Potentials and Challenges of Carbon Knowledge Graph in Sustainable Textile Production for Carbon Traceability:A Review

Abstract

Textile production has received considerable attention owing to its significance in production value, the complexity of its manufacturing processes and the extensive reach of its supply chains. However, textile industry consumes substantial energy and materials and emits greenhouse gases that severely harm the environment. In addressing this challenge, the concept of sustainable production offers crucial guidance for the sustainable development of the textile industry. Low-carbon manufacturing technologies provide robust technical support for the textile industry to transition to a low-carbon model by optimizing production processes, enhancing energy efficiency and minimizing material waste.Consequently, low-carbon manufacturing technologies have gradually been implemented in sustainable textile production scenarios. However, while research on low-carbon manufacturing technologies for textile production has advanced, these studies predominantly concentrate on theoretical methods, with relatively limited exploration of practical applications. To address this gap, a thorough overview of carbon emission management methods and tools in textile production, as well as the characteristics and influencing factors of carbon emissions in key textile manufacturing processes is presented to identify common issues. Additionally, two new concepts, carbon knowledge graph and carbon traceability, are introduced, offering strategic recommendations and application directions for the low-carbon development of sustainable textile production. Beginning with seven key aspects of sustainable textile production, the characteristics of carbon emissions and their influencing factors in key textile manufacturing process are systematically summarized. The aim is to provide guidance and optimization strategies for future emission reduction efforts by exploring the carbon emission situations and influencing factors at each stage. Furthermore, the potential and challenges of carbon knowledge graph technology are summarized in achieving carbon traceability, and several research ideas and suggestions are proposed.

Keywords

sustainable textile production / carbon knowledge graph / carbon traceability / low-carbon development / emission reduction

References

[1] GONÇALVES A, SILVA C. Looking for sustainability scoring in apparel:a review on environmental footprint, social impacts and transparency[J]. Energies, 2021, 14(11):3032.

[2] CAI Y J, CHOI T M. A united nations ’sustainable development goals perspective for sustainable textile and apparel supply chain management[J]. Transportation Research Part E:Logistics and Transportation Review, 2020, 141:102010.

[3] PARHAMFAR M, SADEGHKHANI I, ADELI A M. Towards the net zero carbon future:a review of blockchain-enabled peer-to-peer carbon trading[J]. Energy Science & Engineering, 2024, 12(3):1242-1264.

[4] CHEN R. A review of knowledge mapping-based research on peak carbon and carbon neutrality[J]. Green Finance and Accounting, 2022(7):3-9. (in Chinese)

[5] WANG S H, YANG ZW, ZHANG W, et al. A review of domestic and foreign research on carbon emission under the background of “ double carbon ”:based on visualization of scientific knowledge map and combing of development context[J]. Ecological Economy, 2023, 39(12):222-229. (in Chinese)

[6] HE B, YUAN X, QIAN S S, et al. Product lowcarbon design, manufacturing, logistics, and recycling:an overview[J]. WIREs Energy and Environment, 2023, 12(5):e479.

[7] WANG C X, WANG L H, LIU X L, et al. Carbon footprint of textile throughout its lifecycle:a case study of Chinese cotton shirts[J]. Journal of Cleaner Production, 2015, 108:464-475.

[8] HE B, LIU Y J, ZENG L B, et al. Product carbon footprint across sustainable supply chain[J]. Journal of Cleaner Production, 2019, 241:118320.

[9] SILIŅA L, DĀBOLIŅA I, LAPKOVSKA E. Sustainable textile industry-wishful thinking or the new norm:a review[J]. Journal of Engineered Fibers and Fabrics, 2024, 19:15589250231220359.

[10] HAUKKALA T, NIINIMÄKI K, TURUNEN L L M. Fashion in turmoil:impact of the COVID-19 pandemic on Finland ’ s textile and fashion industry[J]. Sustainability:Science, Practice and Policy, 2023, 19(1):2173424.

[11] CHEN S, ZHU L S, SUN L R, et al. A systematic review of the life cycle environmental performance of cotton textile products[J]. Science of the Total Environment, 2023, 883:163659.

