[From "BP Statistical Review of World Energy 2001: 2000 in review"]

 "World consumption of primary energy rebounded in 2000, rising by
 2.1%, driven by continued strong growth in the world economy and a
 return to colder winter weather patterns. There were again strong
 contrasts in the performance of different fuels. Natural gas and coal
 grew by significantly more than their 1990-2000 annual averages and
 oil and nuclear energy grew effectively in line with their 10-year
 average, while hydroelectricity grew by less."

An annual publication that provides high-quality objective and globally consistent data on world energy markets. Tables include consumption of primary energy; reserves, production, consumption, prices and trade data for oil, natural gas and coal; consumption of nuclear energy, hydroelectricity and renewable energy; electricity generation; and carbon dioxide emissions.

Website: http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html

Global anthropogenic CO2 emissions for 2007 based on EDGARv4.3, fuel type and category specific emissions provided by Greet Janssens-Maenhout (EU-JRC), BP statistics 2016 (http://www.bp.com/content/dam/bp/excel/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-workbook.xlsx), temporal variations based on MACC-TNO (https://gmes-atmosphere.eu/documents/deliverables/d-emis/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011).

The South America Oil and Gas Downstream Market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 3.90% from 2025 to 2033. While the exact market size in 2025 is not provided, considering the CAGR and the involvement of major players like Ecopetrol SA, Shell PLC, YPF SA, Exxon Mobil Corporation, Petrobras, and BP PLC, a reasonable estimation places the market value at approximately $150 billion in 2025. This significant market size reflects the region's considerable energy consumption and its dependence on oil and gas for various sectors, including transportation, electricity generation, and industrial processes. The market's growth is driven by factors such as increasing energy demand from a growing population and expanding industrialization across South American nations. Furthermore, investments in refining capacity and infrastructure upgrades are contributing to market expansion. However, challenges remain, including volatility in global oil prices, environmental regulations aimed at reducing carbon emissions, and geopolitical instability in certain regions. These factors can influence the market's trajectory and present both opportunities and risks for investors and market participants. The market's segmentation (although not specified) likely includes refining, petrochemicals, marketing, and distribution, each with its own growth drivers and challenges. The forecast period of 2025-2033 suggests continued expansion despite the aforementioned headwinds. The continued growth is expected to be fueled by consistent economic growth in some parts of the region, leading to increased energy demand. Government initiatives focused on infrastructure development further contribute to the growth of the downstream sector. Competition among major international and national oil companies is driving innovation and efficiency improvements. However, factors such as fluctuating crude oil prices and governmental policies on emissions will continue to pose significant challenges to the consistent growth of the sector. Analyzing the regional distribution (while data is missing, a logical distribution should be performed based on the GDP and population of each country) is crucial for understanding market dynamics and pinpointing high-growth areas for investment and expansion. This analysis will guide informed decision-making by companies seeking opportunities in this dynamic and significant market. Key drivers for this market are: Rising Industrialization across the Globe, Increasing Utilization of Natural Gas. Potential restraints include: High Cost of Installation and Maintenance. Notable trends are: Refinery Sector to Witness Significant Growth.

Context

Data is compiled by Our World in Data based on two sources: – BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html – Ember: https://ember-climate.org/data/

Generation in THh between 2000 and 2019

Content

World in Data rely on electricity mix data from BP as it's primary source for two key reasons: BP also provides total energy (not just electricity) consumption data, meaning energy and electricity data is consistent from the same source; and it provides a longer time-series. However, BP does not provide data for all countries, but these were removed from this datasets.

Acknowledgements

Ember compiles electricity mix data from numerous international and national sources, but relies on the Energy Information Administration (EIA) as its primary source.

The BP Statistical review of World Energy provides an interactive energy charting tool, with data back to 1965, and a conversion calculator. The 2006 Review includes data through the year 2005 including: - Oil production: Global oil output rose by 900,000 barrels per day in 2005 - Natural gas production: Gas production rose by 2.5%, despite declines in some regions - Coal production: China had 80% of the growth in the world's fastest growing fuel

Global anthropogenic CO2 emissions for 2006-2019 based on EDGARv4.3, fuel type and category specific emissions provided by Greet Janssens-Maenhout (EU-JRC), BP statistics 2019 (https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-economics/statistical-review/bp-stats-review-2019-all-data.xlsx), temporal variations based on MACC-TNO (Denier van der Gon et al. (2011), TNO report, EU FP7 MACC deliverable report D_D-EMIS_1.3), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011)

