Skip to main content

Advertisement

SpringerLink
Log in

Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods

  • S.I. : Artificial Intelligence in Operations Management
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) in general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ahmed, W., Najmi, A., & Khan, F. (2019). Examining the impact of institutional pressures and green supply chain management practices on firm performance. Management of Environmental Quality. https://doi.org/10.1108/MEQ-06-2019-0115.

    Article  Google Scholar 

  • Aldakhil, A. M., Nassani, A. A., Awan, U., Abro, M. M. Q., & Zaman, K. (2018). Determinants of green logistics in BRICS countries: An integrated supply chain model for green business. Journal of Cleaner Production, 195, 861–868.

    Google Scholar 

  • Ameknassi, L., Aït-Kadi, D., & Rezg, N. (2016). Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model. International Journal of Production Economics, 182, 165–184.

    Google Scholar 

  • Bagheri, M., Guevara, Z., Alikarami, M., Kennedy, C. A., & Doluweera, G. (2018). Green growth planning: A multi-factor energy input-output analysis of the Canadian economy. Energy Economics., 74, 708–720.

    Google Scholar 

  • Berry, S., Davidson, K., & Saman, W. (2013). The impact of niche green developments in transforming the building sector: The case study of Lochiel Park. Energy Policy, 62, 646–655.

    Google Scholar 

  • Bouveyron, C., Côme, E., & Jacques, J. (2015). The discriminative functional mixture model for a comparative analysis of bike sharing systems. The Annals of Applied Statistics., 9(4), 1726–1760.

    Google Scholar 

  • Bouveyron, C., Girard, S., & Schmid, C. (2007). High dimensional data clustering. Computational Stats & Data Analysis, 52, 502–519.

    Google Scholar 

  • Bouveyron, C., & Jacques, J. (2011). Model-based clustering of time series in group-specific functional subspaces. Advances in Data Analysis and Classification, 5, 281–300.

    Google Scholar 

  • Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.

    Google Scholar 

  • Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781–793.

    Google Scholar 

  • Chen, D., Ignatius, J., Sun, D., Zhan, S., Zhou, C., Marra, M., et al. (2019). Reverse logistics pricing strategy for a green supply chain: A view of customers’ environmental awareness. International Journal of Production Economics, 217, 197–210.

    Google Scholar 

  • Chhabra, D., Garg, S. K., & Singh, R. K. (2017). Analyzing alternatives for green logistics in an Indian automotive organization: A case study. Journal of Cleaner Production, 167, 962–969.

    Google Scholar 

  • Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.

    Google Scholar 

  • Dai, Y., & Gao, H. O. (2016). Energy consumption in China’s logistics industry: A decomposition analysis using the LMDI approach. Transportation Research Part D: Transport and Environment., 46, 69–80.

    Google Scholar 

  • Davarzani, H., Fahimnia, B., Bell, M., & Sarkis, J. (2016). Greening ports and maritime logistics: A review. Transportation Research Part D, 48, 473–487.

    Google Scholar 

  • Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics—An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219, 671–679.

    Google Scholar 

  • Demir, E., Hrušovský, M., Jammernegg, W., & Van Woensel, T. (2019). Green intermodal freight transportation: Bi-objective modelling and analysis. International Journal of Production Research, 57(19), 6162–6180.

    Google Scholar 

  • Dev, N. K., Shankar, R., & Swami, S. (2020). Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system. International Journal of Production Economics, 223, 107519.

    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, 90–98.

    Google Scholar 

  • Firdausiyah, N., Taniguchi, E., & Qureshi, A. G. (2019). Modeling city logistics using adaptive dynamic programming based multi-agent simulation. Transportation Research Part E, 125, 74–96.

    Google Scholar 

  • Garza-Reyes, J. A., Villarreal, B., Kumar, V., & Ruiz, P. M. (2016). Lean and green in the transport and logistics sector—A case study of simultaneous deployment. Production Planning & Control., 27(15), 1221–1232.

    Google Scholar 

  • Goswami, M., De, A., Habibi, M. K. K., & Daultani, Y. (2020). Examining freight performance of third-party logistics providers within the automotive industry in India: an environmental sustainability perspective. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1756504.

    Article  Google Scholar 

  • Graham, S., Graham, B., & Holt, D. (2018). The relationship between downstream environmental logistics practices and performance. International Journal of Production Economics, 196, 356–365.

    Google Scholar 

  • Gu, W., Wei, L., Zhang, W., & Yan, X. (2019). Evolutionary game analysis of cooperation between natural resource- and energy-intensive companies in reverse logistics operations. International Journal of Production Economics, 218, 159–169.

    Google Scholar 

  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 28(1), 100–108.

