Green Finance, Land Transfer and China’s Agricultural Green Total Factor Productivity

Abstract

Promoting the role of green finance (GF) in agricultural green transformation is essential for easing resource constraints and achieving sustainable agricultural development. Based on provincial-level data from 2006 to 2022, this study considers the China GF reform and innovation pilot zone as a quasi-natural experiment. It empirically examines the impact and mechanism of GF on agricultural green total factor productivity (AGTFP). The following results are obtained: (1) GF exerts a significant enhancement effect on AGTFP. (2) GF can improve AGTFP by increasing the degree of land transfer (LT). (3) The effect of GF on AGTFP is heterogeneous, and GF has a significant enhancement effect on high-environmental-regulation provinces, the eastern region, and nonmajor grain-producing regions. From these findings, this study suggests accelerating the development level of GF, improving LT policies, continuously stimulating rural entrepreneurial vitality, and further leveraging the role of GF in promoting agricultural green transformation through coordinating regional economic development.

1. Introduction

In the process of rapid growth of the agricultural economy, China’s agriculture faces many problems, such as the excessive consumption of resources and damage to the ecological environment [1,2], posing challenges to the sustainability of agricultural development. Since 1970, global agricultural production has more than doubled. The greenhouse gas emissions from agricultural ecosystems account for 7–20% of the world’s total greenhouse gas emissions, with about 17% of China’s carbon emissions coming from agriculture [3]. In 2023, China’s greenhouse gas emissions were 12.6 billion tons of carbon dioxide, an increase of 4.13% from 12.1 billion tons in 2022. Since the 18th National Congress of the Communist Party of China, ecological priority and green development have gradually become the unified consensus of the whole Chinese society. Agricultural green development is the application and deepening of green development theory in the field of agriculture. The 14th Five-Year Plan for national agricultural green development released by China also clearly states the need to comprehensively promote agricultural green development and improve the sustainable development capacity of agriculture. In the process of promoting the transformation of Chinese agriculture toward green development, core production factors such as labor, capital, and land are indispensable, and capital plays a particularly critical role [4]. Finance has an important role in accelerating economic growth. However, traditional finance has not fully considered issues such as environmental pollution that may arise during the process of economic growth. Green finance (GF) is a new financial service model that integrates environmental protection and sustainable development concepts into financial activities. It not only focuses on economic benefits but also emphasizes fully considering the environmental and social benefits of projects in financial activities, such as investment and financing decisions, project operations, and risk management [5]. It balances economic growth and environmental protection, which is highly consistent with the connotation of green development [3]. GF is an important driving force for promoting sustainable agricultural development [6]. It can provide financial support for environmental protection projects in agriculture, encouraging the development of clean and environment-friendly agricultural industries. GF can also reduce the financing costs of green agriculture enterprises and improve the return on investment of environmental protection projects [7]. Furthermore, GF can encourage financial institutions and enterprises to strengthen environmental risk management and improve environmental performance and social responsibility [8]. Therefore, GF is of great significance to agricultural green development.

In 2017, China decided to establish a GF reform and innovation pilot zone (GFRIPZ) in Zhejiang, Guangdong, Guizhou, Jiangxi, and Xinjiang. Its main goals are to improve the GF system and guide financial resources to gather in green industries. GFRIPZ, as one of the important innovative practices in the development of GF, has significant implications for driving regional green and low-carbon transformation. The GFRIPZ policy aims to provide financial support for economic activities that improve the ecological environment, adapt to and resist climate change, and enhance resource utilization efficiency [9]. It conforms to the essential demand of the agricultural sector to practice the concept of green development. Agriculture is deeply influenced by the environment and has strong externalities. It is one of the key areas supported by GF. The Chinese government has always emphasized the need to support new agricultural business entities and new rural forms and industries, actively advocate the flow of green capital to agriculture, and provide direction for GF to support agricultural and rural development. Scholars have conducted sufficient research on GF and agricultural green total factor productivity (AGTFP), but only a few studies have linked the two and found that GF can generally promote the improvement of AGTFP [10,11]. However, such scholars used a comprehensive indicator method to measure GF, which may have certain measurement errors and cause endogeneity problems, which is not conducive to accurately obtaining relevant conclusions on the impact of GF on AGTFP. GFRIPZ, as an important GF policy in China, provides us with an opportunity to use the difference-in-differences (DID) method to study the impact of GF on AGTFP, which can solve potential endogeneity problems and offer accurate conclusions. However, the existing literature has not studied how GFRIPZ affects AGTFP. In addition, the mechanism by which GF affects AGTFP has been hardly examined, which is not favorable for revealing the black box of how GF affects AGTFP. Against the backdrop of China’s GF achieving significant results, has it promoted the green development of agriculture? What is its mechanism of action? These questions are exactly the two important issues that this study aims to explore in depth. To accurately identify the relationship between GF and AGTFP, we empirically test the impact of GF on AGTFP from the perspective of GFRIPZ. We also analyze the impact channels of GF on AGTFP from the perspective of land transfer (LT), further opening up the mechanism black box of the impact of GF on AGTFP. This study has important practical significance for improving the construction of the GF system and achieving green development in agriculture.