Global anthropogenic CO2 emissions based on EDGARv4.3 (Janssens-Maenhout et al., 2019, https://doi.org/10.5194/essd-11-959-2019, fuel type and category specific emissions were provided by Greet Janssens-Maenhout, EU-JRC), BP statistics 2019 (https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-economics/statistical-review/bp-stats-review-2019-all-data.xlsx), temporal variations based on MACC-TNO (Denier van der Gon et al., 2011, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011, https://doi.org/10.5194/acp-11-6855-2011). Gerbig, C., Janssens-Maenhout, G., Karstens, U. (2019). Global anthropogenic CO2 emissions based on EDGARv4.3 and BP statistics 2019, 2012-06-01–2012-06-30, Miscellaneous, https://hdl.handle.net/11676/-3i7GG5XsH3jNZSpLzq53RRP

Anthropogenic CO2 emissions for Europe based on EDGARv4.3 (Janssens-Maenhout et al., 2019, https://doi.org/10.5194/essd-11-959-2019, fuel type and category specific emissions were provided by Greet Janssens-Maenhout, EU-JRC), BP statistics 2021 (https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-economics/statistical-review/bp-stats-review-2021-all-data.xlsx), temporal variations based on MACC-TNO (Denier van der Gon et al., 2011, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011, https://doi.org/10.5194/acp-11-6855-2011) Hourly emissions for 2005-2020

The dataset contains the following information from 12 Middle Eastern countries from 1990 to 2020 (namely Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the United Arab Emirates, and Yemen): • The population (according to the United Nations population statistics) • The GDPs of countries (constant 2010 US dollar) in the studied period (collected from World Bank datasets) • Energy consumption in the Middle East by fuel (collected from the International Energy Agency (IEA) Energy Outlook and British Petroleum (BP) statistical review) • The rate of carbon dioxide emissions in the Middle East by fuel (collected from the International Energy Agency (IEA) Energy Outlook and British Petroleum (BP) statistical review)

Anthropogenic CO2 emissions for Europe adjusted for the impact of the COVID-19 pandemic by using daily factors based on sector- and country-specific emissions read from https://carbonmonitor.org. Anthropogenic CO2 emissions are based on EDGARv4.3 (Janssens-Maenhout et al., 2019, https://doi.org/10.5194/essd-11-959-2019, fuel type and category specific emissions were provided by Greet Janssens-Maenhout, EU-JRC), BP statistics 2021 (https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-economics/statistical-review/bp-stats-review-2021-all-data.xlsx), temporal variations based on MACC-TNO (Denier van der Gon et al., 2011, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011, https://doi.org/10.5194/acp-11-6855-2011). Hourly emissions for 2019-2020. Note that 2019 is also affected due to the interpolation method of daily emissions that avoids step changes between months and years.

Russia is the largest natural gas supplier to the EU. The invasion of Ukraine was followed by a cut-off of gas supplies from Russia to many EU countries, and the EU is planning to ban or dramatically reduce its dependence from Russia. To quantify the magnitude of the Russian gas used for different countries and sectors and the potential solutions to the Russian gas gap, we provide two daily resolution datasets: 1) EU27&UK daily gas supply-consumption (EUGasSC), and 2) EU27&UK daily gas reduction potential (EUGasRP). EUGasSC (from 2016-2022) provides the country- and sector-specific natural gas supply-storage-consumption (including Russian Supply Share) in the EU27&UK at a daily resolution, which is aimed to quantify the shortfalls if Russian imports were to stop. EUGasRP (for 2021) shows the maximal daily gas conservation potentials estimated by reducing demand for heating and/or increasing power generation from other sources, i.e., coal, nuclear, and biomass. They can be used as either input or reference datasets for further research in various fields, such as gas/energy modeling, carbon emission, climate change, geopolitical policy discussions, and the international gas/energy market. The units of the two datasets are KWh.

Preprint of our paper: https://essd.copernicus.org/preprints/essd-2022-246/

Website of our datasets: https://eugas.herokuapp.com/

Github of our work: https://github.com/chuanlongZhou/russia_gas_essd.git

The EUGasSC dataset was developed with a gas network flow simulation based on flow mass balance by combining data from multiple datasets including ENTSO-G, ENTSO-E, and Eurostat energy balance (annual and monthly). The EUGasSC dataset was validated with BP Statistical Review of World Energy and multiple Eurostat datasets. The EUGasSC shows the share of gas supplied by Russia in each country to analyze the ‘gap’ that would result from a stop of all Russian exports to Europe.

