AMPLIFY VOL. 36, NO. 10
The circular economy (CE) aims to minimize waste, maximize the use of resources, and promote sustainability. It is designed as an alternative to the traditional economy, which follows a “take-make-dispose” pattern in which resources are extracted and products are manufactured, used, and then discarded as waste.1,2 The CE goal is to reduce the environmental impact of economic activities, including resource depletion and pollution, while creating economic value and promoting social well-being.
Investing in the circular economy involves financial support of businesses and projects that embrace sustainable practices and aim to reduce waste while maximizing resource efficiency. Unfortunately, a lack of data availability and poor data quality make it difficult for investors to: (1) choose where to invest and (2) evaluate the performance of their investments. Reliable, comprehensive data on resource use, waste generation, and CE practices is limited and is often inconsistently reported by companies, making it challenging to accurately quantify investment effects.
Traditional data sources (e.g., waste/recycling data and energy-consumption data) are frequently criticized for biases, incompleteness, and inconsistency because they tend to be self-reported and lack standardization.3,4 Integrating multiple data sources and adopting a systemic analytics approach can provide a more comprehensive understanding of the circular economy and sustainability performance.
This article identifies modernized data sources that enable big data analytics to help investors better evaluate their CE investments.
IoT & Sensor Data
The Internet of Things (IoT) and sensor data play a pivotal role in the transition from linear economies (make, use, dispose) to circular models. IoT connects everyday objects and devices to the Internet, allowing them to collect and exchange data without human intervention. IoT and sensor data are important in the modern CE5 because they enable automated, efficient, comprehensive data processing and evaluation, which allow businesses to optimize their usage, leading to reduced waste and lower costs.
However, it’s important for a business to identify the problem it aims to solve using IoT and sensor data. For example, a waste management company might want to use that data to track its waste-collection operations and maximize its efficiency. After selecting the appropriate sensors and choosing a data storage location (on a local server or in the cloud), the system would provide real-time data on resource utilization, equipment performance, and environmental conditions, allowing for immediate response to inefficiencies or issues.
The waste management company might install trash bin sensors throughout its system to monitor waste-collection levels in real time to save money on fuel costs, maintenance, and labor by reducing unnecessary trips. Over time, the collected data could be analyzed to identify waste-generation trends and patterns. This information could inform future waste-reduction initiatives, increasing ROI. Although the up-front costs for such a system would be high, there’s an excellent chance the company would save money in the long run.
In summary, IoT and sensor data equip internal business investors (e.g., corporate managers) with real-time insights into their performance and help outside investors make more efficient decisions.
Blockchain & Transparency Data
Blockchain can play a significant role in advancing CE practices by enhancing traceability, transparency, and trust in supply chains and product lifecycles. In a blockchain-based supply chain, when a product is made, a unique digital identity with an embedded RFID (radio-frequency identification) or NFC (near-field communication) chip is created for each product. As the product moves through the supply chain, each transaction or handoff is recorded, creating an unchangeable history.
In this way, every product or material in the circular economy can be tracked from its origin to end of life, ensuring transparency and verifying the authenticity and sustainability of the product.6 Storing lifecycle data on a blockchain results in an immutable record of the entire supply chain, from raw material extraction to production, distribution, and disposal. This allows investors to trace the journey of products and materials, ensuring they meet sustainability and circularity standards.
Using blockchain data to evaluate the performance of CE initiatives involves leveraging various technologies to gather, track, and analyze relevant data that provides insights into sustainability and circularity.7 However, deploying a blockchain network or platform that supports a decentralized and transparent system that tracks products or goods from their origin to the end consumers can be a complex task. For example, network nodes need to be placed strategically in various locations within a supply chain.
Blockchain data can be used with IoT devices to provide effective data sources.8-10 For example, a waste management company could install IoT sensors to track waste and store data on the blockchain, recording and verifying recycling processes to ensure waste materials are properly handled and disposed of or recycled in an eco-friendly manner. Similarly, a manufacturer could use IoT sensor devices to track energy consumption and carbon emissions during the production process and store data on the blockchain to provide transparent and verifiable reporting.
To ensure real-time tracking and reporting, IoT sensors should be integrated across a supply chain to enable automated data input. For example, for perishable fruits planted on a farm and transported to supermarkets, farmers could install temperature sensors and GPS tracking devices in the fruit containers to monitor the freshness of the product across the supply chain. By leveraging blockchain data in this manner, organizations can assess the performance of their CE initiatives to drive accountability, transparency, and innovation in their sustainability efforts.
In summary, blockchain provides an unchangeable, transparent record of products and resources in the circular economy that can strengthen trust and facilitate real-time sustainability assessments for internal business investors.
News & Social Media Sentiment Data
Modern natural language processing (NLP) uses algorithms to analyze large amounts of natural language data.11,12 Sentiment analysis is a subfield of NLP that aims to discern the mood or sentiment behind text.
NLP can help users analyze news and social media sentiment to gain insights into public perception, potential risks, and opportunities related to CE investments.13,14 For example, positive sentiment trends can highlight investment opportunities in companies or sectors that are well received by the public for their CE efforts. Highly positive sentiment can indicate lower reputational risk; negative sentiment may signal reputational or regulatory risks.
