https://doi-002.org/1018/17587832025885
Meiyou Dong1,2, Wenbo Xie3,*.
1, School of Management, Wuhan University of Technology,
2, Changsha social work College
3, Xianning Vocational and Technical College
First author: Meiyou Dong, dongmy2021@163.com
Corresponding author: Wenbo Xie, xiewenbo5188@163.com
Abstract
Carbon neutrality is the new imperative around the globe, and the Dual-Carbon Policy in China a significant driver transforming its manufacturing industry. This paper explores the drive Mechanisms, via which the Dual-Carbon Policy promotes green transition in manufacturing firms. We applied a mixed-method research design and gathered survey data of 317 Chinese manufacturing companies and held 15 expert interviews. Structural Equation Modeling (SEM) was used to analyze the data and study the links among the Regulatory Pressure, Market Incentives, Technological Innovation, Dynamic Capabilities, and Green Transformation Performance. Findings indicate that Regulatory Pressure is the overriding force but Market Incentives play a significant role in enhancing the effect. They discovered that all these effects were mediated by Technological Innovation and Dynamic Capabilities, pointing to the fact that firms need to develop internal competency and the ability to innovate to realize sustainable transformation. The results contribute to the current knowledge of the compatibility of external policy signals and internal organizational aspects to determine green outcomes and offer useful information to policymakers and management aiming to facilitate industrial decarbonization within the scope of the Dual-Carbon Policy regime.
Keywords
Dual-Carbon Policy; Green Transformation; Manufacturing Enterprises; Technological Innovation; Dynamic Capabilities; China; Structural Equation Modeling; Industrial Decarbonization.
Introduction
Growing perils of climate change and environmental degradation have instigated countries all over the world to set ambitious decarbonization goals (IPCC, 2023; UNFCCC, 2022). Of such initiatives, the Dual-Carbon Policy recently proposed in China to peak carbon emissions prior to 2030 and reach carbon neutrality by 2060 is a revolutionary agenda that has significant implications across industrial segments (Liu et al., 2022; Zhang et al., 2023a). Being the largest carbon emitter and the manufacturing powerhouse in the world, China will substantially rely on its decarbonization efforts in the manufacturing sector to achieve its climate targets (IEA, 2023; Wang et al., 2022). With about 28 percent of the total CO 2 emissions in China (IEA, 2023), manufacturing enterprises are facing growing pressure to implement a profound green transformation to achieve the national climate targets (Xie et al., 2021).
Green transformation in manufacturing involves profound structural modification in manufacturing processes, supply chain management, product design, and organization culture, and is all done with an aim to promote resource efficiency and reduction of environmental harm (Porter & van der Linde, 1995; Fan et al., 2021; Seuring & Müller, 2008). This change is a regulatory requirement and a strategic possibility of companies to acquire a competitive advantage in a new low-carbon economy (Chen et al., 2022; Luo & Zhang, 2023). Nevertheless, the channels through which the Dual-Carbon Policy affects the green transformation of manufacturing enterprises are still complicated and multilayered and involve the interaction of regulatory pressure, market incentive, technological innovation, and organizational capacity (Li et al., 2022; Zhu & Sarkis, 2004; Teece, 2007).
This draws on an emerging literature consistently showing the importance of regulations systems in spurring corporate environmental performance (Delmas & Toffel, 2008; Wang et al., 2022). Mandatory reporting requirements, command-and-control regulations, and carbon pricing mechanisms provide an incentive to firms to become greener (Kuo et al., 2022). However, the literature indicates that regulatory pressure cannot exclusively result in long-term change without the involvement of market-based instruments, including green finance, carbon markets, and demand of sustainable products by consumers (Liu et al., 2023; Zhang et al., 2021).
The technological innovations are crucial facilitators of the green transformation. Low-carbon technologies, energy efficiency and cleaner production are the key areas of investment that enable manufacturing companies to pursue environmental objectives and economic performance simultaneously (Porter & van der Linde, 1995; Horbach et al., 2012). Based on empirical research, it is implied that an organisation that is more innovative is more likely to respond to the strict environmental policies and seize new market potential (Rennings, 2000; Zhang et al., 2023b). This process should be hastened by the Dual-Carbon Policy, which exerts an innovation pressure and a system of incentives to adopt green technologies (Zhu & Sarkis, 2004; Fan et al., 2021).
