The effectiveness and prediction analysis of China’s agricultural low-carbon production policy from the perspective of policy tools

The effectiveness and prediction analysis of China’s agricultural low-carbon production policy from the perspective of policy tools | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The effectiveness and prediction analysis of China’s agricultural low-carbon production policy from the perspective of policy tools Yunpeng Liu, Chulin Pan, Shuang Xu, Hongpeng Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7897961/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Government intervention through policy measures to curb carbon emissions from agricultural production is an essential pathway toward achieving sustainable agricultural development in China. However, does the intervention effects of Agricultural Low-carbon Production Policy vary depending on the policy tool types employed? This study compiles 884 policy texts and carbon emission datasets at both national and regional levels in China (1993–2022). Using the Latent Dirichlet Allocation to classify policy tools and Support Vector Machine Regression to predict the effectiveness of policy combinations. We find that (1) Agricultural low-carbon production policies exhibit a lag effect; (2) Coercive policy tools are the most prevalent, accounting for 40.72%, while incentive policy tools are the least common, making up only 16.06%; (3) Empirical results demonstrate that incentive policy tools yield the most effective intervention outcomes from agricultural low-carbon production, followed by coercive, directive, and voluntary policy tools. (4) Predictive results show that a high-growth model combined with incentive policy exerts the most significant suppression effect on agricultural carbon emissions. The findings of this study offer the following insights: Building upon existing research on policy tools, future policy formulation should be guided and informed by strengthening effectiveness, highlighting significance, and enhancing feasibility across three key dimensions. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences evaluation of policy effectiveness incentive policy policy intervention policy tools policy prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Against the backdrop of global climate change and increasing pressure on resources and the environment, how to effectively reduce carbon emissions and realize green development has become a focus of attention for governments. Chins’s agricultural carbon emissions (ACE) account for up to 17% of total carbon emissions (Li et al., 2011), and there is room to reduce agricultural carbon emissions by about 31% (Tang and Ma, 2022 ). Reducing carbon emissions from agricultural production is not only a key part of sustainable agricultural development, but also an important issue for China in realizing the “double carbon target” and transforming the agricultural food system (Yang and Luo, 2020 ). China’s agricultural production activities emit huge amounts of carbon, of which carbon emissions from grain production account for 50% of total agricultural greenhouse gas emissions (Zuo et al., 2023 ). However, due to inefficiencies in resource utilization and production methods, the level of suppression of carbon emissions from agriculture is relatively limited, and there is still a long way to go to reduce carbon emissions from agricultural production (Huang et al., 2025 ). Therefore, constraining carbon emissions from the formulation of Agricultural Low-carbon Production Policy (ALPP) is a necessary way to realize carbon reduction and emission reduction and sustainable development in agriculture. In order to actively respond to global climate change and reduce carbon emissions from agricultural production, China’s Ministry of Agriculture and Rural Development released the Zero Growth Action Plan for Fertilizer Use by 2020, the National Plan for Sustainable Agricultural Development (2015–2030). In 2017, China’s Ministry of Agriculture and Rural Development issued the Notice on Launching the Construction of the First Batch of National Experimental Demonstration Zones for Sustainable Agricultural Development Carrying out Early and Pilot Work on Agricultural Green Development and emphasized that it would take three years to make the concept of agricultural green development deeply rooted in people’s hearts and minds, refine and promote a number of agricultural green development systems, and strive to form an agricultural green production methods and green lifestyles. In 2021, China’s Ministry of Agriculture and Rural Development led the release of the National Green Agricultural Development Plan for the 14th Five-Year Plan and emphasized that by 2025, the intensity of greenhouse gas emissions from major agricultural products would be significantly reduced (Du et al., 2023 ). Chinese government is increasingly developing low-carbon green development in agriculture and has elevated the issue of low-carbon green development and agricultural sustainability to a very important national strategic level. As a result, China’s carbon emissions from agricultural production still face challenges, and new solutions are urgently needed to address the negative impacts of agricultural high carbon emissions (Lei et