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Air pollution has a significant impact on human health and the economy. Air quality in Sydney is usually very good by international standards. For more information about air quality in Sydney, how our ventilation systems work to manage air quality within and outside the tunnels, and what has contributed to improve vehicle emissions visit the [Air Quality Portal](https://v2.communityanalytics.com.au/rms/air-quality/#). This dataset provides standardised measures of: * Carbon Monoxide * Nitrogen dioxide * Nitrogen oxides * Ozone * Sulfur dioxide * Particles < 10μm diameter * Particles < 2.5μm diameter * BTEX * Methane * Non-Methane Hydrocarbons * THC The data captured is from 01/01/2004 - 31/12/2017 and only includes sites where RMS had access to the monitor's data. More information about the sites covered can be found in the Report and associated data files.
The database is envisioned to become the main source of carbon estimating and benchmarking for the UK construction sector and a practical instrument to support the decarbonisation of the built environment. The database has been developed to collect and supply product data and entity level data to the industry through its own portal and by interacting with existing databases and software solutions.
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The CPG Product Supplier Directory includes manufacturers, vendors, and suppliers for each product so you can use it to search for specific companies and products.
Carbon Crops Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Resilient Economic Agricultural Practices in Morris, Minnesota The overall goal of the Carbon Crop study, established in 2000, was to assess strategies for increasing soil C sequestration including converting to no till systems and including perennial grasses (e.g., switchgrass and big bluestem) Overall, the goal of the study has remained constant, although individual treatments were changed after an incremental soil sampling, in response to new hypotheses and questions. Soil sampling is conducted as treatment changes are implemented. In 2012, two of the perennial grass systems (spring harvest of Switchgrass and Big Bluestem) were changed to corn/soybean rotations, beginning with a soybean entry point, to determine if the SOC accrued under the perennial system was lost by converting to a short annual rotation managed without tillage. The second change made was to compare the productivity between recent and traditional switchgrass cultivars. The final change was conversion of autumn harvest of Big Bluestem treatment replaced with an annual biomass crop – Sorghum-Sudan grass. Soil samples were taken to 1 m in 2000, 2006, 2011, and 2016. Nitrous oxide and carbon dioxide fluxes from the soil were measured from June 2009 through March 2012.
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This map shows the rate at which forests could capture carbon from the atmosphere and store it in aboveground live biomass over the first 30 years of natural forest regrowth. It was created by combining ground-based measurements at thousands of locations around the world with 66 co-located environmental covariate layers in a machine learning model to produce a wall-to-wall map. Forest plot data used to train the model are sourced from published literature, which can be found in the Forest Carbon database (ForC, maintained by the Smithsonian Institute (https://github.com/forc-db)), as well as georeferenced data from publicly available national forest inventories. Although rates were estimated over all forest and savanna biomes globally, they are filtered here by “reforestable” area, as defined in Griscom et al. 2017 (PNAS). Reforestable areas exclude areas of native grasslands and croplands to safeguard the production of food and fiber and habitat for biological diversity.Extent: Global, within reforestation extent of Griscom et al. 2017 (which excludes the boreal, grassy biomes, and croplands) Resolution: 1 km x 1 kmCitation: Cook-Patton, S.C., Leavitt, S.M., Gibbs, D. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).Credits: Cook-Patton, S.C., S.M. Leavitt, D. Gibbs, N.L. Harris, K. Lister, K.J. Anderson-Teixeira, R.D. Briggs, R.L. Chazdon, T.W. Crowther, P.W. Ellis, H.P. Griscom, V. Herrmann, K.D. Holl, R.A. Houghton, C. Larrosa, G. Lomax, R. Lucas, P. Madsen, Y. Malhi, A. Paquette, J.D. Parker, K. Paul, D. Routh, S. Roxburgh, S. Saatchi, J.van den Hoogen, W.S. Walker, C.E. Wheeler, S.A. Wood, L. Xu, B.W. Griscom. 2020. Mapping carbon accumulation potential from natural forest regrowth. Nature, in press. https://www.nature.com/articles/s41586-020-2686-x. This work resulted from a collaboration between The Nature Conservancy, World Resources Institute, and 18 other institutions.Date: Applicable to the first 30 years of natural forest regrowth. Related layers: Carbon accumulation potential from natural forest regrowth in forest and savanna biomes, Uncertainty in carbon accumulation potential from natural forest regrowth
This data publication contains the results of chemical and mineral analyses on masticated particles from mixed-conifer forests in 15 study locations. These data were collected from 2012 through 2016 as part of the MASTIDON project. The MASTIDON project was a four-year research project to study how masticated material differs when treated with different cutting machines and how the masticated particles decompose when left on the ground for multiple years. It investigated masticated materials in four states of the western United States. The project was funded by the Joint Fire Sciences Program (JFSP) and RMRS between 2013 and 2016. The masticated particles within this project had been decomposing in situ in wet and dry areas of Idaho, Colorado, New Mexico, and South Dakota since their initial treatment. Particles were tested from four shapes (circular, three-sided, four-sided, and small wood chips) and three size classes. Each shape and size class was ground, dried, and analyzed for percent carbon and nitrogen, cellulose and lignin, heat content, and mineral content (from the duff component) using three pieces of equipment. This data publication includes the results of each of these tests and files describing the MASTIDON project and its goals.