In addition, firm-specific aspects like dynamic capabilities, which comprise the capacity to sense opportunities, grasp these opportunities, and change operations, are important in mediating the effect of external policy signals (Teece, 2007; Eisenhardt & Martin, 2000). Companies possessing high dynamic capabilities have more opportunities to convert policy demands into strategic motion and competitive gain (Li et al., 2022; Chen et al., 2022). By contrast, companies without these abilities might not be able to carry out sophisticated green transformation projects successfully (Zhang et al., 2021).
In spite of these realizations, the extant literature tends to be disjointed, with studies examining one aspect of dimensions, e.g., regulation, technology, or market dynamics, but not systematizing our understanding of how these forces interact within the framework of the Dual-Carbon Policy (Wang et al., 2022; Liu et al., 2023). Additionally, a large portion of the existing literature focuses on developed economies, and there is scarce empirical evidence applied to the Chinese institutional and industrial setting (Zhang et al., 2023a; Fan et al., 2021).
This paper aims to fill these gaps by providing a multidimensional analytical framework to observe how the Dual-Carbon Policy works to achieve green transformation in Chinese manufacturing businesses. Namely, it examines the effects of regulatory pressure and market incentives on the green transformation performance and how the associated effects are mediated by technological innovation and organizational dynamic capabilities (Chen et al., 2022; Teece, 2007). Also embedded in the study are the contributions of institutional theory (Scott, 2001) and the Porter Hypothesis (Porter & van der Linde, 1995) to provide context to the findings of the study.
This study provides a significant survey of Chinese manufacturers and supplements it with semi-structured interviews, thus contributing to the emerging body of knowledge on industrial decarbonization and corporate sustainability (IEA, 2023; Horbach et al., 2012). It can provide theoretical knowledge to academics as well as policymakers and managerial implications to support a resilient and inclusive green transition in the Chinese manufacturing industry (Luo & Zhang, 2023; Xie et al., 2021).
Literature Review
Greening of manufacturing businesses has become an important research topic in the context of the global challenge to counteract climate change and achieve sustainable industrial innovation (Farla et al., 2012; Geels, 2011). In this general area researchers have studied various dimensions, including policy drivers and technological enablers; organizational strategies and market dynamics, which determine how corporations react to the low-carbon transition (Boons & Luedeke-Freund, 2013; Rennings & Rammer, 2011). The mechanisms of impact identified are especially crucial in the discussion of the Dual-Carbon Policy in China, where it is anticipated that the policy will cause systemic shifts throughout the manufacturing industry (Mi et al., 2018). The literature review is a synthesis of current knowledge on four major dimensions including policy influences, market-based drivers, technological innovation, and organizational capabilities in facilitating green transformation.
Policy Influences on Green Transformation
Policy interventions fill a central role to influence corporate environmental strategies. According to the ecological modernization theory, properly aimed environmental policies can become an innovative force and enhance competitiveness (Mol & Sonnenfeld, 2000). This has been supported by the empirical research that finds a very strong effect of command-and-control regulations, including emission limitations and mandatory reporting, on the use of cleaner production methods by firms (Frondel et al., 2007; Testa et al., 2011). The effectiveness of such regulations is enhanced by the fact that in the Chinese setting, the government is centralized and policies are implemented top-down (Qi et al., 2008). Indicatively, research demonstrates that the provincial differences in environmental enforcement are associated with variations in the green performance of manufacturing companies (Wang & Wei, 2016).
These effects can be moderated, however, by policy uncertainty. Companies tend to postpone green investments under the condition of unclear future regulatory paths (Popp et al., 2010). The credibility and stability of the Dual-Carbon Policy is, therefore, of critical importance in achieving long-term corporate commitment (Zhou et al., 2019). Recent commentaries indicate that transparent sectoral targets and schedules within the policy can substantially improve the readiness of firms to undergo deep decarbonization (Li & Du, 2021).
Market-Based Drivers and Incentives
In addition to direct regulation, market-based instruments have become prominent as flexible mechanisms to bring about corporate greening, in particular carbon pricing, emissions trading systems (ETS), and green finance (Calel & Dechezlepretre, 2016; Martin et al., 2014). The carbon market literature demonstrates that involvement in ETS may encourage companies to seek cost-efficient abatement opportunities and invest in energy-efficient equipment (Anderson & Di Maria, 2011). In China, the national carbon market was launched in 2021 and initial indications are that it is having a positive influence on the energy intensity of large emitter entities (Chen et al., 2021).
