Downloads

https://doi.org/10.62836/iaet.v1i1.003
Lei, J. . (2022). Green Supply Chain Management Optimization Based on Chemical Industrial Clusters. Innovations in Applied Engineering and Technology, 1(1), 1–17. https://doi.org/10.62836/iaet.v1i1.003
Volume 1, Issue 1

Green Supply Chain Management Optimization Based on Chemical Industrial Clusters

During the period after the pandemic, the chemical sector, which is a crucial component of national progress, encounters further obstacles. The industry has been affected by the pandemic, leading to a need for faster transformation and upgrading. Additionally, there is an increased demand for green and environmental protection in the chemical sector due to global climate change and the concept of sustainable development. During the time after the pandemic, there will be a continued trend of industrial clustering and scale development in the chemical sector. It is essential to prioritize optimization via green supply chain management in order to facilitate industrial upgrading. In order to achieve this objective, this study administered a questionnaire survey to gather reliable data. The collected data was then analyzed using software tools like SPSS and AMOS. The analysis aimed to investigate the influence of various factors, including regulatory compliance, green procurement, green manufacturing, green logistics, green sales, competitors, internal environmental protection, and cost control, on the awareness and implementation of green supply chain management in chemical enterprises. Additionally, a structural equation model was constructed to further examine these relationships. The findings suggest that green procurement, manufacturing, logistics, sales, and internal environmental protection and cost control have a notable and beneficial influence on the implementation of green supply chain management. Additionally, the awareness and implementation of green supply chain management also contribute positively to both economic and environmental advantages. This paper offers a theoretical framework for enhancing the efficiency of the green supply chain management system in chemical industrial clusters, hence promoting the environmentally friendly and sustainable growth of the chemical sector.

green supply chain management; chemical industrial clusters; green and sustainable development

