Research on Disruptive Green Technological Innovation in Agriculture Driven by Low-Carbon Initiatives

Abstract

Advancing new productive forces in agriculture requires the adoption of disruptive green technological innovation by agricultural enterprises. This study analyzes the generative mechanisms for promoting disruptive green technological innovation based on the DSR model and examines the moderating role of green governance alliances. A total of 302 questionnaires were collected from agricultural enterprises in Guangxi, and structural equation modeling was employed for verification. The results indicate that both low-carbon transformation capability and carbon label credibility have a significantly positive impact on disruptive green technological innovation and organizational green learning, with organizational green learning playing a mediating role. Green governance alliances positively moderate the relationship between low-carbon transformation capability, carbon label credibility, and organizational green learning but do not moderate the relationship between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation. Agricultural enterprises can advance new productive forces and achieve high-quality agricultural economic development by enhancing their low-carbon transformation capability, co-constructing carbon label credibility, and engaging in disruptive green technological innovation. Agricultural enterprises should adopt disruptive green technological innovation, establish carbon certification systems, participate in green learning platforms, and strengthen green governance alliances to promote low-carbon development and enhance market competitiveness.

1. Introduction

President Xi Jinping emphasizes that “Green development is the cornerstone of high-quality development, with new productive forces inherently being green by nature”. New productive forces require the comprehensive enhancement of total factor productivity through new technologies, with a characteristic emphasis on green technological innovation [1,2,3]. Green technological innovation encompasses both incremental and disruptive green technological innovations [4]. Incremental green technological innovation involves improvements within existing frameworks, advocating for minor innovations in existing green technologies and products, which only moderately enhance productivity. In contrast, disruptive green technological innovation breaks traditional frameworks, creating entirely new value chains and business models by introducing new technologies, ideas, and methods, significantly improving productivity and quality. Given the uncertainties in technology, organization, and resources, traditional incremental green innovation is insufficient to meet the demands of industrial upgrading and transformation. Disruptive green technological innovation, characterized by digitization and green development, has become a crucial element in driving the development of new productive forces [5]. Therefore, advancing disruptive green technological innovation in agriculture is of great importance for promoting agricultural modernization, supporting the development of new productive forces, and building a strong agricultural nation.

Disruptive green technological innovation is essentially a process of integrating knowledge, industry, and market factors [6]. Previous literature mainly focuses on three aspects: First, from the perspective of knowledge acquisition, Wang and Liu [7] examined how network embedding and knowledge acquisition strategies promote the development and innovation of disruptive green technologies, while Xu et al. [8] explored the impact of the Simmel Knowledge Innovation Alliance on disruptive green technological innovation. Zhang et al. [9] constructed the B-SECI model from the perspective of internal organizational knowledge innovation to analyze the internal mechanisms of disruptive innovation evolution. From the perspective of industrial impact, Xin [10] examined the impact of digital industry clustering on urban green economic efficiency, identifying the mediating role of disruptive technology innovation; Gao [11] constructed a qualitative analysis framework to study the mechanisms for promoting green technological innovation and suggested that the government should advance disruptive green technological innovation in industrial development. From the market perspective, Xiao and Zhang [12] investigated the adoption process of green disruptive high-tech new products (disruptive green technological innovation products) from the viewpoint of consumer purchase intentions. Gong and Liu [13] analyzed whether start-ups adopting incremental or radical (disruptive) innovation are more competitive in the market. Li [14] studied how start-ups in marginal markets can improve the quality and performance of disruptive innovation within existing innovation ecosystems. Thus, research on disruptive green technological innovation is relatively broad, focusing on the impact of external environments on such innovation, but there is a lack of studies on how internal factors, particularly low-carbon drives, influence disruptive green technological innovation.

Green technological innovation is a long-term, uncertain, complex, and high-risk strategic behavior for enterprises, with disruptive green technological innovation exhibiting even stronger externalities [15,16]. Effectively encouraging agricultural enterprises to engage in disruptive green technological innovation has become a pressing issue for developing green agriculture. However, there is a scarcity of literature that delves deeply into this issue, and empirical studies are particularly lacking. The purposes of this study are twofold: First, it attempts to incorporate low-carbon transformation capability and carbon label credibility into the analytical framework for disruptive green technological innovation, elucidating the driving mechanisms that promote such innovation. By focusing on low-carbon-driven disruptive green technological innovation, this research aims to provide a new analytical perspective and extend the current boundaries of research on promoting new productive forces in agriculture. Second, the study explores the need to establish an innovation ecosystem for disruptive green technological innovation. It examines the generation pathways and interaction mechanisms of green governance alliances and organizational green learning in this context, with the goal of investigating how internal and external green value co-creation within a corporate innovation ecosystem can facilitate disruptive green technological innovation.

