Environmental service provider consolidation and the winning combo

Or, the case against full-stack washing

I wrote about how startups and scaleups with new products in new markets often struggle with the tension between consultancy and saas and their go-to-market (GTM). This essay zooms out to the broader sector, in particular the environmental service provider sector. There are many companies in this sector that do offer ‘similar but not quite’ services. They offer unique, complementary but also (partly) overlapping services. That is inefficient and confusing for buyers and begging for consolidation.

Consolidation in maturing markets is usually driven by the need to improve efficiency, reduce costs, strengthen market positions, and maintain profitability as organic growth becomes harder to achieve.

This essay provides a lens through which to assess the role and place of organisations in the broader market, examines good and bad patterns and suggests a consolidation readiness strategy.

There is a complication. Buyers want end-to-end solutions, and many providers are happy to claim they deliver them, long before they actually do. I call this full-stack washing. It is the shortcut that makes consolidation look unnecessary, and it is the reason consolidation is urgent.

Full-stack washing

As regulatory pressures intensify (for example EU Deforestation Regulation (EUDR), the Corporate Sustainability Due Diligence Directive (CSDDD), and Scope 3 carbon accounting), the demand for “end-to-end” solutions has skyrocketed. This has incentivised service providers to stretch their marketing claims.

The problem with full-stack washing is that it’s a trust-erosion engine. Buyers discover the gaps after signing: the “integrated” carbon module turns out to be a partner’s spreadsheet, the “global coverage” stops at the regions in the demo. They churn, and they tell their peers. Worse, they start distrusting the whole category, including the providers that do deliver. Some businesses even use full-stack washing aggressively as a strategy to conquer as much market share as fast as possible. Then they plan to acquire (buy, merge, build) missing capabilities to close the loops and fill the gaps along the way, praying churn doesn’t outpace sales.

I think full-stack washing is inevitable in any new market with new products. But if it’s not eventually solved (by consolidation), the market will be spoiled before coming to fruition.

Cycles & Chains

My proposal is to move away from the classic “vertical” and “horizontal” consolidation to consolidating cycles and chains. Vertical and horizontal describe the supplier’s value chain: who you buy, who you sell to, who you compete with. Cycles and chains describe the buyer’s problem. A buyer doesn’t want a bigger vendor; a buyer wants the loop closed.

In my view, strengthening market position (adding capabilities) should always drive early consolidation waves. Inefficiency discovery and cost reduction should follow, but they won’t happen by themselves (see anti-pattern 1 below).

Below I describe a few cycles and chains important for service providers in the environmental supply chain risk sector.

Domain cycles

These are the most obvious cycles which are typically served by multiple providers in new markets. For example, the carbon and the water (drought) cycle.

Carbon

A company wants to report on its Scope 3 carbon emissions, reductions, reversals and removals in compliance with the GHG protocol and SBTi FLAG. It needs data on Above Ground Biomass, Below Ground Biomass, Soil Organic Carbon, Dead Organic Matter (and Atmospheric Carbon). And it needs to set up an accounting method. It makes a lot of sense to combine all of these capabilities into one organisation. In reality, that organisation doesn’t exist; there are many companies delivering one or some of these components.

Water

You want to report, monitor and plan on drought risk in your commodity supply chain. Can I still source cocoa or coffee from region X in the coming 10 years or will the yield decrease because of increasing drought risk? There is meteorological drought (lack of rain), hydrological drought (low streamflow, reservoir or groundwater levels) and agricultural drought (soil moisture deficit, often measured as evapotranspiration deficit (PET - AET)). Those are all connected through the water cycle. A company combining these capabilities will have an edge over companies that focus on one aspect.

Data supply chain

Supply chains are a well studied phenomenon. Financial supply chains and commodity supply chains, the classic “vertical” integration domains, are well known. Lesser known and often overlooked are data supply chains.

An example data supply chain is a satellite image of a landscape (an observation), interpreted (forest, non-forest, commodity), spatially aggregated to a supply chain entity (transformed), passed along the chain of custody, and ending up as a deforestation free claim for a commodity shipment or a feature of a digital product passport. Observation, interpretation, claim. First mile, analytics, last mile.

Data is observed, interpreted and flows through a complex system while being transformed and translated multiple times before being sold as a claim. For this claim to be valuable (trustworthy), you need to preserve the epistemic, quality, lineage and governance integrity of the data throughout the chain through a cross-cutting verification (trust) layer.

Controlling the whole data supply chain or the data assurance infrastructure is a consolidation opportunity.