Green financial instruments, including green bonds and green credit policies, complementary to carbon pricing reduce the cost of capital of environmentally friendly projects (Flammer, 2021). Research shows that availability of green finance improves the ability of firms to conduct capital-intensive green innovations (Tang & Zhang, 2020). In addition, shareholder activism, especially by institutional investors with an environmental, social, and governance (ESG) focus, has become an increasingly important force behind corporate sustainability policies (Dyck et al., 2019; Krüger, 2015).
Technological Innovation and Green Capabilities
The connection between technological innovation and green transformation is not new. Eco-innovation, i.e. innovation with a reduced environmental impact, can help firms to comply with the new standards, simultaneously increasing resource productivity (Rennings, 2000). European and Asian studies invariably show a positive correlation between the stringency of environmental policy and the intensity of eco-innovation (Costantini & Mazzanti, 2012; Horbach et al., 2013). The Porter Hypothesis also assumes that properly structured environmental policies may unleash innovation offsets, improving environmental and economic performance (Ambec et al., 2013).
Energy efficiency, low-carbon materials, renewable energy integration, and circular economy solutions are highlighted as the most relevant areas of technological innovation in manufacturing industries (De Jesus & Mendonca, 2018). As another example, IoT, AI, and blockchain digital technologies are becoming common to optimize resource flows and minimize carbon footprints (Bag et al., 2021). The supplier collaboration and open innovation are also noted as aspects that enhance the diffusion of green technologies through industrial networks (Cheng et al., 2014).
Nevertheless, eco-innovation barriers are still high especially with the SMEs (Demirel & Kesidou, 2011). Small firms are usually hindered in their innovation activities by financial constraints, lack of technical know-how as well as unpredictable market demand (Horbach, 2008). Ensuring and overcoming these obstacles with the help of specific policy support and collaboration with the industry is thus a key to realising widespread green change (Marin et al., 2015).
Organizational Capabilities and Strategic Responses
The capability of firms to react to environmental policy and market signs is rooted in their internal organizational capability. Dynamic capabilities framework (Teece, 2007) is cited in the literature to underline the relevance of sensing, seizing, and transforming capabilities in overcoming the uncertainties of green transformation (Albort-Morant et al., 2016). Research indicates that companies blessed with dynamic capabilities can better recognize green business prospects and adjust their operations in such a direction (Lin & Ho, 2011).
Institutional tools ensuring sustainability in corporate routine include environmental management systems (EMS) like the ISO 14001 certification (Darnall et al., 2008). Meta-analyses reveal that the implementation of EMS is associated with the enhanced environmental performance and positive innovational outcomes (Testa et al., 2014). Also, the commitment of top management and corporate culture are considered crucial in motivating proactive environmental strategies (Sharma & Vredenburg, 1998). The role of leadership vision and strategic alignment is especially relevant when it comes to marshalling organization resources towards long-term green transformation (González-Benito & González-Benito, 2006).
The worth of stakeholder engagement is also emphasized in recent research. Companies which actively engage regulators, NGOs and community organizations are much more likely to come up with stronger and socially acceptable green strategies (Delmas & Toffel, 2004; Bansal, 2005). Government-enterprise partnerships and industry alliances are becoming an important driver of collaborative innovation to support decarbonization in China (He et al., 2021).
Gaps and Research Opportunities
Although the literature is informative, there are a number of gaps. First, existing literature mostly concentrates on single aspects or factors such as policy, market, technology, or organization without providing a comprehensive view on the relationship and interaction of these aspects in determining the pathways of green transformation (Wagner, 2007). Second, a significant body of literature relies on the experiences of OECD countries; the institutional specifics of the Dual-Carbon Policy Chinese context would benefit from additional empirical research (Geels et al., 2017).
In addition, there is a relative paucity of longitudinal studies that follow the transformation of firms over time (Johnstone et al., 2012). A major future research direction will be to understand the process through which manufacturing enterprises build up cumulative capabilities and flexible strategies in response to changing policy regimes. Last but not least, the importance of digitalization and industry 4.0 technologies in facilitating the green transformation requires more precise attention (Rojko, 2017).
By offering a multidimensional approach to the issue, this research aims to fill these gaps and offer an insight into the roles of the Dual-Carbon Policy of China in facilitating green transformation among manufacturing enterprises through the lenses of external pressure and internal capacity.
Methodology
Research Design
However, in terms of research design, this paper uses a mixed-methods research design to systematically understand the mechanism of the impact of the Dual-Carbon Policy on the green transformation of manufacturing enterprises. The mixed-methods approach is especially appropriate in this study, since it will be possible to quantitatively test the hypothesized relationships and qualitatively examine the contextual factors that drive green transformation (Creswell & Plano Clark, 2017). The quantitative analysis using surveys and in-depth qualitative interviews complement each other in strengthening and enriching the results, and they are capable of giving an overall picture of how manufacturing enterprises react to the Dual-Carbon Policy in practice.