2. Theoretical Model and Research Hypotheses

The Driving Force-State-Response (DSR) model was developed in 1996 by the United Nations Commission on Sustainable Development as an extension of the Pressure-State-Response (PSR) framework. It explores social sustainability from social, economic, and institutional perspectives and has been widely applied in the fields of social, economic, and business management [17]. This paper applies the DSR model to the study of disruptive green technological innovation in agricultural enterprises, as illustrated in Figure 1. Disruptive green technological innovation is not only crucial for ensuring the sustainable development of agricultural enterprises but also a key step in advancing green agriculture. The model considers driving forces (external carbon label credibility and internal low-carbon transformation capability), state (organizational green learning), and response (disruptive green technological innovation). That is, agricultural enterprises are driven by external pressure from carbon label credibility and internal motivation from low-carbon transformation capability. In response, they engage in organizational green learning to adapt to environmental changes, subsequently taking measures to achieve disruptive green technological innovation. Simultaneously, to enhance the level of cooperation and integration, agricultural enterprises form cohesive green governance alliances with suppliers, distributors, and other stakeholders, where green governance alliances play a moderating role.

2.1. Low-Carbon Transformation Capability and Disruptive Green Technological Innovation

The concept of low-carbon transformation capability refers to the awareness of low-carbon transformation within the decision-making management of an enterprise and the ability to translate this awareness into practical activities that ensure the achievement of low-carbon goals at a strategic level [18]. From the perspective of first-mover technological advantage, agricultural enterprises with low-carbon transformation capability can quickly capture market share, develop consumer preferences, and promote disruptive green technological innovation by establishing technical barriers [19]. From the perspective of reducing innovation costs, agricultural enterprises with low-carbon transformation capability can rapidly accumulate low-carbon resources and benefits, significantly offsetting the initial costs of environmental management and innovation investments, thereby promoting disruptive technology innovation [20]. From the perspective of enhancing corporate competitiveness, as the concept of “lucid waters and lush mountains are invaluable assets” becomes a consensus for sustainable development, agricultural enterprises that cause environmental pollution or lack social responsibility will see a reduction in their social valuation and long-term profitability. In contrast, enterprises with low-carbon transformation capability, possessing more low-carbon information, are more willing to promote low-carbon technology, thus facilitating disruptive green technological innovation [21]. Therefore, the following hypothesis is proposed:

H1. 

Low-carbon transformation capability has a positive impact on disruptive green technological innovation.

2.2. Carbon Label Credibility and Disruptive Green Technological Innovation

A carbon label is an assessment tool designed to measure the efficiency of carbon usage throughout the life cycle of a product or service (from raw material acquisition, production, transportation, and use to final disposal), indicating the total amount of carbon dioxide and other greenhouse gas emissions produced. This aims to promote the reduction of greenhouse gas emissions [22]. Carbon label credibility refers to consumers’ evaluation of the trustworthiness and reliability of carbon labels [23]. For most consumers, who lack the expertise to calculate carbon footprints, the qualifications and carbon labels issued by relevant organizations become objects of trust. In terms of market competition, agricultural enterprises that do not meet carbon label requirements may face a reduction in market share, compelling them to engage in disruptive green technological innovation (as incremental green technological innovation may not meet carbon label requirements). By showcasing their carbon footprint through carbon labels, enterprises send a green signal to the public, gaining a sustainable competitive advantage. As a result, carbon label credibility promotes disruptive green technological innovation [24]. In terms of transaction costs, enterprises that meet carbon label requirements achieve cleaner production, making them more likely to obtain green loans and government environmental subsidies, thereby reducing the transaction costs associated with disruptive green technological innovation. Consequently, carbon label credibility fosters disruptive technology innovation [25]. Therefore, the following hypothesis is proposed:

H2. 

Carbon label credibility has a positive impact on disruptive green technological innovation.

2.3. Low-Carbon Transformation Capability and Organizational Green Learning

Organizational green learning refers to the process by which enterprises acquire environmental knowledge and integrate it into their existing production technologies to address and solve environmental issues [26]. In terms of environmental adaptation, agricultural enterprises with low-carbon transformation capability can rapidly enhance their organizational capacity for green learning and their ability to respond quickly to environmental changes. These enterprises are more likely to apply environmental strategies to improve agricultural technologies or processes, thereby promoting organizational green learning [27]. Regarding resource integration, agricultural enterprises with low-carbon transformation capability can swiftly strengthen their organizational ability to grasp green knowledge and adapt to environmental changes, improving their information tracking capabilities. This, in turn, advances organizational green learning [28]. Therefore, low-carbon transformation capability ensures the strategic realization of low-carbon goals in agricultural enterprises. The following hypothesis is proposed:

H3. 

Low-carbon transformation capability has a positive impact on organizational green learning.

2.4. Carbon Label Credibility and Organizational Green Learning

In terms of government pressure, if agricultural enterprises fail to comply with environmental regulations, they may face pressures such as fines and legal actions from the government. Faced with strict regulatory constraints, some agricultural enterprises may proactively comply with carbon label standards by engaging in green organizational learning, guiding the behavior of members and aligning their thoughts and actions with the correct way [29]. As for consumer pressure, as public environmental awareness increases, consumer preferences for environmentally friendly products and services are growing. To meet this demand, agricultural enterprises may engage in organizational learning, enhancing their market responsiveness and learning to adhere to carbon label standards [30]. In terms of competitive pressure, when industry leaders use carbon labels to enhance their market position, other enterprises, in order to remain competitive, may adopt similar innovative practices. As a result, agricultural enterprises may proactively engage in green organizational learning, creating new knowledge based on existing green knowledge [31]. Therefore, the following hypothesis is proposed:

H4. 

Carbon label credibility has a positive impact on organizational green learning.

