Can Policy-Based Agricultural Insurance Promote Agricultural Carbon Emission Reduction? Causal Inference Based on Double Machine Learning

Abstract

Policy-based agricultural insurance plays a pivotal role in promoting agricultural carbon emissions reduction and driving the development of agricultural modernization. This study, based on panel data from 31 Chinese provinces spanning 2003 to 2021, employs the double machine learning method to conduct theoretical and empirical analyses on the carbon emission reduction effects, implementation mechanisms, and regional heterogeneity in policy-oriented agricultural insurance. The empirical findings indicate that the enforcement of policy-based agricultural insurance exerts a considerable influence on curbing agricultural carbon emissions. This conclusion remains robust across a rigorous suite of robustness checks. Under the “scale–structure–technology” logical framework, the carbon emission reduction effects of policy-oriented agricultural insurance operate through three key mechanisms: the scaling-up of agricultural production, the grain-oriented transformation of planting structures, and the advancement of agricultural technologies. Heterogeneity tests reveal that policy-based agricultural insurance exerts significantly stronger carbon mitigation impacts in major grain-producing areas, the Yangtze River Economic Belt, and regions with stringent environmental regulations.

1. Introduction

The greenhouse effect has manifested as the most imperative environmental predicament on a global scale. The emissions of greenhouse gases, encompassing carbon dioxide, have precipitated the surging frequency of extreme climatic events and have exacted grievous tolls on ecological ecosystems. In response, the international community has widely advocated for carbon reduction initiatives. As the world’s second-largest economy and one of the major carbon dioxide emitters, China plays a significant role in global climate governance [1]. In 2020, China methodically formulated its strategic development blueprint, with the fundamental orientation goal of achieving the dual strategic objectives of “carbon peak” and “carbon neutrality”. In 2024, the Chinese government reiterated its explicit commitment to “proactively and steadily advance the realization of carbon peak and carbon neutrality targets”. The agricultural domain stands as a substantial contributor to the emissions of greenhouse gases and carbon compounds in China [2,3]. Relevant data indicate that carbon emissions, methane, and nitrous oxide emissions from China’s agricultural sector account for 7%, 40.2%, and 59.5% of the national total, respectively. Reducing emissions and enhancing carbon sequestration within the agricultural sector are of paramount importance to realize China’s “Dual Carbon” objectives, as well as serving as an essential requirement for advancing high-quality agricultural development and achieving the green low-carbon transition.

In 2024, China National Financial Regulatory Administration explicitly stated that it unequivocally affirmed that the indispensable role of insurance in expediting the thoroughgoing green metamorphosis of economic and social development should be fully harnessed. It also emphasized actively and prudently supporting the achievement of carbon peak and carbon neutrality. As a vital instrument within the modern financial system, policy-based agricultural insurance possesses the functions of risk pooling, risk dispersion, and economic compensation, providing essential safeguards for the green development of agriculture [4]. According to the data, in 2024, the premium income of agricultural insurance in China reached CNY 152.1 billion, marking a 6% increase from the previous year. It exerts a pivotal influence in promoting rural economic growth and bolstering the resilience of agricultural risk mitigation mechanisms, particularly through numerous beneficial explorations in the field of green finance [5,6]. Against this backdrop, this study will delve into the intrinsic relationship between policy-oriented agricultural insurance and agricultural carbon emissions. Specifically, it will address the following questions: Is it policy-based agricultural insurance specifically that has the potential to effectively mitigate the emissions of agricultural carbon? If so, by means of which transmission mechanisms can this be accomplished? Moreover, what are the critical factors that exert an influence on these outcomes? Through delving into these inquiries, this paper aims to offer significant reference value for propelling the green transition of agriculture and achieving the “Dual Carbon” goals.

2. Literature Review

The articles closely related to this paper predominantly focus on the discussion of the environmental effects of policy-oriented agricultural insurance and carbon emissions in the agricultural sector. Regarding the environmental effects of policy-based agricultural insurance, the academic community has primarily explored the intrinsic connections between policy-based agricultural insurance and the adoption of agricultural green technologies [7,8], as well as the input of agricultural chemicals [9]. Existing research indicates that, due to the high investment risks and long payback periods associated with green agricultural technologies, farmers often prefer traditional agricultural production methods [10,11]. Policy-based agricultural insurance subsidies can provide risk-sharing mechanisms for farmers, stabilize their expected returns, alleviate credit constraints, and incentivize them to improve their agricultural technology investment decisions, thereby promoting the adoption of green agricultural technologies [12,13]. Additionally, some scholars argue that farmers may rely on intensive chemical input. However, agricultural insurance, through its risk-transfer and loss-compensation mechanisms, can stabilize farmers’ income expectations and reduce income volatility, thereby curbing excessive fertilizer applications [14]. Other researchers have demonstrated that the high coverage level of policy-based agricultural insurance may induce moral hazards [15]. Based on cost–benefit considerations, farmers may reduce pesticide and fertilizer inputs when they anticipate that potential losses will be compensated [16,17]. Regarding agricultural carbon emissions, the existing research primarily concentrates on three aspects: firstly, the sources and measurement of agricultural carbon emissions. Early scholars defined the sources of carbon emissions from agriculture in terms of fertilizers, pesticides, livestock farming, and agricultural waste [18], and quantification studies were focused on a single aspect, such as the application of chemical fertilizers, agricultural land use, and straw burning [19,20], with subsequent research focusing on specific areas for carbon emission quantification [21,22]. Secondly, the mechanisms that exert influence on agricultural carbon emissions have been investigated by employing diverse models, such as spatial econometric models, GWR models, and the CSMA-PPE-USSBM model [23,24]. These studies examined the influencing mechanisms of agricultural carbon emissions from macro-level dimensions, including agricultural technological progress, fiscal support for agriculture, economic development levels, and industrial agglomeration [25,26]. Thirdly, research has focused on pathways for agricultural carbon emission reduction. Scholars have predominantly investigated from perspectives including agricultural technological advancement, high-standard farmland construction, and reforms in agricultural subsidy policies. For instance, agricultural subsidy policies can promote green technological innovation in agriculture, substantially curtailing the utilization of electrical energy, raw material inputs, and water resources. This effectively fosters the recycling and reuse of waste, contributing to ecological environmental optimization. Furthermore, the construction of high-standard farmland is considered an effective approach to reducing agricultural carbon emissions [27]. By promoting large-scale agricultural production, improving soil ecosystem services, and enhancing irrigation and drainage conditions, high-standard farmland fosters the intensive and optimized allocation of agricultural resources, thereby significantly augmenting the carbon sequestration potential of agro-ecosystems [28].

