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Article

Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry

by
Chun-Hung Cheng
1,*,
Bau-Jen Tang
2 and
Yea-Rong Cheng
3
1
Program in Electro-Optical and Materials Science, Department of Electro-Optical Engineering, National Formosa University, Huwei 632, Taiwan
2
Industrial Technology Research Institute, Chutung 310, Taiwan
3
Department of Special Education, National University of Tainan, Tainan 700, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(11), 4705; https://doi.org/10.3390/su16114705
Submission received: 27 April 2024 / Revised: 19 May 2024 / Accepted: 29 May 2024 / Published: 31 May 2024
(This article belongs to the Special Issue Sustainable Corporate Governance in Business and Management)
Browse Figure
Versions Notes

Abstract

:
Net-zero carbon reduction has become a global supply chain development trend, and the EU has established CBAM regulations. Industries that fail to effectively reduce carbon emissions will face operational challenges under these regulations. For SMEs, carbon reduction is crucial for sustainable operations. To address this challenge, governments worldwide are formulating relevant policies and investing resources to help SMEs enhance their competitiveness. In Taiwan, the metal industry has an export ratio exceeding 45%, making it a significant global production base for metal products. This study conducted a green operational transformation survey on 230 SMEs in Taiwan’s metal industry. The Taiwanese government has devised a comprehensive carbon reduction approach for the metal industry, which includes environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion as strategies and tools for promoting carbon reduction. According to this study, the aforementioned five promotion strategies have become essential tools for SMEs in their carbon reduction efforts. This study utilized a one-way ANOVA, Pearson correlation analysis, and simple regression analysis, all of which demonstrated significant correlations among these tools. These findings can serve as a reference for other partner countries, accelerating the global industry’s transition toward green operations.

1. Background of This Study

1.1. Motivation of This Study

When implementing global carbon reduction policies, there is a risk of economic disadvantages, including issues such as free-riding and carbon leakage. However, in December 2019, the European Union announced the European Green Deal (EGD) to address these challenges and achieve the goals of the Paris Agreement in the context of insufficient global cooperation and participation. The EU plans to strengthen the implementation of ESG initiatives through a carbon tax mechanism, which will also impact the global supply chain [1]. The core objective of the EGD is to achieve climate neutrality by 2050, decoupling greenhouse gas net emissions from economic growth and resource use. This plan aims to establish viable industries operating under the principles of energy efficiency and circularity while creating a fair, healthy, and environmentally friendly food system [2]. The EGD is not only a climate project but is also seen as the EU’s growth strategy, striving to transform the EU into a fair, prosperous, modern, resource-efficient, and competitive society.
The plan places sustainable development and citizens’ well-being at the heart of EU policymaking and actions, seeking to address global climate security issues in a more geopolitically strategic manner by revitalizing EU investment and competitiveness.
Overall, the European Green Deal provides a concrete framework and strategy for the EU to address the challenges of climate change. Nonetheless, there are still many challenges to overcome in order to achieve the international net-zero emissions target and to ensure the effective implementation of the policy through global cooperation.
SMEs face significant challenges in promoting a low-carbon economy under the targets and policies of achieving carbon neutrality by 2050 set by global governments. These challenges include strong demands from major brands and supply chains to reduce energy use in production processes, lower CO2 emissions, increased energy costs, and changes in environmental and social regulations. Compared to large enterprises, SMEs face significant challenges in terms of operating costs and business strategies. Large enterprises, with abundant financial resources, can more readily comply with relevant global government regulations. However, SMEs lack the resources to cope in the same way. Nonetheless, SMEs have the advantages of operational flexibility and agility. In response to the global trend of green transformation, the Taiwanese government has developed plans and blueprints to guide SMEs toward green transformation and to enhance their global market competitiveness. By following government policies and utilizing the provided resources, SMEs can meet the global green transformation regulations and better align with customer expectations.

1.2. Research Hypotheses

This study intends to compile relevant carbon reduction promotion strategies and tools through a literature review and use the collected literature as the foundation for this research. Further investigation will be conducted on the green operation promotion strategies and tools for SMEs.
Hypothesis 1.
There are differences in the impact of different SME attributes (the number of employees, revenue, and export ratio) on low-carbon preparedness (environmental facilities, digital technology introduction, process transformation, resource recycling, and energy conversion).
Hypothesis 2.
There are correlations among the five measures of low-carbon preparedness (environmental facilities, digital technology introduction, process transformation, resource recycling, and energy conversion).
Hypothesis 3.
There are correlations between environmental facilities, digital technology introduction, process transformation, and resource recycling.
Hypothesis 4.
There are correlations between environmental facilities, digital technology introduction, process transformation, and energy conversion.

