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Clean Technologies and Environmental Policy

, Volume 14, Issue 5, pp 789–803 | Cite as

Sustainability performance evaluation in industry by composite sustainability index

  • Li Zhou
  • Hella Tokos
  • Damjan Krajnc
  • Yongrong Yang
Original Paper

Abstract

The growing importance of sustainable development as a policy objective has initiated a debate about those suitable frameworks and tools useful for policy makers when making a sustainable decision. Composite indicators (CIs) aggregate multidimensional issues into one index, thus providing comprehensive information. However, it is frequently argued that CIs are too subjective, as their results undesirably depend on the normalization method, a specific weighting scheme, and the aggregation method of sub-indicators. This article applies different combinations of normalization, weighting, and aggregation methods for the assessment of an industrial case study, with the aim of determining the best scheme for constructing CIs. The applied methodology gradually aggregates sustainable development indicators into sustainability sub-indices and, finally, to a composite sustainability index. The normalization methods included in this analysis are: minimum–maximum, distance to a reference, and the percentages of annual differences over consecutive years. Equal weightings, the ‘benefit of the doubt’ approach, and budget allocation process were used for determining the weights of individual indicators and sustainability sub-indices. The linear, geometric, and non-compensatory multi-criteria approaches (NCMCs) were used as aggregation methods. The NCMC is modified to fit the two-level aggregation, then to sub-indices, and finally to a composite sustainable index. Also, a penalty criterion is introduced into the evaluation process with the aim of motivating the company to move towards sustainable development. The results are analyzed by variance-based sensitivity analysis. According to the results the recommended scheme for CIs’ construction is: distance to a reference–benefit of the doubt–linear aggregation.

Keywords

Sustainability assessment Composite sustainability index Sensitivity analysis Breweries 

Abbreviations

AHP

Analytical hierarchy processes

BAP

Budget allocation process

BAT

Best available techniques

BOD

Benefit of the doubt

CA

Conjoint analysis

CI

Composite indicator

DEA

Data envelopment analysis

EW

Equal weighting

FAST

Fourier amplitude sensitivity test

GME

Geometric aggregation

GRI

Global reporting initiative

LIN

Linear aggregation

MCDA

Multiple criteria decision analysis

NCMC

Non-compensatory multi-criteria approach

UCM

Unobserved component models

WP

Weighted product

List of symbols

Sets

I

Set of indicator or input quantity of the evaluated model

J

Group of indicators (group of environmental indicators: j = 1; group of societal indicators: j = 2; group of economic indicators: j = 3) or input quantity of the evaluated model

T

The analysed time period (2003–2007)

Parameters

\( I_{i,j,t}^{ + } \)

Indicator i from group of indicator j in year t with positive impact on sustainable development

\( I_{i,j,t}^{ - } \)

Indicator i from group of indicator j in year t with negative impact on sustainable development

\( I_{i,j}^{\text{Benchmark}} \)

Benchmark for indicator i from group of indicators j

\( I_{{_{i,j} }}^{{ + ,{\text{ MAX}}}} \)

The highest value of indicator i with positive impact on sustainable development from group of indicator j for the analyzed time period

\( I_{{_{i,j} }}^{{ - ,{\text{ MAX}}}} \)

The highest value of indicator i with negative impact on sustainable development from group of indicator j for the analyzed time period

\( I_{{_{i,j} }}^{{ + ,{\text{ MIN}}}} \)

The lowest value of indicator i with positive impact on sustainable development from group of indicator j for the analyzed time period

\( I_{{_{i,j} }}^{{ - ,{\text{ MIN}}}} \)

The lowest value of indicator i with negative impact on sustainable development from group of indicator j for the analyzed time period

Xi

Input quantity i of the evaluated model

\( \tilde{X}_{i} \)

The true value of input quantity i

Variables

E(Y|Xi)

Expected value for the output quantity Y for the whole variation interval of the input quantity X i

\( E\left[ {V\left( {Y\left| {X_{ - i} } \right.} \right)} \right] \)

Expected amount of residual variance when X i , and only X i were left free to vary over its uncertainty range, all the other variables are fixed

\( I_{{N_{i,j,t} }}^{ + } \)

Normalized indicator i from group of indicator j in year t with positive impact on sustainable development

\( I_{{N_{i,j,t} }}^{ - } \)

Normalized indicator i from group of indicator j in year t with negative impact on sustainable development

\( I_{{S_{j,t} }} \)

Sustainable sub-indices for group of indicator j in year t

\( I_{{S_{j,t} }}^{\text{Benchmark}} \)

Sustainable sub-indices for group of indicator j determined for the benchmarks in year t

\( I_{{S_{j,t} }}^{*} \)

The highest value for the sustainable sub-indices for group of indicator j in year t

\( I_{{{\text{SUST}}\,_{t} }} \)

Composite sustainability index in year t

\( I_{{_{{{\text{SUST}}\,_{t} }} }}^{*} \)

The highest value for the composite sustainability index in year t

Si

First-order sensitivity index for input quantity i

Si, j

Second-order sensitivity index or two-way interaction for input quantities X i and X j

STi

Total sensitivity index for input quantity i

V(Y)

Unconditional variance for the output variable Y

\( V\left[ {E\left( {Y\left| {\tilde{X}_{i} } \right.} \right)} \right] \)

Conditional variance of the expected value for the output quantity Y when the input quantity is fixed on its true value \( \tilde{X}_{i} \)

\( V\left[ {E\left( {Y\left| {X_{i} ,X_{j} } \right.} \right)} \right] \)

Conditional variance of the expected value for the output quantity Y when input quantities X i and X j are fixed

wj

Weight of the group of sustainability indicator (sub-indices) j

wi,j

Weight of indicator i from group of indicator j

Y

Output quantity of the evaluated model

Notes

Acknowledgment

The authors are grateful to the National Natural Science Foundations of China (Project 21076180) for the financial support.

