A Model to Compare Market Intervention vs. Information to Address Climate Change Using Substitute Goods

dc.contributorRizvanoghlu, Islam
dc.contributor.authorTran, Ryan
dc.date.accessioned2021-02-11T17:49:43Z
dc.date.available2021-02-11T17:49:43Z
dc.date.issued2020-09-29
dc.description.abstractThere exists potential to reduce U.S. GHG emissions by shifting consumer preferences of beef and plant-based substitutes (PBS) due to the disparity in their global warming potential. Market intervention to create new incentive structures using Pigouvian taxation of externalities and price distortion is one method. The second method is to fund a public information campaign to raise awareness on beef's negative impact on climate change and to advocate consideration of PBS as a similar, but more sustainable option. Effective public policy can be interpreted differently, including consumers' elasticities to each method and net change in global warming potential per cost of implementation. The administration of both methods together is possible, or even ideal, but is not explored due to assumed limited budget. To determine optimal tax and optimal information, a welfare model is used that is inclusive of consumer utility, global warming potential externalities, and government revenue. Utility is maximized with respect to consumer gratification from using the consumption bundle to lower their carbon footprint.
dc.description.departmentEconomics, Department of
dc.description.departmentHonors College
dc.identifier.urihttps://hdl.handle.net/10657/7499
dc.language.isoen_US
dc.relation.ispartofSummer Undergraduate Research Fellowship
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.titleA Model to Compare Market Intervention vs. Information to Address Climate Change Using Substitute Goods
dc.typePoster

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