Data Collection
Primary data used in the current study were gathered in two ways: (1) a massive survey of manufacturing businesses in China, and (2) semi-structured interviews with corporate managers and industry specialists.
In the quantitative aspect, an ordered online questionnaire was sent out between January 2025 and April 2025 to manufacturers in various provinces in China. The survey was focused on senior managers (e.g., general managers, sustainability managers, and operations managers) because they are very familiar with the environmental strategies and transformation efforts of their firms. The original sampling frame was composed of 500 firms, which were stratified random sampled to represent various sub-sectors (electronics, automotive, chemicals, machinery and textiles) and firm sizes (large enterprises, medium-sized firms and SMEs). In the end, 317 valid replies were obtained representing a response rate of 63.4%. This sample is larger than the minimum recommended sample size in structural equation modeling (SEM) and it makes the results generalizable.
On the qualitative aspect, 15 in-depth interviews targeting key stakeholders who were the leaders of industry associations, senior policymakers, and academic experts as well as managers of leading manufacturing enterprises actively pursuing green transformation were conducted. The interviewees have been chosen due to their professional knowledge and experience in relation to the Dual-Carbon Policy of China and decarbonization efforts of the manufacturing industry. The interviews lasted 45 to 90 minutes and were organized either in person or through video conferencing. With the permission of the participants, all the interviews were recorded and transcribed verbatim.
Measurement of Variables
The quantitative survey was developed so that it could measure the key constructs of the proposed conceptual model. Such constructs are Regulatory Pressure, Market Incentives, Technological Innovation, Dynamic Capabilities, and Green Transformation Performance.
All of the constructs were operationalized with multi-item scales based on proven instruments in the literature (Hair et al., 2019). As an example, a four-item scale based on Testa et al. (2011) was used to measure Regulatory Pressure, or the degree to which firms feel that there are severe environmental regulations and enforcement in place under the Dual-Carbon Policy. A three-item scale based on access to carbon trading markets, green finance, and government subsidies was used to evaluate Market Incentives (Flammer, 2021). Technological Innovation was assessed using a five item scale that reflects investments by firms in low-carbon technologies, process innovations, and the development of eco-products (Horbach et al., 2012). A six-item scale rated Dynamic Capabilities according to the studies of Teece (2007) and Albort-Morant et al. (2016), sensing, seizing, and transforming capabilities. Lastly, a five-item scale measuring Green Transformation Performance was used to evaluate the outcome of energy efficiency, emissions reduction, circularity practices, and environmental performance outcomes (González-Benito & González-Benito, 2006).
Each item was measured using a five-point Likert-scale (1 = strongly disagree to 5 = strongly agree). Before administering the survey instrument to a large scale, a pilot test involving 25 managers was carried out to give clarity, relevance, and reliability of the survey instrument.
Data Analysis Techniques
The Structural Equation Modeling (SEM) in AMOS 26.0 was applied to analyze quantitative data. SEM was selected because it allows estimating multiple relations between latent constructs simultaneously and testing mediating effects (Byrne, 2016). Before the SEM analysis, the data were checked on missing values, outliers, and normality. Cronbach alpha, Composite Reliability (CR) and Average Variance Extracted (AVE) were used to determine the reliability and the validity of the measurement model. Fornell-Larcker criterion was used to measure discriminant validity.
The model fit was evaluated based on a number of goodness-of-fit indices, such as the chi-square/degrees of freedom ratio ( 2 /df ), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Conventional cut-offs were used: CFI and TLI values beyond 0.90, RMSEA less than 0.08, and 2uchi/df less than 3.0 show an adequate model fit (Hu & Bentler, 1999).
Thematic analysis of the qualitative data collected in interviews was employed (Braun & Clarke, 2006). NVivo 12 software was applied to code transcripts inductively to determine common themes concerning strategic response of the firms to the Dual-Carbon Policy, impediments to green transformation, and facilitating factors. The quantitative results were triangulated and put into context with the qualitative insights, providing a more detailed view of the multifaceted nature of dynamics that occur.
Ethical Considerations
The research was conducted under the strict ethical consideration to safeguard the rights and privacy of the participants. All survey participants and interview participants provided informed consent. Participation was voluntary, and the respondents were guaranteed the anonymity and confidentiality of their answers. The Institutional Review Board (IRB) of the university where the first author is based approved the research protocol and agreed with the decision to publish the research.
