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.

Introduction:

Carbon dioxide (CO₂) is the most abundant greenhouse gas emitted by human activities. The burning of fossil fuels, such as coal, oil, and natural gas, is the primary source of CO₂ emissions. Other human activities, such as deforestation and industrial processes, also contribute to CO₂ emissions.

CO₂ emissions are a major driver of climate change. Climate change is the long-term alteration of temperature and typical weather patterns in a place. Climate change could refer to a particular location or the planet as a whole. Climate change may cause weather patterns to be less predictable. A region might experience lower or higher-than-average temperatures. Climate change may cause more frequent and severe weather events, such as storms, floods, and droughts.

The effects of climate change are already being felt around the world, and they are expected to become more severe in the future. These effects include rising sea levels, more extreme weather events, changes in precipitation patterns, and loss of biodiversity.

Most of the existing carbon emission studies based on the IPAT framework considered the size effect rather than structure effect of population. However, it is proved with the micro-data household evidence that the demographic structure explains the unexpected trends better. To complete the framework, this study integrated the structure effects with the STIRPAT model base on the household life-cycle consumption theory as different age groups differ in carbon consumption behaviors. For further analysis with the frequent extreme weather events caused by global warming and their catastrophic effect on human activities, this study also harmonized Köppen criteria with the theories model by Syukuro Manabe and Klaus Hasselmann and considers climate factors precipitation (PRE), annual degree-day (DD), and temperature anomaly (TA) with the extended model to investigate whether population aging trend provides room for or creates barriers to carbon reduction. NASA night-time light (NTL) data DMSP/OLS and VIIRS/DNB is adopted as the proxy for population density to weight the relevant climate data from over 30,000 weather stations worldwide. The combined dataset is from 150 countries, and the period is during 1970–2013. The Panel Seemingly Unrelated Regression (SUR) method is used to solve the problems of cross-sectional correlation, non-stationarity, and endogeneity since sample countries are closely linked in the global meteorological system which make each cross-sectional disturbance term likely to be contemporaneously correlated, and endogeneity of carbon emission under the same global agreement constraint. The empirical results show that the age structure had significant and different impacts on carbon emissions. The general influence of age growth is an inverted U shape as the younger group consumes less than the older group, and offspring leave the family when the householder turns 50. The EKC theory is also checked with the threshold model of per capita income on carbon emissions to determine how many countries reached carbon peak. This study proved that the aggregated carbon consumption pattern is aligned with the microlevel evidence on household energy consumption. Another distinguished finding is that population aging may generally lead to an increase in heat and electricity carbon emissions, contrary to what some household energy consumption models would predict. We explain the uplifted tail as the “effect caused by the narrowed adaptation temperature range” when people are getting older and vulnerable. It should be noted that as the aging trend becomes severe worldwide and extreme weather events happen with higher frequency, the potential energy spending and thus carbon emission on air conditioning will undoubtfully overgrow. One important method is to improve the building energy efficiency by retrofitting old buildings’ insulations. Implementing new green building standards in carbon reduction must not be ignored. Evidence shows that if the insulation of pre-1990s houses is reconstructed with modern materials, carbon emissions caused by residential cooling and heating can be reduced by about 20% every year. Overall, promoting an efficient building style provides reduction capacity for the industrial sector, and it is a way to achieve sustainable growth.

Vehicle Information Vehicle Type: This column represents the type of vehicle. Possible values include:

Car: A standard passenger vehicle. Truck: A larger vehicle used for transporting goods. Bus: A vehicle designed to carry multiple passengers. Motorcycle: A two-wheeled motor vehicle. Fuel Type: This column indicates the type of fuel the vehicle uses. Possible values are:

Petrol: Also known as gasoline, a common fuel for internal combustion engines. Diesel: A type of fuel used in diesel engines, often found in larger vehicles like trucks and buses. Electric: Vehicles powered by electric batteries. Hybrid: Vehicles that use a combination of an internal combustion engine and electric propulsion. Engine Size: The size of the vehicle's engine, measured in liters. Larger engines typically produce more power and can lead to higher emissions.

Age of Vehicle: The age of the vehicle in years. Older vehicles may have higher emissions due to wear and tear or outdated technology.

Mileage: The total distance the vehicle has traveled, measured in kilometers or miles. Higher mileage can indicate more wear and potentially higher emissions.

