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Under the promotion of sustainable development goals, cleaner production (CP) has become an important strategy for energy-intensive manufacturing industries to maintain their competitiveness. Studies have shown that the implementation of Industry 4.0 (I4.0) can effectively promote the CP process in manufacturing. However, existing studies often focus on specific scenarios, limiting a comprehensive assessment of the industry's overall status. This paper aims to provide a comprehensive overview of the main research areas of I4.0 and its key impacts on CP by employing systematic mapping studies. By reviewing 121 studies retrieved from the Web of Science, a hierarchical analysis framework centered on the product life cycle (PLC) has been introduced. This framework provides a holistic analysis of sustainable intelligent manufacturing, summarizing the application of I4.0 and its impact on CP across the PLC stages. The main findings reveal a growing focus on I4.0 and CP, with the manufacturing and maintenance stages of the PLC being primary research scenarios. Moreover, attention should be directed towards integrating clean technologies, bolstering industrial data security, fostering circular economy practices, and exploring emerging fields like Industry 5.0. Moreover, to better explain the impact of this study, management implications are also provided from the three dimensions of theory, practice, and policy. These viewpoints and conclusions are of great significance for guiding future research and practical applications. • A comprehensive review of Industry 4.0 and cleaner production is conducted. • Systematic mapping and bibliometric methods are employed. • A hierarchical framework based on product life cycle is proposed. • The current challenges and future prospects are discussed. • Management implications from theory, practice, and policy are provided. [ABSTRACT FROM AUTHOR]

• A data-driven cleaner production strategy is proposed for energy-intensive industries. • The data-driven strategy is explained by the cleaner production data lifecycle. • The implementation models are developed to illustrate the proposed strategy. • The effectiveness of the proposed strategy is discussed for companies in China. Cleaner production is an effective strategy for improving material utilization, reducing energy consumption, maximizing product output, and minimizing emissions during the entire manufacturing processes. An increasing number of enterprises promote the implementation of cleaner production. In the Industry 4.0 context, product-embedded information devices are widely used to capture product lifecycle data. Product lifecycle management provides an opportunity to achieve cleaner production strategy. This paper presents a data-driven cleaner production strategy by product lifecycle management for energy-intensive manufacturing industries. The data-driven strategy is proposed and explained by the lifecycle of cleaner production data. Implementation models are developed to illustrate the proposed data-driven cleaner production strategy. Furthermore, two case studies from Southern and Northern China are presented to demonstrate the strategy. Results show that the unit energy consumption and the energy cost of production in Company A are reduced by at least 3% after applying the proposed strategy. In addition, the "cradle-to-gate" lifecycle data analysis indicates that the costs of environmental pollution in Company B are diminished significantly. Finally, energy-intensive manufacturing companies in China are compared and evaluated, and the effectiveness of the proposed strategy is discussed. [ABSTRACT FROM AUTHOR]

The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. • A framework of data-driven sustainable intelligent manufacturing is proposed. • The framework is to achieve a circular economy for energy-intensive industries. • Multi-level demand response models based on PSO are developed to save energy costs. • The framework and models can benefit the environment, economy and society. [ABSTRACT FROM AUTHOR]