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6 Data Characteristics for Achieving Net-Zero Emissions Targets
Sustainability

6 Data Characteristics for Achieving Net-Zero Emissions Targets

By Sebastian Schulz October 30, 2020

We have all seen how, despite reductions in travel and consumption as a result of our response to the COVID-19 pandemic, the Greenland ice sheet is melting at a faster rate than ever before and how wildfires have raged throughout California, Oregon and Washington state among many other locations worldwide. Now, the goal of achieving net-zero emission reduction targets is all the more prescient for the planet.

Even institutional investors, like BlackRock and Primco, are forcing many of the companies they invest in to commit to net-zero targets. And governing bodies, such as the EU and states like California, have greatly restricted fossil-fuel-burning mobility while placing higher prices on carbon emissions.

In this changing global context, companies are often taking the lead by setting their own net-zero emissions goals. Some of the largest and most successful international companies are taking the goal of drastically reducing their emissions very seriously. Just to name a few, IKEA, Walmart, BASF, VW, Mahindra, Bayer and Microsoft all have clear decarbonization strategies and specific emission reduction targets and deadlines, quantified and announced publicly.

When it comes to assessing greenhouse gas (GHG) emissions or the carbon footprints of products, Life Cycle Assessment (LCA) is a powerful tool that provides a baseline, gives insights into scope 3 emissions and helps facilitate a strategy for emissions reductions. Within an LCA, the quality of the underlying Life Cycle Inventory (LCI), including data about materials, processes, energy and transport, is crucial to producing valuable and reliable results. But what should companies actually look for when searching for the right data to act as a basis for a comprehensive assessment of their products and processes?

In conducting a carbon footprint analysis for a particular product, companies need accurate, product specific LCA datasets, preferably from one consistent LCA database. They need frequently updated datasets, based on information and knowhow from their particular industry and region, that are designed for compliance with multiple LCA standards and norms. For example, the electricity grid mix in Germany in 2008 (0,623 kg CO2 Equiv. / 1 kWh) was 10% higher in GHG emissions than in 2016 (0,563 kg CO2 Equiv. / 1 kWh), mainly because of the higher percentage of coal and the low share of renewable energy sources in the mix in 2008 (Source: Sphera’s LCA databases GaBi).

Development grid mix in Germany and EU-28

Development grid mix in Germany (left) and EU-28 (right), Source “GaBi Databases 2020 Edition: Upgrades & Improvements.”

Since then, the rate of renewable energy has increased every year leading to a 20% GHG emissions reduction for the German electricity grid mix from 2016 to 2019 (0,452 kg CO2 Equiv. / 1 kWh in 2019, source: Sphera’s LCA databases GaBi). So, these changes are examples for how, if you are using LCA databases that have not been updated accordingly, you will get inaccurate results and potentially have higher costs from carbon taxes and/or offsetting fees.

To avoid inaccuracies, I’ve outlined the following six characteristics of data that act as the foundation or precondition for airtight assessment, thereby allowing your company to confidently achieve its emissions reduction goals. The LCA data you use needs to:

  1. Be based on primary data sources, meaning LCA data should be cooperatively developed or validated by industry.
  2. Adhere to standardized methods and norms, like ISO 14044, ISO 14064 and ISO 14025 standards, and align with relevant regulations.
  3. Utilize (internal or external) expertise pertinent to your company’s particular sector and supported by on-going professional stakeholders.
  4. Be regularly updated by consistently combining points 1-3 (above) once a year to maintain quality.
  5. Be available on demand. This allows companies, wherever they are along their sustainability journey, to develop net-zero approaches that make the most sense, using the right datasets for specific sectors.
  6. Be expandable on demand. The process of working toward net zero leads to discoveries. With the right partner, companies can identify additional, relevant data outside of their immediate sector. Alternatively, they can request the development of additional datasets specific to their particular scope and to future technologies.

Life Cycle Assessment is not a religion based on belief, but a useful scientifically based, data-driven method for practical applications in the physical world. It is internationally ISO-standardized, and its results and data are comprehensive if you have the appropriate process-chain knowhow, methodological experience and technical and economic information.

At Sphera, we have been collecting and expanding LCA data for 30 years, with more than 100 in-house engineers and environmental scientists, adding up to more than one thousand person-years of work in data acquisition, updates, quality control and expansion. Each of our 15,000 LCA datasets include up to 100,000 individual unit processes for power plants, refineries and conveyor technologies, assuring that you are getting the most reliable LCA data available, with details that have been thoroughly reviewed and audited.

DEKRA, the European technical equipment and vehicle inspection company, has reviewed and audited our LCA data, our continuous improvement processes and our personnel. Sphera’s experts have been selected by 50 industry associations as “trusted experts” to create LCA data for them. Included are also such policy bodies, such as the EU DG Environment and the DG Joint Research Centre, which rely on our LCA data as a basis for sustainability-related regulations and legal frameworks.

If your company has a target for achieving net zero by 2050 or sooner, you’ll want to be sure that the data you use is accurate, so you can correctly calculate your corporate and product carbon footprints. If the data is too far off, it can have enormous financial repercussions for your business. Unreliable data equals financial and reputational uncertainty. To reduce the enormous brand and market risks associated with such uncertainty, take into consideration the six data characteristics necessary for achieving net zero and long-term sustainability success.

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