al., 2025 ).From the current research landscape, the majority of scholars have focused on examining the challenges in agricultural emissions governance through the lenses of ecological sustainability and social governance. However, there remains a notable gap in the exploration of policy tool perspectives, which may hinder the accurate identification of effective measures and tools necessary to achieve overarching policy objectives (Abbasnezhad and Abrams, 2025 ). China’s application of policy tools to conduct an in-depth analysis of the specific mechanisms and evolving trends in ALPP is not only critical for understanding the national government’s top-level design and ensuring the effective implementation of carbon reduction governance in agricultural production, but also serves as a vital platform for sharing China’s low-carbon agricultural governance experience globally, drawing upon the distinctive features of its existing policy framework. 2. Literature review 2.1 Definitions and classifications of policy tools. Policy tools are the means or measures governments adopt to pursue specific social development goals or address particular social and economic issues (Sha et al., 2024 ), it also serves as a key tool for the government to address market failures and enhance social governance (Kern and Howlett, 2009 ). The theoretical framework demonstrates particular analytical utility in public affairs governance, especially concerning environmental security regimes. Moreover, ALPP tool types function as indispensable leverage points for achieving sustainability transitions in contemporary agri-food systems (Wen et al., 2025 ). This study is categorized into four types: incentive, coercive, directive and voluntary (Jiang et al., 2023 ). 2.2 Application of ALPP tools. Existing research demonstrates that although current studies on policy tools have enhanced our comprehension of their capacity to tackle complex challenges and operate in heterogeneous contexts, significant deficiencies persist in the analytical framework and integrated application of ALPP tools (Chai et al., 2024 ). Academic research perspectives on ALPP tools can be categorized into 4 aspects. Firstly, on the environmental impact: scholars believe that ALPP tools play an important role in promoting environmental innovation, and attempt to solve the problem of externalities in environmental innovation by establishing a framework for analyzing the dimensions of policy tool types and policy objectives at two stages of environmental innovation (Liao, 2018 ). Secondly, on the social drivers, scholars have extended their analytical perspectives to the societal level, for example, by exploring the determinants of the preference of policy implementation targets for ALPP tools (Cho and Moon, 2019 ; Pedersen et al., 2020 ). Thirdly, on the economic development: scholars have shifted their analytical perspective to the link between economic development and environmental policy (Ye et al., 2022 ), or categorize environmental regulation into command-and-control and economic incentive policy tools and explore the relationship between environmental regulation, farmers’ willingness and economic development using mediated effects models (Ke and Huang, 2024 ). Fourthly, on the environmental sustainability: scholars have proposed the use of hybrid environmental policy tools to improve ecological governance and socio-economic development, research perspectives have continued to shift, which has also shown that scholars have improved the combination and synergy of policy tools through improved (Olbrich et al., 2024 ), and the organic combination of policy tool types and policy phases to solve practical problems (Yu et al., 2024). 2.3 Evaluating the effectiveness of ALPP tools. Current research focuses on assessing the effectiveness of public policies in combating environmental pollution (Sun et al., 2023 ). In agricultural economics, especially in assessing the effectiveness of ALPP tools, academic research is limited and mainly focuses on qualitative studies. In quantitative studies, some scholars have used the Difference-in-Differences (DID) method to evaluate the effectiveness of policy tools (Levy et al., 2022 ). However, DID requires the experimental and control groups to meet the parallel trends assumption before policy implementation and that no other relevant policies are enacted during the policy evaluation period. This implies that DID can only evaluate the effectiveness of one single policy tool, which is often inconsistent with reality. Policy Modelling Consistency Index (PMC-Index) model has been applied to evaluate policy tool effectiveness, which can break free from the constraints of the parallel trends assumption and simultaneously evaluate the effectiveness of a set of policy tools (Kuang et al., 2020 ). However, the determination of policy scores depends heavily on expert reviews, indicating that the evaluation process is likely to be influenced by subjective factor (Xiong et al., 2023 ). This study situates its analysis within China’s ALPP, a focus justified by three critical considerations: Firstly, agricultural carbon mitigation constitutes a pressing global challenge, and China’s status as one of the world’s leading agricultural producers lends unique empirical significance to this inquiry. Secondly, the Chinese government has implemented a comprehensive policy framework to curb agricultural production emissions, including legal system enhancements, diversification of regulatory tools, and technological innovations in monitoring mechanisms. These governance practices epitomize global efforts toward sustainable agricultural transformation. Thirdly, systematic examination of China’s policy evolution not only informs evidence-based development of its domestic agricultural decarbonization strategies but also provides theoretical foundations for strategic policy design in other nations-particularly developing economies confronting analogous sustainability dilemmas. 