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Article

The Relationship between International Trade in Industry 4.0 Products and National-Level Sustainability Performance: An Empirical Investigation

by
Arindam Das
S.P. Jain Institute of Management & Research (SPJIMR), Mumbai 400058, India
Sustainability 2023, 15(2), 1262; https://doi.org/10.3390/su15021262
Submission received: 14 November 2022 / Revised: 3 January 2023 / Accepted: 4 January 2023 / Published: 9 January 2023
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Abstract

:
In this study, we assessed how Industry 4.0 (I4.0) adoption influences a country’s sustainability performance. Departing from firm-level analyses, we studied international trade of products pertaining to Advanced Industrial Robotics (AIR), Additive Manufacturing (AM), and Industrial Internet of Things (IIoT) and advanced the argument that the intensity of import of these products demonstrates an aspiring level of I4.0 adoption, and the revealed comparative advantage of export of these products demonstrates an advanced level of I4.0 adoption. Consequently, we studied the influence of these factors on national performance in three manufacturing-relevant SDGs, i.e., SDG 8, SDG 9, and SDG 12. Our empirical analysis showed mixed results. Adoption of I4.0 led to sustainable manufacturing practices that further enhanced national-level performance in relevant SDGs, especially in advanced countries. We also identified opportunities for further research on how adoption of I4.0 could avoid unsustainable digitalization and lead to circular economy practices.

1. Introduction

Originating from an initiative of the German government in 2011, Industry 4.0 (I4.0) refers to the intelligent networking of machines and industrial processes that, with the help of digital technologies, could herald the fourth industrial revolution [1,2,3]. This involves the integration of cyber-physical systems, Industrial Internet of Things (IIoT), cloud manufacturing, and additive manufacturing into the industrial value creation processes, enabling manufacturers to harness entirely digitized, connected, smart, and decentralized value chains [4,5]. I4.0 technologies operate at two levels: the base technology level, which comprises the use of IIoT, cloud computing, big data, analytics, and machine learning algorithms, and the front-end technology level, which consists of production processes (smart manufacturing) and its workers (smart working), the external value chain (smart supply chain), and the customers (smart products and services) [6,7,8].
In addition, real-time integration of intra-firm and inter-firm operations leads to the virtual removal of firm boundaries, resulting in end-to-end digital integration in the extended value chain [9]. As a result, I4.0 delivers greater flexibility and robustness to firm competitiveness and enables a firm to build flexible and adaptable business structures. This, in turn, provides a firm with the capability for internal evolutionary developments in order to cope with the changing business environment [5]. Thus, I4.0 represents an “evolution” in factory operations and a potential “revolution” in the manufacturers’ value proposition. However, as of 2020, most manufacturers have failed to seize the revolutionary opportunities of I4.0 [10]. The main obstacles toward the I4.0 transformation are the cost of the implementation process, employees’ attitudes, and a lack of expertise [11].
Even though the promise of I4.0 is yet to be realized completely by businesses, it is necessary to explore how I4.0 ecosystems across organizations are evolving in time and space, as well as how firms are improving their integrative capabilities, removing bottlenecks, and balancing the triad—resources, structure, and governance [6]. Various elements of I4.0 are depicted in Figure 1. A larger question concerns how the adoption of these practices and the development of the capabilities lead to environmentally sustainable manufacturing.
While announcing the sustainable development goals (SDGs) at the UN General Assembly in 2015, the UN Secretary-General sought a complete commitment from businesses and industries to help realize the SDGs [12]. His conviction was that green business could be good business and that transformational change could be achieved only with the help of responsible businesses, investment, and civil society. Though SDGs are designed for nations, the realization of SDGs requires cooperation and alliances among public, private, and not-for-profit entities, with enhanced accountability and financial support for implementation [13,14].
