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This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2019. Moving to the Standard 90.1-2019 edition from Standard 90.1-2016 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.
This publication provides an overview of NREL's active projects in Alaska.
Raw and processed data associated with the Burleyson et al. 2020 paper in Energy. The data needed to reproduce all figures in the paper is also included.
We combine Landsat- and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based products, as well as climate drivers from Phase 2 of the North American Land Data Assimilation System (NLDAS-2) in a Simple Biosphere land surface model (SiB2) to assess the impact of urbanization in continental USA (excluding Alaska and Hawaii). More than 300 cities and their surrounding suburban and rural areas are defined in this study to characterize the impact of urbanization on surface climate including surface energy, carbon budget, and water balance. These analyses reveal an uneven impact of urbanization across the continent that should inform upon policy options for improving urban growth including heat mitigation and energy use, carbon sequestration and flood prevention.
A workshop co-hosted by Pacific Northwest National Laboratory and Seattle City Light was held at the Seattle Municipal Tower on November 8–10, 2022. Participants from research organizations, utilities, professional associations, consultants, and government organizations attended. The purpose of the workshop was to develop a vision for addressing climate extremes that include improving forecasting and characterization, infrastructure resilience modeling, and investment planning and decision support. The workshop also aimed to provide a platform for sharing approaches and information and identifying possible collaborations.
Agricultural land holds tremendous potential to contribute to net zero greenhouse gas emission goals by providing low carbon renewable energy to displace fossil fuels and by serving as a sink for sequestering carbon in the soil with climate‐smart practices. This potential is, however, far from being realized. This paper examines the economic incentives and barriers to implementing land‐based carbon mitigation strategies and discusses the specific features of land‐based carbon mitigation practices on carbon emissions that need to be considered in designing policy incentives to induce adoption. Although a carbon price‐based policy is socially efficient, the more commonly observed policies to promote land‐based carbon mitigation include practice‐based conservation programs, technology mandates, and sector‐specific standards. The paper discusses the rationale for these alternative policy approaches and concludes with a discussion of emerging opportunities for designing policy and market‐based approaches for promoting land‐based carbon‐mitigation and future directions for economics research.
The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On April 24, 2023, PCAST sent a report to the President with recommendations on how science and technology can help inform and empower communities, businesses, and government agencies to prepare, respond, and adapt to extreme weather.
Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.
The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here, we outline a new framework for climate OSSEs that leverages the use of machine learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA’s GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth’s planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.
Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, here we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.
Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.
This document is prepared to help Better Buildings, Better Plants, and Better Climate Challenge partners understand renewable energy certificates (RECs).
Utilities and stakeholders need a standardized mechanism for evaluating how future climate and hydrologic conditions translate to water-related risks for power grid assets and systems to support planning decisions. Yet, no such mechanism exists. To address this need, our goals are to: (1) Develop and execute a state-of-the-art multi-model framework to assess future climate-water impacts and risks to the grid, including sensitivities to varying hydrologic drivers and infrastructure scenarios. (2) Create a standardized interactive visualization platform, using data from the climate-water risk assessments, that enables stakeholders to evaluate climate-water impacts, risks, and adaptation measures for power systems.