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Record W6949565617 · doi:10.5281/zenodo.14931651

Global CO2 emissions from cement production

2025· dataset· en· W6949565617 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsnot available
FundersEuropean Commission
KeywordsFossil fuelCementClinker (cement)Greenhouse gasProduction (economics)CombustionGlobal warming

Abstract

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GCP-CEM: The Global Carbon Project CEMent-process emissions dataset This is an update of the dataset documented in: Andrew, R.M., 2019. Global CO2 emissions from cement production, 1928–2018. Earth System Science Data 11, 1675–1710. https://doi.org/10.5194/essd-11-1675-2019. Data in this release cover the period 1880–2024. Note that emissions from use of fossil fuels in cement production are not included in this dataset since they are usually included elsewhere in global datasets of fossil CO2 emissions. The process emissions in this dataset, which result from the decomposition of carbonates in the production of cement clinker, amounted to ~1.5 Gt CO2 in 2024 while emissions from combustion of fossil fuels to produce the heat required amounted to an additional ~0.9 Gt CO2 in 2024. February 2025 release (250226): Changes The new non-Annex 1 Biennial Transparency Reports submitted to the UNFCCC have been included. This gives a number of new time-series of both clinker production and emissions that were not available previously Various revisions to recent years' estimates based on newly published data Hong Kong: The new BTR indicated zero emissions in 2005. Investigation showed a significant downturn in construction activity in the period 2000-2005, and cement manufacturers switched from producing clinker from imported limestone to importing clinker The Cement Production dataset Annual cement production data by country are assembled from a number of sources. Prioritisation is given to national sources, whether directly from statistical offices or activity data reported in official emissions reports submitted to the UNFCCC. Where official sources are not used, data are sourced from the USGS Minerals Yearbooks. Some data points in the USGS dataset are corrected based on either sense-checks or information from alternative sources. For data before 1990, USGS data are obtained via back-calculation from the 2019 edition of the CDIAC emissions dataset. The first year for most countries in the USGS data is 1928; where the combined dataset shows zeros before 1928 and non-zero data from 1928, these zeros are assumed to be artefacts and are set to NODATA. Using available data for some former Soviet states before the dissolution of the Soviet Union, Soviet states are disaggregated for all years before dissolution. Every data point in the cement production dataset has its source indicated in the accompanying source file. Blank cells should be interpreted as NODATA. The Clinker Production dataset Annual clinker production data by country are assembled from a number of sources. No such multi-country dataset exists elsewhere to our knowledge. Many countries report 'activity data' in their emissions reporting to the UNFCCC, and for the Cement Production sector (2.A.1), this is often clinker production. For all Annex 1 countries this is the case, and clinker production for these countries are obtained from their Excel-format reporting files (CRTs), although New Zealand (and Hungary in recent years) exceptionally has withheld these data for reasons of confidentiality. The new BTRs for non-Annex 1 countries also include CRTs, and these have been used where available. Many other countries report time-series of clinker production in their official emissions reporting, and for some countries data are available (sometimes with monthly frequency) from official websites. Every data point in the cement production dataset has its source indicated in the accompanying source file. Blank cells should be interpreted as NODATA. Not all countries are present in the dataset. Emissions calculation Emissions for all UNFCCC Annex I ("developed") countries are taken directly from their official submissions to the UNFCCC (or EIONET) in Common Reporting Format (structured Excel files), for which data are available from 1990 (slightly earlier for some Economies in Transition). Australia, Austria, Belgium, Bulgaria, Belarus, Canada, Switzerland, Cyprus, Czechia, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Japan, Kazakhstan, Liechtenstein, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Norway, New Zealand, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Sweden, Turkey, Ukraine, United States of America. Country-specific methods are used for Brazil, India, South Africa, Thailand, USA, Vietnam. For Brazil, emissions are published from 1990, and clinker ratios are reported starting in 1970, allowing more accurate estimation before 1990. Little information is available about clinker production in India since the Cement Manufacturers' Association was forced to stop collecting these data. Various sources are used to estimate how the clinker ratio has changed over time in India. South Africa's reported emissions appear to be calculated assuming limestone sales statistics are cement production statistics. An alternative method is used here. For Thailand, cement production and clinker trade data are available from 1990, and these are used to estimate clinker production in the period 1990-2015. The US publishes clinker production data beginning in 1925, and cement production data from 1880. Vietnam is a significant producer but doesn't collect or publish clinker production data. High levels of exports mean that applying a clinker ratio to cement production would be inappropriate. Here we follow Vietnam's own method of using cement production combined with clinker trade data to estimate clinker production. The combined_cement_data.xlsx file is used to overwrite emissions with superior data, in most cases as reported in official reporting to the UNFCCC, e.g. Biennial Update Reports, National Communications, and National Inventory Reports. Where more than one data source has been found for a country (e.g. subsequent reports), a comparison is automatically made of overlapping data, and they are combined only if they are in very close agreement (i.e., significant revisions mean that previous estimates will be ignored). Clinker production data have been obtained for some countries in addition to those available from Annex 1 parties' CRFs, either from reporting to the UNFCCC or directly from official agencies. The period available varies by country. Emissions for these countries are calculated directly from these clinker production data where official emissions estimates are not available. Afghanistan, Argentina, Armenia, Bangladesh, Brazil, Chile, China, Spain, Jamaica, Japan, Moldova, Norway, Paraguay, Poland, Rwanda, Turkey, Saudi Arabia, South Korea, Taiwan, Thailand, Togo, Tunisia, Ukraine, United Kingdom, USA, Uzbekistan. Some countries do not report time-series of emissions, but do supply some isolated estimates in their official reporting to the UNFCCC, and these are used in some cases to constrain estimates. A number of countries state in their official reporting to the UNFCCC that they have never produced clinker, so emissions are set to zero for all years for these countries. In other cases, statements are made that no clinker was produced before or after a certain year, and this information is also incorporated. Never produced clinker: Mauritania, Sierra Leone, Côte d'Ivoire, Brunei Darussalam, Tuvalu, Papua New Guinea, Guinea, Andorra, Singapore, Réunion, Guadeloupe, French Guiana, Martinique, Mayotte, Macao. Stopped or started producing clinker: Cambodia, Estonia, Fiji, Ghana, Iceland, the Netherlands (see file zero_before_after.csv). The information available usually covers a number of years, up to 3 decades. These are then extrapolated by combining available data and assumptions about historical developments in clinker ratios to produce longer time series of emissions based on the longer cement production dataset. More details on this method are given in the accompanying journal paper. For any non-Annex I countries for which time-series data of neither emissions or clinker are available, and cement production is non-zero, clinker ratios derived from the Getting the Numbers Right (GNR) cement sustainability initiative are applied to the cement production dataset to derive approximate clinker production by country, from which emissions are calculated using IPCC default factors. Where emissions are estimated from clinker (or apparent clinker) production data, IPCC default factors are used, with the exception of China and Argentina, for which officially reported factors are used. See also: "Monthly global cement production data": https://doi.org/10.5281/zenodo.10277408

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.287
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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