Limitations
Although the study design is mixed-methods, which makes it robust, there are some limitations. The survey is cross-sectional, which restricts the possibility to make causal inferences across time. Moreover, there is the possibility of social desirability bias on self-reported data. Longitudinal designs and objective performance data would provide value to future research and help to further confirm the results.
Results
Sample Characteristics
The survey obtained valid responses amongst 317 manufacturing enterprises spread across various sub-sectors and regions in China. The descriptive profile of the sample is provided in Table 1. Such characteristics are visually represented in Figure 1. Electronics (29%), Automotive (21.1%), Chemicals (18%), Machinery (14.8%) and Textiles & Others (17%) comprised the largest sectors represented. Regarding the size of the firm, 41.9 percent of respondents worked in large firms (>500 employees), 36 percent in medium-sized firms (100 500 employees), and 22.1 percent in SMEs (<100 employees). In terms of ownership, they included 57.7 percent of private enterprises, 25.9 percent state-owned, and 16.4 percent joint ventures or foreign-invested. Remarkably, 63.1 percent of the sampled companies indicated that they had already started preparing green transformation projects following the Dual-Carbon Policy. As set out in Figure 1, there is a clear indication that most firms are actively responding to green transformation, although differences by sector and size category are still pronounced.
Table 1: Sample Characteristics of Responding Firms (N = 317)
| Variable | Category | Frequency | Percentage (%) |
| Sector | Electronics | 92 | 29.0 |
| Automotive | 67 | 21.1 | |
| Chemicals | 57 | 18.0 | |
| Machinery | 47 | 14.8 | |
| Textiles & Others | 54 | 17.0 | |
| Firm Size (employees) | < 100 | 70 | 22.1 |
| 100–500 | 114 | 36.0 | |
| > 500 | 133 | 41.9 | |
| Ownership Type | Private | 183 | 57.7 |
| State-Owned | 82 | 25.9 | |
| Joint Venture / Foreign-Invested | 52 | 16.4 | |
| Green Transformation | Initiated | 200 | 63.1 |
| Not Initiated | 117 | 36.9 |
Figure 1 Green Transformation Initiation Status
Measurement Model Evaluation
Measurement model was initially tested to determine reliability and validity. Descriptive statistics and inter-construct correlations are presented in Table 2. Such relationships are visually confirmed with the help of the correlation heatmap presented in Figure 2. All the constructs were found to be positively and significantly related to Green Transformation Performance (GTP). Specifically, Regulatory Pressure was correlated with GTP at 0.59, Technological Innovation at 0.63, and Dynamic Capabilities at 0.55 indicating a strong correlation with the predicted model.
The detailed loading of items based on each latent construct is provided in Table 3 to Table 7, and the visual summary of these results can be found in Figures 3 to 7. The items related to Regulatory Pressure (Table 3; Figure 3) showed high loadings (0.79 to 0.85), which implies that companies view regulatory signals within the Dual-Carbon Policy as loud and evident. The greatest loading (0.85) was on perceptions of severity of penalty on non-compliance.
Table 2: Descriptive Statistics and Correlations of Main Constructs
| Construct | Mean | SD | 1 | 2 | 3 | 4 | 5 |
| 1. Regulatory Pressure | 3.92 | 0.78 | 1.00 | ||||
| 2. Market Incentives | 3.71 | 0.84 | 0.41** | 1.00 | |||
| 3. Technological Innovation | 3.85 | 0.73 | 0.52** | 0.45** | 1.00 | ||
| 4. Dynamic Capabilities | 3.79 | 0.75 | 0.44** | 0.40** | 0.53** | 1.00 | |
| 5. Green Transformation Performance | 3.88 | 0.76 | 0.59** | 0.49** | 0.63** | 0.55** | 1.00 |
Note: p < 0.01
Figure 2: Correlation Heatmap of Main Constructs
Items related to Market Incentives (Table 4; Figure 4) also featured good loadings (0.760.81), with the participation in carbon trading and access to green finance standing out as especially relevant incentives. This is in accordance with the market-based mechanisms that the policy presents.