References

  1. Chen T-L, Kim H, Pan S-Y, Tseng P-C, Lin Y-P, Chiang P-C. Implementation of Green Chemistry Principles in Circular Economy System Towards Sustainable Development Goals: Challenges and Perspectives. Science of the Total Environment 2020; 716: 136998.
  2. Deng X, Oda S, Kawano Y. Split–Joint Bull's Eye Structure With Aperture Optimization for Multi–Frequency Terahertz Plasmonic Antennas. In Proceedings of the 2016 41st International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW–THz), Montreal, Canada, 25–30 September 2016.
  3. Desing H, Brunner D, Takacs F, Nahrath S, Frankenberger K, Hischier R. A Circular Economy Within the Planetary Boundaries: Towards a Resource–Based, Systemic Approach. Resources Conservation and Recycling 2020; 155: 104673.
  4. Yu F, Strobel J. Work–in–Progress: Pre–college Teachers’ Metaphorical Beliefs about Engineering. In Proceedings of the 2021 IEEE Global Engineering Education Conference (EDUCON), Vienna, Austria, 21–23 April 2021.
  5. Negri M, Cagno E, Colicchia C, Sarkis J. Integrating Sustainability and Resilience in the Supply Chain: A Systematic Literature Review and a Research Agenda. Business Strategy and the Environment 2021; 30(7): 2858–2886.
  6. Sun G, Zhan T, Owusu BG, Daniel A-M, Liu G, Jiang W. Revised Reinforcement Learning Based on Anchor Graph Hashing for Autonomous Cell Activation in Cloud–RANs. Future Generation Computer Systems 2020; 104: 60–73.
  7. Deng X, Li L, Enomoto M, Kawano Y. Continuously Frequency–Tuneable Plasmonic Structures for Terahertz Bio–Sensing and Spectroscopy. Scientific Reports 2019; 9(1): 3498.
  8. Shen Y, Gu H-m, Zhai L, Wang B, Qin S, Zhang D-w. The Role of Hepatic Surf4 in Lipoprotein Metabolism and the Development of Atherosclerosis in apoE-/- mice. Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids 2022; 1867(10): 159196.
  9. Wang B, Shen Y, Zhai L, Xia X, Gu H-M, Wang M, Zhao Y, Chang X, Alabi A, Xing S, Deng S, Liu B, Wang G, Qin S, Zhang D-W. Atherosclerosis–Associated Hepatic Secretion of VLDL but Not PCSK9 Is Dependent on Cargo Receptor Protein Surf4. Journal of Lipid Research 2021; 62: 100091.
  10. Deng S-j, Shen Y, Gu H-M, Guo S, Wu S-R, Zhang D-w. The Role of the C–Terminal Domain of PCSK9 and SEC24 Isoforms in PCSK9 Secretion. Biochimica et Biophysica Acta (BBA)–Molecular and Cell Biology of Lipids 2020; 1865(6): 158660.
  11. Shen Y, Wang B, Deng S, Zhai L, Gu H-M, Alabi A, Xia X, Zhao Y, Chang X, Qin S, Zhang D-W. Surf4 Regulates Expression of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) but Is Not Required for PCSK9 Secretion in Cultured Human Hepatocytes. Biochimica et Biophysica Acta (BBA)–Molecular and Cell Biology of Lipids 2020; 1865(2): 158555.
  12. Xia D, Alexander AK, Isbell A, Zhang S, Ou J, Liu XM. Establishing a Co–Culture System for Clostridium Cellulovorans and Clostridium Aceticum for High Efficiency Biomass Transformation. J Sci Heal Univ Ala 2017; 14: 8–13.
  13. Shen Y, Gu H–M, Qin S, Zhang D–W. Surf4, Cargo Trafficking, Lipid Metabolism, and Therapeutic Implications. Journal of Molecular Cell Biology 2022; 14(9): mjac063.
  14. M. Wang, Alabi A, Gu H-M, Gill G, Zhang Z, Jarad S, Xia X-D, Shen Y, Wang G-Q, Zhang D-W. Identification of Amino Acid Residues in the MT–Loop of MT1–MMP Critical for Its Ability to Cleave Low–Density Lipoprotein Receptor. Frontiers in Cardiovascular Medicine 2022; 9: 917238.
  15. Dai W. Evaluation and Improvement of Carrying Capacity of a Traffic System. Innovations in Applied Engineering and Technology 2022; 1(1): 1–9.
  16. Dai W. Safety Evaluation of Traffic System With Historical Data Based on Markov Process and Deep–Reinforcement Learning. Journal of Computational Methods in Engineering Applications 2021; 1(1): 1–14.
  17. Narwane VS, Raut RD, Yadav VS, Cheikhrouhou N, Narkhede BE, Priyadarshinee P. The Role of Big Data for Supply Chain 4.0 in Manufacturing Organisations of Developing Countries. Journal of Enterprise Information Management 2021; 34(5): 1452–1480.
  18. Kundu S, Fu Y, Ye B, Beerel PA, Pedram M. Toward Adversary–Aware Non–Iterative Model Pruning Through D Ynamic N Etwork R Ewiring of DNNs. ACM Transactions on Embedded Computing Systems 2022; 21(5): 1–24.
  19. Deng X, Oda S, Kawano Y. Frequency Selective, High Transmission Spiral Terahertz Plasmonic Antennas. Journal of Modeling and Simulation of Antennas and Propagation 2016; 2: 1–6.
