Optimizing environmental costs and economic benefits in new energy vehicle production: A case study of FAW Hongqi in China

Guangzhi Wang, Yixi Wang, Mingze Li

Article ID: 4222
Vol 8, Issue 6, 2024

VIEWS - 1035 (Abstract)

Abstract


This study focuses on the environmental cost accounting and economic benefit optimization of China’s FAW Hongqi New Energy Vehicle manufacturing enterprise under uncertain conditions, within the context of the emission permit system This study calculates the pollution situation throughout the manufacturing and production process of FAW Hongqi new energy vehicles, and constructs a multi-level environmental cost evaluation system for FAW Hongqi new energy vehicle manufacturing projects. Through the interval fuzzy model of FAW Hongqi new energy vehicle manufacturing projects, the maximum economic benefits of the enterprise are simulated. The research results indicate that the pollution emissions of enterprises are mainly concentrated in the three processes of welding, painting, and final assembly. Enterprises use their own exhaust gas and wastewater treatment devices to meet the standards for pollution emissions. At the same time, solid waste generated during the automobile manufacturing process is handed over to third-party companies for treatment. Secondly, based on the accounting results of enterprise pollution source intensity and a multi-layer environmental cost evaluation system, the environmental costs of enterprises are accounted for, and the environmental costs are represented in interval form to reduce uncertainty in the accounting process. According to the accounting results of enterprise environmental costs, the main environmental costs of enterprises are environmental remediation costs caused by normal pollution discharge and purchase costs of environmental protection facilities. Pollutant emission taxes and routine environmental monitoring costs are relatively low. Enterprises can adopt more scientific solutions from the aspects of environmental remediation and environmental protection facilities to reduce environmental costs. After optimization by the fuzzy interval uncertainty optimization model, the economic benefits of the FAW Hongqi new energy vehicle manufacturing project were [101,254.71, 6278.5413] million yuan. Compared with the interval uncertainty optimization model, the lower bound of economic benefits increased by 57.68%, and the upper bound decreased by 12.08%, shortening the results of the economic benefits interval. Clarify the current environmental pollution situation of FAW Hongqi’s new energy vehicle manufacturing enterprise, provide data support for sustainable development of the enterprise, and provide reasonable decision-making space for enterprise decision-makers.


Keywords


environmental cost; economic benefit; new energy vehicles; sustainable manufacturing

