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Evaluating the development path of manufacturing industry under carbon neutralisation

Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
China’s manufacturing industry faces the dual imperatives of supporting economic growth while reducing emissions to achieve national carbon neutrality targets. This study analyses the potential for decarbonisation across manufacturing sub-sectors. Using factor analysis and fuzzy comprehensive evaluation, it assesses industries based on foundational advantages, growth prospects, and sustainability. The results rank sub-sectors and identify promising areas like eco-friendly equipment and agricultural processing. However, carbon-intensive industries require urgent transformation. To optimise China’s industrial structure for low-carbon development, coordinated efforts across policy, industry, and enterprise are needed. Supportive regulations, industrial integration, and technology adoption can incentivise green manufacturing. By strategically promoting circular economy models, China can enhance quality and efficiency, convert waste into value, and contribute to global climate action. This study provides insights into aligning manufacturing growth with carbon neutrality in the new economic era.
Rocznik
Strony
581--593
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • School of Architecture, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China
Bibliografia
  • [1] Lin ZC, Liu YF, Liao WL, Hu WT. Exploring influencing factors and driving mechanisms of public low-carbon behavior in the internet context: An exploratory study using grounded theory. J Logistics Informatics Service Sci. 2023;10(3):280-96. DOI: 10.33168/JLISS.2023.0321.
  • [2] Streck C, Keenlyside P, von Unger M. The Paris Agreement: A new beginning. J Eur Environ Planning Law. 2016;13(1):3-29. DOI: 10.1163/18760104-01301002.
  • [3] Wu CH, Tsai SB, Liu W, Shao XF, Xia YK, Wacławek M. Green environment and sustainable development: methods and applications. Ecol Chem Eng S. 2021;28(4):467-70. DOI: 10.2478/eces-2021-0030.
  • [4] Wu CH, Tsai SB, Liu W, Shao XF, Sun R, Wacławek M. Eco-technology and eco-innovation for green sustainable growth. Ecol Chem Eng S. 2021;28(1):7-10. DOI: 10.2478/eces-2021-0001.
  • [5] Barecka MH, Ager JW, Lapkin AA. Carbon neutral manufacturing via on-site CO2 recycling. iScience. 2021;24:102514. DOI: 10.1016/j.isci.2021.102514.
  • [6] Jin TD, Shi TQ, Park T. The quest for carbon-neutral industrial operations: renewable power purchase versus distributed generation. Int J Prod Res. 2018;56:5723-35. DOI: 10.1080/00207543.2017.1394593.
  • [7] Delnavaz M, Saatchi A. Comparative study on different ratios of foundry and waste foundry sand in concrete. Environ Eng Manage J. 2021;20:1405-14. DOI: 10.30638/eemj.2021.130.
  • [8] Xu W, Sun HY, Awaga AL, Yan Y, Cui YJ. Optimization approaches for solving production scheduling problem: A brief overview and a case study for hybrid flow shop using genetic algorithms. Adv Production Eng Manage. 2022;17:45-56. DOI: 10.14743/apem2022.1.420.
  • [9] Stanujkic D, Karabasevic D, Popovic G, Smarandache F, Zavadskas EK, Meidute-Kavaliauskiene I. Multiple-criteria decision-making based on the use of single-valued neutrosophic sets and similarity measures. Econ Comput Econ Cyb. 2021;55:5-22. DOI: 10.24818/18423264/55.2.21.01.
  • [10] Shahzadi T, Kanwal A, Jabeen H, Riaz H, Zaib M. Eco-friendly synthesis of silver nanopartricles using Gazania rigens and evaluation of activities. Environ Eng Manage J. 2021;20:43-52. DOI: 10.30638/eemj.2021.005.
  • [11] Choi JW, Jeong ER. A multi-output convolutional neural network-based distance and velocity estimation technique. J Logistics Informatics Service Sci. 2022;9:11-25. DOI: 10.33168/LISS.2022.0202.
  • [12] Sun HY, Xu W, Yu YY, Cai GY. An intelligent mechanism for COVID-19 emergency resource coordination and follow-up response. Comput Intell Neurosci. 2022;10:2005188. DOI: 10.1155/2022/2005188.
  • [13] Moghaddas Z, Vaez-Ghasemi M, Hosseinzadeh Lotfi F. A novel DEA approach for evaluating sustainable supply chains with undesirable factors. Econ Comput Econ Cyb. 2021;55:177-92. DOI: 10.24818/18423264/55.2.21.11.
  • [14] Yadollah AH, Matin RK. Centralized resource allocation in two-stage production systems: a network DEA approach. Econ Comput Econ Cyb. 2022;56:279-96. DOI: 10.24818/18423264/56.3.22.18.
  • [15] Jia C, Ding H, Zhang X. Reliability evaluation of direct current distribution system for intelligent buildings based on big data analysis. Tehnički Vjesnik. 2021;28(5):1769-81. DOI: 10.17559/TV-20210507090202.
  • [16] Katariya D, Shukla K. Sustainable economic production quantity (SEPQ) model for inventory having green technology investments - price sensitive demand with expiration dates. Econ Comput Econ Cyb. 2022;56:135-52. DOI: 10.24818/18423264/56.3.22.09.
  • [17] Song YJ, Lee JK. A blockchain-based fog-enabled energy cloud in Internet of Things. J Logistics Informatics Service Sci. 2020;7:45-64. DOI: 10.33168/JLISS.2020.0204.
  • [18] Burinskiene A. The efficiency increase in a two-stage transport system. Int J Simulation Modelling. 2021;20:5-16. DOI: 10.2507/IJSIMM20-1-536.
  • [19] Ištoković D, Perinić M, Borić A. Determining the minimum waiting times in a hybrid flow shop using simulation-optimization approach. Tehnički Vjesnik. 2021;28:568-75. DOI: 10.17559/TV-20210216132702.
  • [20] Nobili C, Cappellaro F. Circular economy good practices in waste management and prevention in the food system. Environ Eng Manage J. 2021;20:1645-54. DOI: 10.30638/eemj.2021.153.
  • [21] Wang YL, Zheng XY, Yin XM, Cai JR. Simulation of financing decisions with behavioural preferences and yield uncertainty. Int J Simulation Modelling. 2022;21:675-83. DOI: 10.2507/IJSIMM21-4-CO16.
  • [22] Chen W, Hao YF. A combined service optimization and production control simulation system. Int J Simulation Modelling. 2022;21:684-95. DOI: 10.2507/IJSIMM21-4-CO17.
  • [23] Zhu S, Song J, Peng W, Sun J. Readability assessment for Chinese L2 sentences: an extended knowledge base and comprehensive evaluation model-based method. Tehnički Vjesnik. 2021;28(1):211-21. DOI: 10.17559/TV-20200914173747.
  • [24] China Statistical Yearbook 2022. Fu LH, editor. China Statistical Publishing House; 2022. DOI: 10.40049/y.cnkiyinfn.2022.00001.
  • [25] China Science and Technology Statistcal Yearbook 2022. Guan XJ, editor. China Statistical Publishing House; 2022. DOI: 10.38606/y.cnki.ybvcx.2023.000001.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b2d9b31b-0572-433a-9b65-5c3c01aea1fb
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