Enhancing Agri-Food Transparent Sustainability - EATS

英国有一个具有法律约束力的2050年温室气体（GHG）净零排放目标（苏格兰，2045年），食品和饮料行业在帮助实现这一目标方面发挥着至关重要的作用。在实现这一目标的同时，还必须改善营养，保护生态系统，减少土壤、水和空气质量的风险。要实现这些雄心勃勃的目标，需要在整个农业食品供应链上采取一系列措施——不仅是初级生产者，还包括加工商、零售商和最终的消费者。在过去的几十年里，收集、监测、披露和传播信息（在“透明度”的概念下广泛分类）的过程取得了快速进展，有助于开发全新的供应链环境监测和治理模式。不幸的是，现有办法在收集和传播数据方面往往受到限制；供应链过度简化；影响信息包容/排除决策的权力动力学；以及关于如何使用信息、由谁使用以及产生何种效果的潜在不良后果。鉴于这些问题，我们需要考虑如何最好地获取有关供应链的信息，以便充分详细地记录现有的可持续性实践；这不仅对支持所有利益相关者的监测和报告需求是必要的，而且还有助于促进更多的环保行为，甚至重新配置供应链。我们的愿景是建立一个可操作的信息生态系统，其目的是通过我们称之为：SEE-SHARE-ACT的三大支柱实现透明的可持续性。其中第一个包括传感器和碳报告工具在获取农业食品过程数据中的作用（SEE）；第二个是一个可信的数字平台，能够管理可持续发展数据并在供应链参与者之间报告（SHARE）；第三是使用数据分析和机器学习来支持决策和行动（ACT）。但是，透明可持续性的可信基础设施是什么样子的？它将如何被更广泛的环境、社会和经济背景所框定（并在其中运作）？此外，这样一个框架如何超越简单地记录供应链的要素（参与者、过程、投入、产出），以实现监测、亲环境决策和行动的整体方法？我们已经组建了一个跨学科的学术团队和用户组织跨越畜牧业，软果和酿造行业来调查透明的可持续性。我们将共同探讨以下问题：哪些数据集、指标和决策过程与参与供应链的不同参与者有关，以实现可持续的食品未来（在DE中）？我们如何制定适当的词汇来描述可持续发展实践及其背景和基本原理，并促进数据的获取和整合？我们能否实现一个基于原产地的供应链可持续发展解决方案，跨越一系列技术和组织边界，是值得信赖的，能够促进亲环境的决策和行动？我们如何利用可持续性数据资产和机器学习/人工智能技术，为实现净零排放的决策提供信息，从而在实践和行为上产生明显的变化？这些问题的答案（以及许多其他肯定会出现的问题）将引导我们开发将与项目合作伙伴一起评估的原型解决方案。我们的目标是创造一种方法，通过这种方法，农民和其他食品和饮料供应链利益相关者可以在产品生命周期历史的可靠数据基础上创造更可持续的经济，以增强食品供应链的环境和产品安全（因此更具弹性）。

The UK has a legally binding target of 'net zero' greenhouse gas (GHG) emissions for 2050 (Scotland, 2045) and the Food and Drink sector has a vitally important role to play in helping to achieve this. This must be done while also improving nutrition, protection of ecosystems, reduced risks to soil, water and air quality. Delivery against these ambitious targets will require a range of measures to be adopted across the agri-food supply chain - not just primary producers but also processors, retailers and ultimately consumers. Over the last few decades rapid advances in processes to collect, monitor, disclose, and disseminate information (broadly classified under the concept of 'transparency') have contributed towards the development of entirely new modes of environmental monitoring and governance for supply chains. Unfortunately, existing approaches often suffer from limitations in terms of collection and dissemination of data; over-simplification of supply chains; power dynamics influencing information inclusion/exclusion decisions; and potentially perverse outcomes regarding how the information is used, by whom and to what effect. Given these issues, we need to consider how best to capture information about supply chains in order to document existing sustainability practices in sufficient detail; this is necessary to not only support monitoring and reporting needs of all stakeholders, but also to promote additional pro-environmental behaviours and even re-configuration of the supply chain. Our vision is built around an actionable information ecosystem whose purpose is to deliver transparent sustainability - realised via three pillars that we refer to as: SEE-SHARE-ACT. The first of these encompasses the role of sensors and carbon reporting tools in capturing data about agri-food processes (SEE); the second is a trusted digital platform able to manage sustainability data and report it across supply chain actors(SHARE); the third is the use of data-analytics and machine learning to support decision-making and action (ACT). But what would a trusted infrastructure for transparent sustainability look like, and how would it be framed by (and operate within) its wider environmental, social and economic context? Also - how would such a framework go beyond simply documenting the elements of a supply chain (actors, processes, inputs, outputs) to enable a holistic approach to monitoring, pro-environmental decision-making and action? We have assembled an interdisciplinary team of academics and user organisations spanning the livestock, soft-fruit and brewing sectors to investigate transparent sustainability. Together we will explore the following questions:What datasets, indicators and decision-making processes are relevant to the different actors participating in supply chains to realize sustainable food futures (in the DE)? How do we formulate appropriate vocabularies with which to characterise sustainability practices, their context and rationale, and facilitate data capture and integration? Can we realize a provenance-based sustainability solution for supply chains, operating across a range of technologies and organisational boundaries, that is trusted and able to facilitate pro-environmental decision-making and action? How do we exploit sustainability data assets and ML/AI technologies to inform decision making towards net-zero, resulting in demonstrable changes to practice and behaviour? Answers to these (and the many other questions that will certainly emerge) will lead us to develop prototype solutions that will be evaluated with project partners. Our ambition is to create a means by which farmers and other food and drink supply chain stakeholders can create a more sustainable economy built upon trusted data regarding the lifecycle history of products for enhanced environmental and product safety in (therefore more resilient) food supply chains.