EPA has over 50 climate change indicators that show changes over time and include more than 100 figures as graphs and maps.
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Farming Systems Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Morris, Minnesota Tillage is decreasing globally due to recognized benefits of fuel savings and improved soil health in the absence of disturbance. However, a perceived inability to control weeds effectively and economically hinders no-till adoption in organic production systems in the Upper Midwest, USA. A strip-tillage (ST) strategy was explored as an intermediate approach to reducing fuel use and soil disturbance, and still controlling weeds. An 8-year comparison was made between two tillage approaches, one primarily using ST the other using a combination of conventional plow, disk and chisel tillage [conventional tillage (CT)]. Additionally, two rotation schemes were explored within each tillage system: a 2-year rotation (2y) of corn (Zea mays L.), and soybean (Glycine max [L.] Merr.) with a winter rye (Secale cereale L.) cover crop; and a 4-year rotation (4y) of corn, soybean, spring wheat (Triticum aestivum L.) underseeded with alfalfa (Medicago sativa L.), and a second year of alfalfa. These treatments resulted in comparison of four main management systems CT-2y, CT-4y, ST-2y and ST-4y, which also were managed under fertilized and non-fertilized conditions. Yields, whole system productivity (evaluated with potential gross returns), and weed seed densities (first 4 years) were measured. Across years, yields of corn, soybean and wheat were greater by 34% or more under CT than ST but alfalfa yields were the same. Within tillage strategies, corn yields were the same in 2y and 4y rotations, but soybean yields, only under ST, were 29% lower in the fertilized 4y than 2 yr rotation. In the ST-4y system yields of corn and soybean were the same in fertilized and non-fertilized treatments. Over the entire rotation, system productivity was highest in the fertilized CT-2y system, but the same among fertilized ST-4y, and non-fertilized ST-2y, ST-4y, and CT-4y systems. Over the first 4 years, total weed seed density increased comparatively more under ST than CT, and was negatively correlated to corn yields in fertilized CT systems and soybean yields in the fertilized ST-2y system. These results indicated ST compromised productivity, in part due to insufficient weed control, but also due to reduced nutrient availability. ST and diverse rotations may yet be viable options given that overall productivity of fertilized ST-2y and CT-4y systems was within 70% of that in the fertilized CT-2y system. Closing the yield gap between ST and CT would benefit from future research focused on organic weed and nutrient management, particularly for corn.
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OverviewThis carbon removals layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2023. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. 2022. Atmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2023) and must be divided by 23 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information. The source of the ratio between belowground biomass carbon and aboveground biomass carbon. Previously used one global constant; now uses map from Huang et al. 2021 The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from Potapov et al. 2022. The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from Tyukavina et al. 2022. The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC Sixth Assessment Report. Removal factors for older (>20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in Table 4.9 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) Version 1.0; now uses SDPT Version 2.0 and associated removal factors. Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel. The first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare. The second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area. The values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude. When estimating removals occurring over a defined number of years between 2001 and 2023 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Related Open Data Portal layers: Forest Carbon Emissions, Net Forest Carbon FluxGoogle Earth Engine asset and visualization scriptResolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: AnnualDate of Content: 2001-2023CautionsData are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. Values are applicable to forest areas (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis. Carbon removals reflect the total removals over the model period of 2001-2023, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual removals. Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. Carbon removals reflect a gross estimate, i.e., carbon emissions from previous or subsequent loss of tree cover are not included. Instead, gross carbon emissions are accounted for in the companion forest carbon emissions layer. Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023. Forest carbon removals reflect those occurring only within forest ecosystems and do not reflect carbon stock increases in the harvested wood products (HWP) pool. Large jumps in removals along some boundaries are due to the use of ecozone-specific removal factors. The changes in removals occur at ecozone boundaries, where different removal factors are applied on each side. This dataset has been updated since its original publication. See Overview for more information.
