How are Industry 4.0 technologies transforming a sustainable society across industries? Open Access

Among global economic growth and rising social inequality, the depletion of natural resources and environmental degradation present unprecedented challenges to modern businesses. Consequently, companies increasingly recognize that sustainability is an ethical responsibility and a cornerstone of long-term prosperity (Lafortune, Fuller, Schmidt-Traub, & Kroll, 2020). In this context, Industry 4.0, representing the fourth industrial revolution, drives businesses towards greater efficiency, environmental friendliness and responsibility through its unique technological advantages and innovative potential.

The core of Industry 4.0 is the integration of advanced digital technologies and human intelligence, such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI) and robotics, to achieve automation, intelligence and personalization in production (Zhong, Xu, Klotz, & Newman, 2017). These technologies significantly improve production efficiency, reduce resource consumption and support environmental protection through intelligent supply chain management, circular economy models and green manufacturing, highlighting the pivotal role of Industry 4.0 in promoting sustainability (Luthra, Mangla, Xu, & Diabat, 2016). Industry 4.0 fosters innovation in business models, leading to concepts such as Product as a Service (PaaS) and the sharing economy, which promote resource sharing, recycling, waste reduction and sustainable social development (Bocken, Short, Rana, & Evans, 2014). Additionally, Industry 4.0 enhances work-life balance by providing remote work and virtual collaboration tools, improving work efficiency and reducing the environmental impact of commuting, embodying a people-centered development philosophy (Bocken et al., 2014).

However, the development of Industry 4.0 is not without challenges. While businesses and society enjoy the dividends of technological progress, they face challenges such as data security, privacy protection and skills gaps. For example, while Lichtenthaler (2019) argues that although AI-driven automation has improved efficiency, its isolated applications cannot sustain competitive advantage without deep integration with human expertise and therefore cannot sustain innovation and long-term growth. Ilbiz and Durst (2019) argues that small and medium-sized enterprises face high costs and technological complexity challenges in adopting blockchain technology and require tailored implementation strategies to avoid resource waste. To fully harness the potential of Industry 4.0, policymakers and industry leaders must work together to establish a supportive framework, ensuring that technological progress benefits all stakeholders and avoids exacerbating social inequality (Peng et al., 2023b).

Existing reviews predominantly focus on single-sector applications of Industry 4.0 technologies and emphasize large enterprises’ sustainability practices. While Oláh, Aburumman, Popp, Khan, Haddad and Kitukutha (2020) mainly discusses environmental issues such as high resource consumption (materials, energy) in the initial deployment phase, increased waste due to equipment upgrades and pollution (such as greenhouse gas emissions) during the production process, but lacks discussion on the economic and social impacts.

Similarly, Ilbiz and Durst (2019) emphasize the fact that the applicability of blockchain depends on whether it meets the specific challenges of SMEs (such as cost, internationalization, data transparency, etc.), and requires customized analysis through structured knowledge management processes. However, it does not discuss in depth the requirements of blockchain’s technical complexity on the technical capabilities of SMEs, and how to solve the practical obstacles in the implementation of technology (such as employee training and infrastructure upgrade costs). In contrast, this review explores how Industry 4.0 is a critical driver of sustainable enterprise development as in Figure 1. To integrate the previous cases studied in a single field, we innovatively adopted holistic thinking to integrate the existing research cases and put forward new insights into the development of Industry 4.0. By synthesizing fragmented research and embedding policy-technology synergies, this work focuses on exploring how Industry 4.0 can become a key driving force for sustainable business development, and highlights the application of Industry 4.0 technologies across various industries and their impact on environmental, social and economic sustainability, developing sector-specific policy recommendations based on the challenges and opportunities, advances a holistic approach to sustainability that prior reviews lack.

This study adopts a systematic literature review approach, following the PRISMA-ScR guidelines (Tricco et al., 2018), to ensure rigorous and transparent literature selection and analysis. It examines peer-reviewed articles, industry reports and case studies published between 2010 and 2023 from sources such as Scopus, Web of Science, IEEE Xplore and ScienceDirect, using keywords like “Industry 4.0,” “sustainability,” “AI,” “IoT,” “blockchain,” and domain-specific terms such as “smart grid” and “precision agriculture.” The inclusion criteria focus on empirical studies and case studies that showcase the application of Industry 4.0 technologies in sustainability, emphasizing environmental, social or economic impacts and limited to English-language literature with full-text access. Exclusion criteria eliminate opinion articles, non-peer-reviewed works and studies lacking industry-specific sustainability indicators. The analysis utilizes a thematic framework to integrate the technological applications and challenges across industries, revealing the impact of Industry 4.0 on sustainability.

This review aims to systematically investigate how Industry 4.0 technologies advance sustainability across industries while addressing implementation challenges and policy implications. Specifically, the study pursues three objectives:

1. To evaluate the technological, environmental and socio-economic impacts of Industry 4.0 applications in energy, manufacturing, agriculture, healthcare and urban development.