However, since the announcement of the SDGs and their respective targets and indicators, the response from business organizations in this regard has been chaotic over the years. A recent analysis of information from 1141 companies indicates that, while 72% of these companies mentioned the SDGs in their reports and publications—with each of them mentioning about nine SDGs on average—only 14% of the companies mentioned specific SDG targets, and only 1% of companies provided quantifiable measures of performance against the targets [15]. It was also observed that businesses demand reduced complexity in SDG-related targets and rally around common ambitions, especially for social goals, in order to advance equal opportunity and foster economic empowerment [13,16,17].
The 17 SDGs, listed in Figure 2, are operationalized through a total of 169 targets, where each of these targets is associated with one or more indicators or measures. In total, 247 indicators have been identified by the UN [18]. While all 247 indicators are equally important, to facilitate global implementation, the indicators are classified into three tiers based on their level of methodological development and the availability of data at the international level [18].
The SDGs represent society’s quest for the triple bottom line (TBL), i.e., balancing the economic, social, and environmental dimensions of our activities on this planet [19]. In this quest, it is necessary to acknowledge that businesses and markets are part of the problem as well as the solution. Businesses can attempt to identify their roles in realizing the SDGs by assessing and re-configuring their products and services, business operations, and social contributions. Analysis of scholarly publications indicates that academic research has explored questions such as how technological innovation is directed toward the realization of SDGs, non-financial reporting, and the roles of businesses in dealing with sustainability issues [20]. Augmenting the extant literature, it is now relevant to evaluate how advanced manufacturing processes, as conceptualized in Industry 4.0, play a role in the realization of SDGs.
Extant literature indicates that innovations related to I4.0 have attracted considerable interest from engineering and management professionals, and on the other hand, sustainable development has become a well-known and broad issue for businesses and society. While adoption of I4.0, in principle, should lead to sustainable manufacturing practices, the strength of the causal relationship between I4.0 adoption and sustainability has not yet been clarified [21,22,23]. Ejsmont et al. [24] found that the current literature has addressed I4.0 from technological perspectives, overlooking its role in addressing the sustainability challenges faced by manufacturing organizations. Consequently, the lack of accurate understanding of the process through which I4.0 technologies enable sustainable manufacturing has become a fundamental barrier for businesses pursuing digitalization and sustainable thinking [25].
In this paper, we address the aforementioned limitations of the existing literature. We study the influence of I4.0 adoption on sustainability. Considering that the true impact of sustainability initiatives can be more appropriately measured at the macro level rather than at the micro level, we take a novel approach to study the relationship between the national-level adoption of I4.0 and the national-level measures of sustainable development. We look at the international trade of a set of final and intermediate products associated with I4.0 technologies, present empirical insights on the adoption of I4.0 at country levels, and establish how they influence SDG performance. We utilize some of the propositions from Castellani et al. [26] and build on the well-established idea in the international economics literature that the trade of capital goods captures technology diffusion and, thus, trade patterns, as observed through exports and imports indicating the intensity and maturity of adoption of these technologies across countries. Our findings augment the academic literature on the critical role of I4.0 adoption in helping to achieve sustainable development goals.

