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Environmental performance evaluation with big data: theories and methods

  • Big Data Analytics in Operations & Supply Chain Management
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Abstract

Traditional theories and methods for comprehensive environmental performance evaluation are challenged by the appearance of big data because of its large quantity, high velocity, and high diversity, even though big data is defective in accuracy and stability. In this paper, we first review the literature on environmental performance evaluation, including evaluation theories, the methods of data envelopment analysis, and the technologies and applications of life cycle assessment and the ecological footprint. Then, we present the theories and technologies regarding big data and the opportunities and applications for these in related areas, followed by a discussion on problems and challenges. The latest advances in environmental management based on big data technologies are summarized. Finally, conclusions are put forward that the feasibility, reliability, and stability of existing theories and methodologies should be thoroughly validated before they can be successfully applied to evaluate environmental performance in practice and provide scientific basis and guidance to formulate environmental protection policies.

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Notes

  1. The UN Climate Change Conference was held on October 23, 2013 in Warsaw, the capital of Poland. Agreements on some important subjects were reached.

  2. NSF has released the plan “Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA)”.

  3. These data include emission loads and emission ratios of oxynitride, sulfur dioxide, carbon dioxide, methane, nitrous oxide, net-generated energy, etc.

  4. International Data Corporation (IDC) presented relevant research in their report “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East”.

References

  • Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.

    Article  Google Scholar 

  • Arabi, B., Doraisamy, S. M., Emrouznejad, A., & Khoshroo, A. (2016). Eco-efficiency measurement and material balance principle: An application in power plants Malmquist Luenberger Index. Annals of Operations Research. doi:10.1007/s10479-015-1970-x.

    Article  Google Scholar 

  • Aronova, E., Baker, K. S., & Oreskes, N. (2010). Big science and big data in biology: From the international geophysical year through the international biological program to the long term ecological research (LTER) network, 1957-present. Historical Studies in the Natural Sciences, 40(2), 183–224.

    Article  Google Scholar 

  • Bagliani, M., Galli, A., Niccolucci, V., & Marchettini, N. (2008). Ecological footprint analysis applied to a sub-national area: The case of the province of Siena (Italy). Journal of Environmental Management, 86(2), 354–364.

    Article  Google Scholar 

  • Bi, G., Luo, Y., Ding, J., & Liang, L. (2012). Environmental performance analysis of Chinese industry from a slacks-based perspective. Annals of Operations Research, 228(1), 65–80.

    Article  Google Scholar 

  • Bizer, C., Boncz, P., Brodie, M. L., & Erling, O. (2012). The meaningful use of big data: Four perspectives-four challenges. ACM SIGMOD Record, 40(4), 56–60.

    Article  Google Scholar 

  • Blengini, G. A., Busto, M., Fantoni, M., & Fino, D. (2012). Eco-efficient waste glass recycling: Integrated waste management and green product development through LCA. Waste Management, 32(5), 1000–1008.

    Article  Google Scholar 

  • Bogetoft, P., & Wang, D. (2005). Estimating the potential gains from mergers. Journal of Productivity Analysis, 23(2), 145–171.

    Article  Google Scholar 

  • Cabeza, L. F., Rincón, L., Vilariño, V., Pérez, G., & Castell, A. (2014). Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renewable & Sustainable Energy Reviews, 29, 394–416.

    Article  Google Scholar 

  • Cai, Y., Lu, Y., Stegman, A., & Newth, D. (2015). Simulating emissions intensity targets with energy economic models: Algorithm and application. Annals of Operations Research. doi:10.1007/s10479-015-1927-0.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Chen, C.-M., & Delmas, M. A. (2012). Measuring eco-inefficiency: A new frontier approach. Operations Research, 60(5), 1064–1079.

    Article  Google Scholar 

  • Cherubini, F., Bargigli, S., & Ulgiati, S. (2009). Life cycle assessment (LCA) of waste management strategies: Landfilling, sorting plant and incineration. Energy, 34(12), 2116–2123.

    Article  Google Scholar 

  • Chevallier, J., & Goutte, S. (2015). Estimation of Lévy-driven Ornstein–Uhlenbeck processes: Application to modeling of CO\(_2\) and fuel-switching. Annals of Operations Research. doi:10.1007/s10479-015-1967-5.

    Article  Google Scholar 

  • Coelli, T., Lauwers, L., & Van Huylenbroeck, G. (2007). Environmental efficiency measurement and the materials balance condition. Journal of Productivity Analysis, 28(1–2), 3–12.

    Article  Google Scholar 

  • Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with nonhomogeneous DMUs. Operations Research, 61(3), 666–676.

