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DOE OSTI2019

Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Prevention of a wrong-location misadministration through the use of an intradepartmental incident learning system

Purpose: A series of examples are presented in which potential errors in the delivery of radiation therapy were prevented through use of incident learning. These examples underscore the value of reporting near miss incidents. Methods: Using a departmental incident learning system, eight incidents were noted over a two-year period in which fields were treated 'out-of-sequence,' that is, fields from a boost phase were treated, while the patient was still in the initial phase of treatment. As a result, an error-prevention policy was instituted in which radiation treatment fields are 'hidden' within the oncology information system (OIS) when they are not in current use. In this way, fields are only available to be treated in the intended sequence and, importantly, old fields cannot be activated at the linear accelerator control console. Results: No out-of-sequence treatments have been reported in more than two years since the policy change. Furthermore, at least three near-miss incidents were detected and corrected as a result of the policy change. In the first two, the policy operated as intended to directly prevent an error in field scheduling. In the third near-miss, the policy operated 'off target' to prevent a type of error scenario that it was not directly intended to prevent. In this incident, an incorrect digitally reconstructed radiograph (DRR) was scheduled in the OIS for a patient receiving lung cancer treatment. The incorrect DRR had an isocenter which was misplaced by approximately two centimeters. The error was a result of a field from an old plan being scheduled instead of the intended new plan. As a result of the policy described above, the DRR field could not be activated for treatment however and the error was discovered and corrected. Other quality control barriers in place would have been unlikely to have detected this error. Conclusions: In these examples, a policy was adopted based on incident learning, which prevented several errors, at least one of which was potentially severe. These examples underscore the need for a rigorous, systematic incident learning process within each clinic. The experiences reported in this technical note demonstrate the value of near-miss incident reporting to improve patient safety.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Pan-YANG/RA-ABM

Crowd-sourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowd-sourcing management for rainfall monitoring. Specifically, we modeled the allocation of incentives (quantitatively measurable and limited rewards) among crowd-sourcing participants for a theoretical rainfall monitoring case. For this purpose, we developed an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulated six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we found that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade‐off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowd-sourcing rainfall monitoring. The proposed crowd-sourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as stormwater management and water availability and biomass assessment for food and energy crops.

Yang, Pan↗

DOE OSTI2020

Knowledge Spillovers and Cost Reductions in Solar Soft Costs

Despite the commonly acknowledged importance of knowledge spillovers in reducing solar soft costs, we are only beginning to answer a fundamental question: who learns what (knowledge acquisition), from whom (knowledge production), and how (spillover mechanisms)? Until recently, this important topic has been largely unexplored in the case of solar soft costs. Thus, this project set out to identify how knowledge spillovers affect soft costs in the U.S. photovoltaic (PV) installation industry, specifically how important spillovers are, what types of knowledge are most likely to spillover, and how networks of actors affect spillovers. Our findings offer insights for designing solutions that address problems associated with knowledge spillovers and that leverage spillovers to reduce solar soft costs. Recognizing the ambiguity in the definition of soft costs, i.e., “non-hardware costs,” and variability in soft cost categories, we developed the Solar Soft Cost Ontology (SSCO) to systematically identify key concepts related to soft costs, network actors, learning processes, and the relationships between them. This ontology served as a foundational organizational structure for the methodology of the remaining tasks: case studies, surveys, pricing analysis, patent analysis, network analysis, and project integration across tasks. While there is substantial learning among installers that is reducing the soft costs for PV installations, most of that learning is retained by firms rather than spread across the industry. The positive relationship between experience accumulation and cost reductions is typically explained as learning by doing (LBD), but we find that LBD effects are mediated by other learning mechanisms, including learning by searching and learning by interacting. Knowledge spillovers have significant potential to reduce solar PV soft costs, but successful knowledge spillover pathways are complex and non-trivial. There are a wide variety of ways to construct an installation business, thus categories of firms that can effectively cross-learn directly are small and what knowledge is relevant to whom is challenging and costly for firms to assess. This fragmentation limits the critical mass needed for spillover related soft cost reductions. Knowledge does not flow directly between installers. Indirect knowledge transfer pathways are critical: distributors, software providers, collaboratives, and hiring. Furthermore, diverse, more integrated knowledge networks tend to promote successful learning by organizations and across the system as a whole. Accordingly, we find the need to supporting the whole ecosystem using an integrated policy and programmatic approach to support installers, distributors, complementary sector, and facilitators. Overall, a deliberate policy-mix design is needed to reduce the solar PV deployment barrier in terms of installation cost reductions, because deployment policies could potentially interact with policies that facilitate network-building and technological innovation. A combination of deployment policies, innovation-support policies, and network-facilitating policies could potentially lead to a more desired market outcome through achieving higher joint learning rates from firms’ cumulative experiences developed in a more integrated production and deployment ecosystem.

