Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This version of the workbook provides the final updates to data for the 2021 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. The data for 2015 - 2020 is located on https://data.nrel.gov. In 2021 and going forward, the data is cloud optimized and provided in the OEDI data lake. A website documents this and future data at https://atb.nrel.gov.

These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

This submission contains several papers, a final report, descriptions of a theoretical framework for two types of control systems, and descriptions of eight real-time flap load control policies with the objective of assessing the potential improvement of annual average capture efficiency at a reference site on an MHK device developed by Resolute Marine Energy, Inc. (RME). The submission also contains an LCOE model that estimates the performance and related energy cost improvements that each advanced control system might provide and recommendations for improving DOE's LCOE model. The two types of control systems are for wave energy converters which transform data into commands that, in the case of RME's OWSC wave energy converter, provide real-time adjustments to damping forces applied to the prime mover via the power take-off system (PTO). The control theories developed were: 1) Model Predictive Control (MPC) or so-called "non-causal" control whereby sensors deployed seaward of a wave energy converter measure incoming wave characteristics and transmit that information to a data processor which issues commands to the PTO to adjust the damping force to an optimal value; and 2) "Causal" control which utilizes local sensors on the wave energy converter itself to transmit information to a data processor which then issues appropriate commands to the PTO. The two advanced control policies developed by Scruggs and Re Vision were then compared to a simple control policy, Coulomb damping, which was utilized by RME during the two rounds of ocean trials it had conducted prior to the commencement of this project. The project work plan initially included a provision for RME to conduct hardware-in-the-loop (HIL) testing of the data processors and configurations of valves, sensors and rectifiers needed to implement the two advanced control systems developed by Scruggs and Re Vision Consulting but the funding for that aspect of the project was cut at the conclusion of Budget Period 1. Accordingly, more work needs to be done to determine: a) means and feasibility of implementing real-time control; and b) added costs associated with such implementation taking into account estimated effects on system availability in addition to component costs.

The TidGen Power System generates emission-free electricity from tidal currents and connects directly into existing grids using smart grid technology. The power system consists of three major subsystems: shore-side power electronics, mooring system, and turbine generator unit (TGU) device. This submission includes the preliminary Installation, Operation & Maintenance (IO&M) and testing plan. In 2012, the first TidGen device was installed in Cobscook Bay utilizing a piled foundation, which required extensive, costly geotechnical survey and on-water effort on the order of several weeks to install the system. The Advanced TidGen 2.0 Power System has adapted the Buoyant Tensioned Mooring System (BTMS) that reduces on-water deployment time to within a tidal cycle. The device has been designed to match the resources typically available in remote regions, such as Igiugig, Alaska, which are the immediate commercial market for ORPC's technology. The system has been designed to meet requirements throughout the entire lifecycle concept of operations.

The calculator gives an indicative cost for connecting a project to an existing water main.

Project baseline levelized cost of energy (LCOE) model for the Centipod WEC containing annual energy production (AEP) data, a cost breakdown structure (CBS), model documentation, and the LCOE content model. This baseline was built for comparison with the resultant LCOE model, built after implementation of the model predictive control (MPC) controller.

The associated excel files hold the cost predictions for nitrate and perchlorate treatment based on a series of assumptions outlined in the paper. No experimental data was generated in this project. This dataset is associated with the following publication: Latham , M. SSWR FY14 Output Summary Report: Performance information and design tools are developed for innovative technologies and approaches for Small Drinking Water and Wastewater Systems. U.S. Environmental Protection Agency, Washington, DC, USA.

This dataset contains all the inputs used and output produced from the modified GEOPHIRES for the economic analysis of base case hybrid GDHC system, improved hybrid GDHC system with heat pump and for hot water GDHC. Software required: Microsoft Notepad, Microsoft Excel and GEOPHIRES modified source code

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

ECAT - Externality Cost Estimates are the estimated costs of highway use externalities including noise, air pollution, crashes, and congestion, broken into various degrees of specificity. For each category of externality the estimates include both the total annual cost (in millions) as well as dollars per vehicle mile traveled, and can be filtered by state, highway functional system, vehicle type, urban and rural. Additionally, every figure has a low, mid, and high estimate that embodies the uncertainty in the parameters and methodology used to construct the estimates. These estimates are useful for any analysis involving the value of negative externalities of highway use, such as benefit-cost analysis or impact analysis of highway projects.