Driving Conditions Speed: The average speed of the vehicle during the measurement period, measured in kilometers per hour (km/h) or miles per hour (mph). Vehicle emissions can vary with speed.

Acceleration: The rate at which the vehicle's speed increases, measured in meters per second squared (m/s²). Rapid acceleration can lead to higher emissions.

Road Type: The type of road the vehicle is driving on. Possible values include:

Highway: Major roads designed for fast travel. City: Urban roads with frequent stops and lower speeds. Rural: Country roads that may have varying conditions. Traffic Conditions: The level of traffic during the measurement period. Possible values include:

Free flow: Minimal traffic, allowing for smooth travel. Moderate: Some traffic, but generally steady movement. Heavy: High traffic, often leading to stop-and-go conditions. Environmental Conditions Temperature: The ambient temperature during the measurement period, measured in degrees Celsius (°C) or Fahrenheit (°F). Temperature can affect engine performance and emissions.

Humidity: The relative humidity of the air during the measurement period, measured as a percentage. Humidity can affect the combustion process and emissions.

Wind Speed: The speed of the wind during the measurement period, measured in meters per second (m/s) or kilometers per hour (km/h). Wind can influence the dispersion of emissions.

Air Pressure: The atmospheric pressure during the measurement period, measured in hectopascals (hPa). Air pressure can affect engine efficiency and emissions.

Emission Data CO2 Emissions: The amount of carbon dioxide emitted by the vehicle, measured in grams per kilometer (g/km). CO2 is a major greenhouse gas contributing to climate change.

NOx Emissions: The amount of nitrogen oxides emitted by the vehicle, measured in grams per kilometer (g/km). NOx contributes to air pollution and can cause respiratory problems.

PM2.5 Emissions: The amount of particulate matter with a diameter of 2.5 micrometers or smaller emitted by the vehicle, measured in grams per kilometer (g/km). PM2.5 can penetrate deep into the lungs and cause health issues.

VOC Emissions: The amount of volatile organic compounds emitted by the vehicle, measured in grams per kilometer (g/km). VOCs contribute to the formation of ground-level ozone and smog.

SO2 Emissions: The amount of sulfur dioxide emitted by the vehicle, measured in grams per kilometer (g/km). SO2 can contribute to acid rain and respiratory problems.

Target Variable Emission Level: This column categorizes the overall emission level of the vehicle into three classes: Low: Vehicles with low emissions. Medium: Vehicles with moderate emissions. High: Vehicles with high emissions. Summary Categorical Features: Vehicle Type, Fuel Type, Road Type, Traffic Conditions, Emission Level. Continuous Numerical Features: Engine Size, Age of Vehicle, Mileage, Speed, Acceleration, Temperature, Humidity, Wind Speed, Air Pressure, CO2 Emissions, NOx Emissions, PM2.5 Emissions, VOC Emissions, SO2 Emissions.

In 2023, global carbon dioxide emissions from fossil fuel combustion and industrial processes reached a record high of 37.8 billion metric tons (GtCO₂). Global CO₂ emissions are projected to have reached record levels in 2024. The world has pumped more than 1,800 GtCO₂ into the atmosphere since the industrial revolution began, though almost 45 percent has been produced since 2000. What is carbon dioxide? CO₂ is a colorless, naturally occurring gas that is released after people and animals inhale oxygen. It is a greenhouse gas, meaning it absorbs and releases thermal radiation which in turn creates the “greenhouse effect”. In addition to other greenhouse gases, CO₂ is also a major contributor to the ability of the Earth to maintain a habitable temperature. Without CO₂ and other greenhouse gases, Earth would be too cold to live on. However, while CO₂ alone is not a harmful gas, the abundance of it is what causes climate change. The increased use of electricity, transportation, and deforestation in human society have resulted in the increased emissions of CO₂, which in turn has seen a rise in earth’s temperature. In fact, around 70 percent of global warming since 1851 is attributable to CO₂ emissions from human activities. Who are the largest emitters worldwide? China is the biggest carbon polluter worldwide, having released almost 12 GtCO₂ in 2023. This was more than the combined emissions of the United States and India, the second and third-largest emitters that year, respectively.

