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

金融机构正面临着越来越大的监管压力，以确保气候风险是投资决策、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条例（SFDR），以及对UCITS/AIFMD/MiFID-II和IFD/IFR的修订。来自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），使其管理的130万亿美元资产达到净零。TCFD将气候风险定义为“气候变化的物理风险与可能造成财务和声誉损害的净零排放过渡风险的组合”。根据TCFD，资产管理公司需要报告治理、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披露。预计其他国家也会效仿。超过99%的资产管理公司的排放是第三类（来自他们的投资组合）。这些数据目前无法完全核实，因此管理人员依赖于不完整的公司报告和不准确的第三方提供商（例如MSCI、Sustainalytics）。这些高且量化不准确的排放量使它们面临未来碳价格监管的“转型风险”，这些监管可能会产生未知的责任。如果资产管理公司能够准确地衡量投资组合公司的排放量，就可以利用碳影子定价计算资产负债表负债，从而降低转型风险。尤其不准确的是，衡量一家公司在供应链（如食品、农业）中土地使用对土壤碳的影响。土地利用占全球二氧化碳排放量的23%，但土地也可以成为一个强大的碳汇（从大气中去除二氧化碳），从而减少公司层面的净排放量。土壤可储存约2500亿吨碳，即80%的陆地碳[8]，人类土地利用过程可显著增加或减少固存。尽管如此，如果没有不可扩展且成本高昂的人工采样，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利用世界首创的多传感器地面真值技术，利用应用于地球观测数据的机器学习技术，准确推断土壤的碳含量，从而解决了这一挑战。这将使金融业能够通过独立量化整个供应链中与土地使用相关的排放，来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.