Abstract
The low-carbon transformation of manufacturing enterprises is considered to be imperative to achieve carbon neutrality. Therefore, we propose a data-driven strategy to achieve a low-carbon transformation of manufacturing enterprises from an eco-efficiency perspective. Following the collection of input (energy, materials, equipment, R&D, and services) and output (waste and products) data from production systems of manufacturing enterprises, an ecological efficiency model of manufacturing enterprise production system was constructed from the perspective of carbon emissions, thus allowing the quantitative evaluation of the ecological efficiency of the production system. Furthermore, a “measurable, evaluable, and optimized” low-carbon transformation and upgrading method for manufacturing enterprise production system was established. Finally, through the production practice data of an enterprise from 2017 to 2021, the feasibility and effectiveness of this method were verified. The results show that this method can effectively improve the ecological efficiency of enterprises by 3.6% and reduce waste emissions by 12%. Our study provides new tools for improving the ecological efficiency of manufacturing systems, along with theoretical and methodological support to manufacturing enterprises for low-carbon transformation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ahlen A, Akerberg J, Eriksson M, Isaksson AJ, Iwaki T, Johansson KH, Knorn S, Lindh T, Sandberg H (2019) Toward wireless control in industrial process automation: a case study at a paper mill. IEEE Control Syst 39:36–57. https://doi.org/10.1109/MCS.2019.2925226
Al-Lami A, Hilmer P, Sinapius M (2018) Eco-efficiency assessment of manufacturing carbon fiber reinforced polymers (CFRP) in aerospace industry. Aerosp Sci Technol 79:669–678. https://doi.org/10.1016/j.ast.2018.06.020
Briese E, Piezer K, Celik I, Apul D (2019) Ecological network analysis of solar photovoltaic power generation systems. J Clean Prod 223:368–378. https://doi.org/10.1016/j.jclepro.2019.03.112
Cai W, Lai KH, Liu CH, Wei FF, Ma MD, Jia S, Jiang ZG, Lv L (2019) Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy. Sci Total Environ 665:23–32. https://doi.org/10.1016/j.scitotenv.2019.02.069
Cao YR, Wang QW, Zhou DQ (2022) Does air pollution inhibit manufacturing productivity in Yangtze River Delta, China? Moderating effects of temperature. J Environ Manage 306:114492. https://doi.org/10.1016/j.jenvman.2022.114492
Chandra M, Shahab F, Kek V, Rajak S (2022) Selection for additive manufacturing using hybrid MCDM technique considering sustainable concepts. Rapid Prototyp J 28:1297–1311. https://doi.org/10.1108/RPJ-06-2021-0155
Chen X, Lin B (2020) Assessment of eco-efficiency change considering energy and environment: a study of China’s non-ferrous metals industry. J Clean Prod 277:123388. https://doi.org/10.1016/j.jclepro.2020.123388
Cuixia Z, Conghu L, Xi Z (2017) Optimization control method for carbon footprint of machining process. Int J Adv Manuf Technol 92:1601–1607. https://doi.org/10.1007/s00170-017-0241-1
Doorasamy M (2016) Using material flow cost accounting (MFCA) to identify benefits of eco-efficiency and cleaner production in a paper and pulp manufacturing organization. Found Manag 8:263–288. https://doi.org/10.1515/fman-2016-0021
Ellis ED, Girardi D, Golden AP, Wallace PW, Phillips J, Cragle DL (2022) Historical perspective on the department of energy mortality studies: focus on the collection and storage of individual worker data. Int J Radiat Biol 98:560–567. https://doi.org/10.1080/09553002.2018.1547851
Fang GC, Gao ZY, Tian LX, Fu M (2022) What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data. Appl Energy 312:118772. https://doi.org/10.1016/j.apenergy.2022.118772
Georgopoulou A, Angelis-Dimakis A, Arampatzis G, Assimacopoulos D (2017) Systemic eco-efficiency assessment of industrial water use systems. Desalin Water Treat 63:343–350. https://doi.org/10.5004/dwt.2017.0523
Gumus S, Egilmez G, Kucukvar M, Shin Park Y (2016) Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing. J Oper Res Soc 67:616–628. https://doi.org/10.1057/jors.2015.88
Horz CM, Marbach M, Steinert CV (2023) Environmental Pollution and Authoritarian Politics. J POLIT. https://doi.org/10.1086/723024
Hu DX, Jiao JL, Tang YS, Han XF, Sun HP (2021) The effect of global value chain position on green technology innovation efficiency: from the perspective of environmental regulation. Ecol Indic 121:107195. https://doi.org/10.1016/j.ecolind.2020.107195
Huang M, Ding R, Xin C (2021) Impact of technological innovation and industrial-structure upgrades on ecological efficiency in China in terms of spatial spillover and the threshold effect. Integr Environ Assess Manag 17:852–865. https://doi.org/10.1002/ieam.4381
