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Construction supply chains are complex and fragmented resulting in sustainability reports relying on estimated carbon data
More than 70% of supply chain data is hidden & complex
Using generic carbon calculators missing sustainability targets
Investors & regulators demanding decarbonisation
Risk to future investment, competitiveness and compliance
Manual processing of supply chain data
Increasing costs and inaccurate reporting eroding confidence
Manage and measure actual material carbon emissions
What we Offer
Discover our sustainability solutions
Analysing supply chains
How Its Work
Customized Energy Assessments Tailored to Your Needs.
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Customized Energy Assessments
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Collaborative Design Workshops
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Seamless Integration of Green Technologies
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Transparent Project Management
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Why Us
Discover the numerous advantages of our carbon chain
Sustainable solutions
Set track and manage emissions in real-time
Compliance ready
Be ready for EU CSRD and ISSB, de-risk your business now
Recommendations engine
Gain supply chain insights in minutes and optimise material costs vs carbon emissions
See your supply chain carbon emissions
News and Updates
How AI and machine learning can help decarbonise supply chains
Accurate carbon management is paramount to reducing supply chain emissions. However, numerous challenges impede the reliability of emissions data, hindering progress towards emission reduction goals. One significant challenge lies in the measurement of supply chain emissions, which constitute a substantial portion of total emissions but are often underestimated or omitted in reporting.
Additionally, the quality of available emissions data is frequently compromised by reliance on inaccurate methods such as spend-based data. Moreover, the reliance on estimates rather than real data further exacerbates the issue, leading to unreliable and misrepresented reporting.
By leveraging AI and machine learning, it becomes possible to enhance the accuracy and reliability of emissions data. AI can facilitate the comprehensive measurement of emissions by analysing vast datasets and identifying previously overlooked sources of emissions.
Furthermore, AI-driven models can improve the quality of emissions data by distinguishing between accurate primary data and unreliable estimates.
AI-powered solutions offer deeper insights into unstructured data, enabling companies to set more realistic emission reduction targets and develop effective strategies for mitigating carbon emissions.