This study takes the practice of GFRIPZ as the starting point and uses the DID method to examine the impact of GF on AGTFP. The marginal contributions of this study are as follows: First, the analysis of the effectiveness of the GF policy is expanded. Existing research has discussed the environmental effects of GFRIPZ and found that GFRIPZ can effectively reduce environmental pollution in pilot areas and improve green innovation levels and green productivity [12,13,14]. However, research on the environmental effects of GFRIPZ from the perspective of AGTFP is lacking. From the viewpoint of the GFRIPZ policy, this study explores whether and how GF affects regional AGTFP. This exploration provides direct empirical evidence from the perspective of AGTFP to verify the existence and applicability of Porter’s hypothesis and enriches the relevant research on GF. Second, the existing literature lacks sufficient research on the impact mechanism of GF on AGTFP. LT is an important variable affecting AGTFP, and no research has explored the role of LT in promoting AGTFP through GF. Thus, LT and AGTFP are placed within the unified analysis scope of the impact of the GFRIPZ policy in this study, providing a comprehensive evaluation of the green development effects of GF. Third, we not only examine the overall impact of GF on AGTFP but also pay attention to the differences in its impact on regions with varying levels of environmental regulation (ER), geographical locations, and major grain-producing areas. This study is beneficial for clarifying the boundary conditions of the effectiveness of GF and providing scientific and reasonable references for improving the GF policy and promoting agricultural green development. Fourth, in the literature about GF and agricultural development, scholars mostly use a comprehensive indicator method to measure GF, which has certain measurement errors and endogeneity issues, and the results obtained are not robust enough. This study uses the DID method to alleviate endogeneity issues, identify the effect of GF on AGTFP, and obtain more accurate results.

2. Literature Review

2.1. Research on GF

We conducted a literature review on the concept of GF and the implementation effects of the GFRIPZ policy. In terms of the concept of GF, Salaza (1998) believed that GF is the combination of multiple disciplines, the integration of the environment and finance, and an important innovation in the financial field. He proposed that unlike developed countries that only focus on the impact of environmental change on the development of GF, developing countries place more emphasis on the economic impact of GF development [15]. Cowan (1999) defined GF as a product of the organic combination of traditional finance and green economy, emphasizing the importance of GF for ecological environment protection [16]. Labatt and White (2002) argued that GF exists as an important financial tool, and this innovative financial tool can play a significant role in diversifying environmental risks, thereby improving poor environmental conditions [17]. White (2006) studied the transmission mechanism between finance and sustainable development from the perspective of financial institutions and deemed that GF can play a positive role in raising funds for financial institutions. Therefore, GF is defined as the optimal solution obtained by financial institutions to solve environmental problems [18]. Berensmann and Lindenberg (2016) stated that investing in green environmental protection and industries is one of the most important purposes for developing GF [19]. That is, when financial institutions provide loans to enterprises, in addition to the economic benefits of the project itself, environmental benefits have become an important factor to consider when lending [20,21]. Sachs et al. (2019) believed that GF, as a financial policy and tool, can tilt funds toward environmental projects and implement the sustainable development goals advocated for in recent years [22]. The existing literature has discussed the relationship between ESG criteria and GF, and research has found that environmental factors are the most important factor in China’s GF investment decisions, followed by governance and social factors [23,24]. Scholars have also explored the relationship between green finance and corporate ESG performance, and found that green finance policies can improve corporate ESG performance by optimizing credit resource allocation and increasing R&D investment [25,26].

In terms of the implementation effect of the GFRIPZ policy, scholars often study the effect from the perspectives of carbon reduction, green innovation, high-quality economic development, industrial structure, green consumption, and energy intensity. The implementation of the GFRIPZ policy has been proven to effectively reduce regional carbon dioxide emissions, demonstrating significant carbon reduction effects [27,28]. GFRIPZ has also stimulated local financial institutions for innovation in products and services, promoting the diversified development of GF products and services with local characteristics [29,30]. The GFRIPZ policy not only promotes the green development of enterprises but also advocates for urban economy growth and suppresses pollutant emissions [31,32,33]. Liu et al. (2024) used nonparametric methods to measure GTFP to represent the level of high-quality economic development. The results showed that the GFRIPZ policy has a promoting effect on GTFP, which is mainly achieved by promoting industrial structure adjustment [34]. Zhang and Zhou (2023) found through the analysis of the DID model that GFRIPZ has promoted the ecological development of regional industrial structure, manifested in the reduction in traditional polluting industries and the expansion of emerging environmental protection industries. Factors such as the economic development level make this promotion effect show differences in various regions [35].

2.2. Related Research on AGTFP

How to achieve green development in agriculture has always been a deeply concerning issue for governments and people worldwide. Green development requires a development path that combines improving output efficiency and ecological protection, which has led to the formation of AGTFP. This indicator not only measures output efficiency but also considers relevant environmental factors. Therefore, scholars have conducted various studies on AGTFP, with the main differences concentrated in the measurement methods and influencing factors.

Solow (1957) proposed the TFP indicator to measure the quality of economic growth, which represents the output efficiency obtained after investing resources, labor, and capital [36]. TFP only considers economic output and does not examine the negative impact of environmental issues; thus, it cannot reasonably reflect the actual quality of the economy [37]. Subsequently, scholars began to incorporate environmental factors into the TFP indicator system [38], also known as GTFP. The research areas of GTFP can be divided into industrial and agricultural fields. Generally, the measurement of the agricultural field is called AGTFP, which involves resource input and environmental pollution in the agricultural production process. Oskam (1991) believed that agriculture can cause certain damage and pollution to the natural environment, such as the atmosphere and soil, during the production process [39]. For effective agriculture productivity, pollution should be included in the measurement indicators. Calculation methods for AGTFP generally comprise two types, namely, parameter and nonparameter estimation methods, with a stochastic frontier analysis and data envelopment analysis (DEA) as typical representatives, respectively [40,41]. Afterward, the Malmquist productivity index and global Malmquist–Luenberger productivity index were successively established [42,43,44], so that the changes in AGTFP can be analyzed. On the basis of the DEA method, Tone (2001) broke through the radial and angular problems and contributed a slack-based measure model based on relaxation variables, which compensated for the shortcomings of traditional methods [45]. The continuous optimization of research methods provides a broad tool foundation for the effective measurement of AGTFP. Hoang et al. (2011) used the parameter method as the analysis method and incorporated the ecological environment into the measurement system, measuring the total factor productivity of agriculture [46]. Ball et al. (2001) included insecticides, nitrogen and phosphorus loss, and other factors in the calculation system and used the Malmquist–Luenberger index to measure the AGTFP in the United States [47]. In terms of selecting input indicators, Wu et al. (2001) included factors such as land, labor, capital, and intermediate inputs in the indicator system. Many applications of environmental pollution indicators as unexpected outputs exist [48]. For example, Xu et al. (2020) identified soil carbon emissions and other pollution emissions as unexpected outputs, and the results showed that AGTFP is an effective indicator of agricultural development efficiency [49].

