SOIL-EO: Mitigating transition risk in supply chains with below-ground biomass estimation using Machine Learning and Earth Observation data

金融机构正面临越来越大的监管压力，以确保气候风险成为投资决策、治理和风险管理的核心，最近的举措包括气候相关财务披露工作组（TCFD）、欧盟可持续财务披露法规（SFDR）以及UCITS/AIFMD/MiFID-II和IFD/IFR的修正案。45个国家的近500家金融机构加入了格拉斯哥净零金融联盟（GFANZ），将管理的130万亿美元资产调整为净零。TCFD将气候风险定义为"气候变化的物理风险和可能造成财务和声誉损失的净零过渡风险的组合“。根据TCFD，资产管理公司应报告治理、战略、风险和指标。虽然TCFD披露目前是自愿的，但到2022年，它们将成为英国大型资产管理公司的强制性要求。超过99%的资产管理公司的排放属于范围3（来自其投资组合）。这些目前无法完全核实，因此管理人员依赖不完整的公司报告和不准确的第三方提供商（例如MSCI，Sustainalytics）。这些高排放量和不准确的量化排放量使它们面临未来碳价格监管的“过渡风险”，这可能会产生未知的负债。如果资产管理公司能够准确衡量投资组合公司的排放量，就可以使用碳影子定价来计算资产负债表负债，从而降低过渡风险。特别不准确的是衡量公司在供应链中使用土地对土壤碳的影响（如粮食、农业）。土地使用占全球二氧化碳排放量的23%，但土地也可以是一个强大的碳汇（从大气中去除二氧化碳），从而减少公司层面的净排放量。土壤储存约2500 Gt，或陆地碳的80%[8]），人类土地使用过程可以显着增加或减少封存。尽管如此，如果没有不可扩展且成本高昂的手动采样，就没有准确的频繁监测方法，这意味着它实际上被排除在当前的报告之外。Sylvera通过利用世界上第一个多传感器地面实况技术来解决这一挑战，该技术使用应用于地球观测数据的机器学习技术来准确推断土壤的碳含量。这将释放金融行业的能力，通过独立量化整个供应链中与土地使用相关的排放，评估其投资组合的净零状态和负债。通过设定土地使用排放标准，SOIL-EO数据将成为行业基准，引发数据质量方面的竞争。

Financial institutions are coming under ever greater regulatory pressure to ensure climate risk is central to investment decisions, governance and risk management, with recent initiatives including the Task Force on CIimate-related Financial Disclosure (TCFD), the EU's Sustainable Finance Disclosure Regulation (SFDR), as well as amendments to UCITS/AIFMD/MiFID-II and IFD/IFR. Almost 500 financial institutions across 45 countries have joined the Glasgow Financial Alliance for Net Zero (GFANZ), aligning $130trillion assets under management with net zero.The TCFD defines climate risk as '_the combination of physical risk of climate change and the transition risks of moving to net zero which may pose financial and reputational damage'_. Under the TCFD, asset managers are expected to report on governance, strategy, risk and metrics. Whilst TCFD disclosures are currently voluntary, they will become mandatory for large UK asset managers in 2022\. Other countries are expected to follow suit.Over 99% of asset managers' emissions are Scope-3 (from their investment portfolio). These are currently impossible to fully verify so managers rely on incomplete corporate reporting and inaccurate third-party providers (e.g. MSCI, Sustainalytics). These high and inaccurately quantified emissions expose them to 'transition risk' from future carbon price regulations that may create unknown liabilities.If asset managers could accurately measure portfolio company emissions, a balance sheet liability could be calculated using carbon shadow pricing, mitigating transition risk.Particular inaccuracy relates to measuring a company's impact on soil carbon from land use in supply chains (e.g. food, agriculture). Land use represents 23% of global CO2 emissions, but land also can be a powerful carbon sink (removing CO2 from the atmosphere), thus reducing company-level net emissions.Soil stores c.2500Gt, or 80% of terrestrial carbon\[8\]), and human land use processes can significantly increase or reduce sequestration. Despite this, no accurate method of frequent monitoring exists without unscalable and cost prohibitive manual sampling, meaning it is effectively excluded from current reporting.Sylvera solve this challenge by leveraging world-first multi-sensor ground truth technique to accurately infer the carbon content of soil using machine learning techniques applied to Earth Observation data. This will unlock the ability of the finance industry to evaluate the net zero status and liabilities of their investment portfolio by independently quantifying the emissions associated with land use throughout supply chains.By setting the standard for land-use emissions, SOIL-EO data will become the industry benchmark, triggering a race to the top in terms of data quality.