Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2019

Abstract

Agency and state grant funding should be disseminated in ways so it will result in better management of household hazardous waste (HHW) and environmental sustainability. Since location seems to matter in HHW collection activities, it is important to consider pro-environmental spatial spillovers that occur, based on agency actions and waste collection behavior taking place in other locations. These may influence HHW-related practices in close-by regions. Using a county-level spatio-temporal dataset that consists of economic, demographic, and HHW data in California from 2004 to 2015, we evaluate the impact of HHW grants on HHW collection activities while considering pro-environmental spillovers. We employ a research design that controls for confounding factors across the North, Central and South Regions, and over time. The research models assess causal relationships using a random effects panel data model with instrumental variables to estimate the grants’ influences, while considering spatial effects and unobservable bias. Several findings were obtained: (1) HHW grants had positive effects on waste collection in a consistent way across multiple models that we tested; (2) positive spatial spillover effects occurred for HHW collection activities due to the pro-environmental activities of nearby counties. This research contributes to the growing body of research on geospatial policy analytics, ways to establish the basis for causal inference, and the use of robustness checks to develop a deeper understanding of how to make waste management grant programs more effective in the regions where they are implemented.

Keywords

Causal inference, Decision bias, Empirical research, Environmental policy, Geospatial policy analytics, Household hazardous waste (HHW), HHW grants, Modeling robustness, Policy analytics, Policy-making, Pro-environmental spillovers, Recycling, Spatial analytics, Sustainability, Waste management

Discipline

Computer Engineering | Data Storage Systems

Research Areas

Data Science and Engineering; Information Systems and Management; Integrative Research Areas

Publication

Applied Geography

Volume

109

First Page

1

Last Page

15

ISSN

0143-6228

Identifier

10.1016/j.apgeog.2019.05.009

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.apgeog.2019.05.009

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