An Analysis of the Pathways for Enhancing Green Total Factor Productivity in Livestock Industry Listed Companies: A Study Based on Dynamic QCA

Abstract

Improving the green total factor productivity (GTFP) of publicly listed companies in the livestock sector is essential for achieving sustainable and high-quality development in China’s agricultural industry. This study proposes an integrated analysis framework for the advancement of GTFP, focusing on internal resource allocation and external business environment configurations. Using panel data from 32 publicly listed companies in China’s livestock sector covering the period 2016 to 2021, we apply the dynamic qualitative comparative analysis (QCA) and necessary condition analysis (NCA) methodologies to explore the configuration pathways for multiple factors that influence GTFP, aiming to identify the mechanisms that drive these pathways in publicly listed livestock companies. The findings reveal that individual antecedent conditions are not essential for achieving high green total factor productivity (GTFP) in firms. Rather, internal and external factors jointly facilitate GTFP enhancement, resulting in three distinct configurational pathways that share the equivalence of “diverse configuration pathways leading to the same objective”. Over time, the consistency level of each configuration pathway fluctuates above 0.94, demonstrating their stability over the study period. In terms of individual companies, the explanatory power of each configuration remains uniform across enterprises, exhibiting no significant differences. This study expands the scope of GTFP-related research and advances the application of the dynamic QCA method. It also provides enlightenment for policymakers to refine sectoral regulations and for companies seeking strategies to improve GTFP.

1. Introduction

The livestock industry is an essential sector connected to the national economy and public welfare that also increases the income of farmers and herders, promoting rural revitalization [1]. Publicly listed companies in the livestock sector are at the forefront of driving industry growth by leveraging advanced technologies and improving market integration. Although they have significant success, these firms face the dual challenges of achieving economic growth and ecological sustainability [2]. To achieve sustainable and high-quality growth of China’s listed livestock companies, a transformation of the development paradigm is necessary. An effective approach to encourage the green development of these firms is to improve green total factor productivity (GTFP) [3,4]. GTFP originates from the basic idea of TFP proposed by Solow [5]. GTFP accounts for both green inputs and undesirable outputs [6] and is widely utilized in academic research to assess the quality of economic growth and development [7]. It measures the efficiency with which factor inputs, such as labor and capital, are converted into an output during economic development. Additionally, GTFP incorporates both positive and negative outputs, including carbon emissions, within its evaluation framework. This measure serves as an indicator of the balanced growth between economic and environmental dimensions at the regional or sectoral level [8,9]. Consequently, improving GTFP is a key objective to achieving the coordinated development of economic and environmental goals, both for individual enterprises and for the whole industry. However, in the current complex environment, firms dependent upon a single factor cannot achieve a significant enhancement in high GTFP levels. To achieve high levels of GTFP, it is necessary for firms to collaborate on both internal and external factors. Therefore, exploring the configuration pathway through which these factors contribute to the GTFP of the livestock industry’s listed companies is essential for achieving their high-quality advancement.

A significant amount of scholarly study has investigated several elements affecting GTFP in the livestock sector. Shuming Ren and Bangle Lv discovered that financial limitations adversely affect the total factor productivity of firms [10]. Their research, using micro-panel data and the bilateral stochastic frontier model, demonstrates that government subsidies may mitigate the adverse impacts of funding limitations for the majority of firms. Nonetheless, for a limited number of enterprises, the beneficial influence of government subsidies does not entirely counterbalance the adverse impacts of these restraints. Meiling Wang et al. used a threshold model at the macro-level to examine the influence of technical innovation on GTFP in OECD member nations [11]. Their research indicates that technical innovation substantially improves GTFP and that enhancing the quality of technological innovation may further accelerate GTFP growth. Jinying Wang and Simin Wang, using the Tobit regression model with panel data from China’s provincial regions, discerned a “U-shaped” correlation between human capital and local GTFP [12]. It was determined that human capital has a favorable influence on GTFP up to a certain threshold, after which the effect wanes. Moreover, the impact of the human capital from adjacent areas on GTFP has an inverted “U” shape. Bingqing Hou and Bing Wang used a quadratic directional distance function model and discovered that the average green total factor productivity (GTFP) of the economy exhibited positive development during the research period, with the business environment identified as a novel catalyst for GTFP enhancement [13]. Jianhong Xia et al. demonstrated using panel data and quantile regression models that environmental regulation first suppresses GTFP before ultimately fostering it [14]. Wei Fu et al. conducted an empirical analysis of the influence of the digital economy on agricultural GTFP using the GMM model, demonstrating that the digital economy substantially enhances the development of agricultural GTFP [15]. Xiaoyu Qu and Zixuan Zhao investigated the mechanisms to improve industrial green total factor productivity (GTFP) in China using the fuzzy set qualitative comparative analysis (fsQCA) methodology [16]. They found ten unique routes for improvement, suggesting that market-based environmental rules and the level of informatization are critical elements in enhancing GTFP, whilst the factor structure and administrative regulations have less influence on its advancement. The mentioned research shows that the factors influencing GTFP are varied and complex, including external factors such as government subsidies, business climate, environmental regulations, and the digital economy, as well as internal factors like financing constraints, technological innovation, and human capital. Improving GTFP is not due to a single factor but results from the combination and coordination of several elements.

While the existing literature provides a strong theoretical foundation for this topic, further research is needed in the following areas. From the perspective of the research subjects, the majority of studies have concentrated on China’s macroeconomics, provinces, and industries, with less research on companies as distinct entities, especially livestock enterprises. From the perspective of influencing factors, the prior study has thoroughly analyzed the correlations between many influencing elements and GTFP, taking into account the “net effect” of these aspects. Nevertheless, this research has inadequately examined the reasons why firms with varying factor endowments may attain comparable GTFP growth. Exclusively concentrating on particular elements is inadequate for comprehending advancements in GTFP. Improving GTFP necessitates the synergistic impact of internal technical innovation, effective resource allocation, reduced transaction costs, and ongoing enhancement of the external business environment with governmental assistance. These elements jointly tackle operational issues arising from internal limitations and encourage enterprises to enhance resource allocation [17], therefore propelling GTFP development. Furthermore, in terms of research methods, conventional regression analysis, while extensively used, is limited to assessing the average impact of individual variables. This method fails to sufficiently account for the intricacies of several interacting variables or their temporal fluctuations. Conventional QCA approaches, while more adept at addressing multi-factor complexity [18], are limited to cross-sectional data and do not effectively capture the dynamic processes of synergistic interactions across components [19].

Guided by the limitations of prior research, such as (1) neglect of the micro-level, (2) excessive focus on individual factors, insufficient attention to factor interactions, and (3) reliance on static analysis, this study explores what configurations of internal and external factors drive high GTFP in listed livestock firms. Specifically, we investigate the following questions: Can a single factor serve as a necessary condition for improving GTFP in publicly listed livestock enterprises? How does the interaction of different factors influence the enhancement of GTFP equivalence? How do these configurations evolve over time or across firms? To investigate these issues, we take a micro-level perspective, integrating internal resource allocation and external business environments. Dynamic qualitative comparative analysis (QCA) is used to identify evolving over time in configuration pathways.