[12] JHANJI Y. Life cycle analysis of textiles and associated carbon emissions[M]//Sustainable Fibres for Fashion and Textile Manufacturing. Amsterdam: Elsevier, 2023:403-431.

[13] LUO Y, WU X Y, DING X M. Carbon and water footprints assessment of cotton jeans using the method based on modularity:a full life cycle perspective[J]. Journal of Cleaner Production, 2022, 332:130042.

[14] LIU J R, LI X, ZHU L S, et al. Discussion on key issues of carbon footprint accounting for bast fiber textiles[J]. Science of the Total Environment, 2023, 897:166272.

[15] PENG S Y, LIU J Y, GENG Y. Assessing strategies for reducing the carbon footprint of textile products in china under the shared socioeconomic pathways framework[J]. Climate Change Economics, 2022, 13(1):2240004.

[16] CHENG Y, LIANG H E. Calculation and evaluation of industrial carbon footprint of cotton denim jacket[J]. Journal of Engineered Fibers and Fabrics, 2021, 16:15589250211020387.

[17] Wang W L. Development of software for carbon footprint assessment and accounting of terylene cotton hoodie[D]. Hangzhou: Zhejiang Sci-Tech University, 2024. (in Chinese)

[18] Hu K L. Study on carbon emissions in key segments of textile enterprises ’ production processes[D]. Xi’an: Xi’an Polytechnic University, 2019. (in Chinese)

[19] ZHANG X J. Analysis and technology of objective management for low carbon emissions in garment sewing process[D]. Suzhou: Soochow University, 2021. (in Chinese)

[20] MAIR S, DRUCKMAN A, JACKSON T. Global inequities and emissions in Western European textiles and clothing consumption[J]. Journal of Cleaner Production, 2016, 132:57-69.

[21] XIAO Y Y, NORRIS C B, LENZEN M, et al. How social footprints of nations can assist in achieving the sustainable development goals[J]. Ecological Economics, 2017, 135:55-65.

[22] LEAL FILHO W, DINIS M A P, LIAKH O, et al. Reducing the carbon footprint of the textile sector:an overview of impacts and solutions[J]. Textile Research Journal, 2024[2024-04-23]. https://journals.sagepub.com/doi/abs/10.1177/00405175241236971.

[23] SUN C W, CHEN L Y, ZHANG F. Exploring the trading embodied CO2 effect and low-carbon globalization from the international division perspective[J]. Environmental Impact Assessment Review, 2020, 83:106414.

[24] GIACOMIN A M, PACCA S A. Input-output analysis as guidance for the Brazilian textile supply chain[J]. Environment, Development and Sustainability, 2024:1-20.

[25] FRACCASCIA L, GIANNOCCARO I, ALBINO V. Green product development:what does the country product space imply?[J]. Journal of Cleaner Production, 2018, 170:1076-1088.

[26] MA X G. Knowledge graph construction and application in geosciences:a review[J]. Computers & Geosciences, 2022, 161:105082.

[27] ZHOU B, BAO J S, CHEN Z Y, et al. KGAssembly:knowledge graph-driven assembly process generation and evaluation for complex components[J]. International Journal of Computer Integrated Manufacturing, 2022, 35(10/11):1151-1171.

[28] WACKERNAGEL M, YOUNT J D. The ecological footprint:an indicator of progress toward regional sustainability[J]. Environmental monitoring and assessment, 1998, 51:511-529.

[29] MOHAN S, OKE N. Waste management in textile industry:a novel application of carbon footprint analysis[C]//Sustainable Environmental Geotechnics:Proceedings of EGRWSE 2019. Berlin: Springer, 2020:125-130.

[30] ZAMANI B, SVANSTRÖM M, PETERS G, et al. A carbon footprint of textile recycling:a case study in Sweden[J]. Journal of Industrial Ecology, 2015, 19(4):676-687.