2. Literature Review

2.1. Site Environment Equipment

The replacement of conventional lighting with LED lamps can reduce energy use. According to Ref. [3] CO2 emission survey, the use of LED lamps reduced building monthly electricity consumption by 1.96%. The study also pointed out that the introduction of LED lamps in middle-income buildings can lead to an overall reduction in energy use. Furthermore, Ref. [4] suggests that the use of LED lighting systems can effectively improve energy efficiency and reduce total power consumption by up to 21.9%. Taken together, these empirical results show that the application of LED lighting in buildings is significant in reducing carbon emissions and conserving energy.
The use of air-conditioning systems is one of the key factors of energy usage in buildings, so the introduction of variable-frequency air-conditioning systems can effectively reduce energy usage. Ref. [5] focused on the use of variable-frequency technology for air-conditioning systems. Ref. [6] found that when using fixed-frequency air-conditioning systems, the energy consumption is relatively high, so the introduction of variable-frequency air-conditioning can further reduce the use of energy. A study by Cai et al. [7] focuses on the high energy consumption of textile factory air-conditioning systems, and it found that the use of inverter air-conditioning systems can significantly reduce the electricity operating cost expenditure. The combination of the above studies indicates that the application of inverter technology in central air-conditioning systems can effectively realize the energy-saving and carbon-reduction objectives in a building.
According to Ref. [8] conventional air compressor systems require high starting currents during operation, which results in energy losses, and, by switching to an inverter system, significant energy savings can be achieved and power demand can be reduced, with energy consumption being reduced to less than half that of the former type. In addition, Ref. [9] found that air compressors account for a significant portion of global electricity consumption, and improving machine performance is crucial for energy conservation. Ref. [10] conducted a comprehensive analysis of industrial air compressor systems, stating that lowering the emission pressure and improving the power factor, which can be reduced, are some of the most cost-effective measures. Synthesizing the above-mentioned energy-saving benefits of air compressor inverter systems is significant. The ability to reduce energy consumption and electricity demand through such systems demonstrates the importance of air compressor inverter systems in energy conservation.
The introduction of variable-frequency pumping systems is one of the most important ways to improve the energy efficiency of water supply systems. According to Ref. [11] pumping and water loss consume a lot of energy; therefore, the safe and efficient operation of water supply systems is essential. The introduction of variable-frequency pumping minimizes operating costs, as well as energy consumption and associated CO2 emissions. In addition, Ref. [12] pointed out that pumping systems are one of the most energy-intensive systems and that the use of an inverter pumping system can reduce carbon emissions in the atmosphere while lowering energy demand. Therefore, the introduction of an inverter pumping system can effectively improve the energy efficiency of a water supply system, showing the importance of inverter pumping systems in energy conservation.
Wind turbines used as energy-saving devices in buildings have significant potential to increase thermal comfort and reduce energy consumption. Ref. [13] showed that wind turbines can reduce the cooling demand in indoor areas, increase thermal comfort, and save energy. Ref. [14] showed that wind turbines are more popular in the Asian region and are used more in mixed-mode and naturally ventilated buildings rather than air-conditioned buildings, and they showed that their use can increase the neutral temperature range, save air-conditioning energy consumption, and increase productivity. In addition, Ref. [15] showed that fans can significantly increase thermal comfort in buildings and save energy, thus reducing energy consumption. In conclusion, using wind turbines as energy-saving devices in buildings can increase thermal comfort and reduce energy consumption.
Boilers play an important role in industrial production, but they are also one of the main sources of energy consumption. Improvements to boilers are essential to increase efficiency and reduce CO2 emissions. A study by Schoeneberger et al. [16] highlights boiler electrification as an important strategy for reducing greenhouse gas emissions. Meanwhile, a study by Ref. [17] indicates that integrating boilers into energy management systems can be effective in improving energy utilization. These studies provide important insights into the in-depth understanding and application of energy-efficient boiler technologies. Boiler improvement is the key to reducing energy use and can increase boiler efficiency and reduce energy consumption, thereby reducing CO2 emissions and further realizing the goals of energy saving and emission reduction.

2.2. Digital Technology Introduction

Ref. [18] states that the introduction of Enterprise Resource Planning (ERP) systems had a significant impact on carbon reduction and energy conservation. Through ERP systems, organizations can integrate environmental and social impacts with business processes to achieve sustainability goals. The use of cloud-based ERP systems saves energy, facilitates information sharing, and effectively manages the carbon footprint, thereby reducing operating costs. Furthermore, a study by Zvezdov et al. [19], highlights the importance of ERP systems in managing the carbon footprint of a product portfolio. Through ERP systems, companies can collect, manage, and analyze carbon information more efficiently, thereby reducing carbon emissions, realizing the comprehensiveness and effectiveness of product carbon footprint management, and facilitating carbon reduction and energy conservation. Overall, ERP implementation has a significant impact on the carbon reduction and energy-saving targets of enterprises and helps to improve sustainability performance, which is in line with the requirements of modern society for the sustainable development of enterprises.
The introduction of Manufacturing Execution Systems (MESs) and Industry 4.0 smart technologies has a significant impact on carbon reduction in companies. The application of these technologies can improve production efficiency and quality while reducing energy consumption and greenhouse gas emissions, thereby effectively achieving sustainability goals. Ref. [20] suggest that, through the combination of digital tools and energy-efficient equipment, companies can effectively reduce carbon emissions while lowering their costs. Ref. [21] combined Industry 4.0 technologies to propose a methodology that can be used to reduce energy consumption in the manufacturing process, combining an overall equipment effectiveness analysis with information technology. To summarize, the introduction of MESs has a significant impact on carbon reduction in enterprises, which can effectively improve energy efficiency and achieve the goal of sustainable development.
Ref. [22] showed that through Internet of Things (IoT) technologies and digital biomass, companies can collect data from the manufacturing process and improve the functionality of their products before production by digitally monitoring and testing them. At the same time, they also highlighted the potential of digital symbiosis in reducing industrial carbon emissions by providing insights into the lifecycle of the production process. Ref. [23] pointed out the importance of a green IoT, which aims to reduce the carbon footprint, conserve resources, and promote proficient energy-using technologies, despite the energy consumption and environmental challenges associated with IoT technologies. Ref. [24] further explored how IoT technologies can realize the operational efficiency improvements and sustainable energy goals of Industry 4.0. In short, through the application of IoT technology in equipment, companies can collect data and monitor energy efficiency in production processes to achieve carbon reduction goals.
Increasing energy costs and demand are driving up the pressure on companies to reduce carbon emissions and save on energy costs. The introduction of energy management systems (EMSs) and the ISO 50001 standard [25] has become key to achieving these goals. A study by Böttcher et al. [26] showed that an EMS has a positive impact on the carbon and economic performance of manufacturing firms, particularly through the adoption of low-carbon production and logistics practices. Similarly, the findings of Ref. [27] suggest that the implementation of ISO 50001 can bring many benefits, including cost savings, productivity gains, and operational improvements. In addition, a retrospective study by Prasetya et al. [28] further examined the impact of ISO 50001 on energy management performance and described its application and impact in different industries through case studies. These findings highlight the importance of energy management systems and the ISO 50001 standard for companies to achieve their carbon reduction and energy-saving targets, and they provide empirical support for corporate decision-making and practice in energy management.