This research presents several significant contributions. An analytical analysis is established using configurational theory to assess the influence of internal and external factors on the GTFP of publicly listed enterprises in the livestock industry. This paradigm elucidates the causal linkages among these factors, providing a thorough comprehension of their synergistic effects and pinpointing essential circumstances within the group path. It further offers theoretical justification for policy enhancements. The dynamic QCA technique analyzes the panel data of publicly listed firms in the livestock industry, addressing the shortcomings of conventional qualitative and quantitative research methodologies. This method methodically examines the effects of multifactor combinations on GTFP, elucidating the dynamics and continuity of preceding circumstances and their internal interaction patterns. This research improves comprehension of the impact of prior circumstances on outcomes from both theoretical and policy viewpoints. The research ultimately investigates the temporal stability of the groups and their significance to different publicly listed firms in the livestock industry. This research gives critical insights into industry advancement and serves as a foundation for local governments to design effective strategies for sector growth.

This study is organized as follows. Section 2 outlines the theoretical analysis. Section 3 describes the research methodology and data. Section 4 starts with a necessity test for a single antecedent condition, followed by a group adequacy analysis, which includes overall, between-group, and within-group tests, as well as a mechanism analysis. Finally, Section 5 presents the conclusions and recommendations based on the study’s findings.

2. Theoretical Analysis

To explore what configurations of internal and external factors drive high GTFP in listed livestock firms, this section offers a theoretical analysis. It examines how internal resource allocation and external business environment factors individually influence GTFP. Then, adopting a configuration perspective, we explore their complex interactions with GTFP. Finally, potential pathways for enhancing GTFP are summarized, laying the groundwork for the subsequent analysis of configuration pathways.

2.1. Theoretical Foundation

Previous studies on GTFP, based on the endogenous economic growth model, have emphasized that innovation-driven technological efficiency and progress are key drivers of GTFP improvement [20]. However, most of these studies have focused on the positive impact of innovation from an internal firm perspective. North posits that traditional economic analyses capture only a portion of economic activity, overlooking the external market transaction costs that firms incur [21]. In an imperfectly competitive market, transaction costs are an inherent challenge in business exchanges. Transaction cost theory, as proposed by North, suggests that effective external policies and institutional frameworks can significantly reduce transaction costs by appropriately regulating enterprises’ production processes and resource exchanges [21]. As a result, a supportive external business environment not only enhances operational efficiency but also works in tandem with internal resource allocation, collectively boosting the enterprise’s GTFP. This highlights the complexity of improving GTFP.

2.1.1. The Endogenous Economic Growth Model: Internal Resource Allocation and GTFP

The endogenous economic growth model identifies technological progress and externalities as the primary drivers of economic growth. Romer emphasized that technological progress is central to economic growth and that knowledge, as a by-product of firms’ investments, generates spillover effects that can be leveraged by other firms, enhancing overall productivity [22]. Ping Li et al. distinguish between technological progress and technological efficiency, with the former driving productivity through innovation and the latter improving efficiency by utilizing existing technologies [23]. Lucas further highlights the importance of specialized human capital and its mobility in driving technological innovation and economic growth [24]. Additionally, the model suggests that the accumulation of knowledge and human capital creates spillover effects, causing the individual returns of firms to fall short of the social returns. Without effective government intervention, firms reduce their investments in knowledge accumulation, leading to suboptimal growth. To achieve optimal productivity, government regulation, such as providing subsidies to encourage technological innovation, is necessary [22].

Based on the aforementioned, the internal resource allocation of publicly traded enterprises in the livestock sector involves integrating key production components, such as technology, capital, and labor, based on demand [25]. In the current context, where environmental sustainability is crucial for development and food safety is a primary concern, improving competitiveness and ensuring product compliance requires innovative business strategies. Enhancing the efficiency of research, development, and application of green animal husbandry technologies is essential. R&D investment generates incremental technology and knowledge, which serve as key drivers of technological upgrading and positively influence productivity [26].

Furthermore, publicly listed livestock enterprises face challenges such as extended production cycles, difficulties in epidemic prevention and control, a heightened risk of environmental pollution, significant capital investment during production, and delayed returns. These factors make firms particularly vulnerable to financing constraints. In a constrained financial environment, internal funds are primarily used for daily operations, but these resources are often insufficient to meet the substantial capital demands of technological research and development. As a result, companies may reduce their R&D investment, diminishing the need for technical personnel and relying on technological spillovers for production technologies, which may ultimately hinder their green total factor productivity (GTFP) [27,28]. In contrast, firms with fewer financing constraints can access the capital required to expand production, increase innovation inputs, strengthen their technological capabilities, and lead industry innovation [29]. These firms can enhance R&D in green technologies, refine environmentally sustainable farming practices, and achieve significant improvements in GTFP.

In technological innovation, specialized human capital and its mobility are crucial for firm growth. Advanced human capital can substantially enhance a company’s GTFP [30,31,32]. Managers with the necessary expertise are better positioned to assess market dynamics, identify innovation opportunities, and make strategic decisions [33]. Skilled teams can quickly and effectively implement new technologies, leveraging their deep knowledge and technical proficiency. This facilitates the rapid dissemination of technology within the organization, significantly improving its capacity for technology absorption and independent innovation [34]. Companies often rely on highly skilled human capital in their innovation efforts, which not only accelerates technological advancement but also enhances the overall knowledge base and practical experience of the workforce [35]. This creates a virtuous cycle, where the interaction between human capital, innovation, and R&D strengthens the company’s GTFP.

2.1.2. Transaction Cost Theory: External Business Environment and GTFP

North divides economic theory into two branches: one that focuses on the theory of transaction benefits and another that examines transaction costs [21]. The former has been the primary focus of economic growth theory since Adam Smith. However, North argues that this approach overlooks another crucial aspect of economic activity—the transaction costs that are inherent in market exchanges [21]. Traditional market economies assume that the economic system is self-regulating through price mechanisms, where supply adjusts to demand, production adjusts to consumption, and production factors flow to higher-paying users. However, Coase argued that this system is incomplete, as many economic activities do not rely solely on price mechanisms for resource allocation [36]. By addressing the question of why firms exist, Coase highlighted the limitations of price mechanisms and the pervasive nature of transaction costs. He further explained the value of entrepreneurs in coordinating resources and reducing these costs. The typical transaction costs in a price mechanism include price assessment, negotiation costs, and the costs associated with setting conditions for each transaction [36]. Moreover, transaction costs do not automatically decrease with technological advancements. In other words, while the introduction of new technology may lower certain costs, it can also generate new costs, sometimes even leading to higher social costs [37]. For instance, when a company introduces new technology that is too advanced for the market, determining a fair price becomes difficult [38]. One party may maximize its own benefits at the expense of the other participants, potentially leading to market failure. North referred to this phenomenon as the ‘self-destructive tendency of market’. Therefore, to address the issue of high transaction costs, effective government intervention becomes particularly necessary [21].