2. To identify critical barriers to adoption, including technical, economic and cultural challenges, through cross-sector comparative analysis.

3. To propose actionable policy frameworks and technological synergies that enhance sustainability while mitigating risks like digital divides and energy inefficiencies.

To address these objectives, the review is structured as follows: Section 2 establishes the theoretical foundation through the Triple Bottom Line and Stakeholder theories, linking Industry 4.0 to sustainable development. Section 3 analyzes core technologies driving digital transformation. Section 4 presents sector-specific case studies, followed by a critical comparative analysis of Industry 4.0 maturity and challenges across industries. Section 5 examines technical, economic and organizational barriers, while Section 6 offers policy recommendations and explores emerging trends like quantum computing and digital twins. Finally, the conclusion synthesizes findings, emphasizing sectoral heterogeneity and the need for collaborative governance to align technological progress with sustainability goals.

This review explores how Industry 4.0 is a critical driver of sustainable enterprise development as in Figure 1. It highlights the application of Industry 4.0 technologies across various industries and their impact on environmental, social and economic sustainability.

Unlike existing studies that focus on single-sector applications or large enterprises, this paper uniquely integrates insights from energy, manufacturing, agriculture, healthcare and urban development, revealing systemic synergies and addressing gaps in cross-industry collaboration. Furthermore, it introduces actionable policy frameworks, such as phased subsidy mechanisms and open-data urban digital twin platforms and explores emerging trends like quantum computing and predictive sustainability. The work provides a forward-looking perspective on how Industry 4.0 technologies can drive sustainable development while addressing adoption barriers and fostering inclusive growth.

The Triple Bottom Line (TBL) framework (Elkington, 1994) and Stakeholder Theory (Freeman, 2010) provide a multidimensional lens to assess sustainability in the Industry 4.0 era. While TBL traditionally emphasizes balancing economic growth, environmental stewardship and social equity, critics argue that its operationalization faces challenges in aggregating non-financial metrics (Norman & MacDonald, 2004). Industry 4.0 technologies, however, redefine these dimensions by embedding real-time data transparency and stakeholder-driven innovation into corporate strategies.

Critics highlight TBL’s difficulty in quantifying environmental impacts (Norman & MacDonald, 2004), yet Industry 4.0 offers measurable solutions. IoT-enabled smart grids optimize renewable energy distribution (Alhaddi, 2015), while AI-driven models simulate urban systems to reduce carbon emissions by 25% (Kramer & Porter, 2011). These innovations counter critiques of TBL’s “aggregation ambiguity” by providing quantifiable environmental outcomes.

Stakeholder Theory demands balancing technological disruption with social equity. Blockchain-based fair-trade platforms like Nestlé’s coffee project in Côte d’Ivoire, ensure farmers receive higher revenues by eliminating intermediaries (Kramer & Porter, 2011), aligning profit motives with social justice.

The “shared value” concept manifests in Industry 4.0 through circular economy models. For instance, automates supply chain contracts to reduce transaction costs by 30% (Kramer & Porter, 2011), directly addressing TBL’s measurement gaps, while blockchain-enabled peer-to-peer energy trading creates new revenue streams for prosumers (Andoni et al., 2019).

In addition to the Triple Bottom Line theory, Stakeholder Theory has profoundly impacted sustainable development practices. This theory suggests that businesses must consider the interests and well-being of all stakeholders, including employees, customers, suppliers, community members and the environment, not just shareholders, to achieve long-term success and sustainability (Freeman, 2010). The Triple Bottom Line theory emphasizes balancing economic, social and environmental goals, while Stakeholder Theory highlights the micro-level responsibilities of enterprises to diverse actors. Recent studies by Geissdoerfer, Vladimirova and Evans (2018) demonstrate that integrating these theories enables companies to adopt sustainable business models such as product-service systems and circular economy frameworks. For example, their research identifies that closed-loop production models reduce material waste by 30% while maintaining profitability. By aligning with these models, businesses can design inclusive strategies that address systemic inequalities and create shared value through resource-efficient practices.

Digital transformation is not merely the adoption of technologies like AI and IoT but a strategic realignment of organizational culture. As argued in Leading Digital (Westerman, Bonnet, & McAfee, 2014), successful digital transformation requires a dual focus (1) leveraging technologies to optimize processes (e.g. AI-driven predictive maintenance): and (2) fostering a culture of agility and data-driven decision-making. Their analysis of 400 companies reveals that firms aligning digital strategies with employee empowerment achieve 26% higher profitability. This transformation is driven by competitive pressures, evolving customer demands and the pursuit of operational efficiency, necessitating significant changes to business models and industry structures (Lucas Jr et al., 2013). The impact of digital transformation is comprehensive, encompassing strategic, technological and human aspects. Integrating these new technologies into core business operations requires a holistic approach.