Table 3: Items and Loadings for Regulatory Pressure
| Item Label | Item Description | Factor Loading | Cronbach’s Alpha |
| RP1 | The firm faces strict carbon emission regulations. | 0.84 | |
| RP2 | The firm is frequently audited for compliance with carbon targets. | 0.79 | |
| RP3 | Government provides clear targets and guidelines for emission reduction. | 0.81 | |
| RP4 | Penalties for non-compliance are substantial. | 0.85 | 0.85 |
Figure 3: Factor Loadings – Regulatory Pressure Items
Table 4: Items and Loadings for Market Incentives
| Item Label | Item Description | Factor Loading | Cronbach’s Alpha |
| MI1 | The firm can participate in a carbon trading system. | 0.81 | |
| MI2 | The firm has received green financing or subsidies. | 0.76 | |
| MI3 | Market demand for green products is growing. | 0.79 | 0.82 |
Figure 4: Radial Chart – Market Incentives Items
Technological Innovation (Table 5; Figure 5) had very high item loadings (0.85–0.91), which highlights the core role of low-carbon technology investments and collective innovation in the green transformation. Process innovations to achieve emission reductions had the highest loading (0.91).
Table 5: Items and Loadings for Technological Innovation
| Item Label | Item Description | Factor Loading | Cronbach’s Alpha |
| TI1 | The firm invests in low-carbon technology R&D. | 0.89 | |
| TI2 | The firm implements advanced process innovations to reduce emissions. | 0.91 | |
| TI3 | The firm develops new eco-friendly products. | 0.88 | |
| TI4 | The firm adopts digital technologies for resource optimization. | 0.85 | |
| TI5 | The firm engages in collaborative green innovation with suppliers. | 0.86 | 0.91 |
Figure 5: Radial Chart – Technological Innovation Items
Dynamic Capabilities (Table 6; Figure 6) displayed stable item loadings (0.790.85), with particular prominence given to cross-functional collaboration and engagement of external stakeholders. This puts an emphasis on organizational agility and the ability to learn.
Table 6: Items and Loadings for Dynamic Capabilities
| Item Label | Item Description | Factor Loading | Cronbach’s Alpha |
| DC1 | The firm monitors new green technologies in the market. | 0.81 | |
| DC2 | The firm rapidly reallocates resources to green initiatives. | 0.84 | |
| DC3 | The firm continuously updates its sustainability strategies. | 0.83 | |
| DC4 | The firm learns from green initiatives of competitors. | 0.79 | |
| DC5 | The firm encourages cross-functional collaboration for green transformation. | 0.82 | |
| DC6 | The firm maintains strong relationships with external stakeholders (government, NGOs, etc.). | 0.85 | 0.87 |
Figure 6: Radial Chart – Dynamic Capabilities Items
Good item loadings (0.83 90) in Green Transformation Performance (Table 7; Figure 7) indicated that surveyed firms are tangibly advancing on various green performance fronts, namely, energy efficiency, emissions reduction, circular economy, and green certification.
Table 7: Items and Loadings for Green Transformation Performance
| Item Label | Item Description | Factor Loading | Cronbach’s Alpha |
| GTP1 | The firm has improved energy efficiency over the past 3 years. | 0.87 | |
| GTP2 | The firm has significantly reduced its carbon emissions. | 0.90 | |
| GTP3 | The firm implements circular economy practices. | 0.84 | |
| GTP4 | The firm has reduced material consumption through eco-design. | 0.83 | |
| GTP5 | The firm has achieved recognized green certifications (e.g., ISO 14001). | 0.85 | 0.89 |
Figure 7: Radial Chart – Green Transformation Performance Items
Structural Model Results
The structural model fitted the data very well ( 2/df = 2.34, CFI = 0.95, TLI = 0.94, RMSEA = 0.048). Table 2 gave some prior correlations; Table 8 shows the main path coefficients and the mediation effects.
The direct effect of Regulatory Pressure on Green Transformation Performance was also significant ( = 0.41, p < 0.001), indicating that a high level of regulatory signals moves firms towards a more profound sustainability transition. Likewise, Market Incentives exerted a sizable direct impact on GTP ( = 0.33, p < 0.001), confirming the importance of carbon markets and green finance as a catalyst of corporate action.
As indicated in Table 8 and visually summarized in Figure 8, Technological Innovation strongly mediated the connection between Regulatory Pressure and GTP (indirect effect = 0.24, p < 0.001). This implies that companies use the policy pressure mainly through improvements in their technology capacity. In the same spirit, Market Incentives were also intermediated by Technological Innovation ( = 0.19), which supports the idea that companies react to market-based opportunities by refilling their innovation funnels.
