A conceptual framework for selecting environmental indicator sets

• Published in: Ecological indicators 2008, Vol.8 (1), p.14-25
• Main Authors: Niemeijer, David, de Groot, Rudolf S.
• Format: Article
• Language: English
• Subjects: Causal chain; Ecological indicators; Environmental assessment; Environmental indicators; Environmental Systems Analysis; Environmental Systems Analysis Group; Indicator selection; Leerstoelgroep Milieusysteemanalyse; Milieusysteemanalyse; WIMEK
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2006.11.012

In recent years, environmental indicators have become a vital component of environmental impact assessments and “state of the environment” reporting. This has increased the influence of environmental indicators on environmental management and policy making at all scales of decision making. However, the scientific basis of the selection process of the indicators used in environmental reporting can be significantly improved. In many studies no formal selection criteria are mentioned and when selection criteria are used they are typically applied to indicators individually. Often, no formal criteria are applied regarding an indicator's analytical utility within the total constellation of a selected set of indicators. As a result, the indicator selection process is subject to more or less arbitrary decisions, and reports dealing with a similar subject matter or similar geographical entities may use widely different indicators and consequently paint different pictures of the environment. In this paper, a conceptual framework for environmental indicator selection is proposed that puts the indicator set at the heart of the selection process and not the individual indicators. To achieve this objective, the framework applies the concept of the causal network that focuses on the inter-relation of indicators. The concept of causal networks can facilitate the identification of the most relevant indicators for a specific domain, problem and location, leading to an indicator set that is at once transparent, efficient and powerful in its ability to assess the state of the environment.