    Article  Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal of Operational Research, 192(1), 1–17.

    Article  Google Scholar 

  • Cook, W. D., & Zhu, J. (2011). Multiple variable proportionality in data envelopment analysis. Operations Research, 59(4), 1024–1032.

    Article  Google Scholar 

  • Cooper, J., Noon, M., Jones, C., Kahn, E., & Arbuckle, P. (2013). Big data in life cycle assessment. Journal of Industrial Ecology, 17(6), 796–799.

    Article  Google Scholar 

  • Cooper, J. S., Kahn, E., & Ebel, R. (2013). Sampling error in US field crop unit process data for life cycle assessment. The International Journal of Life Cycle Assessment, 18(1), 185–192.

    Article  Google Scholar 

  • DeSimone, L. D., & Popoff, F. (2000). Eco-efficiency: The business link to sustainable development. Cambridge, MA: MIT Press.

    Google Scholar 

  • Dong, J., Chi, Y., Zou, D., Fu, C., Huang, Q., & Ni, M. (2014). Energy-environment-economy assessment of waste management systems from a life cycle perspective: Model development and case study. Applied Energy, 114, 400–408.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., & Ali, S. S. (2015a). Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain. International Journal of Production Economics, 160, 120–132.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., & Chakrabarty, A. (2015b). World-class sustainable manufacturing: Framework and a performance measurement system. International Journal of Production Research. doi:10.1080/00207543.2015.1012603.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2015c). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-015-7674-1.

    Article  Google Scholar 

  • Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325.

    Article  Google Scholar 

  • Ellison, A. M. (2010). Repeatability and transparency in ecological research. Ecology, 91(9), 2536–2539.

    Article  Google Scholar 

  • Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249–254.

    Article  Google Scholar 

  • Fadeyi, S., Arafat, H. A., & Abu-Zahra, M. R. (2013). Life cycle assessment of natural gas combined cycle integrated with CO\(_2\) post combustion capture using chemical solvent. International Journal of Greenhouse Gas Control, 19, 441–452.

    Google Scholar 

  • Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90–98.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Lovell, C. K., & Yaisawarng, S. (1993). Derivation of shadow prices for undesirable outputs: A distance function approach. The Review of Economics and Statistics, 75(2), 374–380.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Noh, D.-W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126(2), 469–492.

    Article  Google Scholar 

  • Finnveden, G., Hauschild, M. Z., Ekvall, T., Guinée, J., Heijungs, R., Hellweg, S., et al. (2009). Recent developments in life cycle assessment. Journal of Environmental Management, 91(1), 1–21.

    Article  Google Scholar 

  • Gijzen, H. (2013). Development: Big data for a sustainable future. Nature, 502(7469), 38–38.

    Article  Google Scholar 

  • Glucker, A. N., Driessen, P. P., Kolhoff, A., & Runhaar, H. A. (2013). Public participation in environmental impact assessment: Why, who and how? Environmental Impact Assessment Review, 43, 104–111.

    Article  Google Scholar 

  • Govindan, K., Sarkis, J., Jabbour, C. J. C., Zhu, Q., & Geng, Y. (2014). Eco-efficiency based green supply chain management: Current status and opportunities. European Journal of Operational Research, 2(233), 293–298.

    Article  Google Scholar 

  • Guinée, J., Gorrée, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., et al. (2002). Handbook on life cycle assessment operational guide to the ISO standards. Dordrecht: Kluwer.

    Google Scholar 

  • Hailu, A., & Veeman, T. S. (2001). Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. American Journal of Agricultural Economics, 83(3), 605–616.

    Article  Google Scholar 

  • Halkos, G. E., & Tzeremes, N. G. (2013). A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions. European Journal of Operational Research, 227(1), 182–189.

    Article  Google Scholar 

  • Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., et al. (2013). Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), 156–162.

    Article  Google Scholar 

  • Hashimoto, S., & Moriguchi, Y. (2004). Proposal of six indicators of material cycles for describing society’s metabolism: From the viewpoint of material flow analysis. Resources, Conservation and Recycling, 40(3), 185–200.

    Article  Google Scholar 

  • Hauschild, M., & Pennington, D. (2002). Indicators for ecotoxicity in life cycle impact assessment. In H. A. Udo de Haes, G. Finnveden, M. Goedkoop, M. Hauschild, E. Hertwich, P. Hofstetter, W. Klöpffer, W. Krewitt, E. Lindeijer, O. Jolliet, R. Müller-Wenk, S. L. Olsen, D. Pennington, J. Potting, & B. Steen (Eds.), Life-cycle impact assessment: Striving towards best practice. Pensacola, FL: SETAC Press.

