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Exploring the economic and social impacts of Industry 4.0

Explorer les impacts économiques et sociaux de l’Industrie 4.0
Cécile Cézanne, Edward Lorenz et Laurence Saglietto
p. 11-35

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  • 1 Ardito et al. (2019) listed identical initiatives: “UK CATAPULT—High Value Manufacturing”, the Amer (...)

1After the mass production and automation that characterised the 20th century, recent technological breakthroughs including advanced robotics, artificial intelligence, big data analytics, augmented and virtual reality, the Internet of Things (IoT) and 3D printing are seen as set to change the way we work and live (OECD, 2018, Pereira & Romero, 2017; Frank et al., 2019). The changes are summed up under the notion of Industry 4.0 in reference to the program put in place by the Federal government in Germany in 2011 to drive the digital transformation of its industry. The German initiative inspired similar programs in other European nations and was progressively adopted by countries around the world, thus initiating what is called the 4th industrial revolution.1 (Kagermann et al., 2011, 2013; Ardito et al., 2019). Industry 4.0 is characterised by the integration of different digital-based technologies in the production process in order to increase enterprise efficiency and competitiveness. Based on the connectivity of objects and cyber physical systems, it is the basis for the ‘smart factory’ and a new organisation of productive capacities conducive to improvements in productivity and to a superior and more environmentally sustainable allocation of resources.

2While Industry 4.0 clearly holds out great promise for the economy and society, its deployment has also raised concerns about its possibly negative economic and social impacts. One central concern is the impact of Industry 4.0 on employment and jobs. Several authors have argued that the scope for machine learning, robotics and digitisation to eliminate jobs at all levels of the skill hierarchy could well result in large-scale job losses, undermining the very fundamentals of work and society as we know it today (Brynjolfsson and McAfee, 2014; Frey and Osborne, 2013). Industry 4.0 will also have an impact on international trade and result in the restructuring of global value chains (Strange and Zucchella, 2017, Tjahjono et al., 2017). Digital-based technologies are expected to extend beyond the shop floor to transform supply chain logistics through the use of machine learning, allowing for efficiency gains with real-time production and marketing management over long distances. The differential capacity of regions and nations to deploy these technological changes over the value chain could have a big impact on the international division of labor and on employment growth both within Europe and between Europe and the rest of the world. (Dachs et al., 2019; Szalavetz, 2019).

3The effects of Industry 4.0 on the environment are also debated. Some researchers have pointed out that there are negative environmental impacts attributable to the energy consumption of data centers and to resource consumption in the manufacturing of new devices (Waibel et al., 2017). Others see the 4th industrial revolution as having a major transformative potential that will contribute to the achievement of the United Nations’ Sustainable Development Goals (Sachs et al., 2019). The promise is that 4.0 technologies will allow companies to be competitive in the long run based on an intelligent use of information and data with the identification and tracking of smart products along the entire supply chain, thus reducing overproduction, transport costs, waste and energy consumption (Rajput and Singh, 2019; Waibel et al., 2017).

4This Special Issue contributes to our understanding of the promises and challenges of Industry 4.0. It brings together five of the papers presented at the workshop on ‘Transformative Technologies: The Impact of Industry 4.0 on Employment, Global Value Chains and Sustainable Development’ held at the Université Côte d’Azur in June of 2019. Before summarizing the five contributions published in this special issue, we provide an introductory overview of key issues and debates around the deployment of Industry 4.0 in the three thematic areas addressed in the workshop: the impact of new automation technologies on jobs, employment and earnings; the transformation of logistics and the restructuring of value chains; and the effects on environmental sustainability.

1. The impact of new automation technologies on the labour market

5In popular discourse as well as in policy debates, a central concern around Industry 4.0 is the extent to which people will lose their jobs. Underlying this concern is evidence that the current wave of technological change associated with the adoption of advanced digital automation technologies is increasing the scope for automating not only the tasks of manual workers but also the tasks of occupational categories whose work involves analytical and information processing activities. Much of the discussion and debate concerns the impact of the use of robotics and machine learning and we focus on these two technologies in this section.

1.1. Robots and technological unemployment

6Industrial robots, following the conventions adopted by International Federation of Robotics, may be defined as automatically controlled, reprogrammable, multipurpose manipulators programmable in three or more axes.2 Existing data sources on the adoption of industrial robots are limited. To our knowledge the only available survey-based source of firm-level data on the adoption of robots is that collected though the European Manufacturing Survey (EMS), which focuses on the utilisation of ‘techno-organisational innovations’ including robots in manufacturing and the employment and performance impacts of their adoption.3

7The most recently published results from the EMS are from the 2012 round covering manufacturing industries in Spain, France, Germany, Austria, Sweden, Switzerland and the Netherlands (Jäger et al., 2015). Regression analysis shows that the adoption of industrial robots results in an increase in productivity in adopting firms and is neutral with respect to their employment growth measured over the 2-year period following the adoption. In terms of the determinants of the decision to adopt robots, the results show a positive relation with the size of the firm, with larger batch sizes and with exporting. The positive relation between batch-size and adoption points to the importance of economies of scale as a condition for investing in industrial robots.

  • 4 The ‘price effect’ refers to additional production and employment in adopting firms due a reduction (...)
  • 5 For a summary of the figures on world installations of industrial robots, see: https://ifr.org/indu (...)

8While survey data such as that collected by the EMS can explore the effects of robots at the firm-level, it cannot be used to investigate their aggregate impact on employment because it is unable to take into account what are referred to as the ‘indirect’ or ‘compensating’ effects associated with technological change. These include principally what are called the ‘price’ effect, and the ‘income’ effect.4 The main source of data for investigating the aggregate employment impact of adopting robots are the figures on robot installations collected by the International Federation of Robotics (IFR) since the 1990 at the industry- and country-levels.5 Key contributions using this data covering European nations are the studies by Dauth et al. (2017) for regions in Germany, and Graetz and Michaels (2018) for 14 EU-member countries, Korea, the United States and Austria. Dauth et al. (2017) identify significant direct negative effects of robot adoption on employment in German manufacturing between 1993 and 2014, accounting for almost a quarter of the decline in manufacturing jobs over this period. This negative impact however is fully compensated for by increases in employment in service sectors implying that there are important income effects compensating for the loss of jobs due to robots in adopting industries. Graetz and Michaels (2018) for the larger sample of OECD countries find that robot adoption has a significant positive effect on productivity, a negative effect on output prices and is neutral with respect to employment. Their results also support the idea that after considering the compensating effects there is no negative impact of robot adoption on employment.

