Cleaner Production: Analysis of the Role and Path of Green Finance in Controlling Agricultural Nonpoint Source Pollution

3.2.1 The Role of Government ER

Existing research suggests that although intensifying government ER may initially reduce resource utilization efficiency (Boyd & McClelland, 1999), it plays a pivotal role in enhancing the effectiveness of environmental pollution control (Bu et al., 2022). This influence is evident in two primary ways: first, ER imposes environmental taxes that escalate production costs in high-pollution sectors, effectively reducing their competitive advantage due to their limited capacity for short-term technological innovation. Conversely, sectors engaged in green agriculture benefit from inherent green competitive advantages, mitigating environmental costs through optimized resource allocation and accelerated technological progress (Czyżewski et al., 2020). Second, ER stimulates technological innovation and supports the green transformation of the agricultural industry. This dynamic enhances the quality of factor inputs, phases out obsolete production capacities, and fosters the emergence of technological leaders, promoting the diffusion of innovative agricultural technologies and advancing regional agricultural development. Based on these considerations, this paper proposes the following hypothesis:

Hypothesis 2 (H2): GF inhibits the development of ANPSP through ER.

3.2.2 The Role of Land Transfer

GF can significantly impact ANPSP through the mechanism of land transfer, operating across three dimensions: 1. Optimization of Land Resource Allocation. GF, supported by financial mechanisms, encourages agricultural stakeholders to adopt more efficient farming techniques, reducing the reliance on extensive land and decreasing the use of inefficient inputs such as chemical fertilizers and pesticides. This strategic optimization helps mitigate the negative externalities associated with traditional agricultural practices, thereby enhancing pollution control and emission reduction efforts. 2. Economic Incentives for Land Transfer. By offering economic incentives, GF encourages land transfers to agricultural producers who engage in sustainable practices. Enhanced financial terms such as lower financing costs and more favorable loan conditions support this transition, promoting environmentally friendly agricultural production (Zang et al., 2022). 3. Investment in Agri-Environmental Technologies. GF also encompasses investments in environmentally friendly agricultural technologies. This financial support enables producers to acquire advanced tools like conservation irrigation systems and eco-fertilizers, which further alleviate ecological pressures on the land. Based on these considerations, this paper proposes the following hypothesis:

Hypothesis 3 (H3): GF inhibits the development of ANPSP through the degree of land transfer.

Building on these premises, this paper develops a theoretical model to examine the impact of GF on ANPSP. Within this framework, GF is conceptualized as the “knowledge bandwagon,” with ANPSP as the outcome variable. The model incorporates ER and the mediating effect of land transfer to elucidate the underlying mechanisms, as depicted in Figure 1. This comprehensive analysis aims to provide a robust understanding of the pathways through which GF influences ANPSP, guiding policy formulation and strategic interventions in sustainable agricultural development.

Figure 1

Theoretical analytical framework for the impact of GF on ANPSP.

4.1.1 Benchmark Regression Model

This study conducts a preliminary test to evaluate the effectiveness of GF in promoting pollution control and emission reduction in ANPSP. The framework for the basic model is outlined as follows:

In equation (1), ANPSP i t denotes the level of ANPSP emissions, GF i t is the level of GF development; Controls i t is the set of control variables, σ t is the time fixed effects, ν i is the individual fixed effect, ε i t is random error term, subscript i denotes the provincial individual, and subscript t denotes the year.

4.1.2 Mediating Effects Modeling

Building upon prior analysis, it is posited that GF can influence ANPSP both directly and indirectly, through the promotion of ER and the facilitation of land transfer. This paper advances the basic model (1) to model (2), aiming to explore the mediating roles of ER and land transfer degree in the nexus between GF and ANPSP. The specific formulation of the model is presented as follows:

In equation (2), ln MED i t is the mediating variable after taking logarithm, including two variables of ER and the degree of land transfer. The rest of the variables are the same as in equation (1).

4.2.1 Explained Variable

Agricultural nonpoint source pollution: Different from pollutant emissions from the same point source pollution, ANPSP is mainly composed of soil and sediment particles, nutrients such as nitrogen and phosphorus, pesticides, and various atmospheric particles (Luo et al., 2023). It mainly enters the water, soil, or atmosphere through the way of surface runoff, soil erosion, and farmland drainage, thus causing environmental pollution. Chemical fertilizers, pesticides, agricultural plastic films, and diesel used in agricultural mechanization are the main sources of pollutants in agricultural production. It can be considered that yes, as long as there is the application of agricultural production factors such as fertilizers, pesticides, agricultural plastic films, and diesel, no matter whether it is overused or not absorbed by crops, it will directly become the direct source of water pollution, soil pollution, and other nonpoint source pollution characteristics under multiple effects of rainfall, sediment and irrigation. In this study, representative emissions of pollutants produced in the production and life of the agricultural sector were used as proxy variables to measure agricultural pollution. Agricultural production generates various forms of surface source pollution, predominantly consisting of TN, TP, COD, carbon emissions, and residues from pesticides and AF. This pollution impacts the soil environment directly and also reaches water bodies through a combination of precipitation, topography-driven runoff (both surface and subsurface), and plant interception (Sun et al., 2012). To measure agricultural pollution, this study employs the inventory analysis method based on unit surveys. This method encompasses pollution from both aquaculture and the use of pesticides and AF. It estimates ANPSP across seven dimensions: agricultural fertilizers, livestock and poultry farming, aquaculture, crops, rural population, pesticides, and agricultural plastic films. The index information of this study to measure ANPSP is shown in Table 1.

