How to measure systemic impact

Avoided Emissions Series Part II

9 min readDec 13, 2024

PREVIOUSLY:

In part 1 of our series we described how some methodologies used to assess the environmental impact of one product versus another do not take into account the systemic impacts — often leading to misleading results. The examples we provided on faux leather, bio-based products from biogenic (waste) material, and others illustrated that.

In part two, we highlight key factors we need to explore to get a truly comprehensive picture of the impact an investment decision will have.

WHAT MATTERS WHEN ASSESSING SYSTEMIC ENVIRONMENTAL IMPACTS?

Impact assessments need to look beyond simply quantifying emissions or supply chain effects. For effective investment decisions, a broader perspective is required — one that accounts for market dynamics, supply constraints, competing uses, and economic shifts.

Key considerations include:

1. Market effects and system responses

Scaling dynamics:
When a company scales its production, a critical question arises: Who will supply the additional materials or resources? A new product entering the market might reduce consumption of its alternatives. Great! Or, it may simply shift consumption patterns. Understanding these dynamics is crucial for evaluating systemic impacts.

Marginal suppliers:
The marginal supplier — the one adjusting production to meet demand changes — is often distinct from the average supplier, both geographically and technologically. The marginal supplier is the one that is likely to respond to a change in demand. The impact-profile of this supplier might differ from the average impact-profile of similar products in the market.

The rebound effect:
A systemic phenomenon, the “rebound” effect, can diminish the benefits of innovations. For example, introducing battery-powered electric vehicles might reduce fossil fuel demand, but lower fuel prices could encourage increased usage elsewhere. On a consumer level, greater efficiency — such as a car that uses less fuel — might lead to more driving. These cascading effects must be factored into impact assessments.

2. Supply and demand dynamics

Elastic vs. non-elastic supply:

Elastic Supply: Markets adjust production based on supply and demand changes. This flexibility is influenced by:

Price sensitivity: Higher prices incentivise production increases, while lower prices lead to scaled-back operations.
Regulations: Policy measures, such as emissions caps, can push inefficient suppliers out, fostering elasticity.
Resource availability: Readily accessible resources enable faster adjustments.

Non-Elastic Supply: Some markets or systems cannot adapt quickly due to physical constraints or rigid infrastructures. Limited resource availability (e.g. niche agricultural inputs) or inflexible systems can prevent rapid scaling.

Examples: Startups addressing alternative leather, bitumen substitutes, or cocoa-free chocolate highlight how supply constraints and competing uses affect systemic dynamics (see part 1). Distinguishing between elastic and non-elastic supply and demand gives insights into who might be the marginal supplier.

3. Competing and alternative uses

Materials and resources often serve multiple functions, which can create unintended consequences. For instance, biogenic waste diverted to meet new demand might displace its original uses (e.g., animal feed or energy production), necessitating substitutes that introduce additional impacts. Startups innovating in circular economies face these trade-offs, which should be carefully assessed in impact models.

4. The global ripple effect of local decisions

Systemic impacts are interconnected. For example, biofuel production in one region can displace crops in local markets, leading to increased grain production and exports from another region. This ripple effect shifts environmental burdens globally, influencing biodiversity, land use, and water consumption. Holistic assessments must evaluate these indirect effects alongside direct emissions reductions.

DIFFERENT METHODS FOR DIFFERENT PURPOSES

As we covered in Part I, there are two core scientific methodologies used to evaluate environmental impacts:

Attributional LCA (ALCA):
Provides a snapshot of a product’s environmental footprint, focusing on direct inputs and outputs within defined boundaries. ALCA is suitable for identifying emission hotspots but does not account for systemic effects or market changes.
Consequential LCA (CLCA):
Offers a systemic perspective, analysing broader environmental impacts caused by market responses and changes in supply or demand. CLCA incorporates marginal datasets and is more suitable for understanding innovations’ systemic impacts.

A review of scientific literature on the limitations and applicability of the two approaches underscores a growing consensus in favour of the consequential LCA approach for informed decision-making for investment and policy related decisions. Brander (2022) points out that “attributional methods can lead to actions that unintentionally increase emissions as they only provide information on emissions/removals within the inventory boundary”. The CLCA method clearly aligns with best practices and recommendations from leading environmental standards and frameworks, including the GHG Protocol’s “Estimating and Reporting Avoided Emissions” framework. (For the scientific nerds among us: see further literature at the end of of this article.)

So what about the “avoided emissions approach” that is often used? Most “avoided emissions” studies are based on the attributional LCA methodology: they use emissions factors based on allocated data reflecting average production methods. We discussed in Part 1 why subtracting one attributional style LCA or emission factor from another fails to account for systemic effects. Many practitioners in the investment space are applying this method to answer questions that can only be answered using a systemic view. Instead, their approach introduces some CLCA elements into ALCA frameworks. Doing so creates methodologically meaningless results that neglect systemic effects.

Navigating uncertainty in systemic impact assessments

Assessing systemic environmental impacts offers a broader and deeper understanding of market changes, but it comes with significant challenges, primarily due to uncertainties. These arise from limitations in data, assumptions about market behavior, and the inherent complexity of non-linear systems.

Data uncertainty: Reliable data is the backbone of Life Cycle Assessments, yet it is often incomplete or insufficiently granular. This becomes particularly problematic when analysing systemic impacts, where outcomes hinge on precise context and assumptions. For example, gaps in emission factors, market trends, or technological performance can lead to significant variances in results.

Market response: Systemic impact assessments depend on assumptions about how markets respond to innovation and policy changes. Dynamics like price elasticity — how supply and demand adjust to price shifts — are central but notoriously unpredictable. These uncertainties complicate efforts to model how technologies scale or disrupt existing systems.

Non-linear effects: Unlike linear systems, market responses can exhibit tipping points. A modest increase in production might initially have minimal environmental consequences but could escalate suddenly when resource constraints or regulatory pressures come into play. Predicting these turning points is particularly challenging yet crucial.

Uncertainty is inevitable in any systemic assessment, whether it’s ALCA, CLCA, or approaches like avoided emissions analyses. These challenges cannot be entirely eradicated but can be effectively managed using scientific tools:

Parameterised models: Replacing fixed values with variable parameters in models lays the groundwork for analysing uncertainty dynamically.
Sensitivity analysis: This method identifies key variables that have the greatest influence on outcomes, offering clarity on the robustness of conclusions.
Scenario analysis: Creating best and worst-case scenarios based on variations in policy, technology, or market trends helps practitioners navigate a range of possible futures. This approach is particularly useful for highlighting trajectories likely to lead to desired outcomes.
Monte Carlo simulations: Running thousands of simulations with random variations of key parameters provides probabilistic insights into possible outcomes, highlighting the range and likelihood of impacts.

How to do this in a time and resource constrained world? Prioritise! Rather than striving to resolve every uncertainty — a practically impossible task — efforts should focus on the most impactful factors. By addressing uncertainty strategically, practitioners can deliver more robust assessments and support informed decision-making that aligns with environmental and business goals.

THE TAKEAWAY?

Assessing the impact of changes in a complex world is complicated. Thanks to years of research, there are scientific methods developed for exactly this purpose. These methods provide frameworks to help impact investors like us assess systemic change and answer the critical question: Can a particular innovation deliver the change in the system that we need?

However, challenges remain. As the field continues to evolve, collaborative efforts within the investment and policy communities are essential to standardise approaches, improve data quality, and refine methodologies.

Stay tuned for the final part of this series, where we explore how existing frameworks can be adapted and enhanced to answer these critical questions more effectively.

Authors: Benedikt Buchspies & Kritesh Shridhar