9While research on European nations shows that the overall impact of robots on employment after taking into account the compensating effects is neutral or positive, an influential study by Acemoglu and Restrepo (2017) focusing on local labour markets in the US comes to the opposite conclusion. They find that the combined direct and indirect effects of robot adoption are negative with each additional robot per thousand workers reducing the employment-to-population ratio by between 0.18 and 0.34 percentage points, or that each additional robot displaces about six jobs. While these contrasting results point to the need for further research on the employment impact of robots, they also suggest the need to qualify the view that robots are resulting in massive job destruction. Acemoglu and Restrepo (2017) estimate that only between 360,000 and 670,000 jobs were displaced by robotisation in the US over the 11-year period from 1993 to 2014. To put this in perspective the US Bureau of Labor Statistics (BLS) reports that 2.2 million and 2.6 million new jobs were created in the United States in the years 2017 and 2018 respectively.

1.2. The debate on the impact of artificial intelligence on the future of work

  • 6 Outside of Europe and the US, David (2017) finds that 55% of jobs in Japan are at high risk. For th (...)

10Research on the impact of robots is based on statistical analyses using existing data sets. In contrast to this, a very recent literature focusing mainly on the effects of machine learning (ML) in the form of artificial neural nets adopts a forward-looking perspective and seeks to predict the future impact of this technology on employment and jobs. A seminal paper is that of Frey and Osborne (2013), which came up with the rather alarmist prediction that 47% of persons currently working in the US are at high risk (70% chance or greater) of having their job automated over the upcoming decade and a half. Other studies adopting the same methodology have found that the share of jobs that are at high risk of ML automation in European nations ranges between 45% to more than 60%, with southern European workforces facing the highest exposure to potential automation (Bowles, 2014).6

11The Frey and Osborne approach has been criticised on several grounds. One is that their estimates assume that entire occupations are substituted for and hence fail to consider that occupations are made up of bundles of tasks with varying degrees of susceptibility to automation and that this can vary across countries. Arntz et al. (2016) address these limitations using data from the OECD’s PIAAC survey of adult skills that captures differences in task content for persons with the same occupational category across a sample of OECD countries. They arrive at significantly lower at-risk of automation estimates ranging from a low of about 6% for Korea and Estonia to a high of about 13% for Austria and Germany. In the case of the US they estimate that only about 9% of persons working are at high risk in the sense of a 70% chance or greater of having their jobs automated.

  • 7 See the recent McKinsey report (2018) based on several case studies that identifies collecting and (...)

12Another criticism made of the approach used by Frey and Osborne (2013) is that their at-risk estimates are based on the expert opinions of ML engineers of what is technically or scientifically feasible as opposed to what employers have an interest in and are capable of undertaking (Acemoglu and Restrepo, 2017). Various firm-level factors may constrain or slow the adoption of ML. An important constraint is the difficulties that firms often face in collecting and labelling the large quantities of data necessary for training and testing ML models.7 Further, ML is likely to require costly reorganisation of work and production processes in order to unbundle those tasks that can potentially be automated from those that can’t. A recent study by Brynjolfsson et al. (2018) addresses the issue of work reorganisation and unbundling. The authors use a human intelligence crowdsourcing platform, CrowdFlower, to derive what they refer to as suitability for machine learning (SML) scores at the task level. They consider the 18,156 tasks for 964 occupations in the O*NET database and map them into 2,056 direct work activities that are shared across occupations. The SML scores are based on a rubric that is designed to capture the extent to which the work activity is well-structured in the sense that there is a clear mapping between inputs or actions and outputs that can be learnt by the machine with sufficient data. If there are difficulties in measuring this relation, as is the case for unstructured social interaction or much complex cognitive work, then the work activity receives a low SML score.

13Brynjolfsson et al. (2018) find in general that there is considerable variability across occupations in the susceptibility of their component tasks to automation with only a few having high SML scores for all tasks implying that automation technologies are unlikely to result in the elimination of entire occupations. The authors conclude that significant job redesign will be needed, “for unleashing ML potential” and that, “The focus of researchers, as well as managers and entrepreneurs, should be not (just) on automation, but on job redesign” (Brynjolfsson et al., 2018, p. 44).

14While there remains considerable uncertainty over what share of jobs are at risk of ML automation, there would appear to be an emerging consensus that the estimates of Frey and Osborne (2013), and of those studies that extended their methodology to other counties, overstated the direct impact of new automation technologies which will be more limited than their initial alarmist predictions. Taken in combination with the evidence pointing to a neutral or positive net effect of robots on employment, this implies that the most far-reaching effects of Industry 4.0 may not be on employment levels, but rather on skills needs and gaps, as the task content of occupations are transformed.

1.3. Implications for wage inequality

  • 8 For a critical assessment of the SBTC hypothesis, see Card and DiNardo (2002).

15Closely related to the impact of ML on skills is the question of how its deployment will affect wage inequality. Inequality has been on the rise in the US and in many European nations since the 1980s and the main technology-centered hypothesis to account for this has been the skill-biased technical change (SBTC) hypothesis. This conjectures that the computer revolution from the 1970s has increased the demand for skilled over less skilled workers thus raising the relative wages of the skilled.8 A more recent literature, while not contesting this basic interpretation, has developed a more nuanced view arguing that many of the routine tasks of mid-level clerical and administrative workers have been more amenable to computer-based automation than the tasks of the lowest paid groups composed of service workers and certain of the elementary trades. This view, referred to as the routine-biased technical change (RBTC) hypothesis, provides an explanation for the ‘hollowing’ out of the mid-level occupational categories such as clerks and administrative support staff whose employment shares and relative earnings have declined from the 1980s (Autor et al. 2003; Goos and Manning, 2007).

16A question now being debated is whether the adoption of machine learning will reinforce the trend toward job polarisation or disrupt it due to the ability of machine learning to automate tasks at both the upper and lower ends of the occupational ladder. The RBTC hypothesis was based on the premise that automation depends on the ability of a programmer to model the information processing structure of a task in term of rules sufficiently well understood that they can be codified in a computer program. At the upper end of the occupational ladder, the tasks of managers and professionals whose work depends on abstract reasoning and problem-solving skills were considered out of the scope of automation. This was also the case for the tasks of many low-level service and manual workers whose work depends on visual and motor processing capabilities skills that cannot be described in terms of programmable rules (Autor et al., 2003).

  • 9 For a discussion of these differences, see Levy (2018).