3. Data and Methods This study employes Latent Dirichlet Allocation (LDA) model to achieve objective and efficient classification of central and local-level policy texts (Davidson et al., 2025 ; Huang et al., 2022 ). This methodology is designed to overcome the inherent subjectivity and arbitrariness in traditional policy tool classification studies, thereby addressing a critical research gap in this field. Also introduces Support Vector Machine Regression (SVMR) model and establish seven distinct scenarios to evaluate the effectiveness of ALPP. This analytical framework ultimately enables the identification of optimal policy tool combinations. The study is structured analytical approach comprising 5 components: policy document collection and classification, agricultural carbon emission accounting, policy power measurement, policy effectiveness evaluation, and policy scenario simulation. The specific process is as follows: 3.1 Collection and categorization of policy texts. The policy texts were obtained from the Peking University Law Database (pkulaw), which includes laws, regulations and rules promulgated by China’s central and local governments, and represents a well-established system for retrieving laws and regulations. The government websites of 31 provinces, autonomous regions and municipalities (excluding Hong Kong, Macao and Taiwan) were manually collected, and relevant data was checked and supplemented to ensure comprehensiveness and prevent omissions. When collecting policy text data on the basis of existing research, the keywords “agricultural low-carbon production” “agricultural carbon emission reduction” “low-carbon agriculture” and “green agriculture” were used to accurately search the data. The policy texts were searched from January 1, 1993 to December 31, 2022, covering both the central and local levels. After systematic organization, scientific identification and elimination of duplicated policy texts, 884 valid policy texts were found. Finally, 884 valid policy texts were taken as research objects. 3.2 Calculation of carbon emission. In order to ensure that the data of agricultural carbon emissions are comprehensive and accurate, this study refers to the calculation method of Tian et al (Tian and Yin, 2022). Therefore, the carbon emissions generated by 3 types of agricultural production behaviors: crop cultivation, livestock and poultry breeding, agricultural land utilization. Crop cultivation mainly includes 8 types of planted crops, namely, corn, rice (including early rice, middle rice and late rice), wheat (including spring wheat and winter wheat), legumes, yams, oilseeds, vegetables, and cotton; Livestock and poultry breeding mainly includes 8 types of pigs, cows, sheep, horses, donkeys, mules, camels, and poultry breeding livestock and poultry; Agricultural land utilization mainly includes 5 types of agricultural production methods: fertilizer, agricultural film, diesel fuel, irrigation and ploughing (source: China’s Statistical Yearbook 1993–2022). 3.3 Calculation of policy power. The power of ALPP were calculated by combining the results of policy texts categorization by LDA model. The calculation of policy power includes annual policy power (P), cumulative policy power (TP) and average policy power (AP). P denotes the total power of policies enacted each year, TP denotes the cumulative power of policies enacted up to a given year, and AP denotes the average policy power of each policy each year. Based on existing research on policy power, this study establishes Eq. (1), which shows that policy power is positively related to the level of legal effect and the level of the enacting authority. Considering the long-term and lagged nature of policy effectiveness, policies that are currently legally effective are expected to have a sustained impact on agricultural low-carbon production. Eq. (2) is used to measure the cumulative number of ALPP per year. Equations (3) and (4) are used to measure the TP and AP of ALPP per year, respectively. This quantitative approach provides a structured and objective way to assess the impact and evolution of ALPP over time. \(\:{P}_{i}=\sum\:_{j=1}^{N}{SP}_{j}\times\:{SD}_{j}\) i ϵ (1993,2022) (1) \(\:{TN}_{i}={TN}_{i-1}+{N}_{i}\) i ϵ (1993,2022) (2) \(\:{TP}_{i}={P}_{i-1}+{N}_{i}\) i ϵ (1993,2022) (3) \(\:{AP}_{i}={TP}_{i}/{TN}_{i}\) i ϵ (1993,2022) (4) In the above equation, i denotes the year, j denotes the jth policy, SPj denotes the jth policy legal rating score, SDj denotes the jth policy issuer rating score, Pi denotes the total power of all policies in year i, Ni denotes the number of policies issued in year i, TNi denotes the cumulative number of policies issued in year i, TPi denotes the cumulative policy power in year i, and APi denotes the average policy power. 