Existing scholarly investigations into the environmental effects of policy-based agricultural insurance and the issue of agricultural carbon emissions are quite extensive, providing valuable references for this paper. However, there are still some shortcomings that remain. Firstly, previous studies have empirically demonstrated how policy-driven crop insurance affects the environment and have analyzed the key components, estimation techniques, determinants, and reduction strategies of farm-related carbon emissions. However, within the academic research landscape, there is a dearth of comprehensive and in-depth investigations into the carbon emission reduction effects of policy-oriented agricultural insurance. Secondly, the body of extant scholarly research has neglected to comprehensively account for the heterogeneity in the environmental consequences precipitated by policy-driven agricultural insurance. They have only assessed the overall effectiveness of the policies, which does not reflect the regional characteristics. Thirdly, existing studies have predominantly used the Difference-in-Differences method to assess the environmental effects of policy-based agricultural insurance. However, this approach often struggles to overcome issues such as the uncertainty of confounding factor functional forms, the curse of dimensionality, and regularization bias.

In view of this, the novel academic value of this paper manifests itself through several distinctive dimensions, as outlined below. Firstly, this paper systematically integrates policy-based agricultural insurance and farming-related greenhouse gas emissions into a cohesive analytical framework. It explores the intrinsic connections and pathways of action between policy-based agricultural insurance and agricultural carbon emission reduction from the viewpoints of large-scale agricultural production, the “grain-oriented” shift in planting structures, and advancements in agricultural technology. Secondly, this study conducts an in-depth analysis of the geographical and spatial variation in the influence exerted by the policy-based agricultural insurance on the reduction of carbon emissions through three dimensions—agricultural production, geography, and environmental regulation—thereby accurately grasping the differences in its effects on carbon emission reduction across various regions. This analytical approach enables a precise understanding of how policy-based agricultural insurance reduces carbon emissions across diverse regions. Thirdly, this study utilizes an innovative dual machine learning approach derived from causal inference methodologies as its primary analytical instrument for empirical validation. This method can avoid the estimation biases caused by improper econometric model specifications and the issue of the “curse of dimensionality”, and it is more capable of scientifically distilling the marginal impact effects of policy-based agricultural insurance on farming-related greenhouse gas emissions.

3. Policy Background, Theoretical Analysis, and Research Hypotheses

3.1. Policy Background

As a government-led risk management instrument, policy-based agricultural insurance provides risk compensation for crop production, animal husbandry, and forestry through a combination of fiscal subsidies and market-oriented operations. China began its institutional exploration of policy-based agricultural insurance in 2003. In 2007, the central government proposed launching policy-based agricultural insurance pilots in six provinces, including Jilin and Jiangsu. The premium subsidy standards were set at 25% from the central finance, 25% from provincial finance, with the remaining portion borne by the farmers. This marked the formal commencement of China’s agricultural insurance progressing into the developmental phase of policy-oriented insurance. Since then, China’s policy-oriented agricultural insurance pilot program has continued to expand its coverage area (as illustrated in Figure 1), and the financial support for premium subsidies has been increasing. In 2012, China implemented policy-based agricultural insurance nationwide, with provincial governments providing a 25% premium subsidy, while the central government offered a 35% subsidy for eastern regions and a 40% subsidy for central and western regions. By 2021, China’s agricultural insurance premium scale surpassed that of the United States, ranking first globally. In 2024, the central government allocated CNY 54.7 billion in agricultural insurance premium subsidy funds, supporting a premium scale of CNY 1.521 trillion and providing over CNY 5 trillion in risk protection for 147 million farming households. Given China’s “carbon peak” goal, understanding the ecological and environmental impacts of policy-driven agricultural insurance is of great practical significance. By uncovering how it influences the promotion of low-carbon development in the agricultural sector, we can give a real boost to the growth of agricultural insurance and the transformation and upgrading of agriculture.

3.2. Theoretical Analysis and Research Hypotheses

3.2.1. Direct Effect

As a government-led agricultural risk protection mechanism, policy-oriented agricultural insurance provides risk compensation for crop cultivation, animal husbandry, and forestry production through a combination of fiscal subsidies and market-oriented operations. The implementation of this policy not only contributes to stabilizing farmers’ income and diversifying operational risks, but also influences agricultural production methods by altering the decision-making behaviors of microeconomic entities. Specifically, with the support of risk protection mechanisms, farmers tend to adjust their production organization models, promoting the transition toward large-scale and intensive agricultural operations. This shift further optimizes the allocation of production factors, reducing reliance on high-carbon inputs while increasing the proportion of environmentally friendly technological inputs [29]. Such transformation in production methods helps to reduce the carbon footprint in agricultural processes, ultimately leading to a decrease in agricultural carbon emission intensity. Accordingly, this paper proposes the following hypothesis:

H1: 

Policy-oriented agricultural insurance can promote agricultural carbon emission reduction.

3.2.2. Mediating Effect

Generally, the impact of environmental issues can be attributed to scale effects, structural effects, and technological effects [30]. Therefore, guided by risk management theory and farmer behavior theory, this paper adheres to the logical chain of “scale–structure–technology” and examines the transmission mechanism of the carbon emission reduction effect of policy-based agricultural insurance from three perspectives: large-scale agricultural production, the “grain-oriented” shift in agricultural production, and advancements in agricultural technology.

① Large-scale agricultural production. On the one hand, constrained by geographical and natural environmental factors, China has become one of the countries with a high frequency of natural disasters and relatively severe loss levels. The high-risk production environment not only increases the operational difficulties and costs for farmers, but also elevates the uncertainties associated with agricultural scaling-up. Fiscal subsidies for agricultural insurance can effectively compensate farmers for disaster-induced losses, stabilize their expected income from production and operations, reduce production and operation costs, and thereby incentivize land transfer and scaled agricultural operations. On the other hand, the primary constraint for traditional smallholder farmers transitioning to large-scale and specialized new agricultural business entities is financing. Farmers’ agricultural credit may be secured by the risk-reduction capabilities of policy-oriented agricultural insurance. The certainty of insurance payouts enhances borrowers’ debt-servicing capacity and reduces financial institutions’ lending risks, thereby alleviating financing constraints. This provides solid material foundations and financial support for new-type agricultural operation entities to expand their production scale. Furthermore, the allocation of production components can be optimized through the scaling of agricultural production [31]. Specifically, the scaling-up of farmland operations has transformed the extensive production patterns which are characteristic of traditional smallholder farming. The widespread adoption of mechanization enables the more scientific application of agricultural inputs [32,33]. Furthermore, large-scale farming entities possess stronger risk-bearing capacity and demonstrate a greater propensity to adopt environmentally friendly technologies. The application of green technologies significantly reduces traditional chemical inputs, thereby lowering agricultural carbon emissions. Accordingly, this paper proposes the following hypothesis:

H2: 

Policy-based agricultural insurance can promote agricultural carbon emission reduction by the scaling of agricultural production.

