ABSTRACT Essay 1: In this study, we used agglomerative hierarchical cluster analysis to group 2778 farming-defined counties into six clusters, revealing farm patterns across the contiguous 48 states of the United States. Economists have endeavored to identify patterns in US farming to understand the differences in economic performance and improve farm households' well-being. The US is a leading global producer and exporter of many agricultural and food products. Our primary objective is constructing a policy-relevant farm clustering to characterize agricultural homogeneity in US farms' production potential. We identify six relatively homogeneous clusters in five dimensions: farm size, farm assets, farm labor, farm output, degree of mechanization, and government programs. Minimizing diversity within a cluster allows for analysis of public policy changes on specific clusters and comparison of differential effects of the change across clusters. Essay 2: In this study, we developed the most comprehensive county-level datasets covering the 48 contiguous states of the United States to measure the impact of climate change on the US livestock industry. In the first part of our study, we utilized ordinary least squares (OLS) and Fixed effect (FE) models to perform both cross-sectional and panel analysis on five types of livestock: beef cows, milk cows, layer chickens, broiler chickens, and hogs. Unlike the general Ricardian approach in the literature, we attempted a novel approach using livestock inventory share as our models' dependent variable instead of land value. We found that climate change may or may not affect livestock inventory levels depending on the types of livestock and geographical locations. Increased temperature and precipitation may benefit a particular livestock industry depending on geographical location and production settings. However, we did not predict any adverse effect of climate change on any of the five types of livestock we analyzed. In the second part, we fitted our regression estimates to a climate model and projected the US livestock industry in 2070. Comprehending livestock and region-specific impacts of climate change will allow policymakers to craft better strategies and policies to combat and mitigate the adverse externalities of climate change. Essay 3: This study establishes a statistically significant negative association between public transit funding and private vehicle usage. We used the propensity score matching, genetic matching, and diff-in-diff frameworks to conduct county-level and individual household-level analyses to conclude that increasing public transit funding can successfully decrease private vehicle usage. Our results provide empirical backing for encouraging public transit funding as an intervention strategy to reduce private vehicle usage in communities. More specifically, the counties or households that received public transportation funding have lower average vehicle miles traveled (2.35 miles or roughly 6 percent on the county level and 1306.5 miles or approximately 6 percent on the household level) compared to the counties that did not receive any funding. We also conducted a longitudinal study to understand the causal impact of changes in public transit funding on county and household annual private vehicle mileage. This study uses four datasets. The 2019 National Transit Database Annual Data Products (NTD) provides public transit data. Transportation data are collected from the 2017 and 2009 National Household Travel Survey (NHTS). Data from the 2017 national census provide this study's necessary demographic and geographic data (United States Census Bureau). We matched observations from these four datasets at the county and household levels to create the panel datasets with 3138 counties and 4588 households from 50 states of the United States.