Through reviewing and reading the relevant literature, we found that factors such as natural disasters, human capital, and financial support can all affect AGTFP. Specifically, Appleton (2007) believed that human capital is an important element in the agricultural production process. Through specific research, they observed that human capital development can promote the improvement of agricultural production efficiency [50]. Zhang (2017) selected the average years of education to illustrate rural human capital and concluded through research that as the per capita years of education in rural areas continue to increase, the improvement of AGTFP also accelerates [51]. Through constructing an empirical test model, Ye and Hui (2016) found that agricultural financial support has a positive promoting effect on AGTFP. The production of agriculture is closely related to the natural environment and is therefore greatly affected by environmental factors, especially natural disasters [52]. Wu and Song (2018) empirically tested the relationship between natural disaster factors and AGTFP and determined that agricultural disasters can inhibit the growth of AGTFP [53].

The existing literature provides a solid theoretical foundation and basis for this study. However, most scholars have discussed the research on GF and industrial green total factor productivity separately, while few scholars have studied GF and AGTFP together. Some scholars have used a comprehensive indicator method to calculate the GF indicator and studied the impact of GF on AGTFP. However, the conclusions obtained may have certain endogeneity issues due to measurement errors in the indicator of GF. Furthermore, the impact mechanism of how GF affects AGTFP has not been fully discussed. On this basis, we construct an extended theoretical framework for the important influencing factors of agricultural green development and establish a DID model to empirically test the effect of GF development on AGTFP. Our research can offer theoretical support and an empirical reference for the precise regional implementation of China’s GF policies, enrich and improve the theoretical framework of agricultural green transformation, and provide new methods and ideas for related research.

3. Research Hypotheses

3.1. Effect of GF on AGTFP

The operation of GF, as a new financial development model, mainly gathers government policy support and public investment and then invests and supports the green development of the agricultural sector through differentiated investment and financing, a policy tilt, and other means. GF provides strong financial impetus for the agricultural green industry, which in turn affects agricultural operators’ emphasis on agricultural green development and the enhancement of environment-friendly technologies in management. Such advancements lead to the transformation of traditional extensive agricultural industries and product safety and the promotion of green and low-carbon development [3], thereby enhancing the GTFP of the entire agricultural sector.

Specifically, the impact of GF on AGTFP is mainly reflected in its role in resource allocation and financial regulation. From the perspective of production, utilizing the resource allocation function of GF and financial tools such as green loans and green securities can provide farmers with financial support for agricultural production [2]. Adequate financial security can enable farmers to choose highly efficient methods for agricultural production by using environment-friendly planting techniques and fertilizers, thus reducing carbon emissions. From the perspective of consumption, the purchase of green and environment-friendly technology products in agriculture can provide capital inflows for enterprises, enabling them to develop new green and environment-friendly technologies. Farmers will also obtain environment-friendly agricultural production products, achieving a virtuous cycle between production and consumption and promoting the improvement of AGTFP.

In terms of the financial regulatory role of GF, financial regulation is the process in which financial institutions supervise and control the inflow of funds into financing enterprises, which can encourage agricultural input enterprises to pay considerable attention to production efficiency, enhance their corporate image, and invest the funds they have raised into the production of high-efficiency, environment-friendly technologies [54,55]. The GFRIPZ policy clearly states that financing guidance should be based on the practical requirements of ecological environment protection, such as resource conservation and environmental friendliness, providing assistance for industries. When farmers obtain green financial services, the funds must be used for agricultural green production, ensuring that farmers use the funds obtained to achieve sustainable agricultural development and providing guarantees for promoting the improvement of AGTFP through GF. Hence, this study proposes research hypothesis H1 on the relationship between GF and AGTFP.

H1. 

China’s GF can enhance AGTFP.

3.2. GF, LT, and AGTFP

LT is an important measure in China’s agricultural reform. Through LT, rural households can transfer their land management rights to other entities while retaining their land-contracting rights. This method integrates scattered and fragmented land into large-scale agricultural parks or enterprises, promoting the large-scale operation of agriculture and providing favorable conditions for the mechanization and intensive development of agriculture. In the context of China’s land policy, LT is an important factor in promoting GF to support agricultural green transformation.

GF can enhance the degree of LT. A large number of rural populations have flooded into cities, resulting in idle and abandoned rural land resources. The insufficient utilization of agricultural resources will limit scale production and further hinder rural development. GF can effectively alleviate financing constraints [56], enabling rural households with idle land but not engaged in agricultural production activities to obtain sufficient funds, prompting them to lease idle land while meeting their daily financial needs, activating idle rural resources, improving land circulation, and promoting agricultural resource utilization efficiency. Financing constraints can restrict the production behavior of large-scale agricultural growers who want to achieve large-scale operations through land contracting [57]. GF can provide convenient, efficient, and short-term financial services for green agriculture projects, catering to the diverse financing needs of rural residents and promoting the reintegration and utilization of rural resources.