2.5. Organizational Green Learning and Disruptive Green Technological Innovation

Organizational green learning refers to the process by which enterprises acquire environmental knowledge and integrate it into existing production technologies to address and solve environmental issues [26]. The green attributes of agricultural products are often less visible, yet the cost of green products is 30% to 50% higher than that of conventional agricultural products. Additionally, agricultural technological innovation has significant negative externalities. If agricultural enterprises lack sufficient understanding of green development, their disruptive green technological innovations may encounter the “lemon effect”, leading to their exclusion from the market [32]. Organizational green learning enables agricultural enterprises to update their existing mindsets, perspectives, and cognitive frameworks, helping them recognize the long-term unsustainability of environmental pollution. It also allows them to accumulate extensive environment-related knowledge, including market dynamics and green technologies, providing a technical foundation for disruptive green technological innovation [33]. As the depth of organizational green learning increases, agricultural enterprises continuously improve their environmental knowledge, skills, and proficiency, enabling them to better integrate existing green resources and interact with the market environment. This results in green agricultural products commanding higher price premiums, providing the capital foundation for disruptive green technological innovation [34]. Through green organizational learning, agricultural enterprises transition from traditional production practices to low-carbon production practices, thereby establishing a positive public image, enhancing their reputation and financial performance, and creating a sustainable development trajectory. This provides the organizational foundation for disruptive green technological innovation [35]. Therefore, organizational green learning promotes disruptive green technological innovation, leading to the following hypothesis:

H5. 

Organizational green learning has a positive impact on disruptive green technological innovation.

2.6. The Mediating Role of Organizational Green Learning

Disruptive green technological innovation is a complex process, and its implementation may encounter obstacles such as a lack of green knowledge resources and weak innovative technologies [36]. Merely possessing low-carbon transformation capability is insufficient for agricultural enterprises to overcome these development challenges. In terms of cognitive enhancement, organizational green learning allows enterprises to acquire extensive environmental knowledge, such as information about the green agricultural product market and green production technologies. As the depth of organizational green learning increases, agricultural enterprises continuously improve their environmental knowledge, skills, and cognitive levels, thereby better advancing disruptive green technological innovation [37]. In terms of resource utilization, organizational green learning enables the integration of various resources, facilitating the comprehensive use of green technological innovation resources [38], thereby promoting disruptive green technological innovation. Thus, the following hypothesis is proposed:

H6a. 

Organizational green learning mediates the relationship between low-carbon transformation capability and disruptive green technological innovation.

As for adapting to external demands, agricultural enterprises must adjust their production and operations in response to environmental standards that promote carbon-labeled products. This necessity drives enterprises to recognize the importance of engaging in disruptive green technological innovation, compelling them to undertake organizational green learning. Organizational green learning serves as a mediator, enhancing the enterprise’s ability to adapt to the external market environment, thereby promoting disruptive green technological innovation [39]. As for resource acquisition, when developing carbon-labeled products, agricultural enterprises must engage in green learning to obtain the necessary knowledge and resources for green innovation from the external environment. This process involves reallocating resources and breaking traditional development models, thereby advancing disruptive green technological innovation [40]. Therefore, the following hypothesis is proposed:

H6b. 

Organizational green learning mediates the relationship between carbon label credibility and disruptive green technological innovation.

2.7. The Moderating Role of Green Governance Alliances

Green governance alliances refer to the collaborative efforts of enterprises with suppliers, distributors, and other partners in conducting various environmental business activities, achieving consensus on green governance, and sharing environmental resources and knowledge [41]. Based on stakeholder theory, agricultural enterprises are responsible for leading value creation for a diverse group of stakeholders, including suppliers, distributors, customers, employees, and the community. The stronger the green governance alliance capabilities of agricultural enterprises with low-carbon transformation capabilities, the more effectively they can establish low-carbon value recognition, low-carbon cooperation agreements, and sustainable value-sharing relationships. This process creates comprehensive low-carbon value and shared green outcomes [42], which in turn promote disruptive green technological innovation.

A carbon label is a low-carbon mechanism that provides a foundation of expectations for transactions, effectively reducing transaction costs and friction. Agricultural enterprises that trust carbon label credibility and possess strong green governance alliances capabilities are better able to form green contracts and value recognition. This reduces the costs associated with pre-transaction information gathering, communication during transactions, and post-transaction supervision. Consequently, the production process becomes greener, pollutant emissions decrease, and resource and energy utilization efficiency improves, thereby promoting disruptive green technological innovation [43]. Therefore, the following hypotheses are proposed:

H7. 

Green governance alliances moderate the relationship between low-carbon transformation capability and disruptive green technological innovation.

H8. 

Green governance alliances moderate the relationship between carbon label credibility and disruptive green technological innovation.

Agricultural enterprises engaged in green governance alliances based on low-carbon transformation demands are more capable of sharing and integrating existing green knowledge. They are also better positioned to reflect on the relationship between their development and green, low-carbon practices, which, to some extent, broadens the scope of green knowledge [44], thereby promoting organizational green learning.

Similarly, agricultural enterprises that trust carbon label credibility and engage in green governance alliances are more likely to collaborate and communicate with supply chain partners. This facilitates the integration of knowledge within the organization, strengthens inter-departmental communication, and reduces barriers to the dissemination of low-carbon knowledge [45], thereby promoting organizational green learning. Therefore, the following hypotheses are proposed:

H9. 