Table 8: Summary of Mediation Effects (Bootstrapping Results)
| Mediated Path | Indirect Effect β | 95% CI (Lower–Upper) | p-value |
| Regulatory Pressure → Technological Innovation → Green Transformation Performance | 0.24 | 0.17–0.31 | <0.001 |
| Market Incentives → Technological Innovation → Green Transformation Performance | 0.19 | 0.12–0.27 | <0.001 |
| Regulatory Pressure → Dynamic Capabilities → Green Transformation Performance | 0.17 | 0.11–0.23 | <0.001 |
| Market Incentives → Dynamic Capabilities → Green Transformation Performance | 0.14 | 0.09–0.21 | <0.001 |
Figure 8: Lollipop Chart – Mediation Effects (Bootstrapping Results)
Both routes, Regulatory Pressure Regulatory Pressure → GTP ( = 0.17) and Market Incentives Market Incentives → GTP ( = 0.14), were mediated by Dynamic Capabilities, thus showing that it is the flexibility of the organization and the learning processes that are central to convert outside signals into performance improvement.
Results Interpretation
These results provide some valuable lessons. Firstly, the Regulatory Pressure is the most powerful direct force of green transformation, which is in line with the top-down policy environment and enforcement intensity in China. The companies with more precise and obvious regulatory expectations demonstrate better green performance results.
Second, Market Incentives also have a significant role to play complementarity. The availability of carbon trading and green finance both directly and indirectly contributes to the green initiatives of firms, particularly where technological innovation is concerned. This is emphasized by the relatively high loading of the carbon trading participation and green financing (Table 4; Figure 4).
Third, Technological Innovation plays a key role in the mediation of both the policy and market trajectories. The company performance in terms of green transformation is better in firms that pursue low-carbon technologies and process innovations actively. This is seen in figures 5 and 8. The large loading of process innovation (Table 5; Figure 5) indicates that the companies view technological upgrading as the most feasible way of reacting to the external pressures.
Fourth, the process of transformation is further mediated by Dynamic Capabilities which necessitates organizational preparedness and dexterity. The ability of firms to operate in the dynamic and complicated environment of the Dual-Carbon Policy is facilitated by cross-functional collaboration and external stakeholder engagement, which are high- loading items (Table 6; Figure 6).
Last, the high and significant loadings on the Green Transformation Performance construct (Table 7; Figure 7) suggest that a large number of Chinese manufacturing companies are making actual improvements on numerous levels of energy efficiency and emissions reduction and circularity practices.
On the whole, the findings illustrate that the Dual-Carbon Policy affects green transformation in a multidimensional mechanism. Regulatory Pressure and Market Incentives are the key drivers, and Technological Innovation and Dynamic Capabilities are the mediators that are essential to permit firms to transmit external signals into internal change and green performance enhancement. A combination of the visual trends in Figures 1 to 8, along with the more detailed numerical output in Tables 1 to 8, offer a detailed, cumulative impression of the response of manufacturing enterprises to the ambitious climate targets of China.
Discussion
Overall, the results of this research offer high confidence that the Dual-Carbon Policy of China is already having a strong influence on the practices of manufacturing companies, guiding them to more sustainable and green transformation trajectories. These findings affirm that Regulatory Pressure and Market Incentives are strong exogenous forces of green transformation, whereas Technological Innovation and Dynamic Capabilities intercede these influences and allow companies to attain a better environmental performance. The insights provide recognized progress to the literature on industrial decarbonization and provide practical implications to policymakers and managers.
Firstly, the result that Regulatory Pressure has the most substantial direct effect on Green Transformation Performance confirms a substantial amount of previous literature on the importance of environmental regulation in spurring corporate sustainability performance (Blackman, 2010; Gunningham et al., 2003). Within the framework of the Dual-Carbon Policy in China, this is also in-line with the authoritative governance structure of the country, whereby the top-down decrees and compliance systems have a significant effect on the behavior of the firms (Lo, 2015). The given power of this relationship conforms to the empirical research of the analogous context; Yu et al. (2020) have discovered that the regulatory intensity was the key factor of the low-carbon innovation in the Chinese heavy industry. The large item loadings of penalty severity and compliance audits also indicate that companies recognize tangible and imminent threats should they fail to respond to the Dual-Carbon agenda. These findings further correspond to those presented by Zhao et al. (2019), who demonstrated that visible and credible enforcement encourages firms to transition beyond superficial environmental compliance towards more meaningful process enhancements.
