Technology Transfer for Energy Efficiency in the Industry 4.0 Era: A Systematic Review

Introduction

It is known that energy is an essential resource for human life and its development, as well as for economic growth, especially in the industrial sector, which relies on energy to transform raw materials into products and services. In light of this, the manner in which energy is being utilized is increasingly addressed by representatives of countries, industries, and researchers, particularly concerning the environmental impacts of consumption and climate change (Seow & Rahimifard, 2011; Heideier et al. 2020).

According to the International Energy Agency (IEA, 2024), the energy sector has faced significant challenges in recent years, initially due to the Covid-19 pandemic and subsequently due to the global energy crisis triggered by Russia’s invasion of Ukraine, and of escalating risks in the Middle East and heightened geopolitical tensions globally. As highlighted in the World Energy Outlook, both events have had impacts on energy markets, leaving many producers and consumers feeling adversely affected by the volatility of fuel and electricity prices (IEA, 2024).

With the rise in investment in clean technologies and the rapid growth in electricity demand, the world is embarking on its journey towards a safer and more sustainable energy system, and what more needs to be done to achieve its climate targets. However, the world is still far from a sustainable recovery. Only with an increase in changes to how the world produces and consumes energy can the trend of rising emissions of pollutants be altered in the future. According to the IEA report, investments in clean energy are essential to stimulate global economic growth, generate jobs, and reduce emissions of pollutants, along with investments in improving energy consumption efficiency, low-emission electricity, and more sustainable fuels (IEA, 2024).

According to Cantarero (2020), in addition to post-Covid-19 recovery efforts worldwide, the solution to current climate and economic crises may lie in the rapid transition to low-carbon (sustainable and renewable) energy systems, encompassing assumptions of Energy Efficiency (EE), accessibility, reliability, and energy independence. The pursuit of EE has recently grown due to increasing industry concerns about the environmental impacts of energy consumption. In this context, there are aspects related to EE that can guide organizations toward sustainable production: the economic aspect, directly linked to energy resources and costs, including the cost of treating pollutant emissions; the environmental aspect related to pollutant emissions; and the social aspect that connects energy consumption with employability and the energy security policies of the community in which it operates (Fenerich et al. 2023).

The adoption of EE practices can help reduce the threat of global warming (Brunke, Johansson, & Thollander, 2014). According to Patterson (1996), EE involves minimizing the use of inputs (energy) while producing the same amount of products or services, with the primary function of avoiding the need for new energy sources, contributing to loss reduction, rational energy use, and increased sustainability.

In addition to economic benefits in the use of EE practices in industries, related to the reduction of energy costs, there are also social and environmental benefits (Guedes, Fenerich, & de Oliveira, 2025). Social benefits arise from EE making energy more accessible to the population, due to lower prices, as well as investments in technologies creating jobs, thereby enhancing overall economic and societal development. In terms of environmental benefits, EE enables a reduction in energy use and decreases emissions of energy-related pollutants. Moreover, organizations with lower energy consumption present a greener image, which can influence market competitiveness among environmentally and sustainability-conscious consumers (Kamal, Al-Ghamdi, & Koc, 2019).

Gahm et al. (2016) state that the pursuit of this greener image has led industries to reduce energy consumption and improve the input-output ratio (product) of a production process, resulting in improved EE. One instrument that can be used to achieve sustainability is Technology Transfer (TT), a process in which resources are transferred from one organization to another in pursuit of technological development (Silva, Kovaleski, & Pagani, 2022).

Despite energy management systems presenting sustainable practices in industries, there is still a lack of models that feature TT as a practice for the proper performance of EE. According to Hutchins, Robinson, and Dornfeld (2013), industries often face challenges in measuring the economic, environmental, and social impacts associated with their activities and processes. Therefore, the need for industries to be more sustainable has stimulated the interest of policy developers and decision-makers because, even when sustainability is included in the company’s strategy, current production methods cannot be considered sustainable, necessitating significant changes at the organizational, behavioral, technological, and managerial levels (Neri et al., 2018).

