This study quantifies uncertainties in greenhouse gas (GHG) emissions from petrochemical production, highlighting the need for precise data collection to improve emissions reporting. By analyzing cradle-to-gate emissions of 81 chemicals across 37,379 facilities, the research identifies key sources of uncertainty, including process specificity, feedstock emissions, and indirect energy use. The results show a 34% uncertainty in global GHG emissions of 1.9 ± 0.6 Gt CO₂-e for 2020, with uncertainties ranging from 15–40% across most petrochemicals. The largest uncertainties stem from the inability to assign specific production processes to facilities due to data limitations. Uncertain data on feedstock production and off-site energy generation contribute significantly, while on-site fuel combustion and chemical reactions have smaller roles. Allocation method choices for co-products are generally insignificant. Prioritizing facility-level process specification in data collection for just 20% of facilities could reduce global uncertainty by 80%. This underscores the necessity of quantifying uncertainty in petrochemical GHG emissions globally and outlines priorities for improved reporting. The dataset generated offers independent emissions factor estimates based on facility-specific information for 81 chemicals, supporting future analyses. The petrochemical industry produces nearly 1 billion tonnes of products annually, contributing to approximately 7% of global GDP. Products include 420 ± 40 Mt of plastics and 190 ± 20 Mt of fertilizers. Petrochemical production is energy-intensive, requiring 30% of final industrial energy use, including 14% of global oil demand and 9% of global natural gas demand. Therefore, petrochemical production is a major cause of GHG emissions, with the International Energy Agency (IEA) estimating annual GHGs emitted during petrochemical production, excluding external energy generation, at 1.30 Gt CO₂-e in 2020. This is equivalent to approximately 14% of global industrial GHG emissions and 2.5% of all anthropogenic GHG emissions. Summary environmental assessments provide industry-wide figures describing the scale of action required to reduce GHG emissions in line with climate change mitigation goals. This sector substantially influences global GHG emissions and poses substantial challenges in decarbonization efforts. These have motivated the industry to consider decarbonization as a priority, yet GHG emissions continue to rise year on year. Given the complexity of decarbonizing petrochemical production, complete, accurate and detailed GHG emissions quantification is crucial to identifying opportunities for interventions, assessing their implications and setting credible emissions targets. However, the difficulty of monitoring the operations and emissions of the global petrochemical sector means this cannot be done precisely, and so quantifying the uncertainty of emission estimates is an essential component of a complete and transparent emission inventory. Existing environmental assessments use emission intensity factors (EFThis study quantifies uncertainties in greenhouse gas (GHG) emissions from petrochemical production, highlighting the need for precise data collection to improve emissions reporting. By analyzing cradle-to-gate emissions of 81 chemicals across 37,379 facilities, the research identifies key sources of uncertainty, including process specificity, feedstock emissions, and indirect energy use. The results show a 34% uncertainty in global GHG emissions of 1.9 ± 0.6 Gt CO₂-e for 2020, with uncertainties ranging from 15–40% across most petrochemicals. The largest uncertainties stem from the inability to assign specific production processes to facilities due to data limitations. Uncertain data on feedstock production and off-site energy generation contribute significantly, while on-site fuel combustion and chemical reactions have smaller roles. Allocation method choices for co-products are generally insignificant. Prioritizing facility-level process specification in data collection for just 20% of facilities could reduce global uncertainty by 80%. This underscores the necessity of quantifying uncertainty in petrochemical GHG emissions globally and outlines priorities for improved reporting. The dataset generated offers independent emissions factor estimates based on facility-specific information for 81 chemicals, supporting future analyses. The petrochemical industry produces nearly 1 billion tonnes of products annually, contributing to approximately 7% of global GDP. Products include 420 ± 40 Mt of plastics and 190 ± 20 Mt of fertilizers. Petrochemical production is energy-intensive, requiring 30% of final industrial energy use, including 14% of global oil demand and 9% of global natural gas demand. Therefore, petrochemical production is a major cause of GHG emissions, with the International Energy Agency (IEA) estimating annual GHGs emitted during petrochemical production, excluding external energy generation, at 1.30 Gt CO₂-e in 2020. This is equivalent to approximately 14% of global industrial GHG emissions and 2.5% of all anthropogenic GHG emissions. Summary environmental assessments provide industry-wide figures describing the scale of action required to reduce GHG emissions in line with climate change mitigation goals. This sector substantially influences global GHG emissions and poses substantial challenges in decarbonization efforts. These have motivated the industry to consider decarbonization as a priority, yet GHG emissions continue to rise year on year. Given the complexity of decarbonizing petrochemical production, complete, accurate and detailed GHG emissions quantification is crucial to identifying opportunities for interventions, assessing their implications and setting credible emissions targets. However, the difficulty of monitoring the operations and emissions of the global petrochemical sector means this cannot be done precisely, and so quantifying the uncertainty of emission estimates is an essential component of a complete and transparent emission inventory. Existing environmental assessments use emission intensity factors (EF