The data supply chain is a topic that deserves its own deep dive. I will write about that in a separate essay.

The business cycle: reporting, monitoring, planning

This cycle lives with your clients. They produce products or services and report, monitor and plan on everything around these value streams. Many environmental service providers currently exist by the grace of regulatory reporting and compliance requirements. Which is a good thing. But reporting is seen as a liability, a cost centre. That is not where the money is made. So you have to be able to promote your services to the money making functions of the business, the functions where operational decisions and investment plans are made. Your data should evolve from audit-ready to decision-ready to investment-ready data.

<note: insert business cycle picture>

As sustainability will inevitably move from liability to asset, a company needs to be ready to serve the reporting, monitoring and planning needs of businesses.

The cycles and chains above describe where value flows. The next three are the capability layers you need to serve them: data, modelling and commercial. Each has its own chain, and each is a place where providers are typically incomplete.

Data

The data supply chain above is about the infrastructure, the pipes. This is about what flows through the pipes: the data and how it is sourced (observed) and processed (interpreted).

Sources

Every claim starts with an observation. Observation sources come in many forms: (proprietary) databases, global layers (land cover, soil, elevation, climate reanalysis), satellite and airborne remote sensing, in-situ sensors (weather stations, soil moisture probes, flux towers) and field data collected by humans (plot inventories, farmer surveys, supplier declarations).

For sensor sources, resolution matters:

• Spatial: how small an object can you observe;
• Spectral: which wavelengths;
• Temporal: revisit frequency;
• Radiometric: how small a difference can you detect.

Combining sensors and other data sources will give you an edge: optical with radar, satellite with field data, global layers calibrated and validated with primary data.

Processing

Raw observations are worthless until they are interpreted: preprocessed, corrected, classified, aggregated and linked to supply chain entities. The question is: does it scale? Processing one landscape for one client can be done on a consultant’s laptop. Doing the same for every sourcing region of every commodity, every week, going back 25 years is a data center scale engineering endeavour. I did only one IT course in my life and that was on databases and SQL. That’s where I learned about OLTP and OLAP:

• OLTP (transactional): many (parallel) small, fast reads and writes;
• OLAP (analytical): heavy queries over large volumes, business intelligence.

Most providers optimized for one and kind-of do the other. Reporting can live with overnight batch jobs. Monitoring needs near real time, and planning needs scenario runs on demand. If your processing doesn’t scale, you can’t move up the business cycle from reporting to monitoring to planning.

Processing is also where lineage starts or gets lost. At every transformation step, provenance, uncertainty and quality metadata either travel with the data or get silently dropped. No verification layer downstream can bring them back.

A company with strong sources but weak processing sits on a goldmine it can’t dig. A company with great processing but no privileged sources is a commodity, competing on price for the same public data as everyone else. Put them together and you get much more than the sum of the parts.

Modelling

Since the rise of generative AI, we are familiar with the distinction between deterministic and non-deterministic models. But in the environmental landscape risk sector I think a more meaningful distinction is between data-driven and process-based models. And I think you need both capabilities.

Data-driven models are often effective for describing observed patterns and predicting outcomes under conditions similar to those represented in the training data. Process-based models are particularly useful for simulating future outcomes under changing environmental or management conditions because they explicitly represent the mechanisms governing the system. The relative predictive performance of the two approaches depends on data availability, model assumptions, and the extent to which future conditions differ from historical conditions.

Thinking back about the business cycle: reporting, monitoring, planning, you might cover the reporting function very well with a data-driven approach. But if you want to service the planning functions as well, a process-based approach will probably outperform the data-driven model.

See Table 1 in the appendix for an overview of the model family strengths and weaknesses.

Commercial

Finally, there are commercial chains and cycles that could justify consolidation steps. The commercial chain runs from market access (sales channels) to delivery and back. Market access gets you in the door. Contextual intelligence (last-mile bespoke delivery) makes generic data relevant to one client’s decisions. Support and account management keep you there. Forward-deployed engineers do organisational sensing: they bring what clients actually need back into the product. A consultancy branch takes on bespoke (boutique?) assignments and implementation projects.

A great data product without this chain stays a great data product nobody uses. A strong commercial arm without a product becomes a consultancy.

These are only some of the areas with important cycles and chains of complementary capabilities. There are certainly areas I overlooked. Also, these are areas that are typical for environmental service providers serving the commodity supply chain resilience market. Some of these are irrelevant for other markets and vice versa.

