Author ORCID Identifier

https://orcid.org/0000-0002-1506-3640

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Davis College of Agriculture, Natural Resources and Design

Department

Division of Resource Economics & Management

Committee Chair

Alan R. Collins

Committee Member

Michael P. Strager

Committee Member

Daniel S. Grossman

Committee Member

Donald J. Lacombe

Committee Member

Xiaoli Etienne

Abstract

Spatial interaction and the locational structure between observations play key roles in the field of econometrics for both cross-sectional and panel data analysis. Compared to a non-spatial econometric model, a spatial model relaxes the assumption of independency in observations. This research applies spatial and non-spatial econometrics in three different fields of applied economics: (1) drinking water and air quality violations impacts on lung and bronchus cancer incidence in the contiguous United States (U.S.); (2) spillover effects of non-pharmaceutical interventions (NPIs) on COVID-19 cases across the contiguous U.S. counties; and (3) urbanization impacts on carbon dioxide (CO2) emissions in selected 119 countries.

In Chapter 2, ordinary least squares (OLS) and Spatial Durbin Model (SDM) are applied to data from 48 states plus Washington D.C. in the contiguous U.S. for the period of 2006-2016 to examine the impacts of population-level exposures to environmental quality standards non-compliance on the lung and bronchus cancer incidence. The SDM reveals statistically significant impacts of population-level exposures to violations of environmental pollution standards on lung and bronchus cancer incidence. While impacts are statistically significant (direct effect for water and total effect for air), they are small relative to smoking behavior. Example calculations show that a 10% reduction in population exposure rate to drinking water quality violations across the state of Oklahoma results in a decrease of five cancer cases annually with an estimated annual monetary benefit of $20.6 million. A 10% reduction in population exposure to air quality violations in the state of Utah results in six fewer cancer cases annually with a $24.7 million annual monetary benefit in Utah and three-neighboring states.

Chapter 3 examines the spatial spillover effects of NPIs policies on reductions of COVID-19 cases in the contiguous U.S. Using annual cross-sectional data for the year 2020, I apply a spatial Durbin model (SDM) to find statistically significant spillover effects from stay-at-home mandatory orders and mask mandates on COVID-19 cases per 100,000 people. Nationally, on average, 4 cases per 100,000 people can be reduced within the mandate county while an additional 9 cases per 100,000 people can be reduced in 6-nearest neighboring counties as the spillover effect from implementing a mask mandate policy for a month. On the other hand, the direct effect of mandatory stay-at-home orders is 15 cases per 100,000 people while the spillover (indirect) effect is 38 cases per 100,000 people when this NPI is implemented for one month. Thus, mandatory stay-at-home orders show a greater impact on reducing COVID-19 case rates than mask mandates. Further, the indirect effect in neighboring counties is larger than the direct effect for both NPI policies, showing the importance of accounting for spillover effects when determining NPI policy benefits. Based on the SDM model, an example of mask mandate and mandatory stay-at-home policies in Powder River County in Montana (as a mandate state) with three neighboring non-mandate counties in the state of Wyoming is examined. A month-long implementation of mask mandate, stay-at-home order, and both these policies contributes $0.01 million, $0.04 million, and $0.05 million of direct benefits, respectively from reduced COVID-19 costs in Powder River County in the state of Montana. The spillover benefits of about $0.70 million, $2.95 million, and $3.65 million, respectively are estimated based on reduced COVID-19 costs in the three neighboring counties of Wyoming (Sheridan, Campbell, Crook) from one-month implementation of public mask mandate, mandatory stay-at-home orders and both these policies in Powder River County in the state of Montana. Most of this benefit is the result of reduced hospitalizations in the states of Montana and Wyoming. The results of this research will help to design cost-efficient interventions to tackle future pandemics.

Finally, Chapter 4 relates urbanization to greenhouse gases (GHG) emissions based on data from 105 countries in 1990-2018. Using pooled mean group (PMG) estimation technique, significant nonlinear relationships are found between urbanization and both carbon dioxide (CO2) intensity and CO2 emissions. For CO2 intensity, urbanization impacts hit a turning point at 59.16 percent such that urbanization prior to this percentage increases CO2 intensity while rates of urbanization rate above this percentage decrease CO2 intensity. No such turning point is observed for CO2 emissions. Based on the PMG model, renewable energy consumption significantly reduces both CO2 intensity and emissions. However, at a given rate of renewable energy consumption, urbanization only reduces CO2 emissions. Empirical results from this study highlight the importance of global scale action on urban buildings and transportation to reduce GHG emissions in both developing and under-developed countries with technical support from developed economies.

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