3.4 Assessment of policy effectiveness. Policy effectiveness refers to the direct and indirect impacts on target groups, the economy or society as a result of policy implementation (Wen et al., 2025 ). The effectiveness of ALPP is evaluated by the calculated policy power. Eq. ( 5 ) is introduced to quantitatively analyze the effectiveness of policy interventions, aiming to reveal the impact boundaries of different policy tools. $$\:\text{l}\text{n}{\text{A}\text{C}\text{E}}_{\text{i}\text{t}}={{\beta\:}}_{0}+{{\beta\:}}_{1}{\text{X}}_{\text{t}}+{{\beta\:}}_{2}\text{l}\text{n}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}$$ 5 In the equation, i represents the type of crop cultivation, livestock and poultry breeding and agricultural land utilization, ACEit is the explained variable representing the agricultural carbon emissions in year t for i way, Xt is the explanatory variable representing N, TP, and AP in year t. β 0 represents the constant, ε it represents the error term, Control it represents a set of control variables. In order to control the influence of factors other than policy factors on carbon emissions, based on the Conway et al (1973), this study selected the Gross Domestic Product (GDP), the size of the number of population working in agriculture and the percentage of primary sector structure to measure (Source: China Statistical Yearbook 2008–2022). The data analysis using Stata in this section. 3.5 Prediction of policy scenario. The policy scenarios are projected with 2022 as the base year, the natural year as the cycle node, and the end year of the projections is 2031 to explore the evolution of carbon emissions from agricultural production under different policy scenarios. In order to screen the optimal policy combination, this study uses Matlab software to construct a support vector machine model design and forecasting part in the scenario. It is suitable for solving medium and long term problems learning methods with few parameters and small samples for the prediction problem. This this study mainly uses SVMR has the advantages of good fit and high prediction accuracy (Xi et al., 2024 ). The empirical analysis reveals that the quantitative growth of ALPP between 1993 and 2022 can be classified into three distinct patterns: low-speed, medium-speed, and high-speed growth modes. 4. Results and discussion 4.1 Evolution of policy power and effectiveness. Among the 884 policy texts analyzed, 817 policies scored more than 4 points (out of 8), accounting for 92.42% of the total, and 768 policies scored more than 4 points (out of 5), accounting for 86.76% of the total, demonstrating that the Chinese government attaches great importance to the work of agricultural carbon emission reduction (in Table 1 ). Overall, laws exhibit the highest level of authority; however, only 8 policy tools fall under this category. In contrast, Provincial and ministerial government regulations and normative documents are the most numerous, totaling 484 and accounting for 54.75% of the texts. Local policy documents at the district and county levels are relatively scarce, comprising only 3.62%. This means that at the district and county levels, government departments are mostly policy implementation agencies rather than policy-making institutions. Table 1 Number of China’s ALPP of different effects Index Effectiveness hierarchy Policy power Policy quantity 8-mark Legal and judicial interpretations 8 8 Administrative regulations and State Council normative documents 7 15 Local regulations 6 128 Provincial and ministerial government regulations and normative documents 5 484 Other normative documents 4 182 Military regulations and rules 3 32 Party regulations and systems 2 33 Regulations on social organizations 1 2 5-mark National 5 157 Provincial (autonomous region, municipality directly under the Central Government) 4 611 City 3 83 County 2 19 Commune 1 14 The number of ALPP in China shows a fluctuating growth trend from 1993–2022. The AP and TP of ALPP increases year by year (in Fig. 1 ). Among them, from 1994 to 2006, as the number of policies increased, but the AP showed a decreasing trend, analyzing the main reason is caused by the decline of the policy issuing agency and the legal level of the policy. The policy power in 2011 and 2017 was higher in that year, but there were cyclical fluctuations, with the first trough occurring in 2013, and the second trough occurring in 2019, which indicates that the policy is formulated in the After a certain time of release, the relevant government departments need to reformulate the emission reduction policy in the light of the reality of agricultural development. Note Pi represents the total power of the policy in year i, TPi represents the cumulative power of the policy in year i, and APi represents the average power of the policy in year i. Figure 2 reports the China’s agricultural production carbon emissions has a rising trend, but after 2004 between 1993 and 2003, the Chinese government gradually emphasized the issue of agricultural carbon emission reduction, and introduced and formulated ALPP, due to the fact that the