Trianni, Cagno, and Neri (2017) point out that, to improve sustainability in industries, effective measures need to be implemented, such as the application of EE practices alongside sustainable practices. A TT tool that aids in achieving sustainability through the application of EE practices in an industrial environment can guide efforts and benefit decision-making, making it more accurate, especially in the era of Industry 4.0.

The primary motivation for TT for this purpose lies in achieving sustainability through the implementation of EE practices. Furthermore, this achievement must be evaluated and examined concerning its short to long-term effects on society, the economy, and the environment (Abu-Rayash & Dincer, 2019). Therefore, this article will focus on industrial activities and will specifically address technology transfer for the implementation of EE practices, and aims to develop a systematic review using the Methodi Ordinatio methodology, looking for building the portfolio of articles on the topic, in an effort to answer: How does TT contribute to the implementation of EE practices in industrial activities, and what are the impacts of this implementation on environmental, economic, and social sustainability?

Literature Review

Methodology

The procedures described here were utilized for searching, selecting, and reading articles. For the systematic review with articles aiming to develop a bibliographic portfolio on the topic of Technology Transfer and Energy Efficiency, the Methodi Ordinatio 2.0 methodology was employed, and the steps are presented in Table 1.

To relate the themes of Technology Transfer and Energy Efficiency, a Systematic Review was conducted, resulting in a portfolio of relevant scientific articles that will serve as a data source for the topic. For the construction of the article portfolio, the Methodi Ordinatio 2.0 methodology was employed (Pagani; Kovaleski; & Resende, 2015; 2017; Pagani et al., 2023). The methodology is considered a multicriteria decision-making tool, as it ranks articles in scientific journals based on three criteria: year of publication, impact factor, and citation number, determining their relevance (Corsi; Kovaleski; & Pagani, 2021).

The first step in applying the Methodi Ordinatio 2.0 methodology was to define the research intent. Thus, in this work, the research intent is to build a bibliographic portfolio of scientific articles on the topic of Technology Transfer and Energy Efficiency. After a preliminary search, the terms to be used were also defined, along with their combinations to obtain satisfactory results. This included selecting databases with higher returns and, finally, conducting the search in the chosen databases.

During this stage, reference manager JabRef was used, and, additionally, the databases Science Direct, Scopus, and Web of Science were defined, and the keyword combinations, as well as the selected databases and the search results, are presented in Table 2. Due to the combinations: ‘“anthropotechnology” AND “energy efficiency”’ and ‘“anthropotechnology” AND (“technology transfer” OR “knowledge transfer”) AND “energy efficiency”’ returning no results, the term “anthropotechnology” was searched alone.

After conducting the search in the databases, which resulted in an initial portfolio comprising 535 articles, filtering procedures were initiated to exclude duplicate articles, those not aligned with the theme, and articles from conferences, books, and book chapters. Moreover, the InOrdinatio equation was used to define the portfolio of articles to be read, taking into account the main criteria for selecting an article related to a topic: year of publication, number of citations, and the impact factor (or journal metrics). After collecting the variables, the InOrdinatio Equation (1) was applied, resulting in an ordered portfolio of scientific articles, according to scientific relevance.

The criteria used by the equation are: IF (impact fator - a value of zero was assigned to journals with an unavailable IF value); α (alpha value, ranging from 1 to 10, to be defined by the researcher according to the importance of the novelty of the topic; for this study, the value of α was set to 10, as the topic is the subject of study in very recent articles); ResearchYear (the year the research was developed); PublishYear (the year the article was published); and Ci (the number of times the article was cited- was collected from the Google Scholar platform).

A Methodi Ordinatio 2.0 is a methodology that facilitates the researcher’s work by constructing a portfolio of ordered articles, allowing the selection and prioritization of the articles (Pagani; Kovaleski; & Resende, 2015; 2017; Pagani et al., 2023). This functionality helps reduce long portfolios to more compact ones, while still maintaining scientific influence on the topic, as it allows for the selection and prioritization of the most impactful articles.