② “Grain-oriented” agricultural planting structure. The schemes of policy-based agricultural insurance will alter the economic return ratios among different crops, thereby influencing producers’ cultivation choices and serving as a crucial determinant in their production decision-making processes. In recent years, China’s policy-based agricultural insurance reform has demonstrated a pronounced grain crop protection orientation, with staple crops such as rice, corn, and wheat becoming major insured varieties. Notably, the implementation of comprehensive cost insurance and planting income insurance has significantly enhanced the risk protection levels for these three staple crops. This policy design has effectively reduced farmers’ disaster-induced cost losses and stabilized their grain cultivation income, thereby incentivizing farmers to proactively adjust planting structures by increasing the proportion of staple crops, ultimately promoting a “grain-oriented shift” in agricultural planting patterns. The “grain-oriented shift” in the agricultural planting structure will affect the allocation of agricultural production factors. Compared to cash crops, grain crops require relatively lower inputs of chemical elements such as pesticides and fertilizers. The input of these chemical elements will continue to decline as the percentage of grain crop production rises, contributing to a drop in overall agricultural carbon emissions [34]. Moreover, the “grain-oriented” shift in agricultural planting structures helps farmers to strengthen the agricultural science management. It promotes large-scale and intensive agricultural production and operations; reduces energy consumption, such as diesel for agricultural machinery and electricity for irrigation; and ensures the scientific management of farmland and soil. These factors collectively promote agricultural carbon emission reduction. Accordingly, this paper proposes the following hypothesis:

H3: 

Policy-based agricultural insurance can promote agricultural carbon emission reduction through the “grain-oriented” shift in agricultural planting structures.

③ Agricultural technological advancement. On the one hand, farmers’ production decisions are based on welfare maximization or expected utility maximization. When facing significant uncertainty regarding the costs and benefits of advanced agricultural technologies in the initial investment phase, they often opt for traditional agricultural production methods. Agricultural insurance premium subsidies can compensate for the cost losses incurred by farmers due to disasters, significantly reducing the risks associated with agricultural production cost inputs, stabilizing farmers’ agricultural production and operational income. This, in turn, incentivizes farmers to invest in agricultural socialized services. On the other hand, the synergy between policy-based agricultural insurance and agricultural credit can also promote agricultural technological advancement, consequently facilitating agricultural carbon emission reduction. More funding is needed for the development and use of sophisticated agricultural equipment and intelligent agricultural facilities than for conventional agricultural production techniques. The risk protection function of agricultural insurance fiscal subsidy policies can provide guarantees for agricultural credit, enhancing the willingness and enthusiasm of financial institutions to offer agricultural credit. This helps to alleviate the financial pressure on farmers and agricultural enterprises when purchasing advanced agricultural technologies. The application of advanced agricultural technologies can mitigate the agricultural carbon emission intensity by optimizing resource allocation. For instance, precision farming technologies represented by smart water–fertilizer integrated systems enhance irrigation efficiency and reduce pesticide application through real-time soil moisture monitoring and crop nutrient requirement analysis. Meanwhile, conservation tillage techniques, through the adoption of surface straw mulching and soil-test-based fertilization, can effectively reduce chemical fertilizer inputs while increasing soil organic matter content, thereby enhancing the carbon sequestration potential per unit of arable land. Accordingly, this paper proposes the following hypothesis:

H4: 

Policy-based agricultural insurance can promote agricultural carbon emission reduction through the advancement of agricultural technology.

In summary, the theoretical logic analysis framework for constructing policy-oriented agricultural insurance to reduce agricultural carbon emissions is illustrated in Figure 2.

4. Research Design

4.1. Model Construction

Currently, numerous studies apply the Difference-in-Differences model to assess the efficacy of policy interventions. However, this model presupposes a functional relationship between variables, thereby incurring the risk of functional misspecification. Moreover, the model is extremely stringent in terms of sample selection. Specifically, it requires the assumption that both the treatment and control groups demonstrate parallel trends prior to the implementation of the policy. Simultaneously, traditional linear regression methods, when incorporating control variables, may encounter challenges such as the “curse of dimensionality”, which complicates model estimation, and multicollinearity, which undermines the accuracy of the results. The double machine learning model provides an innovative nonparametric regression approach for causal inference analysis. This method eliminates the need for pre-specified functional forms, instead employing machine learning algorithms to automatically select and optimize control variable combinations. It not only enhances prediction accuracy but also effectively avoids common issues in traditional approaches, such as functional form misspecification and control variable redundancy. By correcting the biases inherent in conventional causal identification methods, the double machine learning model significantly improves the reliability of empirical findings. Therefore, this paper draws on the double machine learning model put forward by Chernozhukov et al. to assess the policy effects [35].

This paper constructs a partially linear double machine learning model, as follows:

$${\mathrm{C}}_{\mathrm{it}+1}={ \mathsf{\theta }}_0{\mathrm{Event}}_{\mathrm{it}}+ \mathrm{g}\left({\mathrm{X}}_{\mathrm{it}}\right)+{ \mathrm{U}}_{\mathrm{it}}$$

(1)

$$\mathrm{E}\left({\mathrm{U}}_{\mathrm{it}}{|\mathrm{Event}}_{\mathrm{it}},{\mathrm{X}}_{\mathrm{it}}\right)=0$$

(2)

Here, the subscript i represents the city and t indicates the temporal dimensions. The response variable ${\mathrm{C}}_{\mathrm{it}+1}$ embodies the aggregate measure of agricultural carbon emissions. The core explanatory variable ${\mathrm{Event}}_{\mathrm{it}}$ represents the policy variable of policy-based agricultural insurance, which is expressed as a dummy variable. The coefficient ${\mathsf{\theta }}_0$ is the estimated treatment effect of primary interest under investigation.${ \mathrm{X}}_{\mathrm{it}}$ denotes a collection of high-dimensional control variables, whose functional format $\widehat{\mathrm{g}}$(${\mathrm{X}}_{\mathrm{it}}$) requires estimation through advanced machine learning techniques. ${\mathrm{U}}_{\mathrm{it}}$ is the random error term, satisfying the assumption that the conditional expectation is zero. Through the concurrent estimation of Equations (1) and (2), the estimated treatment coefficient can be systematically derived and presented in the following fashion:

$${ \widehat{\mathsf{\theta }}}_0={\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}^2\right)}^{-1}{\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}\left({\mathrm{C}}_{\mathrm{it}}-\widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right)$$

(3)

Here, n corresponds to the number of observations in the dataset.