The EUGasRP is developed for the potential solutions to fill the Russian gap in the EU27&UK. We analyze gas reductions for reducing demand for heating and increasing power generation from other sources, i.e., coal, nuclear, and biomass, that can substitute the gas.

For the heating sector, we analyze reduction scenarios for weekdays and weekends of household and public buildings. The reduction estimations are based on empirical temperature-gas-consumption (TGC) curves based on population-weighted air temperatures using the Eurostat population dataset and ERA5 daily 2-meters air temperature data. The values provided in EUGasRP assume the following reduction scenarios: 1) households on weekdays adopt a 2 °C lower critical temperature and follow the lower 20th percentile of TGC curves to define the slope, 2) households on weekends adopt a 2 °C lower critical temperature and the lower 40th percentile of TGC curves, and 3) public buildings adopt a 4 °C lower critical temperature and the lower 20th percentile of the TGS curve.

For the power sector, we assume that the electricity generated with gas can be substituted by boosting the hourly electricity generated with coal, nuclear, and biomass to certain observed higher levels. We estimate the observed higher levels by95% (as maximal gas reduction) of the maximum observed diurnal hourly capacities for coal, nuclear, and biomass for each country based on observed ENTSO-E electricity production data from 2019 to 2021.

We also provide further discussions in our paper for 1) uncertainties of the two datasets, 2) the moderate scenarios for gas reductions, 3) transferring gas savings from countries with surplus to those with deficits, and 4) increasing imports from other countries like Norway, the US, and Australia from either pipelines or LNG. Based on our analysis, we argue that with plausible demand reductions, shifts in power generation towards nuclear and coal, and intra-EU and international coordination, particularly with the UK, the US, Australia, and Norway, it should be possible for the EU to make up for the sudden loss of Russian gas.

Global primary energy consumption has increased dramatically in recent years and is projected to continue to increase until 2045. Only hydropower and renewable energy consumption are expected to increase between 2045 and 2050 and reach 30 percent of the global energy consumption. Energy consumption by country The distribution of energy consumption globally is disproportionately high among some countries. China, the United States, and India were by far the largest consumers of primary energy globally. On a per capita basis, it was Qatar, Singapore, the United Arab Emirates, and Iceland to have the highest per capita energy consumption. Renewable energy consumption Over the last two decades, renewable energy consumption has increased to reach over 90 exajoules in 2023. Among all countries globally, China had the largest installed renewable energy capacity as of that year, followed by the United States.

Snapshot img

Gasification Market Size 2024-2028

The gasification market size is forecast to increase by USD 116.6 billion at a CAGR of 4.36% between 2023 and 2028.

The gasification market is experiencing a surge in growth, propelled by the world's insatiable and ever-increasing energy demands, which necessitate diversified and alternative energy sources. The abundant global supply of coal provides a readily available and cost-effective feedstock for gasification processes, further fueling market expansion. While high operating and maintenance costs remain a significant challenge, they simultaneously present compelling opportunities for innovation and cost reduction strategies.This process is crucial for the chemical, liquid fuel, power, and gaseous fuel segments, as it enables the production of ammonia, methanol, electricity, and hydrogen.
Technological advancements are paramount, with improvements in efficiency and emission reduction crucial for long-term market success and sustainability. These advancements, coupled with the imperative for cost-effective and environmentally sound energy solutions, position the gasification market for continued and significant growth.

What will be the Size of the Gasification Market During the Forecast Period?

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The market encompasses various types of gasifiers, including fixed bed, entrained-flow, and fluidized-bed gasifiers, which convert carbonaceous raw materials into syngas, a mixture of hydrogen, carbon monoxide, and other gases. The coal industry, petroleum, natural gas, biomass/waste, and water are primary feedstocks for gasification. Market dynamics are influenced by industrialization and urbanization, driving the demand for alternative energy sources and reducing reliance on traditional fossil fuels.
The BP Statistical Review indicates a growing trend towards thermochemical conversion, with gasification gaining traction as a viable solution for producing cleaner fuels and reducing greenhouse gas emissions. Carbon dioxide and water are by-products of the process, making gasification an environmentally friendly alternative to conventional fuel production methods.

How is this Gasification Industry segmented and which is the largest segment?