In digital transformation, technologies like AI, the IoT and Blockchain are crucial in optimizing traditional business processes and creating new business models and growth opportunities. For example, AI technology optimizes resource allocation, predictive maintenance and waste reduction, significantly decreasing environmental impact. IoT technology substantially improves resource efficiency through real-time monitoring and intelligent control, particularly in industries like agriculture and logistics (Tzounis, Katsoulas, Bartzanas, & Kittas, 2017). Blockchain technology enhances supply chain transparency, promoting compliant and responsible business practices (Andoni et al., 2019). However, digital transformation also brings challenges. For instance, the energy consumption of data centers and network infrastructure is rapidly increasing, putting pressure on the environment (Tzounis et al., 2017; Zhao, Liu, & Dai, 2021). Moreover, the digital divide means that not everyone can equally access and utilize these advanced technologies, potentially exacerbating social inequality (Legner et al., 2017).

Digital transformation and business sustainability are inherently interconnected, aiming to create value and drive organizational innovation. Companies integrating digital technologies must consider how these innovations contribute to sustainable development goals. For example, digital tools can enhance supply chain transparency, reduce energy consumption and improve waste management practices, contributing to sustainability (Westerman et al., 2014). To ensure the sustainability of digital transformation, companies and policymakers must adopt holistic strategies to leverage digital technologies’ full potential, promoting a win-win scenario for the economy and environment while addressing energy efficiency and digital fairness to ensure that the benefits of transformation reach all of society (Legner et al., 2017).

The rise of Industry 4.0 signifies that the global manufacturing industry is entering a new era, focusing on integrating advanced digital technologies into production processes to drive intelligent and networked manufacturing transformation. Industry 4.0 aims to achieve highly automated, interconnected and intelligent production systems by integrating technologies like smart manufacturing, the IoT, cloud computing, big data analytics and machine learning (Oztemel & Gursev, 2020). This transformation is driving the manufacturing industry towards a more digitized, information-based, customized and environmentally friendly direction. For example, Stock and Seliger (2016) empirically demonstrate that integrating IoT and big data analytics in smart factories reduces energy consumption by 25% and material waste by 30% through real-time resource monitoring. For example, their case study on automotive manufacturing shows how retrofitting legacy equipment with sensors enables predictive maintenance, extending machinery lifespan by 15 years. These practices are characterized by standards and reference architectures, efficient resource management, comprehensive industrial broadband infrastructure, security and privacy measures, continuous learning and innovation and a human-centric design philosophy (Li et al., 2017).

As Industry 4.0 advances, the manufacturing industry is exploring new business models, such as data-driven services and the platform economy, while also placing new demands on human resources, emphasizing skills upgrading and lifelong learning. However, implementing Industry 4.0 faces several challenges, including technological maturity, return on investment, data security and labor market adaptability. Industry 4.0 drives the manufacturing industry’s digital transformation and profoundly impacts society. It drives innovation in products and services, enhances global competitiveness and raises demands on labor skills, prompting reforms in education and training systems (Zhou, Liu, & Zhou, 2015).

The vision of Industry 4.0 is to create a highly integrated and intelligent manufacturing environment, where the convergence and advancement of key technologies are central to its realization. This section examines three core technologies driving the industry 4.0 transformation: AI, the IoT and blockchain. These technologies not only advance innovation within their respective domains but also jointly shape the future of Industry 4.0 through their synergistic interactions.

AI is reshaping the manufacturing industry, particularly in predictive maintenance, resource management and decision-making processes (Porter & Heppelmann, 2014). In Industry 4.0, integrating technologies like smart factories and the Internet of enables real-time data collection, analysis and machine learning (Oztemel & Gursev, 2020). These advancements improve production efficiency and drive the adaptability and intelligence of manufacturing systems. AI predicts equipment failures by analyzing historical data for predictive maintenance, reducing downtime and maintenance costs. In resource management, AI optimizes the supply chain to ensure efficient utilization of raw materials and smooth logistics (Jiang et al., 2017). Enhancing decision-making is another key aspect of AI, as it handles large volumes of complex data to provide real-time recommendations for production scheduling, inventory control and more.

Intelligent algorithms identify production patterns and potential issues, enabling proactive measures to prevent disruptions. Through deep learning and big data analysis, AI systems better understand customer demands, personalize products and achieve mass customization (Kitchin, 2014b). This capability is crucial in smart factories, as they must adapt to market changes, with AI providing key flexibility. However, AI development also presents challenges, including data security, talent shortages and ethical issues. Despite these challenges, technological advancements and the establishment of industry standards are gradually addressing these issues.

