Global carbon dioxide emissions from fossil fuels and industry totaled 37.01 billion metric tons (GtCO₂) in 2023. Emissions are projected to have risen 1.08 percent in 2024 to reach a record high of 37.41 GtCO₂. Since 1990, global CO₂ emissions have increased by more than 60 percent. Who are the biggest emitters? The biggest contributor to global GHG emissions is China, followed by the United States. China wasn't always the world's biggest emitter, but rapid economic growth and industrialization in recent decades have seen emissions there soar. Since 1990, CO₂ emissions in China have increased by almost 450 percent. By comparison, U.S. CO₂ emissions have fallen by 6.1 percent. Nevertheless, the North American country remains the biggest carbon polluter in history. Global events cause emissions to drop The outbreak of COVID-19 caused global CO₂ emissions to plummet some 5.5 percent in 2020 as a result of lockdowns and other restrictions. However, this wasn't the only time in recent history when a major global event caused emissions reductions. For example, the global recession resulted in CO₂ levels to fall by almost two percent in 2009, while the recession in the early 1980s also had a notable impact on emissions. On a percentage basis, the largest annual reduction was at the end of the Second World War in 1945, when emissions decreased by 17 percent.

As a commitment to sustainability, our city joined the Global Covenant of Mayors for Climate and Energy. Tempe is setting a path to sustainability and resilience with our first-ever Climate Action Plan (CAP). This CAP serves as a guideline for the City of Tempe’s path toward a sustainable and resilient future that will benefit the entire city. It is a detailed framework for measuring and reducing GHG emissions and climate change impacts. The CAP includes an inventory of previous years’ GHG emissions, Tempe’s emissions reduction goals, and prioritized actions. This dataset provides the community with Greenhouse Gas emissions for the City of Tempe. Community greenhouse gas emissions inventories are a way for cities to track community greenhouse gas emissions. Currently, cities consume over two-thirds of the world's energy and account for more than 70% of global CO2 emissions. (ICLEI) Tempe conducted a greenhouse gas inventory based on 2015 calendar year data to measure the community's greenhouse gas emissions. City staff worked with a consultant who recommended re-evaluating baseline data, from 2015. 2015 data has been updated accordingly. This page provides data for the Community Carbon Neutrality performance measure. The performance measure dashboard is available at 4.18 Community Carbon Neutrality. Additional Information Source: Various sources including municipal resources, APS, SRP, SW Gas, and Maricopa Association of Governments.Contact (author): Grace Kelly Contact E-Mail (author): Grace_Kelly@tempe.govContact (maintainer): Braden KayContact E-Mail (maintainer): Braden_Kay@tempe.govData Source Type: TablePreparation Method: Transportation, fuel, and energy use data are collected from various sources for residential, commercial, and industrial uses in the community, and estimates of the amount of emissions are calculated using the ICLEI's ClearPath tool.Publish Frequency: Every 5 yearsPublish Method: ManualData Dictionary

While present international CO2 mitigation agreements account for the impact of population composition and structure on emissions, the impact of international migration is overlooked. This study quantifies the CO2 footprint of international immigrants and reveals their non-negligible impacts on global CO2 emissions. Results show that the CO2 footprint of international immigrants has increased from 1.8 gigatonnes (Gt) in 1995 to 2.9 Gt in 2015. In 2015, the U.S. had the largest total and per capita CO2 emissions caused by international immigrants. Oceania and the Middle East are highlighted for their large portions of immigrant-caused CO2 emissions in total CO2 emissions (around 20%). Changes in the population and structure of global migration have kept increasing global CO2 emissions during 1995–2015, while the reduction of CO2 emission intensity helped offset global CO2 emissions. The global CO2 mitigation targets must consider the effects of global migration. Moreover, demand-side measures need to focus on major immigrant influx nations.

Data for Figure SPM.10 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.10 shows global warming as a function of cumulative emissions of carbon dioxide.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has two panels that are closely linked. Data files for the top panel are labelled with 'Top_panel' while data files for the bottom panel are labelled with 'Bottom_panel'.