    Google Scholar 

  • Hong, J., Alzaman, C., Diabat, A., & Bulgak, A. (2019). Sustainability dimensions and PM2.5 in supply chain logistics. Annals of Operations Research, 275, 339–366.

    Google Scholar 

  • Hou, D., Li, G., Chen, D., Zhu, B., & Hu, S. (2019). Evaluation and analysis on the green development of China’s industrial parks using the long-tail effect model. Journal of Environmental Management, 248, 109288.

    Google Scholar 

  • IPCC. (2006). Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change. Retrieved December 2019, from https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html.

  • Jacques, J., & Preda, C. (2014). Functional data clustering: A survey. Advances in Data Analysis and Classification, 8(3), 231–255.

    Google Scholar 

  • James, G. M., & Sugar, C. A. (2003). Clustering for sparsely sampled functional data. Journal of the American Statistical Association., 98(462), 397–408.

    Google Scholar 

  • Ji, S. F., Luo, R. J., & Peng, X. S. (2019). A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics. International Journal of Production Research, 57(11), 3382–3404.

    Google Scholar 

  • Jiahuey, Y., Liu, Y., & Yu, Y. (2019). Measuring green growth performance of China’s chemical industry. Resources, Conservation and Recycling, 149, 160–167.

    Google Scholar 

  • Jin, P., Peng, C., & Song, M. (2019). Macroeconomic uncertainty, high-level innovation, and urban green development performance in China. China Economic Review, 55, 1–18.

    Google Scholar 

  • Khor, K. S., & Udin, Z. M. (2013). Reverse logistics in Malaysia: Investigating the effect of green product design and resource commitment. Resources, Conservation and Recycling, 81, 71–80.

    Google Scholar 

  • Kuo, T. C., Chiu, M. C., Chung, W. H., & Yang, T. I. (2019). The circular economy of LCD panel shipping in a packaging logistics system. Resources, Conservation and Recycling, 149, 435–444.

    Google Scholar 

  • Leroy, A., Marc, A., Dupas, O., Rey, J. L., & Gey, S. (2018). Functional data analysis in sport science: Example of swimmers’ progression curves clustering. Applied Sciences., 8(10), 1766.

    Google Scholar 

  • Li, A., Chen, Y., & Wang, D. (2020). An empirical study of the factors influencing the willingness to implement green coal logistics in China. Journal of Cleaner Production, 245, 118932.

    Google Scholar 

  • Li, W., Wang, J., Chen, R., Xi, Y., Liu, S. Q., Wu, F., et al. (2019). Innovation-driven industrial green development: The moderating role of regional factors. Journal of Cleaner Production, 222, 344–354.

    Google Scholar 

  • Lin, B., & Du, K. (2015). Energy and CO2 emissions performance in China’s regional economies: Do market-oriented reforms matter? Energy Policy, 78, 113–124.

    Google Scholar 

  • Lin, B., & Wu, R. (2020). Designing energy policy based on dynamic change in energy and carbon dioxide emission performance of China’s iron and steel industry. Journal of Cleaner Production, 256, 120412.

    Google Scholar 

  • Lin, B., & Zhu, J. (2019a). Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China. Applied Energy, 239, 12–22.

    Google Scholar 

  • Lin, B., & Zhu, J. (2019b). Fiscal spending and green economic growth: Evidence from China. Energy Economics, 83, 264–271.

    Google Scholar 

  • Marinakis, Y., Marinaki, M., Doumpos, M., Matsatsinis, N., & Zopounidis, C. (2011). A hybrid ACO-GRASP algorithm for clustering analysis. Annals of Operations Research, 188, 343–358.

    Google Scholar 

  • Martínez-Álvarez, F., Schmutz, A., Asencio-Cortés, G., & Jacques, J. (2019). A novel hybrid algorithm to forecast functional time series based on pattern sequence similarity with application to electricity demand. Energies, 12(1), 94.

    Google Scholar 

  • Melkonyan, A., Gruchmann, T., Lohmar, F., Kamath, V., & Spinler, S. (2020). Sustainability assessment of last-mile logistics and distribution strategies: The case of local food networks. International Journal of Production Economics, 228, 107746.

    Google Scholar 

  • Oberhofer, P., & Dieplinger, M. (2013). Sustainability in the transport and logistics sector: Lacking environmental measures. Business Strategy and the Environment, 23(4), 236–253.

    Google Scholar 

  • Rajabi, A., Eskandari, M., Ghadi, M. J., Li, L., Zhang, J., & Siano, P. (2020). A comparative study of clustering techniques for electrical load pattern segmentation. Renewable and Sustainable Energy Reviews, 120, 109628.

    Google Scholar 

  • Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using data envelopment analysis. Transport Policy, 74, 35–46.