17At the high-skilled end, tasks that Autor et al. (2003, p. 1286) identified as out of the scope of automation were medical diagnosis and legal writing, and at the low-skilled end were truck driving and janitorial services. These, of course, are tasks where automation based on the use of ML has registered significant advances. This reflects the fact that ML, unlike conventional automation, has made it possible for a computer, with sufficient data for training purposes, to learn the mapping between inputs and outputs for tasks involving perceptual and pattern recognition skills that cannot be modelled in terms of explicit rules.9

18While recent advances in ML are clearly upending our established assumptions about what kinds of tasks can be automated, as in the case of the overall employment impact there is little consensus over how ML will affect occupational employment shares and wage inequality. Frey and Osborne (2015, p. 59), citing the growing market for AI-enhanced personal and service robots, argue that ML-based automation can be expected to substitute for low-skilled and low paid service and manual jobs that up to now have been impervious to automation. This would result in an increase in inequality due to a decline in the demand for and the relative wages of low-skilled workers.

19Another study, complementary to that of Frey and Osborne (2015), is Felten et al. (2019) who distinguish between the impact of ML in several different skill domains including image and speech recognition and language translation. Unlike Frey and Osborn (2015), they consider the possibility that ML may not only substitute for existing skills but may also complement or enhance them. They find that many of the professions that are most likely to be impacted by ML are highly paid professions and that the main effect of ML for these professions is a complementary one resulting in an increase in their employment and relative earnings. For lower skilled service and manual workers, however, they expect the impact of ML to be neutral with respect to both employment and earnings. Their analysis also implies an increase in wage inequality though unlike the analysis of Frey and Osborn (2015) it results from an increase in the employment and relative wages of the more highly skilled categories.

20The literature on the labour market effects of ML makes a strong case for its disruptive impact. It leaves considerable uncertainty, however, not only over the share of jobs that are at risk of ML automation but also over which occupations will be most affected and what the consequences will be for wage inequality. Much of the uncertainty can be attributable to the speculative nature of the research which is based on assessments of the scientific or technical susceptibility of tasks and occupation in general to ML automation rather than on evidence of what firms are doing. Gaining a better understanding of how ML is impacting on employment, skills and earnings using both qualitative and quantitative studies at the firm-level is, in our view, a priority for future empirical research focusing on Industry 4.0.

2. The restructuring of value chains and transformation of logistics

21The impact of Industry 4.0 on supply chain relations can be examined from a macro and international trade perspective in terms of the restructuring of global value chains (GVCs) and from a micro and managerial perspective in terms of how new technologies contribute to optimizing and allowing for more flexible and speedy delivery of goods and services. We first provide an overview of the literature on the micro and managerial consequences of Industry 4.0 and then turn briefly to the macro and trade consequences.

2.1. The impacts of smart innovations on supply chains

22Prior to Industry 4.0 automation in the logistics sector was based on the computerisation of business processes (EDI, numerical controls, lean production) and production processes (RFID sensors, real-time control, modular organisation). Industry 4.0 is based on the principle of companies being fully integrated into systems where communication between humans and machines is achieved automatically through Cyber-Physical-Systems (CPS) enabled by the Internet of Things (IoT) (Barreto et al., 2017; Hofmann et al., 2017; Winkelhaus and Grosse, 2020). A study by Sorkun (2020) on “Industry 4.0 discourses” identifies several key interdependent enabling technologies which are increasingly being used in logistics operations. According to a recent literature review by Winkelhaus and Grosse (2020), these enabling technologies include IoT-based systems, and robotic applications, Big Data analytics, cloud computing, mobile systems and social media-based applications.

23Cyber-physical production (CPS) systems consist in systems of embedded computers and networks that monitor and control physical processes based on information feedback loops using big data and possibly machine learning. CPS can be seen as a cornerstone of Industry 4.0 that enable smart factories to operate autonomously. In conjunction with such physical systems, “digital twins” or virtual duplicates of the systems may be created to simulate and anticipate risks and uncertainties in the environment in real time (Tjahjono et al., 2017). CPS do not stop at the boundaries of the plant as they interconnect with suppliers’ logistic flow monitoring systems. The IoT plays a key enabling role by providing informational connectivity between the networks of machines in smart factories and external relations and partners. The use of the IoT supports real-time manufacturing flexibility which can substantially change patterns of production and consumption.

2.2. Industry 4.0, supply chain management and performance

24The potential opportunities and benefits of Industry 4.0 are multiple: flexible mass production, real-time coordination and optimisation of value chains, reduction of complexity and costs and the emergence of new services and business models. Industry 4.0 and Logistics 4.0 lead to a new basis for industrial performance which challenges the industrial structures and technological patterns of the 3rd industrial revolution (Tang and Veelenturf, 2019). Industry 4.0 control systems are moving away from vertical, centralised structures to a more resilient approach, where smart technologies are at the center of all processes. Linked to this, the strategy of cost reduction through outsourcing and offshoring to countries with low labor costs is being questioned (Kohler and Weisz, 2018). Personalisation, real-time manufacturing and distribution of products within the hour are now important potential sources of differentiation and value creation. Business performance undeniably depends more and more on the quality of interactions between economic players inside and outside the value chain. In a word, it depends on their business ecosystems.

25In this regard, a review of the literature on the impact of Industry 4.0 on supply chain management performance, conducted by Tjahjono et al. (2017), provides promising leads. The results show that order processing and transport logistics are the most impacted. This is due to the use of a large number of the technologies associated with Industry 4.0, including virtual and augmented reality, 3D printing and simulation. A large survey-based study conducted recently by the consulting firm PWC of 1,155 manufacturing firms in 26 countries ranked the firms surveyed according to their level of digital maturity (Geissbauer et al., 2018). 10% of the sample are classified as digital champions with their success depending on an integrated ecosystem, while 27% are ranked as digital innovators, 42% as digital followers and 22% as digital novices (Geissbauer et al., 2018, p. 14). The implication is that the adoption of Industry 4.0 is progressive and uneven across companies with only a small but significant share of firms displaying at present the most technologically advanced characteristics.

26While Industry 4.0 is clearly becoming a source of economic benefits for a share of firms in the economy, it also carries some risks for the activities of logistics companies. Ivanov et al. (2018) illustrate these risks with this example (pp. 2-3) “additive manufacturing leads to the possibility of producing modules, components, and even end products in one place, and actually in any place in the supply chain. This implies SC design changes, a lower number of supplier layers and suppliers as such, and the reduced need for transportation, which is a threat for logistics companies. UPS and SAP developed a joint technology which allows UPS to manufacture items using 3D printing directly at the distribution centers”. Risks of different kinds are thus intertwined (see the mapping by Kodym et al., 2020, p. 76): economic risks associated with high investments, social risks associated with job losses, technical and IT risks inherent in data security, legal and political risks of data ownership and data protection, and finally environmental risks due to transport pollution.