After applying Equation (1), the articles were ranked according to their scores, and another exclusion criterion was applied: only the top 50 articles with the highest InOrdinatio value would make up the final portfolio, as the number of articles in the portfolio was high. Thus, the final portfolio, ordered by scientific relevance, was composed of 50 articles, as shown in Table 6 (Appendix). The obtained results of the filtering procedures are presented in Table 3.

The 50 articles ordered in the previous step were found in their full version, allowing the start of the final stage of systematic collection and analysis of the articles. Finally, a bibliometric analysis of the portfolio was conducted using the VOSviewer software, identifying the key authors and main keywords, along with systematic readings of the articles to understand the relationship between the topics of Technology Transfer and Energy Efficiency.

Results and Discussion

Bibliometric Analysis

After the filtering procedures, more than 90% of the articles were eliminated. The first bibliometric analysis examined the distribution of the portfolio’s articles over the years. Publications on the topics of Technology Transfer and Energy Efficiency have occurred on the same platforms over the years, but with a significant increase in 2020, showing a growth trend. The last five years accounted for 26 articles in the portfolio, representing over 50% of the total, indicates that the topic is experiencing growing scientific interest, justifying the development of this article, as shown in Figure 2.

Regarding the most influential authors, Barbara Schlomann was the only one with more than one article in the portfolio, having published two papers. The first, in 2015, presented evidence that knowledge transfer from a parent company to a subsidiary increases the diffusion of energy efficiency measures. The second article, published in 2017 (, discusses the potential for directing energy efficiency opportunities in companies through the transfer of knowledge and supporting information.

Other analysis used VOSviewer software to identify the main keywords mentioned in the portfolio, based on the frequency of their occurrence. For this, the software’s Overlay Visualization function was used, which displays the distribution of keywords over the years, highlighting the relevance of the topics, as shown in Figure 3.

Figure 3 presented shows the most frequently used keywords over the years. The words in shades of blue correspond to older articles, while the terms in green, transitioning to yellow, represent more recent publications. It is also evident that the most frequent keyword is “energy efficiency,” followed by “technology transfer,” which are the main topics of this research. Additionally, emerging themes in the field are related to “barriers,” “sustainability,” “facilitators,” and “decision-makers.

Content Analysis

After identifying the key authors and emerging topics in the portfolio articles, full readings of the texts were conducted for the content analysis phase. From this reading, it is possible to understand how Technology Transfer is applied in the implementation of Energy Efficiency in industries. Based on the content analysis, it was possible to develop a set of practices for using Technology Transfer in the implementation of Energy Efficiency in an industry, as shown in Table 4.

To synthesize the literature reviewed in Table 3, Figure 4 presents a conceptual framework in which Technology Transfer for Energy Efficiency emerges from the interaction between Green Energy, Internet of Things, Sustainability, and Decision Support Tools, while being constrained by financial, regulatory, and organizational barriers.

Figure 4 presents a conceptual framework that synthesizes the findings of the systematic review. The framework illustrates TT as a dynamic process driving EE through the integration of Green Energy solutions, Industry 4.0 and IoT technologies, Sustainability principles, and Decision Support Tools. The model explicitly incorporates structural, institutional, and human barriers, highlighting anthropotechnology as a transversal element that enables adaptation, learning, and effective assimilation of transferred technologies.

Several studies have addressed TT and Knowledge Transfer KT as strategic mechanisms supporting the implementation of EE across multiple sectors. As illustrated in the proposed conceptual framework, TT for EE emerges from the interaction among Green Energy, Internet of Things (IoT) - Industry 4.0, Sustainability, and Decision Support Tools, while being constrained by structural and institutional barriers.

In the context of green energy, Ghorbani et al. (2024) discuss best practices for the transition to renewable sources, emphasizing that effective knowledge and technology transfer is essential to expand renewable energy adoption, enhance EE, and reduce carbon emissions. Similarly, Ahmad et al. (2024) propose TT models for clean energy production in industrial environments, demonstrating how structured transfer mechanisms can directly improve energy efficiency outcomes. From an industrial perspective, Rani et al. (2024) analyze the transfer of technologies from laboratory to industrial scale using green fuels, reinforcing the role of TT in scaling EE solutions in the era of Industry 4.0.

