Reliable data about collection, volume, tonnage, stream composition, and disposal price have long been described as key components of successful solid waste management planning. Yet, concerns about data quality and quantity have continued to limit even the most sincere, progressive waste management schemes. This paper examines solid waste management data that has been collected in the US state of New Jersey starting in the 1960s. We present the origins of waste management data collection in New Jersey and trace some of the applications that have been made with the data over time. We compare the New Jersey dataset to waste management data that has been collected in other US states. We then describe our work collecting, cleaning, and preparing for public dissemination and use in a geospatial visualization exercise a digital version of the data spanning approximately 1993 to 2016, before presenting some illustrations of the type of modeling and analysis that researchers or the concerned public would be able to undertake now that the dataset is available. (We are publishing the 1993–2016 dataset alongside this paper). We argue that the New Jersey waste management dataset is much better than most other waste datasets in the US, but despite this fairly high quality, there remain significant gaps which inhibit the ability of planners to design and implement comprehensive waste management plans. That there are limits inherent to the New Jersey dataset suggests, we argue, a ceiling to the usefulness of waste management data as a category of environmental knowledge with possible implications for ‘big’ environmental data more broadly.
Howell, Jordan; Schmidt, Katherine; Iacone, Brooke; Rizzo, Giavanni; and Parilla, Christina, "New Jersey's waste management data: retrospect and prospect" (2019). School of Earth & Environment Faculty Scholarship. 21.
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Howell, Jordan P.; Schmidt, Katherine; Iacone, Brooke; Rizzo, Giavanni; & Parilla, Christina. (2019). New Jersey's waste management data: retrospect and prospect. Heliyon, August 2019, 5(8), E02313.