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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, Seyed Mohammad↗
DOE OSTI2020

A review of coal heating value correlations with application to coal char, tar, and other fuels

Accurately measuring a fuel’s heating value is one of the first steps in the classification of a new fuel. Heating values are widely used in coal combustion research and are becoming more useful in other fuel types as well. Many different empirical correlations to predict heating values based on primary organic (CHONS) elemental composition are found in the literature, many of which were originally created to predict heating values of parent coal, and some of which have been extended for biomass. However, no correlations exist for heating values of coal chars and tars. Thirteen literature heating value correlations (10 model forms with 13 unique sets of coefficients) were evaluated for their predictive accuracy of a wide variety of fuel types, including 353 coals. This comparative analysis showed that the predictive capabilities of many of these literature models are very similar for coal-based fuels, with good correlation of coal char heating values. Correlation of coal tar heating values was hampered by the lack of heating value data. The choice of which model to use becomes more important for biomass fuels as well as for more unique fuel types like propellants and explosives, municipal solid waste, etc. Finally, the best model forms for each fuel type are suggested based on several statistical measures of fit.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based on a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.

adaptive model control↗
DOE OSTI2020

A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Investigation of the potential ultralow Q-value β-decay candidates 89 Sr and 139 Ba using Penning trap mass spectrometry

Background: Ultralow Q-value β decays are interesting processes to study with potential applications to nuclear β-decay theory and neutrino physics. While a number of potential ultralow Q-value β-decay candidates exist, improved mass measurements are necessary to determine which of these are energetically allowed. Purpose: To perform precise atomic mass measurements of 89 Y and 139 La. Use these new measurements along with the precisely known atomic masses of 89 Sr and 139 Ba and nuclear energy level data for 89 Y and 139 La to determine if there could be an ultralow Q-value decay branch in the β decay of 89 Sr → 89 Y or 139 Ba → 139 La. Method: High-precision Penning trap mass spectrometry was used to determine the atomic mass of 89 Y and 139 La, from which β-decay Q values for 89 Sr and 139 Ba were obtained. Results: The 89 Sr → 89 Y and 139 Ba → 139 La β-decay Q values were measured to be Q Sr = 1502.20(0.35) keV and Q Ba = 2308.37(0.68) keV. These results were compared to energies of excited states in 89 Y at 1507.4(0.1) keV, and in 139La at 2310(19) keV and 2313(1) keV to determine Q values of -5.20(0.37) keV for the potential ultralow β-decay branch of 89 Sr and -1.6(19.0) keV and -4.6(1.2) keV for those of 139 Ba. Conclusion: The potential ultralow Q-value decay branch of 89 Sr to the 89 Y (3/2 - , 1507.4 keV) state is energetically forbidden and has been ruled out. The potential ultralow Q-value decay branch of 139 Ba to the 2313 keV state in 139 La with unknown J π has also been ruled out at the 4σ level, while more precise energy level data is needed for the 139 La (1/2 + , 2310 keV) state to determine if an ultralow Q-value β-decay branch to this state is energetically allowed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Comparison of internal dose estimates obtained using organ-level, voxel S value, and Monte Carlo techniques