The business environment is a complex system composed of external factors established by the government that market participants encounter at various stages, including entry, operation, and exit [39]. These factors include government subsidies, the degree of financialization, and the level of marketization. Due to the inherent unpredictability of innovation efforts and the positive externalities of innovation outcomes, organizations often lack sufficient internal motivation for innovation [40,41]. In the development of the livestock sector, the government plays a crucial role in assisting industries and enterprises that struggle to innovate through market forces, thereby guiding their innovative behavior [42]. Government support typically takes two forms: direct financial subsidies and tax incentives. This study focuses on the former. Financial subsidies are a unique category of external funding that can enhance green total factor productivity (GTFP) in several ways: first, by alleviating financing constraints [43], which provides a solid foundation for production and operations; second, by sending positive policy signals to external investors [44], which facilitates the expansion of financing channels, attracts external capital, reduces financing costs, and indirectly eases financing challenges; third, by addressing broader financial limitations; and finally, by mitigating the financial difficulties within the industry and the company [45]. However, while government subsidies can create additional profit margins, they may also encourage rent-seeking behavior in publicly traded companies within the livestock sector, leading them to seek subsidies or over-invest in inefficient projects, potentially undermining their GTFP [46].

The innovative behavior of micro-entities is closely tied to the sophistication of the local financial market and its mechanisms [47]. The level of financialization reflects the extent to which financial intermediaries contribute to societal functions. High levels of financialization can reduce corporate financing costs [48], improve access to finance [49], and increase the speed of capital circulation [50], thereby enhancing credit allocation efficiency to better meet the financial needs of diverse firms [51]. Market orientation refers to a resource allocation system that responds to market demand, facilitating market operations. In an efficient market, resources are allocated effectively through the ‘invisible hand’, which optimizes the functioning of the market. This can lower market entry barriers for enterprises, provide accurate and timely price signals, accelerate the movement of resources, and continuously refine the structure of resource allocation. In an ideal economic system with a perfect market mechanism, the economy serves as the ’trunk’, and finance functions as the ’blood’. The positive relationship between the two is reinforced through the expansion of financing [52], the reduction of financing costs [53], enhanced transaction efficiency within the financial system [54], and the creation of a stable foundation for sustained innovation. These elements alleviate financial burdens and provide the resources necessary for continuous innovation, ultimately driving improvements in GTFP.

2.1.3. Configurational Causality: External and Internal Factors Jointly Drive GTFP

To highlight the important connections between these elements and GTFP enhancement, the linear relationships have been outlined above. However, in practice, the environment, technology, and group of the social domain often interact in complex configurations rather than functioning in isolation [55]. Building on the above discussion, the endogenous economic growth model and transaction cost theory offer a comprehensive theoretical framework for understanding the enhancement of GTFP in listed companies within the livestock industry. The connection between internal and external factors is mediated by the government system. From an internal perspective, a company’s human capital and investment in technological innovation can enhance efficiency. The government system regulates knowledge spillovers, thereby fostering productivity growth. Externally, a favorable business environment allows firms to achieve reasonable returns through independent transactions, while government intervention helps reduce transaction costs.

Capital, human capital, and technical innovation are fundamental strategic elements for the development of publicly traded firms in the livestock sector and key drivers of green total factor productivity (GTFP) enhancement. Capital sustains firm operations. Human capital embodies the reservoir of knowledge, skills, and creativity, and technical innovation serves as the core driver for overcoming developmental barriers and achieving higher GTFP. The livestock sector is characterized by inherent vulnerabilities, including high-risk, long-cycle innovation processes, compounded by information asymmetry. As a result, firms in this sector are susceptible to financing constraints and credit limitations, which complicate the securement and deployment of funds. These constraints negatively impact internal resource allocation [56], dampen innovation enthusiasm, reduce investment levels, and hinder GTFP improvement.

An enabling external business environment is critical to overcoming these challenges. Financial subsidies from government departments not only alleviate financial pressures but also support firms’ R&D investments through targeted policy guidance [57], optimizing the allocation of internal resources. Additionally, government subsidies send positive signals to the market, reduce information asymmetry, enhance market transparency and efficiency, and strengthen the development of market and financial systems. Refining these mechanisms improves firms’ access to capital and technological resources, provides diverse financing options, reduces transaction costs, mitigates financing constraints, attracts top-tier talent, and creates an innovation-friendly environment, all of which contribute to the firm’s GTFP growth.

In summary, efficient integration and use of internal resources are crucial for improving a company’s GTFP, while continuous improvement of the business environment provides external support for its growth. These two factors complement each other and help the company overcome development challenges. This means that the six conditions outlined above do not operate in a linear fashion. Instead, they interact with each other to determine the GTFP development. This is in contrast to the previous studies that examined only the average effect of a single factor on GTFP [10,11].

2.2. Configurational Perspectives: Possible Paths for Internal and External Factors to Promote GTFP in Publicly Listed Livestock Companies

Configuration analysis is based on systems thinking, viewing organizations as complex systems [58]. The complex systems perspective views market agents as competing, coexisting, and adapting to one another, seeking multiple solutions rather than optimal equilibriums when confronted with challenges in the economic system. These agents continuously learn, adapt, and adjust their strategies in response to changing circumstances. As a result, different listed companies in the livestock industry may exhibit varying combinations of mechanisms to enhance GTFP, influenced by differences in resource endowments, such as technology, talent, and location. Additionally, this perspective recognizes innovation as a blend of existing technologies and knowledge. Innovation alone is insufficient to drive GTFP improvement; it requires the coordination of both internal and external factors (e.g., talent, government subsidies, finance, and market conditions) to fully unlock its potential. Consequently, the complexity of GTFP enhancement stems from the potential combinations of these internal and external factors, which together form diverse pathways for improvement.

Table 1. Possible pathways to GTFP enhancement.

GTFP Enhancement Possible Pathways	Mode	Internal	External
		Entrepreneurial Individual	Government	Market
Singl-driven pathway	Enterprise leading	1 a	0 b	0
	Government leading	0	1	0
	Market leading	0	0	1
Diversified synergistic-driven pathway	Enterprise–Government	1	1	0
	Government–Market	0	0	1
	Enterprise–Market	1	0	1
	Enterprise–Government–Market	1	1	1

^a: 1 indicates that the condition has an effect; ^b: 0 signifies no effect.

outlines the possible pathways to GTFP enhancement based on these combinations.

For the single-driven pathway, in the process of enhancing GTFP in listed livestock companies, internal and external factors may interact competitively, with one sometimes playing a dominant role. GTFP varies across enterprises and regions, and strengthening the influence of a specific factor individually can lead to significant improvements. This effect has been validated by numerous studies [10,11].

For the diversified synergistic-driven pathway, enterprises, governments, and markets interact symbiotically within the economic system, collectively fostering entrepreneurial vitality [59]. Listed livestock companies operate in diverse regions, each shaped by distinct governmental and market dynamics. The optimal balance between these influences for GTFP improvement depends on specific contextual factors. To enhance GTFP, enterprises must integrate internal resources with external support, aligning government policies and market intelligence to create a synergistic effect.

To conclude, this paper establishes a theoretical analysis flow, as outlined in Figure 1, highlighting the complex causal mechanisms influencing GTFP enhancement in publicly listed livestock companies. Given that GTFP improvement is shaped by both internal and external factors, a linear perspective is insufficient for capturing the intricacies of these relationships. Instead, a configuration approach provides a more comprehensive examination of how these factors interact. By integrating the endogenous economic growth model and transaction cost theory, this section systematically explores the interplay between internal resource allocation and external business environment factors in influencing GTFP. It identifies key influencing factors and selects financing constraints, R&D investment, human capital, government subsidies, the level of marketization, and the level of financialization as the antecedent conditions, with GTFP as the outcome in the subsequent empirical analysis. Furthermore, the theoretical insights guide the identification and interpretation of the potential configuration pathways. This helps to clarify how different combinations of internal and external factors contribute to GTFP enhancement, ensuring that the empirical findings are grounded in a solid theoretical basis.

