The Challenges of Measuring a Carbon Footprint

Measuring greenhouse gas emissions accurately is becoming crucial for large companies taking climate action. As the impacts of climate change and global warming accelerate, organisations must carefully track their emissions data across Scope 1 and beyond.

Understanding outputs like carbon dioxide and other greenhouse gases is the first step towards setting meaningful reduction targets. Whether aiming for net zero or transitioning to renewable energy, businesses must learn how to calculate your carbon footprint with precision.

This article explores the key challenges in modern carbon footprinting, including the role of fossil fuels and why effective measurement is essential for genuine progress.

Variability in Methodologies

When it comes to measuring a carbon footprint, the variability of methodologies definitely presents challenges.

Because emission measuring gets scrutinisingly granular, and many sectors have their own niche methodologies. Consider dairy farming methods quantifying methane compared to aluminium production measuring CO from industrial processes.

This need for industry-specific precision makes it harder for people to benchmark their environmental performance, track progress over time, identify leaders in the space, and inform stakeholders of consistent data.b12 3

As a fix, software should ensure its methodologies are standardised enough to be applied to a diverse range of companies while ensuring sector-specific accuracy.

Defining the Unit of the Study

When understanding something’s carbon footprint i.e. a product, a service, or an organisation’s footprint, you must first define what’s being measured.

If you’re measuring the footprint of an apple, you can’t just define it as an apple and move on.

Nuance and depths arise in order to gauge the footprint of where this apple has been, walked, and travelled. Is it 100g of apple? Is it one large apple? What if another company defines it as “per crate” or “per £ sold”?

The point here is that different definitions of the “unit” lead to entirely different carbon footprints, making comparison and accuracy difficult.

Defining the Scope of Emissions

When measuring a footprint, defining the emissions’ scope is far from simple. These 3 scopes are the most widely used frameworks for managing GHG emissions, but require extensive data collection and clear boundaries.

Scope 1 includes direct emissions from sources owned or controlled by a company. These emissions, often stemming from fossil fuel combustion, are generally easier to identify, measure, and manage, making them prime targets for reduction or offsetting efforts.

Scope 2 emissions encompass the indirect emissions let off by generating a business area’s electricity, steam, heating or cooling. Instead of direct control over the source, this tends to lie in the hands of the providers who may choose greener energy sources to help reduce emissions indirectly linked to the company.

Scope 3 encompasses all other indirect emission sources. These occur in a company’s value chain, both upstream (e.g., raw material extraction) and downstream (e.g., product use and disposal). Not only do they make up the most significant portion, but they’re also the hardest to track.

Data Collection Challenges

After all sources have been identified, data collection measures must be put in place. This captures the starting point of the reduction process, and puts systems in order to monitor and reduce the overall emission contributions over time. This ensures targets are being met and problematic areas are being attended to.

This could look like fleet telematics systems, utility bills, employee surveys, IoT sensors or smart metres, waste tracking logs, or a range of other automated and manual data collection tools tailored to each emission source.

Challenges arise with low data quality, manual data entry, missing data, time lags and more. To avoid these, opt for software with automated data collection and supplier engagement tools.

Data Accuracy and Reliability

When you have data streaming in from 1193 sources at once—everything from utility bills to supplier spreadsheets—ensuring 100% accuracy and reliability isn’t straightforward.

There can be outdated data sources, inconsistent methods of measurement (i.e. one team using actuals, another using estimates), faulty data, and any inconsistencies arising from the various different reporting boundaries and formats.

These issues are more likely when dealing with hard-to-track Scope 3 emissions or measuring without clear guidelines and data systems.

A strong software avoids this by implementing standardised methodologies and updating regularly in line with GHG Protocol updates and regulatory development.

Identifying Emission Factors

Identifying emission factors is challenging because they can be outdated, heavily variable, and values differ between different sources.

The identification involves selecting numerical values that convert activity data into quantifiable emissions. Emission factors indicate the average emissions produced per unit of activity, enabling more accurate measurement, streamlined compliance, scope relevance, and consistent reporting.

For instance, with electricity usage in the UK, using the DEFRA emission factor specific to the national grid yields a more accurate calculation than a generic global factor, as it reflects the UK’s unique energy mix.

Calculating Emissions from Complex Processes

Calculating emissions follows a simple formula: multiplying the activity data by × emission factor.

For example, multiplying a total electricity consumption of 10,000 kWh by the DEFRA emission factor (0.193 kg CO₂e/kWh) would result in a carbon footprint of 1,930 kg CO₂e for the building’s electricity usage.

For more complex calculations, such as a fleet of vehicles with a range of fuel types, a range of more specific carbon footprint formulas must be used.

Plants with a factory emitting smoke in the background

Emission Factors Are Not Static

If emission factors were static, measurement processes could be streamlined.