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References

  1. Al-Shami, S., & Rashid, N. (2021). A holistic model of dynamic capabilities and environment management system towards eco-product innovation and sustainability in automobile firms. Journal of Business & Industrial Marketing, 37(2), 402–416. https://doi.org/10.1108/jbim-04-2020-0217
  2. Arjmandzadeh, Z., & Safi, M. (2017). A goal programming approach for solving the random interval linear programming problem. Turkish Journal of Mathematics, 41, 775–786. https://doi.org/10.3906/mat-1509-65
  3. Bi, J., Li, J., & Zhang, B. (2011). IMSP: Integrated management system for water pollutant discharge permit based on a hybrid C/S and B/S model. Environmental Modelling & Software, 26(6), 831–833. https://doi.org/10.1016/j.envsoft.2010.11.017
  4. Bowan, P. A., Kayaga, S., & Fisher, J. (2020). A baseline scenario of municipal solid waste management. International Journal of Environment and Waste Management, 26(4), 438. https://doi.org/10.1504/ijewm.2020.110394
  5. Chang, C. T. (2006). Mixed binary interval goal programming. Journal of the Operational Research Society, 57(4), 469–473. https://doi.org/10.1057/palgrave.jors.2601999
  6. Choi, H., & Ahn, Y. (2015). A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model. Transportation Research Part D: Transport and Environment, 41, 228–243. https://doi.org/10.1016/j.trd.2015.10.008
  7. Figueroa-García, J. C., Kalenatic, D., & Lopez-Bello, C. A. (2012). Multi-period Mixed Production Planning with uncertain demands: Fuzzy and interval fuzzy sets approach. Fuzzy Sets and Systems, 206, 21–38. https://doi.org/10.1016/j.fss.2012.03.005
  8. Ge, J., Xiao, Y., Kuang, J., et al. (2022). Research progress of chlorination roasting of heavy metals in solid waste. Surfaces and Interfaces, 29, 101744. https://doi.org/10.1016/j.surfin.2022.101744
  9. Ghandehariun, A., Nazzal, Y., & Kishawy, H. (2016). Sustainable manufacturing and its application in machining processes: a review. International Journal of Global Warming, 9(2), 198. https://doi.org/10.1504/ijgw.2016.074955
  10. Gschösser, F., & Wallbaum, H. (2013). Life Cycle Assessment of Representative Swiss Road Pavements for National Roads with an Accompanying Life Cycle Cost Analysis. Environmental Science & Technology, 130718092515005. https://doi.org/10.1021/es400309e
  11. Gu, J., Liu, X., & Zhang, Z. (2023). Road base materials prepared by multi-industrial solid wastes in China: A review. Construction and Building Materials, 373, 130860. https://doi.org/10.1016/j.conbuildmat.2023.130860
  12. Guo, X., Shi, M., Ni, N., et al. (2021). China pollutant discharge permits as a link between total emission control and water quality: a pilot study of the pesticide industry. Water Policy, 24(1), 19–30. https://doi.org/10.2166/wp.2021.077
  13. Helman, J., Rosienkiewicz, M., Cholewa, M., et al. (2023). Towards GreenPLM—Key Sustainable Indicators Selection and Assessment Method Development. Energies, 16(3), 1137. https://doi.org/10.3390/en16031137
  14. Hu, Q., Li, X., Lin, A., et al. (2018). Total emission control policy in China. Environmental Development, 25, 126–129. https://doi.org/10.1016/j.envdev.2017.11.002
  15. Jane Zhao, Z., & Anand, J. (2009). A multilevel perspective on knowledge transfer: evidence from the Chinese automotive industry. Strategic Management Journal, 30(9), 959–983. https://doi.org/10.1002/smj.780
  16. Kultan, V., Thepanondh, S., Pinthong, N., et al. (2022). Comprehensive Evaluation of Odor-Causing VOCs from the Painting Process of the Automobile Manufacturing Industry and Its Sustainable Management. Atmosphere, 13(9), 1515. https://doi.org/10.3390/atmos13091515
  17. Li, D., & Cheng, C. (2002). Fuzzy multiobjective programming methods for fuzzy constrained matrix games with fuzzy numbers. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(04), 385–400. https://doi.org/10.1142/s0218488502001545
  18. Li, X., Ye, B., & Liu, X. (2022). The solution for type-2 fuzzy linear programming model based on the nearest interval approximation. Journal of Intelligent & Fuzzy Systems, 42(3), 2275–2285. https://doi.org/10.3233/jifs-211568
  19. Li, Y. P., Huang, G. H., Chen, X., et al. (2009). Interval-Parameter Robust Minimax-regret Programming and Its Application to Energy and Environmental Systems Planning. Energy Sources, Part B: Economics, Planning, and Policy, 4(3), 278–294. https://doi.org/10.1080/15567240701620531
  20. Liang, H., & Yao, H. (2022). Construction of China’s automobile financial market network and its sustainability evaluation. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.907311
  21. Motschman, C. A., Warner, O. M., Wycoff, A. M., et al. (2020). Context, acute tolerance, and subjective response affect alcohol-impaired driving decisions. Psychopharmacology, 237(12), 3603–3614. https://doi.org/10.1007/s00213-020-05639-0
  22. Owojori, O., Edokpayi, J. N., Mulaudzi, R., et al. (2020). Characterisation, Recovery and Recycling Potential of Solid Waste in a University of a Developing Economy. Sustainability, 12(12), 5111. https://doi.org/10.3390/su12125111