14 SOLAR ENERGY↗

NASA NTRS1989

Analysis of random drop for gateway congestion control

Lately, the growing demand on the Internet has prompted the need for more effective congestion control policies. Currently No Gateway Policy is used to relieve and signal congestion, which leads to unfair service to the individual users and a degradation of overall network performance. Network simulation was used to illustrate the character of Internet congestion and its causes. A newly proposed gateway congestion control policy, called Random Drop, was considered as a promising solution to the pressing problem. Random Drop relieves resource congestion upon buffer overflow by choosing a random packet from the service queue to be dropped. The random choice should result in a drop distribution proportional to the bandwidth distribution among all contending TCP connections, thus applying the necessary fairness. Nonetheless, the simulation experiments demonstrate several shortcomings with this policy. Because Random Drop is a congestion control policy, which is not applied until congestion has already occurred, it usually results in a high drop rate that hurts too many connections including well-behaved ones. Even though the number of packets dropped is different from one connection to another depending on the buffer utilization upon overflow, the TCP recovery overhead is high enough to neutralize these differences, causing unfair congestion penalties. Besides, the drop distribution itself is an inaccurate representation of the average bandwidth distribution, missing much important information about the bandwidth utilization between buffer overflow events. A modification of Random Drop to do congestion avoidance by applying the policy early was also proposed. Early Random Drop has the advantage of avoiding the high drop rate of buffer overflow. The early application of the policy removes the pressure of congestion relief and allows more accurate signaling of congestion. To be used effectively, algorithms for the dynamic adjustment of the parameters of Early Random Drop to suite the current network load must still be developed.

Hashem, Emam Salaheddin↗

NASA NTRS1998

Preliminary Work for Examining the Scalability of Reinforcement Learning

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Clouse, Jeff↗

DOE OSTI2019

Modeling India’s energy future using a bottom-up approach

India and China are the world's most populous nations, but they have experienced a very different pattern of economic development. As a result, India currently contributes less than one-quarter of the amount of China's carbon dioxide (CO2) emissions. However, India's forecasted economic growth suggests that those emissions will almost quadruple, with much of this rise coming from the industry sector. Whole-economy scenarios for limiting global warming suggest that direct CO2 emissions should decrease significantly, but leave unanswered the question of how this can be achieved by real-world policies. This study describes a bottom-up model that can be used to assess the impacts of emissions mitigation policies and the linkages between the physical drivers and energy growth of India's key industries. It focuses on capturing the main physical drivers of this growth, to identify and prioritize the subsectors to address and develop sustainable, low carbon pathways to support economic growth. This analysis shows that India can achieve its Nationally Determined Contribution (NDC) while achieving substantial economic growth using its currently planned policies. The study describes in detail the methodology and underlying assumptions that are needed by policy makers to inform targeted policy interventions and provide a baseline scenario in the case of no major new technology breakthroughs and no new adopted policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Capital investment requirements for greenhouse gas emissions mitigation in power generation on near term to century time scales and global to regional spatial scales