Per capita carbon dioxide (CO₂) emissions in India have soared in recent decades, climbing from 0.4 metric tons per person in 1970 to a high of 2.07 metric tons per person in 2023. Total CO₂ emissions in India also reached a record high in 2023. Greenhouse gas emissions in India India is the third-largest CO₂ emitter globally, behind only China and the United States. Among the various economic sectors of the country, the power sector accounts for the largest share of greenhouse gas emissions in India, followed by agriculture. Together, these two sectors were responsible for more than half of India's total emissions in 2023. Coal emissions One of the main reasons for India's high emissions is the country's reliance on coal, the most polluting of fossil fuels. India's CO₂ emissions from coal totaled roughly two billion metric tons in 2023, a near sixfold increase from 1990 levels.

There are many technological pathways that can lead to reduced carbon dioxide (CO2) emissions. However, these pathways can have substantially different impacts on other environmental endpoints, such as air quality and energy-related water demand. This study uses an integrated assessment model with state-level resolution of the U.S. energy system to compare environmental impacts of alternative low-carbon pathways. One set of pathways emphasizes nuclear energy and carbon capture and storage (NUC/CCS), while another set emphasizes renewable energy (RE). These are compared with pathways in which all technologies are available. Air pollutant emissions, mortality costs attributable to particulate matter less than 2.5 microns in diameter (PM2.5), and energy-related water demands are evaluated for 50% and 80% CO2 reduction targets in the U.S. in 2050. The RE low-carbon pathways require less water withdrawal and consumption than the NUC/CCS pathways because of the large cooling demands of nuclear power and CCS. However, the NUC/CCS low-carbon pathways produce greater health benefits, mainly because the NUC/CCS assumptions result in less primary PM2.5 emissions from residential wood combustion. Environmental co-benefits differ among states because of factors such as existing technology stock, resource availability, and environmental and energy policies. An important finding is that biomass in the building sector can offset some of the health co-benefits of the low-carbon pathways even though it plays only a minor role in reducing CO2 emissions. This dataset consists of source code, input data, and processed outputs for Ou et al. (2018), published in Applied Energy. This dataset is associated with the following publication: Ou, Y., W. Shi, S.J. Smith, C.M. Ledna, J.J. West, C. Nolte, and D. Loughlin. Estimating environmental co-benefits of U.S. low-carbon pathways using an integrated assessment model with state-level resolution. Applied Energy. Elsevier B.V., Amsterdam, NETHERLANDS, 216: 482-493, (2018).

Due to the gradual threat of climate change to human living environments, the significant use of fossil fuels by humans has led to an increasing concentration of carbon dioxide in the atmosphere, becoming the main cause of global warming. Without effective control of carbon dioxide emissions, the situation could become even more severe. Taiwan Water Corporation actively promotes various energy-saving and carbon reduction policies in coordination with the government, advocating water conservation and raising awareness of the necessary CO2 emissions per cubic meter of water used by consumers. The goal is to promote the concept that water conservation can also reduce carbon emissions and mitigate the impact of global warming on the people of the country. This dataset provides the annual CO2 emissions per cubic meter of water used by Taiwan Water Corporation.