Based on the aforementioned estimator, the estimation bias can be further examined:

$$\begin{array}{l}\sqrt{\mathrm{n}}\left({ \widehat{\mathsf{\theta }}}_0-{\mathsf{\theta }}_0\right)={\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}\mathrm{E}\mathrm{v}\mathrm{e}\mathrm{n}{\mathrm{t}}_{\mathrm{i}\mathrm{t}}^2\right)}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ}\mathrm{T}}\mathrm{E}\mathrm{v}\mathrm{e}\mathrm{n}{\mathrm{t}}_{\mathrm{i}\mathrm{t}}{\mathrm{U}}_{\mathrm{i}\mathrm{t}}+\\ {\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}^2\right)}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ}\mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}\left[\mathrm{g}\left({\mathrm{X}}_{\mathrm{it}}\right)-\widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right]\end{array}$$

(4)

where a = ${\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}^2\right)}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ}\mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}{\mathrm{U}}_{\mathrm{it}}$, and following a normal distribution with a mean of 0, b = ${\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ} \mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}^2\right)}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i}\mathrm{ϵ} \mathrm{I},\mathrm{t}\mathrm{ϵ}\mathrm{T}}{\mathrm{Event}}_{\mathrm{it}}\left[\mathrm{g}\left({\mathrm{X}}_{\mathrm{it}}\right)-\widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right]$. It is crucial to emphasize that utilizing machine learning to approximate the specific functional form $\widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)$ may introduce bias due to the application of regularization techniques. Although this approach effectively controls the variance inflation of the estimators, it simultaneously results in the loss of unbiasedness.

To improve the convergence efficiency of the model under small-sample conditions and ensure that the treatment effect estimator exhibits favorable statistical properties, the following auxiliary model is established:

$${\mathrm{Event}}_{\mathrm{it}}= \mathrm{m}\left({\mathrm{X}}_{\mathrm{it}}\right)+{ \mathrm{V}}_{\mathrm{it}}$$

(5)

$$\mathrm{E}\left({\mathrm{V}}_{\mathrm{it}}{|\mathrm{X}}_{\mathrm{it}}\right)=0$$

(6)

Here, m$\left({\mathrm{X}}_{\mathrm{it}}\right)$ denotes the regression function established between the treatment variable and the high-dimensional control variables. However, the specific form, denoted as $\widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)$, remains to be estimated through the application of machine learning algorithms.${ \mathrm{V}}_{\mathrm{it}}$ is a random error term that satisfies the condition of having a mean expectation of zero.

The specific implementation steps are as follows: First, use machine learning algorithms to estimate the auxiliary regression $\widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)$ and calculate its residual ${ \widehat{\mathrm{V}}}_{\mathrm{it}}$ = ${\mathrm{Event}}_{\mathrm{it}}- \widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)$. Next, we transform the primary regression model into the form ${\mathrm{C}}_{\mathrm{it}+1}- \widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)$ = ${\mathsf{\theta }}_0{\mathrm{Event}}_{\mathrm{it}}+{\mathrm{U}}_{\mathrm{it}}$. Finally, we employ ${ \widehat{\mathrm{V}}}_{\mathrm{it}}$ as an instrumental variable for ${\mathrm{Event}}_{\mathrm{it}}$ in the regression analysis process to derive the unbiased coefficient estimation, which is presented as follows:

$${ \widehat{\mathsf{\theta }}}_0={\left({\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i} \mathrm{ϵ} \mathrm{I},\mathrm{t} \mathrm{ϵ} \mathrm{T}}{ \widehat{\mathrm{V}}}_{\mathrm{it}}{\mathrm{Event}}_{\mathrm{it}}\right)}^{-1} {\displaystyle \frac{1}{\mathrm{n}}}{{\displaystyle \sum }}_{\mathrm{i} \mathsf{\epsilon } \mathrm{I}, \mathrm{t}\mathsf{\epsilon } \mathrm{T}}{ \widehat{\mathrm{V}}}_{\mathrm{it}}\left({\mathrm{V}}_{\mathrm{it}+1}- \widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right)$$

(7)

Similarly, Equation (7) can also be approximately expressed as:

$$\begin{gathered} \sqrt{\mathrm{n}}\left( \widehat{\mathsf{\theta }}-{\mathsf{\theta }}_0\right)={\left[\mathrm{E}\left({\mathrm{V}}_{\mathrm{it}}^2\right)\right]}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i} \mathrm{ϵ} \mathrm{I},\mathrm{t} \mathrm{ϵ} \mathrm{T}}{\mathrm{V}}_{\mathrm{it}}{\mathrm{U}}_{\mathrm{it}} \\ +{\left[\mathrm{E}\left({\mathrm{V}}_{\mathrm{it}}^2\right)\right]}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i} \mathrm{ϵ} \mathrm{I}, \mathrm{t} \mathrm{ϵ} \mathrm{T}}\left[\mathrm{m}\left({\mathrm{X}}_{\mathrm{it}}\right)- \widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right] \left[\mathrm{g}\left({\mathrm{X}}_{\mathrm{it}}\right)- \widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right] \end{gathered}$$

(8)

Here, ${\left[\mathrm{E}\left({\mathrm{V}}_{\mathrm{it}}^2\right)\right]}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i} \mathrm{ϵ} \mathrm{I}, \mathrm{t} \mathrm{ϵ} \mathrm{T}}{\mathrm{V}}_{\mathrm{it}}{\mathrm{U}}_{\mathrm{it}}$ is normally distributed with a zero mean. In light of the fact that the machine learning estimation procedure is executed twice, ${\left[\mathrm{E}\left({\mathrm{V}}_{\mathrm{it}}^2\right)\right]}^{-1}{\displaystyle \frac{1}{\sqrt{\mathrm{n}}}}{{\displaystyle \sum }}_{\mathrm{i} \mathrm{ϵ} \mathrm{I}, \mathrm{t} \mathrm{ϵ} \mathrm{T}}\left[\mathrm{m}\left({\mathrm{X}}_{\mathrm{it}}\right)- \widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)\right]$[g$\left({\mathrm{X}}_{\mathrm{it}}\right)- \widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)$] depends on the convergence rates of $\widehat{\mathrm{m}}\left({\mathrm{X}}_{\mathrm{it}}\right)$ to m$\left({\mathrm{X}}_{\mathrm{it}}\right)$ and $\widehat{\mathrm{g}}\left({\mathrm{X}}_{\mathrm{it}}\right)$ to g$\left({\mathrm{X}}_{\mathrm{it}}\right)$, namely ${\mathrm{n}}^{-\left({\mathsf{\phi }}_{\mathrm{g}}+{\mathsf{\phi }}_{\mathrm{m}}\right)}$. Compared to (4), $\sqrt{\mathrm{n}}\left( \widehat{\mathsf{\theta }}-{\mathsf{\theta }}_0\right)$ demonstrates a faster convergenc. toward 0, ultimately yielding an unbiased treatment effect coefficient estimate that satisfies the unbiasedness requirement.