In smart cities, the IoT enables real-time analysis and optimized management of urban life through comprehensive digitization of city facilities and environmental monitoring (Kitchin, 2014b). This transformation improves urban management efficiency, fosters innovation in economic activities and enhances residents’ quality of life. Deploying IoT technology in agriculture, such as integrating wireless sensor networks and cloud services, significantly enhances the precision and sustainability of agricultural production, allowing farmers to monitor crop conditions in real time and respond to growing food demand.

Meanwhile, the widespread adoption of IoT raises significant concerns about security and privacy, particularly during data transmission and storage. Blockchain technology, as a distributed ledger, is a potential solution to enhance IoT security due to its tamper-resistant characteristics, effectively protecting data integrity and user privacy, especially in smart contracts and secure device transactions (Kshetri, 2017b). In healthcare, integrating IoT with technologies like AI and edge computing drives remote patient monitoring, personalized healthcare, and large-scale data analysis, signaling future healthcare innovation (Qadri, Nauman, Zikria, Vasilakos, & Kim, 2020). As IoT penetrates various aspects of society, addressing ongoing security, privacy and interoperability issues is crucial to realizing its potential.

With its immutable distributed ledger characteristics, Blockchain technology is reshaping transparency and traceability standards in supply chain management and energy transactions. In supply chain management, blockchain ensures that every step, from raw material procurement to finished product delivery, is accurately recorded and tracked (Dutta, Choi, Somani, & Butala, 2020). This transparency reduces fraudulent activities and significantly enhances supply chain efficiency. It allows participants to instantly verify the origin and status of products, simplifies auditing processes and reduces operational costs (Saberi, Kouhizadeh, Sarkis, & Shen, 2019). Blockchain technology heralds a new era in energy transactions, making energy market transactions more open, secure and efficient. Blockchain allows distributed energy producers and consumers to conduct peer-to-peer transactions without traditional intermediaries, lowering transaction costs and accelerating renewable energy adoption. Blockchain technology ensures the authenticity of transaction records, prevents forgery and provides a foundation for issuing and trading green certificates, promoting sustainable development in the energy sector (Andoni et al., 2019).

In cybersecurity and IoT, blockchain boosts the security and privacy of IoT devices by reducing the risk of single-point failures through its decentralized nature. It facilitates automatic transaction execution through smart contracts, minimizing uncertainties due to human intervention (Kshetri, 2017b). Blockchain technology helps trace insecurity sources in IoT supply chains, enabling swift localization and targeted measures after a security breach is detected, avoiding system-wide failures (Kshetri, 2017a).

With its robust data integrity and tamper-resistant capabilities, blockchain technology is embedding itself at the core of supply chain management and energy transactions, ushering in unprecedented transparency, efficiency and security. As blockchain matures, it is set to play an even more pivotal role, leading these sectors into a new era of openness and mutual trust.

With Industry 4.0 sweeping across the globe, its applications in key sectors like energy, manufacturing, agriculture and urban development are transforming production and lifestyles. Through technologies like smart grids, digital twins, the IoT and AI, Industry 4.0 improves energy efficiency, integrates renewable resources, creates lean, automated intelligent factories and promotes precision agriculture and smart cities, demonstrating unprecedented industry potential and social value.

In the energy sector, digital twins, smart grids, IoT devices and other digital technologies provide innovative solutions for optimizing energy distribution and integrating renewable energy. A digital twin is a virtual model that reflects real-time physical entity status, predicting behavior, performance and potential faults by analyzing sensor data (Rasheed, San, & Kvamsdal, 2020). In energy, digital twins simulate power plants, transmission lines and distributed energy systems like solar panels and wind turbines. Major energy companies like Siemens and General Electric use digital twins to optimize wind turbine performance and maintenance (Steiber, Alänge, Ghosh, & Goncalves, 2021). They optimize operational efficiency, reduce maintenance costs and ensure energy supply reliability.

Smart grids upgrade traditional power grids by integrating advanced sensing technology, communication infrastructure and AI to establish bi-directional information and energy flow, achieving a dynamic energy balance and effective renewable energy integration (Bibri & Krogstie, 2017b). Sensors and communication devices in intelligent grids monitor grid status in real-time, automatically adjust loads, prevent overloads and outages and provide consumers with flexible energy options like demand response via smart meters or home energy storage systems. Additionally, blockchain technology revolutionizes this sector by enabling peer-to-peer energy trading, supporting renewable energy and fostering a decentralized, resilient energy system (Andoni et al., 2019).

