Synthetic Meta-Index of Sustainable Development: A DEA Approach Laurens Cherchye (Catholic University of Leuven, Belgium) Timo Kuosmanen (Wageningen University, The Netherlands) INFORMS Annual Meeting, San Jose November 2002

What is Sustainable Development? Various definitions. Vague concept. The Brundlant Commission Report (1987) gives the landmark one SD="Meeting the needs of the present without compromising the ability of future generations to meet their own needs Aspects of SD: Economic, Social/Political, Environmental

Environmental Sustainability Index 2002 An Initiative of the Global Leaders for Tomorrow Environment Task Force, World Economic Forum

From WEF (2002): ESIs Purpose Benchmark environmental performance (!) Identify comparatively environmental results that are above or below expectations Identify best practices (!) Investigate interactions between environmental and economic performance

From WEF (2002): Strengths of ESI + Measures Environmental Sustainability + Permits cross-country comparisons + Method is transparent, reproducible + Enhances capacity to benchmark performance, guide policy, deepen understanding Weaknesses - Assumes particular set of weights (!) - Suffers from gaps in available data - Lacks time series data which limits ability to identify policy drivers

3 dimensions of the study Methodological: A new approach of quantifying Sustainable Development (SD) Operational: New tools and computational procedures related to the weighting methods Empirical: Rank countries in terms of SD to identify benchmarks and pinpoint under- performers.

Components of our meta-index:

SD frontier

DEA-based MISD-index: definition

Weight restrictions Almost all countries perform very well in one of the 14 indices. The basic DEA weighting allows for assigning all weight to just one dimension, so that poor performance on other criteria does not count. Standard solution is to impose additional constraints on weights w.

3 types of weight restrictions: Relative weight between 2 outputs (h,i) for a given country j Relative weight between output categories (k,l) for a given country j Relative weight of the given output i between 2 countries (j,k)

Problem of missing data / blank entries Data of all 14 indices available for 15 countries! We included all countries with the minimum of 6 data entries => lots of blank entries In the basic model, the blanks do not matter: Using 0 for blank entries is equivalent to excluding the missing variable for that particular country. The problem with blank entries arises when we impose weight restrictions enforce a strictly positive weight for the 0 elements in the data matrix.

Modeling the weight restrictions The weight restrictions should can relaxed for the missing data by modeling weight restrictions as disjunctive constraints: This can be equivalently formulated as a linear inequality:

MISD rankings, High-income countries (>$9266/cap.)

Weights

MISD versus GDP/capita r = 0.315

Conclusions DEA appears a promising tool for weighting multiple dimensions of SD to identify benchmarks. Weight flexibility can be restricted across SD outputs, but also across output categories and across countries. DEA seems relatively robust to blank entries, when there are other variables to compensate the missing information. Simple trick to relax weight-restrictions in case of blank entries.

Challenges for future research Constructing a superior SD index directly from measures and indicators, rather than using aggregated indices. Dynamic index: Measuring a rate of change in the stock variables, instead of mixing up stocks and flows. A Malmquist index approach.

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