The Strategic Role of Energy Efficiency and Industry 4.0 Interventions in Manufacturing

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Introduction
Following global commitments to keep the temperature rising by 1.5 degrees above pre-industrial levels (Brecha et al., 2022), climate policies in response to warming comprise, among others, transitions in energy and industrial systems. Moving toward greener industrial systems requires institutional, financial, and industrial efforts aimed at increasing the optimal use of resources. Thus, the roles of energy efficiency (EE) and Industry 4.0 (I4.0) in the sustainability and resilience of manufacturing firms have increased significantly. At a global level, there is a discussion on the validity of policy instruments to incentivize the implementation of EE interventions (Di Foggia, 2016) and investments in I4.0 innovations (Kumar, Bhamu, & Sangwan, 2021). As such, interest in analyzing the relationships between policies that favor the increase of EE and those oriented to business development from an I4.0 perspective has increased (Javied, Bakakeu, Gessinger, & Franke, 2018). EE in the industrial sector plays a key role in improving environmental sustainability and economic performance (Tanaka, 2011), and different approaches can be used to enhance understanding of industrial EE (Palm & Thollander, 2010). Decarbonization and increasing energy efficiency are key economic challenges (Misztal, Kowalska, Fajczak-Kowalska, & Strunecky, 2021). Focusing on the European decarbonization path, EE improvements could bridge the time until low-carbon or even carbon-free technologies mature to commercial scale-their rapid development is essential (Förster et al., 2013). In addition to the desired acceleration of the development of clean technologies in the sectors that most require it, i.e., transport, building, and industry, a reference point for accelerating the transition is the role of I4.0.
Ever since the early stages of industrialization, technological leaps have led to paradigm changes known as industrial revolutions (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014). The emergence and development of I4.0 technologies have been named the fourth industrial revolution. Such improvements are rapid, providing manufacturing firms with new opportunities for digital transformations to offer products and services at more competitive costs (Stentoft, Wickstrøm, Philipsen, & Haug, 2021). Previous studies have examined the effects of I4.0 and found that there are still many knowledge gaps on the uses of I4.0-enabling technologies in manufacturing firms (Zheng, Ardolino, Bacchetti, & Perona, 2021).
In light of commitments to meet the UN 2030 agenda for sustainable development and policies aimed at mitigating climate change, the implementation of more EE interventions in buildings, tertiary, transport, and industrial sectors requires sound information regarding drivers and barriers of EE investments (Di Foggia, Beccarello, Borgarello, Bazzocchi, & Moscarelli, 2022). EE management has become an obligatory step to solve critical issues that require firms directly involving the core business of firms and, therefore, elevation in the priorities and decision-making hierarchy. The United Nations put I4.0 and sustainability in global Sustainable Development Goals 7 and 9 (Hidayatno, Destyanto, & Hulu, 2019). Whereas innovations in the context of I4.0 promote the competitiveness of the production sector by stimulating its technological and managerial growth, the spread of new technologies allows the acquisition, control, and storage of process and consumption data, leading to greater knowledge of production processes, their operation, and consumption (Maggiore et al., 2021). Despite the relevance of the relationship between I4.0 and EE, the existing literature is fragmented, and more insights are needed for steering the complementary implementation of EE and I4.0 measures (Wolniak, Saniuk, Grabowska, & Gajdzik, 2020). Strategic drift may emerge when the interlinkage between EE and I4.0 is omitted.
There are many sectors in which EE and I4.0 may bring substantial benefits (Lasi et al., 2014), and we focused on the industrial sector. This article is based on an empirical survey conducted with the collaboration of 239 manufacturing firms. The survey comprised two main sections: EE and I4.0. We analyzed drivers and barriers for EE and I4.0 on key business variables such as costs, image, reputation, market, and strategy. Sustainability, corporate social responsibility, and economic aspects were considered important due to energy efficiency measures. By contrast, image, reputation, and economic aspects were tagged as highly important outputs of I4.0 interventions.
The rest of the article is organized as follows. Section 2 contains some previous literature on EE interventions and I4.0 investments along with the description of the methodology used to run the analysis and the sample definition and analysis. Section 3 reports the results of our analyses and is divided in two subsections to focus on EE first and I4.0 then. Section 4 discusses the and compare results to extrapolate useful insights for all the interested stakeholders' convenience: scholars, managers, or policymakers alike. The conclusion section closes the article.