2. Literature Review and Hypotheses Development

Until now, businesses’ contributions to sustainability across the world have been inconsistent and disorganized. Businesses can focus on the dual principles of “avoid harm” and “do good” by addressing their negative social and environmental externalities and by offering business solutions that generate positive externalities [27,28,29]. Towards this, the framework provided by the UN SDGs expects businesses to collaborate with a range of other actors, including business partners in the extended supply chain, national and local governments, not-for-profit entities, multilateral bodies, and civil society [27].
The creation of such integrated systems of multiple entities at a global scale is difficult in traditional business environments. However, the promise of I4.0 may enable such integration and, thereby, offers an avenue for businesses to realize sustainability goals. As businesses increase their competitiveness by adopting I4.0 technologies and processes, integrating physical and digital interfaces, leading to lowering the time and cost to market, increasing productivity, and allowing for mass customization, they also contribute towards the realization of the SDGs [30,31].
The main design tenets of I4.0 focus on product innovation, leading to smart products; quality and cost mandates, leading to smart factories; interoperability; modularity; traceability; decentralization; automation and virtualization; real-time capability; product personalization; and service orientation [32,33], and these factors eventually result in the flexibilization of the manufacturing processes [7].
Extant literature on I4.0 covers a wide spectrum of topics related to innovation and performance of manufacturing organizations, as well as the enabling factors internal and external to the organizations. Empirical studies show that advanced manufacturing technologies not only contribute to firm performance, but also enhance green innovation, irrespective of any presence of government subsidies or policy restrictions [34]. However, a key factor that contributes towards improved sustainability performance through the adoption of I4.0 involves superior knowledge processes within the organization (intrinsic) as well as across the organization (extrinsic). When such knowledge is framed in a convincing strategy, it supports direct and indirect improvements to the sustainability dimension [35]. In addition, it has also been established that at operational level, spatial technologies for plant layout, such as 3D mapping, indoor positioning systems, motion capture systems, and immersive reality, contribute to all three pillars of the sustainability concept, namely environmental, social, and economic [36].
In the context of developing countries, the observed factors that inhibit sustainable adoption of I4.0 in developing countries are cultural construct, structural inequalities, noticeable youth unemployment, fragmented task environment, and deficiencies in the education system. Some of the strategies to promote sustainable adoption of I4.0 include understanding context and applying relevant technologies, strengthening policy and regulatory space, overhauling the education system, and focusing on primary manufacturing [37].
Though many of the associated technologies are at nascent stages of adoption, I4.0 signals a transformation in the value chain activities of manufacturing companies. Thus, it is important to combine it with the idea of sustainable development and, more specifically, with the idea of contributing to the SDGs.
In an empirical study, Zengin et al. [38] found that in Turkey, several SDGs, specifically, SDG 9, SDG 10, SDG 11, SDG 12, SDG 13, and SDG 14, have weak relationships with I4.0 and Society 5.0, suggesting a lack of thought leadership in integrating technological advances with societal development in the country. Similarly, in a survey-based study in another developing country, Brazil, Satyro et al. [39] found that executives focus on leveraging I4.0 concepts for the purpose of enhancing competitiveness, and sustainability was considered strategically secondary, with its social dimension ignored. Put differently, the focus on customer demands without stakeholder involvement and the worldview of digitalization as a way of doing “business as usual,” but in a more effective way, may end up reproducing unsustainable economic patterns [40].
Similarly, Olah et al. [1] found a negative relationship between I4.0-related processes and their impacts on the environment, when end-to-end process flow is considered, from the inputs to the final product, including raw materials, energy requirements, information, and waste disposal. However, Olah et al. [1] also argued that it is important to integrate I4.0 objectives with sustainable development goals at the planning and conceptualization level to guarantee higher environmental performance with a more positive impact than before. In other words, the productive synergy between I4.0 and environmentally sustainable manufacturing relies on the careful understanding of a set of critical organizational success factors, such as top leadership commitment, strategic alignment, training and capability development, etc. [4,41].
Some recent research has explored the relationship between sustainable development in a country and the volume of trade. The findings are interesting. It can be observed that international trade positively influences global progress towards achieving several SDG targets in developed countries [42]. However, it can also be noted that international trade reduced the SDG target scores for several developing countries. These results suggest the presence of an environmental Kuznets curve (EKC).
EKC refers to an inverted U-shaped curve, mapping the relationship between economic growth and environmental degradation [43,44,45]. The EKC hypothesis posits that the initial phases of economic development lead to a deterioration in environmental quality, but also to an increase in income level after a certain point, thus contributing to a reduction in environmental degradation. The pattern of this time-based directional relationship resembles an inverted U-shaped function. However, some recent research has also demonstrated that the EKC hypothesis is valid only under certain conditions [46].
Therefore, to improve our understanding of the role I4.0 concepts play in contributing to sustainable development, the research questions we investigate in this paper are as follows: How does the adoption of I4.0 concepts help in improving sustainable development performance as measured through SDGs? Following up on Koilo’s [43] observation, we also ask, is there a difference in contribution to SDG performance by firms who have matured levels of advanced manufacturing technologies and firms who aspire to acquire advanced manufacturing technologies?
We departed from the firm-level analyses that have been present in the literature and moved to a country-level analysis, which provided us with a better understanding of the performance of sustainable development. To identify adoption of the I4.0 concepts, we leveraged insights from de Oliveira et al. [47] and Castellani et al. [26] that export activities, especially when exporting to advanced economies, lead to process innovation, and trade activities for specific product codes can lead to adoption of I4.0 technologies at the country level. Thus, we classified countries based on the import and export of products associated with I4.0 and framed the following hypotheses.
H1. 
Countries in growing stages of I4.0 adoption, measured through the relative intensity of import of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 8.
H2. 
Countries in advanced stages of I4.0 adoption, measured through revealed comparative advantage of export of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 8.
H3. 
Countries in growing stages of I4.0 adoption, measured through the relative intensity of import of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 9.
H4. 
Countries in advanced stages of I4.0 adoption, measured through revealed comparative advantage of export of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 9.
H5. 
Countries in growing stages of I4.0 adoption, measured through the relative strength of import of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 12.
H6. 
Countries in advanced stages of I4.0 adoption, measured through revealed comparative advantage of export of products associated with advanced industrial robotics, additive manufacturing, or IIoT, demonstrate a higher level of realization of UN SDG 12.
Following the relevant extant literature [48], we restricted our study to three SDGs, namely SDG 8, SDG 9, and SDG 12, which would be significant impacted by I4.0 adoption: it is expected to lead to economic growth (SDG 8); new technologies can contribute to the integration of non-conventional energy sources and digitization of production processes (SDG 9); and it can help contribute towards improved resource utilization (SDG 12).