    Google Scholar 

  • Sbihi, A., & Eglese, R. W. (2010). Combinatorial optimization and Green Logistics. Annals of Operations Research, 175, 159–175.

    Google Scholar 

  • Shao, S., Xu, G., Li, M., & Huang, G. Q. (2019). Synchronizing e-commerce city logistics with sliding time windows. Transportation Research Part E, 123, 17–28.

    Google Scholar 

  • Shen, Z., Baležentis, T., Chen, X., & Valdmanis, V. (2018). Green growth and structural change in Chinese agricultural sector during 1997–2014. China Economic Review, 51, 83–96.

    Google Scholar 

  • Sureeyatanapas, P., Poophiukhok, P., & Pathumnakul, S. (2018). Green initiatives for logistics service providers: An investigation of antecedent factors and the contributions to corporate goals. Journal of Cleaner Production, 191, 1–14.

    Google Scholar 

  • Suzanne, E., Absi, N., & Borodin, V. (2020). Towards circular economy in production planning: Challenges and opportunities. European Journal of Operational Research, 287, 168–190.

    Google Scholar 

  • Tang, S., Wang, W., Yan, H., & Hao, G. (2015). Low carbon logistics: Reducing shipment frequency to cut carbon emissions. International Journal of Production Economics, 164, 339–350.

    Google Scholar 

  • Tipping, M. E., & Bishop, C. M. (1999). Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2), 443–482.

    Google Scholar 

  • Yang, J., Tang, L., Mi, Z., Liu, S., Li, L., & Zheng, J. (2019). Carbon emissions performance in logistics at the city level. Journal of Cleaner Production, 231, 1258–1266.

    Google Scholar 

  • Yao, X., Cheng, Y., & Song, M. (2019). Assessment of collaboration in city logistics: From the aspects of profit and CO2 emissions. International Journal of Logistics Research and Applications., 22(6), 576–591.

    Google Scholar 

  • Yi, H., & Liu, Y. (2015). Green economy in China: Regional variations and policy drivers. Global Environmental Change, 31, 11–19.

    Google Scholar 

  • Yuan, B. L., & Xiang, Q. L. (2018). Environmental regulation, industrial innovation and green development of Chinese manufacturing: Based on an extended CDM model. Journal of Cleaner Production, 176, 895–908.

    Google Scholar 

  • Zarbakhshnia, N., Soleimani, H., Goh, M., & Razavi, S. S. (2019). A novel multi-objective model for green forward and reverse logistics network design. Journal of Cleaner Production, 208, 1304–1316.

    Google Scholar 

  • Zhang, Y., Alshraideh, H., & Diabat, A. (2018). A stochastic reverse logistics production routing model with environmental considerations. Annals of Operations Research, 271, 1023–1044.

    Google Scholar 

  • Zhang, N., & Choi, Y. (2013). Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis. Energy Economics, 40, 549–559.

    Google Scholar 

  • Zhang, N., & Choi, Y. (2014). A note on the evolution of directional distance function and its development in energy and environmental studies 1997-2013. Renewable and Sustainable Energy Reviews, 33, 50–59.

    Google Scholar 

  • Zhou, P., Ang, B. W., & Wang, H. (2012). Energy and CO2 emission performance in electricity generation: a non-radial directional distance function approach. European Journal of Operational Research, 221(3), 625–635.

    Google Scholar 

  • Zhou, Y., Liu, W., Lv, X., Chen, X., & Shen, M. (2019). Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China’s construction industry: Based on super-SBM DEA and GVAR model. Journal of Cleaner Production, 241, 118322.

    Google Scholar 

  • Zhu, B., Zhang, M., Huang, L., Wang, P., Su, B., & Wei, Y. M. (2020). Exploring the effect of carbon trading mechanism on China’s green development efficiency: A novel integrated approach. Energy Economics, 85, 104601.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Numbers 71874149, 71934001, 71471001, 41771568, 71533004); the National Social Science Fund of China (Grant Number 20ZDA084); and the National Key Research and Development Program of China (Grant Number 2016YFA0602500).

Author information

Authors and Affiliations

  1. China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China

    Xin Yao & Yuanyuan Cheng

  2. Systems Management and Strategy Department, Business School, University of Greenwich, London, SE10 9LS, UK

    Li Zhou

  3. School of Statistics and Applied Mathematics, Anhui Finance and Economics University, Bengbu, 233030, China

    Malin Song

Authors
  1. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanyuan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Malin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malin Song.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, X., Cheng, Y., Zhou, L. et al. Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods. Ann Oper Res 308, 727–752 (2022). https://doi.org/10.1007/s10479-020-03763-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03763-w

Keywords

Search

Navigation