3. Methodology and Data

3.1. Research Methodology

Based on the previous theoretical analysis, it is clear that the internal and external factors of the enterprise are interdependent, and the interactions among these factors create a complex causal relationship with the outcomes of GTFP enhancement. In order to address the research question regarding how the internal resource allocation and external business environment of publicly traded firms in the livestock sector influence the improvement of GTFP, traditional studies that focus solely on the independent or pairwise correlations of these components are insufficient. It is necessary to go beyond a linear perspective and look at the relationships between systems and elements from a configuration perspective. The QCA method effectively uncovers the concurrent mechanisms linking multiple conditions to outcomes [19], providing an approach to exploring the complex causal relationships between the internal and external factors in enterprises and their influence on green total factor productivity.

QCA, proposed by Ragin, is well-suited for analyzing the multiple and complex causal mechanisms between a firm’s internal and external factors and GTFP enhancement [60]. The configuration analysis of QCA is based on three hypotheses: multiple conjunctural causation, equifinalty, and asymmetry [61]. Through its three hypotheses, QCA analysis effectively addresses the research questions in this study. Multiple conjunctural causation suggests that QCA helps identify specific combinational configurations of these multiple factors and reveals how each configuration contributes to the enhancement of GTFP. Equifinalty suggests that different configuration paths can lead to the same outcome, indicating the existence of multiple equivalent pathways for GTFP enhancement and providing flexibility options for businesses to develop strategies. Asymmetry includes the asymmetry of causation [61] and asymmetry in conditional effect [62]. Asymmetry of causation implies that the factors leading to a desired outcome (high GTFP) differ from those causing its absence (low GTFP). Asymmetry in conditional effect implies that a factor influencing one configuration pathway may be inactive or exert an opposite effect on another. This concept enhances the understanding of case variations and the interdependent nature of configurational effects.

Due to the dynamic, long-term, and time-accumulative nature of the factors influencing GTFP, both internal and external, this study utilizes the QCA method, as proposed by García-Castro and Ariño [63]. This method addresses the limitations of traditional QCA, which tends to focus on cross-sectional data while neglecting the temporal dynamics of causality, thus allowing for a more comprehensive examination of how influences evolve over time. The adequacy of conditional configuration analysis is assessed across three dimensions: pooled analysis, between analysis, and within analysis. While between analysis examines the evolution of these configurations over time, within analysis focuses on their changes across different firms.

Additionally, the study employs the necessary condition analysis (NCA) method to identify the necessary relationships and quantify the extent to which a condition is a prerequisite for a particular outcome [59]. It effectively determines whether a single antecedent condition is necessary for GTFP enhancement. This approach overcomes the limitations of QCA, which can only qualitatively determine whether a condition is necessary or unnecessary, thereby enabling a more precise understanding of the critical factors driving GTFP improvement.

3.2. Data Collection and Processing

3.2.1. Data Resources

The data collection strategy was collected to address the core research question, which focuses on the configurations of the internal and external factors influencing GTFP in listed livestock firms, aligning with the core research question. This study specifically examines publicly traded enterprises within China’s livestock sector, selecting six antecedent conditional variables (financing constraints, human capital, and R&D investment) and one outcome variable (GTFP). This selection aligns with the research question by capturing both the internal and external factors that might contribute to GTFP enhancement. Due to consistency, completeness, and data availability, 32 publicly traded firms were selected as data samples for the research period from 2016 to 2021. The fundamental data used for GTFP computation is sourced from the China Rural Statistical Yearbook, the China Fishery Statistical Yearbook, the China Stock Market & Accounting Research Database, and the manually compiled outcomes of each enterprise’s yearly audit reports. The data about internal resource allocation, government subsidies, and financialization levels under the antecedent circumstances are sourced from the Cathay Pacific database, while the marketization level data are derived from the China Marketization Index Report, edited by Gang Fan et al. [64]. The measurement data for each index were mainly derived from authoritative institutions.

3.2.2. Variables Description

(1) Outcome Variable

Green total factor productivity (GTFP) encompasses resources, energy, the economy, and the environment. The objective of enhancing the GTFP of publicly traded firms is to achieve a mutually beneficial outcome between the economic and ecological advantages of the organization, thereby facilitating high-quality growth. This paper selects the super-efficient SBM model [65], which accounts for non-expected outputs, effectively addressing issues of variable slackness and the differentiation and ranking of decision-making units. It objectively and accurately represents the efficiency of the evaluated units. However, it does not capture dynamic changes in efficiency. Therefore, the GML (global Malmquist–Luenberger) index [66] is employed to assess the GTFP of each publicly traded firm with MaxDEA software (version 8.17). Given that the index represents the growth rate rather than the efficiency value, the cumulative multiplication method, as used by Kun Xie et al. [67], is utilized to derive the annual GTFP of listed firms.

This paper’s GTFP evaluation index system estimates capital inputs using the perpetual inventory method, defines labor inputs by the number of employees engaged in daily production activities, and incorporates energy consumption as a key input consideration. The output consists of both desired and undesired dimensions. First, the company’s primary business income is used as a proxy for desired output, with price fluctuations adjusted using the agricultural production price index. Second, carbon dioxide emission equivalents are employed as a metric for undesired output, following the research methodology of calculating adjustment coefficients proposed by Xinhua Cui et al. [68].

(2) Condition Variable

For financial constraints (FC), the common methods for measuring financial constraints include the SA index [69] and the KZ index [70]. While the KZ index can be influenced by subjective bias, the SA index offers a more objective and comprehensive representation of a firm’s financial limitations. Therefore, this study adopts the SA index to assess the financial constraints faced by enterprises [71]. The SA index can be expressed as follows:

$$SA=-0.737size+0.043siz{e}^2-0.04age$$

(1)

where $size$ is measured using the logarithm of the company’s fixed assets and $age$ is measured using the logarithm of the difference between the company’s corresponding year and the year of its establishment.

For investment in research and development (R&D), several methods are typically used to quantify R&D investment: first, the ratio of R&D expenditure to total assets; second, the ratio of R&D expenditure to operational income; and third, the percentage of R&D spending relative to enterprise value. However, enterprise value in China is affected by multiple factors, making it difficult to assess and verify. Corporations may also hide operational revenue, complicating authenticity checks. To reduce the influence of firm size on R&D investment evaluation, this study follows Zhiyong Xu’s approach [72], using the ratio of R&D investment to total assets at the beginning of the period as the key measure.

For human capital (HC), human capital creation emphasizes education as a fundamental component, and the current research typically measures regional human capital in China by the average years of schooling. However, publicly traded companies in China do not categorize employee education levels uniformly by years of schooling. Some firms classify their workforce into five categories: postgraduate and above, undergraduate, junior college, high school and below, and others. This study uses the ratio of employees with at least an undergraduate education to the total number of employees as a measure of human capital level [73].

For government subsidy (GS), to account for government subsidies, this paper uses government subsidy intensity as a proxy, addressing the impact of heterogeneity in subsidy amounts linked to firm size. Subsidy intensity is measured by the ratio of government subsidies to total assets at the end of the period.