3. Research Method

3.1. Data and Measures

We utilize secondary data on trade, economy, and SDG performance of countries for the period of 2010–2022 to carry out our empirical analysis. As the technologies of advanced manufacturing and the concepts of I4.0 evolved significantly from the first decade of the 21st century, and the term I4.0 was coined in 2011, we felt that this period of study would provide a complete picture of the role of I4.0 in the societal transformation of each country. Although SDGs came into existence in 2015, the prior measures, millennium development goals (MDGs), and SDG-specific performance could be computed for prior years from information on MDGs.
To obtain trade-related information on I4.0-related products, we compiled data on the export and import of product categories that pertain to advanced manufacturing. Following Castellani et al. (2022), we used the six-digit harmonized system (HS) product codes that are associated with three sub-domains of advanced manufacturing: Advanced Industrial Robotics (AIR), Additive Manufacturing (AM), and Industrial Internet of Things (IIoT). The classification of these products by HS code and their corresponding descriptions are listed in Table 1.
Trade-specific data for the identified HS codes were captured for each of the fiscal years from 2010 to 2021 from the International Trade Center’s Trade Map database (www.trademap.org) for all the countries for which data are available. In addition, we also captured total export and import data for each country and global trade data for each of the HS codes during this period. Using this dataset, we computed the relative intensity of imports (RII) and the revealed comparative advantage, demonstrated through exports (RCA), for the three product groups (AIR, AM, and IIoT). RCA is a widely used measure in international business to assess the comparative advantage of a nation for a given product or product category [49].
We use country-wise and goal-wise one-year lagged data (i.e., for the period 2011–2022) of SDG performance for the countries covered in the annual Sustainable Development Report database [50]. The database, built from a vast set of publications from the UN, provides performance trend data across countries over time on all the 17 SDGs, and it has been extensively used in empirical studies in recent years. As mentioned in the earlier section, following Beier et al. [48], we restricted our country-level data collection to select SDGs (i.e., SDG 8, SDG 9, and SDG 12) which can be influenced significantly by I4.0-related initiatives.
In addition, we captured information on each country’s per-capita GDP and population from the UN’s UNCTAD Statistics database in order to use these variables as control variables in our model. The variable definition and data sources are listed in Table 2 and Table 3. The formulas for the calculation of RII and RCA are also specified in Table 3.
Combining these data provided us with an unbalanced pooled dataset of 2884 country-level records that contain trade and SDG information of 242 countries over the period of 11 years. This dataset was used for further analysis.

3.2. Estimation Method

We applied a panel data regression technique to test our hypotheses on the impact of trade volume of I4.0-related products on the realization of three specific SDGs, i.e., SDG 8, SDG 9, and SDG 12. The hypotheses were tested using six models, which are described in the equations below. Here, i represents a country, t represents a time period, α represents country-specific intercepts that capture heterogeneities across countries, and ε is the error term. The independent and dependent variables are defined in Table 2 and Table 3 above.
Goal_8_Perf(i,t+1) = β0 + β1.RI_AIR(i, t) + β2.RI_AM(i,t) + β3.RI_IIoT(i,t) + α(i) + ε(i,t)
Goal_8_Perf(I,t+1) = β0 + β1.RCA_AIR(i,t) + β2.RCA_AM(i,t) + β3.RCA_IIoT(i,t) + + α(i) + ε(i,t)
Goal_9_Perf(i,t+1) = β0 + β1.RI_AIR(i,t) + β2.RI_AM(i,t) + β3.RI_IIoT(i,t) + α(i) + ε(i,t)
Goal_9_Perf(i,t+1) = β0 + β1.RCA_AIR(i,t) + β2.RCA_AM(i,t) + β3.RCA_IIoT(i,t) + α(i) + ε(i,t)
Goal_12_Perf(i,t+1) = β0 + β1.RI_AIR(i,t) + β2.RI_AM(i,t) + β3.RI_IIoT(i,t) + α(i) + ε(i,t)
Goal_12_Perf(i,t+1) = β0 + β1.RCA_AIR(i,t) + β2.RCA_AM(i,t) + β3.RCA_IIoT(i,t) + α(i) + ε(i,t)
While running the statistical analysis, we tested for appropriateness of the fixed effect model over the random effect model through the Hausmann test. As the p-value in the Hausmann test was near 0 for all six of the model specifications mentioned above, we completed our statistical analysis using country-fixed effects and time-fixed effects. In addition, we also log transform the control variables before running the regression.