  23. Peidro, D., Mula, J., & Poler, R. (2010). Fuzzy linear programming for supply chain planning under uncertainty. International Journal of Information Technology & Decision Making, 9(3), 373–392. https://doi.org/10.1142/s0219622010003865
  24. Peidro, D., Mula, J., Poler, R., et al. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640–2657. https://doi.org/10.1016/j.fss.2009.02.021
  25. Podgórniak-Krzykacz, A., Przywojska, J., & Trippner-Hrabi, J. (2022). A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policies. Transport and Telecommunication Journal, 23(2), 180–194. https://doi.org/10.2478/ttj-2022-0016
  26. Quispe, K., Martínez, M., da Costa, K., et al. (2023). Solid Waste Management in Peru’s Cities: A Clustering Approach for an Andean District. Applied Sciences, 13(3), 1646. https://doi.org/10.3390/app13031646
  27. Robers, P. D., & Ben-Israel, A. (1969). Interval Programming. New Approach to Linear Programming with Applications to Chemical Engineering Problems. Industrial & Engineering Chemistry Process Design and Development, 8(4), 496–501. https://doi.org/10.1021/i260032a011
  28. Roos, E. C., & Kliemann Neto, F. J. (2017). Tools for evaluating environmental performance at Brazilian public ports: Analysis and proposal. Marine Pollution Bulletin, 115(1–2), 211–216. https://doi.org/10.1016/j.marpolbul.2016.12.015
  29. Shang, S., & Chen, Z. (2021). Innovation of enterprise environmental cost management path from the perspective of sustainable development. International Journal of Environmental Technology and Management, 24(5/6), 346. https://doi.org/10.1504/ijetm.2021.117298
  30. Shih, T. S., Su, J. S., Yao, J. S. (2009). Fuzzy linear programming based on interval-valued fuzzy sets. International Journal of Innovative Computing, Information and Control, 5, 2081–2090.
  31. Veeramani, C., & Sumathi, M. (2014). Fuzzy Mathematical Programming approach for Solving Fuzzy Linear Fractional Programming Problem. RAIRO - Operations Research, 48(1), 109–122. https://doi.org/10.1051/ro/2013056
  32. Wan, Z., Zhang, S. J., & Teo, K. L. (2011). Two-step based sampling method for maximizing the capacity of V-belt driving in polymorphic uncertain environment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 226(1), 177–191. https://doi.org/10.1177/0954406211412313
  33. Wang, L., Li, C., & Li, S. (2020). Can environmental information disclosure regulate the relationship between environmental cost and enterprise value. International Journal of Environment and Pollution, 67(2/3/4), 95. https://doi.org/10.1504/ijep.2020.117787
  34. Wang, Q., & Yang, Z. (2016). Industrial water pollution, water environment treatment, and health risks in China. Environmental Pollution, 218, 358–365. https://doi.org/10.1016/j.envpol.2016.07.011
  35. Wattage, P., Soussan, J. (2003). Incorporating Environmental Value and Externality in Project Evaluation as a Sustainability Indicator to evaluate Bangladesh Water Development. Water Resources Management, 17(6), 429-446. https://doi.org/10.1023/B:WARM.0000004957.49020.c3
  36. Wei, Y., Wang, Z., Zhang, Z., et al. (2017). Robust methodology of automatic design for automobile panel drawing die based on multilevel modeling strategy. The International Journal of Advanced Manufacturing Technology, 91(9–12), 4203–4217. https://doi.org/10.1007/s00170-017-0082-y
  37. Xu, G., Dong, H., & Shi, X. (2023a). China’s economic development quality grows faster than economic quantity. PLOS ONE, 18(7), e0289399. https://doi.org/10.1371/journal.pone.0289399
  38. Xu, X., Huang, C., Li, C., et al. (2023b). Uncertain design optimization of automobile structures: A survey. Electronic Research Archive, 31(3), 1212–1239. https://doi.org/10.3934/era.2023062
  39. Yang, Z., Huang, X., Fang, G., et al. (2021). Benefit evaluation of East Route Project of South to North Water Transfer based on trapezoid cloud model. Agricultural Water Management, 254, 106960. https://doi.org/10.1016/j.agwat.2021.106960
  40. Zhang, P., Gao, W., Zhao, C., et al. (2023). A parametric distribution model of electrostatic spray rotating bell and application for automobile painting. Journal of Coatings Technology and Research, 20(5), 1727–1745. https://doi.org/10.1007/s11998-023-00777-4
  41. Zhang, W., Liu, H., Chen, Y., et al. (2022). Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak. Science Progress, 105(1), 003685042210754. https://doi.org/10.1177/00368504221075480
  42. Zhao, F., Liu, X., Zhang, H., et al. (2022). Automobile Industry under China’s Carbon Peaking and Carbon Neutrality Goals: Challenges, Opportunities, and Coping Strategies. Journal of Advanced Transportation, 2022, 1–13. https://doi.org/10.1155/2022/5834707
  43. Zhou Y. (2021). A Hierarchical Approach for Inland Lake Pollutant Load Allocation: A Case Study in Tangxun Lake Basin, Wuhan, China. Journal of Environmental Informatics, 37, 16–25.


DOI: https://doi.org/10.24294/jipd.v8i6.4222

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