List of data provided

This dataset contains:

Top panel:

• Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)
• Global surface temperature increase relative to 1850-1900 (1850-2019)
• Estimated human-caused warming relative to 1850-1900 (1850-2019)
• Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)
• Assessed global surface temperature increase relative to 1850-1900 for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)

Bottom panel:

• Cumulative global total anthropogenic carbon dioxide emissions (1850-2019)
• Projected global total anthropogenic carbon dioxide emissions for the five scenarios of the AR6 WGI core set of scenarios (2015-2050)

The illustrative SSP (Shared Socio-economic Pathway) scenarios (referred to here as core scenarios) are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Top panel: • Top_panel_HISTORY.csv: historical CO2 emissions, global surface temperature increase since 1850-1900 for the 1850-2019 period, estimated human-caused warming since 1850-1900 over the 1850-2019 period. [row 1 for black line, grey line and grey range, row 2 for black line, row 3 to 5 range and central grey range] • Top_panel_SSP1-19.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges] • Top_panel_SSP1-26.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges] • Top_panel_SSP2-45.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges] • Top_panel_SSP3-70.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges] • Top_panel_SSP5-85.csv: projected CO2 emissions, assessed projections of global surface temperature increase relative to the 1850-1900 period for the period 2015-2050 [row 1 and 2 for central lines, row 1, 3, and 4 for ranges]

Bottom panel: • Bottom_panel_GtCO2_historical.csv: historical CO2 emissions [grey bars] • Bottom_panel_GtCO2_projections.csv; projected CO2 emissions for the five scenarios in the core set of IPCC AR6 WG1 scenarios [coloured bars]

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record:

• Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) the Technical Summary (Section TS.3.3). and the Supplementary Material for Chapter 5, which co... For full abstract see: https://catalogue.ceda.ac.uk/uuid/cfe938e70f8f4e98b0622296743f7913.

We constructed the time-series of CO2 emission inventories for China, its 30 provinces and 182 cities. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China.China is the world’s top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. We are an international consortium formed by six leading research institutes in the field of green economy. Our GOAL is to develop robust evidence on green growth in both EU and Chinese cities and to draw lessons to facilitate a transition towards sustainable development in EU and Chinese cities. Our team has brought strong and multi-disciplinary expertise into this project from aspects of urban development, environmental economics, economy-energy-environmental modelling, carbon accounting and policy analysis for technology transfers. Green growth means shifting to a development model where environmental protection and economic growth complement each other, rather than being contradictory. Generating 85% of Europe's GDP, 80% of energy consumption and 75% of carbon emissions, cities have a central role to play in this process. European cities are striving for green growth. They are adapting local regulation and raising citizen awareness. Recently, the EU has launched the Europe 2020 strategy that sets out sustainable growth as one of its priorities, alongside smart and inclusive growth: 'making our production more resource efficient while boosting our competitiveness' . On the other hand, China will play a pivotal role in the fight against climate change given due to its immense size and need to develop. Shifting Chinese cities to a green growth path is a critical part of the fight. Chinese cities home 46% of the population and contribute 75% of the Chinese national economy and nearly 85% of CO2 emissions. The nexus between urban evolution and emission mitigation is the key in China's green growth. While the green-growth debate is becoming more prominent at the international level, understanding how to operationalise green-growth strategies is still lacking at more local levels. The key challenges remain: Challenge 1: What are the dynamics of emission trends in Chinese cities at different urbanisation and industrialisation stages? Energy and greenhouse gases (GHGs) emission inventories are usually built at national level. But no such international framework exists requiring measurements of city emissions or providing detailed methodological guidance for conducting an urban emissions inventory. We will construct city level emission inventories. Challenge 2: What factors are driving emission growth in cities? Quantification of emission driving forces has been extensively studies at the national level. Few studies have found at the city level. Understanding the key factors in driving the emission growth, one can target the problem more specific to reduce emissions in cities. Challenge 3: What are the sources of green growth in cities and how can we support green growth? Green growth can open up new sources of growth through increasing resource efficiencies and economic productivities, supporting technology innovations, creation of new market, boosting business confidence in green growth and enhance economic stability. Institutional arrangements and economic incentives are the key to sustain the sources of growth in cities. New institutional arrangements will need to be established to guide the development of green growth strategies and to overcome the institutional inertia and silos that exist around economic and environmental policy making. Challenge 4: How to use interventions to transform cities to green growth? Cities are the centre of transitioning towards green economy. Green growth is already underway in both European and Chinese cities. We identify available interventions for green growth and examine the effectiveness of those interventions. The CO2 emissions were estimated under the IPCC framework with best available emission factors and activity level data for China. For emission factors, they were collected from literature including Liu, Z. et al. 2015, Nature, 524, 335-338. The activity level data includes the fuel consumption and output of cement. They were collected from China's Energy Statistical Yearbook, China's Statistical Yearbook, China Economic Census Yearbook, provincial statistical yearbooks and city-level statistical yearbooks. More details on emission factors and activity level data can be referred to Shan et al. 2018, Scientific Data, volume 5, 170201.

