**1. Introduction**

Industrial energy efficiency is widely recognized as crucial means to mitigate the growing final energy consumption (by more than 25% in the 2018–2040 time span [1]), given that industry is responsible for 35% of global total final energy use [2]. Energy efficiency can also lead to other benefits, such as enhanced security of the energy production systems and a healthier and more comfortable environment [3], plus strategic advantages connected to a less volatile energy market [4], especially in countries strongly dependent on energy imports [5,6]. As discussed by [7,8], previous research has mainly focused on sector-specific energy efficiency measures (EEMs). However, the extreme heterogeneity of the industrial sectors calls for a different approach aimed at promoting specific cross-cutting technologies. Among others, the Compressed Air System (CAS) looks particularly interesting, being widely diffused as ancillary technology within many industrial processes [9] due to its cleanness, practicality, and ease of use [10]. Usually, industrial compressed air (CA) is generated by using electricity as energy source and can account for about 10% of the total electricity bill in some contexts [10]. By taking a life-cycle costs perspective on CAS, the largest portion of costs is covered by operating costs (almost 80% [11]). Therefore, improved energy efficiency in CAS by implementing EEMs (both implying both technological and behavioral changes [12]) should be abundantly cost-effective, and lead to other benefits, such as reduced scrap rates, greater capacity utilization, enhanced safety, and many others [13].

Nonetheless, despite the huge potentials for energy efficiency gains (up to 20% [11,14]) and continuous development in the field [15], EEMs are not diffused as expected, leading to the so-called energy efficiency gap [16,17], particularly critical for small and medium-sized enterprises (SMEs), which everywhere represent the vast majority of companies and are responsible for the largest share of consumption [18,19]. Previous research noted that SMEs particularly suffer from a lack of internal competences as well as standard procedures hindering EEMs adoption [20–22]. This is also confirmed by studies on barriers to energy efficiency [17,20,23], which only partially refers to costs, rather pointing the attention on the lack of awareness and specific knowledge [22–24] as well as unperfect information and irrational behavior [25], therefore suggesting that it is of primary importance to highlight the single factors driving the decision-making process over EEMs. The literature has so far identified assessment factors for EEMs (e.g., [26]); however, they are referred to other technologies other than CAS. Since different technologies are characterized by different EEMs [27], different factors should be analyzed as well.

Classifications of interventions in CAS have been proposed by literature [11,28,29]; nevertheless, a mere technical EEM description does not sufficiently pinpoint some relevant factors, such as specific implications at the operational level that, beyond energy and monetary savings, are crucial for wise decision-making, representing a major research gap. Therefore, starting from an overview of CAS (Section 2) and literature review in Section 3, we offered a novel framework encompassing the most important factors for decision-making over industrial CAS EEMs (Section 4). The framework, which includes the specific EEMs description, broadens the effects of their implementation beyond energy and economic considerations, offering a genuine and innovative contribution to the academic discussion over the impacts of EEMs on industrial operations. Further, the proposed framework also aims to effectively contribute to supporting decision-makers and policymakers in fostering the adoption of EEMs in CAS, as well as technology and service providers in tailoring their services. A validation and preliminary application of the framework was conducted in several manufacturing enterprises (Sections 5 and 6, respectively), giving valuable insights and opening further research avenues (Section 7).

#### **2. EEMs in CAS: An Overview**

Overall, CAS are usually characterized by reduced energy efficiency [10,30]. However, CAS energy efficiency can be improved through well-known EEMs, in terms of technologies and practices available in the market. Understanding the characteristics of CAS EEMs is of primary importance to shed light on the factors driving their adoption and foster their implementation.

A valuable source for the analysis of EEMs in CAS is represented by the US DOE Industrial Assessment Center (IAC) [31], which identified 16 EEMs labelled with an Assessment Recommendation Code (ARC). Such EEMs, as noted by previous literature [7,26,32], represent a broad range of activities to improve the energy efficiency of CAS, including (as summarized in Table 1):


Moreover, e fficiency in CAS may be reached following three directions: preventing energy losses, minimizing energy input, and recovering energy [33]. The IAC database covers the first two areas, however, the latter is partially lacking since the database only refers to the recovery of thermal energy. Hence, to cover the gap, an additional EEM related to the adoption of energy harvesting units was added to Table 1.

With respect to other literature addressing EEMs in CAS (e.g., Nehler [11]), the IAC has been preferred, given that Nehler [11] has clustered EEMs according to their physical local location to recognize the e ffect on the system and their interrelations, however leading to a significant overlapping, since multiple EEMs seem to target the same energy e fficiency issue. Rather, IAC classification allows assessing EEMs with an industrial decision-maker perspective. In fact, as reported in Table 1, the implementation of those EEMs should consider several additional operational issues (e.g., accessibility, location, noise) and impacts on other production resources (e.g., labor through an impact on maintenance activities and/or safety) that are important for industrial decision-makers and other literature, industrial and scientific. Interestingly, the existence of such implications seems to show the need for academic literature to more thoroughly and systematically address the factors that should be considered when adopting an EEM in CAS.


**Table 1.** Industrial Assessment Center (IAC) classification of EEMs in CAS.

