Christoph J. Meinrenken

Fast Carbon Footprinting for Large Product Portfolios

Publicly Available Specification 2050‐2011 (PAS 2050), the Green House Gas Product Protocol (GHGPP) standard and forthcoming guideline 14067 from the International Organization for Standardization (ISO) have helped to propel carbon footprinting from a subdiscipline of life cycle assessment (LCA) to the mainstream. However, application of carbon footprinting to large portfolios of many distinct products and services is immensely resource intensive. Even if achieved, it often fails to inform company‐wide carbon reduction strategies because footprint data are disjointed or dont cover the whole portfolio. We introduce a novel approach to generate standard‐compliant product carbon footprints (CFs) for companies with large portfolios at a fraction of previously required time and expertise. The approach was developed and validated on an LCA dataset covering 1,137 individual products from a global packaged consumer goods company. Three novel techniques work in concert in a single approach that enables practitioners to calculate thousands of footprints virtually simultaneously: (i) a uniform data structure enables footprinting all products and services by looping the same algorithm; (ii) concurrent uncertainty analysis guides practitioners to gradually improve the accuracy of only those data that materially impact the results; and (iii) a predictive model generates estimated emission factors (EFs) for materials, thereby eliminating the manual mapping of a product or services inventory to EF databases. These autogenerated EFs enable non‐LCA experts to calculate approximate CFs and alleviate resource constraints for companies embarking on large‐scale product carbon footprinting. We discuss implementation roadmaps for companies, including further road‐testing required to evaluate the effectiveness of the approach for other product portfolios, limitations, and future improvements of the fast footprinting methodology.

Combining Life Cycle Assessment with Data Science to Inform Portfolio‐Level Value‐Chain Engineering

Life cycle assessment (LCA)‐based analyses of company value chains can inspire profound modifications to products’ design, material procurement, manufacturing, energy/water use, distribution, use, and disposal. However, such modifications often create trade‐offs, improving some aspects while worsening others. How can firms decide whether or not to carry out such modifications? Or prioritize between different options to choose the one delivering the most competitive advantage? Typically, firms’ metrics fall into two groups: (1) product‐level metrics across the life cycle, including up‐ and downstream of facilities (e.g., product carbon footprints); and (2) facility‐level metrics (e.g., plants’ annual energy cost). Neither is sufficient for firm‐wide cost‐benefit analyses of modifications that affect multiple products and value‐chain stages. Whereas facility‐level metrics do not capture up- and downstream effects - where often most cost and environmental impacts originate - life cycle methodologies are currently not mature enough to be applied at the scale of entire product portfolios. We present a pilot system of key performance indicators (KPIs) that evaluate 3,337 products across 211 brands and five countries of PepsiCo, Inc. KPIs are firm‐wide, annual figures (environmental, operational, and financial) across the value chain (cradle to grave) and can be determined at any level (single product, brands, or regions). Uncertainty analysis is included. In addition to KPIs for base cases, the system characterizes KPI impacts for any considered modifications (what‐if scenarios). In a detailed case study, we present background about how and why PepsiCo used the system to evaluate all aspects of a strategic value‐chain modification. For 7 of the 211 brands, this resulted in avoiding an 8% increase in greenhouse gas emissions and a 7% to 10% increase in procurement costs. It also saved PepsiCo an estimated ∼200 years full‐time equivalent employee time (or alternatively ∼US

Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response

30 million in LCA consultant fees) had the LCAs of the 3,337 SKUs been carried out by traditional methods. This cost efficiency of the KPI system enables considering environmental impacts with more‐traditional business metrics side by side. As a result, environmental impacts can be considered on a routine basis as part of integrated strategy and business planning. We discuss implementation considerations of the KPI methodology and future improvements.