Since 1960, the U.S. Department of Agriculture has provided estimates of expenditures on children from birth through age 17. This technical report presents the most recent estimates for married- couple and single-parent families using data from the 2011-15 Consumer Expenditure Survey (all data presented in 2015 dollars). Data and methods used in calculating annual child-rearing expenses are described. Estimates are provided for married-couple and single-parent families with two children for major components of the budget by age of child, family income, and region of residence. For the overall United States, annual child-rearing expense estimates ranged between $12,350 and $13,900 for a child in a two-child, married-couple family in the middle-income group. Adjustment factors for households with less than or greater than two children are also provided. Expenses vary considerably by household income level, region, and composition, emphasizing that a single estimate may not be applicable to all families. Results of this study may be of use in developing State child support and foster care guidelines, as well as public health and family-centered educational programs. i

This folder contains the GEOPHIRES codes and input files for running the base case scenarios for the six deep direct-use (DDU) projects. The six DDU projects took place during 2017-2020 and were funded by the U.S. Department of Energy Geothermal Technologies Office. They investigated the potential of geothermal deep direct-use at six locations across the country. The projects were conducted by Cornell University, West Virginia University (WVU), University of Illinois (U of IL), Sandia National Laboratory (SNL), Portland State University (PSU), and National Renewable Energy Laboratory (NREL). Four projects (Cornell, WVU, U of IL, SNL) investigated geothermal for direct heating of a local campus or community, the project by PSU considered seasonal subsurface storage of solar heating, and the NREL project investigated geothermal heating for turbine inlet cooling using absorption chillers. To allow comparison of techno-economic results across the six DDU projects, GEOPHIRES simulations were set up and conducted for each project. The GEOPHIRES code was modified for each project to simulate the local application and incorporate project-specific assumptions and results such as reservoir production temperature or financing conditions. The base case input file is included which simulates the base case conditions assumed by each project team. The levelized cost of heat (LCOH) is calculated and matches the base case LCOH reported by the project teams.

This dataset contains Saudi Arabia Houses Consumption and Cost of Electricity in the Administrative Regions . Data from General Authority for Statistics . Export API data for more datasets to advance energy economics research.Data from the Household Energy Survey