Green governance alliances moderate the relationship between low-carbon transformation capability and organizational green learning.

H10. 

Green governance alliances moderate the relationship between carbon label credibility and organizational green learning.

3. Research Design

3.1. Variable Measurement

In the questionnaire design phase of this study, all variable items were based on validated original scales. To ensure the comprehensiveness of the questionnaire and the validity of each item, the research team invited over 50 agricultural management experts to participate in a pretest of the questionnaire. A 5-point Likert scale was employed, with scores ranging from 1 (lowest) to 5 (highest) in increasing order. The primary variables in this study include low-carbon transformation capability, carbon label credibility, organizational green learning, disruptive green technological innovation, and green governance alliances. The operational definitions and measurement bases for these variables are as follows (The Source of Scale table is attached with Appendix A):

The dependent variable, “disruptive green technological innovation”, primarily references five items proposed by Xu Jianzhong et al., such as “Our enterprise disrupts the low-end green market by incorporating non-consumers into the new value network and gradually penetrating the high-end market” [8]. The independent variable, “low-carbon transformation capability,” references five items proposed by Li and Yang [46], such as “Our enterprise’s top management has a strong willingness to innovate and reduce emissions”. Another independent variable, “carbon label credibility,” references six items proposed by Mei Lei et al., such as “Our enterprise believes that the information displayed by carbon labels is accurate” [47]. The mediating variable, “organizational green learning,” references seven items proposed by Zhang et al. [48], such as “One of the purposes of our enterprise’s information search is to find more energy-efficient solutions”. The moderating variable, “green governance alliances”, references four items proposed by Yao et al. [41], such as “Our enterprise jointly develops environmental strategies with suppliers”. Control variables include “enterprise acreage”, “enterprise type”, and “public listing status”. “Enterprise acreage” represents the scale of the agricultural enterprise, as economies of scale can influence disruptive green technological innovation. “Enterprise type” (whether state-owned) and “public listing status” may also impact the enterprise’s ability to innovate disruptively in green technology [49].

3.2. Data Collection

This study focuses on agricultural enterprises in Guangxi, a traditional agricultural province where the agricultural industrial system has been initially established and industrial clusters and integration are rapidly developing. Guangxi leads the nation in several agricultural industries, including sugarcane, silkworm, and high-quality chicken, making it an ideal region for studying the need for disruptive green technological innovation and low-carbon transformation in traditional agricultural practices. The research employed a convenience sampling method, distributing an online questionnaire through instant messaging tools, such as WeChat and QQ. The respondents are managers at various levels (senior, middle, and junior) within the enterprises. A total of 311 questionnaires were collected, and after sorting, 302 valid samples were obtained.

The sample results are shown in

Table 1. Basic information.

Variable	Category	Frequency	Percentage (%)
Enterprise Establishment Time	Less than 1 year	41	13.6
	1 to 3 years	55	18.2
	3 to 5 years	126	41.7
	5 to 10 years	45	14.9
	10 to 20 years	28	9.3
	More than 20 years	7	2.3
Enterprise Base Area	Less than 50 mu	106	35.1
	50–100 mu	112	37.1
	100–300 mu	67	22.2
	More than 300 mu	17	5.6
State-Owned Enterprise	Yes	126	41.7
	No	176	58.3
Publicly Listed	Yes	70	23.2
	No	232	76.8
Ownership Type	Sole Proprietorship	68	22.5
	Joint-Stock Company	40	13.2
	Cooperative Enterprise	101	33.4
	Partnership	81	26.8
	Other	12	4
Position	Junior Staff	143	47.4
	Middle Management	85	28.1
	Senior Management	74	24.5
Work Experience	0–5 years	143	47.4
	6–10 years	68	22.5
	11–15 years	46	15.2
	16–20 years	32	10.6
	More than 20 years	13	4.3
Age	20–29 years old	118	39.1
	30–39 years old	126	41.7
	40–49 years old	28	9.3
	50–59 years old	20	6.6
	60 years old and above	10	3.3
Gender	Male	140	46.4
	Female	162	53.6
Education Level	High School or Below	16	5.3
	Associate Degree	79	26.2
	Bachelor’s Degree	137	45.4
	Graduate Degree or Above	70	23.2

. Among the enterprises, those established for less than 1 year accounted for 13.6%, 1 to 3 years for 18.2%, 3 to 5 years for 41.7%, 5 to 10 years for 14.9%, 10 to 20 years for 9.3%, and more than 20 years for 2.3%. Regarding enterprise acreage, 35.1% had less than 50 mu (a unit of area equaling 0.0667 hectares), 37.1% had 50–100 mu, 22.2% had 100–300 mu, and 5.6% had more than 300 mu. In terms of enterprise ownership, 41.7% were state-owned, and 58.3% were non-state-owned. Regarding public listing status, 23.2% were listed, while 76.8% were not. Regarding the nature of ownership, 22.5% were sole proprietorships, 13.2% were joint-stock companies, 33.4% were cooperative enterprises, 26.8% were partnerships, and 4% fell into other categories. Regarding the positions of the respondents, 47.4% were junior staff, 28.1% were middle managers, and 24.5% were senior managers. Concerning work experience, 47.4% had 0–5 years, 22.5% had 6–10 years, 15.2% had 11–15 years, 10.6% had 16–20 years, and 4.3% had more than 20 years. Regarding age, 39.1% were aged 20–29, 41.7% were aged 30–39, 9.3% were aged 40–49, 6.6% were aged 50–59, and 3.3% were 60 years or older. Regarding gender, 46.4% were male, and 53.6% were female. Regarding education level, 45.4% had an undergraduate degree, 26.2% had an associate degree, 5.3% had a high school diploma or lower, and 23.2% had a master’s degree or higher.