    Google Scholar 

  • Hawkins, T., Ingwersen, W., Srocka, M., Transue, T., & Ciroth, A. (2013). Tools to support the widespread application of life cycle assessment: Development of the life cycle assessment harmonization tool and the improvement of openLCA. 2013 International symposium on sustainable systems and technology, May 15–17,2013. http://lcacenter.org/lcaxiii/abstracts/abstract-dynamic.php?id=946. Accessed 17 September 2013.

  • Heidorn, P. B. (2008). Shedding light on the dark data in the long tail of science. Library Trends, 57(2), 280–299.

    Article  Google Scholar 

  • Hjaila, K., Baccar, R., Sarrà, M., Gasol, C., & Blánquez, P. (2013). Environmental impact associated with activated carbon preparation from olive-waste cake via life cycle assessment. Journal of Environmental Management, 130, 242–247.

    Article  Google Scholar 

  • Hunt, R. G., Franklin, W. E., & Hunt, R. (1996). LCA-how it came about. The International Journal of Life Cycle Assessment, 1(1), 4–7.

    Article  Google Scholar 

  • Iribarren, D., Martín-Gamboa, M., & Dufour, J. (2013). Environmental benchmarking of wind farms according to their operational performance. Energy, 61, 589–597.

    Article  Google Scholar 

  • Iribarren, D., Vázquez-Rowe, I., Moreira, M. T., & Feijoo, G. (2010). Further potentials in the joint implementation of life cycle assessment and data envelopment analysis. Science of the Total Environment, 408(22), 5265–5272.

    Article  Google Scholar 

  • ISO 14040:1997. (1997). Environmental management-life cycle assessment-principles and framework. http://www.iso.org/iso/home/store/catalogue_ics/catalogue_detail_ics.htm?csnumber=23151. Accessed 15 September 2012.

  • ISO 14044:2006. (2006). Environmental management-life cycle assessment-requirements and guidelines. http://www.iso.org/iso/catalogue_detail?csnumber=37456. Accessed 9 September 2013.

  • Jang, S. M., & Hart, P. S. (2015). Polarized frames on climate change and global warming across countries and states: Evidence from twitter big data. Global Environmental Change, 32, 11–17.

    Article  Google Scholar 

  • Jawahar, N., Pandian, G. S., Gunasekaran, A., & Subramanian, N. (2015). An optimization model for sustainability program. Annals of Operations Research. doi:10.1007/s10479-015-1928-z.

    Article  Google Scholar 

  • Johnson, J. E. (2012). Strategy: Big data + big analytics = big opportunity. Financial Executive, 28(6), 50.

    Google Scholar 

  • Kao, C., & Lin, P.-H. (2012). Efficiency of parallel production systems with fuzzy data. Fuzzy Sets and Systems, 198, 83–98.

    Article  Google Scholar 

  • Koskela, M., & Vehmas, J. (2012). Defining eco-efficiency: A case study on the Finnish forest industry. Business Strategy and the Environment, 21(8), 546–566.

    Article  Google Scholar 

  • Lausch, A., Schmidt, A., & Tischendorf, L. (2015). Data mining and linked open data-new perspectives for data analysis in environmental research. Ecological Modelling, 295, 5–17.

    Article  Google Scholar 

  • Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). The DEA game cross-efficiency model and its Nash equilibrium. Operations Research, 56(5), 1278–1288.

    Article  Google Scholar 

  • Liu, W., Meng, W., Li, X., & Zhang, D. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194.

    Article  Google Scholar 

  • Liu, W., & Sharp, J. (1999). DEA models via goal programming. In G. Westermann (Ed.), Data envelopment analysis in the service sector (pp. 79–101). New York: Springer.

    Chapter  Google Scholar 

  • Lozano, S. (2015). Technical and environmental efficiency of a two-stage production and abatement system. Annals of Operations Research. doi:10.1007/s10479-015-1933-2.

    Article  Google Scholar 

  • Lozano, S., Iribarren, D., Moreira, M. T., & Feijoo, G. (2010). Environmental impact efficiency in mussel cultivation. Resources, Conservation and Recycling, 54(12), 1269–1277.

    Article  Google Scholar 

  • Lv, W., Zhou, Z., & Huang, H. (2013). The measurement of undesirable output based-on DEA in E&E: Models development and empirical analysis. Mathematical and Computer Modelling, 5(58), 907–912.

    Article  Google Scholar 

  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big Data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation. Accessed 9 October 2014.

  • Medina-Borja, A., Pasupathy, K., & Triantis, K. (2007). Large-scale data envelopment analysis (DEA) implementation: A strategic performance management approach. Journal of the Operational Research Society, 58(8), 1084–1098.

