement are controversial and incompletely understood. This survey reviews what we do and do not know about the overall effectiveness, as well as the cost effectiveness, of pollution monitoring and enforcement. We ask five key questions: what do environmental monitoring and enforcement actionslook like in the real world? How do we assess environmental compliance and deterrence? Do environmental monitoring and enforcement actions get results? How, why, and when do inspections and sanctions achieve compliance and reduce pollution? And, what do the answers to the preceding questions tell us about designing and implementing more effective and more cost effective public policies for the environment? A key contribution is drawing lessons from diverse sources, including insights from theoretical, empirical, and experimental contributions in environmental, tax, and safety settings. We conclude that traditional environmental monitoring and enforcement actions generate important deterrence effects. However, there are limits to such deterrence, and deterrence itself cannot fully explain all patterns of environmental behavior. Encouraging compliance requires both traditional tools and additional tools.

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https://doi.org/https://doi.org/10.1561/0700000048

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@article{james2014,
  title        = {{Environmental Enforcement and Compliance: Lessons from Pollution, Safety, and Tax Settings}},
  author       = {James Alm & Jay P. Shimshack},
  journal      = {Foundations and Trends in Microeconomics},
  year         = {2014},
  doi          = {https://doi.org/https://doi.org/10.1561/0700000048},
}

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Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.