A Parameter Selection Framework for Sustainability Assessment

Abstract

Sustainability assessment of resource recovery from waste is an important prerequisite for informed and sound decision-making. Life Cycle Sustainability Assessment (LCSA) has been developed to support this process, yet its use is still constrained by the difficulty of identifying the most relevant impact parameters. This paper, seeks to inform LCSA for resource recovery from waste based on a parameter identification approach that uses the political, economic, social, technological, environmental and legal (PESTEL) analysis. The novelty of this approach lies in the structured conceptualisation of the resource recovery system and the context within which decisions are made. The anaerobic digestion of source-separated food waste in the UK is used as a case study to trial and demonstrate the approach. Findings suggest that a conceptual, qualitative analysis, although limited in its scope due to the lack of quantitative components, is suitable in integrating different parameters, allowing for a holistic conceptualisation of the system and capturing important issues that could be easily overlooked. This type of analysis can summarise the key interdependencies, contrast the trade-offs and provide a wider understanding of the political and legal context within which the system operates, all important in extending the implementation of LCSA towards the right direction.

How to Achieve Supply Chain Sustainability Efficiently? Taming the Triple Bottom Line Split Business Cycle

“…(v) The established indicators within this DEA Malmquist index calculation -asset and revenue volume as input types as well as EBIT, dividend, GHG emissions, employment and women participation in management as output typeshave been proven to be relevant and meaningful for management science research regarding LSP. Other research projects should connect to these results and further the discussion regarding appropriate TBL sustainability indicators: This could for example be connected to a prior PESTLE analysis as suggested by Iacovidou et al [193]. Also, further trends and developments in the logistics …”

“…(v) The established indicators within this DEA Malmquist index calculation -asset and revenue volume as input types as well as EBIT, dividend, GHG emissions, employment and women participation in management as output types-have been proven to be relevant and meaningful for management science research regarding LSP. Other research projects should connect to these results and further the discussion regarding appropriate TBL sustainability indicators: This could for example be connected to a prior PESTLE analysis as suggested by Iacovidou et al [193]. Also, further trends and developments in the logistics and supply chain management domain (see Fawcett and Waller [194] or Brockhaus, Kersten and Knemeyer [195]) would have to be connected to the efficiency developments of European LSP in a comprehensive sustainability setting; (vi) Method-wise it is suggested that LSP efficiency development could be best analysed by incorporating an index component business cycle frontier shift besides existing technical frontier shift and catch-up index elements.…”

A multi-criteria sustainability assessment framework: development and application in comparing two food waste management options using a UK region as a case study

“…Anaerobic co-digestion's GHG emissions can be offset by renewable energy generation (via biogas production), and partly from chemical fertiliser substitution due to the use of the digestate produced; although the degree to which the latter is used in the UK, is currently constraint by the lack of a coherent regulatory framework in regards to certifying its quality status (Iacovidou et al 2012b). Both alternatives are reported to contribute minimally to acidification, ozone depletion potential, photochemical ozone formation potential and ecotoxicity, although some nitrous oxide (NOx) emissions via manufacturing processes, combustion of the biogas in the combined heat and power (CHP) engines, and emissions from digestate storage have been reported (Iacovidou et al 2017a;Sonesson et al 2000;Whiting and Azapagic 2014). The eutrophication potential may prevail due to the risk of nutrient leaching to surface and ground water from the wastewater effluents and or digestate application to land (Di Maria and Micale 2015).…”

Corporate sustainability performance measurement based on a new multicriteria sorting method

“…In the literature, researchers applied different methodologies to measure corporate sustainability such as balanced scorecard method (Hsu, Hu, Chiou, & Chen, 2011;Junior, de Oliveira, & Helleno, 2018;Kang, Cgiang, Huangthanapan, & Downing, 2015; Medel-Gonzalez, Garcia-Avila, Salomon, Marx-Gomez, & Hernandez, 2016;Nikolaou & Tsalis, 2013;Nurcahyo, Pustiwari, & Gabriel, 2018), life cycle assessment (Guinee et al, 2008;Hu, Kleijn, Bozhilova-Kisheva, & Di Maio, 2013;Iacovidou et al, 2017), and data envelopment analysis (Zhou, Yang, Chen, & Zhu, 2018;Yu, Wang, & Ho, 2016;Sueyoshi & Wang, 2014;Chang, Zhang, Danao, & Zhang, 2013;Lee & Saen, 2012). In the literature, researchers applied different methodologies to measure corporate sustainability such as balanced scorecard method (Hsu, Hu, Chiou, & Chen, 2011;Junior, de Oliveira, & Helleno, 2018;Kang, Cgiang, Huangthanapan, & Downing, 2015; Medel-Gonzalez, Garcia-Avila, Salomon, Marx-Gomez, & Hernandez, 2016;Nikolaou & Tsalis, 2013;Nurcahyo, Pustiwari, & Gabriel, 2018), life cycle assessment (Guinee et al, 2008;Hu, Kleijn, Bozhilova-Kisheva, & Di Maio, 2013;Iacovidou et al, 2017), and data envelopment analysis (Zhou, Yang, Chen, & Zhu, 2018;Yu, Wang, & Ho, 2016;Sueyoshi & Wang, 2014;Chang, Zhang, Danao, & Zhang, 2013;Lee & Saen, 2012).…”

“…To improve corporate sustainability, organizations must measure their performances. In the literature, researchers applied different methodologies to measure corporate sustainability such as balanced scorecard method (Hsu, Hu, Chiou, & Chen, 2011;Junior, de Oliveira, & Helleno, 2018;Kang, Cgiang, Huangthanapan, & Downing, 2015; Medel-Gonzalez, Garcia-Avila, Salomon, Marx-Gomez, & Hernandez, 2016;Nikolaou & Tsalis, 2013;Nurcahyo, Pustiwari, & Gabriel, 2018), life cycle assessment (Guinee et al, 2008;Hu, Kleijn, Bozhilova-Kisheva, & Di Maio, 2013;Iacovidou et al, 2017), and data envelopment analysis (Zhou, Yang, Chen, & Zhu, 2018;Yu, Wang, & Ho, 2016;Sueyoshi & Wang, 2014;Chang, Zhang, Danao, & Zhang, 2013;Lee & Saen, 2012).…”