To use this data source, investors must first identify and subscribe to reputable news outlets and social media platforms that frequently discuss CE topics. Platforms like X (formerly Twitter), Facebook, Bloomberg, and Reddit provide APIs so users can scrape the Web to collect relevant data points from news articles, blogs, and other online publications.
Many times, freshly scraped sentiment data contains irrelevant information due to language nuances. In fact, data cleaning and text processing of text-based sentiment data can be complex. Some investors purchase structured data from providers like Brandwatch to save time on data cleaning and standardizing.
The next step is determining how to implement the sentiment analysis. For this stage, investors need NLP tools that can process and analyze large volumes of news articles, blogs, social media posts, and comments. Python code libraries NLTK (Natural Language Toolkit) and TextBlob are often used for sentiment data analysis, and deep learning frameworks like TensorFlow and PyTorch are popular tools for building neural network–based sentiment models. Platforms like Hugging Face Transformers provide pretrained models that can help users more easily perform sentiment analysis.
It’s important for investors to understand that sentiment analysis results must be carefully interpreted in the context of the domain or industry they are analyzing. For example, the word “degradation” is often interpreted as something that has deteriorated or become worse. However, in biodegradable products or materials, degradation is a desired feature, indicating the material can break down naturally without causing long-term environmental harm.
In summary, news and social media sentiment data provide both internal and external CE investors with clear views of public opinions and the latest sustainability trends in evaluating the sustainability performance of a business.
AI-Driven Solution: Truvalue Labs
Truvalue Labs is one of the first companies to use artificial intelligence (AI) and NLP to provide investors with a more robust perspective on environmental, social, and governance (ESG) performance. The company’s platform uses AI to aggregate and process data from news articles, social media, regulatory findings, and nongovernmental organization reports to give investors near-real-time insights into a company’s ESG activities.
Company-reported data can be framed in a way that highlights the positive and downplays the negative. Truvalue Labs’s emphasis on external data sources offers investors a less biased view of a company’s engagement with the CE principle.
In addition to company-specific data, Truvalue Labs analyzes ESG trends by sector and region. Users can tailor their results on specific aspects of the circular economy, including waste reduction, product longevity, or sustainable sourcing. This gives investors a broader understanding of specific sectors or regions, helps them analyze multiple companies or sectors simultaneously, and lets them tailor their analysis to focus on the ESG issues, sectors, or regions that align with their priorities.
In summary, Truvalue Labs uses AI to analyze large amounts of ESG data in real time, offering both internal and external investors timely insights into companies’ sustainability practices, risks, and opportunities and facilitating informed investment decisions.
Conclusion
Advanced tools and data-driven methodologies not only enhance transparency and precision, they also capture real-time dynamics, helping both internal and external investors make more informed decisions. In a world transitioning from linear to circular models, leveraging technology is key to measuring sustainability impacts and ensuring responsible resource utilization.
References
1 Stahel, Walter R. “The Circular Economy.” Nature, Vol. 531, March 2016.
2 Geissdoerfer, Martin, et al. “The Circular Economy — A New Sustainability Paradigm?” Journal of Cleaner Production, Vol. 143, February 2017.
3 Chatterji, Aaron K., et al. “Do Ratings of Firms Converge? Implications for Managers, Investors and Strategy Researchers.” Strategic Management Journal, Vol. 37, No. 8, June 2015.
4 Berg, Florian, Julian F. Kölbel, and Roberto Rigobon. “Aggregate Confusion: The Divergence of ESG Ratings.” Review of Finance, Vol. 26, No. 6, November 2022.
5 Gubbi, Jayavardhana, et al. “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions.” Future Generation Computer Systems, Vol. 29, No. 7, September 2013.
6 Lim, Ming K., Witold Bahr, and Stephen C.H. Leung. “RFID in the Warehouse: A Literature Analysis (1995–2010) of Its Applications, Benefits, Challenges and Future Trends.” International Journal of Production Economics, Vol. 145, No. 1, September 2013.
7 Saberi, Sara, et al. “Blockchain Technology and Its Relationships to Sustainable Supply Chain Management.” International Journal of Production Research, Vol. 57, No. 7, 2019.
8 Christidis, Konstantinos, and Michael Devetsikiotis. “Blockchains and Smart Contracts for the Internet of Things.” IEEE Access, Vol. 4, May 2016.
9 Panarello, Alfonso, et al. “Blockchain and IoT Integration: A Systematic Survey.” Sensors, Vol. 18, No. 8, August 2018.
10 Wang, Xu, et al. “Survey on Blockchain for Internet of Things.” Computer Communications, Vol. 136, February 2019.
11 Coleman, John. Introducing Speech and Language Processing. Cambridge University Press, 2005.
12 Mishne, Gilad, and Maarten de Rijke. “Language Model Mixtures for Contextual Ad Placement in Personal Blogs.” In Advances in Natural Language Processing, Proceedings of the 5th International Conference (FinTAL). Springer, 2006.
13 Bollen, Johan, Huina Mao, and Xiaojun Zeng. “Twitter Mood Predicts the Stock Market.” Journal of Computational Science, Vol. 2, No. 1, March 2011.
14 Tetlock, Paul C. “Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” The Journal of Finance, Vol. 62, No. 3, May 2007.
