Global oil and gas processing infrastructure already faces a significant threat of disruption due to annual coastal flooding of major refining and petrochemical centers, which is expected to further increase with the effects of climate change. U.S. Gulf Coast refineries account for over half of the total refining capacity of the nation, and Gulf Coast ethylene producing facilities account for approximately 90% of domestic ethylene production. However, less than a third of products refined in this region is used to supply local markets, and as the largest ethylene producer in the world, the U.S. exports the vast majority of ethylene domestically produced. Due to the highly centralized nature of the U.S. petrochemical distribution network, disruptions affecting Gulf Coast oil and gas processing facilities can have widespread impacts. In addition to the adverse economic and supply chain disruption effects of refining and petrochemical processing facility failures, pollutant emissions due to facility disruptions pose a significant threat to the public health of fenceline communities and the surrounding environment. This thesis presents a comprehensive framework for modeling coastal refining and petrochemical infrastructure risk and resilience when subjected to coastal hazards, such as hurricanes, severe storms, or sea level rise. This framework propagates modeled risk and resilience metrics across the regional, domestic, and global product distribution network to gain insight into how local disruptive events might impact the broader supply chain. Additionally, this framework models select community and environmental cascading consequences of oil and gas processing infrastructure failures to demonstrate the ties to potential local health hazards posed by these disruptive events to the vulnerable communities surrounding these facilities. First, this thesis considers the impacts that sea level rise projections might have on the annual flood risk to coastal refineries, and how regional disruptions propagate across the network. Both the annual regional risk in terms of expected production disruption under a range of climate scenarios, as well as the expected production disruption due to a major flood event impacting refining hubs of high importance are assessed throughout the 21st century. These risks are propagated across the network to model the global impact of coastal flood-induced refining disruptions. This analysis provides insights on the relative risks that different climate scenarios and flood events pose globally, informing potential mitigation and adaptation needs of critical facilities. Then, this thesis presents the development of a predictive model for the likelihood and expected duration of refinery shutdowns under hurricane hazards. Such models are currently lacking in the literature yet essential for risk modeling of the cascading consequences of refinery shutdown ranging from resilience analyses of petroleum networks to potential health effects on surrounding communities tied to startup and shutdown activities. A database of empirical refinery idle time duration and storm hazards data is developed, and statistical analyses are conducted to explore the relationship between refinery and storm characteristics and shutdown duration. The proposed method with the highest predictive accuracy is found to be a model comprised of a logistic regression binary classification component related to refinery shutdown potential and a Poisson distribution generalized linear model component related to idle time duration determination. To illustrate the utility of the newly developed model, a case study is conducted exploring the impact of two storms affecting the Houston Ship Channel and surrounding region. Both the regional refining resilience as well as the distribution network resilience are quantified. Next, this thesis presents the development of predictive models for likelihood and expected duration of petrochemical facility idle and restart times and expected resulting excess emissions quantities are proposed. As is the case with refinery infrastructure, these models are also presently lacking in the literature for petrochemical processing infrastructure. Development of these models would similarly afford opportunities for risk and resilience modeling of the cascading consequences of petrochemical complex shutdowns, which might also include applications in resilience analyses of regional petrochemical processing infrastructure or quantification of negative health impacts on fenceline communities. A database of empirical petrochemical facility characteristics, downtime, and hurricane hazards data is developed, and statistical analyses are conducted to investigate the relationship between facility and storm features and shutdown duration. The proposed method for expected shutdown modeling with the highest predictive accuracy is determined to be one comprised of a logistic regression binary classification component related to facility shutdown potential and a gamma distribution generalized linear model component related to idle time duration determination. Finally, these developed models are employed as input to a culminating framework for cascading consequence modeling of petrochemical processing infrastructure subjected to hurricane hazards. Overall, the proposed framework leverages Bayesian networks for predictive modeling and potential updating of facility shutdown and excess emissions quantification due to hurricane-induced facility failures. To illustrate the utility of the proposed framework, a case study is performed investigating the potential mitigative impact of the proposed Galveston Bay Park Plan on Houston Ship Channel regional refining and petrochemical processing risk and resilience and cascading air pollutant emissions risk. Such analyses expose community and regional impacts of facility failures and can support resilience improvement decisions.