The manufacturing industry embraces Industry 4.0 by using sensors, data analytics and cloud technologies to enhance efficiency and sustainability. Companies like Bosch and Schneider Electric lead in smart factory solutions that significantly reduce resource consumption (Stock & Seliger, 2016; Luthra & Mangla, 2018; Frank, Dalenogare, & Ayala, 2019). Smart factories achieve automation and intelligence by connecting machines, products and supply chains, reducing waste and improving energy efficiency. IoT sensors collect data on equipment operation status, product quality and production processes, while AI analyses the data to identify patterns, predict faults and optimize production schedules. Additive manufacturing, or 3D printing, adjusts production based on real-time demand, improves energy efficiency, enables complex products with minimal waste, reduces the environmental impact of traditional manufacturing and promotes green manufacturing practices (Ford & Despeisse, 2016; Zhong et al., 2017). Sensors predict accidents before they occur by monitoring machinery performance and minimizing downtime and resource waste. Cloud-based analytics enable manufacturers to gain insights into operations and identify areas for improvement.

Precision agriculture enhances resource use and crop yield through technologies like soil sensors, satellite imagery and advanced robotics. For example, IoT-enabled drones monitor field conditions in real time, enabling targeted irrigation and fertilization. Agricultural robots equipped with AI-driven vision systems demonstrate selective harvesting capabilities for high-value crops like strawberries and grapes. In vineyard operations, robotic systems like Vine Robot autonomously collect data on grape maturity and soil health, enabling dynamic adjustments to irrigation schedules (Hajjaj & Sahari, 2016).

In urban development, smart cities are revolutionizing city management. Smart buildings use technologies to adjust heating and lighting, significantly lowering energy consumption dynamically. Urban data platforms integrate various data sources to support sustainable urban planning, improve waste management and conserve water (Kitchin, 2014a; Bibri & Krogstie, 2017a). Integrating big data analysis and IoT technology allows city planners to optimize traffic flow, reduce energy consumption, improve public service efficiency and create a more sustainable urban environment.

The tourism and hotel industry uses digital tools like virtual reality (VR) and augmented reality (AR) to promote cultural heritage and reduce travel emissions (Guttentag, 2010; Higgins-Desbiolles, 2018). VR and AR provide immersive experiences that let tourists virtually explore destinations and historical sites, reducing the need for physical travel and the associated carbon footprint. These technologies enhance the visitor experience by providing interactive content that deepens understanding and appreciation of cultural heritage.

The healthcare sector is undergoing significant transformation through digital innovation. AI-driven diagnostic tools, telemedicine and electronic health records enhance patient care and improve health outcomes. AI algorithms analyze medical images and data with high accuracy, aiding in the early diagnosis and treatment of diseases. Telemedicine expands healthcare access, particularly in remote areas, while electronic health records improve coordinated care and reduce administrative burdens (Gomber, Kauffman, Parker, & Weber, 2018). These advancements improve healthcare efficiency and accessibility, improving public health and well-being.

The financial services industry is undergoing a paradigm shift due to fintech innovations. Blockchain technology enhances transaction transparency and security, while AI and machine learning improve risk assessment and fraud detection. These technologies streamline operations, reduce costs and improve customer services. Digital banking and mobile payment systems increase financial inclusion by providing banking access to underserved populations.

While Industry 4.0 applications demonstrate sector-specific successes, a comparative analysis reveals systemic gaps and untapped synergies. In energy and manufacturing, blockchain and IoT integration has matured in optimizing decentralized energy trading and smart factories (Andoni et al., 2019; Stock & Seliger, 2016), yet agriculture and healthcare lag due to fragmented data ecosystems and low digital literacy (Tzounis et al., 2017; Kaminski & Malgieri, 2021). For instance, blockchain’s traceability potential in food supply chains remains underexplored compared to its energy sector dominance, despite similar needs for combating fraud (Saberi et al., 2019). In order to further improve the application of Industry 4.0 in various industries, more consideration should be given to cross-industry technology collaborative application and cross-disciplinary data sharing in the future.

However, the advancement of Industry 4.0 heralds a revolution in manufacturing but also brings complex challenges at both the technological implementation level and within organizational culture and structure.

Implementing technologies like AI, IoT and blockchain requires high technical expertise and substantial infrastructure, which can be cost-prohibitive, especially for SMEs with limited IT resources and financial capital (Kane, 2015). The financial burden of digital transformation is a significant barrier, particularly for smaller enterprises. High initial investment costs for advanced technologies, such as AI-driven analytics or blockchain systems, can be prohibitive (Peng et al., 2023a). These costs include hardware, software, training, system integration and ongoing maintenance (Fitzgerald et al., 2014).

For instance, studies indicate that the production and operational energy costs of ICT devices account for 8% of global electricity consumption in 2015, a figure projected to reach 14% by 2040 if adoption rates remain unchecked. This is primarily due to manufacturing energy and short device lifespans. While modular solutions like low-code platforms are proposed to reduce customization costs, their effectiveness hinges on transparent lifecycle assessments of ICT devices, including material extraction and manufacturing energy (Belkhir & Elmeligi, 2018).

