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This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources in the Mowry formation of the Wind River Basin Province in Wyoming. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

By using a liquid desiccant ventilation system for dehumidification and an air-handling unit for cooling, the liquid desiccant cooling system (LDCS) system became a promising alternative for traditional technology. Solar thermal energy is suitable to deal with the heat requirement of LDCS in buildings, especially in the areas with abundant solar radiation. The energy saving of solar-assisted liquid desiccant air-conditioning system is significantly affected by various operation conditions, and multi-parameter optimization was necessary to improve the system applicability. In this paper, we investigated the impact of five main parameters on the system performance via self-developed system modelling, including the solution mass flow rate, concentration, cooling tower flow rate, and solar water flow rate and installation area of solar collector. A typical commercial building in Hong Kong was selected as a case study, which air-conditioning load was obtained by Energy-plus. The results indicated that the installation area of solar collector showed the greatest impact, and the effect of heating water flow rate was also important. The effect of desiccant flow rate was significant, but the influence of solution concentration was slight. Then, the multi-parameter optimization was conducted for obtaining a maximum annual electricity saving rate based on the Multi-Population Genetic Algorithm. The optimized installation area of solar collector was 72 m2, and the heating water flow rate was 0.66 kg/s. The optimized solution flow rate was 0.17 kg/s. The required cooling water flow rate was around 0.8 kg/s.

DataForAssessingSeasonalEffectsOnElectricityAndWaterForRepositoryThis Excel file contains data used to conduct a seasonal analysis to assess whether seasonal patterns exist in electricity use in dorms and whether these patterns differ by latitude. The first worksheet contains metadata.Fall 2010 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Fall 2010 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_F10_survey.pdfSpring 2012 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Spring 2012 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_Spring12_survey.pdfFall 10 Campus Conservation Nationals electricity, water, webhit, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Fall 2010 CCN competition. The first sheet contains metadata defining all variable names.Fall10_CCN_elec_water_webhits_commitments.xlsxSpring 2012 Campus Conservation Nationals electricity, water, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Spring 2012 CCN competition. The first sheet contains metadata defining all variable names.Spring12_CCN_elec_water_commitments_no.xlsxFall 10 CCN data aggregated at dorm level with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Fall10_CCN_dormagg_with_psych_variables.xlsxSpring 2012 CCN data with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Spring12__CCN_dormagg_with_psych_variables.xlsx “Campus Conservation Nationals” (CCN) is a recurring, nation-wide electricity and water-use reduction competition among dormitories on college campuses. We conducted a two year empirical study of the competition’s effects on resource consumption and the relationship between conservation, use of web technology and various psychological measures. Significant reductions in electricity and water use occurred during the two CCN competitions examined (n = 105,000 and 197,000 participating dorm residents respectively). In 2010, overall reductions during the competition were 4% for electricity and 6% for water. The top 10% of dorms achieved 28% and 36% reductions in electricity and water respectively. Participation was larger in 2012 and reductions were slightly smaller (i.e. 3% electricity). The fact that no seasonal pattern in electricity use was evident during non-competition periods suggests that results are attributable to the competition. Post competition resource use data collected in 2012 indicates that conservation behavior was sustained beyond the competition. Surveys were used to assess psychological and behavioral responses (n = 2,900 and 2,600 in 2010 and 2012 respectively). Electricity reductions were significantly correlated with: web visitation, specific conservation behaviors, awareness of the competition, motivation and sense of empowerment. However, participants were significantly more motivated than empowered. Perceived benefits of conservation were skewed towards global and future concerns while perceived barriers tended to be local. Results also suggest that competitions may be useful for “preaching beyond the choir” – engaging those who might lack prior intrinsic or political motivation. Although college life is distinct, certain conclusions related to competitions, self-efficacy, and motivation and social norms likely extend to other residential settings.

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data.  R is required to open the models.