4. Empirical Results and Discussion

Table 4 lists the descriptive statistics pertaining to all of the variables in the sample. It is important to note that the lack of data points for some variables has resulted in the dataset becoming unbalanced panel data. Final statistical analysis was conducted for 189 countries.
Bivariate correlations among dependent and control variables used in the regression models are documented in Table 5. We observed that all RI- and RCA-related computed measures had low correlation among themselves.
Results from fixed-effect panel data regression are listed in Table 6 and Table 7. We documented the key information for each of the regression runs, including adjusted R2.
The results showed that the models were statistically significant; however, our trade level predictor variables were found to be only partially significant. For the hypotheses we have articulated, we found partial support for H1, H2, H3, H4, and H6, and no support for H5.
The performance on SDG 8 was positively impacted by the import of AM and IIoT products. The import of IIoT products also positively influenced the performance on SDG 9. However, the performance on SDG 12 was not influenced by the import of any products of the I4.0 category.
On the other hand, the performance on SDG 8 was positively impacted by the export of IIoT products. The performance on SDG 9 was also positively impacted by the export of IIoT products, but was negatively impacted by the export of AIR products. Lastly, the performance on SDG 12 was impacted by the export of AIR products.
These results provide interesting insights into the influence of trade of I4.0 products on the realization of relevant SDGs. Broadly speaking, as hypothesized, I4.0-related products in trade (either in import or in export) have some positive influence on the performances of the SDGs we have studied. However, there are deeper insights. First, countries aspiring to excel in advanced manufacturing (as operationalized through relative intensity of import of I4.0 products) and countries that maturing in advanced manufacturing (as operationalized through revealed comparative advantage of export of I4.0 products) demonstrate different performance characteristics on these SDGs. Our results suggest that a higher level of technological maturity in I4.0, as demonstrated through comparative advantage in exports of IIoT products, leads to a stronger performance on SDG 8 and SDG 9. In other words, advanced economies that have excelled in I4.0 technological innovation and have developed an edge regarding the revealed comparative advantages of exporting these products demonstrate improvements in the performance of manufacturing-related SDGs, in comparison to the countries which are aspiring to acquire I4.0 technologies and have higher level of import intensity in these products. This finding also lends support to the concept of the EKC, i.e., the relationship between environmental degradation and economic growth tends to follow an inverted U-shaped curve [43,45].
However, it is also important to note the lack of influence the import intensity of I4.0 products has on the realization of SDG 12. If we investigate further, it is clear that realization of SDG 12 hinges on waste management activities in production processes (see Table 2), and a higher level of production activities does not seem to address waste management issues effectively. This finding provides credence to Chiarini’s [51] observation on the concerns about the disposal of electrical and electronic waste produced by these technologies. Therefore, this finding calls for follow-up research on how I4.0 can contribute to implementation of the circular economy.
We also observed the negative impact of the import of AIR products on SDG 9 performance in countries aspiring for excellence in I4.0. This anomaly requires further investigation. This observation relates to “unsustainable digitalization types,” as noted by Niehoff [40], which are difficult to address with soft policy instruments and require a more regulated approach.
The mixed results that we obtained in our statistical analysis revealed another critical issue related to measures of SDG performance. As Beier et al. [52] have noted, some of the conceptual formulations of SDG measures are “inexact” and do not necessarily align with the premise of I4.0. For example, additive manufacturing (AM) may not contribute to addressing the unemployment rate that is calculated as part of SDG 8 performance. Therefore, it may be important to establish rigorous mapping between I4.0 objectives and granular-level SDG measures.

5. Conclusions

I4.0 is considered to be the new industrial stage, in which several emerging technologies are converging to provide digital solutions that are more efficient than traditional solutions [7]. In this study, we assess how I4.0 adoption across industries within a country influences a country’s overall performance on select SDGs. The study is unique in many ways. First, we departed from firm-level I4.0 analyses and instead looked at country-level adoption by analyzing international trade data on an exclusive set of carefully identified product codes. By conducting a macro-level analysis, this study was designed to complement the growing literature on I4.0′s role in sustainable manufacturing and the larger purpose of sustainable development [34,35,48]. Second, our large sample, comprising data from 189 countries over 11 years, provides us with rich insights not discussed in the extant literature. Research on such large-scale panel data on international trade of I4.0 products (classified under AM, AIR and IIoT) is rare and valuable. Third, our approach to quantification of variables, required for statistical analyses, used unambiguous and objective metrics. For I4.0 adoption, we looked at relative import intensity and revealed comparative advantage through exports, a well-established measure in international business. For country-level SDG performance, we utilized well-defined indexed data, curated from publications by the UN as well as other multilateral organizations and research centers [50].
Supporting the observations of some of the extant research [42,48], our results show that the adoption of I4.0 across countries (both mature in advanced technology adoption as well as aspiring in advanced technology adoption) may lead to some of the economic dimensions of sustainability, such as growth and productivity, as outlined in SDG 8 and 9, and may not address responsible and sustainable production activities, as outlined in SDG 12. Therefore, holistic integration of I4.0 with the SDGs and its targets may result in truly sustainable performance on all three pillars (environmental, social, and economic). Therefore, we conclude that managing and leveraging the technological resources embedded in the trade of I4.0 products results in sustainable manufacturing, which contributes to sustainable development.
Our work contributes to the growing knowledge base on the interface of I4.0 and sustainability. Considering that the world, collectively, has less than 3000 days to realize the ambitious goals outlined in the 17 SDGs, it is important that we strengthen our understanding of the role of emergent technological innovation in economic activities such as international trade and sustainable development, which can guide the policy-makers. This work can be enhanced by investigating some of the anomalies observed in our results. First, considering the very limited influence of I4.0 product trade activities on SDG 12, it is important to find out how I4.0 can contribute to the implementation of a circular economy. In addition, as Niehoff has articulated [40], unsustainable digitalization types that negatively impact SDG performance can be explored with respect to the adoption of I4.0 technologies. Additionally, researchers can work on developing an integrative framework between I4.0 objectives and SDG targets, and such work will contribute to developing national-level policies on sustainable manufacturing.