Not Available

Chaturvedi, Vaibhav↗

DOE OSTI2021

How to design a zero-emissions vehicle mandate? Simulating impacts on sales, GHG emissions and cost-effectiveness using the AUtomaker-Consumer Model (AUM)

We report although numerous studies investigate the impacts of policy on zero-emissions vehicle (ZEV) sales, few address the impacts of a ZEV sales mandate and its particular design features. We use the AUtomaker-consumer Model (AUM) to simulate varying designs of a ZEV mandate in Canada's light-duty vehicle sector, requiring 30% ZEV sales by 2030. Design features include: different penalties for non-compliance, different allocations of credits per ZEV type, and the allowance of banking credits across years. For each policy design, we consider impacts on ZEV adoption, GHG emissions, consumer surplus, automaker profits, and overall policy cost-effectiveness. Results demonstrate trade-offs among the policy designs. Generally, ZEV mandate designs that are more effective at increasing ZEV adoption and decreasing GHG emissions also induce larger reductions in consumer surplus and automaker profits. To achieve the ZEV sales goal, simulations show that a higher non-compliance penalty (CDN$ 10k per credit) is needed. ZEV adoption and GHG reductions are also improved by a “One credit per ZEV” scheme, rather than a “California-style” scheme that gives multiple credits per battery-electric vehicle. Further, allowing automakers to bank credits softens the policy impact on profit and increases automaker compliance in initial years. Across these different design combinations, we find that the most cost-effective design ($CDN/tonne mitigated, including consumer surplus and automaker profits impacts) utilizes a $10k non-compliance penalty and “One credit per ZEV” system that allows banking. Our analysis demonstrates the importance of various ZEV mandate design features, and the complex trade-offs involved among various policy goals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

State Energy Efficiency Resource Standards: Design, Status, and Impacts

An energy efficiency resource standard (EERS) is a policy that requires utilities or other entities to achieve a specified amount of energy savings through customer energy efficiency programs within a specified timeframe. EERSs may apply to electricity usage, natural gas usage, or both. This paper provides an overview of the key design features of EERSs for electricity, reviews the variation in design of EERSs across states, and provides an estimate of the amount of savings required by currently specified EERSs in each state. As of December, 2013, 23 states have active and binding EERSs for electricity. We estimate that state EERSs will require annual electricity savings of approximately 8-11% of total projected demand by 2020 in states with EERSs, however the level of savings targeted by the policies varies significantly across states. In addition to the variation in targeted savings, the design of EERSs varies significantly across states leading to differences in the suite of incentives created by the policy, the flexibility of compliance with the policy, the balance of benefits and costs of the policy between producers and consumers, and the certainty with which the policy will drive long-term savings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

U.S. Renewables Portfolio Standards: 2017 Annual Status Report

Berkeley Lab’s annual status report on U.S. renewables portfolio standards (RPS) provides an overview of key trends associated with U.S. state RPS policies. The report, published in slide-deck form, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on renewables development, and compliance costs. The 2017 edition of the report presents historical data through year-end 2016 and projections through 2030. Key trends from this edition of the report include the following: -Evolution of state RPS programs: Significant RPS-related policy revisions since the start of 2016 include increased RPS targets in DC, MD, MI, NY, RI, and OR; requirements for new wind and solar projects and other major reforms to the RPS procurement process in IL; and a new offshore wind carve-out and solar procurement program in MA. -Historical impacts on renewables development: Roughly half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is associated with state RPS requirements. Nationally, the role of RPS policies has diminished over time, representing 44% of all U.S. RE capacity additions in 2016. However, within particular regions, RPS policies continue to play a central role in supporting RE growth, constituting 70-90% of 2016 RE capacity additions in the West, Mid-Atlantic, and Northeast. -Future RPS demand and incremental needs: Meeting RPS demand growth will require roughly a 50% increase in U.S. RE generation by 2030, equating to 55 GW of new RE capacity. To meet future RPS demand, total U.S. RE generation will need to reach 13% of electricity sales by 2030 (compared to 10% today), though other drivers will also continue to influence RE growth. -RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique state-specific policy designs. -REC pricing trends: Prices for renewable energy certificates (RECs) used to meet general RPS obligations fell in most markets in 2016, as surplus RPS supplies emerged in many regions. Price trends for solar RECs were more varied, with a particularly pronounced drop in MD. -RPS compliance costs and cost caps: RPS compliance costs totaled $3.0 billion in 2015 (the most-recent year for which relatively complete data are available), which equates to 1.6% of average retail electricity bills in RPS states. Though total U.S. RPS compliance costs rose from 2014, future cost growth in most RPS states will be capped by cost containment mechanisms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Nuclear Power's Future Role in a Decarbonized U.S. Electricity System

