Sustainability Intelligence

Introduction

Over 900 Earth-observing satellites currently peer down at us from space. Simultaneously, an emerging network of ground-based sensor technologies tracks the movement of water, the sounds of ecosystems, and the chemicals that permeate Earth’s soils and the atmosphere above it.. Thousands of Internet of Things (IoT) devices trace millions of products around the globe through increasingly complex supply chains. Although there is more data and powerful systems to process it, reporting and monitoring risks and impacts through value chains could be a daunting task, which relies heavily on second-party audits and siloed databases. 

From big companies´ standpoint, supply chains contain the most significant social and environmental risks, which may have a great impact on companies´ reputations and business continuity. Some of these risks can arise from poor labor practices, health and safety issues, unfair wages, and modern slavery, in the case of social risks. On the other hand, environmental risks may be associated with improper handling of toxic waste or greenhouse gas emissions, among others .

The aforementioned has major implications when it comes to modeling the complexity and diversity of actors in a global supply chain, as well as when companies should navigate the heterogeneous regulatory framework in the countries where their value chains operate. On the other hand, the difficulties in the implementation of concepts and tools related to the Circular Economy or Environmental, Social and Corporate Governance (ESG) standards, suggest that tools are required that can facilitate the deployment of these frameworks, creating incentives to increase the transparency of all actors and to give access to historically marginalized communities into international trade flows.

Sustainable Intelligence is not a unique tool, instead, it is a set of methodologies, processes, architectures, and technologies by which organizations can transform raw data into meaningful and useful information for decision making. Likewise, Sustainable Intelligence can be understood as the process of gathering, cleaning, and analysis of data regarding organizations´ social, environmental, and economic issues in order to provide insights on risks, impacts, and opportunities. This supports companies’ strategies, including sustainability disclosure according to monitoring and reporting frameworks .

Applications

Sustainable Intelligence systems are being designed to manage sustainability issues (i.e., Environmental, Social, and Corporate Governance issues), thus creating organizational capabilities to transform ESG-related risks into value and opportunities . Also, these systems support supply chains’ performance improvement , as well as, monitor for emerging and disruptive global trends that impact sustainability .  

In addition, Sustainability Intelligence systems screen and map supply chains’ ESG-related risks at different levels. To do that, these systems integrate measures, methodologies, and guidelines from the most widespread frameworks on sustainability measurement and reporting such as :

• United Nations Global Compact – UNGC
• CDP
• Global Reporting Initiative (GRI).
• Corporate Carbon Footprint calculations.
• Product Carbon Footprint and Life Cycle Analysis (LCA).
• Carbon emissions from transport and logistics.
• Sustainability accounting standards (The Sustainability Accounting Standards Board - SASB).
• Task Force on Climate-Related Financial Disclosures (TCFD).
• The Sustainable Development Goals (SDG).
• Global Real Estate Sustainability Benchmark (GRESB).
• MSCI ESG Rating.
• Sustainalytics' ESG Risk Ratings.
• Principles for Responsible Investment (PRI).
• The Dow Jones Sustainability Index (DJSI).
• ISS ESG rating
• International Integrated Reporting Council (IIRC)

The benefits of Sustainability Intelligence applications are

• Identify potential social and environmental hotspots.
• Collect data easily through the supply chain.
• Share and communicate supply chain KPIs within the company and its stakeholders.
• Compare sustainability performance within the value chain and across industries.
• Track continuous improvement.
• Facilitate compliance assessments and audits.
• Improve communications and trust among stakeholders.
• Support supply chain management decisions.
• Simplify sustainability or ESG reporting.

Emerging Developments

Sustainability Intelligence evolves to increasingly engage all levels of value chains, especially marginal partners (e.g., small producers and wage workers) potentially creating a data ecosystem that offers multi-layered benefits to diverse stakeholders (financial institutions target subjects of credit, input suppliers can custom blend from QR codes holding soil analysis, etc.) . However, there are several roadblocks such the lack of interoperability of information systems, lack of trust and incentives to share data, and the transactional and operational cost of collecting, processing, storage, and monitoring all the data produced through value chains.