The emission intensity of ANPSP is calculated as:

In equation (3), TE is the total emission of ANPSP, E TP , E TN , and E COD are the total emission of TN, TP and COD, respectively. In equation (4), the EU i is the statistic of the pollution unit i , ρ i is the pollution production coefficient of the pollution unit, and θ i is the emission coefficient or loss rate. The pollution intensity of various units differs due to distinct influencing factors. For the calculation of emissions from ANPSP, the coefficients for the seven pollution units are utilized as follows:

Agricultural fertilizers: The primary pollutants from agricultural fertilizers include nitrogen, phosphorus, and compound fertilizers. This paper employs the output coefficient method to account for the variances in fertilizer loss rates due to different planting methods. Since the focus is on TN and TP pollution from fertilizer inputs, and the phosphorus fertilizer inputs in statistical yearbooks are indicated as phosphorus pentoxide (P₂O₅), these inputs are adjusted by multiplying them by 43.66%. Additionally, in line with recent domestic fertilizer practices and prior research findings, the compound fertilizer is converted to TN at 40% and P₂O₅ at 32%. The fertilizer loss coefficient is derived by averaging the results from different regional samples, based on existing studies (Wang et al., 2019). This specific accounting approach is in accordance with the methods used in the second national pollution source census.

Livestock and poultry farming: The pollution emissions are calculated as the product of the total quantity of livestock and poultry (in stock or slaughtered), multiplied by both the pollution discharge coefficient and the wastage coefficient. The discharge coefficients for feces and urine of livestock and poultry are sourced from SEPA data (2022). The formula applied is: Livestock and poultry pollution intensity (kg per head per annum) = Rearing cycle × Fecal (urine) emission factor × Fecal (urine) pollutant excretion coefficient. In this study, livestock and poultry statistics encompass cattle, sheep, and pigs. For cattle and sheep, which have a rearing period of more than 1 year, the total breeding amount is based on the year-end stock. For pigs, due to their rearing period of less than 1 year, the total breeding amount is determined by the current year’s output.

Aquaculture: ANPSP primarily arises from bait residues, aquaculture excreta, and chemicals. The extent of this pollution is contingent on the aquaculture type and method. The China Statistical Yearbook classifies aquaculture production into marine and freshwater categories. Given that artificial aquaculture is a significant pollution contributor, this paper exclusively utilizes data from freshwater aquaculture for its analyses. The primary aquaculture species include freshwater fish, crustaceans, shellfish, and other aquatic organisms. The production and discharge coefficients for aquaculture are derived from the First National Pollution Source Census: Handbook of Production and Discharge Coefficients for Pollution Sources in Aquaculture, supplemented by additional literature (Feng et al., 2023).

Crops: The primary pollutants from crops include residues, vegetable wastes, and other debris from agricultural production (Norse, 2005). Given the diverse range of crops, this paper focuses on the seven most representative ones for analysis: rice, wheat, maize, beans, potatoes, oilseeds, and vegetables. The estimation of surface source pollution from agricultural solid waste involves calculating the crop residue yield based on the grass-to-grain ratio and determining the TN, TP, and COD content from the nutrient composition of the straw. Recognizing the varied straw utilization methods in rural areas, each with different nutrient loss rates, the final emission formula for farmland solid waste pollution is: Emissions (tons) = Total crop production (tons) × Production coefficient × Straw utilization structure × Straw nutrient loss rate, where the production coefficient equals the grass-to-grain ratio multiplied by the straw nutrient content (Ma et al., 2019).

Rural life: Pollution in rural life primarily comprises domestic sewage and human feces. The annual production coefficients per capita for COD, TN, and TP in domestic wastewater are 5.84 kg/person, 0.584 kg/person, and 0.146 kg/person, respectively, with an emission factor of 100%. For human feces, the corresponding coefficients are 19.8 kg/person, 3.06 kg/person, and 0.64 kg/person, respectively, with an emission factor of 10% (Luo et al., 2019).

Pesticides: Pesticide residues are calculated as the amount of pesticides applied multiplied by a residue factor of 0.5.

Agricultural film: The amount of AF residue is determined by multiplying the quantity of AF used by a residue factor of 0.1.