The contribution of the Industry 4.0, IoT and smart technologies to EE is highlighted by several authors. Lai et al. (2024) demonstrate how the optimization of wireless sensor networks can be enhanced through the transfer of knowledge from previous projects, leading to improved energy performance. Huang, He, and Su (2024) focus on real-time knowledge transfer to optimize energy consumption in urban transport vehicles, aiming to increase EE in the mobility sector. Likewise, Wang, Shang, and Lei (2023) apply knowledge transfer principles to algorithmic optimization of resource allocation, contributing to improved EE and grid stability.

From a sustainability and collaboration perspective, Amaral et al. (2023) emphasize inter-university cooperation as a vector for promoting EE through knowledge exchange. Johansen and Werner (2022) also underline the importance of collaborative knowledge transfer in improving efficiency in district heating systems in Denmark. These studies reinforce the framework’s sustainability dimension, showing that long-term EE impacts depend on institutional cooperation and shared learning processes.

However, several studies also identify barriers to technology transfer, which are explicitly incorporated into the conceptual model. Decuypere et al. (2022) discuss the challenges faced by intermediaries in implementing heat pumps for residential EE, highlighting regulatory and institutional constraints. Fořt and Černý (2022) demonstrate how insufficient knowledge transfer can hinder sustainable building retrofits, proposing interdisciplinary cooperation as a solution to overcome these barriers. In a broader industrial context, Qiu et al. (2022) show that TT can enhance the technological capacity of energy companies, provided that organizational and knowledge-related obstacles are addressed.

The role of decision support tools is also strongly linked to TT for EE. Benedetti, Giordano, and Salvio (2022) focus on heat recovery in textile processes, illustrating how knowledge transfer improves EE in industrial applications. Salvia et al. (2021) present decision-support tools for EE in public buildings, emphasizing capacity building and structured knowledge transfer as critical factors for their effectiveness. Similarly, Luna-Navarro et al. (2020) discuss decision-making processes aimed at making buildings smarter and more energy efficient.

On an international scale, Ghouchani et al. (2021) analyze how TT can enhance Iran’s capacity to deploy renewable energy technologies, while Khosla et al. (2021) examine improvements in EE for air-conditioning systems in India. Durán-Romero et al. (2020) propose a circular economy model for sustainable practices, explicitly emphasizing the enabling role of TT. Finally, Wang et al. (2020) and Ghadaksaz and Saboohi (2020) highlight TT as a strategic pathway for reducing greenhouse gas (GHG) emissions while simultaneously improving EE, stressing the importance of international cooperation in deploying clean energy technologies.

An important contribution of this review lies in emphasizing anthropotechnology as a critical enabler of TT for EE. Beyond technical feasibility, the successful implementation of EE solutions depends on workers’ skills, behavioral adaptation, organizational culture, and learning processes. Anthropotechnology allows anticipating human-related barriers, facilitating the customization and assimilation of transferred technologies, and reducing resistance to change. This perspective differentiates the present review from purely engineering-oriented studies by positioning EE as a socio-technical process rather than a purely technological outcome (Kovaleski, Picinin, & Kovaleski, 2022).

Overall, the literature demonstrates that Technology and Knowledge Transfer are central enablers of EE, operating across sectors such as industry, buildings, urban mobility, and energy systems. In addition to its theoretical contributions, managerial implications may be relevant for professionals involved in implementing energy efficiency initiatives:

• Managers should invest in continuous training programs focused on energy management and digital competencies to ensure effective absorption of transferred technologies.
• Companies should adopt anthropotechnological approaches when implementing EE solutions, aligning technologies with workers’ capabilities, routines, and cultural contexts.
• Strategic partnerships with universities and research centers can accelerate technology transfer and reduce implementation risks.
• Managers should actively engage in policy advocacy to access public incentives and regulatory support for EE and clean technology adoption.

