Theses and Dissertations from UMD
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Item ESSAYS ON INTERNATIONAL TRADE AND THE ENVIRONMENT(2022) Lim, Heehyun Rosa; Limão, Nuno; Lee, Eunhee; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the relationship between international trade and environmental outcomes. In particular, I study the impact of international trade on airborne pollutants, including the change in emissions and concentration as well as their welfare consequences. In the first chapter, I suggest the intermediate import channel as a new perspective to understand the linkage between international trade and air pollutant emissions. I first review the existing literature's understanding of the impact of trade on emissions. The review shows that the literature mostly focuses on the increased market access but overlooks the increased access to imported inputs. Using the data on the US manufacturing industries, I then document a few stylized facts that are suggestive of the linkage between intermediate imports, input usage, and emissions. I show that in the US, the import penetration among inputs used has increased while the energy intensity of US manufacturing has declined, the latter of which explains a third of the within-industry reduction in $NO_x$ emission intensity. To analyze the channels by which trade in intermediate inputs affects emission intensity, I build a model of heterogeneous firms, intermediate trade, and inputs with different emission profiles. By focusing primarily on the emissions linked with input usage, my model examines the effect of improved access to foreign intermediates on firms' input choices and emission outcomes. The model shows that with lower intermediate import costs, firms become less energy-intensive by either increasing their intermediate intensity, using energy-saving technology, or both. Moreover, the general equilibrium force, as well as amplification through the input-output linkage, bring a further decrease in emission intensity in all firms. The model also presents the selection and reallocation effect which further amplifies the within-firm improvements. In the second chapter, I run empirical and quantitative analyses to test the theoretical model from the first chapter against the US manufacturing data. In the empirical analysis, I estimate the model prediction, which states that industry-level emission intensity can be expressed in the producer price index when the cost of energy and market access are controlled,using the industry-level panel data between 1998 and 2014. By using the import price of intermediates as an instrumental variable for the producer price index, I find evidence that a lower producer price, driven by a lower intermediate import price, leads to lower $NO_x$ emission intensity. The reduced-form evidence supports the model mechanism that states that a lower import price of intermediates decreases emission intensity. I then calibrate the model to 1998 aggregate US manufacturing and quantify the change in emission intensity driven by the change in intermediate import cost. The quantification shows that the fall in intermediate import cost between 1998 and 2014 explains about 8-10\% of the observed technique effect in $NO_x$ emissions. 68\% of the decrease comes from the within-firm changes via firms' substituting away from energy inputs, global sourcing, and adopting energy-saving technology, which highlights the importance of taking within-firm channels into account to understand the effects of trade policies on emissions. The third chapter (co-authored) re-examines the welfare gains from international trade by incorporating the transboundary nature of air pollutants.\footnote{This chapter is from a joint work with Eunhee Lee.} We run country-level panel regressions and find that concentration is correlated with transboundary pollution, constructed as the weighted sum of other countries' emissions. We then build a general equilibrium model of international trade and environmental externality from local pollutants of transboundary nature, in which the concentration of a country is affected by both its own and other countries' emissions. The model shows that the change in welfare can be decomposed into the change in real income and the change in air pollutant concentration, the latter of which can further be decomposed into that driven by own emissions and by other countries' emissions. We use this model to quantify the welfare implications of two trade shocks -- China shock and the EU 2004 enlargement. The results show multiple channels that shape heterogeneous welfare consequences across countries. First, liberalizing countries experience an increase in emissions due to an increase in production. Second, the emissions of other countries move in either direction, depending on the effects of pollution relocation and increased production due to cheaper inputs. Third, the levels of concentration increase in liberalized countries and some other countries due to the increase in own emissions or transboundary pollution, or both. We run additional counterfactual exercises with stricter environmental regulations imposed on liberalized countries and show that there can be welfare gains in many countries by lowering emissions and transboundary pollution, suggesting the potential effects of combining trade and environmental policies.Item Application of Advanced Statistical Methods to Assess Atmospheric and Soil Pollution Mitigation and Potential Risks(2020) Yang, Zijiang; Torrents, Alba; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In environmental engineering field studies, data