Purpose: The authors’ objective was to compare internal dose estimates obtained using the Organ Level Dose Assessment with Exponential Modeling (OLINDA/EXM) software, the voxel S value technique, and Monte Carlo simulation. Monte Carlo dose estimates were used as the reference standard to assess the impact of patient-specific anatomy on the final dose estimate. Methods: Six patients injected with{sup 99m}Tc-hydrazinonicotinamide-Tyr{sup 3}-octreotide were included in this study. A hybrid planar/SPECT imaging protocol was used to estimate {sup 99m}Tc time-integrated activity coefficients (TIACs) for kidneys, liver, spleen, and tumors. Additionally, TIACs were predicted for {sup 131}I, {sup 177}Lu, and {sup 90}Y assuming the same biological half-lives as the {sup 99m}Tc labeled tracer. The TIACs were used as input for OLINDA/EXM for organ-level dose calculation and voxel level dosimetry was performed using the voxel S value method and Monte Carlo simulation. Dose estimates for {sup 99m}Tc, {sup 131}I, {sup 177}Lu, and {sup 90}Y distributions were evaluated by comparing (i) organ-level S values corresponding to each method, (ii) total tumor and organ doses, (iii) differences in right and left kidney doses, and (iv) voxelized dose distributions calculated by Monte Carlo and the voxel S value technique. Results: The S values for all investigated radionuclides used by OLINDA/EXM and the corresponding patient-specific S values calculated by Monte Carlo agreed within 2.3% on average for self-irradiation, and differed by as much as 105% for cross-organ irradiation. Total organ doses calculated by OLINDA/EXM and the voxel S value technique agreed with Monte Carlo results within approximately ±7%. Differences between right and left kidney doses determined by Monte Carlo were as high as 73%. Comparison of the Monte Carlo and voxel S value dose distributions showed that each method produced similar dose volume histograms with a minimum dose covering 90% of the volume (D90) agreeing within ±3%, on average. Conclusions: Several aspects of OLINDA/EXM dose calculation were compared with patient-specific dose estimates obtained using Monte Carlo. Differences in patient anatomy led to large differences in cross-organ doses. However, total organ doses were still in good agreement since most of the deposited dose is due to self-irradiation. Comparison of voxelized doses calculated by Monte Carlo and the voxel S value technique showed that the 3D dose distributions produced by the respective methods are nearly identical.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Capacity Value of Concentrating Solar Power Plants

This study estimates the capacity value of a concentrating solar power (CSP) plant at a variety of locations within the western United States. This is done by optimizing the operation of the CSP plant and by using the effective load carrying capability (ELCC) metric, which is a standard reliability-based capacity value estimation technique. Although the ELCC metric is the most accurate estimationtechnique, we show that a simpler capacity-factor-based approximation method can closely estimate the ELCC value. Without storage, the capacity value of CSP plants varies widely depending on the year and solar multiple. The average capacity value of plants evaluated ranged from 45%?90% with a solar multiple range of 1.0-1.5. When introducing thermal energy storage (TES), the capacity value ofthe CSP plant is more difficult to estimate since one must account for energy in storage. We apply a capacity-factor-based technique under two different market settings: an energy-only market and an energy and capacity market. Our results show that adding TES to a CSP plant can increase its capacity value significantly at all of the locations. Adding a single hour of TES significantly increasesthe capacity value above the no-TES case, and with four hours of storage or more, the average capacity value at all locations exceeds 90%.

14 SOLAR ENERGY↗
DOE OSTI2025

Empirical Indicators of Transmission Value in the Southeast United States

Concurrent differences in energy price between different parts of the electric grid are a key indicator of the value of additional transmission. In areas without a wholesale electricity market, such as the Southeast, an alternative indicator to price is the Federal Energy Regulatory Commission’s (FERC) system lambda data. This economic metric represents the minimized marginal production costs of thermal generators, including fuel and other variable operation and maintenance expenses. Balancing Authorities report a single system lambda for their entire balancing area. Most Southeastern lambdas exhibit sufficient price variation to support a transmission valuation analysis, although incomplete accounting of congestion costs or scarcity rents during peak load hours may underestimate the true value of transmission capacity. With transmission value defined as the annual average hourly absolute price difference between two regions and FERC’s system lambda data used as a price proxy, we find the following results in the Southeast region during 2012-2023 (reported in $\$2024$/MWh): Intra‐regional findings: Annual averages historically span $\$2$–$\$28$/MWh and average $\$12$/MWh in SERTP and span $\$4$–$\$19$/MWh and average $\$9$/MWh in FRCC, disregarding transmission value driven by anomalous data. The ranges of transmission value reported here are large, spanning an order of magnitude in some cases. Much of this variation is driven by year-to-year changes, with 2022 having a particularly high intra-regional transmission value due to elevated natural gas prices. Inter‐regional corridors: Annual average transmission values across three broader regions range from $\$6$ to $\$28$/MWh with a long-term average of $\$11$/MWh. Much of the transmission value is concentrated in a small portion of hours. Across all regions, severe weather—particularly polar vortex events in January 2018, February 2021, and December 2022—drives the largest price spreads. Seasonal patterns also emerge, with summer afternoons and fall mornings contributing consistently to transmission value, as for example between MISO and SOCO in 2023.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

