Abstract
The new era requires port sectors cannot only promote economic development, but also need to achieve social harmony and ecological environment protection, which is in accordance with the initiative of ecological civilization construction proposed by the 17th National Congress of the Communist Party of China. Yet most performance-evaluated metrics related to social and environmental dimensions are qualitative not quantitative and hence not easy to be observed imprecisely, which makes it more complex to evaluate the ecological performance of port sectors. To address this issue, this paper introduces uncertainty theory to data envelopment analysis to deal with the imprecise data. Meanwhile, considering that the port industries usually reveal the characteristics of economies of scale and ignoring it would result in incomplete evaluation and wrong decision-making, this paper examines the ecological efficiency of port industries from the two perspectives with both technical efficiency and scale efficiency. Through the empirical analysis of China’s 17 port sectors, two major categories have emerged representing ecological efficiencies at different levels. That is, technically eco-efficient and eco-inefficient ports group as well as scale eco-efficient and eco-inefficient ports group. Moreover, the improvement radii of eco-inefficient port sectors are further calculated by models. The empirical implications can help policymakers to formulate and adjust policies on ecological civilization construction in China’s port industry.
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References
Hu J (2007) Full text of Hu Jintao’s Report at 17th Party Congress. http://news.xinhuanet.com/english/2007-10/24/content_6938749.htm. Accessed 22 Aug 2012
Hu J (2012) Full text of Hu Jintao’s Report at 18th Party Congress. http://news.xinhuanet.com/english/special/18cpcnc/2012-11/17/c_131981259_ 9.htm. Accessed 22 Jan 2013
Li GJ (2011) Vigorously promotes ecological civilization construction. Environ Prot 14:14–19
Zhang XH, Wang YQ, Qi Y et al (2016) Evaluating the trends of China’s ecological civilization construction using a novel indicator system. J Clean Prod 133:910–923
Xiao L, Zhao R (2017) China’s new era of ecological civilization. Science 358:1008–1009
Tongzon J (2001) Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transp Res A Policy Pract 35(2):107–122
Itoh H (2002) Effeciency changes at major container ports in japan: a window application of data envelopment analysis. Rev Urban Region Dev Stud 14(2):133–152
Cui Q (2017) Environmental efficiency measures for ports: an application of RAM-Tobit-RAM with undesirable outputs. Marit Policy Manag 44:551–564
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2:429–444
Jiang B, Li Y, Lio W et al (2018) Sustainability efficiency evaluation of seaports in China: an uncertain data envelopment analysis approach. Soft Comput. https://doi.org/10.1007/s00500-018-3559-1
Liu B (2012) Why is there a need for uncertainty theory. J Uncertain Syst 6:3–10
Lio W, Liu B (2018) Uncertain data envelopment analysis with imprecisely observed inputs and outputs. Fuzzy Optim Decis Mak 17:357–373
Zu T, Wen M, Rui K (2017) An optimal evaluating method for uncertainty metrics in reliability based on uncertain data envelopment analysis. Microelectron Reliab 75:283–287
Wen ML, Zu TP, Guo MM et al (2018) Optimization of spare parts varieties based on stochastic DEA model. IEEE Access 6:22174–22183
Zahra MN, Alireza GH (2018) A novel DEA model based on uncertainty theory. Ann Oper Res 264:367–389
Jiang B, Lio W, Li X (2018) An uncertain DEA model for scale efficiency evaluation. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2018.2883546
Liu B (2007) Uncertain theory. Springer, Berlin
Liu B (2010) Uncertainty theory: a branch of mathematics for modeling human uncertainty. Springer, Berlin
Liu Y, Ha M (2009) Expected value of function of uncertain variables. J Uncertain Syst 4:181–186
Choi Y, Zhang N, Zhou P (2012) Efficiency and abatement costs of energy-related \(\text{ CO}_{{2}}\) emissions in China: a slacks-based efficiency measure. Appl Energy 98:198–208
Chang YT (2013) Environmental efficiency of ports: a data envelopment analysis approach. Marit Policy Manag 40:467–478
Gupta AK, Gupta SK, Patil R (2005) Environmental management plan for port and harbour projects. Clean Technol Environ Policy 7:133–141
Dinwoodie J, Tuck S, Knowles H et al (2012) Sustainable development of maritime operations in ports. Bus Strateg Environ 21:111–126
Cooper D (2003) Exhaust emissions from ships at berth. Atmos Environ 37:3817–3830
Bailey D, Solomon G (2004) Pollution prevention at ports: clearing the air. Environ Impact Assess Rev 24:749–774
Grifoll M, Jordà G, Espino M et al (2011) A management system for accidental water pollution risk in a harbour: the Barcelona case study. J Mar Syst 88:60–73
Kroger K, Gardner J, Rowden A et al (2006) Long-term effects of algal bloom on subtidal soft-sediment macroinvertebrate communities in Wellington Harbour New Zealand. Estuar Coast Shelf Sci 67:589–604
Murphy E, King EA (2014) An assessment of residential exposure to environmental noise at a shipping port. Environ Int 63:207–215
Lee TC, Lam JSL, Lee PTW (2016) Asian economic integration and maritime \(\text{ CO}_{{2}}\) emissions. Transp Res Part D Transp Environ 43:226–237
Beskovnik B, Bajec P (2015) Application of environmental and social sustainable measures by port of Koper: the basis for the regional approach. Problemy Ekorozw 10:99–106
Lu CS, Shang KC, Lin CC (2016) Examining sustainability performance at ports: port managers’ perspectives on developing sustainable supply chains. Marit Policy Manag 43:1–19
Kang D, Kim S (2017) Conceptual model development of sustainability practices: the case of port operations for collaboration and governance. Sustainability 9:2333
Shiau TA, Chuang CC (2015) Social construction of port sustainability indicators: a case study of Keelung port. Marit Policy Manag 42:26–42
Picazo-Tadeo AJ, Reig-Martinez E, Gomez-Limon JA (2012) Assessing farming eco-efficiency with directional distance function. Eur J Oper Res 220:798–809
Camarero M, Castillo J, Picazo-Tadeo AJ et al (2013) Eco-efficiency and convergence in OECD countries. Environ Resource Econ 55:87–106
Vasant P, Zelinka I, Weber G (2018) Intelligent computing and optimization. Springer, Cham
Thomas JJ, Karagoz P, Bazeer AB, Vasant P (2019) Deep learning techniques and optimization strategies in big data analytics. IGI Global, Nov 29, 2019—computers. https://doi.org/10.4018/978-1-7998-1192-3
Vasant P, Zelinka I, Weber G (2019) Intelligent computing and optimization. In: Proceedings of the 2nd international conference on intelligent computing and optimization 2019 (ICO 2019). Advances in intelligent systems and computing
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This study was funded by the Social Science Foundation of Shandong Province (Grant No. 17CCXJ19).
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Jiang, B., Yang, C., Dong, Q. et al. Ecological efficiency evaluation of China’s port industries with imprecise data. Evol. Intel. 17, 189–200 (2024). https://doi.org/10.1007/s12065-021-00638-2
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DOI: https://doi.org/10.1007/s12065-021-00638-2