Jiang ZG, Ding ZY, Zhang H, Cai W, Liu Y (2019) Data-driven ecological performance evaluation for remanufacturing process. Energy Convers Manag 198:111844. https://doi.org/10.1016/j.enconman.2019.111844
Jingjing Z, Jiaxin W, Ta L, Dongping H (2019) Intuitionistic fuzzy measures of enterprise eco-efficiency and its influencing factors. J Intell Fuzzy Syst 37:185–192
Kaza S, Yao L, Bhada-Tata P, et al (2018). What a waste 2.0: a global snapshot of solid waste management to 2050. Washington, World Bank Publications. https://openknowledge.wordbank.org/handle/10986/2174
Kluczek A (2019) Assessment of manufacturing processes eco-efficiency based on MFA-LCA-MFCA methods. Environ Eng. Manag J (EEMJ) 18:465–477. https://doi.org/10.30638/eemj.2019.044
Krug HF (2022) Collection of controlled nanosafety data-the CoCoN-database, a tool to assess nanomaterial hazard. Nanomaterials (basel) 12:441. https://doi.org/10.3390/nano12030441
Kuhnle A, May MC, Schäfer L, Lanza G (2022) Explainable reinforcement learning in production control of job shop manufacturing system. Int J Prod Res 60:5812–5834. https://doi.org/10.1080/00207543.2021.1972179
Li Z, Shao S, Shi XP, Sun YP, Zhang XL (2019) Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: evidence of a threshold effect from China. J Clean Prod 206:920–927. https://doi.org/10.1016/j.jclepro.2018.09.241
Liu F (2017) The statue and difficult problems of research on energy efficiency of manufacturing systems. J Mech Eng 53:1–11. https://doi.org/10.3901/JME.2017.05.001
Liu C, Chen J, Cai W (2021a) Data-driven remanufacturability evaluation method of waste parts. IEEE Trans Industr Inf 18(7):4587–4595
Liu C, Gao M, Zhu G, Zhang C, Zhang P, Chen J, Cai W (2021b) Data driven eco-efficiency evaluation and optimization in industrial production. Energy 224:120170
Liu C, Chen J, Wang X (2023). Quantitative evaluation model of the quality of remanufactured product. IEEE Transactions on Engineering Management (Early Access). 1–12. https://doi.org/10.1109/TEM.2023.3268618.
Malone SM, Weissburg MJ, Bras B (2018) Industrial ecosystems and food webs: an ecological-based mass flow analysis to model the progress of steel manufacturing in China. Engineering 4:209–217. https://doi.org/10.1016/j.eng.2018.03.008
Mikulčić H, Cabezas H, Vujanović M, Duić N (2016) Environmental assessment of different cement manufacturing processes based on energy and ecological footprint analysis. J Clean Prod 130:213–221. https://doi.org/10.1016/j.jclepro.2016.01.087
Morato A, Vitturi S, Tramarin F, Cenedese A (2021) Assessment of different OPC UA implementations for industrial IoT-based measurement applications. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2020.3043116
Peng S, Li T, Li M, Guo Y, Shi J, Tan GZ, Zhang H (2019) An integrated decision model of restoring technologies selection for engine remanufacturing practice. J Clean Prod 206:598–610. https://doi.org/10.1016/j.jclepro.2018.09.176
Pereira CP, Prata DM, Santos LdS, Monteiro LPC (2018) Development of eco-efficiency comparison index through eco-indicators for industrial applications. Braz J Chem Eng 35:69–90. https://doi.org/10.1590/0104-6632.20180351s20160370
Schaltegger S, Burritt R (2000) Contemporary environmental accounting: issues, concepts and practice. Greenleaf Publishing, Sheffield Pubns
Shao L, Yu X, Feng C (2019) Evaluating the eco-efficiency of China’s industrial sectors: a two-stage network data envelopment analysis. J Environ Manage 247:551–560. https://doi.org/10.1016/j.jenvman.2019.06.099
Shi Y, Liu J, Shi H, Li H, Li Q (2017) The ecosystem service value as a new eco-efficiency indicator for industrial parks. J Cleaner Prod 164:597–605. https://doi.org/10.1016/j.jclepro.2017.06.187
Stergiou E, Kounetas KE (2021) Eco-efficiency convergence and technology spillovers of European industries. J Environ Manage 283:111972. https://doi.org/10.1016/j.jenvman.2021.111972
Stergiou E, Kounetas K (2022) Heterogeneity, spillovers and eco-efficiency of European industries under different pollutants’ scenarios. Is there a definite direction? Ecol Econ 195:107377. https://doi.org/10.1016/j.ecolecon.2022.107377
Su J, Shen T, Jin SX (2022) Ecological efficiency evaluation and driving factor analysis of the coupling coordination of the logistics industry and manufacturing industry. Environ Sci Pollut Res Int 29:62458–62474. https://doi.org/10.1007/s11356-022-20061-z
Townsend M, Le Quoc TL, Kapoor G, Hu H, Zhou W, Piramuthu S (2018) Real-time business data acquisition: how frequent is frequent enough? Inf Manag 55:422–429. https://doi.org/10.1016/j.im.2017.10.002
Vásquez J, Aguirre S, Fuquene-Retamoso CE, Bruno G, Priarone PC, Settineri L (2019) A conceptual framework for the eco-efficiency assessment of small- and medium-sized enterprises. J Clean Prod 237:117660. https://doi.org/10.1016/j.jclepro.2019.117660