4. Data Analysis

4.1. Descriptive Statistics

The results of the descriptive statistics are summarized in

Table 2. Means, standard deviations, composite reliability, convergent validity, and correlation coefficient matrix.

Variable	M	SD	1	2	3	4	5	6	7	8
1. Low-Carbon Transformation Capability	3.370	1.120	0.823
2. Carbon Label Credibility	3.310	1.160	0.357 ***	0.812
3. Organizational Green Learning	3.750	0.940	0.534 ***	0.489 ***	0.774
4. Green Governance Alliances	3.580	1.080	0.381 ***	0.377 ***	0.511 ***	0.807
5. Disruptive Green Technological Innovation	3.740	0.940	0.450 ***	0.414 ***	0.523 ***	0.399 ***	0.762
6. Base Area	1.980	0.900	−0.035	−0.047	−0.087	0.080	−0.043	1
7. State-Owned	0.420	0.490	−0.040	−0.066	−0.063	−0.036	−0.068	0.113 *	1
8. Publicly Listed	0.230	0.420	0.062	0.082	0.022	0.020	0.032	0.001	0.188 **	1

Note: N = 302; Off-diagonal values represent the correlation coefficients between constructs; diagonal bolded values represent the square root of the average variance extracted (AVE) for each construct; * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001.

. There are significant correlations among the variables, and the partial correlation coefficients between each pair of variables are all smaller than the square root of the average variance extracted (AVE). The multicollinearity test shows that all tolerance values are greater than 0.5, and the variance inflation factor (VIF) values range between 1.044 and 1.965, indicating that multicollinearity is within an acceptable range.

The common method bias was tested for post-hoc examination of common method variance using the single method latent factor technique. The fit indices for the model with the method latent factor (χ²/df = 1.081, RMSEA = 0.016, SRMR = 0.016, CFI = 0.996, TLI = 0.995) did not significantly outperform the fit indices of the original confirmatory factor analysis model (χ²/df = 1.167, RMSEA = 0.024, SRMR = 0.028, CFI = 0.991, TLI = 0.989). Therefore, the control for common method bias in this study is adequate.

4.2. Confirmatory Factor Analysis

This study utilized Mplus 8.7 for confirmatory factor analysis (CFA). The results indicated that the five-factor model had significantly better-fit indices compared to the alternative factor models (χ²/df = 1.156, RMSEA = 0.023, SRMR = 0.028, CFI = 0.991, TLI = 0.990), demonstrating good fit and adequate discriminant validity among the variables.

The Cronbach’s α coefficients for the five scales—low-carbon transformation capability, carbon label credibility, green governance alliances, organizational green learning, and disruptive green technological innovation—were all above 0.70, indicating that the scales are reliable, with good stability and credibility of the survey results. The composite reliability (CR) values for each scale exceeded 0.7, demonstrating good composite reliability. The AVE values for each scale were above 0.5, indicating adequate convergent validity. The square roots of the AVE values were higher than the correlations between variables, confirming good discriminant validity. The reliability test results are shown in

Table 3. Reliability test.

Factor	Measurement Items	Loading Value
Low-Carbon Transformation Capability (α = 0.913, AVE = 0.677, CR = 0.913)	DTBG1	0.831
	DTBG2	0.819
	DTBG3	0.807
	DTBG4	0.834
	DTBG5	0.824
Carbon Label Credibility (α = 0.851, AVE = 0.659, CR = 0.853)	TBQ1	0.771
	TBQ2	0.853
	TBQ3	0.809
Green Governance Alliances (α = 0.882, AVE = 0.652, CR = 0.882)	LSZY1	0.816
	LSZY2	0.791
	LSZY3	0.816
	LSZY4	0.806
Organizational Green Learning (α = 0.913, AVE = 0.599, CR = 0.913)	LSZZ1	0.768
	LSZZ2	0.760
	LSZZ3	0.746
	LSZZ4	0.780
	LSZZ5	0.762
	LSZZ6	0.801
	LSZZ7	0.799
Disruptive Green Technological Innovation (α = 0.874, AVE = 0.581, CR = 0.874)	DFX1	0.768
	DFX2	0.788
	DFX3	0.771
	DFX4	0.709
	DFX5	0.774

.

4.3. Hypothesis Testing

The structural model testing results showed that the fit indices (χ²/df = 1.217, RMSEA = 0.027, SRMR = 0.033, CFI = 0.987, TLI = 0.985) met the required standards, indicating a good fit between the model and the data.

First, the impact of low-carbon transformation capability and carbon label credibility on disruptive green technological innovation was tested. Low-carbon transformation capability had a significant positive effect on disruptive green technological innovation (β = 0.220, p = 0.002), confirming Hypothesis H1. Carbon label credibility also had a significant positive effect on disruptive green technological innovation (β = 0.198, p = 0.012), confirming Hypothesis H2. These results suggest that both low-carbon transformation capability and carbon label credibility positively influence disruptive green technological innovation, indicating that agricultural enterprises need to enhance their internal and external drivers to promote such innovation.