Funding

There is no funding received for this research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: www.trademap.org and https://www.sdgindex.org/.

Conflicts of Interest

The author declares no conflict of interest.

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Sustainability 15 01262 g001 550
Figure 1. Building blocks of Industry 4.0 (adapted from [6]).
Figure 1. Building blocks of Industry 4.0 (adapted from [6]).
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Figure 2. UN SDGs and targets.
Figure 2. UN SDGs and targets.
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Table
Table 1. Classification of advanced manufacturing technology products associated with Advanced Industrial Robotics (AIR), Additive Manufacturing (AM), and Industrial Internet of Things (IIoT) by 6-digit HS codes.
Table 1. Classification of advanced manufacturing technology products associated with Advanced Industrial Robotics (AIR), Additive Manufacturing (AM), and Industrial Internet of Things (IIoT) by 6-digit HS codes.
I4.0 CategoryHS CodeDescription
AIR
847950Industrial robots, n.e.s. in heading no. 8479
AM
846390Machine tools; for working metal, sintered metal carbides, or cermets, without removing material, n.e.s. in heading no. 8463
847720Machinery; extruding, for rubber or plastics exports
847759Machinery; for molding or forming, other than for molding or retreading pneumatic tires, or for molding or otherwise forming inner tubes
847780Machinery; for working rubber or plastics, n.e.s. in heading no. 8477
851580Welding machines and apparatus; n.e.s. in heading no. 8515, whether capable or not of cutting
IIoT
847180Units for automatic data-processing machines (excluding processing units, input or output units, and storage units)
851762Machines for the reception, conversion, and transmission or regeneration of voice, images, or other data, incl. switching and routing apparatus (excluding telephone sets, telephones for cellular networks, or for other wireless networks)
852691Radio navigational aid apparatus
852692Radio remote control apparatus
854231Electronic integrated circuits as processors and controllers, whether or not combined with memories, converters, logic circuits, amplifiers, clock and timing circuits, or other circuits
854239Electronic integrated circuits (excluding such as processors, controllers, memories, and amplifiers)
903210Regulating or controlling instruments and apparatus; automatic type, thermostats
903220Regulating or controlling instruments and apparatus; automatic, manostats
903281Regulating or controlling instruments and apparatus; automatic, hydraulic, or pneumatic
903289Regulating or controlling instruments and apparatus; automatic, other than hydraulic or pneumatic
Source: ITC Trade Map (www.trademap.org (accessed on 1 November 2022)) and World Integrated Trades Solution (https://wits.worldbank.org/ (accessed on 1 November 2022)).
Table
Table 2. Variable definitions, formulas, and data sources—dependent variables.
Table 2. Variable definitions, formulas, and data sources—dependent variables.
VariableTypeDescription/CalculationData Type, Range and PolaritySource
Goal 8 PerfDependentSDG 8 promotes sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. This is measured through adjusted GDP growth (%), victims of modern slavery (per 1000 population), adults with an account at a bank or other financial institution or with a mobile-money-service provider (% of population aged 15 or over), unemployment rate (% of total labor force, ages 15+), whether fundamental labor rights are effectively guaranteed (0–1, worst to best), fatal work-related accidents embodied in imports (per 100,000 population), employment-to-population ratio (%), and youth not in employment, education, or training (NEET) (% of population aged 15 to 29).Numerical, 0–100, a higher value indicates that the country, for the given year of reporting, was able to improve its overall performance in SDG 8.Sustainable Development Report
Goal 9 PerfDependentSDG 9 intends to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation. This is measured through the population using the internet (%), mobile broadband subscriptions (per 100 population), logistics performance index, i.e., quality of trade and transport-related infrastructure (1–5, worst to best), The Times Higher Education Universities Ranking—average score of top three universities (0–100, worst to best), articles published in academic journals (per 1000 population), expenditure on research and development (% of GDP), researchers (per 1000 employed population), triadic patent families filed (per million population), gap in internet access by income (percentage points), and female share of graduates from STEM fields at the tertiary level (%).Numerical, 0–100, a higher value indicates that the country, for the given year of reporting, was able to improve its overall performance in SDG 9.Sustainable Development Report