2.3. Improvement of Process and Production Technology

The use of green or low-carbon materials has a significant impact on carbon reduction, a view supported by several studies. Among them, Ref. [29] emphasize the importance of the government’s promotion of green building strategies to achieve carbon reduction targets through measures such as reducing energy use, using renewable energy, and adopting low-carbon materials. Meanwhile, a study by Ref. [30] pointed out that the adoption of lightweight metals and new materials by automobile manufacturers can reduce the weight of automobiles, which, in turn, reduces CO2 emissions. In addition, Ref. [31] mentioned that the use of new materials such as carbon fiber can significantly reduce the weight of vehicles, save fuel, and reduce carbon emissions. Finally, a study by Ref. [18] emphasized that the use of technologies such as bio-carbon sequestration can ultimately eliminate excessive carbon dioxide emissions and, thus, have a positive impact on carbon reduction.
Ref. [32] suggested that improving operational efficiency can effectively reduce energy consumption, which, in turn, protects natural resources, reduces production costs, and delays investment in infrastructure. Ref. [33] proposed a methodology for evaluating the carbon emissions of industrial motors, which can help to reduce carbon emissions, material waste, and energy consumption. Ref. [34] emphasized the importance of reducing the power consumption of machines to reduce power consumption in factories. Overall, the use of low-energy-consuming equipment has a significant and necessary impact on carbon reduction benefits.
Ref. [35] proposed an innovative control method for path planning using a digital gremlin model to achieve energy-saving and emission-reduction goals in industrial robotic arms. Their study showed that the energy consumption of industrial robotic arms can be effectively reduced by directly manipulating the digital gremlin model, thus realizing the energy-saving goal in the manufacturing process. Furthermore, Ref. [36] proposed a method to minimize energy consumption by considering robotic units as a whole. Their study utilized a hybrid heuristic approach and a multi-core processor to optimize the energy consumption of a robot cell with promising results. The work of these researchers highlights the importance of robotic arms in carbon reduction and provides concrete methods and approaches for energy saving and emission reduction in the manufacturing industry.
Ref. [37] pointed out that process carbon emissions can be reduced by eliminating manufacturing defects and improving process yield and casting quality. Ref. [38] proposed a mixed-integer linear planning model that minimizes the total production time and total energy consumption from the perspective of process planning to reduce energy consumption and carbon emissions. Ref. [39] proposed a method to quantify the carbon emissions of CNC tooling machines and to reduce carbon emissions and machining time by optimizing the machining process. Finally, Ref. [40] illustrated an implementation framework for the Lean Energy Saving and Emission Reduction (LESER) strategy and demonstrated its usefulness in real-world cases in energy-intensive enterprises. The work of these researchers highlights the importance of process improvement for carbon reduction and energy saving, providing practical solutions for the sustainable development of the manufacturing industry.
In a study by Zhang et al. [41] it was pointed out that the path planning of AGVs, as an important piece of transportation equipment in modern manufacturing systems, is crucial to reducing energy consumption. They proposed an energy-efficient AGV path planning (EAPP) model that effectively reduces the transportation distance and the corresponding energy consumption, which promotes energy saving and emission reduction in the manufacturing industry. In a study by Riazi et al. [42] the energy efficiency of AGVs was further emphasized and it was found that by optimizing the cruising speed and travel distance of the AGV system, it is possible to significantly reduce energy consumption while maintaining production efficiency. Ref. [43] stated that the AGV systems widely used in supply chain management could enhance sustainability by increasing efficiency, improving worker safety, and minimizing energy costs.
Ref. [44] emphasized the environmental characteristics of green packaging materials, which are not only harmless to human health but also have a good protective effect on the ecological environment. The use of green packaging materials can save resources and energy, and these materials can be naturally degraded or reused after disposal, thus reducing the negative impact on the environment and improving resource utilization.
Ref. [45] optimized the recycling and reuse process of plastic drums. By optimizing decision-making and distribution, their model not only maximizes the number of recycled plastic buckets but also considers the goal of minimizing carbon emissions, thus achieving waste recycling and sustainable management.
In addition, Ref. [46] analyzed the impact of green packaging from the perspectives of businesses and consumers. The results of their study show that there is an increasing interest in green packaging among academics and researchers, and a large number of publications reflecting the concern for sustainable development have been published in recent years. Through green packaging, companies can realize the greening of waste management, the circular economy, logistics, and supply chain management, thereby reducing carbon emissions and improving energy efficiency.