[31] BEVILACQUA M, CIARAPICA F E, GIACCHETTA G, et al. A carbon footprint analysis in the textile supply chain[J]. International Journal of Sustainable Engineering, 2011, 4(1):24-36.

[32] Q IAN W, JI X, XU P, et al. Carbon footprint and water footprint assessment of virgin and recycled polyester textiles[J]. Textile Research Journal, 2021, 91(21/22):2468-2475.

[33] ZHANG Y, KANG H, HOU H, et al. Improved design for textile production process based on life cycle assessment[J]. Clean Technologies and Environmental Policy, 2018, 20(6):1355-1365.

[34] CASTELLANI V, SANYÉ-MENGUAL E, SALA S. Environmental impacts of household goods in Europe:a process-based life cycle assessment model to assess consumption footprint[J]. The International Journal of Life Cycle Assessment, 2021, 26(10):2040-2055.

[35] WOOD R, MORAN D, STADLER K, et al. Prioritizing consumption based carbon policy based on the evaluation of mitigation potential using input output methods[J]. Journal of Industrial Ecology, 2018, 22(3):540-552.

[36] HE B, CAO X, GU Z. Kinematics of underactuated robotics for product carbon footprint[J]. Journal of Cleaner Production, 2020, 257:120491.

[37] GARCIA R, FREIRE F. Carbon footprint of particleboard:a comparison between ISO/TS 14067, GHG protocol, PAS 2050 and climate declaration[J]. Journal of Cleaner Production, 2014, 66:199-209.

[38] HU Y, DU H, XU L, et al. Life cycle environmental benefits of recycling waste liquor and chemicals in production of lignocellulosic bioethanol[J]. Bioresource Technology, 2023, 390:129855.

[39] PANG B, SUN Z, WANG L, et al. Improved value and carbon footprint by complete utilization of corncob lignocellulose[J]. Chemical Engineering Journal, 2021, 419:129565.

[40] PERKINS J, SUH S. Uncertainty implications of hybrid approach in LCA:precision versus accuracy[J]. Environmental Science & Technology, 2019, 53(7):3681-3688.

[41] YASIN S, BEHARY N, ROVERO G, et al. Statistical analysis of use-phase energy consumption of textile products[J]. The International Journal of Life Cycle Assessment, 2016, 21(12):1776-1788.

[42] SHREE V, NAUTIYAL H, GOEL V. Carbon footprint estimation for academic building in India[M]//LCA Based Carbon Footprint Assessment. Singapore: Springer Singapore, 2021:55-70.

[43] ORMAZABAL M, JACA C, PUGA-LEAL R. Analysis and comparison of life cycle assessment and carbon footprint software[C]//Proceedings of the Eighth International Conference on Management Science and Engineering Management:Focused on Computing and Engineering Management. Berlin: Springer, 2014:1521-1530.

[44] ZHENG H, YAN Y F. Research on carbon footprint analysis model of mechanical and electrical products from the perspective of LCA[C]//Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Singapore: Springer Singapore, 2019:21-28.

[45] TURCONI R, BOLDRIN A, ASTRUP T. Life cycle assessment(LCA)of electricity generation technologies:overview, comparability and limitations[J]. Renewable and Sustainable Energy Reviews, 2013, 28:555-565.

[46] WANG Y, CHAU C K, NG W Y, et al. A review on the effects of physical built environment attributes on enhancing walking and cycling activity levels within residential neighborhoods[J]. Cities, 2016, 50:1-15.

[47] BOSCO S, DI BENE C, GALLI M, et al. Soil organic matter accounting in the carbon footprint analysis of the wine chain[J]. The International Journal of Life Cycle Assessment, 2013, 18(5):973-989.

[48] CHEN J L, WEN Y H. Developing a simple carbon footprint calculation tool with the criteria of inventory data selection[C]//Leveraging Technology for a Sustainable World:Proceedings of the 19th CIRP Conference on Life Cycle Engineering. Berlin: Springer, 2012:429-434.

[49] ABBATE S, CENTOBELLI P, CERCHIONE R, et al. Sustainability trends and gaps in the textile, apparel and fashion industries[J]. Environment, Development and Sustainability, 2024, 26(2):2837-2864.