For the level of marketization (LM), to evaluate the extent of marketization, Fan Gang and other scholars have developed a comprehensive index system, which enables vertical and horizontal comparisons of marketization processes across provinces and cities. The marketization index for each province reflects the effectiveness of regional market mechanisms. This study uses the marketization index from the “Report on China’s Marketization Index” by Fan Gang et al. [64] as a proxy to assess marketization levels.

For the level of financialization (LF), to measure the extent of financial contribution from the social financial system to the real economy and broader society, the study by Jia Li et al. uses the ratio of incremental social financing in a province to its provincial GDP as an indicator of financialization [74].

(3) Data Calibration

Data must be harmonized and standardized before conducting necessity and sufficiency analyses, which are basic steps in dynamic QCA. Clear criteria for distinguishing between high and non-high GTFP are not readily available. The evaluation of internal resource allocation within enterprises, along with the assessment of the external business environment’s advantages and disadvantages, depends on relative levels. The GTFP level of enterprises serves as a relative indicator based on the sample; the data presented in this paper are suitable for calibration using the sample-based relative position method [75]. A direct calibration method is employed, with the 95th percentile, 50th percentile, and 5th percentile of the descriptive statistics for the case samples representing the entire affiliation point, the crossover point, and the entire unaffiliated point, respectively [59]. Additionally, to address the issue of group-state attribution when the case affiliation for the antecedent condition is exactly 0.5, this paper recalibrates the affiliation of 0.5 by incorporating a constant of 0.001 [76,77].

Table 2. Calibration of dependent variable and independent variable.

	Variable	Calibration
		Complete Membership	Cross Point	Complete Non-Membership
Outcome Variable	GTFP (High)	1.0441	0.0919	0.0028
Internal resource allocation factors	FC (high)	−1.9501	−2.5728	−3.2036
	RD (high)	0.1037	0.0024	0.0000
	HC (high)	0.4330	0.1173	0.0205
External business environment factors	GS (high)	0.0420	0.0037	0.0003
	LM (high)	11.4137	9.1910	5.3200
	LF (high)	0.4099	0.2226	0.1170

provides the calibration anchors for the antecedent conditions and outcomes.

4. Results of Dynamic Analysis of Configurations Driving High GTFP in Listed Livestock Firms

This section examines the necessity of individual antecedent conditions for GTFP using QCA necessity analysis and NCA. Subsequently, dynamic QCA is applied to explore the condition pathways through pooled, between, and within analyses. The following subsections present the results.

4.1. Necessary Analysis of Single Conditions

4.1.1. QCA Necessity Analysis

According to the QCA theoretical framework, it is essential to verify the necessity of the antecedent conditions influencing the GTFP of publicly traded companies in the livestock sector before performing the sufficiency analysis. If the overall consistency level of a condition exceeds the threshold of 0.9, it is considered a necessary condition for the outcome. To ensure the reliability of the results, the same test is applied to assess the presence of missing variables (indicated by a ‘~’). In the QCA panel data analysis, an adjusted distance of less than 0.2 indicates a high degree of feasibility for aggregated consistency, serving as a benchmark for evaluation. Conversely, further investigation into the necessity of conditional variables is required, alongside year-to-year variations in panel data, to make a comprehensive judgment [63].

The necessity test for individual conditions in R language, as shown in

Table 3. Single-variable necessity analysis.

Variable	GTFP				~GTFP
	Pooled Consistency	Pooled Coverage	Between Adjustment Distance	Within Adjustment Distance	Pooled Consistency	Pooled Coverage	Between Adjustment Distance	Within Adjustment Distance
FC	0.624	0.603	0.040	0.474	0.578	0.68	0.040	0.521
~FC	0.669	0.566	0.037	0.438	0.663	0.682	0.086	0.444
R&D	0.634	0.577	0.128	0.462	0.657	0.728	0.076	0.491
~R&D	0.701	0.627	0.058	0.403	0.618	0.673	0.031	0.491
HC	0.574	0.632	0.061	0.509	0.518	0.695	0.107	0.592
~HC	0.723	0.552	0.070	0.343	0.726	0.675	0.055	0.373
GS	0.633	0.621	0.312	0.355	0.554	0.662	0.394	0.409
~GS	0.655	0.546	0.238	0.361	0.683	0.694	0.296	0.296
LM	0.743	0.633	0.052	0.361	0.629	0.653	0.092	0.474
~LM	0.592	0.567	0.211	0.480	0.646	0.754	0.238	0.444
LF	0.625	0.655	0.082	0.444	0.548	0.701	0.150	0.515
~LF	0.715	0.565	0.049	0.343	0.730	0.703	0.070	0.343

, reveals that the overall consistency level for each antecedent condition falls below the threshold of 0.9. However, a scenario exists where the adjustment distance between and within the groups exceeds the threshold of 0.2. This can be analyzed from two perspectives. First, the research samples consist of 32 publicly traded companies within the livestock industry, covering diverse primary business types, such as pig farming, poultry farming, and fishery farming. These companies exhibit notable variations in production technology, infrastructure, resource endowment, and locational conditions. As a result, enterprises face significant differences in both external and internal environmental factors while striving for improved GTFP and high-quality development, which may explain the substantial adjustment distance within the group. Second, when the adjustment distance exceeds 0.2, it is crucial to deepen the examination of inter-group consistency and the conditions’ coverage, while conducting thorough analyses related to the relevant years. The analysis of the six types of intergroup adjustment distances that exceed the threshold shows that the consistency level across all years reaches the benchmark of 0.9. As a result, no individual antecedent condition is identified as necessary for the improvement of GTFP among publicly traded companies in the livestock sector (see

Table 4. Combination of cases where the adjustment distance between groups is greater than 0.2.

Group			Year
			2016	2017	2018	2019	2020	2021
Group1	LF and GTFP	Between Consistency	0.454	0.626	0.419	0.724	0.867	0.741
		Between Coverage	0.695	0.698	0.718	0.680	0.489	0.597
Group 2	LF and ~GTFP	Between Consistency	0.400	0.485	0.298	0.621	0.817	0.673
		Between Coverage	0.662	0.675	0.625	0.680	0.690	0.617
Group 3	~LF and GTFP	Between Consistency	0.779	0.709	0.781	0.659	0.451	0.525
		Between Coverage	0.546	0.525	0.476	0.599	0.621	0.586
Group 4	~LF and ~GTFP	Between Consistency	0.815	0.783	0.865	0.708	0.396	0.561
		Between Coverage	0.617	0.723	0.646	0.750	0.817	0.712
Group 5	~LM and GTFP	Between Consistency	0.709	0.691	0.639	0.562	0.515	0.427
		Between Coverage	0.592	0.582	0.585	0.566	0.505	0.554
Group 6	~LM and ~GTFP	Between Consistency	0.823	0.754	0.658	0.667	0.522	0.466
		Between Coverage	0.743	0.792	0.737	0.783	0.768	0.687

).

4.1.2. NCA Analysis

The NCA approach addresses the question, “What is the minimum threshold of preconditions required for a business to achieve a certain level of GTFP?” NCA provides methods for estimating ceiling regression (CR) and ceiling envelope (CE). These two estimation techniques handle continuous and discrete variables, respectively, and determine the necessary conditions through antecedent condition necessity effect sizes. A single conditional effect size (d) exceeding 0.1, accompanied by p-value tests indicating a significant effect (p < 0.05) [78], can be interpreted as a necessary condition for the outcome.

