Are Google and Amazon Underestimating Their Own Carbon Footprints?

Two of the world’s leading technology companies are under fire for underestimating data centers’ carbon footprints amid claims they use an obsolete tool for calculating emissions from electricity they purchase off the power grid.  

Lux Research, an independent research and advisory firm, went after the two tech giants for using tools that make broad generalizations about power production in the regions where Google and Amazon have large data facilities – reporting that the two companies may be underestimating their carbon footprints by 42,000 MT CO2e per year and 85,000 MT CO2e per year, respectively.

It’s pretty clear that Lux is using Google’s and Amazon’s data – data based on the EPA’s Emissions & Generation Resource Integrated Database (eGRID) – to tout its own analytical tool that estimates GHG emissions from electricity use.

What is important to note here is: the world of sustainability tools out there is rapidly moving. What you report today can be disputed tomorrow as new analytical tools, calculators, and data sets are developed.  

It’s not that eGRID is a terrible tool, or that Lux has built a surefire new solution, it’s more about choosing the right tool, at the right time, and at the right level of detail for your individual case.

Not every company needs a power-plant-by-power-plant analysis of its power sourcing, as the cost of a microscopic look at GHG emissions in this area may outweigh the overall variation in results. In other words, for many companies, the eGRID analysis would be absolutely acceptable based on moderate use of electricity in a given area as the overall data is within an acceptable margin of error.

However, power-intense companies like Google and Amazing, using vast amounts of energy, should absolutely be looking for the most refined and detailed tool to analyze power use impact. Being off by just a small percentage can represent tens of thousands of tons of CO2 being left un-reported, and more accurate data should help inform locations of future data centers to optimize clean power use.

If an organization is new to sustainability reporting, GHG calculating or meeting industry standards for environmental data, it is highly unlikely that that organization is going to be able to navigate these ever-changing waters without help.

Partnering with an experienced consulting firm like SSC, with the background knowledge and experience, to choose the best-fit reporting tool for every individual case is critical. Contact us today to talk about your carbon footprint analysis.  

3 Sustainability Tools that got our Attention in 2015

We appreciate good calculation tools. We are constantly looking for the most comprehensive or best combinations of calculation tools to cross check and ensure our clients are getting the best possible data. 

Here were three tools that got our attention in 2015:

GCSP-ITC Quick Scan Tool – Launched in June, this open-source tool allows companies to compare their compliance policies against best practices in order to inform improvements in supply-chain management. Provided by the Global Social Compliance Programme (GCSP), it is open to GCSP members and others free of charge.

• How it works: Buying companies can identify standards others use in purchasing. Suppliers can create a self-assessment, benchmark the assessment against peers, and identify immediate steps to move toward best practice.
• Who should use it: Anyone with a medium to lengthy supply chain or who is a supplier.

Water Risk Valuation Tool – Launched in September by Bloomberg ESG Data and Tols, this calculator illustrates how water risk can be valuated in corporate mining valuation models. Based on the gold and copper mining industries, this tool can inform all mining companies on how water risk might effect earnings and operations.

• How it works: The tool models potential “asset stranding” based on estimated future water scarcity and risk factors related to that scarcity.
• Who should use it: Mining companies, especially in precious metals

RiskHorizon – Launched in October by Anthesis Group, this web-based toold quantifies and monetizes environmental, social, and governance risk over 25 political, economic, social, and environmental areas, aggregating 100 different datasets.

• How it works: The tool is designed to help “futurecast” risks and opportunities in assets, supply chain, and business model and then quantify and prioritize the value of that risk. A big job.
• Who should use it: Investors, risk management professionals, supply chain managers, and strategic leaders should all be interested in a company’s risk profile.

One thing to remember - data out of context or too generalized really won't do anyone any good. Ensure you're working with a sustainability professional that can help validate and contextualize your data in your reporting process and sustainability planning programs.  

Have you used a calculator, but aren’t quite sure how to take action on results? Let us know. We can help assess your findings and customize a plan to help your company align with best practice in sustainability.

Data Management Concepts for Sustainability - Part 4

This article was written as an expansion of our white paper “Choosing Sustainability Management Software for your Business” published in July 2011.  If you’re looking for information on how to make your software selection, check out the full article.  If you just want to make sense of this particular topic, keep reading.  Whether you like this article or not, we want to hear from YOU so that we can continue to provide the best insight for YOU, our readers… 

Our series on Sustainability Software continues with “Data Management Concepts for Sustainability”.  In this article (Part 4 of 4), we’ll complete the introduction and definition of key Data Management terms (read Part 3 here).  Our end goal with this series is to enable YOU, as the Business Leader, to feel more comfortable in a technical discussion related to the various areas of Data Management, especially as related to the care and feeding of Sustainability Software packages. Being able to “talk the talk” is the best defense in the technology wilderness.  Just remember, at the basis of any technical term is a common sense business notion, and staying grounded to this notion will help keep your conversations from drifting astray.

Data Integration

Data Integration is one of the most difficult of the activities covered in this series because it involves most of the different activities working in concert with each other.  For example, it is implicit in Data Movement between systems where the Data models are different.  Suppose we have data in our Accounting system that will be used in a cost calculation algorithm (method) in our Sustainability Software.  To do this, we need to copy the Accounting data, then reshape it to conform to the load utilities for our package and proceed with the load.  This setup entails numerous subtleties including the cross referencing of the source data model in the Accounting System with the format of the import utility.  This is called Field Mapping and it’s not just an easy matching question where you can get the first few right and guess the rest.  Examples will help us here.

• Suppose we need to deal with quantity shipment data and the target model is asking for unit prices and volumes.  We might need to deduce the carbon content per gallon from the available carbon content per fifty five gallon barrel, or just divide by 55. 
• A more complex example involves translation from the English System to the Metric System (raise your hand if you can do this without a calculator).
• Another example would be the rules concerning the potential for rounding errors for large quantities.
• A final classic example is how to deal with Asian names (commonly listed with the surname first) being transferred into a system with a European paradigm (where the surname is listed last).

Data Integration is expensive to build and more expensive to operate.  SaaS is a way to avoid the Integration Tax to the extent possible since it has already been built into many of the downstream systems you’ll be using.

Data Mining

Data Mining is the last major topic to be introduced.  It also involves smatterings of the others, but has a unique ad-hoc character at its essence.  

Suppose we have a database that describes product production events in a manufacturing setting.  Suppose also that we wish to learn more about root causes of some recurring problem that has escaped previous attempts to solve it and choose to “look at all the occurrences at once”.  Someone who is expert in the data itself, as well as all the business processes it describes could attempt to construct queries that will reveal common conditions that led to the problem occurrences.  For example, he might notice they all tend to fall in the first half hour of their respective production runs.  Further drill down might reveal they all involve late shipments from the same raw material vendor, while production runs with timely shipments from the same vendor seem to go without mishap.  This would lead us to suspect potential spoilage or lack of maturity in the late arriving material.  Data Mining is a spiral learning discipline.  One spirals in to a common cause, or spirals out to learn the nature of the Cosmos. 

Conclusion

We hope that as a result of this information, albeit somewhat high-level, you’ll find a greater degree of ease in approaching Data Management problems and their solutions with any Sustainability Software package that you may be considering.   As with the rest of this series, our goal is to guide you through each of these complex topics and bring them safely toward a solution that will provide you with robust and accurate data and data management practices that will last for years to come. 

Now that you’ve read this article, tell us what you think!  And be sure to check out the full white paper.