The Asia-Pacific region produced 19 billion metric tons of carbon dioxide (GtCO₂) from energy use in 2024. China's CO₂ emissions are by far the highest in the Asia-Pacific region, at more than 10 GtCO₂ per year. The second most polluting region in 2024 was North America, where 5.6 GtCO₂ were generated, the majority of which came from the U.S. Global CO₂ emissions growth Global CO₂ emissions from energy consumption have more than doubled since 1970, reaching a record high of 35.5 GtCO₂ in 2024. The rise in emissions is mainly due to rapidly growing economies and increasing energy demand in developing regions. This is especially the case in the Asia-Pacific region, where emissions have almost tripled since the turn of the century. The Middle East has also seen a dramatic rise in emissions, going from producing the lowest CO₂ emissions worldwide in 1965, to the fourth-highest as of 2024. Atmospheric carbon dioxide concentrations The increased burning of fossil fuels - as well as deforestation and other human activities - has seen atmospheric CO₂ concentrations surge in recent decades. In 2023, global atmospheric concentrations of CO₂ reached a record high of 424.61 parts per million, which is roughly 50 percent higher than before the industrial revolution.

Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. Reducing emissions of CH4 and N2O will decrease concentrations in the atmosphere more quickly. Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. Reducing emissions of CH4 and N2O will decrease concentrations in the atmosphere more quickly. Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

The map contains greenhouse gas (GHG) data reported to Ecology as of April 24, 2017. The reported emissions are preliminary and have not been fully verified by Ecology. This information is subject to change.

Except where noted, emissions are reported in metric tons of carbon dioxide equivalent (CO2e). CO2e is a useful measure for comparing the emissions from various greenhouse gases based upon their global warming potentials.

Organizations that have emissions spread throughout the state instead of at a single location, such as petroleum product producers/importers and natural gas distributors, are not included in this map. See the complete report to view emissions for all Washington organizations that emit at least 10,000 metric tons of carbon dioxide equivalent.

For more information see the complete 2012-2015 Washington Mandatory Greenhouse Gas Report. http://www.ecy.wa.gov/programs/air/permit_register/ghg/ghg.html

Detailed New Zealand greenhouse gas emissions data for 1990 and 2015 for Energy and Agriculture sectors. Data are sourced from the 1990–2015 New Zealand Greenhouse Gas Emissions Inventory. Includes sub–sub–sector data. Emissions are in kt and have not been standardised by conversion to CO2 equivalents. Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. Reducing emissions of CH4 and N2O will decrease concentrations in the atmosphere more quickly.Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. More information on this dataset and how it relates to our Environmental reporting indicators and topics can be found in the attached data quality pdf.