LT can improve AGTFP [5]. With the increase in the LT rate, agricultural production tends to be intensive, large-scale, and intelligent, which enhances the green production literacy of farmers and drives farmers without green production willingness to transform their agricultural production methods [58]. LT can improve the utilization efficiency of factors, promote the integration of agricultural resources, enhance the efficiency of production factor allocation [59], increase agricultural desirable output, and expand AGTFP. It is an important manifestation of agricultural management entities participating in agricultural socialized services and seeking outsourcing services, which can effectively unleash the green development effect of agricultural socialized services [60]. LT can have a positive impact on the adoption of green agricultural technologies by new agricultural operators, significantly improving the GTFP of the transferee through marginal output equilibrium, transaction income, and technology adoption effects [61] and promoting agricultural green transformation. Thus, we propose the following hypothesis:

H2. 

GF promotes LT to boost AGTFP.

4. Research Design

4.1. Model Setting

This study regards the GFRIPZ policy implemented in 2017 as an exogenous policy shock to identify the effect of GF on AGTFP. This study employs a DID method to examine whether GF can improve AGTFP. The province-level data in China for the analysis come from the China Statistical Yearbook, China Agriculture Yearbook, China Rural Statistical Yearbook, China Environmental Statistical Yearbook, and National Bureau of Statistics database. In this study, panel data from 2006 to 2022 are utilized, encompassing 30 provinces. The specific model is as follows:

$${AGTFP}_{it}={\alpha }_0+{\alpha }_1{P*T}_{it}+{\alpha }_2{Conl}_{it}+{\mu }_i+{\gamma }_{\mathrm{t}}+{\epsilon }_{it}.$$

(1)

Here, AGTFP denotes the AGTFP of provinces; i and t represent the province and year, respectively. P*T denotes the GFRIPZ pilot policy as a proxy variable for GF. The dummy variable P is a grouping variable for provinces, with a value of 1 for the experimental group and 0 for the control group. The dummy variable T is a time grouping variable. The GFRIPZ pilot policy was proposed in 2017. The year 2017 and later are set as 1, and the years before are set as 0. This study focuses on the coefficient α₁ of the cross-term P*T, which empirically tests the impact of GF by comparing the AGTFP of two groups of provinces before and after the implementation of GFRIPZ. To control for potential sequence correlation and heteroscedasticity issues, this study selects a series of control variables that would affect AGTFP. Conl represents a control variable; μ and γ indicate the time and province fixed effects, respectively.

4.2. Variable Definitions

AGTFP: The AGTFP is measured using the SBM-GML model to avoid the deviation caused by the selection of the radial angle in the traditional DEA and compare the efficiency differences among multiple effective decision-making units, referring to the research of Tone (2001) [45] and Oh (2010) [44]. The specific indicator system design for AGTFP based on Qin et al. (2024) [62] is shown in

Table 1. Measuring indicators of AGTFP.

Indicator Type	Indicator Name	Indicator Connotation
Input	Land	Total sown area
	Energy	Total power of agricultural machinery
		Water inputs
	Labor	Number of people employed in agriculture
	Agricultural	Reduced amount of chemical fertilizer application
		Pesticide application rate
		Amount of agricultural film used
Desirable output	Economic	Gross agricultural output
Undesirable output	Pollutant emission	Carbon emissions from agricultural production

Note: In accordance with the study of Ge et al. (2018) [63], agricultural carbon emissions are calculated on the basis of sources such as fertilizers, pesticides, agricultural films, diesel, tillage, irrigation, etc.

.

GF: We use the GFRIPZ policy established by China in 2017 as a proxy indicator for GF. The GFRIPZ policy is obtained by multiplying the dummy variables of pilot areas and policy implementation time. The dummy variable of pilot areas is a grouping variable for provinces, with a value of 1 for the experimental group and 0 for the control group. The dummy variable is a time grouping variable. The year 2017 and later are set as 1, and the years before are set as 0.

Control variables: Referring to Han et al. (2023) [5] and Qin et al. (2024) [62], we choose the following province characteristics as control variables. Natural disaster rate (ND): Agricultural disasters mean that the output due to production inputs is not received because of force majeure, so the occurrence of natural disasters inhibits the growth of agricultural production. Therefore, we use the ratio of affected crop area to total sown area to represent ND. Fiscal support for agriculture (FSA): Fiscal support for agriculture can provide necessary financial support for the development of agriculture in the region and is a key factor in improving agricultural output levels. We use the ratio of expenditure for agriculture, forestry, and water conservancy to general public expenditure to measure FSA. Industrialization rate (IN): The development of industrialization can create material conditions and the market environment for agricultural development and stimulate the growth of agricultural economy and the optimization and upgrading of agricultural production structure. We use the ratio of industrial added value to regional GDP to measure IN. Human capital level (HC): The improvement of human capital is conducive to the enhancement of technological innovation, thereby affecting agricultural productivity. We use the ratio of the population of college students to total population to measure HC. Transport infrastructure construction (TI): The more complete the transportation facilities are, the more conducive they are to reducing the transportation time and production costs of agricultural products, hence improving agricultural competitiveness. The logarithm of highway mileage is used to measure TI. Economic development (EC): Agriculture is the fundamental industry of a region’s economy, and it is influenced by the level of economic development. The logarithm of per capita GDP is used to measure EC. Agricultural opening up (AOP): Increasing the level of openness to the outside world is beneficial for China to absorb foreign green development concepts, promote the improvement of green development technologies, and advance the realization of agricultural green development. The ratio of the total value of agricultural imports and exports to the gross output value of agriculture, forestry, animal husbandry, and fisheries is used to represent AOP. To reduce the impact of price factors, we deflate output-related indicators, such as GDP and gross agricultural output (with 2005 as the base year). The definitions and descriptive statistics are presented in

Table 2. Descriptive statistics.