[50] SCRUCCA F, BARBERIO G, FANTIN V, et al. Carbon footprint:concept, methodology and calculation[M]//SUBRAMANIAN S M. Carbon Footprint Case Studies:Municipal Solid Waste Management, Sustainable Road Transport and Carbon Sequestration. Singapore: Springer Singapore, 2021:1-31.

[51] MORRIS J, KOEP L, DAMERT M. Labels in the textile and fashion industry:communicating sustainability to effect sustainable consumption[M]//MATTHES A, BEYER K, CEBULLA H, et al.Sustainable Textile and Fashion Value Chains:Drivers, Concepts, Theories and Solutions. Berlin: Springer, 2021:257-274.

[52] LI Y, LUO Y, HE Q. Chemical footprint of textile and apparel products:an assessment of human and ecological toxicities based on USEtox model[J]. The Journal of the Textile Institute, 2020, 111(7):960-971.

[53] LIU M, LI X, LI J, et al. A knowledge graphbased data representation approach for IIoTenabled cognitive manufacturing[J]. Advanced Engineering Informatics, 2022, 51:101515.

[54] QIN Z, LU Y. A knowledge graph-based knowledge representation for adaptive manufacturing control under mass personalization[J]. Manufacturing Letters, 2023, 35:96-104.

[55] SIDDHARTH L, BLESSING L T M, WOOD K L, et al. Engineering knowledge graph from patent database[J]. Journal of Computing and Information Science in Engineering, 2022, 22(2):021008.

[56] LAI X. Bottleneck prediction and resilience improvement for manufacturing systems[D]. Ann Arbor: University of Michigan, 2022.

[57] TANG J, LIU Y, LIN K, et al. Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph[J]. Advanced Engineering Informatics, 2023, 55:101862.

[58] WU T, LI J, BAO J, et al. CarbonKG:industrial carbon emission knowledge graph-based modeling and application for carbon traceability of complex manufacturing process[J]. Journal of Computing and Information Science in Engineering, 2024, 24:081001.

[59] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C/OL]. The 31st Annual Conference on Neural Information Processing Systems(NIPS). La Jolla: Neural Information Processing Systems(NIPS), 2017, 30[2024-03-01].

[60] WU Z, PAN S, CHEN F, et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1):4-24.

[61] WANG L. From intelligence science to intelligent manufacturing[J]. Engineering, 2019, 5(4):615-618.

[62] ZHOU J, CUI G Q, HU S D, et al. Graph neural networks:a review of methods and applications[J]. AI Open, 2020, 1:57-81.

[63] VELIČ KOVIC' P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL].(2018-02-04)[2024-07-13]. https://arxiv.org/abs/1710.10903.

[64] LUO L H, FANG Y X, LU M L, et al. GSim:a graph neural network based relevance measure for heterogeneous graphs[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12):12693-12707.

[65] SENTHIL KUMAR P, JANET JOSHIBA G. Energy footprints of textile products[M]//SUBRAMANIAN S M.Energy Footprints of the Food and Textile Sectors. Singapore: Springer Singapore, 2019:45-61.

[66] KARTHIK T, GOPALAKRISHNAN D. Environmental analysis of textile value chain:an overview[M]//SUBRAMANIAN S M. Roadmap to Sustainable Textiles and Clothing:Environmental and Social Aspects of Textiles and Clothing Supply Chain. Singapore: Springer Singapore, 2014:153-188.

[67] ROY CHOUDHURY A K. Environmental impacts of the textile industry and its assessment through life cycle assessment[M]//SUBRAMANIAN S M. Roadmap to Sustainable Textiles and Clothing:Environmental and Social Aspects of Textiles and Clothing Supply Chain. Singapore: Springer Singapore, 2014:1-39.

[68] ZAMAN S U, UMAIR M, JAVID A. Sustainable production practices in textiles[M]//BATOOL S R, AHMAD S, NAWAB Y, et al. Circularity in Textiles. Berlin: Springer, 2023:129-157.