Goal 12 PerfDependentSDG 12 attempts to ensure sustainable consumption and production patterns. This is measured through municipal solid waste (kg/capita/day), electronic waste (kg/capita), production-based SO₂ emissions (kg/capita), SO₂ emissions embodied in imports (kg/capita), production-based nitrogen emissions (kg/capita), nitrogen emissions embodied in imports (kg/capita), exports of plastic waste (kg/capita) and non-recycled municipal solid waste (kg/capita/day).Numerical, 0–100, a higher value indicates that the country, for the given year of reporting, was able to improve its overall performance in SDG 12.Sustainable Development Report
Table
Table 3. Variable definitions, formulas, and data sources—control and independent variables.
Table 3. Variable definitions, formulas, and data sources—control and independent variables.
VariableTypeDescription/CalculationData Type, Range and PolaritySource
YearControlFiscal year for reporting economic data and lagged year for reporting SDG performance data.Numerical, 2010–2021 for economic data and 2011–2022 for the corresponding SDG performance data.-
GDP Per Capita (USD)ControlPer capita GDP in US dollars at constant prices (2015) for the given fiscal year.NumericalUNCTAD
PopulationControlAverage population of the country for the given fiscal year.NumericalUNCTAD
Tot Trade (USD Thousands)ControlTotal import and export of all products by a country for the given fiscal year, after factoring in reexports.NumericalITC Trade Map
AIR Import (USD Thousands)PredictorTotal import of products by a country for the given fiscal year under the product code classified as Advanced Industrial Robotics (AIR), as per classification in Table 1.NumericalComputed from ITC Trade Map data
AIR Export (USD Thousands)PredictorTotal export of products by a country for the given fiscal year under the product code classified as Advanced Industrial Robotics (AIR), as per classification in Table 1.NumericalComputed from ITC Trade Map data
AM Import (USD Thousands)PredictorTotal import of products by a country for the given fiscal year under the product code classified as Additive Manufacturing (AM), as per classification in Table 1.NumericalComputed from ITC Trade Map data
AM Export (USD Thousands)PredictorTotal export of products by a country for the given fiscal year under the product code classified as Additive Manufacturing (AM), as per classification in Table 1.NumericalComputed from ITC Trade Map data
IIoT Import (USD Thousands)PredictorTotal import of products by a country for the given fiscal year under the product code classified as Industrial Internet of Things (IIoT), as per classification in Table 1.NumericalComputed from ITC Trade Map data
IIoT Export (USD Thousands)PredictorTotal export of products by a country for the given fiscal year under the product code classified as Industrial Internet of Things (IIoT), as per classification in Table 1.NumericalComputed from ITC Trade Map data
RI Imp AIRPredictorRelative Intensity of Import of AIR products for a given year for a country is computed as (import of products classified as AIR by the country/total import by the country)/(total import of products classified as AIR globally/total global imports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of import of these products among countries.Computed from ITC Trade Map data
RCA Exp AIRPredictorRevealed Comparative Advantage through Export of AIR products for a given year for a country is computed as (export of products classified as AIR by the country/total export by the country)/(total export of products classified as AIR globally/total global exports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of export of these products across countries.Computed from ITC Trade Map data
RI Imp AMPredictorRelative Intensity of Import of AM products for a given year for a country is computed as (import of products classified as AM by the country/total import by the country)/(total import of products classified as AM globally/total global imports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of import of these products among countries.Computed from ITC Trade Map data
RCA Exp AMPredictorRevealed Comparative Advantage through Export of AM products for a given year for a country is computed as (export of products classified as AM by the country/total export by the country)/(total export of products classified as AM globally/total global exports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of export of these products across countries.Computed from ITC Trade Map data