Consolidation Patterns & Antipatterns

Now that we know how to look for consolidation candidates, we are done. We can have a look at what a winning combination could look like in the environmental service provider landscape. A potential winner could for example focus on owning the global carbon cycle. Access to all relevant sensors (including humans collecting field data) to collect observations in near-real time on a global scale. Owning the processing capabilities to (pre)process that data, and a fast query and storage engine for analytics. It should be able to look backward (report) to now (monitor) and look forward (plan) through a combination of data-driven and process-based models to serve the full business cycle of clients. It should control the data supply chain through data assurance infrastructure. And finally it should be able to support implementation projects and bespoke assignments through a consultancy branch.

Yes, that is a full stack. The difference with full-stack washing is that every capability is actually owned or integrated, and the data stays assured from first mile to last mile.

The problem is, there are no companies doing exactly those things. Companies are usually a mix of the above-mentioned capabilities: satellite based carbon estimation with data-driven models for scope-3 reporting, first mile specialists offering compliance services on top of their verified field data, consultancies doing their own data analytics.

The pattern should be: look for complementary capabilities to improve the top line (revenue, market share); look for redundant or overlapping capabilities to reduce inefficiencies and costs and improve the bottom line.

Closing a loop doesn’t always mean buying the company that holds the missing piece. There is a spectrum: ownership (merge, acquire), partnership (white-label, revenue share, joint offering) and integration (APIs, shared standards). Choosing the right mode for each gap is part of the pattern. Ownership gives control but brings coordination cost; integration keeps you lean but leaves you dependent. Early in a market, integration and partnership are usually the smarter first step. They also turn out to be the best preparation for ownership later.

New product in new market anti-pattern 1: look for complementary capabilities to increase the top line, but forget to get rid of overlapping capabilities. Total costs will not be the sum of the original costs but (sum of original costs) × coordination factor, probably ruining your bottom line.

New product in new market anti-pattern 2: focus on the bottom line by reducing costs and inefficiencies through merging overlapping and redundant capabilities, but fail to grow the top line because complementary capabilities are missing.

New product in new market anti-pattern 3: claim the full stack first, then buy, merge or build the missing capabilities while the contracts come in. This is full-stack washing (see above).

The obvious counterargument: why not let buyers assemble best-of-breed specialists themselves? In mature markets with shared standards, that works. In a new market it pushes the integration problem onto the buyer, and buyers are not equipped for it. Every handoff between providers is a place where lineage, uncertainty and accountability get lost. When an auditor asks how a deforestation-free claim was derived, “three vendors and a spreadsheet” is not a good answer. Until standards mature, someone has to close the loop, and buyers will pay for it to be someone else.

There must be many more patterns and anti-patterns.

Strategies

I developed this model based on the environmental service providers sector. But the model is applicable to any sector. Every new sector has its own cycles and chains. Every new sector has its players excelling at parts of those cycles and chains. From this the strategy becomes obvious:

1. Be aware of the cycles and chains in your sector and your place in them;
2. Make sure you are ready to connect to other players or be connected to.

Having a strategy allows you to have a seat at the table. And you have to be at the table, else you’re on the menu.

Close the loops, fill the gaps

Find cycles and chains in your domain, determine your role and position in them, and identify missing capabilities to close the loop. Look around in the market and find (perceived) competitors with those capabilities and reach out to them. Find areas of complementarity, overlap and redundancy. Figure out the best way to collaborate to optimise revenue growth and minimise cost.

Do the same for any chains in your domain.

Chance favours the prepared mind

Being aware of your position in the system is one thing, the other is being prepared to close the loops and fill the gaps (without resorting to full-stack washing).

• Connected by Default (API and MCP first): if you can’t be plugged in, you won’t close any loop, including your own.
• Build for Reporting, Monitoring, Planning: design for the whole business cycle from day one, even if you only sell reporting today.
• Design Services as Modular Components: modules can be combined, swapped and acquired; monoliths can only be replaced.
• Embrace & Advocate Continuous Compliance: continuously assured data travels further than a report.

All of these are pre-competitive, no regret strategies that will both strengthen your position in the system and strengthen the system itself. I will write about each of these strategies in more detail in separate articles.

Hurray

A new way of looking at consolidation in markets. Patterns, anti-patterns. A new, hip term, “full-stack washing”, ready to go viral. And mitigation strategies. All for you. All for free.

Done.

Success.

Well, maybe.

Appendix

Table 1. Model family strengths and weaknesses.