This dataset contains the annual historical series of CO2 Emissions and Drivers ( Kaya Decomposition) from 1971-2020Note: Identifying drivers of CO2 emissions trends This table presents the decomposition of CO2 emissions into four driving factors following the Kaya identity1, which is generally presented in the form: Kaya identity C = P (G/P) (E/G) (C/E) where: "C = CO2 emissions; P = populationG = GDPE = primary energy consumption" "The identity expresses, for a given time, CO2 emissions as the product of population, per capita economic output (G/P), energy intensity of the economy (E/G) and carbon intensity of the energy mix (C/E).Because of possible non-linear interactions between terms, the sum of the percentage changes of the four factors, e.g. (Py-Px)/Px, will not generally add up to the percentage change of CO2 emissions (Cy-Cx)/Cx. However, relative changes of CO2 emissions in time can be obtained from relative changes of the four factors as follows:" Kaya identity: relative changes in time Cy/Cx = Py/Px (G/P)y/(G/P)x (C/E)y/(C/E)x where x and y represent for example two different years. In this table, the Kaya decomposition is presented as: "CO2 emissions and driversCO2 = P (GDP/P) (TES/GDP) (CO2/TES) " where: "C = CO2 emissions; P = populationGDP/P = GDP/population *TES/GDP = Total primary energy consumption per GDP *CO2/TES = CO2 emissions per unit TES" * GDP in 2015 USD, based on purchasing power parities. "The Kaya identity can be used to discuss the primary driving forces of CO2 emissions. For example, it shows that, globally, increases in population and GDP per capita have been driving upwards trends in CO2 emissions, more than offsetting the reduction in energy intensity. In fact, the carbon intensity of the energy mix is almost unchanged, due to the continued dominance of fossil fuels - particularly coal - in the energy mix, and to the slow uptake of low-carbon technologies.However, it should be noted that there are important caveats in the use of the Kaya identity. Most important, the four terms on the right-hand side of equation should be considered neither as fundamental driving forces in themselves, nor as generally independent from each other."

New Zealand greenhouse gas emissions data for 1990 and 2015. Data are sourced from the 1990–2015 New Zealand Greenhouse Gas Emissions Inventory. Emissions are provided by sector (Energy, Indistrail processes and product use, Agriculture, Land–use, land–use change and Forestry; and Waste) and sector subcategory. IPCC 2004 global warming potential values were used during conversion to CO2 equivalents. Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. Reducing emissions of CH4 and N2O will decrease concentrations in the atmosphere more quickly.Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. More information on this dataset and how it relates to our Environmental reporting indicators and topics can be found in the attached data quality pdf.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

New Zealand greenhouse gas emissions source and sink summary data by sector and gas for 1990-2015. Data are sourced from the 1990-2015 New Zealand Greenhouse Gas Emissions Inventory. Greenhouse gases (GHGs) absorb heat from Earth’s surface, warming the atmosphere and changing our climate. New Zealand’s share of GHG emissions is very small, but our gross emissions per person are high. Emissions mainly come from combustion of fossil fuels that emit carbon dioxide (CO2), and agriculture which emits methane (CH4) and nitrous oxide (N2O). Carbon dioxide remains in the atmosphere much longer than other major GHGs. Because of this, today’s global CO2 emissions will continue to influence atmospheric CO2 concentrations for a very long time. Methane and N2O trap heat better than CO2 but leave the atmosphere faster. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Details are in : https://doi.org/10.1016/j.ecmx.2022.100299

The Kingdom of Saudi Arabia (KSA) is among the countries that committed to taking measures to cut greenhouse gas emissions in accordance with the 2015 Paris Climate Agreement. KSA has rolled out the 2030 Vision aiming at creating a more diverse and sustainable economy that cascaded into a series of initiatives, including the circular carbon economy, Saudi green initiative, and the national renewable energy program. Furthermore, KSA has recently announced an ambitious goal to reach net-zero goal by 2060. In its updated nationally determined contribution (NDC), the Kingdom committed to reducing its carbon emissions by 278 million tons of CO2eq (equivalent) annually by 2030. This ambition is more than a two-fold increase versus the previously announced target (130 million tons of CO2eq). With no current plans to change its hydrocarbon production rates, this reduction in emissions would be achieved mainly through diversifying its energy mix, increasing the efficiency of industrial processes, and deploying carbon capture utilization and storage (CCUS). To achieve this goal, it is vital to establish a detailed register for CO2 emissions from stationary industrial sources to design optimum and effective CCUS applications. This register includes details about the emission source locations, rates, and characteristics. For the first time, this paper provides a country-wide extensive study that maps out CO2 emissions from stationary industrial emitters associated with the leading six industries in the country, which are electricity generation, desalination, oil refining, cement, petrochemicals, and iron & steel. Moreover, CO2 concentrations within the emitted flue gas from these resources are estimated, which is crucial to determine the capture cost. This study aims to provide a vital resource for researchers and policymakers who seek to reduce greenhouse gas emissions by promoting renewable energy, improving the efficiency of existing fossil-fuel-based industries, and evaluating the potential of CCUS in KSA.