2.4. Resource Recycling

Ref. [47] pointed out that waste heat recycling plays an important role in improving economic efficiency, saving energy, and reducing emissions. Ref. [48] further investigated the potential of waste heat in reducing GHG emissions and proposed a corresponding environmental impact assessment and technological changes. Ref. [49] proposed a framework for waste heat energy recovery that aims to help manufacturers select appropriate recovery technologies to realize carbon reduction and energy efficiency benefits. The work of these researchers highlights the importance of waste heat recovery technologies in carbon reduction and provides concrete methods and approaches for companies to achieve sustainable development.
Water reuse is an important way to address freshwater scarcity and environmental stress. Ref. [50] developed a regional multisectoral model to explore water quantity and quality interactions between the urban, agricultural, and environmental sectors, emphasizing the feasibility of wastewater reuse. Ref. [51] indicated that industrialization and reductions in water stress can reduce water conservation and wastewater treatment costs. Ref. [11] findings suggested that improving water use efficiency can help reduce carbon dioxide emissions, providing policy recommendations accordingly. Finally, Ref. [52] proposed a water costing-based approach to incentivize water reuse projects with low energy and carbon emission targets. Together, these studies emphasize the importance of water reuse for carbon reduction and energy conservation, providing guiding policy and technical support.
Using Shanghai as an example, a study by Dong et al. [53] assessed the potential of waste recycling for energy-saving and carbon reduction, especially the recycling of scrap steel and non-ferrous metals, which contributed the most to energy-saving and emission reduction benefits. Additionally, Ref. [54] found that solid waste recycling not only saves a lot of electricity but also reduces greenhouse gas emissions; however, it is important to note that some recycling processes may have negative impacts on the environment. Ref. [55] focused on the potential contribution of waste transformation and recycling to the realization of zero-waste manufacturing, emphasizing that, through the application of innovative technologies, the environmental impact of waste can be minimized. Finally, Ref. [56] suggested the importance of waste energy recovery for the global steel industry.

2.5. Energy Conversion

Ref. [57] stated that natural gas has a higher H/C ratio and can help reduce CO2 emissions from energy systems and industrial processes. This is supported by research by Ref. [58] who found that switching to the use of natural gas can reduce system CO2 emissions in certain scenarios. Although methane leakage may partially offset the CO2 emission reduction benefits, overall, the use of natural gas reduces the global warming potential of a system. In addition, Ref. [59] proposed a chemical-free process that can be used to compress natural gas without generating additional CO2 emissions. This technology significantly reduces GHG emissions after compression, further supporting the notion that natural gas can reduce CO2 emissions in manufacturing.
According to Ref. [60] the application of biomass-based reductants in the steel production process can reduce lifecycle emissions. Similarly, Ref. [61] stated that a combination of CO2 capture and biomass utilization in the industrial sector could result in net-negative emissions. The exploration of biomass as an alternative source of energy is seen as a promising opportunity that could play a significant role in reducing CO2 emissions. A study by Ref. [62] further highlighted the potential of biomass in steel production. They showed that maximizing the use of biomass products can reduce CO2 emissions from existing steel production technologies by 43%. Ref. [63] showed that replacing a portion of coal with alternative fuels, such as biomass, can significantly reduce CO2 emissions while also having important economic benefits.
A model proposed by Franco et al. [64] analyzes the economic and environmental feasibility of implementing solar photovoltaic (PV) systems in industry through a case study of a parking lot in Brazil. They emphasize the environmental impacts avoided by the use of PV systems. A study by MacDonald et al. [65] focused on the U.S. situation, calculating the cost-optimized configuration of variable generators through weather data with a high spatial and temporal resolution and pointing out the potential of wind and solar energy in reducing CO2 emissions from the U.S. power sector. Finally, the Marchi et al. [66] study of Siena, Italy, highlighted the leading role of cities in reducing global CO2 emissions, especially through measures such as energy efficiency and transition to renewable energy sources, for example, the installation of photovoltaic panels on rooftops. Their study also highlighted the importance of renewable energy sources such as solar energy in achieving carbon neutrality targets.

2.6. Summary of Tools for Low-Carbon Initiatives

Based on the above literature review, this study compiles a list of technical items that can be used as tools for green transformation and low-carbon initiatives in SMEs, as shown in Table 1.

3. Research Design

3.1. Research Framework

This study focuses on Taiwan’s metal industry as a key indicator of decarbonization and green transformation to promote research and investigation. The conceptual framework of this study is shown in Figure 1.

