Risky Business: The Economic Risks of Climate Change in the United States

The American economy could face significant and widespread disruptions from climate change unless U.S. businesses and policymakers take immediate action to reduce climate risk. This report summarizes findings of an independent assessment of the impact of climate change at the county, state, and regional level, and shows that communities, industries, and properties across the U.S. face profound risks from climate change. The findings also show that the most severe risks can still be avoided through early investments in resilience, and through immediate action to reduce the pollution that causes global warming. The Risky Business report shows that two of the primary impacts of climate change -- extreme heat and sea level rise -- will disproportionately affect certain regions of the U.S., and pose highly variable risks across the nation. In the U.S. Gulf Coast, Northeast, and Southeast, for example, sea level rise and increased damage from storm surge are likely to lead to an additional $2 to$3.5 billion in property losses each year by 2030, with escalating costs in future decades. In interior states in the Midwest and Southwest, extreme heat will threaten human health, reduce labor productivity and strain electricity grids. Conversely in northern latitudes such as North Dakota and Montana, winter temperatures will likely rise, reducing frost events and cold-related deaths, and lengthening the growing season for some crops. The report is a product of The Risky Business Project a joint, non-partisan initiative of former Treasury Secretary Henry M. Paulson, Jr., Mayor of New York City from 2002-2013 Michael R. Bloomberg, and Thomas P. Steyer, former Senior Managing Member of Farallon Capital Management. They were joined by members of a high-level "Risk Committee" who helped scope the research and reviewed the research findings

Health Problems Heat Up: Climate Change and the Public's Health

Examines the health effects of climate change, the needed public health response, concerns for communities at high risk, and state planning and funding for climate change assessments and strategies. Makes federal, state, and local policy recommendations

Congressional Action on Resilient Infrastructure - Areas of Progress and Future Needs

Even though the 115th Congress did not enact a comprehensive infrastructure bill as many had hoped, lawmakers passed and advanced several pieces of legislation that address resilience in homes, defense facilities, airports, and water infrastructure. Going forward, resilience should be a central goal for the new construction, repair, or modernization of any infrastructure project, from early planning, budgeting, and design, through the duration of a project's life cycle. At a minimum, Congress can require resilience metrics and mitigation strategies for federally-funded projects. Prioritizing resilience in planning decisions can help meet the challenges posed by climate change-driven events, facilitate greater resource efficiency, and promote safe, healthy, and enduring infrastructure where people can thrive. Future infrastructure investments should reflect a triple bottom line of economic, social, and environmental sustainability in a manner that equitably serves the community

Climate Change and Health: Transcending Silos to Find Solutions

Background: Climate change has myriad implications for the health of humans, our ecosystems, and the ecological processes that sustain them. Projections of rising greenhouse gas emissions suggest increasing direct and indirect burden of infectious and noninfectious disease, effects on food and water security, and other societal disruptions. As the effects of climate change cannot be isolated from social and ecological determinants of disease that will mitigate or exacerbate forecasted health outcomes, multidisciplinary collaboration is critically needed. / Objectives: The aim of this article was to review the links between climate change and its upstream drivers (ie, processes leading to greenhouse gas emissions) and health outcomes, and identify existing opportunities to leverage more integrated global health and climate actions to prevent, prepare for, and respond to anthropogenic pressures. / Methods: We conducted a literature review of current and projected health outcomes associated with climate change, drawing on findings and our collective expertise to review opportunities for adaptation and mitigation across disciplines. / Findings: Health outcomes related to climate change affect a wide range of stakeholders, providing ready collaborative opportunities for interventions, which can be differentiated by addressing the upstream drivers leading to climate change or the downstream effects of climate change itself. / Conclusions: Although health professionals are challenged with risks from climate change and its drivers, the adverse health outcomes cannot be resolved by the public health community alone. A phase change in global health is needed to move from a passive responder in partnership with other societal sectors to drive innovative alternatives. It is essential for global health to step outside of its traditional boundaries to engage with other stakeholders to develop policy and practical solutions to mitigate disease burden of climate change and its drivers; this will also yield compound benefits that help address other health, environmental, and societal challenges

Power Grid Management in Response to Extreme Events

Power system management in response to extreme events is one the most important operational aspects of power systems. In this thesis, a novel Event-driven Security Constrained Unit Commitment (E-SCUC) model and a statistical method, based on regression and data mining to estimate the system components outages, are proposed. The proposed models help consider the simultaneous outage of several system components represented by an N-1-m reliability criterion and accordingly determine the proper system response. In addition, an optimal microgrid placement model with the objective of minimizing the cost of unserved energy to enhance power system resilience is proposed. The numerical simulations on the standard IEEE 30-bus and IEEE 118-bus test systems exhibit the merits and applicability of the proposed E-SCUC model, as well as the advantages of the data mining approach in estimating component outage, and the effectiveness of the optimal microgrid placement in ensuring an economic operation under normal conditions and a resilient operation under contingency cases

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Bark beetle population dynamics in the Anthropocene: Challenges and solutions

Tree-killing bark beetles are the most economically important insects in conifer forests worldwide. However, despite N200 years of research, the drivers of population eruptions and crashes are still not fully understood and the existing knowledge is thus insufficient to face the challenges posed by the Anthropocene. We critically analyze potential biotic and abiotic drivers of population dynamics of an exemplary species, the European spruce bark beetle (ESBB) (Ips typographus) and present a multivariate approach that integrates the many drivers governing this bark beetle system. We call for hypothesis-driven, large-scale collaborative research efforts to improve our understanding of the population dynamics of this and other bark beetle pests. Our approach can serve as a blueprint for tackling other eruptive forest insects