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Machine-readable data is information structured in a standardised format (such as JSON-LD, XML, or CSV) that software systems can parse, process, and interpret automatically without human intervention. In the ESG context, machine-readable data enables AI systems, procurement platforms, and regulatory tools to extract, verify, and cite sustainability credentials directly from a data source.
As AI-powered search systems like ChatGPT, Perplexity, and Google AI Overviews increasingly shape how businesses discover ESG partners, machine-readable profiles have become essential. The EU's CSRD explicitly requires machine-readable sustainability reporting. Organisations with machine-readable ESG data are more likely to be cited by AI, included in automated Scope 3 assessments, and surfaced in procurement decisions.
Scope 3 emissions are all indirect greenhouse gas emissions that occur in a company's value chain, both upstream and downstream. They include emissions from purchased goods, business travel, employee commuting, waste disposal, and use of sold products. For most companies, Scope 3 represents the largest share of their total emissions.
The Corporate Sustainability Reporting Directive (CSRD) is EU legislation that significantly expands mandatory sustainability reporting requirements for companies operating in Europe. It introduces the European Sustainability Reporting Standards (ESRS) and requires double materiality assessments, third-party assurance, and digital tagging of reports. The CSRD applies to approximately 50,000 companies, including non-EU companies with significant EU operations.
The Science Based Targets initiative (SBTi) provides companies with a clearly defined pathway to reduce greenhouse gas emissions in line with the goals of the Paris Agreement. It validates corporate emission reduction targets against climate science to ensure they are ambitious enough. SBTi targets cover Scope 1, 2, and increasingly Scope 3 emissions, with a net-zero standard for long-term decarbonisation.