2.3. The debate on the impact of Industry 4.0 on global value chains and the international division of labor

27The pursuit of performance gains in the context of slow productivity growth has motivated the move towards Industry 4.0. This has required a transformation of supply, production and transport models. Based on the same technologies as Industry 4.0, Logistics 4.0 offers more integrated models in which information flows in multiple directions within internal planning processes, business ecosystems and global value chains (GVCs). Industry 4.0 offers new growth opportunities for international trade through globalised production networks involving multiple industrial, commercial and service companies in the value creation process.

  • 10 China annually installs more industrial robots that any other single country in the world. See Inte (...)

28The adoption of Industry 4.0 technologies for the management of trade flows through global value chains may well have an impact on patterns of the location of industrial activity and the international division of labor. From a developed country perspective, this locational impact has mainly been considered in terms of the increased opportunities it offers for reshoring production located in developed nations. Indeed, Logistics 4.0 technologies allow for the reduction of many steps in the supply chain or their realisation at lower costs. As a result, labor costs, which have been a key driving factor in the search for comparative advantage, promise to become less important relative to the advantages of proximity to markets. This may encourage the relocation of labor-intensive activities to developed countries at the expense of developing countries. Moreover, as China has emerged from the 2000s as the single largest producer of manufacturing goods in in world, there are concerns that due to the rapid adoption of robots and other advanced automation technologies in China, Chinese producers will not follow the previous pattern displayed by developed country multinationals of progressively offshoring the more labor intensive parts of the production process to lower wage economies (Hallward-Driemeier and Nayyar, 2017; Hollweg, 2019, p. 74).10

  • 11 See case studies on reshoring provided by the European Foundation for the Improvement of Living and (...)
  • 12 According to a recent U.S Bureau of Labor Statistics study, the most dynamic areas of US job creati (...)
  • 13 See Hollweg (2019, p. 73) who observes, “3-D printing may lower transport costs, lessen the importa (...)

29While there is a variety of anecdotal evidence that developed-country multinational enterprises are reshoring production from their sites located in less developed and lower wage countries,11 the extent of this and its overall impact on the international division of labor remain in doubt. A recent OECD survey of the empirical evidence, using the share of domestic demand that is served by imports as a rough measure of the importance of reshoring, found that while the share has decreased recently in Japan, Germany and the United Kingdom, it has increased in the United States, Italy and France (de Backer et al., 2016).12 The report concludes that reshoring is still more ‘a trickle than a flood’ and observes that an increase in reshoring does not mean an end to offshoring since proximity to markets is an argument for both reshoring and offshoring. The ambiguous effect of new technologies on the location of production is also discussed by Hollweg (2019, p. 73) who observes that while new technologies such as 3-D printing are necessarily disruptive, they can bring new opportunities for developing countries to engage in and achieve the benefits of GVCs participation by reducing entry costs into manufacturing and by reducing the impact of distance.13

3. Industry 4.0 and the challenge of environmental sustainability

30Industry 4.0 not only has potential to improve the productivity and competitiveness of industrial firms, it can also contribute to more efficient energy use and to more sustainable patterns of consumption and production (Stock and Seliger, 2016; Stock et al., 2018). In line with recent empirical evidence on the link between big data and predictive analytics on the one hand and environmental sustainability on the other (Dubey et al., 2019), the major technologies of Industry 4.0 can be viewed as strong predictors of environmental performance. There is the promise that Industry 4.0 transformative technologies can favor the circular economy principles at the firm-level (cleaner production and eco-design) and at the territorial level (industrial ecology and smart cities).

3.1. Improving corporate environmental performance

31At the firm-level, cleaner production involves changing corporate processes, management and maintenance practices, aimed at “reducing production and use of material resources; reducing waste and pollutant emissions; and developing products that can easily go through recycling processes” (De Guimarães et al., 2017, p. 881). Unlike the traditional approach of investing in end-of-pipe pollution reduction, which increases capital and operating costs, cleaner production projects can reduce direct input costs, cleaning costs and pollution. In this context, all types of companies (multinational firms, small and medium-sized enterprises, public players…) are encouraged to develop cleaner production and/or service processes in their main field of activity both independently of each other and across sectors (for manufacturing see Ramos et al., 2018; for agrifood Notarnicola et al., 2017; for construction Ghisellini et al., 2018; and for utilities Garonne et al., 2018). Investing in Industry 4.0 technologies can lower manufacturing costs by reducing the consumption of resources and by reducing the volume of waste. For example, cyber-physical systems can support productive activities while generating less waste. In the same vein, IoT by facilitating a strategy of mass customisation of production can help meet demand with reduced production of surplus inventory. As for cloud manufacturing, it ensures controlled consumption of resources (energy, water, raw materials) and additive manufacturing contributes to proactive maintenance by saving energy and reducing waste from defective products (de Sousa Jabbour et al., 2018). Cleaner production methods can also deliver higher value-added and more competitive products. For example, Asif et al. (2016) develop a multi-method simulation-based tool to evaluate economic and environmental performance of circular product systems. They conclude that mutual interactions among critical factors of the business model, product design and supply chain contribute to improve global performance of cleaner production methods. Therefore, cleaner production technologies and/or management related approaches should be promoted to help industries, governments, and society to speed up the transition to sustainable patterns.

  • 14 For evidence on the trend towards eco-design in Europe, see the results of the 2014 Community Innov (...)

32At the firm-level, eco-design consists in the integration of environmental aspects into product (or service) design and development, with the aim of reducing adverse environmental impacts throughout a product’s (or service) life cycle (ISO 14006:2020). Eco-design can improve the degree of product sustainability with consequences for all the other stages of the production process throughout and beyond the lifecycle of existing products or solutions (Charter and Tischner, 2017). In many industries, firms shift towards this ‘bottom-up’ process to enhance their environmental performance rather than choosing to ‘clean-up’ manufacturing processes (Karlsson and Luttropp, 2006).14 More broadly, eco-design serves to promote acceptable societal demands and needs (in relation to the eco-designed product’s functionalities) in part through the productive coordination amongst all the stakeholders along a chain of vertically interdependent activities. Industry 4.0 technologies provide relevant operational tools to achieve this coordination along the production process (Bovea and Pérez-Belis, 2012). They can be viewed as enabling factors to develop circular business models for the transition to green industrial dynamics (Garcia-Muiña et al., 2019). Industry 4.0 solutions can allow for programming at competitive prices smaller production series adapted to the specific demands of consumers contributing to fewer stocks, less transport-related energy costs and lower procurement costs thus significantly improving energy use. For example, cyber-physical systems can enhance customer satisfaction by allowing for product development based on accurate and timely consumption information. This makes it possible to design products with extended life spans, with a priority on reducing waste, repairing, reusing, recycling and remanufacturing (de Sousa Jabbour et al., 2018a, 2018b).