Blockchain

Blockchain has the potential to solve problems that involve a significant number of actors and interests, creating incentives to work collaboratively and thus improve transparency. For example, Blockchain can be used to fully monitor the life cycle of a product, including its final disposal or the impact on different ecosystems. Such is the case of WasteLedge, which combines Blockchain with artificial intelligence to track waste such as batteries, packaging, and parts of electronic equipment through supply chains. Likewise, this platform can monitor industrial, domestic, and construction waste .

Within the framework of transparency, Blockchain allows improving the measurement of the performance of each one of the activities of the supply chain and the verification of the quality of the products (for example, of the cold chain) during their transport and transformation . In terms of verification, it is also possible to record all actions (transactions) during the life cycle of a product, including who is developing these actions, when, and where . This is vital to identify possible negative externalities that are being generated in the chain, and in this way facilitate decision-making concerning the carbon or water footprint, and in the future, to implement a more holistic approach such as Circular Economy.

One of the most promising features of blockchain are Smart Contracts. These are self-executing and self-enforcing code that can establish a set of conditions that once are met automatically trigger one or more transactions within the Blockchain . This allows executing contracts and agreements without a third party to enforce them, making transactions faster, cost-effective, irreversible, and traceable . Some of the solutions combine Blockchain with Artificial Intelligence (neural networks and deep learning) to make the information processing of the forest more efficient. For example, the D-Wave artificial intelligence platform can recognize three different species of trees in aerial photographs with 90% accuracy . This, added to the installation of RFID antennas in the trees, would allow control of the forest harvesting process.

Besides the use of Internet of Thing devices, there are chemical markers that can be used to identify, trace, and verify characteristics of raw materials and final products. The Crypto Anchor Verifier project from IBM Research Lab uses a lens attachment for a standard smartphone and app that run artificial intelligence (AI) technology to perform light spectral analysis against a physical asset. This allows users to capture microscopic properties, viscosity, and other identifiers to produce a unique digital identifier for physical goods. For example, creating “a digital cryptographic fingerprint of an aspirin on a blockchain so that the pill can be verified for authenticity as it progresses across the supply chain” . This type of marker is being used to trace products from regions or suppliers, whose data and processes are not rigorous or trustworthy. Some human rights coalitions have estimated that almost one in five cotton products are connected to the Xinjiang Uygur Autonomous Region (XUAR) in China. The Yarn-level Chokepoint program (YCP) uses isotope and microbiome tracing to determine the provenance of yarn in stages of the garment supply chain outside China .

To sum up, Blockchain can create more robust chain-of-custody systems that require fewer face-to-face audits, less use of physical documents, and the management of risks in the supply chain more efficiently and at a lower cost .

Related Concepts

Environmental Intelligence

Environmental Intelligence (EI) integrates environmental data and knowledge with Artificial Intelligence to provide timely and meaningful insights supporting decision-making and improving risk management . This integration involves the development of frameworks that require the integration of two distinct aspects of research: first, an "end-to-end integration of research across the linked and iterative steps of problem identification, environmental observing, understanding, prediction, and decision support"; and second, a holistic view of the decision-making contexts across the ecosystems' components or elements (e.g., the Arctic System)

University of Exeter has developed a Climate Impacts Mitigation, Adaption and Resilience (CLIMAR) framework, which integrate multiple sources of data to identify and quantify the risks of climate change on populations, infrastructure, and industries. The resulting match between climate-related hazards with data for individual assets (e.g., roads, facilities, communities) reveals to what extent those assets are exposed to climate change risk and what are their potential vulnerabilities. This is key to enhance the capabilities of policymakers and decision-takers to design and deploy solutions to tackle the effects of climate change.

References

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