analysis plays an important role when presenting data into useful information that can be used by engineers and policy makers. However, traditional and currently used approaches have significant limitations due to the nature of the field data, such as high temporal variability, high spatial variability, and high heterogeneity. Such uncertainty may be better handled with more realistic statistical models than traditional statistical models with normal approximation. Additionally, a more robust incorporation of heterogeneity and variability may help to modify environmental fate models to achieve more accurate predictions. Therefore, this dissertation applied some advanced data analysis techniques to four case studies.First, reparameterization was applied to modify the Gaussian plume model to predict dispersion of air pollutant emission from a ground-level active-discharge releasing source. Cross-validation was applied for model selection. The results showed that predictive accuracy of the modified GPM was greatly improved compared with the original model. Second, dispersion of particulate matter was accessed, and a dispersion correction factor was developed to enhance the performance of the regulatory air dispersion model (AERMOD) for low-level sources. Cross-validation was used for model comparison. The results showed that predictive accuracy of the corrected model was greatly improved. Third, carbon amendments were applied to a historically contaminated field to investigate the feasibility for mitigating bioaccumulation. The effect of carbon amendments on bioaccumulation were evaluated. The results showed some evidence of the mitigation effect of compost, and in the meanwhile, the need of a robust statistical method was highlighted due to great spatial variability. Lastly, the Bayesian hierarchical model (BHM) was applied to the field measurement dataset to characterize pollutant concentrations and bioaccumulation. Cross-validation and information criteria were used to evaluate model performance between the BHM and traditional model. The results showed that the BHM was preferred for smaller predictive errors and ability to handle data with larger observational error. These case studies demonstrate the capability of advanced statistical methods for dealing with different environmental research problems. Such statistical methods will be useful for model modification with more specific situations, for data analysis with limited sample size and/or great variability and observational error, for environmental and ecological risk assessment, for evaluation of environmental mitigation strategies, for simulation of real-time pollutant distribution and forecasting with integration of monitoring and modelling approaches, and for minimization of sample size to meet with the accuracy requirement and lower the cost. In conclusion, advanced statistical methods are useful tools for environmental research.Item Exposure to Ambient Air Pollution and Correlates of Cardiovascular Disease among Youth with Type 1 Diabetes(2019) Montresor-Lopez, Jessica Anne; Puett, Robin C; Maryland Institute for Applied Environmental Health; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Air pollution from traffic-related sources is associated with cardiovascular disease (CVD), potentially through changes in systemic inflammatory responses, vascular function and oxidative stress leading to atherosclerosis, thrombosis or endothelial dysfunction. Individuals with type 1 diabetes (T1D) have a greater risk of CVD-related morbidity and mortality than the general population, and they may be more susceptible to the effects of air pollution on CVD. Although these increased risks begin during childhood, very few studies have assessed the impact of air pollution on children and youth with T1D. This dissertation directly addresses gaps in the epidemiologic evidence by: 1) evaluating the relationship of short-term exposures to traffic-related air pollutants with pulse wave velocity (PWV), a measure of arterial stiffness, 2) assessing the effects of changes in air pollution exposures on changes in inflammatory biomarkers, including C-reactive protein, fibrinogen and interleukin-6 (IL-6), and 3) examining the relationship of long-term exposures to traffic-related air pollution with allostatic load (AL), a measure of cumulative biological risk, among a cohort of youth with T1D. Data were obtained from the SEARCH for Diabetes in Youth (SEARCH) study. SEARCH was initiated in 2000 and includes a diverse population of US youth diagnosed with diabetes prior to age 20 years. Anthropometric and laboratory measures were taken at SEARCH study visits, and standardized questionnaires were used to collect information on important covariates. Air pollution exposures were estimated using spatio-temporal models and assigned to residential addresses for each participant. In the first study, we identified a significant association between increased exposure to PM2.5 on the day of the examination with higher PWV using generalized linear models adjusted for lifestyle and demographic characteristics. In the second analysis, we found consistent positive effects of increases in PM2.5 over the week prior to the examination with IL-6 using longitudinal mixed models. In the third study, no significant associations were observed for monthly and annual PM2.5 exposures or proximity to major roadways with AL in fully adjusted linear mixed models. However, effects differed by race/ethnicity and gender. Overall, this research indicates that youth with T1D may be at higher risk for air pollution-related cardiovascular impacts.