References

  1. Abusam A, Keesman K, Spanjers H, van Straten G (2004) Benchmarking procedure for full-scale activated sludge plants. Control Eng Pract 12(3):315–322CrossRefGoogle Scholar
  2. Beloff BR, Tanzil D (2006) Assessing impacts: overview on sustainability indicators and metrics. Environ Qual Manag 15(4):41–56CrossRefGoogle Scholar
  3. Chan K, Saltelli A, Tarantola S (1997) Sensitivity analysis of model output: variance-based methods make the difference. In: Proceedings of the 1997 winter simulation conference, pp 261–268Google Scholar
  4. Cherchye L (2007) An introduction to ‘benefit of the doubt’ composite indicators. Soc Indic Res 82(1):111–145CrossRefGoogle Scholar
  5. Cherchye L, Moesen W, Rogge N, Van Puyenbroeck T (2006) Creating composite indicators with DEA and robustness analysis: the case of the technology achievement index. J Oper Res Soc 59(2):239–251CrossRefGoogle Scholar
  6. De Carvalho SCP, Carden KJ, Armitage NP (2009) Application of a sustainability index for integrated urban water management in Southern African cities: case study comparison—Maputo and Hermanus. Water SA 35(2):144–151Google Scholar
  7. Frangopoulos CA, Keramioti DE (2010) Multi-criteria evaluation of energy system with sustainability considerations. Entropy 12(5):1006–1020CrossRefGoogle Scholar
  8. Gasparatos A, El-Haram M, Horner M (2008) A critical review of reductionist approaches for assessing the progress towards sustainability. Environ Impact Assess Rev 28(4–5):286–311CrossRefGoogle Scholar
  9. Geneletti D (2008) Impact assessment of proposed ski areas: a GIS approach integrating biological, physical and landscape indicators. Environ Impact Assess Rev 28(2–3):116–130CrossRefGoogle Scholar
  10. Hatefi SM, Torabi SA (2010) A common weight MCDA-DEA approach to construct composite indicators. Ecol Econ 70(1):114–120CrossRefGoogle Scholar
  11. Henning FPT, Muruvan S, Feng AW, Dunn CR (2011) The development of a benchmarking tool for monitoring progress towards sustainable transportation in New Zealand. Transp Policy 18(2):480–488CrossRefGoogle Scholar
  12. Krajnc D, Mele M, Glavič P (2007) Fuzzy Logic Model for the performance benchmarking of sugar plants by considering best available techniques. Resour Conserv Recycling 52(2):314–330CrossRefGoogle Scholar
  13. Mateos-Espejel E, Savulescu L, Maréchal F, Paris J (2011) Base case process development for energy efficiency improvement, application to a Kraft pulping mill. Part II: benchmarking analysis. Chem Eng Res Des 89(6):729–741CrossRefGoogle Scholar
  14. Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman A, Giovannini E (2008) Handbook on constructing composite indicators: methodology and user guide. OECD Publishing, ParisGoogle Scholar
  15. Niemeijer D (2002) Developing indicators for environmental policy: data driven and theory driven approaches examined by example. Environ Sci Policy 5(2):91–103CrossRefGoogle Scholar
  16. Phylipsen D, Blok K, Worrell E, de Beer J (2002) Benchmarking the energy efficiency of Dutch industry: an assessment of the expected effect on energy consumption and CO2 emissions. Energy Policy 30(8):663–679CrossRefGoogle Scholar
  17. Saisana M, Saltelli A (2008) Expert panel opinion and global sensitivity analysis for composite indicators. Comput Sci Eng 62:251–275Google Scholar
  18. Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297CrossRefGoogle Scholar
  19. Sikdar SK (2003) Sustainable development and sustainability metrics. AICHE J 49(8):1928–1932CrossRefGoogle Scholar
  20. Sikdar SK (2009) On aggregating multiple indicator into a single metric for sustainability. Clean Technol Environ Policy 11(2):157–161CrossRefGoogle Scholar
  21. Singh RK, Murty HR, Gupta SK, Dikshit AK (2007) Development of composite sustainability performance index for steel industry. Ecol Ind 7(3):565–588CrossRefGoogle Scholar
  22. Singh RK, Murty HR, Gupta SK, Dikshit AK (2009) An overview of sustainability assessment methodologies. Ecol Ind 9(2):189–212CrossRefGoogle Scholar
  23. Tokos H, Novak Pintarič Z, Krajnc D (2011) An integrated sustainability performance assessment and benchmarking of breweries. Clean Technol Environ Policy. doi: 10.1007/s10098-011-0390-0
  24. von Bahr B, Hanssen OJ, Vold M, Pott G, Stoltenberg-Hansson E, Steen B (2003) Experiences of environmental performance evaluation in the cement industry. Data quality of environmental performance indicators as a limiting factor for Benchmarking and Rating. J Clean Prod 11(7):713–725CrossRefGoogle Scholar
  25. Zhou P, Ang BW, Poh KL (2007) A mathematical programming approach to constructing composite indicators. Ecol Econ 62(2):291–297CrossRefGoogle Scholar
  26. Zhou P, Ang BW, Zhou D (2010) Weighting and aggregation in composite indicator construction: a multiplicative optimization approach. Soc Indic Res 96(1):169–181CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Chemical Engineering, Department of Chemical and Biochemical EngineeringZhejiang UniversityZhejiangPeople’s Republic of China
  2. 2.Laboratory for Process Systems Engineering and Sustainable Development, Faculty of Chemistry and Chemical EngineeringUniversity of MariborMariborSlovenia

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