Digital technologies also raise significant data security and privacy issues due to the vast amounts of sensitive information they process, complicating their adoption (Kaminski & Malgieri, 2021). Deploying IoT devices, integral to smart manufacturing and smart cities, involves continuous data collection and transmission, creating numerous entry points for potential cyber-attacks (Roman, Lopez, & Mambo, 2018). This requires robust cybersecurity measures, which are both technically challenging and costly to implement (Taddeo, McCutcheon & Floridi, 2019). It is essential to implement robust data protection measures, such as Europe’s GDPR and California’s CCPA, which mandate strict data privacy standards (Voigt & Von Dem Bussche, 2017). Non-compliance can lead to severe penalties and damage an organization’s reputation (Quach, Thaichon, Martin, Weaven, & Palmatier, 2022).

Cultural and organizational resistance to change also pose a formidable barrier, often stemming from fears of job displacement and a lack of understanding of the benefits of digital transformation within the workforce. Employees accustomed to traditional ways of working may resist adopting new technologies due to fears of job displacement or lack of confidence in their digital skills (Frey & Osborne, 2017). Overcoming this resistance requires effective change management strategies, including clear communication of digital transformation benefits, employee involvement in the transformation process and investment in training and development to enhance digital literacy (Bessen, 2019).

The skill gap in digital competencies within organizations complicates the implementation and management of new technologies, requiring significant investments in training and development (Cappelli, 2015; Berman, 2019). Organizations often face difficulties recruiting and retaining employees with the necessary digital skills, particularly in emerging fields like AI and data analytics (Bughin et al., 2018). Addressing this skills gap requires sustained efforts in education and workforce development, including partnerships with educational institutions and ongoing professional development programs (Brynjolfsson & McAfee, 2014).

Regulatory barriers can hinder the adoption of digital technologies as frameworks often lag behind advancements, creating uncertainties and compliance challenges for businesses (He & Harris, 2020). The lack of clear regulations for emerging technologies like blockchain and AI can slow adoption as businesses navigate legal uncertainties and potential risks (Fenwick, Kaal, & Vermeulen, 2017). Developing adaptive regulatory frameworks that keep pace with technological innovation is crucial for facilitating digital transformation, ensuring compliance and mitigating risks (Gawer & Phillips, 2013).

The environmental sustainability of digital technologies is another concern. While these technologies drive efficiencies and reduce waste, their production and use can have significant environmental impacts (Belkhir & Elmeligi, 2018). Data centers, for example, consume large amounts of energy, contributing to carbon emissions (Jones & Kierzkowski, 2018). The lifecycle of digital devices—from production to disposal—raises environmental concerns due to raw material extraction and e-waste generation (Baldé et al., 2017). If improperly managed, these impacts may endanger the environment and human health. Mitigating these impacts requires implementing environmentally sustainable practices, such as using renewable energy for data centers and developing recycling programs for e-waste.

Over 50% of manufacturing workers require reskilling by 2025 to adapt to Industry 4.0 technologies, including IoT and AI. Dynamic capabilities frameworks, such as “sensing” emerging skills and “transforming” organizational learning structures, are critical to addressing this gap. For example, Academic-industry partnerships, supported by AR-based training programs, can accelerate skill acquisition by simulating real-world scenarios, though their scalability depends on policy incentives.

Developing and implementing policies to encourage sustainable technologies and support the digital transformation of small and medium-sized enterprises are crucial for driving Industry 4.0 and the broader digital transformation of the economy. By enacting regulations to encourage sustainable technologies and align digital transformation with broader sustainability goals, policies can help address the digital divide and promote equitable access to technology, ensuring the benefits of digital transformation are widely distributed (Kshetri, 2017b). We provide some possible policy recommendations:

Governments can encourage businesses to invest in green technologies and digital transformation through tax breaks, subsidies and funding programs. For instance, enterprises can receive financial incentives for adopting energy-efficient equipment, renewable energy systems or implementing circular economy practices. To ensure accountability, governments could require annual sustainability audits for subsidy recipients and link incentives to measurable emission reduction targets. Governments and public sectors can priorities procuring low-carbon and environmentally friendly products and services, establishing a centralized “Green Procurement Database” with real-time supplier ESG metrics to guide purchasing decisions, demonstrating a commitment to drive the market towards sustainable solutions.

Educational institutions should design and implement education and training programs that focus on future skills like data analysis, programming and intelligent systems management. Curriculum development should align with industry certifications and include internship quarters in tech enterprises. In this process, educational institutions should actively seek cooperation with the government and enterprises.

Strengthening international cooperation is vital. Close collaboration between countries, sharing best practices and coordinating international standards-especially in data protection, privacy and cross-border data flows will promote digital transformation in global supply chains.

Energy Sector should prioritize funding for grid modernization to integrate decentralized renewable energy systems, leveraging blockchain-enabled peer-to-peer trading platforms. Pilot cities could mandate renewable energy trading through such platforms, with grid operators required to provide open access for decentralized transactions. For instance, tax incentives could be offered to households and businesses adopting solar panels paired with smart meters, reducing reliance on fossil fuels while lowering energy costs.