  20. Liu Y, Hajj M, Bao Y. Review of Robot–Based Damage Assessment for Offshore Wind Turbines. Renewable and Sustainable Energy Reviews 2022; 158: 112187.
  21. Qiu Y. Estimation of Tail Risk Measures in Finance: Approaches to Extreme Value Mixture Modeling; Johns Hopkins University: Baltimore, MD, USA, 2019.
  22. Liu Y, Bao Y. Review on Automated Condition Assessment of Pipelines With Machine Learning. Advanced Engineering Informatics 2022; 53: 101687.
  23. Deng X, Dong Z, Ma X, Wu H, Wang B. Active Gear–Based Approach Mechanism for Scanning Tunneling Microscope. In Proceedings of the 2009 International Conference on Mechatronics and Automation, Changchun, China, 9–12 August 2009.
  24. Sugaya T, Deng X. Resonant Frequency Tuning of Terahertz Plasmonic Structures Based on Solid Immersion Method. In Proceedings of the 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW–THz), Paris, France, 1–6 September 2019.
  25. Luo Z, Xu H, Chen F. Audio Sentiment Analysis by Heterogeneous Signal Features Learned from Utterance–Based Parallel Neural Network. In Proceedings of the AffCon@AAAI 2019, Honolulu, HI, USA, 27–28 January 2019.
  26. Chen F, Luo Z, Xu Y, Ke D. Complementary Fusion of Multi–Features and Multi–Modalities in Sentiment Analysis. 2019. arXiv:1904.08138.
  27. Luo Z, Zeng X, Bao Z, Xu M. Deep Learning–Based Strategy for Macromolecules Classification With Imbalanced Data From Cellular Electron Cryotomography. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019.
  28. Horne J, Beddingfield E, Knapp M, Mitchell S, Crawford L, Mills SB, Wrist A, Zhang S, Summers RM. Caffeine and Theophylline Inhibit β–Galactosidase Activity and Reduce Expression in Escherichia coli. ACS Omega 2020; 5(50): 32250–32255.
  29. Mock MB, Zhang S, Pniak B, Belt N, Witherspoon M, Summers RM. Substrate Promiscuity of the NdmCDE N7–Demethylase Enzyme Complex. Biotechnology Notes 2021; 2: 18–25.
  30. Yu F, Milord J, Orton SL, Flores L, Marra R. The Concerns and Perceived Challenges Students Faced When Traditional in–Person Engineering Courses Suddenly Transitioned to Remote Learning. In Proceedings of the 2022 ASEE Annual Conference, Minneapolis, MN, USA, 26–29 June 2022.
  31. Milord J, Yu F, Orton S, Flores L, Marra R. Impact of COVID Transition to Remote Learning on Engineering Self–Efficacy and Outcome Expectations. In Proceedings of the 2021 ASEE Virtual Annual Conference, Virtual Meeting, 26–29 July 2021.
  32. Yu F, Milord JO, Flores LY, Marra R. Work in Progress: Faculty Choice and Reflection on Teaching Strategies to Improve Engineering Self–Efficacy. In Proceedings of the 2022 ASEE Annual Conference, Minneapolis, MN, USA, 26–29 June 2022.
  33. Yu F, Milord J, Orton S, Flores L, Marra R. Students’ Evaluation Toward Online Teaching Strategies for Engineering Courses during COVID. In Proceedings of the 2021 ASEE Midwest Section Conference, Virtual Meeting, September 13–15 2021.
  34. Li S, Singh K, Riedel N, Yu F, Jahnke I. Digital Learning Experience Design and Research of a Self–Paced Online Course for Risk–Based Inspection of Food Imports. Food Control 2022; 135: 108698.
  35. Chen F, Luo Z. Learning Robust Heterogeneous Signal Features From Parallel Neural Network for Audio Sentiment Analysis. 2018. arXiv:1811.
  36. Luo Z, Xu H, Chen F. Utterance–Based Audio Sentiment Analysis Learned By a Parallel Combination of CNN and LSTM. 2018; arXiv:1811.08065.
  37. Pinto L. Green Supply Chain Practices and Company Performance in Portuguese Manufacturing Sector. Business Strategy and the Environment 2020; 29(5): 1832–1849.
  38. Gold S, Schleper MC. A Pathway Towards True Sustainability: A Recognition Foundation of Sustainable Supply Chain Management. European Management Journal 2017; 35(4): 425–429.
  39. Wu J, Xu M, Zhang P. The Impacts of Governmental Performance Assessment Policy and Citizen Participation on Improving Environmental Performance Across Chinese Provinces. Journal of Cleaner Production 2018; 184: 227–238.
  40. Liu K, Wang X, Yan Y. Network Analysis of Industrial Symbiosis in Chemical Industrial Parks: A Case Study of Nanjing Jiangbei New Materials High–Tech Park. Sustainability 2022; 14(3): 1381.

Supporting Agencies

  1. Funding: Not applicable.