## 1 Introduction  
Reducing carbon emissions is currently one of the most pressing trends in business, technology, innovation, and many other sectors. According to scientific studies, carbon emissions and their long-lasting impact, often persisting for hundreds of years, have noticeable effects on the environment, air temperature, overall climate, and the behavior of various complex ecosystems on our planet. Consequently, CO2 emissions leave behind a carbon footprint. Considering that a significant proportion of CO2 emissions are anthropogenic--generated by human activities due to industrialization in developed countries--it is society's responsibility to minimize the impact of industrialization on CO2 emissions. Research suggests that the effect of technological innovation on CO2 emissionsvaries depending on a country's economic development level, highlighting the need for tailored long-term policies that account for these differences [1]. However, a common issue is that developed countries, aware of the existence and causality of carbon footprints, are often not the primary sources of CO2 emissions. Nonetheless, it is necessary to regulate and monitor these emitting activities and find innovative, efficient, and sustainable solutions to reduce CO2 emissions. Various international organizations, negotiations, conferences, and treaties are actively involved, obliging countries to adopt sustainable practices. Advanced computational techniques, such as Multi-Objective Mathematical Modeling, Mixed-Integer Linear Programming (MILP), and Adaptive Fuzzy-Based Particle Swarm Optimization (AFPSO), can help minimize energy consumption, implementation costs, and demand forecast errors while maximizing sustainability. Despite having access to a wide range of technologies that significantly reduce or even negate their carbon footprint, many nations still lag in adopting sustainable innovation projects [2]. This brings us to the issue addressed by this research. Small and medium-sized enterprises (SMEs), from a macroeconomic perspective, may not individually contribute significantly to the carbon footprint, but collectively form a network generating thousands of tons of emissions that could be prevented if provided with practical and low-cost monitoring and optimization tools. Just as decentralizing specific operations in supply chains has been shown to significantly reduce carbon footprint without compromising service levels or lead time, SMEs could benefit from similarly scalable, efficiency-preserving strategies tailored to their collective impact [3]. In line with this, we consider up-to-date scientific approaches and innovative technologies that may aid in developing roadmaps to reduce climate-active gas emissions, control rising temperatures, decarbonize economies, and promote sustainable societal development. Therefore, we have developed a universal simulation and reference digital model for optimizing and managing the carbon footprint in the mechanical engineering sector using available software solutions and accumulated knowledge. Just as dynamic game models compare equilibrium solutions and optimal trajectories across variables like commission rates, our simulation framework identifies emission-reduction pathways by testing scenarios where production parameters, energy sources, and circular economy practices are systematically varied [4; 5]. An example of one of the currently employed methods for regulating and detecting CO2 emissions is the CEMS, or Continuous Emission Monitoring System. This system consists of a set of online analyzers (most commonly NDIR or TDLAS lasers) that measure the concentration of CO2 and other gases directly in the flue gas 24 h a day. From the measured concentration and known flow rate of the flue gases, the mass flow of CO2 is calculated every minute. The system complies with the EN 14181 standard [6] for large combustion sources and sectors falling under the Industrial Emissions Directive and the EU ETS.