3.2. Acting for the sustainability of territorial areas

33At the territorial-level, industrial ecology can be defined as an operational strategy for the implementation of sustainable development around the collective and cooperative dynamics of actors whose productive and/or service activities may be separate or independent (Lifset and Graedel, 2002). Industrial ecology aims at developing industrial ecosystems that function in a manner comparable to biological ecosystems (Frosch and Gallopoulos, 1989). The idea is to pursue “industrial symbiosis—defined to include physical exchanges of materials, energy, water, and by-products among diversified clusters of firms” (Chertow, 2007, p. 11). Firms engaged in an industrial ecology approach seek to self-organize by carrying out a series of operations to rationalize production including the optimisation of energy and material consumption, minimisation of waste at source and the reuse of waste to serve as raw materials for other production processes. (Frosch, 1995; Erkman, 1998). This recent practice of environmental management depends on structuring inter-firm relations both within as well as across industrial boundaries through the sharing of infrastructure, the exchange of equipment, services, materials, energy and resource flows, as well as through the use of smart data on a territorial level (Côté, 1998; Mallawaarachchi et al., 2020). Industrial ecology then requires an efficient coordination throughout the product life cycles between the different organisations forming the network (Stock and Seliger, 2016). Industry 4.0 tools can support this by offering new opportunities for realizing closed-loop product life cycles and industrial symbiosis based on remanufacturing or reuse (Wen and Meng, 2015). Cross-industry networks of multiple supply chains can be optimised through innovative efficient information systems. More precisely, data-driven and computer-optimisation solutions can be used to identify sustainability gains and develop indices for resilience and reliability, thus providing decision-making support through the production of reliable information among the supply chain and industrial networks (Tseng et al., 2018). Inspired by the experience of industrial symbiosis in Kalundborg, Denmark15 in 1989, eco-industrial parks have flourished all over the world based on national programmes to implement industrial ecology principles, with more or less success (see for example, Chertow et al., 2008; Dong et al., 2017; Domenech et al., 2019). The growing development of industrial ecology depends on the continuous promotion of technological innovations based on the use of newer information technology tools (Maqbool et al., 2019).

  • 16 For an overview of smart city definitions and dimensions, see Albino et al. (2015).

34At the scale of urban conurbations, the use of Industry 4.0 technologies is closely associated with the development of ‘smart cities’ initiatives seeking improvements in urban transport and a reduction in negative environmental impacts though economizing on energy and water use. There is no consensus in the literature on how smart cities should be defined or on what dimensions should be taken into account.16 It is possible to find studies promoting a technocratic vision of smart cities based exclusively on the massive collection of data with sensors combined with modeling and the use of data analytics for optimizing the city’s use of physical infrastructure and resources (Harrison et al., 2010; Al Nuami et al., 2015). Several studies, however, make the case for a more integrated approach also focusing on such social dimensions as better access to educational services and improved communication with citizens fostering more transparent and participative governance. Chouarbi et al. (2012), for example, propose an integrated framework for understanding smart cities initiatives including not only the use of technology in relation to infrastructure and the environment but also the dimensions of urban management, organisation and governance. Digital technologies in this more integrated vision are seen as tools that can be used to actively engage citizens in decision-making. Zanella et al. (2014, p. 22), with respect to the data collected through the IoT observe, “the availability of different types of data, collected by a pervasive urban IoT, may also be exploited to increase the transparency and promote the actions of the local government toward the citizens, enhance the awareness of people about the status of their city, stimulate the active participation of the citizens in the management of public administration.” The ultimate promise is that Industry 4.0 technologies can contribute to multiplying the possibilities for responding to new emerging demands of consumers including distance work, more personally tailored transport public transport solutions and the diffusion eco-innovations in ways that enhance democratic governance.

4. Contributions to the Special Issue

35This Special Issue of the Review of Industrial Economics brings together five of the papers presented at the 2019 University of Côte d’Azur workshop on ‘Transformative Technologies: The Impact of Industry 4.0 on Employment, Global Value Chains and Sustainable Development’. These papers address the three thematic areas discussed above, with the first 3 papers focusing on the impact of Industry 4.0 on production, jobs and earning, and the 4th and 5th papers focusing on its impact on value chains and environmental sustainability respectively.

36Bernhard Dachs (Austrian Institute of Technology, Vienna) and Iztok Palčič (University of Maribor, Slovenia) in a paper titled, “Not for everyone? Product characteristics and digital production technologies in manufacturing” fill an important gap in the literature by investigating the basis for firm heterogeneity in the use of new digital technologies. As observed above, most of the empirical work on the impact of robotics and artificial intelligence has been conducted at the sector and national levels with little attention to what takes place inside the firm. New technologies, however, are adopted by firms and not by sectors and nations. The authors make use of a unique data set, the European Manufacturing Survey (EMS) to explore the uptake of advanced digital technologies in a sample of 2,120 manufacturing firms in Austria, Germany and Switzerland in 2015. The main result shows that Industry 4.0 technologies, often considered in the literature as general-purpose technologies, are rather specific in their use and depend on firm heterogeneity in terms of such characteristics as the degree of standardisation and complexity of the main good they produce.

37In the second paper titled, “Artificial Intelligence and the Future of Work”, Salima Benhamou (France Stratégie, Paris) investigates the impact of artificial intelligence in the form of machine learning on work and skills through case studies carried out in the health, banking and logistics and transport sectors. While much of the research on the impact of artificial intelligence on jobs and employment in economics has focused on its potential for substituting for the tasks and skills of human workers, the study by Benhamou shows that the effects are more complicated and varied, with machine learning applications often complementing and transforming the skills of exiting occupations as opposed to simply substituting for them. Moreover, Benhamou shows that these effects may vary across occupations in the same sector of activity. The article concludes by considering what forms of work organisation are most suited to fostering machine-human complementarity in work.

38In the third contribution titled, “The Fourth Industrial Revolution, Technological Innovation and Firm Wages: Firm-level Evidence from OECD Economies”, Liu Shi, Shaomeng Li and Xiaolan Fu (Technology and Management Centre for Development, University of Oxford) focus on the impact of digital technologies on wages and earnings. The authors present one of the first studies looking at the impact of new digital technologies on average earnings at the level of the firm, focusing on differences between advanced technology adopters and non-adopters. Challenging the implicit assumption in much of the literature that firms act as wage takers, they argue that firms have some latitude in setting wages. They adopt a ‘rent-sharing’ perspective based on the premise that some of the quasi-rents derived from being the first to commercialize an innovation are captured by the firm’s employees in the form of higher wages. The authors develop this argument using a unique matched firm-patent panel data set for 27 OECD countries between 2010 and 2018. Their analysis both provides insight into the basis for cross-firm wage differentials and sketches out a research agenda for extending the analysis so as to address the issue of wage and earnings inequality more generally.

