M&V Understood - at last

A Semiotic Theory of Counterfactual Governance:

Meaning, Structure, and Optimization in Measurement & Verification

Abstract

This paper argues that Measurement & Verification (M&V) and counterfactual design are not merely technical domains concerned with quantifying energy and carbon impacts. They are semiotic and structural systems in which numerical values, modeling conventions, and regulatory rules function as signs within a rule-governed grammar. Impacts are not discovered but produced through these sign systems and structural frameworks. Recognizing this semiotic dimension clarifies why cooperation, communication, and trust are central to M&V—and why artificial intelligence, with its capacity to model latent structures, is accelerating the need to understand counterfactual governance as a fundamentally meaning-making enterprise.

1. Introduction — From Engineering to Semiotics

M&V is typically presented as a technical practice: collect baseline data, model counterfactual conditions, evaluate performance, and assign impacts. Yet practitioners quickly learn that the results do not arise solely from mathematics or physics. The key quantities—baselines, savings, adjustments, weather normals, model coefficients, confidence intervals—carry meanings that depend on shared understandings, institutional context, and negotiated trust.

In this sense, the field is semiotic.

Semiotics, the study of sign systems and meaning-making, provides a lens for understanding how M&V practitioners, regulators, and stakeholders collectively produce the meanings of quantitative values. Structuralism, with its focus on deep rule-governed systems, provides the complementary insight that frameworks such as IPMVP, ASHRAE Guideline 14, utility tariffs, and ESG protocols serve as the “grammar” within which these signs operate.

This paper proposes that M&V is best understood as counterfactual governance: a semiotic–structural system through which institutions define what counts, how meaning is produced, and what forms of optimization are legitimate.

2. Semiotics in M&V: Numbers as Signs

From a semiotic perspective, a sign is any entity that conveys meaning. In M&V:

• A baseline energy model is a sign.

• A weather adjustment coefficient is a sign.

• The phrase “savings” is a sign.

• A normalized consumption value is a sign.

• Even the selection of a baseline period signifies assumptions about relevance and fairness.

Numbers do not speak for themselves; they acquire meaning through human interpretation and convention. A kilowatt-hour saved under a performance contract is not the same sign as a kilowatt-hour avoided under an ESG disclosure. The same number, embedded in different sign systems, means different things.

Interpreting these values requires understanding the conventions that govern them—not unlike interpreting words within a language. Semiotics reminds us that the meaning of the impact is not inherent in the data but produced through structured communication between parties.

3. Structuralism in M&V: Protocols as Grammar

If semiotics examines signs, structuralism examines the rules that structure those signs. In M&V, structure appears in the form of:

• regulatory protocols (IPMVP, ASHRAE Guideline 14),

• tariffs and incentive rules,

• contract language,

• audit procedures,

• ESG disclosure frameworks,

• methodologies baked into legislation such as the IRA.

These structures play the same role that grammar plays in language: they define permissible statements, restrict transformations, and set the boundaries of what can be said or claimed. They are largely invisible, naturalized through practice, and treated as objective—even though they emerge from negotiation, politics, and institutional history.

Calling them “deep structure” is not metaphorical; it is descriptive. M&V runs on a structural grammar that defines what a counterfactual is allowed to be. This is why identical projects across jurisdictions can yield different impacts: the governing structure, not the equipment, determines the meaning of the outcome.

4. Optimization as a Semiotic–Structural Process

Optimization is often framed as a mathematical activity, but in counterfactual governance it is fundamentally semiotic and structural.

Optimization requires:

1. a set of variables that matter (semiotic choice),

2. a system defining which variables may be manipulated (structural grammar),

3. a rule for evaluating better/worse outcomes (semiotic meaning),

4. a legitimizing institution that stabilizes these choices (structural authority).

Thus, optimization is not simply “solving a model.” It is choosing which sign system and which structural grammar define the field of action. In this sense, optimization is a cultural and institutional commitment before it is a technical one.

5. Counterfactuals as Communicative Acts

The counterfactual—“what would have happened in the absence of the intervention”—is not an observable fact. It is a communicative act bound to a shared structure of reasoning. That is why debates about baselines are debates about meaning, not physics.

A counterfactual is credible only because parties agree to understand it through the same structural rules. This agreement is a social achievement. It requires trust, transparency, and responsibility, not unlike shared linguistic understanding.

Thus counterfactual design is a semiotic practice. It aligns more closely with law, narrative, and cooperative reasoning than with raw measurement.

6. Artificial Intelligence and the Latent Structures of M&V

LLMs and machine learning systems reinforce this semiotic–structural view.

They do not “discover truth.” They model latent structures in corpora and generate new signs according to those learned structural relations. This mirrors what M&V practitioners already do:

• infer a structure from empirical data,

• define a counterfactual using that structure,

• negotiate the meaning of the resulting signs,

• and formalize those meanings into rules.

AI exposes that M&V is not about certifying a single truth but about constructing stable meaning in complex systems. This is counterfactual governance.

7. Conclusion — Toward a Semiotics of Impact

Understanding M&V as semiotics and structuralism clarifies what the field actually does: it creates the conditions for meaningful and trustworthy value exchange in situations where outcomes cannot be directly observed.

This reframing does not diminish the technical rigor of M&V; it explains it. It shows why cooperation, communication, and transparency are essential: the counterfactual is a communicative construct, and impact is a meaning negotiated within a structured system.

Recognizing the semiotic nature of the field prepares practitioners to integrate new tools, navigate complexity, and build institutions capable of managing energy and carbon impacts in a world where the meaning of a kilowatt-hour, a ton of CO₂, or a regression coefficient is never simply given but always produced.

How to Layer in Lessig (the “Code Is Law” structure)

Lessig’s framework offers the perfect structural complement. His central claim: behavior is shaped by four forces:

1. Law – formal rules

2. Markets – incentives and prices

3. Norms – shared expectations

4. Architecture – the built environment or technological structure

You can adapt this directly into counterfactual governance:

1. Law → Regulatory protocols structure meaning

IPMVP, Guideline 14, IRA rules, carbon accounting standards.

These define the grammar of permissible counterfactuals.

2. Markets → Incentives create semiotic pressure

Optimization is always shaped by market signals—rebates, avoided cost rates, ESG premiums.

3. Norms → Professional culture governs interpretation

Engineering norms, utility norms, contractor expectations, acceptable accuracy bands.

These stabilize meaning across practitioners.

4. Architecture → Models, software, AI systems

The physical and computational environment constrains choices:

weather files, meter granularity, ML models, default assumptions, embedded templates.

These four forces collectively govern the meaning of impacts.

You can articulate this as:

Lessig’s model becomes the structural deep grammar of counterfactual governance: it shapes which signs (data, models, claims) can exist, how they are interpreted, and which optimizations are seen as legitimate.

If you want, I can integrate Lessig into the full paper as a new section, or produce a “Lessig + Semiotics for M&V” slide deck.