    Article  Google Scholar 

  • Mestre, A., & Vogtlander, J. (2013). Eco-efficient value creation of cork products: An LCA-based method for design intervention. Journal of Cleaner Production, 57, 101–114.

    Article  Google Scholar 

  • Millie, D. F., Weckman, G. R., Young, W. A., Ivey, J. E., Fries, D. P., Ardjmand, E., et al. (2013). Coastal ‘big data’ and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric. Estuarine, Coastal and Shelf Science, 125, 57–67.

    Article  Google Scholar 

  • Mohammadi, A., Rafiee, S., Jafari, A., Dalgaard, T., Knudsen, M. T., Keyhani, A., et al. (2013). Potential greenhouse gas emission reductions in soybean farming: A combined use of life cycle assessment and data envelopment analysis. Journal of Cleaner Production, 54, 89–100.

    Article  Google Scholar 

  • Mouter, N., Annema, J. A., & Van Wee, B. (2013). Ranking the substantive problems in the Dutch cost-benefit analysis practice. Transportation Research Part A: Policy and Practice, 49, 241–255.

    Google Scholar 

  • Nahorski, Z., & Ravn, H. F. (2000). A review of mathematical models in economic environmental problems. Annals of Operations Research, 97(1–4), 165–201.

    Article  Google Scholar 

  • Oggioni, G., Riccardi, R., & Toninelli, R. (2011). Eco-efficiency of the world cement industry: A data envelopment analysis. Energy Policy, 39(5), 2842–2854.

    Article  Google Scholar 

  • Ohlhorst, F. J. (2012). Big data analytics: Turning big data into big money. New York: Wiley.

    Book  Google Scholar 

  • Özdemir, V., Badr, K. F., Dove, E. S., Endrenyi, L., Geraci, C. J., Hotez, P. J., et al. (2013). Crowd-funded micro-grants for genomics and big data: An actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy. OMICS: A Journal of Integrative Biology, 17(4), 161–172.

    Article  Google Scholar 

  • Paco, A., & Raposo, M. (2009). Identifying the green consumer: A segmentation study. Journal of Targeting, Measurement and Analysis of Marketing, 17(1), 17–25.

    Article  Google Scholar 

  • Pérez-López, P., González-García, S., Ulloa, R. G., Sineiro, J., Feijoo, G., & Moreira, M. T. (2014). Life cycle assessment of the production of bioactive compounds from tetraselmissuecica at pilot scale. Journal of Cleaner Production, 64, 323–331.

    Article  Google Scholar 

  • Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: Configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling & Software, 51, 250–268.

    Article  Google Scholar 

  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight restrictions and free production in data envelopment analysis. Operations Research, 61(2), 426–437.

    Article  Google Scholar 

  • Ramli, N. A., Munisamy, S., & Arabi, B. (2013). Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector. Annals of Operations Research, 211(1), 381–398.

    Article  Google Scholar 

  • Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. The International Journal of Life Cycle Assessment, 13(5), 374–388.

    Article  Google Scholar 

  • Reichman, O., Jones, M. B., & Schildhauer, M. P. (2011). Challenges and opportunities of open data in ecology. Science, 331(6018), 703–705.

    Article  Google Scholar 

  • Russom, P. (2011). Fourth quarter: Big data analytics. TDWI Best Practices Report.

  • Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400–410.

    Article  Google Scholar 

  • Schleisner, L. (2000). Life cycle assessment of a wind farm and related externalities. Renewable Energy, 20(3), 279–288.

    Article  Google Scholar 

  • Schnase, J. L., Duffy, D. Q., Tamkin, G. S., Nadeau, D., Thompson, J. H., Grieg, C. M., et al. (2014). Merra analytic services: Meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service. Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2013.12.003.

    Article  Google Scholar 

  • Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P., (2012). Analytics: The real-world use of big data. http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-big-data-at-work.html. Accessed 3 May 2014.

  • Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (2005). A response to comments on modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 161(2), 579–581.

    Article  Google Scholar 

  • Slagstad, H., & Brattebø, H. (2014). Life cycle assessment of the water and wastewater system in Trondheim, Norway—A case study. Urban Water Journal, 11(4), 323–334.

    Article  Google Scholar 

  • Snijders, C., Matzat, U., & Reips, U.-D. (2012). Big data: Big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1–5.

    Google Scholar 

  • Song, M., An, Q., Zhang, W., Wang, Z., & Wu, J. (2012). Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 16(7), 4465–4469.