Table 5. NCA necessity analysis for individual conditions.

Condition 1	Method 2	Accuracy	Ceiling Line	Scope	Effect Size	p-Value
HC	CR	92.20%	0.040	0.879	0.046	0.371
	CE	100%	0.004	0.879	0.004	0.563
FC	CR	99.50%	0.000	0.890	0.000	0.991
	CE	100%	0.000	0.890	0.000	0.992
RD	CR	100%	0.000	0.876	0.000	0.730
	CE	100%	0.000	0.876	0.000	0.703
LF	CR	93.80%	0.068	0.918	0.074	0.214
	CE	100%	0.007	0.918	0.008	0.648
LM	CR	95.30%	0.014	0.977	0.015	0.377
	CE	100%	0.006	0.877	0.006	0.527
GS	CR	99.00%	0.000	0.884	0.000	0.846
	CE	100%	0.000	0.884	0.000	0.910
HC	CR	92.20%	0.040	0.879	0.046	0.371
	CE	100%	0.004	0.879	0.004	0.563

¹ Using calibrated fuzzy set affiliation values; ² repeated sampling times = 10,000 using displacement experiments.

presents the results of the necessary assessments using the NCA method under individual circumstances, with the CE method chosen based on Duer’s suggestions [79]. The findings indicate that none of the factors meet the criterion of d > 0.1 with a significant p-value, suggesting that no single condition is essential for achieving high GTFP.

A necessary condition is a critical constraint for the existence of the outcome, and additional criteria cannot compensate for the absence of this condition [79].

Table 6. Analysis of NCA’s necessity bottleneck level (%) for individual conditions under the CE approach.

Condition
GTFP	HC	FC	R&D	LF	LM	GS
0	NN	NN	NN	NN	NN	NN
10	NN	NN	NN	NN	NN	NN
20	NN	NN	NN	NN	NN	NN
30	NN	NN	NN	NN	NN	NN
40	NN	NN	NN	NN	NN	NN
50	NN	NN	NN	NN	NN	NN
60	NN	NN	NN	1.2	NN	NN
70	NN	NN	0.6	1.2	NN	NN
80	NN	NN	0.6	1.2	NN	NN
90	NN	NN	0.6	1.2	NN	2.4
100	28.8	1	76	71.3	27.6	70.1

displays the results of the bottleneck-level analysis using the CE approach. For a given GTFP value, the figures in the bottleneck table must be achieved. Otherwise, reaching that level becomes unattainable. The results show that financialization initially restricts a high GTFP, albeit at a low threshold. As the GTFP levels increase, the impact of bottleneck factors associated with necessary conditions becomes more pronounced, leading to multiple constraints. Specifically, R&D investment, marketization, and government subsidies emerge as the most significant obstacles to attaining higher GTFP in enterprises.

4.2. Condition Configuration Analysis

The histogram analysis examines how different combinations of antecedent conditions lead to the same outcomes. This paper uses a consistency threshold of 0.80, a frequency threshold of 3, and a PRI threshold of 0.70 to construct the truth table, following the methodologies of Yunzhou Du et al. [59] and Fiss [76], as well as observations from the sample set, which includes a total of 165 enterprises. Given China’s vast geographical expanse and the considerable variation in resource conditions across enterprises, assessing the impact of antecedent conditions on outcomes proves to be challenging. As a result, in the analysis of sufficiency data, the direction of the condition variables is not predetermined. After the analysis, solutions can be classified as simple, intermediate, or complex. In this paper, the intermediate solution is identified as the primary solution, with the simple solution serving as a supplementary option. Core and marginal conditions are distinguished through a nested comparison of the two [59].

4.2.1. Interaction Effects of Multiple Factors: Pooled Analysis of GTFP Enhancement Configuration Pathways

Four conditional configurations of publicly traded companies in the livestock sector show high GTFPs.

Table 7. Realization of high GTFP group analysis results.

Condition	GTFP (high) 1
	S1	S2a	S2b	S3
HC
FC
RD
LF
LM
GS
Consistency	0.940	0.967	0.960	0.963
PRI	0.724	0.736	0.754	0.781
Original Coverage	0.271	0.261	0.214	0.219
Unique Coverage	0.077	0.052	0.044	0.056
Between Consistency Adjustment Distance	0.073	0.049	0.046	0.064
Within Consistency Adjustment Distance	0.130	0.124	0.118	0.107

¹ represents the presence of a core condition; represents the absence of a core condition; represents the presence of a borderline condition; represents the absence of a borderline condition, and blank indicates that the condition may or may not be present.

presents the results of the overall grouping analysis, revealing a coverage of 0.461 and a consistency of 0.946. The overall PRI is 0.777, and both the between and within consistency adjustment distances for each pathway are below 0.2. This indicates that the grouping results are relatively stable, with no significant cross-temporal or cross-case effects. Overall, the pathways exhibit high explanatory power, suggesting that these four paths are key strategies for promoting high GTFP in listed companies within the livestock sector.

The four pathways demonstrate strong consistency and PRI values, accounting for 46.1% of the overall sample cases. The critical conditions for S1 include human capital, financing constraints, R&D investment, and government subsidies. For S2a and S2b, the key conditions are financing constraints, R&D investment, and the level of marketization. In S3, the primary conditions are R&D investment and government subsidies. S2a and S2b are grouped based on the core conditions, forming a second-order equivalent state. The analysis identifies three path-driven modes. First, government subsidies are internally driven and reinforced (S1). Second, R&D and innovation dynamics are driven by the market environment (S2a and S2b). Third, R&D and innovation are stimulated by the business environment (S3). These models facilitate the synergy between internal enterprise resources and the external environment, enhancing GTFP and supporting high-quality development. The following section provides an in-depth examination of the configurations that lead to high GTFP, along with representative cases for each.

M1 is the endogenous enhancement of the government subsidy (S1). The key conditions driving enterprises toward a higher GTFP include strong human capital, significant financing constraints, substantial R&D investment, notable government subsidies, and moderate marketization levels. In environments with low marketization, firms can leverage robust internal human capital, alongside external government subsidies, to overcome financing challenges, boost innovation, and improve GTFP outcomes. One explanation is that, in regions with underdeveloped market mechanisms, businesses rely more on government intervention, which offers both policy support and financial subsidies to foster the development of the livestock sector. This support not only mitigates the negative impacts of a challenging external market environment but also accelerates technological innovation within companies, integrating high-quality internal human capital into the process. The government’s role in steering innovation is crucial for GTFP improvement. In this context, the primary firms along this path are located in the Xinjiang Uygur Autonomous Region. For instance, Western Livestock, a prominent company in this region, has faced a marketization index that remained low from 2016 to 2021 (28/31). Despite this, the livestock industry, their primary focus, has received considerable support from national policies. Given the long payback periods and substantial upfront capital required for investment in this industry, the Xinjiang government has provided subsidies to new projects. Over time, as the state’s financial capacity has grown, tax incentives and capital subsidies for livestock enterprises have also increased. The firm prioritizes attracting talent, offering competitive salaries to R&D personnel, and strengthening its “production, learning, and research” model, positioning itself as the main beneficiary of research institutes. This collaboration allows the firm to obtain critical technologies, solve technical issues, and improve product quality, which directly contributes to higher GTFP. The consistency of this path (S1) is 0.940, meaning that 94% of sample cases in this group meet the high GTFP target. Additionally, this group covers 27.1% of the high GTFP cases, and 7.7% of these cases are exclusively attributed to this path. The histogram path is the sole explanation for these high GTFP outcomes.