39The fourth paper in the special issue by Vincent Frigant (University of Bordeaux, Gretha) turns to the issue of the impact of Industry 4.0 on supply chains and on industrial geography. His paper is titled « L’industrie 4.0, vers une dé-globalisation des chaines de valeur ? Effets attendus de la robotique industrielle avancée et de la fabrication additive sur le système de coordination ». Frigant argues that the centripetal or centrifugal forces that are weighing on the organisation of global value chains in Industry 4.0 should be identified. To do this, the author suggests breaking down Industry 4.0—a complex and uncertain set of forces—into different technological ‘bricks’ or components that are easier to analyse. Based on a coordination system approach, he advances the hypothesis that vertically related activities must regulate three types of separation: physical, contractual and cognitive. He provides insights into the roles played by the different technological bricks in managing the three separations and discusses the consequences in terms of the conflicting trends towards the globalisation and deglobalisation of value chains.

40In the fifth paper Adel Ben Youssef (Université Côte d’Azur, CNRS, GREDEG) offers a first analysis of the role of Industry 4.0 in climate change mitigation. His article titled “How Can Industry 4.0 Contribute to Combatting Climate Change?” provides an overview of the basic principles and main technological changes of Industry 4.0 and highlights the conditions under which they can be consistent with and supportive of climate sustainability. His qualitative analysis concludes that in order to achieve this goal, transformative technologies must (1) achieve efficiency gains and ensure energy efficiency; (2) contribute to the shift from a linear to a circular economy focusing on reuse and recovery within closed loop supply chains; (3) develop eco-innovations (i.e. technologies improving environmental performance); and (4) be transferred to the least developed countries which must participate both in Industry 4.0 and sustainable development.

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Bibliographie

ASIF, F. M., LIEDER, M., & RASHID, A. (2016). Multi-method simulation based tool to evaluate economic and environmental performance of circular product systems. Journal of Cleaner Production, 139, 1261-1281.

ACEMOGLU, D., & RESTREPO, P. (2017). Robots and jobs: Evidence from US labor markets. National Bureau of Economic Research Working Paper 23285, Cambridge MA. Retrieved from https://www.nber.org/papers/w23285.pdf.

ALBINO, V., BERARDI, U., & DANGELICO, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of urban technology, 22(1), 3-21.

AL NUAIMI, E., AL NEYADI, H., MOHAMED, N., & AL-JAROODI, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 25.

ARDITO, L., PETRUZZELLI, A. M., PANNIELLO, U., & GARAVELLI, A. C. (2019). Towards Industry 4.0: Mapping digital technologies for supply chain management-marketing integration, Business Process Management Journal, 25(2), 323-346.

ARNTZ, M., T. GREGORY and U. ZIERAHN (2016). “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis”, OECD Social, Employment and Migration Working Papers, No. 189, OECD Publishing, Paris.

AUTOR, D. H., LEVY, F., & MURNANE, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics118(4), 1279-1333.

BARRETO, L., AMARAL, A., & PEREIRA, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252.

BOVEA, M., & PÉREZ-BELIS, V. (2012). A taxonomy of ecodesign tools for integrating environmental requirements into the product design process. Journal of Cleaner Production, 20(1), 61-71.

BOWLES, J. (2014). The computerisation of European jobs, Bruegel, Brussels.

BRYNJOLFSSON, E., & MCAFEE, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.

BRYNJOLFSSON, E., MITCHELL, T., & ROCK, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108, 43-47.

CARD, D., and J. E. DINARDO. “Skill-biased technological change and rising wage inequality: Some problems and puzzles.” Journal of labor economics 20.4 (2002): 733‑783 Chertow, M. R. (2007). “Uncovering” industrial symbiosis. Journal of Industrial Ecology, 11(1), 11-30.

CHERTOW, M. R., ASHTON, W. S., & ESPINOSA, J. C. (2008). Industrial symbiosis in Puerto Rico: Environmentally related agglomeration economies. Regional studies, 42(10), 1299-1312.

CHOURABI, H., NAM, T., WALKER, S., GIL-GARCIA, J. R., MELLOULI, S., NAHON, K., … & SCHOLL, H. J. (2012, January). Understanding smart cities: An integrative framework. In 2012 45th Hawaii international conference on system sciences (pp. 2289‑2297). IEEE.

CHARTER, M., & TISCHNER, U. (Eds.) (2017). Sustainable solutions: developing products and services for the future. Routledge.

CÔTÉ, R. P. (1998). Thinking like an ecosystem. Journal of Industrial Ecology, 2(2), 9-11.

DACHS, B., KINKEL, S., & JÄGER, A. (2019). Bringing it all back home? Backshoring of manufacturing activities and the adoption of Industry 4.0 technologies. Journal of World Business, 54(6), 101-17.

DAUTH, W., FINDEISEN, S., SÜDEKUM, J., & WOESSNER, N. (2017). German robots-the impact of industrial robots on workers, IAB Discussion Paper 30/2017, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg.

DE BACKER, K., MENON, C., DESNOYERS-JAMES, I., & MOUSSIEGT, L. (2016). Reshoring: Myth or reality?, OECD.

DE GUIMARÃES, J. C. F., SEVERO, E. A., & VIEIRA, P. S. (2017). Cleaner production, project management and strategic drivers: an empirical study. Journal of Cleaner Production, 141, 881-890.

DE SOUSA JABBOUR, A. B. L., JABBOUR, C. J. C., FOROPON, C., & GODINHO FILHO, M. (2018a). When titans meet–Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25.

DE SOUSA JABBOUR, A. B. L., JABBOUR, C. J. C., GODINHO FILHO, M., & ROUBAUD, D. (2018b). Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations. Annals of Operations Research, 270(1‑2), 273-286.

DOMENECH, T., BLEISCHWITZ, R., DORANOVA, A., PANAYOTOPOULOS, D., & ROMAN, L. (2019). Mapping Industrial Symbiosis Development in Europe_ typologies of networks, characteristics, performance and contribution to the Circular Economy. Resources, Conservation and Recycling, 141, 76-98.

DONG, L., LIANG, H., ZHANG, L., LIU, Z., GAO, Z., & HU, M. (2017). Highlighting regional eco-industrial development: Life cycle benefits of an urban industrial symbiosis and implications in China. Ecological Modelling, 361, 164-176.

