Evolving Telecoms scope 3 decarbonisation: an open-access emissions datasource powered by Vision Machine Learning

**问题：**英国电信部门每年340万吨二氧化碳排放量中的大部分属于“范围3”。解决这些间接排放问题相当于每年从英国道路上减少170万辆汽车。为了脱碳，电信运营商迫切需要可靠的供应链排放数据——但目前，存在的直接数据远比容易获取的数据多。大量重要的供应商数据以非结构化的可视化格式隐藏在在线公司PDF报告中，现有昂贵的人工智能方法无法准确地优先提取这些数据。因此，对于运营商和供应商来说，实现零排放的努力并没有得到优化或奖励，因为核算仍然基于行业平均排放系数：这是一个巨大的拖累因素。**创新：**用开创性的视觉机器学习转换电信范围3数据：一个新的开放获取平台。通过开创性的基于视觉的机器学习技术，Mycelium从企业报告中定位和提取难以访问的“非结构化”排放数据，并促进无缝访问，以减少对行业估计的依赖，提高准确性，并帮助道德参与者参与竞争。菌丝体目前正处于与谢菲尔德大学合作开发的第二阶段，是第一个训练基于视觉的机器学习技术的平台，专门用于处理非结构化排放数据。它还将率先培训一个模块，专门识别电信范围3中与脱碳工作相关的数据，并通过开放访问平台和订阅应用程序编程接口（API）为大量用户提供这些数据。Mycelium是一个由专有机器学习创新技术支持的专业模块，在提取数据方面，它有可能超过ChatGPT等成本效益较低的服务的准确性，电信运营商需要认真对待脱碳范围3\。**使数据访问民主化，加速实现零净值**与专注于数据访问货币化的平台不同，菌丝体优先考虑开放访问和用户对数据配置文件的控制。那些以前数据被“掩埋”的公司会被激励去改进他们的上市，添加他们自己的详细数据，并相应地竞争；承诺提高排放报告的质量和透明度，并要求现有数据公司改进其服务。**优化产品**我们的机器学习模块已经构建，测试正在进行中。接下来，我们将训练模块识别电信范围3中的排放数据，并提取可以支持这一棘手领域脱碳的非结构化数据。这些数据将通过我们即将推出的开放访问平台提供给任何需要它的人，我们将开发一个API，通过订阅为大量企业用户提供这些数据。

**The problem:**The majority of the UK telecoms sector's 3.4 million tons of CO2e annual emissions fall in 'Scope 3'. Addressing these indirect emissions would be comparable to removing 1.7 million cars from UK roads annually.To decarbonise, telecoms operators urgently need reliable data on supply chain emissions - but currently, far more direct data exists than can be readily accessed. Vast amounts of important supplier data are buried in unstructured visual format in online corporate PDF reports that existing, expensive AI methods don't prioritise extracting accurately. So for operators _and_ suppliers, work towards net zero isn't optimised _or_ rewarded because accounting is still based on industry average emissions factors: a huge drag factor.**The innovation:**Transforming Telecoms Scope-3 data with pioneering vision Machine Learning: a new open-access platform.Through pioneering vision-based Machine Learning techniques, Mycelium locates and extracts hard-to-access 'unstructured' emissions data from corporate reports and facilitates seamless access, to reduce reliance on industry estimates, transform accuracy and help ethical actors compete.Currently in its second phase of development in partnership with the University of Sheffield, Mycelium is the first platform to train vision-based machine learning techniques specifically to process unstructured emissions data. It will also be first to train a module specifically to recognize data relevant to decarbonisation efforts in Telecoms scope 3, and make it available via an open-access platform, and subscription Application Programming Interface (API) for large-volume users. A specialist module supported by proprietary ML innovations, Mycelium has the potential to surpass the accuracy of less cost-effective services like ChatGPT in extracting the data Telecoms operators need to get serious about decarbonising scope 3\.**Democratising data-access to accelerate Net Zero**Unlike platforms focused on monetising data access, Mycelium prioritises open access and user control of data profiles. Companies whose data was previously 'buried' are incentivised to improve their listing, add their own detailed data and compete accordingly; promising higher quality and transparency in emissions reporting, and challenging incumbent data companies to evolve their services.**Refining the offering**Our Machine Learning module is built, and testing is ongoing. Next we'll train the module to recognise emissions data in Telecoms scope 3 and extract the kind of unstructured data that can support decarbonisation in this tricky area. This data will be available to anyone who needs it via our forthcoming open-access platform, and we'll develop an API to make it available by subscription for large-volume corporate users.