3.2. Research Subjects

3.2.1. Sample Population

This study predominantly focuses on the metal industry of Taiwan’s small- and medium-sized enterprises (SMEs). According to the Ministry of Economic Affairs (MOEA) of Taiwan, the upper limit for the number of employees in SMEs is 200, and, according to the Census and Statistics Department (C&SD) of the MOEA, the number of registered companies in Taiwan’s metal industry is 23,000, 95% of which are SMEs. Additionally, the proportion of exports accounts for 45% of the overall revenue of the metal industry.

3.2.2. Sample Size

Given the validity and credibility of the complete survey, this study adopts a purposive sampling method, targeting SMEs and compiling the email responses from senior executives, with a total of 230 valid samples recovered.

3.3. Research Questionnaire

Questionnaire Development

The survey period for this study was from January 2024 to March 2024. To ensure that the respondents were representative and significant within the industry, the questionnaires were distributed to the directors and supervisors of enterprises that are members of relevant associations in Taiwan’s metal industry, totaling 520 enterprises. For a valid and reliable survey, the questionnaires were sent via email to senior managers (managers of the Management Department, Manufacturing Department, or R&D Department). A total of 230 valid samples were collected, resulting in a response rate of 44.2%.

3.4. Research Questionnaire

Questionnaire Design

Through the collection of the relevant literature, this study evaluated the technical indicators that can be used as low-carbon introduction technologies for SMEs. It also incorporated the policies and technical projects promoted by relevant Taiwanese government departments for the low-carbon and green transformation of SMEs. Additionally, five experts from related fields were invited to provide expert opinions for reference. With the assistance of these experts, a valid questionnaire was established. The contents of the questionnaire, including the company backgrounds, are shown in Table 2.
For the technical items that can be introduced by SMEs, such as “environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion”, this study used the technical items compiled in Table 1 from the literature review as exploration indicators. A five-point Likert scale was employed, consisting of the options “Strongly Agree”, “Agree”, “Neutral”, “Disagree”, and “Strongly Disagree”, scored as 5, 4, 3, 2, and 1, respectively. Higher scores indicate a higher degree of actual compliance.
A pilot questionnaire was formed based on these indicators, and a total of 23 questionnaires were collected and subjected to a Pearson correlation analysis. The results showed significant correlations for environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion. On this basis, the main study questionnaire survey was conducted.

3.5. Data Processing and Statistical Methods

After the questionnaire survey data from this study were recovered, all the valid questionnaire data were sorted and coded, and they were entered into a computer for storage and filing. The following statistical analyses were carried out by using the statistical package software SPSS for WINDOWS, version 27 7, for the research questions.

3.5.1. Dimensional ANOVA Analysis

By using business operation as the independent variable, divided into the number of employees, business revenue, and export ratio, and then by using field environment equipment, digital technology introduction, manufacturing process, production technology improvement, resource recycling, and energy conversion as dependent variables, we carried out a single-factor variance analysis. When the one-way variance analysis reached a significant difference, we used the snow fee method again for an ex-post comparison to understand the difference.

3.5.2. Dimensional Correlation Analysis

We conducted a two-by-two correlation analysis of the environmental equipment of the site, the introduction of digital technology, the improvement of manufacturing processes and production technology, the recycling of resources, and the conversion of energy to understand the correlation between the variables.

3.5.3. Dimensional Simple Regression Analysis

We used a simple regression model to analyze the correlations between environmental equipment, digital technology introduction, and the effect of process conversion on resource recycling.
We used a simple regression model for the correlations between environmental equipment, digital technology introduction, and the effect of process conversion on energy conversion.