Background and Research Methods
One of the most important energy policy objectives is energy efficiency, which is crucial for limiting climate change and meeting decarbonization targets (Pérez-Lombard, Ortiz, & Velázquez, 2013). It is acknowledged that the paths toward decarbonization have to be complemented by energy efficiency improvements (Román-Collado & Economidou, 2021). However, energy efficiency investment decisions may remain vague despite the large potential for enhancing EE in different sectors (Cooremans & Schönenberger, 2019). Firms struggle to identify digital energy services that best suit their strategies to stay competitive and to align with energy efficiency policy targets (Goldbach, Rotaru, Reichert, Stiff, & Gölz, 2018).
To leverage the potential of EE and I4.0 interventions, an empirical analysis was performed based on a robust sample of Italian firms. We designed and ran an online survey to obtain empirical insights from firms. The survey was designed following common rules (Brace, 2004;Couper, 2008) and included both closed and open questions; 307 firms participated in the survey. The number of valid responses was 239 and the threshold we chose to consider valid answers obtained by firms was 75% of the questionnaire completed. Most of the questions did not require a mandatory response to avoid forcing any response from the firms that were willing to participate. The survey focused on manufacturing firms that represent the cohort (DeForge, 2010) of this analysis.
Slightly more than half of the sample, 52.8%, had only one factory in Italy, whereas 25% had two factories and approximately 20% had three or more factories. Regarding ownership, 76 firms belonged to a multinational group, and in 51.3% of the cases, the parent company of these groups was foreign; 63% of firms had more than 50 employees, and approximately half had more than 250 employees. The size of the firms is shown in Table 1 and the energy cost on the turnover of the samples is in Table 2. Our research method merged quantitative and qualitative analysis. We used an online questionnaire to collect the data (Couper, 2008). The survey was designed to guarantee clearness, correctness in items, order, and effectiveness of the items contained (Brace, 2004). Questions of the questionnaire were combined into different sections according to their main domains: general information, EE, and I4.0. Over the 2 months, we completed two data collection campaigns. For questions related to EE and I4.0, most of the variables were designed to get ordinal answers, typically using 1−2 or 1−5 Likert scale questions.

Energy Efficiency
Considering the EE, the most frequent EE interventions implemented over the last 10 years were those related to lighting, followed by measures aimed at electric motors and inverters and those applied to compressed air systems. On average, interventions on thermal renewables were less frequent, as well as those on cogeneration or trigeneration and pumping systems ( Figure 1). The three b problem w analyzed drive by the firms w ortance were t l drivers ident m of 3.5 to a m eed from pro shorter lead

Figure 8. Impact on consumption and costs
Next, we consider the impact of EE interventions and I4.0 on the reduction of costs. In the case of thermal consumption, we note that the EE intervention impact was slightly higher than that of I4.0; in both cases, firms with more than 250 employees considered this impact higher than did firms with fewer than 250 employees. A similar situation can be seen in electricity consumption, where the difference was even higher-the average was 3.55 in the case of EE interventions compared to 3.1 for I4.0. Considering only EE interventions in Table 5, firms with more than 250 employees considered the reduction of thermal consumption to be more significant than firms with fewer than 250 employees. Considering electrical consumption, smaller firms indicated greater savings in electricity. For I4.0 interventions, there tended to be a greater impact for firms with more than 250 employees. Regarding the impact of EE and I4.0 policies on cost reduction, Table 6 presents a cross-section based on whether the company has an energy manager. For thermal consumption, there were no large differences in the answers. For electrical consumption, the values are slightly higher in firms that do not have an energy manager. The competence of the energy manager is important in the evaluation of I4.0 interventions. Unlike for EE interventions, in all five types of costs considered, there is a higher value where there is an energy manager. impact of the intervention on the company's core business, and the second is the technological risk. An explanation is that larger firms tend to have a more complex organizational structure. Therefore, the strategic pact of a single intervention is not directly visible (as in the cases of smaller firms). The same could be said for technological risks. Larger firms have to face higher investments; therefore, they have a higher sensitivity to this type of risk, which is a more important barrier than smaller firms. The article has policy implications deriving from the fact that investments in EE-related innovations bring positive externalities from environmental and economic points of view .

Conclusions
The article provided a combined analysis of barriers and drivers of EE and I4.0 interventions. The article focused on the impacts of such measures and interventions on key business variables, specifically production costs, business image and reputation, market positioning, and strategy. The article also reported information on the role of energy managers, the size of firms, and certification. Regarding the impact on costs, energy efficiency measures were indicated as critical, whereas I4.0 mainly impacts competitiveness and production optimization variables.
The importance of barriers was also investigated. Economic feasibility ranked first in both EE and I4.0, whereas regulatory uncertainty ranked second in EE. In I4.0 the role of enabling infrastructure emerged to be particularly important. Another important result related to the drivers for I4.0 investments-the first three drivers related to production improvement; ranked first was production optimization, second was the reduction of production times, and third was production flexibility.
Considering the impact of energy efficiency and I4.0 interventions on key competitiveness drivers, noteworthy differences arose. Sustainability, corporate social responsibility, and economic aspects were considered highly important due to energy efficiency measures. By contrast, image, reputation, and economic aspects were highly important as a consequence of I4.0 interventions. This paper has both managerial and policy implications. Managers may benefit from new insights provided to compare the impact of EE and I4.0 interventions. This is useful to design data-driven strategies on sustainability and innovation. Similarly, results also help policymakers to design or fine-tune supporting policies and incentives based on insights from firms. Economic feasibility and policy uncertainty are common barriers to overcome. Future research should focus on the complementarity, overlap, and contrasting effects of measures to limit the risk of strategic drift and increase the probability of meeting sustainable development goals and work toward decarbonization.