M2 is the market environment and R&D innovation dynamism driven (S2a and S2b). The S2a path shows that firms can achieve a high GTFP despite significant financing constraints, substantial R&D investments, high marketization levels, and limited government subsidies, even in the absence of strong human capital and financialization. On the other hand, the S2b path highlights that, when high human capital and financialization are present alongside these core conditions, firms are more likely to reach high GTFP. This suggests that, in certain cases, additional government subsidies may not be necessary to overcome financing constraints. Instead, higher marketization levels and adequate R&D investments can mitigate these challenges, facilitating high GTFP outcomes. The high marketization level creates a more competitive environment, enabling firms to increase product value and market share, compensating for financing limitations through technological innovation and product quality improvements. For instance, Longda Meat in Shandong Province has benefited from high marketization levels from 2017 to 2019, providing a favorable external environment for growth. With a less concentrated meat product market and rising consumer demand for healthier options, the company has successfully aligned its products with market needs. This alignment is supported by significant R&D investments, such as a 62.01% increase in R&D spending in 2019. Additionally, Longda Meat’s strategic integration of its supply chain, with R&D centers in Shanghai, Sichuan, and Shandong, gives it a competitive edge. By linking raw material sourcing, production capacity, new product development, and nationwide distribution, the company has enhanced product quality and production efficiency, ultimately achieving a high GTFP. The consistency of the S2a and S2b groupings is 0.967 and 0.960, respectively, meaning 96.7% and 96.0% of the sample cases in these categories meet the high GTFP target. The original and unique coverages for S2a are 0.261 and 0.052, respectively, suggesting that S2a accounts for 26.1% of high GTFP cases, with 5.2% uniquely explained by this path. Similarly, the original and unique coverages of S2b are 0.214 and 0.044, indicating that S2b explains 21.4% of high GTFP cases, with 4.4% exclusively attributed to this path.

M3 is the stimulation of R&D innovation in the business environment (S3). The S3 path shows that firms can achieve high GTFP under conditions of low human capital, minimal financing constraints, and substantial R&D investment when supported by government subsidies and high levels of financialization and marketization. A robust financial and market environment creates a conducive external development ecosystem, reducing transaction costs, fostering technological innovation, and ensuring a fair distribution of benefits between all parties involved. Additionally, government support through subsidies and policies addresses the firm’s human capital gaps, facilitating effective R&D with sufficient funding, which maximizes the impact of technological advancements on GTFP. Huatong Stock, a leading enterprise in agricultural industrialization, exemplifies this path. Based in Zhejiang Province, it integrates the entire supply chain, including feed production, breeding, slaughtering, and meat product processing. Zhejiang’s favorable business environment, marked by a leading marketization index (3/31) and a competitive financialization level (9/31), has greatly benefited the company. Despite Huatong’s labor-intensive structure, its workforce is highly specialized in key areas, giving it a significant human capital advantage. By collaborating with Zhejiang University and other institutions, attracting new talent, acquiring advanced technologies, and leveraging government subsidies, the company has maintained strong R&D investments, addressing diverse market needs. Its efficient distribution system further enhances operational performance, contributing to high GTFP. The consistency of S3 is 0.963, indicating that 96.3% of the sample cases meet the criteria for this grouping to achieve a high GTFP. The original and unique coverages of S3 are 0.219 and 0.056, respectively, meaning S3 accounts for 21.9% of high GTFP cases, with 5.6% uniquely explained by this path.

An analysis of the individual antecedent conditions across the four paths reveals that financing constraints and R&D investment are key factors present in all groupings. Notably, R&D investment serves as a common core condition, playing a crucial role in each category. This underscores that technological innovation is the main driver behind the improvement of GTFP in publicly listed companies within the animal husbandry sector, especially in the face of market, epidemic, and environmental uncertainties affecting the industry. These challenges, along with the uncertainties surrounding innovation activities, contribute to financing difficulties for most firms. Government subsidies and a supportive business environment are essential to alleviating these constraints. Such measures help optimize product structures, reduce pollutant emissions, and foster innovation, thereby ensuring the sustainable enhancement of green total factor productivity (GTFP) through efficient resource and technology utilization. A comparison of paths S2a and S2b reveals that, while both share the same core conditions, they differ only in marginal conditions. Specifically, the presence or absence of human capital and the level of financialization do not significantly impact the outcome in M2. This suggests that the firm’s core competitiveness is primarily driven by R&D and innovation capabilities, with human capital and financialization playing a secondary role in achieving high GTFP. Additionally, comparing the paths of S1 and S2b shows that, for firms focused on R&D and innovation, government subsidies have a limited impact on enhancing GTFP when financialization or marketization levels are high. Therefore, local governments should prioritize improving the broader business environment, as this approach is more effective in driving a high GTFP than implementing subsidy policies aimed at encouraging technological innovation.

4.2.2. Between Analysis: Temporal Evolution of Configuration Pathways

This study explores the temporal effects that drive high GTFP groupings by examining variations in inter-group consistency, aiming to address the time blindness typically present in conventional QCA methods when applied to cross-sectional data. Inter-group consistency measures the robustness of conditions that lead to results across each year of the sample period, reflecting the extent of cross-sectional consistency over time in the panel data. Table 7 shows that the adjusted distance of inter-group consistency for each of the four histogram paths does not exceed 0.2, indicating a lack of significant cross-sectional effects and the relative stability of the condition histograms throughout the study period.

An analysis of inter-group consistency across various histogram paths over the years (Figure 2) reveals that, overall, the consistency level for each histogram exceeds 0.7, with the values consistently around 0.9 from 2016 to 2021. The fluctuation is minimal, indicating benign deviations. The between-group analysis compensates for the lack of cross-sectional data groupings over time, demonstrating that the four groupings maintain strong explanatory power for the sample cases from 2016 to 2021. The decline in collective magnitude in 2017 and 2020 can be attributed to the 2015 revision of the People’s Republic of China Environmental Protection Law, which mandates environmentally friendly standards for the siting and construction of livestock and poultry farms. The same year saw the introduction of the National Plan for Sustainable Agricultural Development (2015–2030), which established regulations for the livestock and poultry farming sectors, including quantitative targets for the comprehensive utilization of waste. These regulations require enterprises to comply with environmental standards, including the installation and operation of pollution control equipment. Increased costs associated with these regulatory requirements, along with the reallocation of funds, may have hindered innovation investments and led to a decline in GTFP [80].

In 2020, the COVID-19 pandemic resulted in stringent social isolation measures in China, reducing the efficiency of market resource allocation and leading to temporary economic stagnation. The government’s prioritization of public health responses and economic recovery adversely impacted the regional innovation environment. Enterprises faced difficulties in resuming operations, with reduced participation from R&D personnel in innovation activities. This setback in human capital hindered technological innovation, thus impeding the advancement of GTFP. However, despite these challenges, the overall explanatory strength of the group stage remains intact, demonstrating its continued relevance in improving GTFP under normal circumstances.

