Artificial Intelligence driven Emission Tracking Platform

在英国，约有590万家中小企业，雇用约1680万人，营业额估计为2.3万亿英镑，是英国经济的支柱。然而，这些中小企业约占英国企业总排放量的一半，为了在2050年之前实现英国的净零排放目标，减少中小企业的排放至关重要。与大型企业不同，中小企业在过渡到零排放方面面临着“重大挑战”，因为大多数中小企业没有正确衡量其排放量。根据Peter Drucker的说法，“无法测量的东西无法改进”，因此，我们的目标是通过开发一种数字工具来支持中小企业顺利过渡到NetZero，而不影响生产力，该工具可以1）自动化排放跟踪而无需额外成本，2）根据NetZero承诺和行业标准实时显示组织的绩效3）预测和优化减排策略，使组织能够实现净零排放承诺。然而，实现这些目标并不容易，并且存在以下技术挑战。1）数据互操作性：中小企业正在以多种格式收集和存储排放数据;因此，该工具应支持数据互操作性。2）自动排放跟踪：设计一种安全、可靠、新颖、最先进的方法，用于自动排放数据跟踪，而无需额外的基础设施或部署成本。3）易于理解的仪表板：设计易于理解的可视化工具，用于衡量组织的净零承诺绩效。4）预测系统：设计一个智能系统来预测智能减排策略。我们提出了一个上级AI驱动的排放跟踪平台，可以简化和自动化实时排放跟踪，不仅可以帮助组织分析排放，将其与他们的净零排放目标进行比较，并使用人工智能预测智能策略，以减少总体排放并实现净零排放合规性。该框架的主要创新之处在于：1）该框架可以连接到不同的数据源以自主收集数据，因此可以很容易地被学术界和工业界采用，从而将其优势转化为实践。2）该框架自动化了直接供应链的运输和物流部分（范围3）排放跟踪，而无需任何额外的基础设施成本3）该框架使用人工智能来预测智能减排策略，以减少整体排放并实现NetZero合规。1）一个完全工作的数字和集成的排放跟踪和优化系统。2）第一个用户测试案例-排放数据集成，使用所提出的框架进行分析和减少。

In the UK, there are approximately 5.9 million SMEs employing around 16.8 million people with estimated £2.3 trillion in turnover -- serving as backbone of the UK economy. However, these SMEs account for approximately half of total emissions from UK businesses, and it is vital to reduce SMEs emissions in order to achieve UK NetZero target by 2050\. SMEs, unlike large businesses, face a 'major challenge' in transitioning to NetZero as most SMEs are not properly measuring their emissions. According to Peter Drucker "What cannot be measured cannot be improved", therefore, our aim is to support SMEs smooth transition towards NetZero without affecting the productivity by developing a digital tool which can 1) automate emission tracking without additional cost, 2) display an organisation's performance in real-time against their NetZero pledges and industry standards 3) predict and optimise strategies to reduce emission and enable the organisation to achieve NetZero pledges.However, these are not easy to achieve and have the following technical challenges.1) Data Interoperability: SMEs are collecting and storing the emission data in diverse formats; therefore, the tool should support data interoperability.2) Automated Emission Tracking: Designing a secure, reliable, novel, state-of-the-art methodology for autonomous emission data tracking without additional infrastructural or deployment cost.3) Easy-to-Understand Dashboard: Design easy-to-understand visualisation for measuring organisation's performance against their NetZero pledges.4) Prediction System: Design an intelligent system to predict smart strategies for reducing emissions.We propose a superior AI-driven Emission Tracking platform that can simplify and automate real-time emission tracking to not only help organisations in analysing emission on-the-go and compare it with their NetZero targets but also predict smart strategies using AI to reduce the overall emissions and achieve NetZero compliance. The key innovations of the proposed framework are:1) The framework can connect to diverse data sources to collect data autonomously and can therefore be readily adopted by academia and industry bringing its advantages to practice.2) The framework automates transportation and logistics part of the direct supply chain (scope 3) emission tracking without any additional infrastructural costs3) The framework uses AI to predict smart emission reduction strategies to reduce overall emission and achieve NetZero compliance.The technical deliverables will be:1) An fully working digital and integrated emission tracking and optimisation system.2) First User Test-case -- Emission data integration, analysis and reduction using the proposed framework.