What data your Sustainable Mobility Plan needs to comply with Royal Decree-Law 7/2026

The challenge is no longer understanding the regulation, but knowing what data a Sustainable Mobility Plan needs to comply with it. With the timeline brought forward by Royal Decree-Law 7/2026, many organizations have realized that the real difficulty is not designing the plan, but building a solid data foundation. Because without data, there is no diagnosis—and without a diagnosis, there is no plan that can withstand an audit.

In practice, most organizations are not starting from scratch, but neither do they have a solid baseline. Information is scattered across HR, operations, and external providers. There are outdated surveys, poorly updated internal policies, and, above all, a lack of traceability that makes it difficult to quantify impact.

This article addresses that critical point: what data a Sustainable Mobility Plan actually requires, where companies tend to fall short, and how to structure information to comply with Spain’s Sustainable Mobility Law 9/2025 without turning the process into an endless project.

What data does a Sustainable Mobility Plan require under the law?

The regulation does not call for a theoretical document, but for a plan grounded in real commuting patterns. This means working with concrete, up-to-date, and, above all, representative data.

It also does not affect all organizations equally. The obligation is particularly focused on companies with more than 200 employees, or those with more than 100 workers per shift, where the scale and complexity of commuting make a detailed analysis essential.

This is where the first friction appears. Many companies try to build their plan based on estimates or industry averages. This is understandable, but insufficient. The regulation points toward measurable and justifiable actions—requiring a much deeper level of detail.

In practice, the data for a Sustainable Mobility Plan is structured into four main blocks, enabling organizations to understand, measure, and improve commuting patterns.

1. How people actually commute

This is the core of the diagnosis. It’s not enough to know where the office is—you need to understand how people get there.

Key data:

• Employees’ place of residence (at least by postal code)
• Usual mode of transport
• Distance traveled and commuting frequency
• Start and end times

The issue here is usually not technical, but methodological. Many companies rely on internal surveys with low participation or poorly designed questions, while others don’t even have that starting point. Without this layer, any subsequent measure becomes a hypothesis.

2. The impact of those commutes

Once mobility patterns are understood, the next step is to translate them into impact.

Required data:

• Emissions associated with each transport mode
• Energy consumption linked to commuting
• Direct and indirect costs for the company

This is where technical complexity comes in. It’s not just about collecting data, but applying the correct emission factors and consistent methodologies. This is often where organizations lose precision or comparability.

3. What viable alternatives exist

The plan is not limited to describing the current situation—it must propose realistic measures.

This requires data such as:

• Public transport availability in areas where employees operate
• Existing infrastructure (parking, bike lanes, charging points)
• Current internal policies (remote work, flexible hours, incentives)

This block is often fragmented across different departments or not documented at all.

4. How improvement will be measured

Regulation emphasizes monitoring. It’s not enough to define actions—you must demonstrate their impact over time.

This involves:

• Clear indicators (emissions reduction, modal shift, use of shared transport)
• Systems for periodic data collection
• Reporting capabilities aligned with ESG objectives

Another common issue appears here: many companies design the plan, but not the measurement system. And without that system, compliance remains uncertain.

The problem isn’t a lack of data—it’s how it’s structured

One of the most common mistakes is assuming there is no data. In reality, most organizations do have it—but it’s scattered, incomplete, or stored in formats that make it unusable.

HR has employee addresses, finance tracks transport expenses, and operations understands logistics. But none of it is connected.

The challenge, therefore, is not just collecting information, but shaping it—turning isolated data points into a system that enables analysis, decision-making, and reporting. This is where many companies lose time. And right now, time is exactly what they don’t have. Royal Decree-Law 7/2026, which approves the Response Plan to the Middle East Crisis, has shortened deadlines: companies must now have their mobility plan ready before the end of 2026.

How to structure data for a Sustainable Mobility Plan

The real shift is not about measuring more, but measuring better—building a data model that can answer three key questions:

• Where are we now?
• What can we change?
• What impact will those changes have?

As regulatory pressure increases, so does the need for tools that can structure this process—not to replace analysis, but to make it feasible within tight timelines.

Centralizing data, automating emissions calculations, and maintaining continuous tracking is no longer an operational improvement—it’s a requirement for compliance.

Don’t let data block your compliance. At Manglai, we centralize your scattered information so you can have your plan ready before the end of 2026.

Request a technical demo here.

Because in this new scenario, the risk is not failing to understand the regulation—it’s failing to have the data ready in time to comply with it.