Next, the effects of low-carbon transformation capability and carbon label credibility on organizational green learning were examined. Low-carbon transformation capability had a positive impact on organizational green learning (β = 0.433, p < 0.001), confirming Hypothesis H3. Carbon label credibility also positively impacted organizational green learning (β = 0.370, p < 0.001), confirming Hypothesis H4. These findings indicate that both low-carbon transformation capability and carbon label credibility positively influence organizational green learning, suggesting that agricultural enterprises should enhance their internal and external drivers to promote organizational green learning.

Finally, the mediating role of organizational green learning was tested. Organizational green learning had a significant positive effect on disruptive green technological innovation (β = 0.348, p = 0.002), confirming Hypothesis H5. The mediation test was conducted using the bootstrap method with 5000 resamples. The regression test results are shown in

Table 4. Regression test results.

Effect Type	Pathway	β	t-Value	95% Confidence Interval
Direct Effects	Low-Carbon Transformation Capability → Organizational Green Learning	0.433	0.064	-
	Carbon Label Credibility → Organizational Green Learning	0.370	0.070	-
	Low-Carbon Transformation Capability → Disruptive Green Technological Innovation	0.220	0.073	-
-	Carbon Label Credibility → Disruptive Green Technological Innovation	0.198	0.078	-
	Organizational Green Learning → Disruptive Green Technological Innovation	0.348	0.113	-
Mediating Effects	Low-Carbon Transformation Capability → Organizational Green Learning → Disruptive Green Technological Innovation	0.151	0.056	[0.059, 0.282]
	Carbon Label Credibility → Organizational Green Learning → Disruptive Green Technological Innovation	0.129	0.056	[0.040, 0.257]
Total Effects	Low-Carbon Transformation Capability → Disruptive Green Technological Innovation	0.371	0.072	[0.230, 0.517]
	Carbon Label Credibility → Disruptive Green Technological Innovation	0.326	0.072	[0.183, 0.463]
Model Fit Indices: χ2/df = 1.237, CFI = 0.987, TLI = 0.985, RMSEA = 0.027, SRMR = 0.033

. The findings indicate that organizational green learning significantly mediates the relationships between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation (p < 0.05), with the 95% confidence intervals not containing 0, thereby confirming Hypotheses H6a and H6b.

Finally, the moderating role of green governance alliances on the relationships between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation was tested using PROCESS 4.1. The results are presented in

Table 5. Moderation test results.

Variable	Organizational Green Learning				Disruptive Green Technological Innovation
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Base Area	−0.113	−0.104	−0.113 *	−0.113 *	−0.054	−0.054	−0.054	−0.054
State-Owned	−0.044	−0.045	−0.026	−0.020	−0.084	−0.084	−0.069	−0.067
Publicly Listed	−0.012	−0.058	−0.025		0.032	0.033	0.020	0.015
Low-Carbon Transformation Capability	0.389 ***	0.383 ***			0.343 ***	0.343 ***	0.300 ***	0.300 ***
Carbon Label Credibility			0.336 ***	0.338 ***
Green Governance Alliances	0.370 ***	0.288 ***	0.392 ***	0.320 ***	0.271 ***	0.272 ***	0.289 ***	0.269 ***
Low-Carbon Transformation Capability × Green Governance Alliances		−0.180 ***				0.003
Carbon Label Credibility × Green Governance Alliances				−0.146 **				−0.039
R2	0.407	0.446	0.374	0.396	0.268	0.268	0.244	0.246
ΔR2	0.407	0.039	0.374	0.022	0.268	0.000	0.244	0.002
F	40.653 ***	39.559 ***	35.375 ***	32.195 ***	21.667 ***	17.996 ***	19.134 ***	16.026 ***

* represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001.

. The interaction between low-carbon transformation capability and green governance alliances was not significant (β = 0.003, p > 0.05), so Hypothesis H7 was not supported. Similarly, the interaction between carbon label credibility and green governance alliances was not significant (β = −0.039, p > 0.05), so Hypothesis H8 was not supported. These findings suggest that green governance alliances do not moderate the relationships between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation. Policymakers and enterprises should place greater emphasis on the potential value of green governance alliances in enhancing organizational green learning and promoting low-carbon transformation capabilities rather than relying solely on their direct impact on disruptive green technological innovation.

As shown in Table 5, the interaction between low-carbon transformation capability and green governance alliances was significant (β = −0.180, p < 0.001), indicating that green governance alliances moderate the relationship between low-carbon transformation capability and organizational green learning, thereby supporting Hypothesis H9. Similarly, the interaction between carbon label credibility and green governance alliances was significant (β = −0.146, p = 0.001), indicating that green governance alliances moderate the relationship between carbon label credibility and organizational green learning, thereby supporting Hypothesis H10.