Variable	Obs.	Definition	Mean	SD	Min	Max
AGTFP	510	AGTFP index based on SBM-GML model	1.029	0.069	0.946	1.693
ND	510	Natural disaster rate (Affected crop area/total sown area)	0.182	0.144	0.003	0.458
FSA	510	Fiscal support for agriculture (Expenditure for agriculture, forestry, and water conservancy/general public expenditure)	0.106	0.037	0.061	0.238
IN	510	Industrialization rate (Industrial added value/regional GDP)	0.336	0.086	0.1001	0.559
HC	510	Human capital level (Population of college students/total population)	0.019	0.006	0.006	0.0436
TI	510	Transport infrastructure construction (Logarithm of highway mileage)	11.632	0.861	9.249	12.916
EC	510	Economic development (Logarithm of per capita GDP)	9.184	0.527	7.898	10.806
AOP	510	Agricultural opening up (Total value of agricultural imports and exports/gross output value of agriculture, forestry, animal husbandry, and fisheries)	0.33	1.024	0.0498	8.397

.

5. Research Results and Discussion

5.1. Benchmark Results

5.1.1. Parallel Trend Test

To test whether the sample has parallel trends, we construct the dummy variable T_−i (i = 1, 2, 3) to represent 1 and 2 years before the GFRIPZ policy implementation and the dummy variable T_i (i = 0, 1, 2, 3, 4) to represent the first to fifth years of the GFRIPZ policy implementation. Subsequently, we multiply the dummy variables with T to form new interaction terms for the regression. If the coefficient of P*T_−i (i = 1, 2, 3) is not significant, then the sample has parallel trends and can be regressed by using the DID method.

Table 3. Results of parallel trend.

	(1)	(2)	(3)
	AGTFP	AGTFP	AGTFP
P*T−3	0.0394	0.0455	0.0401
	(0.0466)	(0.0500)	(0.0474)
P*T−2	−0.00649	0.00392	−0.00997
	(0.0149)	(0.00623)	(0.0150)
P*T−1	0.0859	0.0962	0.0821
	(0.0820)	(0.0836)	(0.0788)
P*T0	0.0969 ***	0.108 ***	0.0941 ***
	(0.0191)	(0.0133)	(0.0211)
P*T1	0.111 ***	0.122 ***	0.109 ***
	(0.0244)	(0.0212)	(0.0254)
P*T2	0.113 ***	0.124 ***	0.113 ***
	(0.0286)	(0.0239)	(0.0298)
P*T3	0.0970 ***	0.109 ***	0.0980 ***
	(0.0302)	(0.0274)	(0.0314)
P*T4	0.0920 ***	0.103 ***	0.0901 ***
	(0.0318)	(0.0271)	(0.0307)
Conl	NO	YES	YES
Province FE	YES	NO	YES
Year FE	YES	YES	YES
Observations	510	510	510
R-squared	0.366	0.320	0.387

Note: *** represents the significance level at 1%, with robust standard errors in parentheses.

reports the corresponding results. In 2014–2016, the regression coefficients of P*T were not significant, which supports the parallel trend hypothesis. To display the parallel trend results intuitively, we draw a parallel trend test chart, as shown in Figure 1. From this figure, a significant difference in AGTFP exists between the experimental and control groups after 2017. Passing the parallel trend test indicates that the construction of the DID model in this study is reasonable. Next, we will analyze the benchmark regression results.

5.1.2. DID Results

Table 4. Effect of GF on AGTFP.

	(1)	(2)	(3)
	AGTFP	AGTFP	AGTFP
P*T	0.109 ***	0.118 ***	0.107 ***
	(0.0188)	(0.0153)	(0.0196)
ND		0.181	0.397
		(0.143)	(0.272)
FSA		−0.0124	−0.107
		(0.110)	(0.136)
IN		0.000304	−0.101
		(0.0398)	(0.108)
HC		1.060	3.008 **
		(0.697)	(1.476)
TI		0.0146 *	−0.0132
		(0.00880)	(0.0501)
EC		0.0212 *	0.00413
		(0.0115)	(0.0450)
AOP		−0.0614	0.0736
		(0.0475)	(0.0774)
Province FE	Y	N	Y
Year FE	Y	Y	Y
Observations	510	510	510
R-squared	0.379	0.333	0.399

Note: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, with robust standard errors in parentheses.

shows the effect of GF on AGTFP. P*T coefficients are positive, which means that GF can, to some extent, improve the level of regional AGTFP. With the implementation of the GFRIPZ policy, substantial green funds are being invested in the field of agricultural green development. Enterprises in various regions can use the funds to invest in agricultural green environmental protection projects, thereby introducing clean and efficient agricultural projects, increasing agricultural output, and reducing avoidable carbon emissions and other pollutants. At the same time, given the society’s emphasis on the added value of green agricultural products, high-energy-consuming industries have decreased; instead, they are catering to the public’s continuous innovation and development of high-value-added green products, promoting the improvement of AGTFP. Therefore, GF can improve AGTFP. Similarly, existing research based on a two-way fixed effects model and provincial panel data has found that GF can enhance AGTFP [10,11], which proves the scientific validity of the conclusion presented in this paper. The difference between the current study and previous studies is that we use a quasi-natural approach to explore the key relationship between GF and AGTFP, which can provide a new method and idea for future research.