Environment Modeling Using Runtime Values for JPF-Android

Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

Verification↗
DOE OSTI2016

The Value of Regression Models in Determining Industrial Energy Savings

Determination of industrial facility energy savings is commonly undertaken to comply with legal requirements, meet sustainability goals, and quantify greenhouse gas emissions, cost savings, and as one way to demonstrate energy performance improvement per the requirements of an energy management system standard, such as ISO 50001. At their most basic level, energy savings are determined through a process of accounting for baseline and reporting period energy consumption followed by a calculation of energy savings as the difference in the levels of energy consumption between the two periods. The determination of industrial facility-wide energy savings can be undertaken in a wide variety of ways and in relation to energy performance improvement, energy savings can be expressed as any number of energy performance indicators (EnPIs). Energy savings values will vary depending upon if an absolute, intensity, or regression basis of calculation is selected for use. The use of one of the three basis of energy savings calculation will alter the relevance, meaning, and comparability of the resulting energy savings values To increase consistency in the determination of energy savings values, standards such as the International Performance Measurement and Verification Protocol (IPMVP), ISO 17747 – Determination of Energy Savings in Organization, and the U.S. Department of Energy (U.S. DOE) Superior Energy Performance (SEP) program Measurement and Verification Protocol (SEP M&V Protocol) have been published. Industrial facilities in the United States, Mexico, and Canada are certified to the U.S. DOE SEP program after becoming ISO 50001 certified and verification by a third party of calculated energy savings per the SEP M&V Protocol. This paper discusses some of the different meanings energy savings values will have when calculated by using the three different bases. Additionally, data from five SEP certified industrial facilities are used to calculate energy savings values using the three bases and compared as energy performance improvement percentage (EPI %) values. Discussion of why a large degree in variation in EPI % values are seen for any given set of data is provided. Ultimately, while each energy saving calculation basis has merit and can be used appropriately in its own context, the regression basis is shown to best translates energy savings values into contextualized energy performance improvement values by conveying comparable information regarding operational and behavioural improvements not otherwise observable with the absolute or intensity bases.

Therkelsen, Peter↗
DOE OSTI2017

Distributed metadata servers for cluster file systems using shared low latency persistent key-value metadata store

A cluster file system is provided having a plurality of distributed metadata servers with shared access to one or more shared low latency persistent key-value metadata stores. A metadata server comprises an abstract storage interface comprising a software interface module that communicates with at least one shared persistent key-value metadata store providing a key-value interface for persistent storage of key-value metadata. The software interface module provides the key-value metadata to the at least one shared persistent key-value metadata store in a key-value format. The shared persistent key-value metadata store is accessed by a plurality of metadata servers. A metadata request can be processed by a given metadata server independently of other metadata servers in the cluster file system. A distributed metadata storage environment is also disclosed that comprises a plurality of metadata servers having an abstract storage interface to at least one shared persistent key-value metadata store.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Residual value analysis of plug-in vehicles in the United States