The Smithsonian's "Eruptions, Earthquakes, & Emissions" web application (or "E3") is a time-lapse animation of volcanic eruptions and earthquakes since 1960. It also shows volcanic gas emissions (sulfur dioxide, SO2) since 1978 — the first year satellites were available to provide global monitoring of SO2. The eruption data are drawn from the Volcanoes of the World (VOTW) database maintained by the Smithsonian's Global Volcanism Program (GVP). The earthquake data are pulled from the United States Geological Survey (USGS) Earthquake Catalog. Sulfur-dioxide emissions data incorporated into the VOTW for use here originate in NASA's Multi-Satellite Volcanic Sulfur Dioxide L4 Long-Term Global Database. Please properly credit and cite any use of GVP eruption and volcano data, which are available via a download button within the app, through webservices, or through options under the Database tab above. A citation for the E3 app is given below.Clicking the image will open this web application in a new tab.Citation (example for today)Global Volcanism Program, 2016. Eruptions, Earthquakes & Emissions, v. 1.0 (internet application). Smithsonian Institution. Accessed 19 Oct 2018 (https://volcano.si.edu/E3/).Frequently Asked QuestionsWhat is the Volcanic Explosivity Index (VEI)?VEI is the "Richter Scale" of volcanic eruptions. Assigning a VEI is not an automated process, but involves assessing factors such as the volume of tephra (volcanic ash or other ejected material) erupted, the height the ash plume reaches above the summit or altitude into the atmosphere, and the type of eruption (Newhall and Self, 1982). VEIs range from 1 (small eruption) to 8 (the largest eruptions in Earth's entire history).What about eruptions before 1960?For information about volcanic eruptions before 1960, explore the GVP website, where we catalog eruption information going back more than 10,000 years. This E3 app only displays eruptions starting in 1960 because the catalog is much more complete after that date. For most eruptions before the 20th century we rely on the geologic record more than historical first-hand accounts — and the geologic record is inherently incomplete (due to erosion) and not fully documented.What are "SO2 emissions" and what do the different circle sizes mean?The E3 app displays emissions of sulfur dioxide gas (SO2) from erupting volcanoes, including the mass in kilotons. Even though water vapor (steam) and carbon dioxide gas (see more about CO2 below) are much more abundant volcanic gases, SO2is the easiest to detect using satellite-based instruments, allowing us to obtain a global view. There is no universally accepted "magnitude" scale for emissions; the groupings presented here were chosen to best graphically present the relative volumes based on available data.What am I seeing when I click on an SO2 emission event?You are seeing a time-lapse movie of satellite measurements of SO2 associated with a particular emission event. These SO2 clouds, or plumes, are blown by winds and can circle the globe in about a week. As plumes travel, they mix with the air, becoming more dilute until eventually the concentration of SO2 falls below the detection limit of satellites. Earth's entire atmosphere derives from outgassing of the planet — in fact, the air you breathe was once volcanic gas, and some of it might have erupted very recently!Why are there no SO2 emissions before 1978?E3 shows volcanic gas emissions captured from satellite-based instruments, which were first deployed in 1978. NASA launched the Total Ozone Mapping Spectrometer (TOMS) in 1978, which provided the first space-borne observations of volcanic gas emissions. Numerous satellites capable of measuring volcanic gases are now in orbit.Why don't you include H2O and CO2 emissions?The most abundant gases expelled during a volcanic eruption are water vapor (H2O in the form of steam) and carbon dioxide (CO2). Sulfur dioxide (SO2) is typically the third most abundant gas. Hydrogen gas, carbon monoxide and other carbon species, hydrogen halides, and noble gases typically comprise a very small percentage of volcanic gas emissions. So why can't we show H2O and CO2 in the E3 app? Earth's atmosphere has such high background concentrations of H2O and CO2 that satellites cannot easily detect a volcano's signal over this background "noise." Atmospheric SO2 concentrations, however, are very low. Therefore volcanic emissions of SO2 stand out and are more easily detected by satellites. Scientists are just beginning to have reliable measurements of volcanic carbon dioxide emissions because new satellites dedicated to monitoring CO2 have either recently been launched or have launches planned for the coming decade.How much carbon is emitted by volcanoes?We don't really know. CO2, carbon dioxide, is the dominant form of carbon in most volcanic eruptions, and can be the dominant gas emitted from volcanoes. Humans release more than 100 times more CO2 to the atmosphere than volcanoes (Gerlach, 2011) through activities like burning fossil fuels. Because of this, the background levels of CO2 in the atmosphere have risen to levels that are so high (greater than 400 parts per million, or 0.04%) that satellites cannot easily detect the CO2 from volcanic eruptions. Scientists are able to estimate the amount of carbon flowing from Earth's interior to exterior (the flux) by measuring carbon emissions directly at volcanic vents and by measuring the carbon dissolved in volcanic rocks. Scientific teams in the Deep Carbon Observatory (one of the supporters of E3) are working to quantify the flux of carbon from Earth's interior to exterior.Do volcanic emissions cause global warming?No, not in modern times. The dominant effect of volcanic eruptions is to cool the planet in the short term. This is because sulfur emissions create aerosols that block the sun's incoming rays temporarily. While volcanoes do emit powerful greenhouse gases like carbon dioxide, they do so at a rate that is likely 100 times less than humans (Gerlach, 2011). Prior to human activity in the Holocene (approximately the last 10,000 years), volcanic gas emissions did play a large role in modulating Earth's climate.Volcanic eruptions and earthquakes seem to occur in the same location. Why?Eruptions and earthquakes occur at Earth's plate boundaries — places where Earth's tectonic plates converge, diverge, or slip past one another. The forces operating at these plate boundaries cause both earthquakes and eruptions. For example, the Pacific "Ring of Fire" describes the plate boundaries that surround the Pacific basin. Around most of the Pacific Rim, the seafloor (Earth's oceanic crust) is "subducting" beneath the continents. This means that the seafloor is being dragged down into Earth's interior. You might think of this as Earth's way of recycling! In this process, ocean water is released to Earth's solid rocky mantle, melting the mantle rock and generating magma that erupts through volcanoes on the continents where the plates converge. In contrast, mid-ocean ridges, chains of seafloor volcanoes, define divergent plate boundaries. The Mid-Atlantic Ridge that runs from Iceland to the Antarctic in the middle of the Atlantic Ocean is one example of a divergent plate boundary. Earth's crust is torn apart at the ridge, as North and South America move away from Europe and Africa. New lava erupts to fill the gap. This lava cools, creating new ocean crust. All these episodes where solid rock collides or is torn apart generate earthquakes. And boom! You have co-located eruptions and earthquakes. To learn more about plate margins using E3, watch this video.Is this the first time eruptions, emissions, and earthquakes have been animated on a map?E3 is a successor to the program Seismic/Eruption developed by Alan Jones (Binghamton University). That program was one of the first to show the global occurrence of earthquakes (USGS data) and eruptions (GVP data) through space and time with animations and sound. The program ran in the Smithsonian's Geology, Gems, and Minerals Hall from 1997 to 2016, and was also available on the "Earthquakes and Eruptions" CD-ROM. E3 builds upon Seismic/Eruption with the addition of emissions data and automated data updates.How many eruptions and emissions are shown, and from how many volcanoes?The application is currently showing 2,065 eruptions from 334 volcanoes. It is also showing 360 emission activity periods from 118 different volcanoes. In addition, there are 67 animations available showing the movement of SO2 clouds from 44 volcanoes.How often do you update the data represented in the web application?The application checks for updates once a week. Earthquake data, being instrumentally recorded, is typically very current. Eruption data, which relies on observational reports and analysis by GVP staff, is generally updated every few months; however, known ongoing eruptions will continue through the most recent update check. Emissions data is collected by satellite instruments and also must be processed by scientists, so updates will be provided as soon as they are available following an event, on the schedule with eruption updates.Is my computer system/browser supported? Something isn't working right.To run the map, your computer and browser must support WebGL. For more information on WebGL, please visit https://get.webgl.org to test if it should work.Source Obtained from http://volcano.si.edu/E3/