Emission factors—the numerical values that convert activity data into carbon emissions—change over time to account for advances in technology, shifts in sources of energy like lessening coal reliance, or new methods of data collection.

On top of this, the activities being measured rarely stay the same either. An employee isn’t going to always wash their hands for exactly 43.8 seconds every day.

To fix this, software must account for these unavoidable fluctuations and be adaptable, versatile and future-proofed.

Time Lag and Real-Time Monitoring

Most data collection happens monthly, quarterly or annually for simplified operations (and because data is being collected from an array of sources).

While streamlined in theory, in action this can cause major complications. What if major inefficiencies across an entire new fleet of vehicles are only identified 3 months after their integration?

To counteract this asynchronicity, software can utilise real-time data collection inputs tools that capture continuous data, such as smart metres or integrated IoT systems.

Verification and Assurance

When calculations are totalled, data must be verified for reliability and accuracy. This poses a challenge as third-party auditors can be expensive, monitoring reports independently, securing credibility and enhancing the quality of the data.

This could include internal audits and reviews, scouring for errors in calculations, validating data sources and double-checking methodology. Calculations should be benchmarked against industry standards.

Carbon accounting software should offer automated validation tools, able to notice and flag anomalies or inconsistencies to further enhance data strength.

Attribution and Shared Responsibility

The nature of emission measurement tracks indicates frameworks of a company, product, or service. This level of nuance can cause complications when it comes to attribution and taking the blame.

Without a clearly identified attribution, double-counting is at risk, and with too light an attribution, an organisation could easily be targeted and incorrectly encouraged to offset, while the true emitter avoids accountability entirely.

The fix here is that emission boundaries must be clearly outlined, with little room for blur and error.

Lack of Real-Time Tracking Systems

As recently covered, time lags in data collection can cause significant challenges like delayed action or misinformed reporting.

In some cases, it’s just not as simple as getting real-time tracking in place.

For some, the technology isn’t there yet, such as process-level emissions in complex manufacturing (i.e. chemical reactions or batch-level energy use). In other industries, it’s just too impractical or expensive, such as energy and fuel use monitoring at a small site.

Moreover, for some, it’s just not possible, such as product use and disposal, or waste decomposition.

Inconsistent Reporting Standards Across Regions

Due to vast differences in regulatory frameworks and disclosure expectations across regions, carbon reporting requirements vary significantly, from mandatory reporting in the UK to voluntary guidelines in the US.

While some frameworks like SECR enforce stringent disclosure rules and penalties, others may be more niche, more relaxed, or even non-existent.

This creates serious challenges for multinational companies, as their operations in one region may be exposed to higher compliance risks, calling for customised reporting strategies.

In time, a globalised framework should recognise the need for simplified, consistent reporting for companies operating beyond national borders.

Impact of Assumptions and Estimations

Some real data isn’t readily available—think supplier energy use or end-of-life product disposal. In these hard-to-track cases, an educated guess must be made, leaving room for vast inaccuracies and ineffective emissions reduction.

An estimation of average freight distances when the truth was even 20% off could result in thousands of kilograms of CO₂e misreported over the course of the annual reporting period.

Software should always document assumptions transparently, using conservative, worst-case estimations to assure meaningful emission reduction is being made without risk of underreporting.

Improving Carbon Footprint Measurement

Carbon footprint measurement has come a long way from basic input calculators and rough estimations. Modern software now offers a range of capabilities, from real-time data collection to automated compliance reporting.

The best platforms also include regulatory-specific features, such as SECR or CSRD reporting modules, keeping companies ahead of tightening legislation.

As climate regulations rapidly evolve, more businesses will fall under mandatory carbon accounting requirements.

When choosing your software, it’s essential to opt for a forward-thinking, adaptable solution like Gaia’s built for today’s needs and tomorrow’s demands.

Using Carbon Footprint Software

Businesses typically either hire an in-house consultant, build internal systems, or—most commonly—use specialised carbon footprint software like Gaia’s. It allows them to track, measure and report business-wide greenhouse gas (GHG) emissions.

The software allows businesses to understand their carbon footprint and exactly what is contributing to carbon emissions, crucially recognising the strongest contributors and determining key areas to reduce.

Following the GHG protocol, Scope 1 covers direct emissions, Scope 2 covers indirect emissions from energy, and Scope 3 covers other indirect emissions produced across the value chain. This allows for corporate transparency, demonstrates to clientele, audiences, stakeholders and employees that they are socially responsible for their carbon footprint.

It’s aligned with the Greenhouse Gas Protocol framework as the leading industry standard, including the annual UK GHG conversion factors provided by DEFRA and DESNZ. This allows you to calculate your carbon emissions while ensuring best practices.

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