Greenhouse gas data and tools

At Endrava, we take a fact-based, data-driven approach for making recommendations. Climate-related decisions can have significant impacts on business operations and lifestyles and therefore we want our clients to make their decisions based on the best available knowledge.

The issue is, although there is plenty of climate-related data available out there, much of it is unfortunately not readily available. By that we mean available for decision-making, in an easy to understand manner, for non-technical people. Insights are often hidden in complex excel spreadsheets, or worse, locked in difficult to use, outdated software or websites. We remove these barriers by helping our customers collect, process and present data in a way that is easily accessible and understandable by most. That is how we turn complicated data into actionable advice.

We understand the data

We are a multidisciplinary team, with background in research, engineering and consulting. Through our past experience with various organizations, we have built up an understanding of the pipeline for emission data. That is how we understand the science behind the data we work with.

Some of this data is also produced by us, through work on greenhouse gas emission calculations for the oil and gas sector, on reporting to authorities (e.g. EU ETS quotas), when aggregating and quality-assuring the data (e.g. at the Norwegian NOx-fund and the process industries SOx fund), and when synthesizing and communicating the findings. We understand the data, the methodology used to obtain it, and what it is used for.

We have the tools to collect and analyse the data

Our best day at the office is when someone sends us a well-organized Excel spreadsheet to work with. But life is not always that easy, and over the years we have developed our own scripts and tools to collect and process data. We use Python or VBA to connect to web APIs, automate data harvesting online, and simply reformat data tables into something more appropriate to our tools. We further analyse and augment the data by finding new connections between datasets, and by processing geographical data in dedicated tools (e.g. geographic information systems – GIS).

We turn the data into meaningful insights

Let’s face it, most people prefer looking at a nice map or chart rather than at a large Excel spreadsheet. We pay attention to details when producing our results, and we always try to find the representations that make the most out of the data we have, whether that be charts, maps, or interactive dashboards in PowerBI. Data literacy is a skill in itself, and we pay particular attention to it when working on projects.
How do our results look in practice? Here are a few examples of projects we have completed. Further below you will also find a short list of some of our favorite data sources.

Example of projects

Some of our favorite data sources