Green governance alliances negatively moderate the relationship between low-carbon transformation capability and organizational green learning. Slope analysis shows that when green governance alliances are low (M − SD), low-carbon transformation capability has a significant positive effect on organizational green learning (β = 0.564, p < 0.001). When green governance alliances are high (M + SD), the positive effect of low-carbon transformation capability on organizational green learning weakens (β = 0.203, p = 0.001). The moderation effect is illustrated in Figure 2. This indicates that when external green governance alliances are low, enterprises can enhance organizational green learning through low-carbon transformation capability. However, when green governance alliances are high, the impact of low-carbon transformation capability on organizational green learning diminishes. High involvement in green governance alliances may hinder the influence of low-carbon transformation capability on organizational green learning. Enterprises should strengthen their low-carbon transformation capabilities, especially when green governance cooperation levels are low, to promote organizational green learning through internal efforts. Since high levels of green governance cooperation may weaken the effect of low-carbon transformation capability on organizational green learning, enterprises should carefully evaluate the degree and manner of their involvement in green governance cooperation.

Green governance alliances negatively moderate the relationship between carbon label credibility and organizational green learning. Slope analysis shows that when green governance alliances are low (M − SD), carbon label credibility has a significant positive effect on organizational green learning (β = 0.483, p < 0.001). When green governance alliances are high (M + SD), the positive effect of carbon label credibility on organizational green learning weakens (β = 0.192, p = 0.004). The moderation effect is illustrated in Figure 3. This indicates that when external green governance alliances are low, enterprises can enhance organizational green learning through carbon label credibility. However, as green governance alliances increase, the impact of carbon label credibility on organizational green learning gradually diminishes. High involvement in green governance alliances may reduce the influence of carbon label credibility on organizational green learning. Enterprises should focus on building the credibility of carbon labels, particularly when green governance cooperation levels are low, as carbon labels can enhance organizational green learning. However, excessive participation in green governance cooperation may reduce the influence of carbon label credibility on organizational green learning. Enterprises should adopt a moderate level of participation in green governance cooperation to avoid undermining the role of carbon labels.

4.4. Summary of Research Findings

Most hypotheses related to the direct impact of low-carbon transformation capability and carbon label credibility on green technological innovation, organizational green learning, and the mediating role of green learning were supported.

However, the hypotheses regarding the moderating role of green governance alliances in the relationship between low-carbon transformation and innovation, as well as between carbon label credibility and innovation, were not supported. Green governance alliances did have a moderating effect in the relationships between low-carbon transformation capability and organizational green learning, and between carbon label credibility and organizational green learning (

Table 6. Summary of Research Findings.

Hypothesis	Conclusion
H1	Supported
H2	Supported
H3	Supported
H4	Supported
H5	Supported
H6a	Supported
H6b	Supported
H7	Not Supported
H8	Not Supported
H9	Supported
H10	Supported

).

5. Conclusions and Suggestions

5.1. Research Findings

The results align with the DSR model, demonstrating that agricultural enterprises are driven by external pressures such as carbon label credibility and internal motivations like low-carbon transformation capability. These drivers foster organizational green learning as an adaptive mechanism to environmental changes, ultimately leading to disruptive green technological innovation. The DSR model provides a robust framework for understanding the interplay between internal and external factors in promoting sustainable development within agricultural enterprises. This study underscores the importance of recognizing and responding to external environmental changes (e.g., market demand and policy shifts) and leveraging internal capacities (e.g., technological and managerial resources) to adapt through green learning. This process facilitates dual achievements in environmental and economic sustainability via disruptive green technological innovation.

Low-carbon transformation capability and carbon label credibility have a significant positive impact on disruptive green technological innovation, supporting and validating existing research literature [20,21,24,25]. These findings further confirm the positive and significant influence of low-carbon transformation capability and carbon label credibility on disruptive green technological innovation, aligning with the conclusions of prior studies. The internal low-carbon transformation capability of agricultural enterprises contributes to driving disruptive green technological innovation. Carbon label credibility enhances this innovation by promoting a unified product carbon labeling system and certification standards, which strengthen the carbon measurement and certification system. Therefore, agricultural enterprises can effectively advance disruptive green technology innovation by enhancing internal motivation from both internal and external market perspectives. Organizational green learning plays a mediating role between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation [27,28,29,30,31]. These findings align with existing research literature and highlight the mediating function of organizational green learning in the process of advancing disruptive green technological innovation within agricultural enterprises. Agricultural enterprises’ organizational green learning acts as a critical intermediary, facilitating the transformation of internal and external driving forces into innovative green technological outcomes.

Green governance alliances moderate the relationship between low-carbon transformation capability and organizational green learning. When green governance alliances are low, enterprises need to rely on low-carbon transformation capability to enhance organizational green learning. This capability allows enterprises to identify external environmental factors; introduce new knowledge, technologies, and processes; and then convert them through organizational learning. However, as green governance alliances increase, the influence of low-carbon transformation capability and carbon label credibility on organizational green learning weakens. The reason behind this is that, in a high green governance alliances context, the green learning among agricultural enterprises gradually shifts from a competitive logic to a symbiotic logic. Enterprises become interdependent and mutually reinforcing, forming an integrated organizational green learning ecosystem, thereby diminishing the impact of low-carbon transformation capability on organizational green learning. Thus, high involvement in green governance alliances may impede the influence of low-carbon transformation capability on organizational green learning.

