The LoCaT project’s study has been released and we are thrilled to have been a part of this significant project, bringing together broadcasters from across Europe to analyse the energy consumption of different methods of watching TV – antennas, Freeview boxes, satellites and online apps.

We developed our own approach, in collaboration with leading academics in this area. We are pleased that the findings from our independent methodology was aligned with other studies in this area, whilst also providing some unique perspectives. We are very proud to have been a part of it and are excited to finally share the findings with you!

The bottom line is that, for individual viewers, the emissions per hour across all viewing methods are still quite small when compared with other every day activities like driving to the shops. When you’re comparing delivery methods of TV content, the many-to-one distribution of terrestrial TV comes out most efficient when compared to linear TV distributed over the internet. What’s still to be explored is the consequential effects of significant increase in internet traffic from viewing TV will affect the internet energy consumption in the longer-term.

In 2018 Carnstone analysed the first round of Gender Pay Gap reporting from the 350 largest listed UK companies. We have repeated and updated that analysis for 2019, again producing simple comparison tables for the FTSE 100 and FTSE 250, and again comparing companies on a sectoral basis.

Want to know who has the largest or smallest gap in your sector? This report collates the mean and median gaps, the mandatory quartile data and boils it all down into a simple single-figure rating.

Our 2018 report was widely used by HR and Governance teams wanting to know how they compared against peers. So this year, we have expanded the basic data with an analysis of the biggest movers; those companies reporting the largest rises or falls in their data, and their quoted reasons for the change.

Lastly, the report also contains some initial modelling work to try and link data to cause, by exploring some of the most common drivers of gender pay inequality and investigating the tell-tale signatures they might leave in company data.