DUBEY, R., GUNASEKARAN, A., CHILDE, S. J., PAPADOPOULOS, T., LUO, Z., WAMBA, S. F., & ROUBAUD, D. (2019). Can big data and predictive analytics improve social and environmental sustainability?. Technological Forecasting and Social Change, 144, 534‑545.

ERKMAN S. (1998), Vers une écologie industrielle : comment mettre en pratique le développement durable dans une société hyper-industrielle. Editions Charles Leopold Mayer, Paris.

FELTEN, E. W. and RAJ, M. and SEAMANS, R. (2019). The Occupational Impact of Artificial Intelligence: Labor, Skills, and Polarisation. NYU Stern School of Business. Available at SSRN: https://ssrn.com/abstract=3368605.

FERRANTINO M., and ELCIN KOTEN E. (2019), Understanding Supply Chain 4.0 and its potential impact on global value chains, Chapter 5, pp103-119. in Global value chain development report 2019. Technological innovation, supply chain trade, and workers in a globalised world, OECD, World Trade Organisation, Genova.

FRANK, A. G., DALENOGARE, L. S., & AYALA, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15-26.

FREY, C. B., & OSBORNE, M. (2013). The future of employment. Oxford Martin Programme on Technology and Employment, Oxford University.

FREY, C. B., & OSBORNE, M. (2015). Technology at work: The future of innovation and employment. Citi GPS, Oxford University.

FROSCH, R. (1995). L’écologie industrielle du XXIe siècle. Pour la science, (217), 148-151.

FROSCH, R. A., & GALLOPOULOS, N. E. (1989). Strategies for manufacturing. Scientific American, 261(3), 144-153.

GARBIE, I. (2016). Sustainability in manufacturing enterprises: Concepts, analyses and assessments for industry 4.0. Springer, Cham, Swizterland.

GARCIA-MUIÑA, F. E., GONZÁLEZ-SÁNCHEZ, R., FERRARI, A. M., VOLPI, L., PINI, M., SILIGARDI, C., & SETTEMBRE-BLUNDO, D. (2019). Identifying the equilibrium point between sustainability goals and circular economy practices in an Industry 4.0 manufacturing context using eco-design. Social Sciences, 8(8), 241-263.

GARRONE, P., GRILLI, L., GROPPI, A., & MARZANO, R. (2018). Barriers and drivers in the adoption of advanced wastewater treatment technologies: a comparative analysis of Italian utilities. Journal of Cleaner Production, 171, S69-S78.

GEISSBAUER R., LÜBBEN E, SCHRAUF S., PILLSBURRY, S., (2018). How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions, Global Digital Operations 2018 Survey, PricewaterhouseCoopers Strategy & Germany.

GHISELLINI, P., JI, X., LIU, G., & ULGIATI, S. (2018). Evaluating the transition towards cleaner production in the construction and demolition sector of China: A review. Journal of Cleaner Production, 195, 418-434.

GOOS, M., & MANNING, A. (2007). Lousy and lovely jobs: The rising polarisation of work in Britain. The review of economics and statistics89(1), 118-133.

GRAETZ, G., & MICHAELS, G. (2018). Robots at work. Review of Economics and Statistics, 100(5), 753-768.

HALLWARD-DRIEMEIER, M., & NAYYAR, G. (2017). Trouble in the Making?: The Future of Manufacturing-led Development. World Bank Publications.

HARRISON, C., ECKMAN, B., HAMILTON, R., HARTSWICK, P., KALAGNANAM, J., PARASZCZAK, J. and WILLIAMS, P. (2010). Foundations for Smarter Cities, IBM Journal of Research and Development, 54(4), 1-16.

HOFMANN, E., & RÜSCH, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34.

HOLLWEG C. (2019). Global value chains and employment in developing economies, Chapter 3, pp 63-81 in Global value chain development report 2019. Technological innovation, supply chain trade, and workers in a globalised world, OECD, World Trade Organisation, Genova.

IVANOV, D., DOLGUI, A., & SOKOLOV, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.

INTERNATIONAL FEDERATION OF ROBOTICS (2018). Executive Summary World Robotics 2018 Industrial Robots.

JACOBSEN, N. B. (2006). Industrial symbiosis in Kalundborg, Denmark: a quantitative assessment of economic and environmental aspects. Journal of Industrial Ecology, 10(1-2), 239-255.

JÄGER, A., MOLL, C., SOM, O., ZANKER, C., KINKEL, S., & LICHTNER, R. (2015). Analysis of the impact of robotic systems on employment in the European Union. Final report, A study prepared for the European Commission, DG Communications Networks, Content & Technology, Fraunhofer ISI.

KAGERMANN, H., LUKAS, W. D., & WAHLSTER, W. (2011). Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI nachrichten, 13(11), 2.

KAGERMANN, H., HELBIG, J., HELLINGER, A., & WAHLSTER, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion.

KARLSSON, R., & LUTTROPP, C. (2006). EcoDesign: what’s happening? An overview of the subject area of EcoDesign and of the papers in this special issue. Journal of Cleaner Production, 14(15-16), 1291-1298.

KODYM, O., KUBÁČ, L., & KAVKA, L. (2020). Risks associated with Logistics 4.0 and their minimisation using Blockchain. Open Engineering10(1), 74-85.

KOHLER, D., & WEISZ, J. D. (2018). Industrie 4.0, une révolution industrielle et sociétale. Futuribles, (424), 47-68.

LEVY, F. (2018). Computers and populism: artificial intelligence, jobs, and politics in the near term. Oxford Review of Economic Policy, 34(3), 393-417.

LIFSET, R., & GRAEDEL, T. E. (2002). Industrial ecology: goals and definitions. A Handbook of Industrial Ecology, E. Elgar, 3-15.

MALLAWAARACHCHI, H., SANDANAYAKE, Y., KARUNASENA, G., & LIU, C. (2020). Unveiling the conceptual development of industrial symbiosis: Bibliometric analysis. Journal of Cleaner Production, 258 (20618), 2-10.

MAQBOOL, A. S., MENDEZ ALVA, F., & VAN EETVELDE, G. (2019). An assessment of European information technology tools to support industrial symbiosis. Sustainability, 11(1), 131.

MCKINSEY (2018). Note from the AI Frontier: Insights from Hundreds of Use Cases. McKinsey Global Institute.

NOTARNICOLA, B., SALA, S., ANTON, A., MCLAREN, S. J., SAOUTER, E., & SONESSON, U. (2017). The role of life cycle assessment in supporting sustainable agri-food systems: A review of the challenges. Journal of Cleaner Production, 140, 399-409.

