This Executive Summary is an excerpt from the full Methodology and Results report, which can be accessed at https://www.nrel.gov/docs/fy22osti/80889.pdf. The author list, acknowledgments, appendices, and sections referenced below can all be accessed in the full report.

"While the energy sector is a primary target of efforts to arrest and reverse the growth of greenhouse gas emissions and lower the carbon footprint of development, it is also expected to be increasingly affected by unavoidable climate consequences from the damage already induced in the biosphere. Energy services and resources, as well as seasonal demand, will be increasingly affected by changing trends, increasing variability, greater extremes and large inter-annual variations in climate parameters in some regions. All evidence suggests that adaptation is not an optional add-on but an essential reckoning on par with other business risks. Existing energy infrastructure, new infrastructure and future planning need to consider emerging climate conditions and impacts on design, construction, operation, and maintenance. Integrated risk-based planning processes will be critical to address the climate change impacts and harmonize actions within and across sectors. Also, awareness, knowledge, and capacity impede mainstreaming of climate adaptation into the energy sector. However, the formal knowledge base is still nascent?information needs are complex and to a certain extent regionally and sector specific. This report provides an up-to-date compendium of what is known about weather variability and projected climate trends and their impacts on energy service provision and demand. It discusses emerging practices and tools for managing these impacts and integrating climate considerations into planning processes and operational practices in an environment of uncertainty. It focuses on energy sector adaptation, rather than mitigation which is not discussed in this report. This report draws largely on available scientific and peer-reviewed literature in the public domain and takes the perspective of the developing world to the extent possible."

The Rapid Visual Screening (RVS) handbook can be used by trained personnel to identify, inventory, and screen buildings that are potentially seismically vulnerable. The RVS procedure comprises a method and several forms that help users to quickly identify, inventory, and score buildings according to their risk of collapse if hit by major earthquakes. The RVS handbook describes how to identify the structural type and key weakness characteristics, how to complete the screening forms, and how to manage a successful RVS program.

The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.