OverviewThis emissions layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2023). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GFW. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information. The source of the ratio between belowground carbon and aboveground carbon. Previously used one global constant; now uses map from Huang et al. 2021 The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from Potapov et al. 2022. The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from Tyukavina et al. 2022. The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC Sixth Assessment Report. Removal factors for older (>20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in Table 4.9 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) Version 1.0; now uses SDPT Version 2.0 and associated removal factors. Emissions are available for download in two different area units: 1) megagrams of CO2 emissions/ha, and 2) megagrams of CO2 emissions/pixel. The first is appropriate for visualizing (mapping) emissions because it represents the density of emissions per hectare. The second is appropriate for calculating the emissions in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. The values in the latter were calculated by adjusting the emissions per hectare by the size of each pixel, which varies by latitude. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Related Open Data Portal layers: Forest Carbon Removals, Net Forest Carbon FluxGoogle Earth Engine asset and visualization scriptResolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: AnnualDate of Content: 2001-2023CautionsData are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. Values are applicable to forest areas only (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis. Although emissions in each pixel are associated with a specific year of disturbance, emissions over an area of interest reflect the total over the model period of 2001-2023. Thus, values must be divided by 23 to calculate average annual removals. Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation. Emissions reflect a gross estimate, i.e., carbon removals from any regrowth that occurs after disturbance are not included. Instead, gross carbon removals are accounted for in the companion forest carbon removals layer. Emissions data contain temporal inconsistencies. Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer here for additional information. Forest carbon emissions do not reflect carbon transfers from ecosystem carbon pools to the harvested wood products (HWP) pool. This dataset has been updated since its original publication. See Overview for more information.
OverviewThis net flux layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Net forest carbon flux represents the net loss of forest ecosystem carbon, calculated as the between carbon emitted by forests and removed by (or sequestered by) forests during the model period. Net carbon flux is calculated by subtracting average gross removals from annual gross emissions in each forested pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of carbon between 2001 and 2023. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. 2022. This layer reflects the cumulative net flux during the model period (2001-2023) and must be divided by 23 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information. The source of the ratio between belowground carbon and aboveground carbon. Previously used one global constant; now uses map from Huang et al. 2021 The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from Potapov et al. 2022. The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from Tyukavina et al. 2022. The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC Sixth Assessment Report. Removal factors for older (>20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in Table 4.9 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) Version 1.0; now uses SDPT Version 2.0 and associated removal factors. Net flux is available for download in two different area units over the model duration: 1) megagrams of CO2 emissions/ha, and 2) megagrams of CO2 emissions/pixel. The first is appropriate for visualizing (mapping) net flux because it represent the density of carbon fluxes per hectare. The second is appropriate for calculating the net flux in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total carbon flux for that area. The values in the latter were calculated by adjusting the net flux per hectare by the size of each pixel, which varies by latitude. When estimating net flux occurring over a defined number of years between 2001 and 2023, divide the values by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Related Open Data Portal layers: Forest Carbon Emissions, Forest Carbon RemovalsGoogle Earth Engine asset and visualization scriptResolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: AnnualDate of Content: 2001-2023CautionsData are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. Net flux reflects the total over the model period of 2001-2023, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual net flux. Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. These uncertainties are propagated to the uncertainty in net flux. Values are applicable to forest areas (canopy cover >30 percent and >5 m height). See Harris et al. (2021) for further information on the forest definition used in the analysis. Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation. Activity data used as the basis of the estimates contain temporal inconsistencies: Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023. Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer here for additional information. Large jumps in net flux along some boundary are due to the use of ecozone-specific removal factors. The changes in net flux occur at ecozone boundaries, where different removal factors are applied on each side. This dataset has been updated since its original publication. See Overview for more information.
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ICAP’s Allowance Price Explorer allows you to explore allowance prices of emissions trading systems around the world
The ICAP ETS Map provides and visualises up-to-date information on emissions trading systems around the world – including systems that are in force, under development and under consideration. The interactive map features downloadable factsheets and gives granular information on individual design aspects.
The NABERS ESC Estimator will help you understand whether your building is eligible to participate in the NSW Energy Savings Scheme (ESS) under the NABERS baseline method. It will also provide an estimation of how many ESCs your building could potentially generate by reducing its energy consumption. For this tool you will need an accurate estimation of your energy consumption and your predicted NABERS Energy rating. To generate ESCs, you will need to assign an Accredited Certificate Provider (ACP) before the end of the rating period. Note: results predicted by the ESC Estimator are an indication only.