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 contains details on the input data used in Table 5.SM.6 (Figure 5.31) - Link to related publications for input data

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)

Embodied carbon emissions research is an important branch of climate change study. Some scholars have noted the value-added chains associated with the carbon emissions embodied in international trade. But they have not covered the global scale and the entire demand-supply chains. This paper tries to investigate this issue and answer how much value-added is gained by countries, especially developing regions that are the main carbon emissions suppliers in the world, and how this value-added changed during 2000–2014, based on the multi-regional input-output table. The conclusions are, on a global average, the value-added gained per unit of carbon emissions embodied in the global demand-supply chain had increased, but it had not brought net value-added to developing regions but instead caused them a net loss of wealth, mainly because developing regions should pay more value-added for their increasingly external demand.

Most of the previous studies of environmental innovation focus on the impact of environmental innovation on carbon emissions. This study rarely examines the internal causes and mechanisms of influence of low-carbon innovation. This study focuses on the effect of carbon emissions on low-carbon innovation in firms. Using a panel data set of Chinese A-share firms, this study finds that the increase in carbon emissions promotes low-carbon innovation. This promoting effect comes from high carbon emissions increasing the pressure to reduce carbon emissions in firms and prompting firms to increase R&D investment, and the effect is more pronounced in firms with lower equity concentration or high-tech firms. It is also found that indirect carbon emissions do not promote low-carbon innovation, while other types of carbon emissions do. This study expands the research on the internal causes of low-carbon innovation in firms, examines the logic influencing low-carbon innovation in firms from the perspective of emission reduction motives and methods, reveals that global warming contains opportunities for the development of low-carbon innovation in firms, and provides a reference for optimizing the carbon emissions calculation system.

