Avoiding Data Drivenness

Many indicator projects are driven by the availability of relevant and reliable data because indicators are useful only when there are sufficient data to give meaningful

Figure 2.1. Six different baselines for one indicator value (1,000 dolphins present). A current population of 1,000 dolphins means different things when compared with historical data (1,300 in 1970), viability of the population (250), threat status (750) or the natural state (10,000) (Brink 2000).

results. In assembling indicator sets for sustainable development, data availability usually is a selection criterion to ensure rigorous quantitative underpinning. Even so, the limited quantity and quality of data underlying indicators of sustainability leave them open to criticism. Data collection is expensive, and countries are already under great pressure to supply data to international organizations from which they often receive multiple, overlapping, and uncoordinated requests for information. They are reluctant to accept indicators that imply new data collection.

This creates another conceptual challenge by producing biased and incomplete indicator sets that fall far short of measuring sustainability. We are forced to use indicators that were created for other purposes and describe only limited parts of the human—environment system. There are still extensive gaps in our knowledge, often reflecting inadequate supporting data. The result is both spatial and temporal bias. Scientific research and statistical data collection are strongest in industrialized countries, whose concerns and priorities dominate existing indicators.12 Temporal biases come from the lack of long-term data sets and the concentration of most research on a very narrow time frame linked to the present.

It takes a long time to initiate new data collection processes, often 5—10 years, even in wealthy countries. Thus indicators being implemented now reflect issues identified at least 5 years ago. Finding the best indicators of sustainability entails breaking away from data availability constraints and determining the appropriate phenomena to monitor and the indicators needed. This implies switching from a deductive to an inductive research process. Data gaps can initially be filled with pilot collections and sampling, the use of remote sensing data, or the use of proxies (see Chapter 3, this volume).

Once development processes and sustainability issues are better understood and modeled with suitable indicators, it should be possible make data collection simpler and more flexible, for example with optimal spatial and temporal sampling, as a guide to institutionalizing long-term monitoring. However, changing data collection practices requires political and legal authorization and significant resources, calling for more flexibility and careful consideration of costs and benefits.

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