4.2.3. Within Analysis: Firm-Level Variation in Configurations

The within-group analysis is derived from the company-level analysis, which examines the trend of each grouping pattern within the case dimension, as shown in Figure 3. The results indicate that the groupings of various firms demonstrate considerable stability throughout the study period. Over 90% of firms within each path exhibit an intra-group consistency exceeding 0.7 across the four groupings, consistently aligning with the four path classifications. This suggests that multiple pathways are available for firms to enhance their GTFP. While some individual firms show a group consistency level below 0.7, the intra-group adjustment distance remains under 0.2, implying that the interpretative strength of each grouping is consistent across firms, with no significant differences. Consequently, the overall explanatory power, though somewhat weaker than anticipated, remains robust. Despite the presence of some firms with a consistency below 0.7, the intra-group adjustment distance consistently stays below 0.2, indicating that the explanatory strength of each grouping is generally uniform across firms, without significant variations that would undermine the overall interpretative validity. Therefore, the study’s results retain strong applicability.

4.3. Robustness Testing

This study analyzes the stability of the conditional groupings that result in an elevated GTFP. QCA, as an ensemble theory method, produces robust results, even when the relevant thresholds are marginally adjusted. The findings remain consistent before and after the adjustments, or there exists a subset of relationships that do not alter the substantive interpretation of the results [59,81]. The case frequency threshold was increased from three to four, and the PRI consistency was raised from 0.7 to 0.75. Additionally, fuzzy set qualitative comparative analysis (fsQCA) was employed, and the GTFP for 2021 was selected to align with the antecedent conditional means from 2021 and 2019, following the approach outlined in the study by Jinwu Xiong and Guanyu Hou [82]. The results of the four tests (

Table 8. Robustness test result.

Frequency of Original Configuration Cases/PRI/Consistency	Types of Robustness Tests
	Increased Case Frequency	Increased PRI	Replacement of fsQCA Method
3/0.7/0.8	4/0.7/0.8	3/0.75/0.8	3/0.7/0.8 (2021 GTFP Matching 2021 and 2020 Antecedent Conditional Means)
Changes in the configuration results	Disappearance of configuration S2a	Disappearance of configuration S2a	invariably

) show a subset relationship with the core grouping conditions from the original criteria, demonstrating a similar explanatory mechanism. This further confirms the robustness of the findings presented in this paper.

5. Conclusions and Implications

5.1. Conclusion of the Research

This study employs dynamic QCA in combination with the NCA method, analyzing 32 publicly listed companies in China’s animal husbandry sector from 2016 to 2021. The findings reveal the relationship between the group states established by six antecedent conditions and GTFP, focusing on internal resource allocation and the external business environment (shown in

Table 9. Results of dynamic analysis.

Process of QCA Analysis			Key Findings
(1) Necessary Analysis of Single Conditions			QCA necessity analysis: the overall consistency level for each antecedent condition falls below the threshold of 0.9. However, a scenario exists where the adjustment distance between and within the groups exceeds the threshold of 0.2. The analysis of the six types of inter-group adjustment distances that exceed the threshold shows that the consistency level across all years reaches the benchmark of 0.9. No individual antecedent condition is identified as necessary for the improvement of GTFP NCA analysis: none of the factors meet the criterion of d > 0.1 with a significant p-value, suggesting that no single condition is essential for achieving high GTFP.
Condition Configuration Analysis	(2) Impact of Factor Interactions on GTFP Equivalence (Pooled Analysis)		Three configuration pathways can help firms improve their GTFP. R&D investment and business environment are the keys to enhancing GTFP.
	(3) Analysis of time-firm	Between Analysis	Temporal evolution of configuration pathways: the fluctuation in configuration paths over time is minimal, reflecting a mild deviation with no clear time effect.
		Within Analysis	Firm-level variation in configurations: The configurations show similar explanatory power across companies, with little evidence of individual differences.

).

(1) No single factor within the dimensions of internal resource allocation or the external business environment serves as a necessary condition for high GTFP. That finding is consistent with prior research by Sun et al. [83] and Fang et al. [84]. Instead, a company’s GTFP is the result of the combined influence of multiple factors. The interaction of various factor groupings adds to the complexity of GTFP changes [17,85].

(2) Four equivalence configuration paths enable enterprises to achieve high GTFP. These paths can be categorized into three main types: government-subsidized internal reinforcement, market-driven R&D and innovation, and business–environmental-driven R&D stimulation. Financing constraints and R&D investment are key factors influencing a company’s GTFP. R&D investment acts as the foundation for technological innovation within firms, playing a critical role in improving GTFP [86]. This relationship is prevalent across the sample, and a favorable business environment enhances enterprise innovation, which, in turn, boosts GTFP [32].

(3) The overall explanatory strength of the four groupings remains stable throughout the study period, with no significant cross-period effects observed. However, slight variations in the explanatory strength of each grouping for individual companies are noted.

5.2. Implications

Based on the above conclusions, the following insights are drawn:

(1) Livestock enterprises must recognize the importance of the interactions between internal and external factors. From a systemic perspective, they should leverage their resource advantages and developmental needs to effectively integrate resources. By maximizing comparative advantages and formulating strategies suited to local conditions, enterprises can drive high-quality development [83,84]. Initially, it is vital to optimize internal resource allocation and increase investment in innovation, particularly in green farming technology. Additionally, companies should optimize their financing structures and plan fund utilization to meet the capital needs of key projects. Strengthening human resource development and attracting high-quality talent is also crucial for the long-term success of the enterprise. These efforts will maximize internal resource advantages, improve production efficiency, enhance product quality, and strengthen core competitiveness. Externally, enterprises should adapt to and leverage the business environment [17], staying informed about government policies related to animal husbandry at both the national and local levels. They should actively pursue subsidies and grants to support technological upgrades. Aligning with market demand, companies can develop regionally distinctive animal husbandry brands to increase market share. Financial strategies should diversify funding sources and manage market risks effectively to ensure the enterprise’s operational success.

(2) Government should continue optimizing the business environment [87,88], enhancing the market system and supporting brand development. To facilitate the transformation and upgrading of enterprises, the government should focus on eliminating outdated production capacity, improving the cold-chain distribution system, and expanding the sales networks. Encouraging the integration of listed companies in the livestock sector can promote deep processing and the comprehensive utilization of byproducts. Moreover, fostering green food certification, organizing participation in large-scale exhibitions, and increasing promotion via new media platforms will enhance product competitiveness. Innovative financial products, such as “livestock and poultry live mortgages”, should be developed to address financing challenges. Dynamic subsidy policies should be tailored to the development needs of enterprises, with continuous follow-up to assess effectiveness, curb rent-seeking behavior, and enhance the efficiency of government support [89].

5.3. Limitations and Prospects

This study has several limitations. It focuses on traditional production factors and does not consider the emerging role of data in livestock firms’ green development. Future research should incorporate data as a production factor to evaluate its impact on sustainability. While this study selects influencing factors based on the existing literature, a broader range of economic, technological, environmental, policy, and market factors should be explored. Additionally, GTFP growth is influenced by time-lagged effects, and future research could examine these temporal dynamics. The study’s short sample period limits its scope, and future work could extend the period and use time-segmented QCA to deepen understanding of GTFP evolution.