RI Imp IIoTPredictorRelative Intensity of Import of IIoT products for a given year for a country is computed as (import of products classified as IIoT by the country/total import by the country)/(total import of products classified as IIoT globally/total global imports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of import of these products among countries.Computed from ITC Trade Map data
RCA Exp IIoTPredictorRevealed Comparative Advantage through Export of IIoT products for a given year for a country is computed as (export of products classified as IIoT by the country/total export by the country)/(total export of products classified as IIoT globally/total global exports).Numerical, lowest possible value 0; a higher value indicates a relatively higher level of export of these products across countries.Computed from ITC Trade Map data
Table
Table 4. Descriptive statistics of key variables.
Table 4. Descriptive statistics of key variables.
VariablesNMinimumMaximumMeanStd. Deviation
Year288420102021
GDP Per Capita229993.67053108,531.2753612,871.6035618,113.04368
Ln_Tot_Trade27070.0000024.5111916.036863.26118
Ln_Population19919.2940421.0908316.170011.67813
Goal 8 Score19910.0000091.8330065.8181613.37009
Goal 9 Score19910.0000099.0915038.6002128.04919
Goal 12 Score19910.0000098.7678683.3261115.99607
RI Imp AIR26870.0000013.111840.379680.79189
RI Imp AM26870.0000013.009750.822750.99894
RI Imp IIoT26870.000005.656940.314300.57155
RCA Exp AIR26930.0000019.577500.268041.13012
RCA Exp AM26930.0000012.403590.283960.83122
RCA Exp IIoT26930.0000016.476170.349561.15454
Valid N (listwise)1928
Table
Table 5. Bivariate correlation between variables in the regression model.
Table 5. Bivariate correlation between variables in the regression model.
VariablesGDP Per CapitaLn_Tot_TradeLn_PopulationRI Imp AIRRI Imp AMRI Imp IOTRCA Exp AIRRCA Exp AMRCA Exp IIoT
GDP Per Capita1
Ln_Tot_Trade0.4671
Ln_Population−0.1150.5961
RI Imp AIR0.3590.3750.1961
RI Imp AM−0.1960.3530.4150.1751
RI Imp IIoT0.2450.3500.2070.2510.1331
RCA Exp AIR0.4820.2250.0260.361−0.0460.1121
RCA Exp AM0.2420.2470.0730.2170.0160.1340.2741
RCA Exp IIoT0.2300.1000.0300.138−0.0330.6740.1140.1041
Table
Table 6. Panel data regression models—effect of import of I4.0 products.
Table 6. Panel data regression models—effect of import of I4.0 products.
ParameterModel 1—Dep Var: Goal 8 PerfModel 2—Dep Var: Goal 9 PerfModel 3—Dep Var: Goal 12 Perf
BStd ErrBStd ErrBStd Err
(Constant)120.26316.778279.8896.42741.4253.369
GDP Per Capita8.52 × 10−52.76 × 10−5−0.0017.18 × 10−5−2.06 × 10−55.56 × 10−6
Ln_Population−4.0441.033−16.2562.6832.4540.208
Ln_Tot_Trade0.5520.1961.5520.510.1260.039
RI Imp AIR−0.0810.089−0.1620.2310.0020.018
RI Imp AM0.1610.079 **0.1370.2040.0120.156
RI Imp IIoT0.5890.232 **0.990.601 *0.0490.047
Periods: 12, Cross-sections: 189N: 1928, Adj R2 = 0.984, F-Statistic = 564.245N: 1928, Adj R2 = 0.975, F-Statistic = 365.093N: 1928, Adj R2 = 0.999, F-Statistic = 20,567.06
** significant at the 5% level; * significant at the 10% level.
Table
Table 7. Panel data regression models—effect of export of I4.0 products.
Table 7. Panel data regression models—effect of export of I4.0 products.
ParameterModel 4—Dep Var: Goal 8 PerfModel 5—Dep Var: Goal 9 PerfModel 6—Dep Var: Goal 12 Perf
BStd ErrBStd ErrBStd Err
(Constant)128.39916.951295.56643.41441.653.372
GDP Per Capita9.36 × 10−52.78 × 10−5−0.0017.12 × 10−5−2.16 × 10−55.53 × 10−6
Ln_Population−4.0671.053−16.7242.6962.4490.209
Ln_Tot_Trade0.1140.1831.0910.4690.1190.036
RCA Exp AIR−0.0130.076−0.8610.194 ***0.0320.015 **
RCA Exp AM−0.0940.1340.2980.3420.0010.027
RCA Exp IIoT0.2690.102 ***0.530.261 **0.0070.02
Periods: 12, Cross-sections: 189N: 1929, Adj R2 = 0.983, F-Statistic = 554.099N: 1929, Adj R2 = 0.978, F-Statistic = 368.929N: 1929, Adj R2 = 0.999, F-Statistic = 20,611.30
*** Significant at the 1% level; ** significant at the 5% level.
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Das, A. The Relationship between International Trade in Industry 4.0 Products and National-Level Sustainability Performance: An Empirical Investigation. Sustainability 2023, 15, 1262. https://doi.org/10.3390/su15021262

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Das A. The Relationship between International Trade in Industry 4.0 Products and National-Level Sustainability Performance: An Empirical Investigation. Sustainability. 2023; 15(2):1262. https://doi.org/10.3390/su15021262

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Das, Arindam. 2023. "The Relationship between International Trade in Industry 4.0 Products and National-Level Sustainability Performance: An Empirical Investigation" Sustainability 15, no. 2: 1262. https://doi.org/10.3390/su15021262

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