The Two Engines of SIP: Qualitative and Quantitative Modelling

This blog is the fifth in the SIP blog series, a set of blogs shared among the partners of the Sustainable Innovation Pathways project. This cross-border, collaborative effort brings together foresight, financial forecasting and technology readiness levels to understand where companies, industries, and countries can best decarbonise.

The majority of net zero and decarbonisation strategies fail not because the goal is incorrect but rather because the approach is insufficient. Some strategies mainly rely on rich, creative, and politically conscious future narratives, but they have trouble turning those insights into tangible investment or policy decisions. Others rely on complex numerical models that produce accurate results, but they frequently make flimsy assumptions about how the world will act.

The Sustainable Innovation Pathways (SIP) framework was specifically designed to steer clear of this poor decision. Its two main analytical engines are quantitative modelling and qualitative foresight. Every engine is capable of something that the others are not. Importantly, SIP is made to function as a single system rather than as separate exercises.

This method expands upon a long-standing tradition of considering the practical integration of futures, scenarios, and models. Decades of experience in operational research and scenario planning demonstrate that qualitative and quantitative approaches are complementary tools that address different aspects of uncertainty rather than being a replacement for one another.

Why a single engine is never sufficient

The present is where quantitative models start. They define variables and relationships based on current data, then project those findings. They are therefore effective for investigating sensitivities, verifying hypotheses, and comprehending how results alter when inputs change. However, the way the world is currently understood inevitably shapes them.

Qualitative scenarios begin in a different location. They create vivid images of how the future might be structured differently in terms of politics, society, technology, and the economy. These are not predictions. They are instruments for investigating discontinuity, questioning presumptions, and controlling uncertainty. They are strong because they can reveal dynamics in data that are not yet apparent.

For decades, there has been discussion about the conflict between these methods. Research from a variety of disciplines has demonstrated that qualitative and quantitative approaches do not examine the same phenomena and cannot be used to "prove" each other right. However, they can cooperate for complementary goals, making up for each other's shortcomings.

SIP is based on this insight.

First Engine: Qualitative foresight

Investigating potential changes to the world

Qualitative futures foresight is the first SIP engine. Its goal is disciplined, structured imagination rather than prediction.

Horizon scanning, systems thinking, scenario building, and Three Horizons analysis are all well-established foresight traditions that are incorporated into SIP's qualitative work. By combining these resources, SIP is able to investigate futures that are significantly different from the present rather than merely extrapolating existing patterns.

The SAFIRE scenario framework, created for the European Commission, is a key component. These scenarios specifically examine how various global conditions, such as fragmentation, protectionism, cooperation, and systemic stress, may affect research, innovation, governance, and economic behaviour. They are especially useful because they incorporate institutional and political change—factors that significantly influence decarbonisation but are frequently absent from techno-economic models—into the future.

Additionally, SIP employs Three Horizons thinking to differentiate between:

• today's prevailing technologies and systems,

• the innovations and transition dynamics currently in development, and

• Future states with distinct structures that might eventually take the place of current systems.

By doing this, a common modelling error is avoided: assuming that tomorrow's world is just today's world with cleaner technologies.

Lastly, interdependencies are mapped using systems thinking. In addition to technology costs, decarbonisation pathways also depend on supply chains, infrastructure, capital markets, public acceptance, governance, and skills. Even when these relationships are not yet confidently quantifiable, qualitative foresight makes them clear.

This first engine produces a collection of logical, believable futures. Likelihood is not used to rank these futures. They are responsible for exposing uncertainty, questioning "official futures," and determining whether strategies are still workable in the event that the world does not behave as anticipated.

Second engine: Testing pathways under constraints through quantitative modelling

Those futures are converted into numbers by SIP's second engine. This is the point at which foresight becomes important for making decisions.

Pathways, not point forecasts, are the main focus of SIP's quantitative modelling. It asks "what sequences of decisions work under different conditions" rather than "what will happen?"

There are a few key components.

Cost curves for dynamic abatement

Conventional abatement curves are static snapshots. SIP views them as changing over time. As technologies advance through readiness levels, scale up, and take advantage of learning effects, costs shift. As supporting systems develop and deployment speeds up, abatement potential increases.

Learning rates and the rate of innovation

The speed at which technologies advance from demonstration to widespread implementation distinguishes them. Learning rates are dependent on skills, capital availability, market design, and policy support; they are neither uniform nor guaranteed.

Credibility of policies and efficient carbon pricing

SIP encodes scenario-specific effective prices that represent market confidence, enforcement strength, and political viability rather than assuming a single global carbon price. By doing this, the typical modelling error of treating policy ambition as policy reality is avoided.

Exploration of probabilistic pathways

There isn't just one "optimal" pathway produced by SIP. It identifies options that are strong, brittle, or extremely sensitive to assumptions by examining a wide range of potential investment sequences across several futures.

Therefore, precision in and of itself is not the focus of the quantitative engine.

Its objective is to expose trade-offs, stress-test strategies, and indicate areas where risks accumulate.

Translation, not validation, is where the engines meet.

SIP is distinguished by the connections between qualitative and quantitative methods rather than by the fact that it employs both.

Innovation speed, learning dynamics, workforce availability, policy strength, and institutional effectiveness are among the metrics and constraints that parameterise the quantitative model based on qualitative scenarios. These parameters change over time and depending on the situation.

The strategy is then informed by the quantitative results. They show where investment bottlenecks appear, which qualitative futures require implausible assumptions, and which are economically coherent.

Crucially, triangulation is not what this is. Numbers are not used by SIP to "prove" scenarios or to support model outputs. Rather, in accordance with decades of experience integrating scenarios and models in practice, each engine communicates with the other in a complementary loop.

Why this is important for decarbonisation

Decarbonisation is not a single optimisation issue. It is a series of capital allocation choices made in the face of extreme political uncertainty, technological advancement, and uncertainty.

Narrative-only frameworks frequently fail to direct actual investment. When assumptions go wrong, frameworks that only use models frequently fall apart.

SIP is intended for the interim.

SIP makes the following possible by fusing quantitative pathway modelling with qualitative foresight:

• tactics that work in a variety of political and economic contexts,

• routes that are comparable and financially explicit,

• and a framework that is adaptable to changing contexts, data, and technologies.

For this reason, SIP views its assumptions and data as dynamic components that are constantly improved rather than set in stone.

Converting futures into plans

SIP's central concept can be summed up as follows:

We convert numbers into strategy and futures into numbers.

Strategies are based on realistic perspectives of how the world might change thanks to qualitative foresight. Quantitative modelling guarantees that those tactics are accountable, explicable, and investable.

On its own, neither engine is adequate. When combined, they enable decision-makers to go beyond rigid plans and toward resilient, adaptable routes that make sense in an uncertain world.

Written by Dr. Christian Spindler, Co-Founder and CEO of Sustainaccount

The views expressed are those of the author(s) and not necessarily of SAMI Consulting.

Achieve more by understanding what the future may bring. We bring skills developed over thirty years of international and national projects to create actionable, transformative strategy. Futures, foresight and scenario planning to make robust decisions in uncertain times. Find out more at www.samiconsulting.co.uk

Image by Gerd Altmann from Pixabay