Sustainability Raw Data Disclosed & Modelled

ESG metrics for up to 44 impact KPIs for 50 leading companies, with flags for whether the data is from company disclosures or GIST Impact models. The full data set is available for over 13,000 companies worldwide. Description Raw data is crawled from publicly available company disclosures using our cognitive search engine. The data undergoes validation by our team of expert analysts to identify, verify and document outliers. Following reprocessing and data appending, the data undergoes algorithmic assurance before final approval by team leads specialising in each area of impact. The combination of human and machine quality control delivers a high level of confidence in the accuracy of the data. Disclosure is incomplete even for GHG emissions, which is the most standardised non-financial disclosure. The lack of data is significantly higher in emerging markets compared with developed and certain sectors particularly lacking disclosure. Water usage and waste generation data are much sparser than GHG emissions. Benchmark-based models are commonly used in the industry. The benchmarking approach uses the average emissions of the companies belonging to a particular business activity to predict the emissions of companies with no disclosures, by extrapolating the emissions based on revenues. Estimations are linearly proportional to the revenue generated by the company. This approach ignores that most large business have revenues from multiple sectors and that location is particularly important when assessing impact. Statistically, each estimated value has the same standard deviation, which leads to very limited co-relation between estimated and actual values. GIST Impact’s machine learning approach to modelled data takes into consideration unique financial and non-financial data points for each company. A unique list of companies is identified to create the nearest neighbour group and then an average value is generated for extrapolation. Six different unique operational parameters are taken into consideration to identify the peer group. The model accounts for multiple business activities and the peer group accurately captures the variability from operations, location, and performance, thereby reducing the deviation between actual emissions and estimates. Customers have the option of access to analysts for questions and clarifications of the data. Data Meta-data Fields: • ISIN (licence required) • ISO Code • Company Name • Reporting year Impact Data: 1. Scope 1 emissions 2. Scope 2 emissions – location based 3. Scope 2 emissions – market based 4. Total Scope 2 emissions 5. Total GHG emissions 6. Total energy consumption 7. Coal 8. Diesel 9. Motor Gasoline 10. Natural Gas 11. Particulate matter 12. Oxides of Nitrogen 13. Oxides of Sulphur 14. Air Cadmium 15. Air Mercury 16. Total water withdrawal 17. Total freshwater withdrawal 18. Total water consumption 19. Total water discharge, wastewater generation 20. Recycled water, reuse water, treated water 21. Chemical oxygen demand 22. Total non-hazardous waste generated 23. Total hazardous waste generated 24. Total waste generated 25. Waste incinerated 26. Waste composted 27. Waste to landfill 28. Disposed – non-hazardous waste 29. Disposed – hazardous waste 30. Disposed – total waste 31. Recovered – non-hazardous waste 32. Recovered – hazardous waste 33. Total waste recovered / recycled 34. Fly ash 35. Construction debris 36. Overburden in mining 37. Number of employees 38. Number of female employees 39. Number of male employees 40. Percentage of female employees 41. Average age 42. Annual revenue 43. Start date of accounting period 44. End date of accounting period Flags are provided to show whether impact data is from disclosed sources or modelled.

搜集汇总

背景与挑战

背景概述

该数据集提供全球超过13,000家公司的ESG指标，涵盖44个关键影响指标，并标注数据来源为公司披露或GIST Impact模型。它通过结合人工验证与机器学习方法进行质量控制，以应对非财务披露不完整的问题，特别是在新兴市场和特定行业。