This file contains the road segments from FHWA's HPMS dataset with estimates of the cost of air pollution from the traffic on that segment. The cost of air pollution is estimated using a combination of traffic volumes and speeds from FHWA's HPMS, the emission rate of PM2.5 from EPA's MOVES, the dispersion rate and pollutant concentration from EPA's AERMOD, the per unit health costs from a series of studies cited in EPA's COBRA, and the population affected from the US Census ACS 2019. The resulting cost per road segment is transformed into cost per 1/10 mile length of roadway and binned into 7 different categories to reduce the file size. For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the road segments from FHWA's HPMS dataset with estimates of the cost of air pollution from the traffic on that segment. The cost of air pollution is estimated using a combination of traffic volumes and speeds from FHWA's HPMS, the emission rate of PM2.5 from EPA's MOVES, the dispersion rate and pollutant concentration from EPA's AERMOD, the per unit health costs from a series of studies cited in EPA's COBRA, and the population affected from the US Census ACS 2019. The resulting cost per road segment is transformed into cost per 1/10 mile length of roadway and binned into 7 different categories to reduce the file size. For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the US county polygons with estimates of the air pollution cost from roads within that county, population counts for different demographics, estimates of how much air pollution cost from roads each demographic is absorbing, and the air-equity ratio for each demographic--the percent of air pollution cost borne divided by the percent of population within the county. The cost of air pollution is estimated in a separate shapefile and the results are aggregated together through FHWA's Inequity Identification Tool (IIT). For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the US county polygons with estimates of the air pollution cost from roads within that county, population counts for different demographics, estimates of how much air pollution cost from roads each demographic is absorbing, and the air-equity ratio for each demographic--the percent of air pollution cost borne divided by the percent of population within the county. The cost of air pollution is estimated in a separate shapefile and the results are aggregated together through FHWA's Inequity Identification Tool (IIT). For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the road segments from FHWA's HPMS dataset with estimates of the cost of noise pollution from the traffic on that segment. The cost of noise pollution is estimated using a combination of traffic volumes and speeds from FHWA's HPMS, the noise produced through FHWA's TNM3.1, the number of households affected from the US Census ACS 2019, and the per unit noise costs from a series of studies cited in FHWA's ECAT. The resulting cost per road segment is transformed into cost per 1/10 mile length of roadway and binned into 7 different categories to reduce the file size. For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the road segments from FHWA's HPMS dataset with estimates of the cost of noise pollution from the traffic on that segment. The cost of noise pollution is estimated using a combination of traffic volumes and speeds from FHWA's HPMS, the noise produced through FHWA's TNM3.1, the number of households affected from the US Census ACS 2019, and the per unit noise costs from a series of studies cited in FHWA's ECAT. The resulting cost per road segment is transformed into cost per 1/10 mile length of roadway and binned into 7 different categories to reduce the file size. For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the US county estimates of the noise cost from roads within that county, population counts for different demographics, estimates of how much noise cost from roads each demographic is absorbing, and the noise-equity ratio for each demographic--the percent of noise cost borne divided by the percent of population within the county. The cost of noise pollution is estimated in a separate shapefile and the results are aggregated together through FHWA's Inequity Identification Tool (IIT). For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This file contains the US county estimates of the noise cost from roads within that county, population counts for different demographics, estimates of how much noise cost from roads each demographic is absorbing, and the noise-equity ratio for each demographic--the percent of noise cost borne divided by the percent of population within the county. The cost of noise pollution is estimated in a separate shapefile and the results are aggregated together through FHWA's Inequity Identification Tool (IIT). For more information on this data please visit: https://maps.dot.gov/fhwa/iit/

This submission includes all the data to support an LCOE baseline assessment for the Resolute Marine Energy (RME) Surge WEC device.

This is an LCOE (levelized cost of energy) baseline assessment for the Wave Carpet.

This is the LCOE analysis spreadsheet and content model for the heaving point absorber buoy developed for controls purposes. The cost assessment was done on a wave-farm of 100-units.

The overarching project objective is to demonstrate the feasibility of using an innovative PowerTake-Off (PTO) Module in Columbia Power's utility-scale wave energy converter (WEC). The PTO Module uniquely combines a large-diameter, direct-drive, rotary permanent magnet generator; a patent-pending rail-bearing system; and a corrosion-resistant fiber-reinforced-plastic structure

Useful information and tools for calculating the Levelized Cost of Energy (LCOE) and MHK Cost Breakdown Structure. Includes a structure for calculating the capital expenditures and operating costs of a marine energy technology or device, reference resource data for both wave and tidal, and LCOE reporting guidance. These tools are meant to be used to help calculate the Levelized Cost of Energy (LCOE) for an MHK or MRE technology or device.

Data included in this submission support the analysis conducted for the report "Nontechnical Barriers to Geothermal Development" which is linked bellow. These data include information about the power purchase agreements (PPAs) analyzed for the report, inputs and model results for the pro forma economic analysis, and outputs from the regression analysis conducted on PPAs comparing geothermal and other power generation technologies.

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index.