4.2. Variable Setting and Description

4.2.1. Dependent Variable

Total Agricultural Carbon Emissions (C). Drawing upon the existing academic research [36,37], this paper focuses on the field of crop farming to assess the total agricultural carbon emissions. The formula for the calculation is shown below:

$${\mathrm{C}}_{\mathrm{it}}={{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{S}}_{\mathrm{ijt}}={{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{ijt}}{\mathrm{Q}}_{\mathrm{j}}.$$

(9)

${\mathrm{C}}_{\mathrm{it}}$ represents the total agricultural carbon emissions. ${\mathrm{P}}_{\mathrm{ijt}}$ indicates the amount of the j-th carbon source in province i for the year t. ${\mathrm{Q}}_{\mathrm{j}}$ represents the emission coefficient corresponding to the j-th carbon source. Here, carbon sources encompass chemical fertilizers, pesticides, agricultural films, machinery usage, tillage practices, and irrigation systems. The agricultural carbon emission coefficients are referenced from authoritative guidelines.

4.2.2. Core Explanatory Variables

Policy-Oriented Agricultural Insurance (Event). This study identifies 400 treatment regions and 189 control regions based on the six batches of policy-oriented agricultural insurance pilot programs officially published on Chinese government websites. A policy dummy variable is constructed according to the programs’ enrollment timelines to serve as the proxy variable for policy-oriented agricultural insurance implementation. Specifically, if a province is approved for the pilot program of policy-oriented agricultural insurance, the corresponding policy dummy variable is given a value of 1. Conversely, non-pilot regions receive a value of 0.

4.2.3. Control Variables

To enhance the reliability of the research findings, this paper controls for key variables that are likely to influence agricultural carbon emissions: agricultural diesel (Diesel), plastic film usage (Plastic), the irrigated area (Irrigated), chemical fertilizer usage (Fertilizer), the total sown area of crops (Crop), and the disaster-affected area (Disaster).

4.2.4. Mechanism Variables

This paper develops a multidimensional analytical framework via three pathways: the agricultural production scale, the “grain-oriented” trend in the agricultural planting structure, and agricultural technological advancement. The scale of agricultural production is proxied by the per capita sown area of crops, calculated by dividing the total sown area by the rural population, and then subjected to logarithmic transformation. The ratio of the grain crop sown area to the total crop sown area is used to measure the “grain-oriented” trend. This proportion is also logarithmically transformed. Drawing upon established methodologies [38], this study selects five input indicators: effectively irrigated area, agricultural labor force, total agricultural machinery power, total sown area, and chemical fertilizer application. The gross output value of the primary sector serves as the desirable output indicator in constructing the agricultural total factor productivity measurement framework. Using MaxDEA 9 software and applying the DEA-Malmquist method with global reference and constant returns to scale, the agricultural total factor productivity is measured. This method also yields indicators such as agricultural total factor productivity, technological progress index, and efficiency change index. Among these, the technological progress index is used as a proxy variable to gauge advancements in agricultural technology.

4.3. Data Sources and Descriptive Statistics

To investigate the carbon emission reduction effects of policy-oriented agricultural insurance, this paper establishes an analytical timeframe covering 2003 through 2021, incorporating data from 31 Chinese provinces (excluding the Hong Kong, Macao, and Taiwan regions). The data sources of this paper are mainly classified into three categories: (1) Based on the agricultural carbon emission Formula (9), this study manually compiles provincial-level agricultural carbon emission data across China, with carbon source data derived from the National Bureau of Statistics, the China Agricultural Statistical Yearbook. (2) Based on official websites of provincial finance departments, publicly available reports from relevant media platforms, and the provincial statistical bulletins, we manually collect and compile the pilot timelines and cities for policy-oriented agricultural insurance programs. (3) Other provincial-level data are sourced from the China Environmental Statistical Yearbook, the EPS database, and the provincial statistical bulletins. To address the limited data unavailability, missing values are imputed utilizing interpolation techniques.

Table 1. Descriptive statistical results of original variables.

Variable Type	Variable Symbol	Std	N	P50	Mean	Min	Max
Dependent variable	C	1.097	589	5.668	5.325	2.214	6.905
Core independent variables	Event	0.467	589	1	0.679	0	1
Threshold variable	Scale	0.193	589	0.222	0.268	0.0320	1.405
	Structure	0.146	589	0.670	0.664	0.354	1.065
	Technology	0.0810	586	1.067	1.066	0.811	1.488
Control variable	Diesel	64.73	589	48.50	63.85	0.800	487
	Plastic	65,000	589	53,000	72,000	441	340,000
	Lrrigated	1574	589	1527	2008	109.2	6178
	lnFert	1.191	589	4.974	4.691	1.435	6.575
	Crop	3719	589	4997	5427	213.1	15,000

provides the basic characteristics of each statistical variable to avoid outlier-induced distortion in regression outputs. Overall, the data from 2003 to 2021 exhibit a relatively stable pattern, with the standard deviations of all variables, except for agricultural diesel usage, being lower than their respective means.

5. Analysis of Empirical Results

5.1. Parallel Trend Test

This paper employs a causal inference framework to evaluate the impact of the pilot policy on agricultural carbon emissions using double machine learning. A key assumption is that there exist no marked discrepancies in the time-based trends between the experimental group and the control group. To mitigate the potential endogeneity concerns stemming from the non-randomized assignment of regions under the agricultural insurance subsidy policy, the event study technique is employed. This method helps to analyze the evolving patterns and dynamic impacts of policy implementation. Specifically, a new dummy variable is created by constructing an interaction term between the year dummy variable and the treatment group dummy variable within the sample time window. By setting the start period of the designated time window as the benchmark, a rigorous empirical analysis is conducted on the dynamic evolution of the agricultural insurance fiscal subsidy policy. The regression outcomes are presented in Figure 3. We can observe that before the policy is put into practice, the estimated coefficients of the pilot policy are not statistically significant. This suggests that the pilot and non-pilot provinces’ agricultural carbon emissions do not differ significantly. This observation validates that the assumption of the parallel trend is indeed tenable.

