We didn’t enter sustainability to build the world’s most accurate spreadsheets. Yet increasingly, the profession is becoming more about reporting perfection than real-world reductions. A perfect emissions inventory with no emissions reductions is a failure. But today’s ESG industry is dangerously close to making that tradeoff — and few seem willing to question it.

Sustainability was meant to drive real-world change — cutting emissions, improving systems, and transforming industries. But somewhere along the way, the focus shifted. Today, an entire business ecosystem thrives on the complexity of emissions reporting, assurance, and benchmarking. While these tools have value, they’ve increasingly become the center of gravity, often delaying action in the name of perfecting data. If we don’t recalibrate, we risk building ever more sophisticated models while emissions continue rising in the real world.

Sustainability began as a response to clear and urgent environmental threats — pollution, resource depletion, and the realization that unchecked economic growth could destabilize ecosystems and societies. Over the decades, the field expanded dramatically, incorporating social, environmental, and governance dimensions into what is now broadly called ESG. Global frameworks, voluntary initiatives, and regulatory pressures evolved to shape corporate behavior, driving significant awareness and progress.

At the heart of much of this work lies carbon emissions — the most visible and universally recognized metric of environmental impact. As climate change moved to the top of the global agenda, measuring, managing, and reporting carbon footprints became a central pillar of sustainability practice.

The irony is: while carbon accounting methods are fundamentally straightforward — rooted in clear activity data and well-established emission factors — the practice has become increasingly burdened by layers of unnecessary complexity. In many cases, we’re complicating what could be simple, delaying action in pursuit of data perfection that adds little real-world value.

This is where we begin.

The original purpose of carbon accounting was simple: measure emissions well enough to know where to act. The aim was never to create the most intricate or mathematically precise inventory, but to identify hotspots, set priorities, and drive reductions. Yet somewhere along the way, this goal was overshadowed by an obsession with perfection.

Many organizations now spend disproportionate time refining models, debating methodologies, and chasing marginal improvements in data precision — while tangible reductions remain on hold. In reality, most companies already know where the bulk of their emissions come from: energy, materials, transportation, and supply chains. They don’t need decimal-point accuracy to replace fossil fuels, redesign products, or shift procurement decisions.

In a world where the climate crisis is accelerating, imperfect action beats perfect analysis every time.

Carbon accounting is not — and has never been — about achieving perfect numbers. Even the GHG Protocol, the global standard often referred to as the "bible" of emissions reporting, makes this clear. It defines five guiding principles that every calculation should follow: relevance, completeness, consistency, transparency, and accuracy. The key is balance. These principles often pull in different directions, and there will always be tradeoffs between them.

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For example, pushing for extreme accuracy may come at the cost of relevance or completeness. Gathering hyper-detailed data from every supplier may be technically possible, but impractical, slow, and resource-intensive. Instead, the goal is to provide decision-useful information that reflects reality well enough to guide meaningful action.

We must remember that models are models. They are useful approximations, but they do not replicate the full complexity of real-world supply chains or emissions pathways. Even the most sophisticated databases — ecoinvent, Sphera, and others — are built on assumptions, averages, and estimates. Mistaking model output for absolute truth is both dangerous and counterproductive.

At its core, the calculation is actually quite simple:
Emissions = Activity Data × Emission Factor.

For example, if a facility consumes electricity, its emissions are calculated by multiplying the amount of electricity used by the applicable emission factor. That factor could be specific to the supplier (market-based), to the country or grid average (location-based), or drawn from public databases. These factors are updated regularly as energy mixes change — but they always remain estimates. The same applies to almost every activity: fuel consumption, materials production, transportation, etc.

Crucially, carbon dioxide equivalent (CO₂e) is itself a modeling construct — a way to standardize and aggregate the climate impact of different gases and sources into one comparable number. It is not a direct measurement of molecules, but an estimation tool designed to enable decision-making.

Throughout the entire carbon accounting process, layers of approximation and evolving data introduce unavoidable inaccuracies. That is not a flaw — it is the nature of modeling complex systems. The danger arises when we start mistaking these approximations for absolute truth, and when the drive for marginally better estimates overshadows the real goal: emissions reduction.

I still remember when I first started as a working student back in 2020. One of my first tasks was to build an Excel-based tool for calculating emissions. The logic was straightforward: gather activity data and apply the correct emission factors, The tool was simple, accurate, and fully compliant. More importantly, it was usable. It allowed the team to focus on understanding where emissions were coming from — and what could be done to reduce them.

That’s what carbon accounting is supposed to be: a means to guide action, not an endless technical exercise.

Yet, over time, the push for hyper-accuracy has turned what should be a fairly simple task into something that feels increasingly burdensome. With each new demand for greater granularity — supplier-specific data, dynamic emission factors, deep-tier supply chain estimates — the entry barrier rises. What once could be managed with basic data and a spreadsheet now often requires expensive software platforms, external consultants, costly databases, and constant updates.

Around carbon accounting, a massive business ecosystem has grown — consultancies, software providers, data vendors, verifiers, rating agencies, auditors, and training programs. This is not inherently a problem. In fact, many of these players provide valuable services that help organizations build competence, ensure credibility, and stay compliant with emerging regulations.

The problem arises when complexity becomes the product. The more complicated the rules, the more specialized expertise is required. The more granular the data demands, the more expensive software licenses become. The more intricate the methodologies, the more frequent the need for consulting services, data subscriptions, and verification audits.

Maintaining this complexity serves an obvious business interest: it ensures a continuous flow of revenue from companies trying to navigate an increasingly convoluted system. Simplification, on the other hand, threatens that revenue model. A simpler, more transparent approach to carbon accounting would empower more organizations to take action independently, reduce their reliance on external support, and shift resources toward actual decarbonization — not just reporting.

The risk is clear: we are building a profitable system designed around measuring emissions, rather than reducing them. The more money flows into managing the reporting process, the less is available for real, meaningful climate action. And the more the ecosystem resists simplification, the more we drift away from the true goal that brought us here in the first place.

There’s also a new layer emerging: AI. I often find myself wondering how many of the service providers building ever-more complex sustainability platforms plan to approach this technology. On one hand, AI has enormous potential to simplify calculations, automate data collection, and make emissions reporting more accessible to organizations with limited resources. On the other hand, the growth of AI itself — with its massive compute requirements — is becoming a growing source of emissions. Ironically, the very tools being developed to manage carbon footprints could end up contributing to them. Yet, despite these challenges, I see many sustainability service providers doubling down on AI-supported systems — not necessarily to simplify, but often to expand the scope, increase the granularity, and deepen the dependency on their platforms. Once again, complexity becomes the product.

We need to remind ourselves why carbon accounting exists in the first place: not to create perfect numbers, but to guide meaningful action. The methods are not inherently complex. The formula remains simple — activity data multiplied by emission factors. The complexity has been layered on by choice, often driven by commercial interests that benefit from maintaining technical opacity.

Sustainability professionals must resist the temptation to equate complexity with rigor, or precision with progress. The real test of effective carbon accounting is not how many decimal points we can report, but how much emissions we actually reduce. Simplification is not a threat to the profession — it’s an evolution toward impact.

As AI and digital tools reshape the industry, we have a choice: we can either use them to democratize access, streamline calculations, and focus resources on action — or we can allow them to become just another layer in the growing complexity business.

The mission was never about building the most sophisticated models. The mission was — and still is — to decarbonize. And every layer of unnecessary complexity only delays that mission.

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