This study explores the potential future role of nuclear energy in a decarbonized U.S. electricity system through a multi-model comparison approach. We employ four state-of-the-art CEMs with native and harmonized input assumptions, layered with different policy and technology trajectories. Comparing outputs across models, technology assumptions, and policy scenarios informs model understanding, interpretation, and development decisions. Under current policies, models differ in their projections for nuclear retirements, but nuclear power plants consistently run with high capacity factors and new builds only occur in scenarios with very low nuclear costs. String power sector carbon policies drive models to align in keeping existing nuclear capacity and employing nuclear plant flexibility, but they may not be enough to bring new nuclear capacity online in the absence of significant cost declines. Therefore, significant economic deployment of new nuclear capacity requires both a stringent electric sector CO2 policy and very low cost assumptions for new nuclear. While these scenarios should not be interpreted as predictions, they are informative for understanding differing model assessments of the relative competitiveness of nuclear energy under a range of policy and technology conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

U.S. State Renewables Portfolio & Clean Electricity Standards: 2024 Status Update [Slides]

This report provides an overview and status update on U.S. state renewables portfolio standards (RPS) and has been expanded from previous editions to also cover 100% clean electricity standards (CES) adopted by a growing number of states. The report, published in slide-deck form along with accompanying data files, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on clean electricity development, and compliance costs. The 2023 edition presents historical data through year-end 2023 and projections out to 2050. Key trends from this edition of the report include the following: -Evolution of state RPS and CES programs: States continue to refine and revise their RPS policies, often by adopting higher targets and/or broader CES policies. Among the 29 states plus DC with an RPS, 16 have RPS targets of at least 50% of retail sales, and 4 states have a 100% RPS. An additional 16 states have adopted a broader 100% CES. -Historical impacts on renewables development: Almost half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is nominally associated with state RPS requirements. That percentage has declined over time to 35% of all U.S. RE capacity additions in 2023, though in certain regions RPS policies continue to play a dominant role in driving RE growth. -Future RPS and CES demand and incremental needs: The combined demand for clean electricity from RPS and CES policies will grow from roughly 500 TWh today to 1700 TWh by 2050. Accounting for current supplies—including existing nuclear and hydroelectric generation eligible for CES targets—RPS and CES policies will require 900 TWh of new clean electricity by 2050, equivalent to roughly 3x the historical rate of RPS-buildout. -RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique, state-specific issues. Most CES targets are not yet in force, and so little compliance experience to-date. -REC pricing trends: Prices for NEPOOL Class I RECs remained at roughly $\$40$/MWh over the past year, just below ACP rates in the larger state markets, while PJM Tier I REC prices continued to rise, reaching $\$35$/MWh by year-end 2023 and surpassing ACP levels in some states. Prices for solar RECs remained relatively stable, and continue to exhibit wide variation across states, with the highest prices ($200-450/MWh) in NJ, MA, and DC. -RPS compliance costs: RPS compliance costs average roughly 4% of retail electricity bills across RPS states, though vary widely from state to state, with the highest costs (11-12% of retail bills) in states with solar carve-outs and high SREC prices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗

