# Sustainability Requirements of Digital Twin-Based Systems: A Meta Systematic Literature Review
## 3 Literature Review and Results
### Digital Twins and Sustainability Requirements  
In our set of selected papers, there are 4 SLR papers and 4 non-SLR papers mainly related to DTs and sustainability. [PERSON], [PERSON], and [PERSON] [3] (S3) study the definition of DT and \"state-of-the-art\" on the development of DT, including reported work on the usability of a DT for cybersecurity using the SRL methodology. Regarding incident prediction, the cases of the reported use of DTs are: intrusion detection; anomaly detection; monitoring (remote and on-site); virtual commissioning; autonomy; predictive analytics; documentation; and communication. Security is a major dimension of sustainability; for example, if equipment is dangerous its daily usage is probably impossible; their paper is an example of relevant SLR application in the field. [PERSON] et al. [10] (S5) assess the relationships between CE and I4.0 using the SLR methodology. They stress the hybrid categories like Circular I4.0 and Digital CE, but move forward to the identification of the main benefits of integrating CE and I4.0, such as production technologies, financial performance, market expansion, supply chain management, product life-cycle management, workforce empowerment, and business models. [PERSON], [PERSON], and [PERSON] [33] (S6) use a bibliometric literature review to evaluate the impact of Industry 4.0 on sustainability. They find that authors who deal with CE usually also study sustainable supply chains; nevertheless, I4.0 concepts such as sustainability, big data, smart manufacturing, IoT, sustainable development, digital transformation, and industrial IoT are frequently addressed. Cyber-physical systems, sustainable manufacturing, the smart factory, and digitalization are also popular concepts. Possibly, the main conclusion of their paper is that the positive sustainability outcome of these technologies is not guaranteed, and so, success requires supportive measures and specific policies to ensure the competitiveness of local actors. [PERSON] and [PERSON] [34] (S7) present an Industry 4.0 model for CE and cleaner production. Their model is built with mixed-integer linear programming (MILP) to optimize product machine allocation, e.g., optimizing the trade-off between energy consumption and machine processing cost. In this model, sensors are also deployed to capture real-time information in the Industry 4.0 facility. [PERSON] [41] (NS2) stresses the complexity of the new I4.0 and the new tools associated with it, for example, 3D printing, to allow the optimization of manufacturing. Because there is no production without energy (meaning electricity at the production factory level, non-renewable sources of energy still being the paramount contribution to electricity production), the next three papers (i.e., NS1, NS3, and NS4) look to the general requirement of the \"responsible use of energy\". [PERSON] et al. [8] (NS1) and [PERSON] et al. [43] (NS4) present a framework for quantifying the energy and productivity benefits of smart manufacturing technologies. Breweries are the example used to demonstrate this framework and the implementation of smart manufacturing technologies. To determine the feasibility of a set of smart manufacturing interventions, the framework uses the cost of conserving energy (CCE) as a complementary measure. The quantification and analysis of energy productivity is its focus, and a strategic analysis framework to estimate cost-effective improvements in energy efficiency and productivity, using smart manufacturing, has been developed. [PERSON], [PERSON], and [PERSON] [42] (NS3) study the effective and efficient implementation and design of DTs for energy cyber-physical systems. With the emergence of distributed energy resources (DERs), with communication and control complexities, it is fundamental to guarantee an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system.