    Article  Google Scholar 

  • Song, M., & Guan, Y. (2014). The environmental efficiency of Wanjiang demonstration area: A Bayesian estimation approach. Ecological Indicators, 36, 59–67.

    Article  Google Scholar 

  • Song, M., Zhang, L., An, Q., Wang, Z., & Li, Z. (2013). Statistical analysis and combination forecasting of environmental efficiency and its influential factors since China entered the WTO: 2002–2010–2012. Journal of Cleaner Production, 42, 42–51.

    Article  Google Scholar 

  • Stamp, A., Althaus, H.-J., & Wäger, P. A. (2013). Limitations of applying life cycle assessment to complex co-product systems: The case of an integrated precious metals smelter-refinery. Resources, Conservation and Recycling, 80, 85–96.

    Article  Google Scholar 

  • Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and Systems Engineering, 22(2), 127–151.

    Article  Google Scholar 

  • Tiruta-Barna, L., Benetto, E., & Perrodin, Y. (2007). Environmental impact and risk assessment of mineral wastes reuse strategies: Review and critical analysis of approaches and applications. Resources, Conservation and Recycling, 50(4), 351–379.

    Article  Google Scholar 

  • Tone, K. (2003). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. GRIPS Research Report Series. http://ci.nii.ac.jp/naid/120005571101/. Accessed 5 January 2014.

  • Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131.

    Article  Google Scholar 

  • Tyteca, D. (1996). On the measurement of the environmental performance of firms—A literature review and a productive efficiency perspective. Journal of Environmental Management, 46(3), 281–308.

    Article  Google Scholar 

  • UN Global Pulse. (2012). Big data for development: Challenges and opportunities. http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf. Accessed 3 May 2014.

  • Vázquez-Rowe, I., Villanueva-Rey, P., Iribarren, D., Moreira, M. T., & Feijoo, G. (2012). Joint life cycle assessment and data envelopment analysis of grape production for vinification in the RíasBaixas appellation (NW Spain). Journal of Cleaner Production, 27, 92–102.

    Article  Google Scholar 

  • Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.

    Article  Google Scholar 

  • Wikle, C., Holan, S., & Hooten, M. (2013). Guest editor’s introduction to the special issue on modern dimension reduction methods for big data problems in ecology. Journal of Agricultural, Biological, and Environmental Statistics, 3(18), 271–273.

    Article  Google Scholar 

  • Wilson, B. P., Lavery, N. P., Jarvis, D. J., Anttila, T., Rantanen, J., Brown, S. G., et al. (2013). Life cycle assessment of gas atomised sponge nickel for use in alkaline hydrogen fuel cell applications. Journal of Power Sources, 243, 242–252.

    Article  Google Scholar 

  • Wu, J., Chu, J., Sun, J., & Zhu, Q. (2016). DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research, 248(2), 571–579.

    Article  Google Scholar 

  • Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.

    Article  Google Scholar 

  • You, S., & Yan, H. (2011). A new approach in modelling undesirable output in DEA model. Journal of the Operational Research Society, 62(12), 2146–2156.

    Article  Google Scholar 

  • Zhou, G., Chung, W., & Zhang, Y. (2014). Measuring energy efficiency performance of China’s transport sector: A data envelopment analysis approach. Expert Systems with Applications, 41(2), 709–722.

    Article  Google Scholar 

  • Zhou, P., Ang, B., & Poh, K. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1–14.

    Article  Google Scholar 

  • Zhou, P., Poh, K. L., & Ang, B. W. (2007). A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 178(1), 1–9.

    Article  Google Scholar 

  • Zhu, D., & Ru, J. (2008). Strategic environmental assessment in China: Motivations, politics, and effectiveness. Journal of Environmental Management, 88(4), 615–626.

    Article  Google Scholar 

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Acknowledgments

We appreciate the support of the Program for the Major Projects in Philosophy and Social Science Research of the Ministry of Education of China (No. 14JZD031), National Natural Science Foundation of China (Nos. 71471001, 71171001 and 71503001), and New Century Excellent Talents in University (No. NCET-12-0595).

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Authors and Affiliations

  1. Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu, 233030, Anhui, China

    Ma-Lin Song & Lian-Biao Cui

  2. Department of International Business and Asian Studies, Griffith Business School, Griffith University, Gold Coast Campus, Parklands Drive, Southport, QLD, 4222, Australia

    Ron Fisher

  3. Center for Industrial and Business Organization, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China

    Jian-Lin Wang

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Song, ML., Fisher, R., Wang, JL. et al. Environmental performance evaluation with big data: theories and methods. Ann Oper Res 270, 459–472 (2018). https://doi.org/10.1007/s10479-016-2158-8

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