5.2. Robustness Test

5.2.1. Placebo Test

The establishment time of the GFRIPZ policy is 2017; hence, we can advance the occurrence time of this policy by 1, 2, and 3 years and regress in accordance with Formula (1). If the DID coefficient is still significant in the test after the random policy time point, then the improvement in AGTFP may not be caused by the GFRIPZ policy because the same conclusion is obtained in the false policy time point regression results. The credibility of the basic regression conclusion still needs to be discussed. On the contrary, if the DID coefficient is not significant, then the experimental results of the benchmark regression are not arbitrarily obtained and have a certain degree of robustness. Columns (1)–(4) in

Table 5. Results of placebo test.

	(1)	(2)	(3)	(4)
	Assuming the Policy Was Implemented in 2016		Assuming the Policy Was Implemented in 2015
	AGTFP	AGTFP	AGTFP	AGTFP
P*T	0.0961	0.0972	0.0503	0.0525
	(0.0805)	(0.0792)	(0.0432)	(0.0427)
ND		0.0693		0.0523
		(0.242)		(0.245)
FSA		−0.0408		−0.0322
		(0.106)		(0.105)
IN		−0.191 **		−0.200 **
		(0.0892)		(0.101)
HC		0.137		0.267
		(1.630)		(1.560)
TI		0.0395		0.0397
		(0.0252)		(0.0251)
EC		0.0108		0.0116
		(0.0172)		(0.0167)
AOP		0.0185		0.0136
		(0.0533)		(0.0526)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Observations	330	330	330	330
R-squared	0.260	0.275	0.225	0.241

Note: ** represents the significance level at 5%, with robust standard errors in parentheses.

show the regression results of the GFRIPZ policy 1 and 2 years ahead of schedule. The DID coefficients are not significant, indicating that the benchmark empirical results presented earlier have a certain degree of robustness.

5.2.2. Exclusion of Impact of Environmental Policy

Other environmental policies related to agricultural green development during the sample period, such as the carbon trading policy, may interfere with the causal identification effect of the study. Given the overlapping and parallel nature of various environmental policies issued by the government, eliminating the interference of each policy one by one is difficult. We introduce the interaction fixed effect of time and province dummy variables into the benchmark model on the basis of the approach of Zhou et al. (2023) [64], which helps eliminate the interference of policy factors at the province level on the estimation results in the linear dimension. The regression result is shown in

Table 6. Exclusion of impact of environmental policy.

	(1)	(2)	(3)
	AGTFP	AGTFP	AGTFP
P*T	0.103 ***	0.116 ***	0.103 ***
	(0.0182)	(0.0150)	(0.0189)
ND		0.114	0.385
		(0.172)	(0.276)
FSA		0.0348	−0.105
		(0.125)	(0.134)
IN		−0.00472	−0.0903
		(0.0378)	(0.111)
HC		1.252 **	2.399
		(0.589)	(1.704)
TI		0.0148 *	−0.0437
		(0.00872)	(0.0390)
EC		0.0236 **	0.00480
		(0.0119)	(0.0454)
AOP		−0.0655	0.0458
		(0.0490)	(0.0812)
Province FE	N	N	Y
Year FE	N	Y	Y
Observations	510	510	510
R-squared	0.386	0.335	0.403

Note: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, with robust standard errors in parentheses.

. The DID coefficients are positive, indicating that the results of this study remain robust after the influence of province-level policy factors is excluded.

5.2.3. Exclusion of Impact of Important Events

The global financial crisis that occurred in 2008 might have a significant impact on China’s GF and agricultural green development. COVID-19 in 2020 would also greatly affect China’s AGTFP. To eliminate the interference of the important events mentioned above, we set the sample interval from 2009 to 2019 for robustness testing. After the sample size is narrowed further, the DID coefficients in

Table 7. Change in sample interval.

	(1)	(2)	(3)
	AGTFP	AGTFP	AGTFP
P*T	0.101 ***	0.118 ***	0.100 ***
	(0.0183)	(0.0104)	(0.0187)
ND		0.100	0.163
		(0.142)	(0.265)
FSA		−0.0251	−0.0313
		(0.105)	(0.108)
IN		−0.0133	−0.0819
		(0.0274)	(0.125)
HC		0.128	−0.496
		(0.391)	(1.310)
TI		0.00711	0.0258
		(0.00744)	(0.0388)
EC		0.0145	0.00618
		(0.0118)	(0.0181)
AOP		−0.0437	0.0872
		(0.0448)	(0.0557)
Province FE	N	N	Y
Year FE	N	Y	Y
Observations	330	330	330
R-squared	0.438	0.360	0.444

Note: *** represents the significance level at 1%, with robust standard errors in parentheses.

remain significantly positive.

5.3. Mechanism Analysis

To test the intermediate channels of GF on AGTFP, we construct the following model to verify the mediating effect of LT.

$${LT}_{it}={\beta }_0+{\beta }_1{P*T}_{it}+{\beta }_2{Conl}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(2)

Here, LT represents the mediating variable of land transfer. We use the ratio of the area of household contracted farmland to the total area of household contracted farmland as a proxy variable for LT. As shown in

Table 8. Effect of GF on LT.

	(1)	(2)	(3)
	LT	LT	AGTFP
P*T	0.0166 **	0.0154 **
	(0.00736)	(0.00675)
LT			0.0951 *
			(0.0528)
ND		−0.147	0.337
		(0.103)	(0.273)
FSA		0.0365	−0.044
		(0.0754)	(0.143)
IN		0.0819 **	−0.153
		(0.0400)	(0.104)
HC		−0.840	3.61 **
		(2.149)	(1.639)
TI		−0.0203	−0.011
		(0.0148)	(0.051)
EC		0.0399 ***	−0.107
		(0.0116)	(0.044)
AOP		0.249 ***	0.073
		(0.0646)	(0.089)
Province FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	510	510	510
R-squared	0.927	0.934	0.326

Note: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, with robust standard errors in parentheses.