The synthesis of the literature and the proposed conceptual framework demonstrate that the success of TT processes depends not only on technological readiness, but also on managerial decisions related to organizational structure, skills development, and human adaptation. In this sense, managers play a critical role in aligning transferred technologies with operational routines, institutional contexts, and workforce capabilities. The following managerial implications translate the main findings of this review into practical recommendations to support decision-makers in facilitating effective Technology Transfer for Energy Efficiency in industrial environments.

The studies collectively reinforce the proposed conceptual framework, showing that effective TT for EE depends on the integration of technological innovation, sustainability goals, decision-support mechanisms, and collaborative networks among universities, industries, and governments, while actively addressing financial, regulatory, and cultural barriers.

Research Agenda & Future Gaps

Based on the systematic review, several research gaps were identified. While technological solutions for EE are widely explored, limited attention has been given to human adaptation, organizational learning, and anthropotechnological aspects. Table 5 summarizes current knowledge and proposes a structured research agenda to guide future investigations.

The research agenda summarized in Table 4 highlights that, although TT and EE have been extensively explored from a technological and policy-oriented perspective, several critical gaps remain. Current studies predominantly focus on the deployment of digital technologies, such as IoT-based monitoring systems and decision-support tools, as well as on regulatory mechanisms that foster energy-efficient practices (Kornarius et al., 2025). However, these contributions are often limited to short-term assessments and technical performance indicators, overlooking the long-term dynamics of human adaptation and organizational learning.

A major gap identified in the literature concerns the limited incorporation of anthropotechnology as an analytical lens in TT for EE. While technological readiness is frequently addressed, insufficient attention is given to human-centered factors, including workers’ skills, resistance to change, training processes, and cultural alignment within organizations. Future research should therefore adopt longitudinal and interdisciplinary approaches to examine how anthropotechnological factors influence the effectiveness, scalability, and sustainability of energy-efficient technologies over time.

Furthermore, there is a lack of comparative empirical studies across regions, industrial sectors, and institutional contexts, particularly in emerging and developing economies. Such studies would contribute to understanding how regulatory environments, institutional capacities, and socio-cultural conditions shape technology transfer outcomes. Integrating qualitative methods with quantitative performance metrics could offer more robust insights into the interaction between technological systems and human behavior.

Finally, future research is encouraged to develop and validate human-centered decision-support frameworks that explicitly integrate anthropotechnological principles into TT processes (Liaudat, Zukerfeld, & Terlizzi, 2025). By bridging technological innovation with social and organizational dimensions, forthcoming studies can provide more comprehensive models for advancing Energy Efficiency and supporting sustainable industrial transitions.

Conclusion

This systematic review provides a comprehensive understanding of how TT serves as a central enabler for EE within the Industry 4.0 era. A primary contribution of this revised draft is the conceptual framework which synthesizes the finding that TT drives EE through the dynamic integration of Green Energy solutions, Industry 4.0 and IoT technologies, Sustainability principles, and Decision Support Tools. This framework explicitly acknowledges that while these elements propel efficiency, the process is simultaneously constrained by financial, regulatory, and organizational barriers.

Central to overcoming these challenges is the critical role of Anthropotechnology, which is presented as a transversal element within the framework. By focusing on human factors such as behavioral adaptation, organizational culture, and learning processes, anthropotechnology allows for the anticipation of human-related barriers. This perspective is vital as it redefines the implementation of EE solutions as a socio-technical process rather than a purely engineering outcome, ensuring that transferred technologies are effectively assimilated and customized to the workforce.

Finally, the study highlights essential managerial implications for practitioners. Success in TT for EE depends not only on technological readiness but on strategic managerial decisions, including investing in continuous training programs, adopting anthropotechnological approaches to align innovations with worker capabilities, and fostering strategic partnerships with universities. Ultimately, achieving global sustainability goals will depend on the ability of decision-makers to scale these technological solutions through effective, human-centered transfer mechanismsIn summary, the results of this systematic review confirm that TT is an essential factor for the successful implementation of EE in various spheres. The continuous exchange of knowledge, innovation and good practices between stakeholders is essential to achieve global sustainability and energy efficiency goals. The future of energy policies will depend, to a large extent, on the ability to integrate and scale up technological solutions through effective transfer mechanisms.

Appendix