4. Research and Discussion

4.1. ANOVA Method Analysis

The results of the analysis of variance (ANOVA) of the effect of the number of employees on the five variables shown in Table 3 indicate significant differences in environmental equipment, digital technology introduction, and process and production technology improvement. Specifically, environmental equipment (p < 0.001) shows a significant impact of different employee numbers, with the third group scoring significantly higher than the first and fourth groups. Digital technology introduction (p < 0.001) indicates that different employee numbers have a significant impact, with the second group scoring significantly higher than the third and first groups. Process and production technology improvement (p < 0.001) also shows a significant impact, with the third group scoring significantly higher than the fourth group. Resource recycling (p = 0.053) approaches significance, suggesting that the impact of different employee numbers is nearly significant. Energy conversion (p = 0.493) shows no significant impact with different employee numbers. In summary, the number of employees has a significant impact on environmental equipment, digital technology introduction, and process and production technology improvement.
The results of the analysis of variance (ANOVA) of the effect of company revenue on the five variables shown in Table 4 indicate significant differences in environmental equipment, digital technology introduction, process and production technology improvement, and resource recycling. Specifically, environmental equipment (p < 0.001) shows a significant impact on company revenue, with the second group scoring significantly higher than the third and first groups, and the fourth group scoring significantly higher than the fifth group. Digital technology introduction (p < 0.001) similarly shows a significant impact, with the second group scoring significantly higher than the third and first groups, and the fourth group scoring significantly higher than the fifth group. Process and production technology improvement (p < 0.001) also shows a significant impact on company revenue, with the second group scoring significantly higher than the third group, and the fourth group scoring significantly higher than the fifth group. Resource recycling (p < 0.01) indicates a significant impact on company revenue, with the second group scoring significantly higher than the third group, and the fourth group scoring significantly higher than the fifth group. However, energy conversion (p = 0.157) shows no significant impact on company revenue. Overall, company revenue has a significant impact on environmental equipment, digital technology introduction, process and production technology improvement, and resource recycling.
The results of the analysis of variance (ANOVA) of the effect of the proportion of export sales on the five variables shown in Table 5 indicate significant differences in environmental equipment, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion. Specifically, environmental equipment (p < 0.001) shows a significant impact on the proportion of export sales, with the second group scoring significantly higher than the third and first groups, the third group scoring significantly higher than the fourth group, and the fourth group scoring significantly higher than the fifth group. Digital technology introduction (p < 0.001) also shows a significant impact, with the second group scoring significantly higher than the third and first groups, the third group scoring significantly higher than the fourth group, and the fourth group scoring significantly higher than the fifth group. Process and production technology improvement (p < 0.001) indicates a significant impact on the proportion of export sales, with the second group scoring significantly higher than the third and first groups, the third group scoring significantly higher than the fourth group, and the fourth group scoring significantly higher than the fifth group. Resource recycling (p < 0.01) demonstrates a significant impact on the proportion of export sales, with the second group scoring significantly higher than the third and first groups, the third group scoring significantly higher than the fourth group, and the fourth group scoring significantly higher than the fifth group. Energy conversion (p < 0.01) similarly shows a significant impact, with the second group scoring significantly higher than the third and first groups, the third group scoring significantly higher than the fourth group, and the fourth group scoring significantly higher than the fifth group. Overall, the proportion of export sales has a significant impact on all five variables.

4.2. Correlation Method Analysis

The analysis of variance in Table 6 shows the correlation coefficients between environmental equipment and various other variables. The correlation coefficient between environmental equipment and digital technology introduction is 0.830 (p < 0.001), indicating a high positive correlation. Additionally, the correlation coefficient between environmental equipment and process and production technology improvement is 0.546 (p < 0.001), indicating a moderate positive correlation. Furthermore, there is a weaker positive correlation between environmental equipment and resource recycling (r = 0.255, p < 0.001) and energy conversion (r = 0.243, p < 0.01). The correlation coefficient between digital technology introduction and process and production technology improvement is 0.619 (p < 0.001), indicating a high positive correlation. There are weaker positive correlations between digital technology introduction and resource recycling (r = 0.289, p < 0.001) and energy conversion (r = 0.260, p < 0.001). The correlation coefficient between process and production technology improvement and resource recycling is 0.168 (p < 0.05), indicating a weak positive correlation, while the correlation between process and production technology improvement and energy conversion (r = 0.154, p < 0.05) is even weaker. Finally, the correlation coefficient between resource recycling and energy conversion is 0.426 (p < 0.001), indicating a moderate positive correlation.

4.3. Simple Regression Analysis Method

The simple regression analysis indicates that environmental equipment has a significant impact on resource recycling, as shown in Table 7 (R = 0.255, R2 = 0.065, adjusted R2 = 0.061). The estimated standard error is 0.476, and the model’s variance is significantly increased (ΔR2 = 0.065). The F-value is 15.853 (p < 0.01). Although the R2 value is relatively low, the model still confirms the important role of environmental equipment in promoting resource recycling.
The simple regression analysis shows that digital technology introduction has a significant impact on resource recycling, as indicated in Table 8 (R = 0.297, R2 = 0.088, adjusted R2 = 0.084). The estimated standard error is 0.47014, and the model’s variance is significantly increased (ΔR2 = 0.088). The F-value is 22.042 (p < 0.01). Although the R2 value is relatively low, the model still confirms the important role of digital technology introduction in promoting resource recycling.
The simple regression analysis shows that process and production technology improvement have a significant impact on resource recycling, as indicated in Table 9 (R = 0.168, R2 = 0.028, adjusted R2 = 0.024). The estimated standard error is 0.48531, and the model’s variance is significantly increased (ΔR2 = 0.028). The F-value is 6.646 (p < 0.01). Although the R2 value is relatively low, the model still confirms the important role of process and production technology improvement in promoting resource recycling.
The simple regression analysis indicates that environmental equipment has a significant impact on energy conversion, as shown in Table 10 (R = 0.243, R2 = 0.059, adjusted R2 = 0.055). The estimated standard error is 0.70566, and the model’s variance is significantly increased (ΔR2 = 0.059). The F-value is 14.370 (p < 0.01). Although the R2 value is relatively low, the model still confirms the important role of environmental equipment in promoting energy conversion.
The simple regression analysis shows that digital technology introduction has a significant impact on energy conversion, as indicated in Table 11 (R = 0.26, R2 = 0.068, adjusted R2 = 0.063). The estimated standard error is 0.70254, and the model’s variance is significantly increased (ΔR2 = 0.068). The F-value is 16.526 (p < 0.01). Although the R2 value is relatively low, the model still confirms the important role of digital technology introduction in promoting energy conversion.
The simple regression analysis indicates that process and production technology improvement have a significant impact on energy conversion, as shown in Table 12 (R = 0.154, R2 = 0.024, adjusted R2 = 0.020). The estimated standard error is 0.71884, and the model’s variance is significantly increased (ΔR2 = 0.024). The F-value is 5.561 (p < 0.05). Although the R2 value is relatively low, the model still confirms the important role of process and production technology improvement in promoting energy conversion.

