Environmental econometrics is the application of economic analysis to support decision making in natural resources and environmental management. Despite its proven track record of success in various scientific fields, econometrics has not been exploited to its fullest for environmental engineering applications, such as the prediction of municipal solid waste generation. Traditionally, environmental econometrics heavily relied on regression methods such as Ordinary Least Squares (OLS). While these methods are popular due to their easy implementation and simple mathematical analysis, they have fundamental limitations. Typically, these methods perform poorly when dealing with outliers, non-linearities, a large number of variables, or when a subset of independent input variables is highly correlated to each other. Despite their limitations, these methods can still provide robust estimations that could assist environmental policy-makers. Over the past few years, there has been a growing interest in the use of Artificial Intelligence (AI) in general and Machine Learning (ML) in particular for waste management. ML algorithms address some of the limitations associated with classical methods, but they also present new challenges. A brief review of the traditional methods, as well as the ML algorithms, is presented, and the pros and cons of each are discussed. Lastly, specific practical cases in waste management are discussed.

Practical application of econometrics for environmental management: Review of challenges and opportunities

Macromolecular Chemistry: The Second Century

[Author Benefits](https://scimeetings.acs.org/?utm_source=pubs_content_marketing&utm_medium=website&utm_campaign=0320_MCF_NPI_Launch_Spring_Homepage&ref=pubs_content_marketing)

[How to use SciMeetings](https://storage.googleapis.com/dl.morressier.com/acs/SciMeetings-ACSSpring2020-HowToGuide_M3020-Final.pdf)

[Frequently Asked Questions](https://storage.googleapis.com/dl.morressier.com/acs/SciMtng_ACSNMSp2020_FAQ_M3120_Final.pdf)

ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.