The Greening Government Strategy establishes climate and environmental commitments for the Government of Canada’s internal operations. The Government of Canada’s operations will be net-zero emissions by 2050 including: • Government owned and leased real property • Mobility: fleets, business travel and commuting • Procurement of goods and services • National safety and security (NSS) operations To implement net-zero in real property and fleet operations, the Government of Canada will reduce absolute Scope 1 and Scope 2 GHG emissions by 40% by 2025 and at least 90% below 2005 levels by 2050. On this emissions reduction pathway, the government will aspire to reduce emissions by an additional 10% each 5 years starting in 2025. The Government of Canada tracks its energy use and its GHG emissions across 29 departments and agencies. As of fiscal year 2023-24, we have reduced GHG emissions from federal facilities and conventional fleet (excluding NSS) operations by 42% from 2005 levels. The Government of Canada’s Scope 1 and 2 GHG emissions result from the energy used for its facilities and fleets: • facilities comprise office space, defence bases, laboratories, warehouses and other building types • fleets comprise on-road vehicles and off-road fleets, including cars, vans, trucks, boats, ships and planes. It consists of vehicles and equipment primarily used to transport people and cargo in the conduct of government business. Updated data for fiscal year 2023 to 2024 shows that GHG emissions continue a downward trend and remain below the pre-pandemic levels of 2019-2020. Operational improvements (e.g. portfolio rationalization, increased energy efficiency), clean electricity procurement and a warm winter in southern Ontario and Quebec contributed to a reduction of GHG emissions from the previous year (2022-2023). In addition, some year-to-year changes in GHG emissions may be due to data collection gaps, methodology or error correction refinements, while others may be the result of one-time or specific events or actions (such as natural disasters or operational disruptions). Additionally, variations in seasonal weather conditions (for example, the effect of heating or cooling days on building energy use) also influence annual GHG emissions. Data for some facilities have been excluded for operational reasons. Therefore, the results of calculations using this data may not align with other published results.

Background

Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.

Summary

This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.

In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.

Datasets used

Forest and biomass carbon distribution

The Global Forest Change dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.

The WCMC Above and Below Ground Biomass Carbon Density (Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.

Generalized deforestation drivers

Tree cover loss by dominant driver (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.

EarthStat pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.

Detailed deforestation drivers

The Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.

The Spatial Production Allocation Model (SPAM) physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.

The Gridded Livestock of the World (GLW3) (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.

Data processing

Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:

Proportional driver distribution strategy: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).

Main driver strategy: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.

Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.

Files

This repository contains the following files:

deforested_area_by_LUC_driver_2014_2023.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.

carbon_emissions_by_LUC_driver_2014_2023.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.

spatial_grid.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.

summary_showcase.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.

How to cite

Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514

Authors and contact

Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts

*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)

VITAL SIGNS INDICATOR
Greenhouse Gas Emissions (EN3)

FULL MEASURE NAME
Greenhouse gas emissions from primary sources

LAST UPDATED
December 2022

DESCRIPTION
Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels.

DATA SOURCE
US Energy Information Administration: Carbon Dioxide Emissions Coefficients - https://www.eia.gov/environment/emissions/co2_vol_mass.php
1990-2021

California Energy Commission: Retail Fuel Outlet Annual Reporting (Form CEC-A15) - https://www.energy.ca.gov/data-reports/energy-almanac/transportation-energy/california-retail-fuel-outlet-annual-reporting
2010-2021

California Energy Commission: Electricity Consumption by County - http://www.ecdms.energy.ca.gov/elecbycounty.aspx
1990-2021

California Energy Commission: Natural Gas Consumption by County - http://www.ecdms.energy.ca.gov/gasbycounty.aspx
1990-2021

California Department of Finance: Population and Housing Estimates, E-4 - http://www.dof.ca.gov/research/demographic/
1990-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Emissions in this dataset are reported in metric tons. For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the California Energy Commission (CEC) corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Data from the CEC is limited to retail sales, therefore Vital Signs surface transportation emissions estimates are limited to GHG from retail fuel sales. Retail gasoline sales represent most of the gasoline consumed for surface transportation, but retail diesel sales are just a fraction of all diesel consumed for surface transportation. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. Future refinements to the Vital Signs methodology for monitoring GHG emissions from all surface transportation will seek to more closely align monitoring data with estimates from the California Air Resources Board's EMFAC model.