5.2. Baseline Regression Results

This paper employs a dual machine learning approach to assess the policy effects. The research sample is divided into a 1:4 ratio, and the main and auxiliary regression models are predicted separately using the random forest method. The regression outcomes are reported in

Table 2. Baseline regression results.

	(1)
	C
Event	−0.020 ***
	(0.007)
_cons	0.001
	(0.002)
Control	Yes
Fix Year	Yes
Fix City	Yes
N	589

Note: Robust standard errors are in parentheses; *** p < 0.01.

. According to the findings, the estimated coefficient of the policy-oriented agricultural insurance variable is −0.020 and is statistically significant at the 1% level after adjusting for time and province fixed effects. This demonstrates that the application of policy-oriented agricultural insurance significantly reduces carbon emissions from agriculture; H1 is validated. The reason is that policy-oriented agricultural insurance serves multiple functions, including risk pooling, risk dispersion, and economic compensation. By mitigating the financial losses farmers experience due to catastrophic events or market price volatility, this policy stabilizes farmers’ income expectations. It also facilitates the adoption of environmentally sustainable production techniques and drives the evolution of agricultural practices towards eco-friendly and low-emission paradigms [39,40].

5.3. Robustness Tests

5.3.1. Placebo Test

To tackle the potential disruption that omitted variables or stochastic factors might exert on the regression findings, a placebo test was implemented in this paper. Specifically, a computational randomization procedure was employed to reassign the treatment and control group, thereby generating a reconstituted treatment cohort. We then performed 1000 simulated regression analyses to obtain the estimated coefficients and p-values of the virtual policy. Based on these results, a kernel density estimation plot was constructed to visualize the distribution of the p-values, as depicted in Figure 4. The results show that the kernel density distribution of the estimated coefficients is approximately symmetrical around zero, while the p-value of the estimated coefficients associated with the placebo policy is close to zero. This demonstrates the robustness of our baseline regression results, which remain unbiased despite potential confounding variables or stochastic disturbances.

5.3.2. Exclusion of Municipality Samples

Given that municipalities (Beijing, Shanghai, Tianjin, and Chongqing) exhibit distinct agricultural economic development levels and carbon emission intensity compared to other regions, this study excludes their data from the initial sample when analyzing the impact of policy-based agricultural insurance on agricultural carbon emissions. This approach aims to eliminate potential biases, ensuring the stability and reliability of the research findings. The results are reported in

Table 3. Further robustness test results.

	(1) Exclusion of Municipality Samples	(2) Alteration of Sample Interval	(3) Winsorization	(4) Lagged Explained Variable by One Period
	C	C	C	C
Event	−0.013 *	−0.021 **	−0.023 ***	−0.035 **
	(0.007)	(0.008)	(0.007)	(0.008)
_cons	0.001	0.001	−0.000	0.000
	(0.001)	(0.002)	(0.002)	(0.002)
Control	Yes	Yes	Yes	Yes
Fix Year	Yes	Yes	Yes	Yes
Fix City	Yes	Yes	Yes	Yes
N	513	496	589	558

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

(1). The findings indicate that policy-based agricultural insurance remains significantly and negatively correlated with agricultural carbon emissions at the 10% level, indicating the robustness of our baseline regression findings.

5.3.3. Alteration of Sample Interval

Given the methodological revisions in agricultural carbon emission calculations after 2006, the analysis focuses on the 2006–2021 period. Table 3 (2) presents the findings. The findings show that there is a substantial negative correlation between policy-oriented farm insurance and agricultural carbon emissions at the 5% level. The results remain robust, with no significant changes observed.

5.3.4. Winsorization

To address potential outlier effects, a 1% winsorization procedure is implemented on key explanatory variables, followed by re-estimation utilizing the dual machine learning model. The results are displayed in Table 3 (3). The regression coefficient for policy-based agricultural insurance is −0.023, indicating a strong negative correlation that is significant at the 1% level. Importantly, the parameter estimation coefficients and significance levels remain unchanged. This demonstrates that the estimation results of the previously presented baseline regression model are robust, as outliers do not affect the results, thereby underscoring their credibility.

5.3.5. Lagged Explained Variable by One Period

Considering that the significance of the test results for policy treatment effects has a certain time cycle, this paper re-regresses by lagging the dependent variable by one period. This approach aims to more accurately assess the long-term and time lag effects of policy adjustments on agricultural carbon emissions. As meticulously presented in Table 3 (4), the regression coefficient of the lagged variable remains significantly negative at the 1% level. This indicates that the emission reduction effect remains statistically significant even after accounting for the time lag in policy implementation. Thus, the research conclusions demonstrate strong robustness and effectively handle any endogeneity problems that can arise from reverse causality.

6. Further Analysis

6.1. Mechanism Analysis

An analysis of the existing relevant literature reveals that the scaling of agricultural production, the “grain-oriented” shift in agricultural planting structure, and advancements in agricultural technology are three potential pathways explaining the carbon emission reduction effects of policy-based agricultural insurance. In this regard, this paper adopts the methodological approach proposed by Wan and Yu (2024) to validate the causal mediation effects [41]. The results are displayed in

Table 4. Test of the impact mechanism.

	(1)	(2)	(3)
	Scale	Structure	Technology
Event	0.031 ***	0.019 **	0.052 ***
	(0.006)	(0.008)	(0.009)
_cons	0.001	−0.001	−0.001
	(0.003)	(0.002)	(0.003)
Control	Yes	Yes	Yes
Fix Year	Yes	Yes	Yes
Fix City	Yes	Yes	Yes
N	589	589	586

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05.

.

Scale of Agricultural Production. By taking the scale of farmland operation as the dependent variable and regarding policy-oriented agricultural insurance as the core independent variable, the results are displayed in Table 4 (1)–(3). As is evident from the table, the regression coefficient for policy-driven agricultural insurance shows a statistically significant positive value at the 1% level. This suggests that policy-oriented agricultural insurance can remarkably facilitate the large-scale operation of farmland. The reason is that the fiscal subsidy policy for agricultural insurance can provide risk protection for farmers, reducing the uncertainties in their production and operation. This, in turn, encourages farmers to transition from decentralized to large-scale operations. Large-scale farmland operations can drive the development of farmland towards industrialization, intensification, and modernization. These measures facilitate improved factor allocation efficiency and encourage judicious agrochemical utilization, consequently mitigating farming-related greenhouse gas emissions while fostering sustainable agricultural ecosystems. Thus, Hypothesis H2 is validated.