Green governance alliances also moderate the relationship between carbon label credibility and organizational green learning. When green governance alliances are low, enterprises rely on the credibility of government or industry carbon labels to enhance organizational green learning. Carbon label credibility, as part of government or industry environmental regulations, requires agricultural enterprises to comply with carbon label standards through organizational green learning. However, when enterprises are highly involved in green governance alliances, the influence of carbon label credibility on organizational green learning weakens. In a high green governance alliance environment, enterprises establish close, long-term, low-carbon cooperation and trust within their supply chains. This deep understanding of each other’s low-carbon practices turns carbon labels into a marker of competitiveness and brand influence, enabling agricultural enterprises to co-create value through organizational green learning.

Green governance alliances do not significantly moderate the relationship between low-carbon transformation capability and disruptive green technological innovation. This may be because enterprises with strong low-carbon transformation capabilities focus their green governance alliances more on enhancing low-carbon and green capabilities. To protect core technologies, they may share less information about disruptive technologies and transformations, making it difficult to advance disruptive green technological innovation. Similarly, green governance alliances do not significantly moderate the relationship between carbon label credibility and disruptive green technological innovation. The reason may be that, after adopting carbon label credibility and engaging in green governance alliances, suppliers, downstream customers, research institutions, industry associations, and competitors collaborate to fully exploit mature green resources and develop carbon-labeled products and services. However, due to concerns about trade secrets, the exchanges between enterprises primarily focus on green improvements rather than disruptive innovations in core green processes, resulting in an insignificant impact on disruptive green technological innovation.

Furthermore, regression analysis conducted by dividing agricultural organizations into large-scale and small-scale enterprises reveals that low-carbon transformation capability has a significant positive effect on disruptive green technological innovation in large-scale enterprises, with its influence being greater than in small-scale enterprises (regression coefficients of 1.312 and 0.896, respectively). In large-scale enterprises, low-carbon transformation capability significantly promotes organizational green learning. In small-scale enterprises, however, the effect of green organizational learning on disruptive green technological innovation is not significant. This highlights significant differences in the driving mechanisms of green innovation between large-scale and small-scale enterprises. Large-scale enterprises place greater emphasis on internal low-carbon transformation capability and organizational learning, while small-scale enterprises rely more on external market factors, particularly carbon label credibility.

For the first research objective, the DSR model was employed to construct an analytical framework where low-carbon transformation capability and carbon label credibility are conceptualized as driving forces, organizational green learning as the state, and disruptive green technological innovation as the response. The results confirm that low-carbon transformation capability and carbon label credibility significantly and positively influence disruptive green technological innovation, thereby validating their roles as critical drivers.

For the second research objective, the findings reveal that organizational green learning mediates the relationships between low-carbon transformation capability, carbon label credibility, and disruptive green technological innovation. This highlights the generative pathways and interactive mechanisms of green governance alliances and organizational green learning in advancing disruptive green technological innovation.

5.2. Policy Implications

For the Government: Strengthen carbon information disclosure, establish a comprehensive system for carbon information disclosure to enhance the credibility of carbon label market information, and build a unified and transparent carbon emissions management system; Improve carbon emission management in agricultural enterprises, guide enterprises in low-carbon transformation, enhance their carbon reduction capabilities, and initiate pilot projects for product carbon emission benchmarking and carbon footprint certification; Foster a platform for agricultural enterprises to aggregate and facilitate green organizational learning, encouraging them to better acquire, absorb, and integrate knowledge, technologies, and experiences related to green innovation from external sources; Offer technical support, green finance, and talent resources to promote low-carbon and green development in agricultural enterprises.

For Enterprises: Pursue disruptive green technological innovation, enhance awareness of low-carbon innovation, introduce disruptive green technologies, allocate research and development funds for green technology breakthroughs, implement low-carbon transformation throughout the process, and promote the market application of green innovations; Establish a carbon certification system for agricultural products, develop and implement pilot projects for product carbon footprint registration and certification, actively develop carbon-labeled products, improve carbon data quality, and seize early opportunities in the low-carbon market; Join platforms for agricultural enterprises to engage in organizational green learning, continuously increase the stock of green knowledge, and strengthen the integration of green knowledge within the organization; Enhance green governance alliances, build trust among enterprises based on a shared commitment to low-carbon development, strengthen collaboration in green technologies among members, improve the integration and cohesion of green cooperation, and form a cohesive green governance community within the enterprise.

While this study contributes to the understanding of low-carbon-driven disruptive green technological innovation in agriculture, it has certain limitations. First, the sample is restricted to agricultural enterprises in Guangxi, which may limit the generalizability of the findings. Future research should consider expanding the sample to include enterprises from diverse regions and of varying sizes to enhance representativeness. Second, the reliance on questionnaire data introduces potential subjective bias. Incorporating both quantitative data and qualitative interviews in future studies could provide more comprehensive insights. Moreover, international comparative studies could be conducted to examine differences and shared experiences in disruptive green technological innovation across agricultural enterprises worldwide.

In summary, the analytical framework constructed through the DSR model not only validates low-carbon transformation capability and carbon label credibility as critical driving forces of disruptive green technological innovation but also reveals the generative pathways and interactive mechanisms of green governance alliances and organizational green learning in advancing such innovations. These findings align with existing perspectives in the literature and provide a new viewpoint for understanding how agricultural enterprises achieve sustainable development through the interaction of internal and external factors.