Wang YY, Zhang YY (2020) Regional differences in ecological efficiency and their interactive spatial spillover effects with industrial structure upgrading. Geogr Sci 40:58–66
Wang Y, Zhu Z, Ma Z (2018) Eco-efficiency evaluation of petrochemical enterprises: an application of 3D state-space model. Energy Sci Eng 6:272–280. https://doi.org/10.1002/ese3.205
Wang Y, Li P, Zhu Z, Liu Z (2019) The evaluation of eco-efficiency of the industrial coupling symbiosis network of the eco-industrial park in oil and gas resource cities. Energy Sci Eng 7:899–911
Wang JI, Xu DN, Liu CY (2020) Analysis of ecological efficiency of China’s iron and steel industry based on DEA method. Ecol Econ 36:63–68
Wang SH, Sun XL, Song ML (2021a) Environmental regulation, resource misallocation, and ecological efficiency. Emerg Markets Fin Trade 57:410–429. https://doi.org/10.1080/1540496X.2018.1529560
Wang EZ, Lee CC, Li YY (2022) Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Econ 105:105748. https://doi.org/10.1016/j.eneco.2021.105748
Wang M, Pang SC, Yu SH, Qiao SB, Zhai X, Yue H (2022b) An optimal production scheme for reconfigurable cloud manufacturing service system. IEEE Trans Ind Inform 18:9037–9046. https://doi.org/10.1109/TII.2022.3169979
Wang Y, Wang ZY, Wang Z, Li XC, Pang XL, Wang SL (2021b). Application of discrete choice experiment in health care: a bibliometric analysis. Front. Public Health. 591. https://doi.org/10.3389/fpubh.2021.673698.
Xu LJ, Liu YJ, Zhang BD, Xiang BB (2023) Study on the impact of green finance on low carbon development of manufacturing industry from the perspective of multidimensional space: evidence from China. Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-023-25690-6
Xu L, Yang Z, Chen J, et al. Spatial-temporal heterogeneity of global ports resilience under pandemic: a case study of COVID-19[J]. Maritime Policy & Management: 1–14. https://doi.org/10.1080/03088839.2023.2224811.
Yang YP, Wu D, Xu M, Yang MT, Zou WJ (2022) Capital misallocation, technological innovation, and green development efficiency: empirical analysis based on China provincial panel data. Environ Sci Pollut Res 29:65535–65548. https://doi.org/10.1007/s11356-022-20364-1
Yuan HX, Feng YD, Lee CC, Cen Y (2020) How does manufacturing agglomeration affect green economic efficiency? Energy Econ 92:104944. https://doi.org/10.1016/j.eneco.2020.104944
Yuan BL, Xiang QL (2018). Environmental regulation, industrial innovation and green development of Chinese manufacturing: based on an extended CDM model. J Cleaner Prod. 176, 895–908/j.jclepro.2017.12.034.
Zhang HH (2019) Study on the evaluation of industrial ecological efficiency and its influencing factors in China. J Hebei Univ Econ Trade 40:51–58
Zhang Z (2020) Analysis on the spatiotemporal evolution characteristics of China’s regional industrial ecological efficiency in the New Era. Explor Econ Issues 01:92–101
Zhang RL, Liu XH (2021) Evaluating ecological efficiency of Chinese industrial enterprise. Renew Energy 178:679–691. https://doi.org/10.1016/j.renene.2021.06.119
Zhang Z, Hu J, Khan AA, Khan SU, Ali MA, Wang ZL, Ali M, Luo JC (2021) Analysis of financial support efficiency and influencing factors of listed seed companies from the perspective of energy consumption and carbon emissions. Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-023-26303-y
Zhao D, Chen J (2021) An analysis of the impact of service inputs in manufacturing industries on eco-efficiency: evidence from China. Environ Sci Pollut Res Int 28:61825–61840. https://doi.org/10.1007/s11356-021-15092-x
Zhouji (2015) Intelligent manufacturing – the main direction of “made in China 2025”. China Mech Eng 26:2273–2284
Zou FQ, Li TH (2022) The impact of agricultural ecological capital investment on the development of green circular economy. Agriculture 12(461):6371. https://doi.org/10.3390/agriculture12040461
Funding
This research was funded by Research Project on the Innovative Development of Social Sciences in Anhui Province (2021CX069), Special Project of Anhui Province Social Science Innovation and Development Research Project (2023CXZ018), and Anhui Provincial Natural Science Foundation (2008085ME150).
Author information
Authors and Affiliations
Contributions
Conceptualization, G.T. and C.L.; methodology, F.L.; software, F.L.; validation, C.Z., C.L., and F.L.; formal analysis, G.T.; investigation, C.Z.; resources, C.L.; data curation, C.L.; writing—original draft preparation, F.L.; writing—review and editing, F.L.; visualization, C.L.; supervision, G.T.; project administration, C.Z. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Ilhan Ozturk
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, C., Liu, F., Liu, C. et al. Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective. Environ Sci Pollut Res 30, 102519–102530 (2023). https://doi.org/10.1007/s11356-023-29573-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-29573-8