OECD (2018). OECD Science, Technology and Innovation Outlook 2018: Adapting to Technological and Societal Disruption, OECD Publishing, Paris.

PEREIRA, A. C., & ROMERO, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13, 1206-1214.

RAJPUT, S., & SINGH, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98-113.

RAMOS, A. R., FERREIRA, J. C. E., KUMAR, V., GARZA-REYES, J. A., & CHERRAFI, A. (2018). A lean and cleaner production benchmarking method for sustainability assessment: A study of manufacturing companies in Brazil. Journal of Cleaner Production, 177, 218-231.

SORKUN M.F. (2020). Digitalisation in Logistics Operations and Industry 4.0: Understanding the Linkages with Buzzwords, Chapter 9. pp 177-199. In: Hacioglu U. (eds) Digital Business Strategies in Blockchain Ecosystems. Contributions to Management Science. Springer, Cham, Swizterland.

SACHS, J. D., SCHMIDT-TRAUB, G., MAZZUCATO, M., MESSNER, D., NAKICENOVIC, N., & ROCKSTRÖM, J. (2019). Six transformations to achieve the sustainable development goals. Nature Sustainability, 2(9), 805-814.

SKILTON, M., & HOVSEPIAN, F. (2017). The 4th industrial revolution: Responding to the impact of artificial intelligence on business. Springer, Cham, Swizterland.

STOCK, T., OBENAUS, M., KUNZ, S., & KOHL, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267.

STOCK, T., & SELIGER, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536-541.

STRANGE, R., & ZUCCHELLA, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25(3), 174-184.

SZALAVETZ, A. (2019). Industry 4.0 and capability development in manufacturing subsidiaries. Technological Forecasting and Social Change, 145, 384-395.

TADDY, M. (2018). The technological elements of artificial intelligence (No. 24301). National Bureau of Economic Research.

TJAHJONO, B., ESPLUGUES, C., ARES, E., & PELAEZ, G. (2017). What does industry 4.0 mean to supply chain?. Procedia Manufacturing, 13, 1175-1182.

TSENG, M. L., TAN, R. R., CHIU, A. S., CHIEN, C. F., & KUO, T. C. (2018). Circular economy meets industry 4.0: can big data drive industrial symbiosis? Resources, Conservation and Recycling, 131, 146-147.

VALENTINE, S. V. (2016). Kalundborg Symbiosis: Fostering progressive innovation in environmental networks. Journal of Cleaner Production, 118, 65-77.

WAIBEL, M. W., STEENKAMP, L. P., MOLOKO, N., & OOSTHUIZEN, G. A. (2017). Investigating the effects of smart production systems on sustainability elements. Procedia Manufacturing, 8, 731-737.

WEN, Z., & MENG, X. (2015). Quantitative assessment of industrial symbiosis for the promotion of circular economy: a case study of the printed circuit boards industry in China’s Suzhou New District. Journal of Cleaner Production, 90, 211-219.

WINKELHAUS, S. & GROSSE, E. (2020). Logistics 4.0: a systematic review towards a new logistics system, International Journal of Production Research, 58(1), pp 18-43.

ZANELLA, A., BUI, N., CASTELLANI, A., VANGELISTA, L., & ZORZI, M. (2014). Internet of things for smart cities. IEEE Internet of Things journal, 1(1), 22-32.

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Notes

1 Ardito et al. (2019) listed identical initiatives: “UK CATAPULT—High Value Manufacturing”, the American “Manufacturing USA”, the French “Industrie du Futur”, and the Dutch “Smart Industry”, etc.

2 See: International Federation of Robotics, ‘Industrial: https://ifr.org/industrial-robots.

3 See: https://www.isi.fraunhofer.de/en/themen/industrielle-wettbewerbsfaehigkeit/fems.html.

4 The ‘price effect’ refers to additional production and employment in adopting firms due a reduction in unit costs from the productivity increases due to technological change. The ‘income effect’ refers to how cost savings due to technological change may result in higher incomes and hence increased consumption which may impact positively on production and employment in the adopting industry and in other sectors.

5 For a summary of the figures on world installations of industrial robots, see: https://ifr.org/industrial-robots.

6 Outside of Europe and the US, David (2017) finds that 55% of jobs in Japan are at high risk. For the case of Australia, Edmonds and Bradley (2015) estimate 44% jobs to be at risk. For developing countries, see: World Bank Development Report, 2016.

7 See the recent McKinsey report (2018) based on several case studies that identifies collecting and labelling the massive data sets needed for supervised training models as two of the most important obstacles firms face in using ML.

8 For a critical assessment of the SBTC hypothesis, see Card and DiNardo (2002).

9 For a discussion of these differences, see Levy (2018).

10 China annually installs more industrial robots that any other single country in the world. See International Federation of Robotics, 2018.

11 See case studies on reshoring provided by the European Foundation for the Improvement of Living and Working Conditions (Eurofound): https://reshoring.eurofound.europa.eu/reshoring-cases.

12 According to a recent U.S Bureau of Labor Statistics study, the most dynamic areas of US job creation in supply chains are not directly connected to the reshoring of manufacturing activities but are in warehousing, storage and delivery and involve moving goods around either in warehouses or delivery vehicles (Ferrantino and Elcin Koten (2019, p. 105).

13 See Hollweg (2019, p. 73) who observes, “3-D printing may lower transport costs, lessen the importance of achieving economies of scale for manufacturers, and make it easier to manufacture high-quality products.”

14 For evidence on the trend towards eco-design in Europe, see the results of the 2014 Community Innovation Survey module for 22 EU-member countries measuring environmental innovations. The survey results show that slightly over 15% of enterprises design products so as to facilitate recycling by end users, ranging from a high of over 25% of enterprises in Finland and Slovenia to a low of about 6% in Malta.

15 For empirical details on the Kalundborg Symbiosis, see http://www.symbiosis.dk/en/. For academic discussion, see for example Jacobsen (2006) and Valentine (2016).

16 For an overview of smart city definitions and dimensions, see Albino et al. (2015).

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Cécile Cézanne, Edward Lorenz et Laurence Saglietto, « Exploring the economic and social impacts of Industry 4.0 »Revue d'économie industrielle [En ligne], 169 | 1er trimestre 2020, mis en ligne le 05 janvier 2023, consulté le 09 février 2026. URL : http://journals.openedition.org/rei/8643 ; DOI : https://doi.org/10.4000/rei.8643

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Cécile Cézanne

Université Côte d’Azur, CNRS, GREDEG

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Laurence Saglietto

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