After having heard Jonathan Porritt at the Anglo-German Foundation, I studied their Creating Sustainable Growth in Europe research programme and applied for a small grant with this proposal:
A recent analysis of on-line statistics of the Bank of England has resulted in a submission to the Treasury Select Committee entitled Green Credit for Green Purposes. It was written in response to an inquiry into the Stern Review. Our key recommendation is the use of the Cash : Credit Ratio in the money supply as a new indicator and economic measure.
The report recommends ‘green credit’ as a mechanism for Government so that it can lead by ‘green governance’ and fuel the economy by funding environmentally beneficial activities.
By commissioning a similar study of data from the European Central Bank and the Bundesbank, new comparative insights would become available that would be applicable to both countries in terms of measuring sustainable growth.
The communication and dissemination of results is aimed at the City and Westminster in London and the German Government in Berlin. However, experience exists only in organising Forum meetings at the Palace of Westminster since 1998. Hence it is hoped that active collaboration with the Anglo German Foundation would contribute to the dissemination.
The proposed ‘matching analysis’ is also based on 10 years of independent research that resulted in an innovative framework for measuring new qualities called ‘3D Metrics’. In its deeply fundamental nature, it cuts across science, finance and economics through new mathematical methods embedded in proprietary software.
Description
As a first step towards further contributions to the Creating Sustainable Growth in Europe programme of the Anglo-German Foundation, the proposer it is suggested to analyse Euro data to compare the financial economics between the two countries.
In addition to her experience as a systems and data analyst, ‘3d metric’ software methods allow for
- comparing data sets that are hitherto not comparable – by visualizing ‘data layers’
- forecasting data over short, medium and long time intervals – by using methods that have been tested and found well above average on financial market data.