Knowledge about the value of used plug-in electric vehicles (PEVs), known as residual value, is critical for potential PEV purchasers, corporations, and governments who wish to consider the lifetime cost effectiveness of PEVs. In this study, we investigate the residual values of PEVs and compare them with those of other vehicle powertrain technologies using the “true market value” data from Edmunds.com, and considering U.S. federal incentives. We define the adjusted retention rate as the ratio between resale value and the manufacturer’s suggested retail price, subtracting incentives. We develop statistical models to quantify the effects of powertrain technologies on residual value while controlling key factors, including vehicle size, brand, and market segments. We find that the long-range high-performance Tesla Model S holds value significantly better than any other vehicle type evaluated, including internal combustion engine vehicles. Hybrid electric vehicles and plugin hybrid electric vehicles are comparable to one another. They hold slightly less value than internal combustion engine vehicles, but significantly more than short-range (<125 miles) battery electric vehicles. However, short-range battery electric vehicles have a faster improvement in 3-year adjusted retention rate than any other powertrain technology from model year 2013–2014. Finally, the other factor that significantly influences adjusted retention rate is the home country of the vehicle manufacturer (e.g., United States, Japan, or Germany).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Direct determination of the La 138 β -decay Q value using Penning trap mass spectrometry

Background: The understanding and description of forbidden decays provides interesting challenges for nuclear theory. These calculations could help to test underlying nuclear models and interpret experimental data. Purpose: Compare a direct measurement of the 138 La $\beta$-decay $Q$ value with the beta-decay spectrum end-point energy measured by Quarati et al. using LaBr 3 detectors [Appl. Radiat. Isot. 108, 30 (2016)]. Use new precise measurements of the 138 La $\beta$-decay and electron capture (EC) $Q$ values to improve theoretical calculations of the $\beta$-decay spectrum and EC probabilities. Method: High-precision Penning trap mass spectrometry was used to measure cyclotron frequency ratios of 138 La, 138 Ce and 138 Ba ions from which beta-decay and EC Q values for 138 La were obtained.Results: The 138 La beta-decay and EC Q values were measured to be Q$_\beta$ = 1052.42(41) keV and $Q_{EC}$ = 1748.41(34) keV, improving the precision compared to the values obtained in the most recent atomic mass evaluation [Wang, et al., Chin. Phys. C 41, 030003 (2017)] by an order of magnitude. These results are used for improved calculations of the 138 La $\beta$-decay shape factor and EC probabilities. New determinations for the 138 Ce 2EC $Q$ value and the atomic masses of 138 La, 138 Ce, and 138 Ba are also reported. Conclusion: The 138 La $\beta$-decay Q value measured by Quarati et al. is in excellent agreement with our new result, which is an order of magnitude more precise. Uncertainties in the shape factor calculations for 138 La $\beta$-decay using our new $Q$ value are reduced by an order of magnitude. Uncertainties in the EC probability ratios are also reduced and show improved agreement with experimental data.

138La↗
DOE OSTI2021

Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented “best-case scenarios” with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Representation of Solar Capacity Value in the ReEDS Capacity Expansion Model

An important issue for electricity system operators is the estimation of renewables' capacity contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to the resource variability. Reliability-based methods, particularly assessment of the Effective Load-Carrying Capacity, are considered to be the most robust and widely-accepted techniques for addressing this resource variability. This report compares estimates of solar PV capacity value by the Regional Energy Deployment System (ReEDS) capacity expansion model against two sources. The first comparison is against values published by utilities or other entities for known electrical systems at existing solar penetration levels. The second comparison is against a time-series ELCC simulation tool for high renewable penetration scenarios in the Western Interconnection. Results from the ReEDS model are found to compare well with both comparisons, despite being resolved at a super-hourly temporal resolution. Two results are relevant for other capacity-based models that use a super-hourly resolution to model solar capacity value. First, solar capacity value should not be parameterized as a static value, but must decay with increasing penetration. This is because -- for an afternoon-peaking system -- as solar penetration increases, the system's peak net load shifts to later in the day -- when solar output is lower. Second, long-term planning models should determine system adequacy requirements in each time period in order to approximate LOLP calculations. Within the ReEDS model we resolve these issues by using a capacity value estimate that varies by time-slice. Within each time period the net load and shadow price on ReEDS's planning reserve constraint signals the relative importance of additional firm capacity.