The “Grain-Oriented” Trend in Agricultural Planting Structure. This study employs the agricultural planting structure as the dependent variable and policy-oriented agricultural insurance as the independent variable for empirical analysis. The results are displayed in Table 4 (2). As is evident from the table, the regression coefficient for policy-driven agricultural insurance shows a statistically significant positive value at the 5% level. This finding suggests that policy-oriented agricultural insurance effectively drives the “grain-oriented” transformation of the agricultural planting structure. The reason is that in recent years, China’s policy-oriented agricultural insurance reform has increasingly focused on staple food crops, with innovative insurance products significantly enhancing risk protection levels. This effectively reduces the relative production costs of grain crops and stabilizes farmers’ expected income, thereby incentivizing farmers’ enthusiasm for grain cultivation and promoting the “grain-oriented” trend in the agricultural planting structure. Compared to cash crops, grain crops have relatively stable planting densities and growth cycles, and possess strong carbon sequestration capabilities during their growth process. Therefore, the “grain-oriented” trend in the agricultural planting structure helps to expand the planting scale of grain crops, thereby enhancing the carbon sequestration capacity of the entire agricultural ecosystem and promoting agricultural carbon emission reduction; H3 is validated.

Agricultural Technological Advancement. This paper takes agricultural technological progress as the dependent variable, with policy-oriented agricultural insurance as the independent variable for empirical analysis. The results are displayed in Table 4 (3). As is evident from the table, the regression coefficient for policy-driven agricultural insurance shows a statistically significant positive value at the 1% level. This demonstrates that policy-oriented agricultural insurance can markedly boost agricultural technological advancement. The reason is that government-subsidized agricultural insurance can compensate for farmers’ risk losses in technological investments, stabilize grain-growing income, and thereby incentivize the adoption of new production technologies. Moreover, policy-oriented agricultural insurance can also increase the availability of financing for farmers from financial institutions through the “credit + insurance” interaction model, alleviate the financial constraints faced by farmers, and enhance their willingness to adopt green agricultural technologies, thus aiding in the reduction of carbon emissions from agriculture; H4 is validated.

6.2. Heterogeneity Analysis

6.2.1. Agricultural Production Function Dimension

There are substantial disparities between China’s major grain-producing regions and non-major grain-producing regions in terms of agricultural production methods, resource endowments, and policy support. These disparities may result in the different carbon emission reduction effects of policy-oriented agricultural insurance in these two types of regions. This study refers to the delineation criteria for agricultural production functional zones outlined in government public documents and divides the sample into two categories: primary grain production regions and non-primary grain production regions for subgroup regression. The results are displayed in

Table 5. Heterogeneity test results of primary grain-producing regions.

	(1) Primary Grain-Producing Regions	(2) Non-Primary Grain-Producing Regions
	C	C
Event	−0.038 *	−0.009
	(0.022)	(0.008)
_cons	−0.002	0.000
	(0.002)	(0.003)
Control	Yes	Yes
Fix Year	Yes	Yes
Fix City	Yes	Yes
N	247	342

Note: Robust standard errors are in parentheses; * p < 0.1.

. The results demonstrate that in major grain-producing regions, policy-oriented agricultural insurance greatly lowers agricultural carbon emissions, but it has no discernible effect in non-major grain-producing regions. A plausible explanation is that agricultural operations in primary grain-producing regions are conducted on a more extensive scale, coupled with more substantial efforts in promoting policy-driven agricultural insurance and government fiscal subsidies. These factors can provide stronger risk protection for farmers, reduce the uncertainty of agricultural production, and encourage farmers to embrace green techniques, ultimately contributing to a significant reduction in agricultural carbon emissions. The implementation of green production technology is hampered in non-major grain-producing regions by the comparatively small scale of agricultural production and extremely fragmented land parcels, resulting in limited carbon emission reduction effects.

6.2.2. Geographical Location Dimension

The Yangtze River Economic Belt, compared to other regions, exhibits notable differences in the level of support for policy-oriented agricultural insurance and in the degree of agricultural modernization, which may result in varying carbon emission reduction effects of policy-oriented agricultural insurance. This paper conducts a grouped regression analysis for the Yangtze River Economic Belt and the areas outside it; the results are displayed in

Table 6. Heterogeneity test results of the Yangtze River Economic Belt.

	(1) Non-Yangtze River Economic Belt Areas	(2) Yangtze River Economic Belt
	C	C
Event	−0.010	−0.027 ***
	(0.011)	(0.009)
_cons	−0.001	−0.003
	(0.003)	(0.004)
Control	Yes	Yes
Fix Year	Yes	Yes
Fix City	Yes	Yes
N	380	209

Note: Robust standard errors are in parentheses; *** p < 0.01.

. The results reveal that policy-oriented agricultural insurance significantly reduces agricultural carbon emissions in the Yangtze River Economic Belt, while exhibiting no substantial impact in non-Yangtze River Economic Belt areas. The possible reasons are that the Yangtze River Economic Belt has a favorable geographical position and encompasses six major commodity grain bases, such as the Jianghuai region and the Dongting Lake Plain. As the core area of modern agricultural development in China, it features a high degree of agricultural scale and intensification. The implementation of policy-oriented agricultural insurance in this region is more conducive to the application of green agricultural production technologies, curbing excessive agrochemical inputs and thereby effectively inhibiting agricultural carbon emissions. In contrast, agricultural production in non-Yangtze River Economic Belt regions is relatively decentralized and less modernized, resulting in the policy having limited carbon emission reduction effects.

6.2.3. Environmental Regulation Dimension

Environmental regulation indicates the level of importance different regions assign to the agricultural ecological environment, and the differences in environmental regulation intensity may result in varying boundary conditions for the impact of policy-oriented agricultural insurance on agricultural carbon emissions. This paper draws on the research findings of Morgenstern et al. to measure the evaluation indicators of environmental regulation intensity [42]. This ratio is then divided by 10,000 to obtain an evaluation indicator of environmental regulation intensity. Subsequently, it carries out grouped regression based on the median. The results are displayed in

Table 7. Heterogeneity test results of environmental regulation intensity.

	(1) Low Environmental Regulation Intensity	(2) High Environmental Regulation Intensity
	C	C
Event	−0.0003	−0.027 **
	(0.016)	(0.011)
_cons	0.004	−0.000
	(0.003)	(0.004)
Control	Yes	Yes
Fix Year	Yes	Yes
Fix City	Yes	Yes
N	278	311

Note: Robust standard errors are in parentheses; ** p < 0.05.

