Pitfalls of Green Economy Indicators: How to detect when “good” is actually not “good”

Publication Date: 

Monday, 23 February 2015 - 10:30am


Marius Hasenheit
Good indicator interpretations for good decisions & policies (license: CC, source: http://www.flickr.com/photos/90400155@N04/10674547284)


All indicators measure an object or field of interest, per se. Hence, a significant reason for misinterpretation occurs when indicators are used for a scope wider than intended. A prominent example might be GDP, which still is used by some policy actors as a measurement for the overall wellbeing of a nation.

Another reason for the misinterpretation of an indicator is that the assumptions about a direction of the indicator by the indicator are wrong. For example, greenhouse gas emissions are a very important green economy indicator, but emissions can decrease for various reasons - and not all of them are indications of progress in the transition towards green economy. Also, increasing environmental tax revenue could be automatically perceived as positive but it may well be due to (taxed) rising pollution, indicating that a potentially existing environmental policy is not achieving its aims. These misinterpretations are referred to as pitfalls (of interpretation).

Measuring-progress.eu combines experience, literature review and interviews with key Green Economy stakeholders to collect knowledge about cases where many different causal chains could be at work. By pointing out these potentially different causal chains the user can avoid pitfalls of interpretation. Measuring-progress.eu also provides additional indicators to assist the user in obtaining a clearer picture of underlying causal chains.

How to ensure consistency & objectivity in the identification of cases where multiple causal chains are at work?

First, it is crucial to incorporate the knowledge of many experts and stakeholders, which came from the project partners but also interviewees. During the pitfall identification process, all indicators were grouped according to their respective policy issue(s). Since similar indicators tend to have similar misinterpretations, this facilitaes the identification process.

Furthermore, all indicators are first examined with regard to their definition and contribution toward the transition towards Green Economy, separately. As a screening exercise for example, the definition incorporate threshold, where available are examined. Urban air quality indicators for example often incorporate the minimum diameter of particulate matters. A broken causal chain would appear, if the amount of particulate matter above the boundary value is decreasing, while the amount of smaller diameter particulate matter increases.

When the direction of the indicator is examined with its contribution to green Economy, multiple causal chains appear. If air quality indicators are improving, it would be useful to assess whether it is due to shifting of industrial production sites, leading to leakage effects. This can not only simulate a positive trend, but it could also hide negative impacts on employment and income levels.

One could conclude that the detection of interpretation pitfalls is challenging and impossible to be done objectively. However, it is possible to describe which pitfalls can occur under which assumptions after examining the contribution to Green Economy of the respective indicator.

Identifying the pitfalls is the condition to relate the respective indicators to the indicators, which show considerable pitfalls. This can help the user combine indicators which neutralize each other’s weaknesses. The Measuring-progress.eu tool will be firstly presented on the 11th of March and testing will start afterwards. The workshop will have no fee and registration is not restricted. Experiences and curiosity of other actors as workshop participants or user tools are very welcomed. The team behind Measuring-progress.eu is looking forward to insights and warning signs to help identify pitfalls in the “indicator matrix”.

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