1,728 research outputs found

    Valuing Environmental Factors in Cost-Benefit Analysis Using Data Envelopment Analysis

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    Environmental cost-benefit analysis (ECBA) refers to social evaluation of investment projects and policies that involve significant environmental impacts. Valuation of the environmental impacts in monetary terms forms one of the critical steps in ECBA. We propose a new approach for environmental valuation within ECBA framework that is based on data envelopment analysis (DEA) and does not demand any price estimation for environmental impacts using traditional revealed or stated preference methods. We show that DEA can be modified to the context of CBA by using absolute shadow prices instead of traditionally used relative prices. We also discuss how the approach can be used for sensitive analysis which is an important part of ECBA. We illustrate the application of the DEA approach to ECBA by means of a hypothetical numerical example where a household considers investment to a new sport utility vehicle.Cost-Benefit Analysis, Data Envelopment Analysis, Eco-Efficiency, Environmental Valuation, Environmental Performance, Performance Measurement

    The change of the Spanish tourist model: From the Sun and Sand to the Security and Sand

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    There is evidence of specialisation in tourism destinations, but also a lack of literature regarding itsimpact on tourism regional performance. This study aims to contribute to the analysis of thedeterminants of tourism performance. To this end, the efficiency of 17 Spanish regions has beenestimated by meta-frontier data envelopment analysis techniques over the 2008-2018 period. In thesecond stage, we adopt the bootstrapping method proposed by Simar and Wilson to measure theimpact of explanatory factors on tourism efficiency. The results suggest that regions specialised intourism may achieve higher efficiency levels. However, there is evidence of a catching-up process inthe tourism technology of the Spanish regions over the last 10 years. Results also suggest thatsand(kilometres of beaches) andinsecurityare the key drivers of tourism efficiency. Moreover,naturalattractionsis the factor that most positively influences efficiency in non-specialised region

    Industry 4.0 enabling sustainable supply chain development in the renewable energy sector:A multi-criteria intelligent approach

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    The aim of this paper is to provide a multi-criteria decision-making intelligent approach based on Industry 4.0 and Triple Bottom Line principles for sustainable supply chain development in the renewable energy sector. In particular, the solar photovoltaic energy supply chain is used as a case study, encompassing the entire energy production process, from supply to disposal. An exhaustive literature review is conducted to identify the main criteria affecting social, economic and environmental sustainability in the photovoltaic energy supply chain, and to explore the potential impact of Industry 4.0 on sustainability. Subsequently, three Fuzzy Inference Systems combining quantitative and qualitative data are built to calculate the supply chain's social, economic and environmental sustainability. Experts' opinions are used to identify the impact of Industry 4.0 technologies on the three pillars of sustainability for each supply chain stage. Finally, a novel sustainability index, Sustainability Index 4.0, is formulated to compute the overall sustainability of the photovoltaic energy supply chain in seven countries. The results show the applicability and usefulness of the proposed holistic model in helping policy makers, stakeholders and users to make informed decisions for the development of sustainable renewable energy supply chains, taking into account the impact of Industry 4.0 and digital technologies

    An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

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    To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.This article belongs to the Special Issue Sustainability Assessmen