NASA NTRS2024

Acquisition of and Access to Research Omics Data

Omics↗

DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

The Time Needed to Implement the Blue Ribbon Commission Recommendation on Interim Storage - 13124

The report of the Blue Ribbon Commission on America's Nuclear Future [1] makes a number of important recommendations to be considered if Congress elects to redirect U.S. high-level radioactive waste disposal policy. Setting aside for the purposes of this discussion any issues related to political forces leading to stopping progress on the Yucca Mountain project and driving the creation of the Commission, an important recommendation of the Commission was to institute prompt efforts to develop one or more consolidated storage facilities. The Blue Ribbon Commission noted that this recommended strategy for future storage and disposal facilities and operations should be implemented regardless of what happens with Yucca Mountain. It is too easy, however, to focus on interim storage as an alternative to geologic disposal. The Blue Ribbon Commission report does not go far enough in addressing the magnitude of the contentious problems associated with reopening the issues of relative authorities of the states and federal government with which Congress wrestled in crafting the Nuclear Waste Policy Act [2]. The Blue Ribbon Commission recommendation for prompt adoption of an interim storage program does not appear to be fully informed about the actions that must be taken, the relative cost of the effort, or the realistic time line that would be involved. In essence, the recommendation leaves to others the details of the systems engineering analyses needed to understand the nature and details of all the operations required to reach an operational interim storage facility without derailing forever the true end goal of geologic disposal. The material presented identifies a number of impediments that must be overcome before the country could develop a centralized federal interim storage facility. In summary, and in the order presented, they are: 1. Change the law, HJR 87, PL 107-200, designating Yucca Mountain for the development of a repository. 2. Bring new nuclear waste legislation to the floor of the Senate, overcoming existing House support for Yucca Mountain; 3. Change the longstanding focus of Congress from disposal to storage; 4. Change the funding concepts embodied in the Nuclear Waste Policy Act to allow the Nuclear Waste fund to be used to pay for interim storage; 5. Reverse the Congressional policy not to give states or tribes veto or consent authority, and to reserve to Congress the authority to override a state or tribal disapproval; 6. Promulgate interim storage facility siting regulations to reflect the new policies after such changes to policy and law; 7. Complete already underway changes to storage and transportation regulations, possibly incorporating changes to reflect changes to waste disposal law; 8. Promulgate new repository siting regulations if the interim storage facility is to support repository development; 9. Identify volunteer sites, negotiate agreements, and get Congressional approval for negotiated benefits packages; 10. Design, License and develop the interim storage facility. The time required to accomplish these ten items depends on many factors. The estimate developed assumes that certain of the items must be completed before other items are started; given past criticisms of the current program, such an assumption appears appropriate. Estimated times for completion of individual items are based on historical precedent. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

How to Design and Implement an Equitable Building Performance Standard: Lessons from the Building Performance Standards Technical Assistance Network

States and local governments seek to accomplish the intersecting goals of reducing greenhouse gas emissions, improving building operations, and bettering the daily lives of their communities. Building Performance Standards (BPS) have emerged as a critical policy lever to reach these intertwined climate and societal goals. These policies, if shaped and implemented well, have the chance to not only help reach our nation's climate, energy, and livability goals, but to do so with the active participation of those traditionally excluded from policy processes. This nascent policy movement provides jurisdictions across the country the opportunity to shape these policies from the outset to deliver comfort, health, and safety in our built environment for all. The US Department of Energy in partnership with our National Laboratories have been providing technical assistance for jurisdictions interested in, adopting, and implementing Building Performance Standards. Through this work, the BPS Technical Assistance Network (TA Network) has tracked and documented the innovative and equitable approaches to BPS across the country. The TA Network has crafted foundational technical analysis, such as building stock and emissions impacts, equity prioritization and peak load impacts, and aggregated cost benefit analysis. And by combining powerful technical analysis with dissemination of best practices resources to support equitable implementation, the TA Network provides jurisdictions with the tools and support necessary to embark on their ambitious policy goals.

building performance standards↗