To accelerate the green transformation of the manufacturing industry, it is recommended to establish a phased subsidy mechanism, giving priority to supporting small and medium-sized enterprises to transform traditional equipment with IoT sensors to achieve real-time monitoring of energy consumption and emissions. Third-party verification bodies should be accredited to audit sensor data authenticity before subsidy disbursement. At the same time, digital twin technology should be incorporated into the carbon trading system, and additional carbon quotas should be issued to companies that achieve a waste reduction of ≥20% through this technology.

To bridge the digital divide highlighted in Section 5.3, governments should launch co-funded IoT adoption programs for smallholders, providing low-interest loans for precision agriculture tools. Extension services must train farmers in data literacy to interpret IoT-generated insights, while establishing regional demonstration farms to showcase best practices. Public–private partnerships should develop open-access data platforms aggregating market and weather information, enabling informed planting decisions.

Urban planning projects should adopt open data standards to break down departmental data silos in transportation, energy and environmental management. For example, a cross-departmental “city digital twin platform” can be built to integrate real-time sensor data and geographic information systems (GIS) to simulate carbon emissions and resource flows, with mandatory cybersecurity protocols for sensitive infrastructure data. In addition, special funds can be set up to subsidize AI-based public facility predictive maintenance projects (such as water pipe leak warnings) to reduce municipal operation and maintenance costs. Citizen co-creation platforms should be established, allowing residents to submit urban issues via AR-enabled 3D maps, while universities could be funded to develop localized digital twin training modules for municipal staff.

Regulatory bodies must mandate GDPR-compliant frameworks for AI diagnostics (Kaminski & Malgieri, 2021; Voigt & Von Dem Bussche, 2017), requiring hospitals to implement edge computing solutions for anonymizing patient data at the source. Public–private partnerships should fund regional telemedicine hubs in underserved areas, reducing healthcare disparities while mitigating data privacy risks.

The digital transformation driven by Industry 4.0 presents unprecedented opportunities for innovation across sectors, reshaping industry operations and promoting sustainable practices crucial for environmental and economic health. Cutting-edge technologies like quantum computing, advanced robotics, digital twinning, AI, the IoT and big data are gradually converging to create a more intelligent, efficient and sustainable future. Integrating these technologies will drive a new wave of innovation and profoundly change our lives and work, promoting optimal resource utilization, reducing environmental impact and achieving green development.

There are some examples:

Quantum computing, with its superior processing capabilities compared to classical computers, has the potential to revolutionize data processing and analysis. It could enable the simulation of complex systems and optimize resource usage on an unprecedented scale. In the financial services industry, quantum computing can enhance the accuracy and speed of risk analysis, optimize algorithms for complex investment portfolios and support the development of more precise quantitative trading strategies (Bova, Goldfarb, & Melko, 2021).

Advanced robotics technology is evolving towards greater autonomy and intelligence, integrating deep learning and perception technology to enable robots to perform more refined and complex tasks. In the agricultural sector, integrating advanced robotics with AI can enhance precision, reduce waste and improve efficiency (Hajjaj & Sahari, 2016). Robotic applications in the medical field include surgery, rehabilitation therapy, medical assistance and long-term care (Riek, 2017). Combining emerging technologies like quantum computing and advanced robotics with AI, blockchain and IoT holds immense potential for further innovation.

Digital twinning technology, which maps physical entities, systems or processes in the physical world through exact digital models, has become a powerful tool for optimizing resource usage, predicting maintenance and enhancing operational efficiency. The concept of “digital and green twins” will be further explored to manage natural resources more precisely, reduce waste, minimize the carbon footprint of production processes and achieve higher sustainable development goals (Rasheed, San, & Kvamsdal, 2020).

The deep integration of technologies like AI, IoT and big data is giving rise to super-intelligent models with unprecedented data processing and analysis capabilities, demonstrating enormous potential in medical diagnosis, disease prevention and personalized treatment plan design. AI can improve diagnostic accuracy, predict patient hospitalization, optimize resource allocation and develop personalized treatment plans. IoT devices can monitor patient health in real time and provide data for healthcare decision-making. By collecting and analyzing real-time health data from patients, these models can give early warnings, customized health management plans and accelerate research processes in areas like drug discovery and gene editing, bringing revolutionary changes to human health.

Industry 4.0 technologies hold significant potential to promote sustainability, reshape industries and foster more efficient and environmentally responsible practices. Advanced technologies such as AI, the IoT and blockchain enable more effective resource management, reduced waste and enhanced operational efficiency across key sectors, including energy, manufacturing and agriculture.