4.2.2 Explanatory Variable

Green finance: GF primarily aims to adhere to market economy principles while focusing on building an ecological civilization. It employs a range of financial tools, including credit, securities, insurance, and funds, to foster energy conservation, reduce consumption, and achieve a harmonious balance between economic resources and the environment. In the realm of existing literature, methodologies such as principal component analysis, the entropy value method, and hierarchical analysis are commonly used to determine the weights of GF development indicators. Following the approach of Li and Shao (2022), this paper develops indicators in seven domains: green credit, green investment, green insurance, green bonds, green support, green fund, and green rights and interests. These indicators are then integrated using the entropy method to formulate a GF index, which assesses the level of GF development. For this assessment, raw data is initially standardized, followed by the computation of the indicators. The detailed measurement methodology is presented in Table 2.

4.2.3 Mediator Variables

Environmental regulation: The selection of the ER variable follows the methodology of Chen et al. (2018). This approach utilizes the frequency of terms related to “environmental protection” in local government work reports compared to the total word count of the report as an indicator. A higher frequency indicates a stronger commitment to environmental governance, thus reflecting the intensity of ER and addressing endogeneity concerns. Relevant terms include ecology, green, low-carbon, pollution, energy consumption, emission reduction, sewage, sulfur dioxide, and carbon dioxide. As local government work reports are typically published early in the year, they predate and thus are not influenced by that year’s environmental conditions, further mitigating endogeneity issues.

Land transfer: For the degree of land transfer (LT), this paper adopts the rate of agricultural land transfer (calculated as the total area of family-contracted arable land transferred divided by the total area of family-contracted arable land operated) as the proxy variable. This rate is an effective measure of agricultural land transfer levels and is widely used in inter-provincial level studies.

4.2.4 Control Variables

In alignment with existing literature (Abid et al., 2022), this study selects seven indicators as control variables: industrial structure (IS), economic development (ED), fixed investment (INV), research and development (R&D) intensity (RD), marketization level (MAR), human capital (HC), and openness to foreign trade (FT). The specific methodologies for these measures are detailed in Table 3.

5.4.1 Heterogeneity of Economic Development Level

To investigate whether regional economic disparities influence the effectiveness of GF in mitigating ANPSP, this study calculated the average per capita GDP across 30 provinces within the sample year. By comparing each province’s total per capita GDP against the average value of 12078.75, the sample was divided into two groups: high and low economic development. The high economic development group consists of ten provinces: Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Hubei, Guangdong, and Chongqing. In contrast, the low-economic development group includes 20 provinces: Hebei, Liaoning, Anhui, Jiangxi, Henan, Hunan, Sichuan, Shaanxi, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Guangxi, Hainan, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang. As shown in Table 7, a comparison of the estimated coefficients’ magnitude and significance reveals that GF’s impact on ANPSP is only pronounced in regions of low-economic development and is notably positive. In these areas, agricultural practices may rely more on outdated technologies and methods, which are typically less environmentally sustainable. Furthermore, these regions might lack efficient resource allocation mechanisms, leading to suboptimal use of GF for pollution reduction. Additionally, areas with lower economic development often have weaker ER and enforcement, posing challenges to effective implementation of environmental protection measures, even with GF support (Wang & Wang, 2023).

5.4.2 Heterogeneity Analysis of GF Policy Support

Considering the heterogeneity of GF standards, development levels, policy arrangements, and regulatory policies in the GF Reform and Innovation Pilot Zone, there are significant differences in the implementation effectiveness of GF. This study further explores the differential impact of GF on ANPSP across provinces within the policy framework of GF reform and innovation pilot zones. According to the Overall Plan for the Construction of Green Financial Reform and Innovation Pilot Zone jointly issued by seven ministries and commissions including the People’s Bank of China in 2017 and the Overall Plan for the Construction of Green Financial Reform and Innovation Pilot Zone in Lanzhou New Area of Gansu Province approved by the State Council in 2019, 30 provinces are divided into green financial reform and innovation pilot zones and nongreen financial reform and innovation pilot zones, of which the provinces of green financial reform and innovation pilot zones include Zhejiang, Guangdong, Jiangxi, Guizhou, Xinjiang, Gansu, and Chongqing.

The regression results in Table 7 indicate that in policy-supported provinces, GF and ANPSP are negatively correlated, with a GF coefficient of −1.340, which is significant at the 1% level. However, in nonpolicy-supported regions, GF and ANPSP are not significant. This indicates that GF policies have effectively promoted the improvement of environmental quality in policy-supported provinces, while the effect is not significant in nonpolicy-supported provinces. The possible reason is that in nonpolicy-supported areas, the incentive mechanism and policy support for GF are insufficient, resulting in low efficiency in financial resource allocation and thus failing to fully stimulate the enthusiasm of the market and enterprises to support green development.