The gasification industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Feedstock

  Coal
  Biomass/waste
  Natural gas
  Petroleum


Application

  Chemical
  Liquid fuel
  Gaseous fuel
  Power


Gasifier Medium

  Air
  Oxygen
  Vapor


Type

  Fixed Bed
  Fluidized Bed
  Entrained Flow



Geography

  APAC

    China
    India


  Europe

    Germany
    UK


  North America

    US


  Middle East and Africa



  South America

By Feedstock Insights

The coal segment is estimated to witness significant growth during the forecast period.

Coal gasification is a critical process In the global energy sector, particularly In the context of increasing coal demand and the push towards environmental sustainability. According to the International Energy Agency (IEA), global coal demand grew by over 1.4% in 2023 due to increase economic expansion, driving industrial output and electricity consumption. However, countries are focusing on net-zero emissions to mitigate environmental concerns. Coal gasification offers a solution by transforming coal into clean chemicals, liquid and gaseous fuels, power, and blends, utilizing indigenous carbonaceous feedstocks. This approach enables nations to meet their energy security and environmental objectives while using coal.

The gasification process involves thermochemical conversion, producing syngas, which is a mixture of hydrogen, carbon monoxide, and other impurities like sulfur and particulate matter. This syngas can be further processed to generate ammonia, methanol, electricity, and hydrogen. Coal, biomass, and municipal solid waste are common feedstocks. Gasifiers include fixed bed, fluidized bed, and entrained-flow types. The chemical segment includes Dimethyl ether, synthetic natural gas, and higher alcohols, while the liquid fuel segment encompasses urea, pet coke, and ash. The power segment utilizes carbon dioxide (CO2), steam, and oxygen for power generation. Gasification's environmental benefits include reduced greenhouse gas emissions and improved waste management through carbon capture technologies.

Companies like Larsen & Toubro are investing in coal gasification plants to meet the growing demand for cleaner energy sources.

Get a glance at the Gasification Industry report of share of various segments Request Free Sample

The Coal segment was valued at USD 248.60 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estima

Recommended citation

Gütschow, J.; Günther, A.; Jeffery, L.; Gieseke, R. (2021): The PRIMAP-hist national historical emissions time series v2.2 (1850-2018). zenodo. doi:10.5281/zenodo.4479172.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1850 to 2018, and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO₂, CH₄, and N₂O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture is available. Due to data availability and methodological issues, version 2.2 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF).

The PRIMAP-hist v2.2 dataset is an updated version of

Gütschow, J.; Jeffery, L.; Gieseke, R.; Günther, A. (2019): The PRIMAP-hist national historical emissions time series v2.1 (1850-2017). GFZ Data Services. doi:10.5880/pik.2019.018.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset.

Sources

• Global CO₂ emissions from cement production v4 (Andrew 2019) data, paper Andrew (2019a), Andrew (2019b)
• BP Statistical Review of World Energy website: British Petroleum (2020)
• CDIAC data: Boden et al. (2017)
• EDGAR versions 4.2 and 4.2 FT2010: EDGAR v4.2, EDGAR v4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)
• EDGAR version 5.0: data, paper: JRC and PBL (2017), Crippa et al. (2019)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2020)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries website, data: UNFCCC (2020b), Gieseke and Gütschow (2020)
• UNFCCC Biennial Update Reports website: UNFCCC (2020a)
• UNFCCC Common Reporting Format (CRF) website, paper, data: Gütschow et al. (2020b) UNFCCC (2019a) (processed as described in Jeffery et al. (2018a). PRIMAP-hist contains updated CRF data for Hungary, compared to the published PRIMAP-crf dataset)

Files included in the dataset

• PRIMAP-hist_v2.2_19-Jan-2021.csv: With numerical extrapolation of all time series to 2018.
• PRIMAP-hist_no_extrapolation_v2.2_19-Jan-2021.csv: Without numerical extrapolation of missing values and not including the country groups mentioned in section country.
• PRIMAP-hist_v2.2_data-description.pdf: Data description including changelog.
• PRIMAP-hist_v2.2_updated_figures.pdf: Updated figures from the PRIMAP-hist paper published in ESSD.

Notes

• Emissions from international aviation and shipping are not included in the dataset.
• Emissions from Land Use, Land-Use Change, and Forestry (LULUCF) are not included in this dataset.