[69] KOPF S, ÅKESSON D, SKRIFVARS M. Textile fiber production of biopolymers:a review of spinning techniques for polyhydroxyalkanoates in biomedical applications[J]. Polymer Reviews, 2023, 63(1):200-245.

[70] FARHANA K, KADIRGAMA K, MAHAMUDE A S F, et al. Energy consumption, environmental impact, and implementation of renewable energy resources in global textile industries:an overview towards circularity and sustainability[J]. Materials Circular Economy, 2022, 4(1):15.

[71] RAJ A, CHOWDHURY A, ALI S. Green chemistry:its opportunities and challenges in colouration and chemical finishing of textiles[J]. Sustainable Chemistry and Pharmacy, 2022, 27:100689.

[72] DE OLIVEIRA C R S, DA SILVA A H, MULINARI J, et al. Textile re-engineering:ecoresponsible solutions for a more sustainable industry[J]. Sustainable Production and Consumption, 2021, 28:1232-1248.

[73] MOAZZEM S, CROSSIN E, DAVER F, et al. Assessing environmental impact reduction opportunities through life cycle assessment of apparel products[J]. Sustainable Production and Consumption, 2021, 28:663-674.

[74] STANESCU M D. State of the art of postconsumer textile waste upcycling to reach the zero waste milestone[J]. Environmental Science and Pollution Research, 2021, 28(12):14253-14270.

[75] HE B, YUAN X, QIAN S S, et al. Carbon neutrality:a review[J]. Journal of Computing and Information Science in Engineering, 2023, 23(6):060809.

[76] MA S Y, DING W, LIU Y, et al. Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries[J]. Applied Energy, 2022, 326:119986.

[77] ZHAO X, MA X W, CHEN B Y, et al. Challenges toward carbon neutrality in China:strategies and countermeasures[J]. Resources, Conservation and Recycling, 2022, 176:105959.

[78] HE B, MAO H Y. Digital twin-driven product sustainable design for low carbon footprint[J]. Journal of Computing and Information Science in Engineering, 2023, 23(6):060805.

[79] KWON S, MONNIER L V, BARBAU R, et al. A new implementation of ontostep:flexible generation of ontology and knowledge graphs of express-driven data[J]. Journal of Computing and Information Science in Engineering, 2022, 22(2):024502.

[80] GENG Z Q, ZHANG Y H, LI C F, et al. Energy optimization and prediction modeling of petrochemical industries:an improved convolutional neural network based on crossfeature[J]. Energy, 2020, 194:116851.

[81] DING S, XU N, YE J, et al. Estimating Chineseenergy-related CO2 emissions by employing a novel discrete grey prediction model[J]. Journal of Cleaner Production, 2020, 259:120793.

[82] ZHU J W, HAN X, DENG H H, et al. KSTGCN:a knowledge-driven spatial-temporal graph convolutional network for traffic forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9):15055-15065.

[83] HANS A, CHAUDHARI A M, BILIONIS I, et al. A Bayesian hierarchical model for extracting individuals’ theory-based causal knowledge[J]. Journal of Computing and Information Science in Engineering, 2023, 23(3):031011.

[84] ZHENG H B, LIU S M, ZHANG H J, et al. Visual-triggered contextual guidance for lithium battery disassembly:a multi-modal event knowledge graph approach[J]. Journal of Engineering Design, 2024:1-26.

[85] FU T J, LI P Y, LIU S M. An imbalanced small sample slab defect recognition method based on image generation[J]. Journal of Manufacturing Processes, 2024, 118:376-388.

[86] SHEN X W, LIU S M, ZHANG C, et al. Intelligent material distribution and optimization in the assembly process of large offshore crane lifting equipment[J]. Computers & Industrial Engineering, 2021, 159:107496.

[87] SHI Y L, ZHANG N, SONG X L, et al. Novel approach for industrial process anomaly detection based on process mining[J]. Journal of Process Control, 2024, 136:103165.

Funding

Natural Science Foundation of Shanghai, China(21ZR1400800)