    Sustainability in universities: DEA-Greenmetric

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    [EN] Many universities are currently doing important work not only on environmental issues, but also on social and economic matters, thereby covering the three dimensions of sustainability. This paper uses Data Envelopment Analysis to construct a synthetic indicator based on the variables that make up the UI GreenMetric. The aim is to quantify universities' contribution to sustainability, rank all the campuses accordingly, and evaluate specific aspects of their related institutional policies. First, cluster analysis is applied, yielding four homogeneous groups of universities. DEA is then applied to these clusters in order to construct the synthetic indicator. The proposed indicator, DEA-GreenMetric, reveals that the US and the UK are the countries that are home to the greatest number of universities actively involved in all aspects of sustainability. In addition, this new index provides a complete ranking of universities, circumventing the issue of the duplicate scores assigned by UI GreenMetric. Lastly, it can be seen that greater efforts are required for universities to improve their performance relating to environmental variables (energy, water use and waste treatment) than to make improvements in infrastructure, transport or education.Puertas Medina, RM.; Martí Selva, ML. (2019). Sustainability in universities: DEA-Greenmetric. Sustainability. 11(14):1-17. https://doi.org/10.3390/su11143766S1171114Kuhlman, T., & Farrington, J. (2010). What is Sustainability? Sustainability, 2(11), 3436-3448. doi:10.3390/su2113436Castellani, V., & Sala, S. (2010). Sustainable performance index for tourism policy development. Tourism Management, 31(6), 871-880. doi:10.1016/j.tourman.2009.10.001Togtokh, C. (2011). Time to stop celebrating the polluters. Nature, 479(7373), 269-269. doi:10.1038/479269aSharp, L. (2002). Green campuses: the road from little victories to systemic transformation. International Journal of Sustainability in Higher Education, 3(2), 128-145. doi:10.1108/14676370210422357Shriberg, M. (2002). Institutional assessment tools for sustainability in higher education. International Journal of Sustainability in Higher Education, 3(3), 254-270. doi:10.1108/14676370210434714Balsas, C. J. . (2003). Sustainable transportation planning on college campuses. Transport Policy, 10(1), 35-49. doi:10.1016/s0967-070x(02)00028-8Lukman, R., Krajnc, D., & Glavič, P. (2010). University ranking using research, educational and environmental indicators. Journal of Cleaner Production, 18(7), 619-628. doi:10.1016/j.jclepro.2009.09.015Baboulet, O., & Lenzen, M. (2010). Evaluating the environmental performance of a university. Journal of Cleaner Production, 18(12), 1134-1141. doi:10.1016/j.jclepro.2010.04.006Alshuwaikhat, H. M., & Abubakar, I. (2008). An integrated approach to achieving campus sustainability: assessment of the current campus environmental management practices. Journal of Cleaner Production, 16(16), 1777-1785. doi:10.1016/j.jclepro.2007.12.002Lukman, R., & Glavič, P. (2006). What are the key elements of a sustainable university? Clean Technologies and Environmental Policy, 9(2), 103-114. doi:10.1007/s10098-006-0070-7León-Fernández, Y., & Domínguez-Vilches, E. (2015). Environmental management and sustainability in higher education. International Journal of Sustainability in Higher Education, 16(4), 440-455. doi:10.1108/ijshe-07-2013-0084Velazquez, L., Munguia, N., Platt, A., & Taddei, J. (2006). Sustainable university: what can be the matter? Journal of Cleaner Production, 14(9-11), 810-819. doi:10.1016/j.jclepro.2005.12.008Grindsted, T. S. (2011). Sustainable Universities From Declarations on Sustainability in Higher Education to National Law. SSRN Electronic Journal. doi:10.2139/ssrn.2697465Suwartha, N., & Sari, R. F. (2013). Evaluating UI GreenMetric as a tool to support green universities development: assessment of the year 2011 ranking. Journal of Cleaner Production, 61, 46-53. doi:10.1016/j.jclepro.2013.02.034Sonetti, G., Lombardi, P., & Chelleri, L. (2016). True Green and Sustainable University Campuses? Toward a Clusters Approach. Sustainability, 8(1), 83. doi:10.3390/su8010083Drahein, A. D., De Lima, E. P., & Da Costa, S. E. G. (2019). Sustainability assessment of the service operations at seven higher education institutions in Brazil. Journal of Cleaner Production, 212, 527-536. doi:10.1016/j.jclepro.2018.11.293Parvez, N., & Agrawal, A. (2019). Assessment of sustainable development in technical higher education institutes of India. Journal of Cleaner Production, 214, 975-994. doi:10.1016/j.jclepro.2018.12.305Ragazzi, M., & Ghidini, F. (2017). Environmental sustainability of universities: critical analysis of a green ranking. 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Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Angulo-Meza, L., & Lins, M. P. E. (2002). Annals of Operations Research, 116(1/4), 225-242. doi:10.1023/a:1021340616758Hashimoto, A., & Ishikawa, H. (1993). Using DEA to evaluate the state of society as measured by multiple social indicators. Socio-Economic Planning Sciences, 27(4), 257-268. doi:10.1016/0038-0121(93)90019-fHashimoto, A., & Kodama, M. (1997). Social Indicators Research, 40(3), 359-373. doi:10.1023/a:1006804520184Zhu, J. (2001). Multidimensional quality-of-life measure with an application to Fortune’s best cities. Socio-Economic Planning Sciences, 35(4), 263-284. doi:10.1016/s0038-0121(01)00009-xMurias, P., Martinez, F., & De Miguel, C. (2006). An Economic Wellbeing Index for the Spanish Provinces: A Data Envelopment Analysis Approach. 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    Environmental and Energy Efficiency Evaluation Based on Data Envelopment Analysis (DEA)

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    Efficiency and productivity assessment are essential to ensure the long-term financial sustainability of countries, services and processes. In the last few years, there has been an increasing interest in the environmental effects of economic activities, and the need to assess the environmental and energy efficiency has been internationally recognized. Energy and environmental efficiency assessments of decision-making units (DMUs), such as countries, utilities, processes and services are relevant and have strong implications for companies, regulators, stakeholders, policy makers, and customers. To improve both the decision-making process and the management of DMUs, fundamental and practical knowledge about energy and environmental efficiency and productivity is essentia

    An additive two-stage DEA approach creating sustainability efficiency indexes

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    In this paper we apply an additive two-stage data envelopment analysis (DEA) estimator on a panel of 27 Annex I countries for the time period 2006-2010 in order to create sustainability efficiency indexes. The sustainability efficiency indexes are decomposed into economic and eco-efficiency indicators. The results reveal inequalities among the examined countries between the two stages. The eco-efficiency stage is characterized by large inequalities among countries and significantly lower efficiency scores than the overall or/and the economic efficiency stages. Finally, it is reported that a country’s high economic efficiency level does not ensure a high eco-efficiency performance
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