5. Conclusions and Recommendations

5.1. Conclusions

The metal industry in Taiwan is dominated by small- and medium-sized enterprises (SMEs). The production process of metal products covers a wide range of areas, from raw material sourcing, processing, handling, manufacturing, and assembly to finished product testing, all of which require specialized technology and equipment. SMEs are often unable to cover all the manufacturing processes and, therefore, specialize in different areas, dividing labor to enhance production efficiency and product quality. For example, in the fastener industry, there are SMEs specializing in the production of components such as screws, nuts, and washers, and there are also SMEs specializing in processes such as surface treatment, plating, and heat treatment. These SMEs work together to produce complete fastener products. This study can be summarized as follows:
  • This study shows that there are significant correlations between different numbers of employees, company revenue, and the proportion of export sales with respect to environmental equipment, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion. It was observed that companies with more than 40 employees, a revenue exceeding 200 million, and an export ratio of over 40% place significantly more emphasis on various low-carbon and green transformation initiatives.
  • For SMEs to move toward low-carbon and green transformation, a gradual approach is required. It was observed that environmental equipment has a significant impact on other areas such as digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
  • In the professional division of labor situation of small- and medium-sized enterprises, the surface-treatment and heat-treatment industries should focus on resource recycling and energy conversion because these industries consist of enterprises with high energy consumption and high carbon emissions. Thus, for small- and medium-sized enterprises to maintain competitiveness in the sustainable operation of the situation, the production processes of the industrial chain ring need to move toward green transformation, where environmental equipment, the introduction of digital technology, and the manufacturing process and production processes have a high degree of relevance in improving resource recycling and energy conversion.

5.2. Recommendations

  • Most SMEs need strong support from the government and a complete set of low-carbon promotion roadmaps to effectively and gradually move toward low-carbon production and operation. However, this study shows that the Taiwan government can guide SMEs to move toward green transformation with its policy and capital investment. In the future, this study can be shared through various international exchange activities to promote the results and benefits, which can be used as a model for other international partners to learn from.
  • Sustainable management is the only way for enterprises to maintain a high degree of competitiveness; however, SMEs will move toward green transformation mainly due to the development trend of the supply chain in the global market. This crisis forces the need to move toward green transformation in order to survive, and the future proposal of the government’s relevant policies and resources to SMEs should be the top priority. After all, in Taiwan, SMEs account for 95% of the total enterprises, and they need strong support from the government to cope with business challenges. Furthermore, SMEs are also an important pillar of the economy.