. The results demonstrate that policy-oriented agricultural insurance significantly reduces agricultural carbon emissions in regions with stringent environmental regulations, while exhibiting no substantial impact in areas with weaker environmental regulation enforcement. The possible reasons are that in areas with stringent environmental regulation, the government often introduces strict policies and regulations to standardize agricultural production environmental behaviors, regulate farmers’ overuse of pesticides and chemical fertilizers, and encourage the adoption of organic fertilizers and biological control technologies. Therefore, in response to the policy environment, policy-oriented agricultural insurance offers more favorable premium subsidies to farmers who adopt green ecological planting models. This facilitates the transition to green agricultural production and helps to curb agricultural carbon emissions. Conversely, in regions with more relaxed environmental regulations, farmers have a lower willingness to invest in green agricultural production factors, and the inhibitory effect of the policy implementation on agricultural carbon emissions is limited.

7. Conclusions and Policy Recommendations

7.1. Research Conclusions

This paper employs panel data from 31 Chinese provinces over the period from 2003 to 2021 and utilizes the double machine learning methodology to conduct comprehensive theoretical and empirical analyses. The findings are as follows: Firstly, the baseline regression results demonstrate that policy-oriented agricultural insurance exerts a significant inhibitory effect on agricultural carbon emissions. This conclusion remains robust after lagging the dependent variable by one period, adjusting the research interval, conducting placebo tests, and performing winsorization. Secondly, based on risk management theory and farmer behavior theory, this paper investigates the mechanism of policy-oriented agricultural insurance in carbon emission reduction within the “scale–structure–technology” analytical framework. The findings demonstrate that agricultural insurance fiscal subsidies can guide farmers to promote land transfer and scale operations. This facilitates a “grain-oriented” transition in cropping patterns; drives agricultural technological progress; optimizes the rational allocation of production factors including pesticides fertilizers, and green technologies; and ultimately mitigates agricultural carbon emissions. Thirdly, heterogeneity analysis reveals significant regional variations in the inhibitory effect of policy-based agricultural insurance on agricultural carbon emissions. In major grain-producing regions, where agricultural production operates at larger scales and benefits from relatively higher fiscal subsidies for policy-based agricultural insurance, the carbon emission reduction effect of such insurance proves more pronounced. The Yangtze River Economic Belt, as a core area of China’s agricultural modernization, benefits from a higher agricultural scale and levels of intensification, along with a more solid foundation for green transformation, leading to more significant carbon reduction outcomes from policy-oriented agricultural insurance. Moreover, stringent environmental regulations complement policy-oriented agricultural insurance that more effectively curbs agricultural carbon emissions. Consequently, provinces with a higher environmental regulation intensity demonstrate more evident emission reduction effects from policy-oriented agricultural insurance.

7.2. Policy Recommendations

Drawing on the aforementioned research findings, this article puts forward the following policy recommendations: firstly, agricultural insurance products should be innovated, and diversified value-added services should be provided. On one hand, innovative agricultural insurance products related to carbon emission reduction should be developed in accordance with different agricultural development models and carbon reduction needs [43]. For example, agricultural carbon sink insurance could be introduced to provide economic compensation for reductions in carbon sink volume or income losses caused by natural disasters. Additionally, innovative specialized insurance products for low-carbon agricultural production technologies should be developed, covering dedicated equipment such as conservation tillage implements and smart irrigation systems, to provide financial compensation for losses caused by technical equipment failures and related risks. On the other hand, agricultural insurance institutions should provide diversified value-added services for agricultural carbon emission reduction. Specifically, it is recommended that insurance companies collaborate with agricultural equipment manufacturers to establish a low-carbon equipment sharing platform. By integrating equipment leasing with technical insurance services, this platform can provide insured farmers with professional equipment rentals such as intelligent monitoring devices and smart water-saving systems, along with supporting services. This innovative service model offers dual benefits: it not only alleviates the financial pressure on farmers when purchasing agricultural machinery but also facilitates the widespread adoption and application of low-carbon agricultural technologies. Secondly, the policy layout should be optimized, and agricultural insurance premium subsidy policies should be implemented according to the local conditions. On one hand, building upon the advantages of major grain-producing regions and the Yangtze River Economic Belt in terms of intensive farming practices and agricultural technology extension, the policy orientation of agricultural insurance in these regions should be further strengthened. Specifically, it is imperative to enhance fiscal subsidies for agricultural insurance in these key areas, actively implementing the coverage and promotion of crop revenue insurance. By elevating the level of risk protection, these measures will guide farmers to expand their agricultural production scale while incentivizing the adoption of intelligent and green technologies. Through the combined effects of economies of scale and green technology applications, the carbon emission intensity per unit of agricultural product can be substantially reduced. On the other hand, the policymaking process should prioritize the insurance needs of low-coverage regions. For areas outside major grain-producing zones and the Yangtze River Economic Belt, innovative specialty crop insurance products [44] could be developed, featuring differentiated policy terms tailored to specific crops’ growth characteristics and carbon emission profiles, thereby promoting low-carbon agriculture. Furthermore, strengthening environmental regulation’s role in cutting the carbon emissions of policy-based agricultural insurance is urgent. Policy-oriented agricultural insurance should be closely integrated with environmental regulation, and carbon emission limit standards should be set for insured agricultural operators. Those who fail to meet these standards should see their premium subsidy amount affected. Thirdly, a green agricultural insurance premium subsidy mechanism should be established to promote low-carbon agricultural production. On one hand, the content of the subsidy policy should be precisely formulated, clearly defining the subsidy targets and standards. Green production and operation entities should include farmers or corporate organizations that adopt green production methods and emphasize the judicious utilization of ecological resources [45]. For example, small farmers who use organic fertilizers for green production and agricultural cooperatives that adopt ecological recycling farming models should be included. For agricultural production and operation entities that adopt green production methods, key premium subsidy support should be provided. This will increase the proportion of fiscal subsidies for agricultural insurance, thereby reducing the risks and costs associated with agricultural production and operations. Consequently, this will encourage their participation in green agricultural production. On the other hand, a diversified “online + offline” publicity approach should be established to enhance farmers’ participation enthusiasm in agricultural insurance. Online efforts should leverage new media channels such as agricultural information service platforms and short video platforms to produce concise and intuitive animated videos and graphic explanations about green agricultural insurance. Offline initiatives should involve local governments and financial institutions jointly organizing “Green Insurance into Rural Areas” promotional campaigns, involving setting up policy consultation points in townships to explain key information such as fiscal subsidy standards and claims procedures. This will deepen farmers’ understanding of green agricultural insurance and encourage greater participation.