For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overestimated. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

For natural gas consumption, the dataset is based on natural gas consumption data for the nine Bay Area counties; note that this is different than natural gas production as the region imports electricity. Certain types of liquefied natural gas shipped into the region or "makegas" produced at oil refineries during their production process may not be fully reflected in this data. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

CO2 Emissions in Nigeria decreased to 122750 KT in 2022 from 123180 KT in 2021. This dataset includes a chart with historical data for Nigeria CO2 Emissions.

Introduction and Carbon Budget Explorer This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. Some of these rules can be considered fair and can be used as information in the debate on just transitions. Beyond this dataset, we also published the Carbon Budget Explorer: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at www.carbonbudgetexplorer.eu. Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will updated with version 0.2 (i.e., the version presented in this data repository) during summer 2024. Disclaimer The research behind this dataset is still under development and therefore this dataset is not final. A preprint of a scientific publication is being drafted and will be published in summer 2024, along with potential updates of this dataset. Subsequently, the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence this release of a "version 0.2". Data description Default (DefaultAllocations.zip and DefaultReductions.zip) For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The reductions_default_*.csv files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations. Global pathways (GlobalPathways.zip) Allocating emissions to countries starts with determining global emissions pathways. The files in GlobalPathways.zip contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the emissionspathways_default.csv datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in emissionspathways_all.csv. Emission allocations (Allocations.zip -> allocations_*.nc) The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format allocations_region.nc indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written, using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as nccreate, ncread), R (e.g. using the ncdf4 package) and Python (e.g. using the xarray package). Input data (Inputdata.zip) Additional input data coming from third parties, such as population and GDP data, is stored in Inputdata.zip. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'. Allocation rules Below you can find a summarized description of all allocation rules. More detailed information can be found in Van den Berg et al. (2020), as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details. The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis. 'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see Rajamani et al. 2021). It is include here for reference only. ...

Details of current or terminated Climate Change Agreements (CCA) for the UK under the Climate Change Agreements (Administration Facilities) Regulations 2012. The Climate Change Agreement scheme has 51 sectors of various business types. Each sector has its own umbrella agreement which sets out improvement targets for each sector. A sub-sector is a practical method of partitioning within a sector such that the target units (Operators) in each sub-sector carry out the same or similar processes and/or have either all energy targets or all carbon targets. A trade association or other body may represent a sector of industry. Some sectors have set up (or plan to set up) subsidiary or separate organisations to represent them in the agreements. This sector/trade association will look after the management of all the operators which fall within their sector. For example a supermarket chain would be the Operator, each store which is eligible to be in the scheme would be a facility and all the facilities together would form a target unit. A target unit can just hold one facility or it may be a multi-facility target unit. The target unit holds an underlying agreement which sets out the targets that the target unit must meet. Eligibility to hold a CCA depends on the operator carrying out a listed ‘eligible process’. The CCA scheme runs from 1 April 2013 to 31 March 2023. Industrial operators that enter into and abide by the terms and conditions of their CCA are entitled to a discount on the Climate Change Levy (CCL), a tax added to electricity and fuel bills to encourage operators to reduce the amount of carbon dioxide they emit. Operators holding CCAs must monitor and report their energy consumption against agreed targets across four two-year target periods – each covering two calendar years – running from 2013 to 2020. At the end of each target period, operators meeting their targets will be certified to continue to receive the CCL discount. CCAs are voluntary agreements containing targets to increase energy efficiency and reduce carbon dioxide (CO2) emissions. Facility and Target Unit addresses for 6 specific sectors are not released, for National Security/Site Security reasons. The sectors are NFU1 (Pigs), NFU5 (Eggs & Poultry Meat), BMPA (Meat), BPC1 (Poultry Meat), BPC2 (Poultry Meat Processing and Feed and DATC (Data Centres). Energy efficiency improvements and emission reductions data may not be included for some sites where a case for commercial confidentiality has been accepted. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.