Author Contributions

Conceptualization, C.-H.C.; methodology, C.-H.C. and B.-J.T.; formal analysis, C.-H.C.; investigation, B.-J.T.; data curation, Y.-R.C.; writing—review and editing, C.-H.C.; visualization, Y.-R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 16 04705 g001
Figure 1. Research Framework of This Study.
Figure 1. Research Framework of This Study.
Sustainability 16 04705 g001
Table 1. Technical items for green transformation and low-carbon initiatives in enterprises.
Table 1. Technical items for green transformation and low-carbon initiatives in enterprises.
DimensionsIndicatorsData Sources
Site Environment EquipmentEnergy-saving Indoor Lighting System (LED Fixtures)[3,4]
Replacement of Energy-Saving Air-Conditioning System (Inverter)[5,6,7]
Replacement of Energy-Saving Compression System (Inverter)[8,9,10]
Replacement of Energy-Saving Pump System (Inverter)[11,12]
Replacement of Energy-Saving Fan System (Inverter)[13,14,15]
Replacement of Energy-Saving Boiler System[16,43]
Digital Technology AdoptionImplementation of ERP System[18,19]
Implementation of MES System[20,21]
Implementation of IoT for Machinery[22,23,24]
Implementation of ISO 50001[26,27,28]
Process and Production Technology ImprovementUse of Low-Carbon or Green Materials[29,30,31]
Use of Energy-Saving Equipment (Including Replacement of Inverter Motors)[32,33,34]
Implementation of Robotic Arms[35,36]
Process Optimization (Including Reduction in Procedures)[37,38,39,40]
Use of Electric AGVs (Automated Guided Vehicles) for Transportation[41,42,43]
Use of Green Packaging Materials[44,45,46]
Resource Recycling and UtilizationImplementation of Waste Heat Recovery and Utilization[47,48,49]
Implementation of Water Recycling and Reuse[11,50,51]
Implementation of Waste Recycling and Reuse[53,54,55,56]
Energy ConversionUse of Natural Gas[57,58,59]
Use of Biomass Energy[60,61,62,63]
Installation of Rooftop Solar Photovoltaic Systems[64,65,66]
Source: compiled by this study.
Table 2. Company backgrounds.
Table 2. Company backgrounds.
Number of EmployeesRevenue (100 Million)Percentage of Export (%)
10~200~10~20
221~401~221~40
341~602~341~60
461~803~461~80
580 or more4 or more81~100
Table 3. Number of workers in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Table 3. Number of workers in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
VectorSource of VariationSSdfMSFpPost Hoc
Environmental FacilitiesNumber of workers19.35944.8409.6170.000 ***3, 4, 5 > 1
3, 4 > 2
3 > 1, 2
4 > 1, 2
Digital Technology IntroductionNumber of workers32.36848.09215.1120.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Process and Production Technology ImprovementNumber of workers12.98743.2478.3500.000 ***3, 4, 5 > 2
Resource RecyclingNumber of workers2.23940.5602.3750.053
Energy ConversionNumber of workers1.80440.4510.8530.493 *
N = 230. *, p < 0.05; ***, p < 0.001.
Table 4. Revenue in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Table 4. Revenue in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
VectorSource of Variation SSdfMSFpPost Hoc
Environmental FacilitiesRevenue58.568414.64244.5060.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Digital Technology IntroductionRevenue75.928418.98255.5260.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Process and Production Technology ImprovementRevenue28.16647.04121.9120.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Resource RecyclingRevenue4.66041.1655.1800.001 **3, 4, 5 > 1
Energy ConversionRevenue3.48440.8711.6720.157 *
N = 230. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Table 5. Percentage of export in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Table 5. Percentage of export in ANOVA of environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Vector Source of Variation SSdfMSFpPost hoc
Environmental FacilitiesPercentage of Export61.861415.46549.1980.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Digital Technology IntroductionPercentage of Export76.588419.14756.4920.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
2 > 1
Process and Production Technology ImprovementPercentage of Export27.15146.78820.8310.000 ***2, 3, 4, 5 > 1
3, 4, 5 > 2
Resource RecyclingPercentage of Export7.99241.9989.5090.001 **3, 4 > 1
3, 4, 5 > 2
5 > 1
Energy ConversionPercentage of Export8.91142.2284.4840.002 **3, 4, 5 > 1
N = 230. **, p < 0.01; ***, p < 0.001.
Table 6. Cumulative differences in environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Table 6. Cumulative differences in environmental facilities, digital technology introduction, process and production technology improvement, resource recycling, and energy conversion.
Related
Factor		Environmental FacilitiesDigital Technology IntroductionProcess and Production Technology ImprovementResource RecyclingEnergy Conversion
1. Environmental Facilities1-
2. Digital Technology Introduction0.830 ***1
3. Process and Production Technology Improvement0.546 ***0.619 ***1
4. Resource Recycling0.255 ***0.297 ***0.168 *1
5. Energy Conversion0.243 ***0.260 **0.154 *0.426 ***1
N = 230. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Table 7. Summary of simple regression analysis of role of environmental equipment in promoting resource recycling.
Table 7. Summary of simple regression analysis of role of environmental equipment in promoting resource recycling.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.2550.0610.4760.06515.85312280.000 ***
Explanation of variables: (regression constant) environmental facilities. ***, p < 0.01.
Table 8. Summary of simple regression analysis of role of digital technology introduction in promoting resource recycling.
Table 8. Summary of simple regression analysis of role of digital technology introduction in promoting resource recycling.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.2970.0840.470140.08822.04212280.000 ***
Explanation of variables: (regression constant) digital technology introduction. ***, p < 0.01.
Table 9. Summary of simple regression analysis of role of process and production technology improvement in promoting resource recycling.
Table 9. Summary of simple regression analysis of role of process and production technology improvement in promoting resource recycling.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.1680.0240.485310.0286.64612280.000 ***
Explanation of variables: (regression constant) process and production technology improvement. ***, p < 0.01.
Table 10. Summary of simple regression analysis of role of environmental equipment in promoting energy conversion.
Table 10. Summary of simple regression analysis of role of environmental equipment in promoting energy conversion.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.2430.0550.705660.05914.37012280.000 ***
Explanation of variables: (regression constant) environmental equipment. ***, p < 0.01.
Table 11. Summary of simple regression analysis of role of digital technology introduction in promoting energy conversion.
Table 11. Summary of simple regression analysis of role of digital technology introduction in promoting energy conversion.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.260.0630.702540.06816.52612280.000 ***
Explanation of variables: (regression constant) digital technology introduction. ***, p < 0.01.
Table 12. Summary of simple regression analysis of role of process and production technology improvement in promoting energy conversion.
Table 12. Summary of simple regression analysis of role of process and production technology improvement in promoting energy conversion.
RAdj R2Standard Errors of EstimateChange Statistics
ΔR2ΔFdf1df2F
0.1540.0200.718840.0245.56112280.019
Explanation of variables: (regression constant) process and production technology improvement.
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Cheng, C.-H.; Tang, B.-J.; Cheng, Y.-R. Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry. Sustainability 2024, 16, 4705. https://doi.org/10.3390/su16114705

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Cheng C-H, Tang B-J, Cheng Y-R. Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry. Sustainability. 2024; 16(11):4705. https://doi.org/10.3390/su16114705

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Cheng, Chun-Hung, Bau-Jen Tang, and Yea-Rong Cheng. 2024. "Strategies and Tools for Small- and Medium-Sized Enterprises (SMEs) to Move toward Green Operations: The Case of the Taiwan Metal Industry" Sustainability 16, no. 11: 4705. https://doi.org/10.3390/su16114705

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