NABERS has released prediction tools to support building owners in understanding the impact of NABERS Energy ratings on individual assets across different sectors. The tool includes forecasted scenarios estimating the impact on ratings in 2025 and 2030. The benchmarks which calculate NABERS Energy ratings are updated every five years with the next update scheduled for 2025. These changes are implemented in a way that does not affect the average rating of the sector at that time.
Use the NABERS rating calculators to get an idea of how well your building or tenancy is performing. Results are an indication only and cannot be promoted or published.
NABERS reverse calculators give you an indication of the maximum amount of energy and water your building can use to achieve your desired rating.
PHACE Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Cheyenne, Wyoming
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The Rangeland Analysis Platform ( rangelands.app) is a free online application that provides simple and fast access to geospatial vegetation data for U.S. rangelands. The tool was developed to provide landowners, resource managers, conservationists, and scientists access to data that can inform land management planning, decision making, and the evaluation of outcomes. The Rangeland Analysis Platform (RAP) uses innovative cloud computing technology to provide maps and analysis opportunities straight to your desktop, delivered securely and instantaneously. The maps and data provided by RAP are intended to be used alongside local knowledge and site-specific data to inform management actions that improve rangelands and wildlife habitat. Biomass The Rangeland Analysis Platform’s vegetation biomass product provides annual and 16-day aboveground biomass from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, and herbaceous (combination of annual and perennial forbs and grasses). Estimates represent accumulated new biomass throughout the year or 16-day period and do not include biomass accumulation in previous years. Aboveground biomass was calculated by separating net primary production (paritioned by functional group) to aboveground and converting carbon to biomass (Jones et al. 2021, Robinson et al. 2019). Estimates are provided in United States customary units (lbs/acre) to facilitate use. Although these data were produced across a broad region, they are primarily intended for rangeland ecosystems. Biomass estimates may not be suitable in other ecosystems, e.g., forests., and are not to be used in agricultural lands, i.e., croplands. Cover The Rangeland Analysis Platform’s vegetation cover product provides annual percent cover estimates from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, shrubs, trees, and bare ground. The estimates were produced by combining 75,000 field plots collected by BLM, NPS, and NRCS with the historical Landsat satellite record. Utilizing the power of cloud computing, cover estimates are predicted across the United States at 30m resolution, an area slightly larger than a baseball diamond. Partitioned NPP The Rangeland Analysis Platform provides net primary productivity (NPP) estimates from 1986 to present. Estimates are partitioned into the following functional groups: annual forb and grass, perennial forb and grass, shrub, and tree. NPP is the net increase (i.e., photosynthesis minus respiration) in total plant carbon, including above and below ground. NPP data download Partitioned NPP is available as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-npp/ and in Google Earth Engine (ImageCollection ‘projects/rap-data-365417/assets/npp-partitioned-v3’).
EPA created the Recycled Content (ReCon) Tool to help companies and individuals estimate embodied carbon, the climate footprint across the full lifecycle of purchasing and/or manufacturing materials with varying degrees of post-consumer recycled content. Estimates provided by the ReCon Tool are intended to support voluntary reporting initiatives, as well as EPA's Comprehensive Procurement Guidelines (CPG) Program and other Environmentally Preferable Purchasing activities.
Residue Removal Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Resilient Economic Agricultural Practices in Brookings, South Dakota
This Tool is intended to enable companies to develop appropriate science-based emissions reductions targets, as well as to assist companies understand and implement the level of climate ambition required to meet the 1.5°C goal of the Paris Agreement.
The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The core of the 1997 experiment involves the deployment of the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) for daily mapping of surface soil moisture. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The temporal coverage for this dataset is as follows: Begin datetime: 1995-10-01 00:00:00, End datetime: 2001-03-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Organic Carbon and Organic Matter Soils Data Set is one of the various sub-surface data sets developed for the GCIP (Global Energy and Water Cycle Experiment [GEWEX] Continental-scale International Project) Enhanced Observation Period (EOP) Data Set. This data set contains a summary table of the measured organic carbon percentage and the estimated organic matter percentage in the near surface soil at each of the ARM SWATS (Soil Water and Temperature System) sites at the SGP site. The soil characterizations were performed by Oklahoma State University.