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.

This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. The analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window. This page provides monthly mean values, worldwide. Original 3-hourly outputs can be accessed here.

Data for Figure SPM.7 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.7 shows the cumulative anthropogenic CO2 emissions taken up by land and ocean sinks by 2100 under the five core scenarios. --------------------------------------------------- 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. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains cumulative anthropogenic (human-caused) carbon dioxide (CO2) emissions taken up by the land and ocean sinks under the five core scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), simulated from 1850 to 2100 by Earth System Models that contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The five illustrative SSP (Shared Socio-economic Pathway) 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 --------------------------------------------------- Data file: SPM7_data.csv: each column corresponds to a single scenario, in which rows 2-7 are the bar values, rows 8-10 are the pie chart values and row 11 is the central value in the pie chart. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblink is 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 CRC Order 2013 provides for publication of information on the basis of participants' annual reports plus details submitted when they registered for the scheme. The CRC Energy Efficiency Scheme (CRC) is a mandatory scheme which aims to improve energy efficiency and cut emissions for large public and private sector energy users across the UK. The scheme aims to encourage organisations to develop energy management strategies that promote a better understanding of energy usage, and lead to improved energy efficiency. The scheme is split into phases: Phase 1 from 1 April 2010 until 31 March 2014 and Phase 2 from 1 April 2014 until 31 March 2019. Each year, CRC participants have to monitor their energy consumption and report this information into the CRC Registry, an online IT system, which calculates their carbon dioxide (CO2) emissions. Participants must purchase and surrender 'allowances' to cover these emissions - one allowance for each tonne of CO2. Further information on the scheme can be found on the CRC pages of GOV.UK. The ARP format was introduced for the 2012 to 2013 reporting year and replaces the performance league tables (PLT). Removal of the PLT was part of the government’s simplification of the CRC scheme. These annually produced data sets contain the information provided by participants to the CRC Registry including organisational type, energy use and CO2 emissions. The data should be read in combination with the equivalent years ‘CRC Energy Efficiency Scheme: annual report publication which are located on GOV.UK. CRC performance league tables were published for the 2010 to 2011 and 2011 to 2012 compliance years and ranked participants according to their energy efficiency performance against a range of metrics.

This table contains figures on the total Dutch emissions of the greenhouse gases CO2, N2O and CH4 by both stationary and mobile sources. It offers an insight in the emissions as reported within the framework of reporting commitments for the United Nations Climate Treaty (UNFCCC) and the European Union Monitoring Mechanism for Greenhouse gases. The emissions haven been calculated according to the IPCC-guidelines. The IPCC (Intergovernmental Panel on Climate Change) takes care of the supervision of the implementation of the Kyoto-protocol. The CO2 figures include the emissions caused by the use of. This source accounts for about 0.1 billion kilograms. According to the activity classification (SIC 2008) the coke factory of Tata Steel belongs to the energy sector. In this table it’s emissions are assigned to the category manufacture of iron and steel.

Data available from: 1990

Status of the figures: In order to obtain a consistent time series the complete data set is (re)calculated when necessary, so as to be able to include the latest insights in the survey, especially in the case of emission factors.

Changes as of 10 September 2018: Addition of 2017 provisional figures.

Changes as of 9 March 2018: Stationary Sources: - The latest insights related to the use of diesel oil in the sectors Services and Construction have been applied in the time series (1990-2016). - The CBS survey on renewable energy has been revised; the results have been used in the emission calculations.

Mobile sources: - Until now CBS applied fixed heating values valid for the entire time series of petrol and diesel fuel for the Dutch market. These values were introduced before 1975. Measurements by TNO in 2016 and RIVM in 2004 showed that these heating values, and also the carbon contents, have changed considerably during the past decades. This made it necessary to adjust the values used in the Dutch CO2 emission calculations. This has led to a new time series 1990-2016 of heating values, CO2 emission factors, and CO2 emissions for petrol and diesel (see paragraph 4). - The emissions from mobile machinery have been recalculated for the entire time series. This is due to revised CBS data on the use of gas oil and adjustments to the TNO emission model. - The CO2 emission figures now include the indirect CO2 emission caused by VOC emissions by transport.

When will new figures be published? Definitive figures of 2017 are published in April 2019. The 2018 provisional figures will be published in September 2019.