In the energy sector, AI algorithms can accurately predict demand, enabling better integration of renewable sources into the power grid and reducing dependency on fossil fuels. In agriculture, IoT devices—such as soil moisture sensors and weather stations—deliver real-time data for precision farming, helping to minimize resource waste and mitigate environmental harm. Similarly, in urban environments, IoT sensors track traffic flows, air quality and energy use, offering valuable data for urban planners to design more innovative and sustainable cities.

Blockchain technology contributes to sustainable manufacturing by tracing product lifecycles, ensuring responsible sourcing of materials and minimizing waste through recycling and reuse. In the energy sector, it facilitates peer-to-peer energy trading, allowing consumers to exchange excess renewable energy directly with nearby users. This decentralized approach promotes renewable energy adoption, reduces transmission losses and enhances grid resilience.

The digital transformation driven by Industry 4.0 presents several critical challenges. The high upfront investment required to implement advanced digital technologies poses a significant barrier, particularly for small and medium-sized enterprises (SMEs) lacking financial resources and technical expertise. Integrating new technologies into legacy systems is often complex and disruptive, demanding extensive planning and coordination. Regulatory frameworks frequently lag technological advancements, creating uncertainty for businesses, while existing data privacy laws often fall short of addressing the concerns raised by IoT devices that collect vast volumes of personal data.

Digital technologies also introduce environmental challenges, such as increased energy consumption by data centers and the accumulation of electronic waste from short-lived devices. Internal organizational resistance can further impede digital transformation efforts. Overcoming these barriers requires robust change management strategies, including digital literacy programs and initiatives that actively engage employees in the transformation process. Fostering a culture of innovation and continuous learning enables organizations to overcome resistance and fully capitalize on the benefits of digital transformation.

Despite these obstacles, Industry 4.0 offers significant opportunities for innovation. Future trends suggest that digital solutions will increasingly combine sustainability goals with predictive technologies essential for energy management and resource optimization. AI and the IoT will continue to evolve, offering advanced tools for monitoring environmental impacts. AI can optimize supply chains by identifying efficient transportation routes, thereby reducing carbon footprints. IoT-enabled smart grids dynamically adjust energy flows based on real-time demand, improving system efficiency and reliability.

Emerging technologies such as quantum computing and robotics also hold promise for sustainable industrial transformation. Quantum algorithms like Grover’s search can solve complex optimization problems in logistics, enabling real-time decision-making and improving industrial performance (Awan, Hannola, Tandon, Goyal, & Dhir, 2022). In energy distribution, combining quantum-enhanced information density with blockchain technology can strengthen the security and efficiency of smart grids and decentralized energy systems (Awan et al., 2022). Advanced robotics, by automating precision tasks and minimizing resource waste, supports green manufacturing and enhances sustainability across industrial operations (Javaid, Haleem, Singh, & Suman, 2021). Furthermore, integrating digital twins with green twins embeds environmental parameters into virtual models, forming ecological digital closed-loop systems. This transition facilitates the shift from linear consumption to circular economy models and demonstrates a “predictive sustainability” approach that aligns digital innovation with ecological responsibility (Steiber et al., 2021).

Policymakers are advancing the adoption of sustainable technologies through targeted legislation, aiming to align digital transformation with environmental objectives, bridge the digital divide and accelerate the diffusion of innovation. To support the digitalization of small and medium-sized enterprises (SMEs), they provide financial and technical assistance. In parallel, international cooperation and standardization promote cross-border interoperability, enabling the pooling of global resources and knowledge to drive innovation. Digital transformation must progress in tandem with economic growth and environmental management, guided by policy and enabled by advanced technologies to support global ecological balance.

In the future, the integration between digitalization and sustainability will deepen, requiring all stakeholders to proactively address emerging challenges, advance technology responsibly and safeguard both societal welfare and environmental integrity. Key priorities include developing energy-efficient technologies, reducing energy consumption in infrastructure such as data centers and cutting carbon emissions using renewable energy and optimized algorithms.

Fostering a culture of sustainability within organizations is essential. Companies should embed sustainability into their strategies and operations, establishing clear goals to evaluate both environmental and social impacts. This process requires leadership commitment, employee engagement and stakeholder collaboration to implement sustainable practices across the supply chain. Empowering employees to contribute to sustainability goals requires targeted education to enhance their digital competencies and environmental awareness.

Collaboration between the public and private sectors accelerates sustainable digitalization, with governments facilitating progress through incentives, investment in digital infrastructure and innovation-enabling environmental policies. Public-private partnerships harness the strengths of both sectors to promote the innovation and adoption of sustainable technologies aimed at achieving shared objectives. In summary, organizations must internalize sustainability, while governments and businesses should work collaboratively to advance green digital transformation through education, policy alignment and cross-sector cooperation.

The work is supported by Twin (digital and green) transition for corporations under our project EU Project and we thank university of Twente and Turiba University for support. The authors appreciate Mr. Jianxun Feng’s assistance during the writing and revising stages.