Jumping into the world of sustainability can be intimidating. New concepts and buzzwords are far and between, though many industries lack standardization, resulting in a thousand different pathways. The addition of new technologies and products further complicate the situation, overwhelming even sustainability experts. Today, we’re simplifying how an important concept in sustainability—Life Cycle Assessments (LCAs)—can be enhanced and powered by the groundbreaking new technology of Artificial Intelligence (AI). AI-driven LCAs are both affordable and scalable, making them a powerful tool for achieving sustainability goals.
Raw Material Extraction: The process of sourcing and obtaining materials needed for the product.
Manufacturing and Processing: Converting raw materials into the finished product through various production methods.
Transportation: Involves delivering the final product to retailers or customers.
Consumer Usage: The stage where the product’s environmental impact is determined by how it’s used by consumers.
End of Life: The disposal phase, which determines how the product is managed after its use.
How do the Benefits of AI Address LCA Challenges?
While there are only five stages of an LCA, each one requires a comprehensive understanding of the product. This can lead to challenges, especially in the CPG industry with complicated supply chains. Luckily, emerging technologies like AI are streamlining the LCA process. The benefits of AI-powered LCAs are helping companies overcome these challenges, allowing them to leverage their findings to create more sustainable products:
Data Collection: Obtaining accurate and reliable data, or any data at all, is often the largest barrier to performing an LCA. Without the data, there’s nothing to analyze. Companies may not have access to data at every stage. They must communicate with suppliers to obtain this information, though they may not have it either. Companies that do obtain data may be missing data points that are required to fit the nuances of their product. The more detailed and accurate the data, the more accurate the LCA.
Leveraging modeling, AI can fill in data gaps, especially hard to find emissions factors like those that are location dependent. AI can model complex subcomponents that are necessary for accurate analysis, but may not be accessible by companies or suppliers. Furthermore, the use of AI can help ensure data is accurate and consistent for the product based on a number of factors such as product type, material, and location.
Expertise: Performing an LCA often requires a high level of knowledge and is often time consuming, which companies may not have the resources for. Ensuring that your LCA is accurate is important for obtaining a useful report to make informed decisions. Furthermore, outsourcing LCA experts can be expensive, time consuming, and shifting supply chains can quickly outdate your LCA. This can make it difficult and expensive to continuously update LCAs.
Leveraging AI powered LCA software reduces the need for highly skilled expertise. AI can help fill in data gaps and perform complex analysis for you, so companies can focus their resources elsewhere. For those that need assistance with complicated products, software companies provide the support and expertise needed to ensure your LCA is accurate.
Scalability: Performing an LCA for a single product can be challenging, and performing one for every product in your portfolio can be resource intensive and time consuming. A company with only a few products may be able to perform LCAs if they have the proper resources. This time consuming and resource intensive method is more or less out of the question for large portfolios.
The use of AI powered LCAs are streamlining the process, making it scalable. This enables small and large companies to perform LCAs for their entire portfolio, quicker and more affordably. Additionally, Speeding up the data collection process, analysis, and results allows companies to obtain simple reports to share with stakeholders. This enables them to make informed decisions and focus resources on actionable changes.
AI-powered LCAs streamline the process, enabling companies to scale assessments across their entire portfolio more affordably. Faster data collection and analysis help companies make informed decisions and focus on actionable changes.
Scope 1: This includes direct emissions from sources that are company-owned such as onsite fuel combustion, company-owned offices or factories, and company-owned vehicles.
Scope 2: Scope 2 includes indirect emissions from the consumption of purchased electricity, heat, or steam. This includes emissions from sources like electricity consumption. Although the electricity is consumed onsite, it is produced offsite at a power plant (unless you have onsite generation) which is not a company owned asset.
Scope 3: This category encompasses all indirect emissions resulting from activities of the company. This can include waste disposal, employee travel, and purchased goods.
Scope 3 emissions can be responsible for around 95% of a company’s overall footprint, making it the highest impact category. However, it’s also the most complicated category to calculate and report. Performing LCAs provides the necessary information to understand your company’s Scope 3 impact, leveraging useful insights that help your company reach environmental goals, social goals, and help you stay compliant with regulation, all while giving your company a competitive edge.
Scope 3 emissions can be responsible for around 95% of a company’s overall footprint, making it the highest impact category
Benefits of AI-Driven LCAs for Companies
Brand Reputation: Changing markets are demanding sustainable options. Adopting sustainable practices early can cement your company as an industry leader. Leveraging AI powered LCAs enhances your transparency to stakeholders who seek sustainable options with data to back up their claims.
Operational Savings: Designing efficient products can reduce operational and material costs. LCAs can identify high impact hotspots to reduce resource consumption. Examples include more efficient manufacturing processes, eliminating unnecessary materials in the product and packaging, and using sustainable materials.
Regulation Compliance: Existing and impending regulation is here to stay. As governments and institutions work to meet climate goals, they are adopting regulation and policies that require companies to reduce their environmental impact. Leveraging LCAs can provide the necessary information to make impactful reductions.
Key Takeaways: AI-Driven LCAs
In summary, AI-driven Life Cycle Assessments (LCAs) provide an effective and scalable approach for companies aiming to understand and minimize their environmental impact. By automating data collection and analysis, AI enhances the accessibility, accuracy, and cost-efficiency of LCAs. This makes them suitable for organizations with intricate supply chains and diverse product lines. The LCA process evaluates a product’s footprint across five key stages: raw material extraction, manufacturing, transportation, consumer usage, and end-of-life disposal, encompassing a comprehensive view of its environmental effects.
Utilizing AI-powered LCAs can bolster brand reputation through increased transparency, promote operational savings by optimizing resource use, and ensure compliance with emerging sustainability regulations. Additionally, these assessments help organizations address critical environmental issues, particularly Scope 3 emissions.
AI technology makes performing the key steps in conducting an LCA more efficient. By reducing the need for specialized expertise and filling data gaps, AI empowers companies to analyze their entire product portfolios quickly and effectively. Ultimately, embracing these technologies enables companies to achieve their sustainability objectives, maintain a competitive edge, and contribute significantly to global climate action initiatives.
CarbonBright’s AI-powered LCA software helps organizations accurately measure emissions and meet regulatory standards—at a fraction of the time and cost of traditional methods.