Simultaneously, the large and positive marginal impact of Market Incentives on green transformation indicates the complementary nature of market-based instruments. This observation aligns with those literature that support the significance of carbon market and green finance in driving corporate decarbonization (Fankhauser et al., 2013; Carattini et al., 2017). Specifically, the expanding presence of the national carbon market in China can give companies a financial reason for efficient energy use and low-carbon technology (Zhang et al., 2014). The calculated indirect effect via Technological Innovation implies that companies tend to use the opportunities available on the market to make a case of R&D investment that would not be otherwise economically feasible (Schmidt et al., 2012). This interaction is particularly pertinent to industries with narrow margins, whereupon green investments upfront cannot be justified unless there are long run cost reductions or market incentives (Fowlie et al., 2012).
The findings also affirm Technological Innovation as core in enabling green transformation. The high process innovation and collaborative innovation loadings are consistent with an emerging literature that states that systemic technological change is required to realize deep decarbonization (Grubler et al., 2012; Kohtz et al., 2019). In addition the findings of this study resonate with those of Hekkert et al. (2007) who contend that the innovation system dynamics are a factor of policy and market pressures, which determine the diffusion of green technologies. The profound mediation results found in the present study imply that without a respective investment into innovation, the influence of the external pressures on green transformation can be significantly dampened. This is in line with findings by Bergek et al. (2008) that companies that have more established technological capacities are in a better position to react strategically to alterations in environmental policies.
Together with that, the prominent mediating role of Dynamic Capabilities assures that organizational agility is an essential facilitator of green transformation. This result builds on previous research into dynamic capabilities in sustainability settings (Pitelis & Teece, 2010; Bhatti et al., 2021). The great loadings of cross-functional collaboration and stakeholders engagement imply that the green transformation cannot be achieved only through technological upgrading, but shifts in organizational routines, culture, and external relations (Gualandris & Kalchschmidt, 2014). These findings speak in favor of the position taken by Hart & Dowell (2011) who believe that developing environmental dynamic capabilities are the way firms can continue gaining competitive advantage in a market that is increasingly focusing on sustainability. The observation is also consistent with empirical research like Lin & Wu (2014), which indicated that dynamic capabilities mediate the effect of the external environmental condition on green supply chain performance.
Notably, the findings of this study also support the contention that green transformation is an interactive, multidimensional process (Bocken et al., 2014). Both policy requirements and market signals are not enough to achieve deep transformation, and instead, firms should build internal capabilities and innovation skills to operate in this complex environment (Horbach, 2016). The conclusion that both Technological Innovation and Dynamic Capabilities are the mediating mechanisms of the impacts of both regulatory and market driver variables accentuates the role of organizational preparedness and the learning orientation. This observation is similar to that of Marcus & Fremeth (2009) who showed that the heterogeneity of firms in their green transformation outcomes was mostly influenced by disparities in internal capability building.
The second significance of the study is to present the empirical evidence of China, which has been relatively insufficient in the worldwide literature on the green transformation of corporations (Wang et al., 2018; Dai et al., 2015). With its distinctive governance system and industrial organization, how policy-based decarbonization engages with market liberalization dynamics and innovation system development is a particularly important question in the Chinese context. The results indicate that the regulatory drivers are still prevalent but the developing green finance ecosystem in China and the emerging carbon markets are establishing strong new motives that encourage firms to participate in sustainability transitions (Ren et al., 2020). Besides, the presence of a profound importance of dynamic capabilities shows that despite the state-led context, agency and strategic management at the firm level remain essential factors that define transformation paths (Zhu et al., 2018).
Lastly, the excellent Green Transformation Performance results recorded by a high number of companies indicate that the Dual-Carbon Policy is having an already quantifiable effect on manufacturing sustainability. High item loadings associated with energy efficiency, emissions reduction, and circular economy practices indicative of substantive advancement in many areas of performance. These results are promising considering the criticisms of scholars like Lo & Fryxell (2005) who complained that numerous Chinese companies were involved in symbolic green practices with little substantive change in operations. The current study shows that under the Dual-Carbon Policy, more and more firms are shifting beyond symbolic compliance towards actual green innovation and process change.
Overall, this paper establishes that the Dual-Carbon Policy is having a sturdy and multidimensional impact on the work of manufacturing companies in China, where regulatory and market pressures interplay with internal organizational drivers to promote green transformation. The findings are relevant to both theoretical knowledge of policy-driven industrial decarbonization and the practical action plan to meet national and global climate goals. Further studies ought to further track these dynamics as they develop through time, especially the deepening roles played by digitalization, industry alliance, and transnational supply chain pressures in defining the next step of Chinese green manufacturing transformation.
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