14 SOLAR ENERGY↗
DOE OSTI2015

The effects of storage on the net calorific value of wood pellets

The wood pellet export from Canada to Europe has been increasing steadily in recent years (roughly 1.8 million ton in 2013). Due to distances involved, wood pellets remain in transit and storage for months before their final consumption. The net calorific value determines the price of wood pellet purchase in Europe. There have been concerns about the changes of net calorific values over time. In this study, the effects of storage time, storage configuration, storage temperature, and wood pellet quality on the net calorific value of wood pellets for a period of 6 months were investigated. Storage configurations were open or closed and storage temperatures were 25 °C, 35 °C and 45 °C. Two types of wood pellets used were whitewood and mixed. The results in closed storage indicated that storage time had a positive effect on the net calorific value where the net calorific value increased by 1% to 2% over the storage period. In open storage, the moisture content had the most significant impact on the net calorific value. The net calorific values of the two types of wood pellets were found to be significantly different at p < 0.001. A multivariable linear regression and analyses of variance performed verified the graphical results. Lastly, the authors postulated that the higher energy potential compounds, such as aldehyde and ketone, produced during pellet storage, caused the increase in net calorific values.

09 BIOMASS FUELS↗
DOE OSTI2013

Retrospective evaluation of exposure index (EI) values from plain radiographs reveals important considerations for quality improvement

Following X-ray exposure, radiographers receive immediate feedback on detector exposure in the form of the exposure index (EI). To identify whether radiographers are meeting manufacturer-recommended EI (MREI) ranges for routine chest, abdomen and pelvis X-ray examinations under a variety of conditions and to examine factors affecting the EI. Data on 5000 adult X-ray examinations including the following variables were collected: examination parameters, EI values, patient gender, date of birth, date and time of examination, grid usage and the presence of implant or prosthesis. Descriptive statistics were used to summarize each data set and the Mann–Whitney U test was used to determine significant differences, with P < 0.05 indicating significance for all tests. Most examinations demonstrated EI values that were outside the MREI ranges, with significantly higher median EI values recorded for female patient radiographs than those for male patients for all manufacturers, indicating higher detector exposures for all units except for Philips digital radiography (DR), where increased EI values indicate lower exposure (P = 0.01). Median EI values for out of hours radiography were also significantly higher compared with normal working hours for all technologies (P ≤ 0.02). Significantly higher median EI values were demonstrated for Philips DR chest X-rays without as compared to those with the employment of a grid (P = 0.03), while significantly lower median EI values were recorded for Carestream Health computed radiography (CR) chest X-rays when an implant or prosthesis was present (P = 0.02). Non-adherence to MREIs has been demonstrated with EI value discrepancies being dependent on patient gender, time/day of exposure, grid usage and the presence of an implant or prosthesis. Retrospective evaluation of EI databases is a valuable tool to assess the need of quality improvement in routine DR.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Uplifting winds: The surprisingly positive community-wide impact of wind energy installations on property values

A primary concern of stakeholders when considering a new wind project is the potential negative effects wind turbines may have on home values. Yet, what has been surprisingly overlooked in the literature and general discourse around wind energy is that the well-researched positive economic development and fiscal and amenity benefits of wind energy (e.g., increased tax base, tax revenue, better public services and employment gains) might positively affect jurisdiction-wide housing values. With a focus on school districts in the United States, we compare home values in school districts with wind energy installations, before and after a wind energy installation becomes operational, to home values in other school districts located in the same county but without a wind energy installation to provide some of the first causal evidence on the relationship between wind energy projects and district-wide property values. We find that wind projects lead to economically meaningful increases in district-wide housing values of approximately 3 %, when those values are compared to similar homes located in school districts in same-county without wind energy. The effect is strongly correlated with wind project size. The mechanisms, our research suggests, are likely related to relatively large increases in school district per-pupil revenues and expenditures, which are also correlated with wind project size. We suggest other possible mechanisms for the increased values as well.

Attitudes↗