Find reports on emissions from operations include those arising from energy and transport, as well as emissions arising from treatment of our water and wastewater (termed process emissions
About the Project This study examines potentially efficient global decarbonisation pathways that incorporate mitigation and adaptation to achieve more cost-effective outcomes. It forms part of a joint research program into practical approaches to climate change policy being undertaken by KAPSARC and the Institute of Energy Economics, Japan (IEEJ). This research program aims to help inform the debate around the development of more practical and cost-effective ways to help close the gap between national contributions and agreed global goals in the context of future climate negotiations. Key Points This paper presents an analysis of the climate and economic impacts of four different carbon emission scenarios. The scenarios include: a ‘business as usual’ reference scenario; a carbon emission mitigation scenario designed to meet the Paris Agreement goal of limiting average global temperature increases to no more than 2o Celsius (C) by 2100; and two scenarios that seek to optimize global welfare taking into account the total costs associated with carbon mitigation, adaptation and damage, one with more rapidly declining low-carbon or zero-carbon technology costs after 2050. Key insights include the following: Under the optimal global welfare case with more rapidly declining technology costs after 2050, global average temperature increases peak at between 2.3oC and 2.7oC, which is above the level achieved under the 2oC by 2100 scenario. However, the 2o C scenario, which relies exclusively on mitigation responses, requires very high carbon prices to achieve its goals; above $250/ton carbon dioxide equivalent (CO2 e) by 2050 and over $1,200/ton CO2 e in 2100 (prices are in real 2014 U.S. dollars). This is reflected in a disproportionately high total economic cost between now and 2100, reaching around 4 percent of global gross domestic product (GDP) by 2090. By comparison, carbon prices associated with the optimal global welfare cases are less than $50/ ton CO2 e by 2050, and between $175/ton CO2 e (standard optimal case) and $300/ton CO2 e (optimal case with more rapidly declining technology costs) in 2100. The total economic costs under the optimal global welfare scenarios never exceed 3 percent of global GDP, with the cost peaking at 2.6 percent of global GDP around 2130 under the optimal scenario, with more rapidly declining technology development costs after 2050. This analysis suggests that a more pragmatic approach to tackling climate change (referred to as a practical approach hereafter) which balances mitigation, adaptation and damage is likely to minimize the overall cost to society. It also highlights the potential economic benefits associated with accelerating the development and deployment of cost effective low- and zero-carbon technologies. Governments have a crucial role to play to support effective research and development in this context. Scope remains to develop more practical and flexible approaches to climate policy that are clear, predictable and able to effectively evolve as the transition to a decarbonized global economy unfolds.
Estimates the uncertainty in carbon accumulation potential from natural forest regrowth. Specifically, the uncertainty metric is calculated as the modeled sequestration rate (mean of 100 random forest models) divided by the standard error of the 100 random forest models. Uncertainty is presented as a ratio because areas with higher sequestration rates tend to have high standard errors for their sequestration rates; presenting error as a ratio standardizes the model error by the sequestration rate. Higher numbers represent greater uncertainty in the model. For reference, an error ratio of 0.5 means that the standard error of the random forest models is half as large as the mean output of the models for that pixel. The rate uncertainties were estimated over all forest and savanna biomes.Extent: Global, within forest and savanna biomesCitation: Cook-Patton, S.C., Leavitt, S.M., Gibbs, D. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).Credits: Cook-Patton, S.C., S.M. Leavitt, D. Gibbs, N.L. Harris, K. Lister, K.J. Anderson-Teixeira, R.D. Briggs, R.L. Chazdon, T.W. Crowther, P.W. Ellis, H.P. Griscom, V. Herrmann, K.D. Holl, R.A. Houghton, C. Larrosa, G. Lomax, R. Lucas, P. Madsen, Y. Malhi, A. Paquette, J.D. Parker, K. Paul, D. Routh, S. Roxburgh, S. Saatchi, J.van den Hoogen, W.S. Walker, C.E. Wheeler, S.A. Wood, L. Xu, B.W. Griscom. 2020. Mapping carbon accumulation potential from natural forest regrowth. Nature, in press. https://www.nature.com/articles/s41586-020-2686-x. This work resulted from a collaboration between The Nature Conservancy, World Resources Institute, and 18 other institutions.Resolution: 1 x 1 kmDate: Applicable to the first 30 years of natural forest regrowth. Related layers: Carbon accumulation potential from natural forest regrowth in reforestable areas, Carbon accumulation potential from natural forest regrowth in forest and savanna biomes
The Zero Tool is used to compare an existing or planned building’s energy use intensity (EUI) with similar building types, understand how a building achieved its EUI (via energy efficiency, on-site renewable energy, and/or green power purchases), and set EUI targets.