Data format description (columns)

“scenario”

• HISTCR: In this scenario country-reported data (CRF, BUR, UNFCCC) is prioritized over third-party data (CDIAC, FAO, Andrew, EDGAR, BP).
• HISTTP: In this scenario third-party data (CDIAC, FAO, Andrew, EDGAR, BP) is prioritized over country-reported data (CRF, BUR, UNFCCC)

“country”

ISO 3166 three-letter country codes or custom codes for groups:

Code    Region description
----    -------
EARTH   Aggregated emissions for all countries.
ANNEXI   Annex I Parties to the Convention
NONANNEXI Non-Annex I Parties to the Convention
AOSIS   Alliance of Small Island States
BASIC   BASIC countries (Brazil, South Africa, India and China)
EU28    European Union
LDC    Least Developed Countries
UMBRELLA  Umbrella Group

Table: Additional “country” codes.

“category”

IPCC (Intergovernmental Panel on Climate Change) 2006 categories for emissions. Some aggregate sectors have been added to the hierarchy. These begin with the prefix IPCM instead of IPC.

-----------------------------------------------------------------------
Category code Description




IPCM0EL    National Total excluding LULUCF
IPC1     Energy
IPC1A     Fuel Combustion Activities
IPC1B     Fugitive Emissions from Fuels
IPC1B1    Solid Fuels
IPC1B2    Oil and Natural Gas
IPC1B3    Other Emissions from Energy Production
IPC1C     Carbon Dioxide Transport and Storage
       (currently no data available)
IPC2     Industrial Processes and Product Use (IPPU)
IPC2A     Mineral Industry
IPC2B     Chemical Industry
IPC2C     Metal Industry
IPC2D     Non-Energy Products from Fuels and Solvent Use
IPC2E     Electronics Industry
       (no data available as the category is only used for
       fluorinated gases which are only resolved at the level
       of category IPC2)
IPC2F     Product uses as Substitutes for Ozone Depleting Substances
       (no data available as the category is only used for
       fluorinated gases which are only resolved at the level
       of category IPC2)
IPC2G     Other Product Manufacture and Use
IPC2H     Other
IPCMAG    Agriculture, sum of IPC3A and IPCMAGELV
IPC3A    

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

British Petroleum (BP) - Navigating the Energy Transition: A Look at BP's Stock Outlook

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset contains information about world's primary energy consumption for 1965-2020. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes:* In this Review, primary energy comprises commercially-traded fuels, including modern renewables used to generate electricity.# Excludes Estonia, Latvia and Lithuania prior to 1985 and Slovenia prior to 1990.

This is an updated version of Gütschow et al. (2018, http://doi.org/10.5880/pik.2018.003). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2016, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.0 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.0_11-Dec-2018.csv: With numerical extrapolation of all time series to 2016. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.0_11-Dec-2018.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.0_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.0_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v2: Andrew (2018)- BP Statistical Review of World Energy: BP (2018)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2018)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2018)- UNFCCC Biennal Update Reports: UNFCCC (2018)- UNFCCC Common Reporting Format (CRF): UNFCCC (2017), UNFCCC (2018), Jeffery et al. (2018) Full references are available in the data description document. Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.

Please use the updated version of this dataset which incorporates updates to input data as well as correction of errors in the original dataset. For a detailed description of the changes please consult the CHANGELOG included in the data description document of the new version. This dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2014 and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 1996 categories. For CO₂ from energy and industry time series for subsectors are available. List of datasets included in this data publication:PRIMAP-hist_v1.0_14-Apr-2016.csv: With numerical extrapolation of all time series to 2014. PRIMAP-hist_no_extrapolation_v1.0_14-Apr-2016.csv: Without numerical extrapolation of missing values. When using this dataset or one of its updates, please cite the precise version of the dataset used. Please consider also citing the relevant original sources. Sources: UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2015) UNFCCC Biennal Update Reports: UNFCCC (2016) UNFCCC Common Reporting Format (CRF): UNFCCC (2013), UNFCCC (2014) BP Statistical Review of World Energy: BP (2014) CDIAC: Boden et al. (2015) EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012) FAOSTAT database: Food and Agriculture Organization of the United Nations (2015b) Houghton land use CO2: Houghton (2008); RCP historical data: Meinshausen et al. (2011) EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001), HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011) SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999) FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015a) Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and calculations for the needed agricultural land.

China's daily biofuel production reached ** thousand barrels of oil equivalent in 2023, an increase by eight thousand barrels of oil equivalent per day in comparison to the year prior. Between 2002 and 2023, production of biofuels in the East Asian country experienced a growth of ** thousand barrels of oil equivalent per day. As of 2022, China's production corresponded to *** percent of the global biofuel production.