, the coefficients of P*T are significant, indicating that GF can significantly enhance LT. Geng et al. (2024) used a mediation effect model to determine whether LT is an important channel for GF to reduce agricultural pollution, which also indicates whether GF can significantly improve the level of LT in the region [65]. The research of Ge et al. (2024) [65] ensured the robustness of the result in this section. To further verify the relationship between LT and AGTFP, we conduct a regression analysis with LT as the explanatory variable and AGTFP as the dependent variable. The results are shown in the third column of Table 8, presenting a significant positive relationship between LT and AGTFP. Existing research has also empirically verified that LT is beneficial for improving AGTFP [4,66]. Therefore, LT is the mediating variable for GF to improve AGTFP. These results show that GF mainly affects AGTFP through LT, which validates H2. Our study is the first to incorporate LT into the research framework of GF and AGTFP, enriching the analysis of the impact mechanism of GF and expanding the boundaries of related research.

5.4. Heterogeneity Analysis

5.4.1. Heterogeneity Analysis of Geographical Location

To investigate the heterogeneous impact of GF on AGTFP under different geographical locations, we divide the samples into the central western (CW) region and eastern (EA) region. Then, we regress these two sets of samples separately.

Table 9. Heterogeneity test.

	(1)	(2)	(3)	(4)	(5)	(6)
	CW Region	EA Region	NMGP Region	MGP Region	High ER Level	Low ER Level
	AGTFP	AGTFP	AGTFP	AGTFP	AGTFP	AGTFP
P*T	0.0900 ***	0.128 ***	0.127 ***	0.0799 ***	0.131 ***	0.0853 **
	(0.0280)	(0.0166)	(0.0233)	(0.0172)	(0.0132)	(0.0407)
ND	0.439	0.261	−0.0528	0.957 *	−0.153	0.727 **
	(0.295)	(0.322)	(0.290)	(0.494)	(0.298)	(0.346)
FSA	−0.0917	−0.0564	−0.0179	0.0921	−0.0144	0.0135
	(0.147)	(0.309)	(0.185)	(0.186)	(0.166)	(0.186)
IN	−0.104	−0.174	0.184	−0.343 **	0.251	−0.305 **
	(0.118)	(0.146)	(0.175)	(0.154)	(0.191)	(0.138)
HC	3.389	3.456	1.382	1.403	0.0405	2.035
	(2.821)	(2.139)	(1.341)	(3.303)	(1.799)	(3.340)
TI	−0.0154	−0.0200	0.0982 **	−0.00599	0.158 **	−0.0429
	(0.0653)	(0.0432)	(0.0483)	(0.0295)	(0.0756)	(0.0581)
EC	−0.0707	0.0883 *	0.0735 ***	−0.0940	0.0964 **	−0.0855
	(0.0711)	(0.0450)	(0.0275)	(0.0864)	(0.0384)	(0.0773)
AOP	−0.00452	0.165	−0.0331	0.0804	0.0216	−0.104
	(0.107)	(0.149)	(0.103)	(0.117)	(0.113)	(0.148)
Province FE	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Observations	340	170	289	221	255	255
R-squared	0.409	0.536	0.491	0.408	0.524	0.445

Note: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, with robust standard errors in parentheses.

displays the geographical location heterogeneity result. The P*T coefficient in column 2 is greater than that in column 1, indicating that GF can improve the AGTFP of the EA region compared with that of the CW region. This result may be due to the superior geographical location and high degree of agricultural digitization in the EA region, which has a relatively good foundation for an agricultural green development system. The transmission effect of GF in the field of AGTFP is faster and stronger in this region. Compared with that in the EA region, the economic development level in the CW region is lagging behind, the level of digital infrastructure construction is relatively low, the degree of digital development is low, the green development of agriculture is relatively backward, and the financial and digital literacy of farmers is low. Therefore, the effect of GF on improving AGTFP is not high. The development level of GF in the EA region is higher than that in the CW region, and GF is more likely to flow into the agricultural sector in the EA region, thereby promoting the development of AGTFP. The higher the level of GF, the more it can promote the improvement of AGTFP through direct and indirect impacts.

5.4.2. Heterogeneity Analysis of Major Grain-Producing Regions

To investigate the heterogeneous impact of GF on AGTFP under different grain-producing regions, we establish a dummy variable of nonmajor grain-producing (NMGP) regions. If the sample province is located in NMGP regions, the NMGP value is 1. Otherwise, it is 0. Then, the interaction term (NMGP*P*T) between the NMGP dummy variable and GF is added to Model 1. Table 9 displays the NMGP heterogeneity result. The regression coefficient of NMGP*P*T is positive, indicating that GF can improve the AGTFP of NMGP regions compared with that of major grain-producing regions. The main grain-producing areas are facing greater pressure on food security development. In the process of grain production, they often need to consume more agricultural resources and emit more pollutants, resulting in a lower overall level of agricultural green development in the regions. Consequently, GF faces more obstacles in promoting agricultural green transformation. NMGP areas have less pressure to grow crops and relatively lower levels of agricultural resource consumption and carbon emissions, leading to a higher level of green agricultural development. GF faces fewer obstacles in promoting agricultural green transformation in NMGP areas.