This submission includes an Electricity Generation Summary, Maintenance Logs, Detailed Operations Data, Operating Cost Summary, and an Operations overview at the Paisley Oregon Geothermal Plant. Data uploaded for SVEC by Tom Williams, NREL

The U.S. Department of Energy Geothermal Vision (GeoVision) Study is currently looking at the potential to increase geothermal deployment in the U.S. and to understand the impact of this increased deployment. This paper reviews 31 performance, cost, and financial parameters as input for numerical simulations describing GDH system deployment in support of the GeoVision effort. The focus is on geothermal district heating (GDH) systems using hydrothermal and Enhanced Geothermal System resources in the U.S.; ground-source heat pumps and heat-to-electricity conversion technology were excluded. Parameters investigated include: 1) capital and operation and maintenance costs for both subsurface and surface equipment; 2) performance factors such as resource recovery factors, well flow rates, and system efficiencies; and 3) financial parameters such as inflation, interest, and tax rates. Current values as well as potential future improved values under various scenarios are presented. Sources of data considered include academic and popular literature, software tools such as GETEM and GEOPHIRES, industry interviews, and analysis conducted by other task forces for the GeoVision Study, e.g., on the drilling costs and reservoir performance.

Columbia Power LCOE (levelized cost of energy) Model for the Stingray H1 at the DOE Reference Site of Humboldt, CA. The model is integrated with and reports LCOE from DOE Cost Breakdown Structure

DOE System and LCOE (levelized costs of energy) Content Models completed for a utility-scale Stingray WEC.

The current uncertainty in the global supply of rare earth elements (REEs) necessitates the development of novel extraction technologies that utilize a variety of REE source materials. Herein, we examined the techno-economic performance of integrating a biosorption approach into a large-scale process for producing salable total rare earth oxides (TREOs) from various feedstocks. An airlift bioractor is proposed to carry out a biosorption process mediated by bioengineered rare earth-adsorbing bacteria. Techno-econmic asssements were compared for three distinctive categories of REE feedstocks requiring different pre-processing steps. Key parameters identified that affect profitability include REE concentration, composition of the feedstock, and costs of feedstock pretreatment and waste management. Among the 11 specific feedstocks investigated, coal ash from the Appalachian Basin was projected to be the most profitable, largely due to its high-value REE content. Its cost breakdown includes pre-processing (primarily leaching) (8077.71%), biosorption (1619.04%), and oxalic acid precipitation and TREO roasting (3.35%). Surprisingly, biosorption from the high-grade Bull Hill REE ore is less profitable due to high material cost and low production revenue. Overall, our results confirmed that the application of biosorption to low-grade feedstocks for REE recovery is economically viable.

Workbooks showing Annualized Energy Production, Cost Breakdown Structure, Levelized Cost of Electricity for DOE Reference Tidal Project 1) Baseline TidGen Power System 2) TidGen Power System with the application of Advanced Controls 3) Advanced TidGen Power System with several enhancements These files are provided as a zipped set. Files are linked together and must be viewed in the same folder.

Data included as supplementary information for the article for consideration in Nature Water, titled "Treatment of brackish water for fossil power plant cooling: Tradeoffs in freshwater savings, cost, and capacity shortfalls".

The 2018 Irrigation and Water Management Survey (formerly called the Farm and Ranch Irrigation Survey) is a follow-on to the 2017 Census of Agriculture by the U.S. Department of Agriculture (USDA). This survey provides the only comprehensive information on irrigation activities and water use across American farms, ranches, and horticultural operations. In responding to the survey, producers provide information on topics such as water sources and amount of water used, acres irrigated by type of system, irrigation and yield by crop, and system investments and energy costs. The full reports for the 2003, 2008, 2017, and 2018 surveys are provided in this submission. By following the link to the USDA Census of Irrigation, a specific year can be selected, in which the tables and figures of each report are provided.

This report describes "churning" as a policy concern in regards to the Supplemental Nutrition Assistance Program (SNAP). “Churning” in the Supplemental Nutrition Assistance Program (SNAP) is defined as when a household exits SNAP and then re-enters the program within 4 months. Churning is a policy concern due to the financial and administrative burden incurred by both SNAP households and State agencies that administer SNAP. This study explores the circumstances of churning in SNAP by determining the rates and patterns of churn, examining the causes of caseload churn, and calculating costs of churn to both participants and administering agencies in six States.