5.4.3. Heterogeneity Analysis of ER

To investigate the heterogeneous impact of GF on AGTFP under varying ER intensities, we establish a dummy variable of ER. We use the proportion of regional environmental pollution investment to GDP to measure ER. On the basis of the average level of ER, the sample provinces are divided into high- and low-ER-level provinces, with high-ER-level provinces defined as 1 and low-ER-level provinces defined as 0. Then, the interaction term (ER*P*T) between ER and GF is added to Model 1. Table 9 displays the ER heterogeneity result. The regression coefficient of ER*P*T is positive, indicating that GF can improve the degree of AGTFP of high-ER-level provinces compared with that of low-ER-level provinces. ER often requires farmers to adopt clean agricultural production practices and technologies, such as investing in expensive green technologies, reducing the use of fertilizers and pesticides, improving waste disposal facilities, and increasing energy efficiency. The development of GF can provide financial support for farmers to carry out green projects. The pressure of ERs prompts farmers to seek highly environment-friendly and efficient production methods, which will stimulate the development of agricultural green technology innovation activities. The cost savings and production efficiency improvements brought by these innovations will not only offset early environmental compliance costs but also help improve agricultural resource utilization efficiency, reduce environmental costs per unit product, and ultimately enhance AGTFP. Therefore, compared with that in areas with low ER intensity, GF can promote the level of AGTFP in areas with high ER intensity.

6. Conclusions and Policy Recommendations

The essence of green agricultural development is to enhance AGTFP, and the financial support for the enhancement of AGTFP is crucial. GF, which is a kind of innovation engine, can provide strong financial support and guarantee for it. Therefore, we explore the impact of GF on AGTFP, which is of practical significance for China to promote the quality and efficiency of agricultural economy. The following conclusions are drawn: (1) GF exerts a significant enhancement effect on AGTFP. (2) GF can improve AGTFP by increasing the degree of LT. (3) The effect of GF on AGTFP is heterogeneous, and GF has a significant enhancement effect on high-ER provinces, the EA region, and NMGP regions. The high-emission traditional agricultural production growth model has seriously hindered the sustainable development of China’s economy and the livability of the ecological environment. All sectors of society have deeply recognized the necessity of green agricultural development. GF, as the essential content of the orderly development of the green economy, is the most important engine for accelerating the implementation of AGTFP growth. Current researchers still mainly focus on GF and TFP, and few scholars have combined green finance and AGTFP. This study not only enriches the research on the impact of GF on AGTFP in China, but also helps to correctly understand the impact of GF on agricultural green economy, providing theoretical reference for sustainable agricultural development.

Drawing from the theoretical analysis and empirical results on the impact of GF on AGTFP, we propose the following countermeasures and suggestions.

First, the beneficial experience of promoting the construction of GFRIPZ should be summarized, and the construction of the pilot policy should be deepened. The first batch of GFRIPZ successfully completed the 6-year trial phase and achieved significant results. Following Zhejiang, Guangdong, Jiangxi, Guizhou, and Xinjiang provinces, Gansu and Chongqing also launched the construction of GFRIPZ. At present, GFRIPZ is undergoing a stage of expansion and development. The government needs to be aware of the important connection between the construction of GFRIPZ and AGTFP. The best practice cases of innovative financial products and services in the construction of the first batch of GFRIPZ should be comprehensively summarized to promote the improvement of regional AGTFP and form effective experiences that can be replicated and endorsed in other provinces to create a good atmosphere for GF to promote agricultural green transformation throughout the country. From the perspective of different regions, significant differences in the impact of GF on AGTFP exist. That is, each region should implement differentiated GF development strategies through consolidating the benefits of GF for agricultural green development. Efforts should be made to merge the role of GF in areas and fields where it has been demonstrated and to pursue advancement in areas and fields where it has not been demonstrated.

Second, the implementation of policies related to LT should be promoted, and the continuous advancement of rural entrepreneurial activities should be encouraged. Standardizing the procedures related to LT; refining the laws and regulations associated with land management rights; ensuring the openness and transparency of the LT process; strengthening the publicity, implementation, and subsidy of LT; building a complete mechanism for linking interests; and encouraging farmers to actively transfer land are necessary. Meanwhile, GF should be used to support and promote rural entrepreneurial activities and encourage farmers to engage in diversified entrepreneurial activities, improve their entrepreneurial enthusiasm, strive to broaden their income channels, and stimulate rural entrepreneurial vitality.

Third, a favorable environment for promoting AGTFP through GF should be created. In response to the human resources needed for rural development, a reasonable agricultural talent development policy should be formulated, a farmer education and training system should be established, financial knowledge should be promoted to rural areas, GF knowledge should be vigorously popularized, financial risk identification ability should be enhanced, and the rural education level should be improved. The government needs to withdraw funds to support agricultural technology innovation, provide conditions for the development of green agriculture, reduce the risks of agricultural technology innovation, and draw on advanced technologies at home and abroad to expand the research and investment level of agricultural enterprises. Strengthening farmers’ technical training, promoting the improvement of AGTFP, and achieving sustainable agricultural development are further recommendations.

Our hypothesis regarding the interaction between GF and AGTFP is based on a literature review and has not been further validated using theoretical models. We will attempt to construct theoretical models in the future to explore the impact mechanism between GF and AGTFP, expanding the research boundaries. The existing literature has found that the impact of GF on AGTFP has spatial spillover effects, but this study did not use a spatial DID model to identify this impact, resulting in incomplete results. We will adopt a spatial DID model in the future to study the spatial spillover effects of GF on AGTFP. In addition, GF may affect AGTFP through other channels of influence, but we have not had a comprehensive discussion on this. In the future, we will further analyze the impact channels of GF on AGTFP through the advancement of green technology and optimization of resource allocation.