number of ALPP and the TP have increased year by year, resulting in a decreasing trend of China’s agricultural production carbon emissions from 2004 to 2022, which indicates that agricultural carbon emissions are affected by ALPP and the evolution is consistent. During the period 1993–2022, the overall carbon emissions from China’s agricultural production show a fluctuating state, which is closely related to policy formulation (Fig. 3 a, 3 b). The northern region of China has promulgated a substantial number of ALPP, primarily due to Northeast China’s status as the country’s primary agricultural and grain production base, coupled with Inner Mongolia’s well-developed livestock industry. These regional characteristics have necessitated the formulation of a comparatively larger volume of ALPP (Zhang et al., 2022 ). The higher carbon emissions in the central and eastern regions are mainly related to the region’s higher economic level and the development of smart agricultural practices, in which farmers improve crop production efficiency through improved rice varieties, farmyard fertilizers, water-saving technologies and bio-pesticides, which lead to higher carbon emissions while promoting growth in crop yields (Vatsa et al., 2023 ) 4.2 Policy power of different policy tools. Through computational determination of topic categories, the analysis encompasses various keywords reflected in ALPP (Kim et al., 2024 ). Drawing on Howlett’s categorization method, this study classified the different policy tools categories into four types: incentive, coercive, directive and voluntary policy tools. Table 2 and Fig. 4 report that among the 884 ALPP analyzed, coercive policies constitute the largest proportion at 40.72%, followed by directive policies and incentive policies, while voluntary policies account for the smallest share at 20.25%. Statistical analysis reveals that over 20% of voluntary policies originate from municipal-level or lower administrative agencies, with virtually no policies being formulated by societal actors. Nevertheless, the potential contribution of social forces to agricultural emission reduction merits serious consideration. As Fabi et al ( 2021 ) have argued, social organizations can leverage their technical expertise to provide assistance. Table 2 Classification and quantity distribution of China’s ALPP Policy type Incentive policy Coercive policy Directive poliy Voluntary policy Number 142 360 203 179 Main document type Departmental normative documents; local normative documents; provincial local regulations, etc. Laws; departmental regulations and normative documents; local government regulations; provincial local regulations, etc. Departmental normative documents; local normative documents; provincial local regulations, etc. Provincial local regulations; local normative documents, etc. Percentage of policies 16.06% 40.72% 22.96% 20.25% Table 3 shows the local normative documents are the main source of ALPP, with the highest number of coercive policy tools and the most diverse issuing organizations. As shown in equations (3) and (4), there are statistically significant differences between the three types of policy tools when calculating the TP and AP of coercive, directive, incentive and voluntary policy tools. Combining TP, AP and number of policies, the order from highest to lowest is coercive, incentive, directive and voluntary policy tools. Table 3 Multiple regression descriptive statistics of N, TP, and AP of ALPP Tool type of policy Categorization Mean Std.Dev Min Max Total TP AP 6580.467 21.057 4958.515 .824 1496 19.760 17503 22.250 Coercive policy CN CTP CAP 21.600 1884.800 19.625 23.058 2019.706 .646 2 72 18 70 6430 20.419 Incentive policy IN ITP IAP 9.200 865.467 21.778 9.405 815.562 1.288 1 40 20 34 2775 24.375 Directive poliy DN DTP DAP 13.133 1389.533 20.349 11.526 1196.259 3.234 1 40 10.053 45 3820 24.333 Voluntary policy VN VTP VAP 11.2 1156.667 21.784 9.872 971.109 2.979 2 90 19.190 33 3224 30 Note:N = numbers of year, TP = Cumulative policy power, AP = Average policy power, I represents incentive policy, V represents voluntary policy, D represents directive policy, C represents coercive policy. 4.3 Analysis of policy interventions effectiveness. Since the number of ALPP in China from 1993 to 2007 was relatively small, with only 57, and the regression results were not significant, this part adopts 827 policies from the 15 years from 2008 to 2022 for analysis, the TP is analyzed in multiple linear regressions for four different policy power types. Table 4 shows the cumulative coercive policy power (CTP) has the most significant effect on carbon emission suppression (-0.082), followed by cumulative incentive policy power (ITP) (-0.050), cumulative directive policy power (DTP) (-0.033), and cumulative voluntary policy power (VTP) (-0.027). This is primarily because incentive policies mostly focus on areas such as pesticide and fertilizer reduction, while coercive policies are more concentrated in aspects like mechanization in crop production. By controlling variables to analyze the impact on regression results, it was found that the population working in agriculture can increase carbon emissions from agricultural activities, whereas an increase in the percentage of primary sector structure can suppress carbon emissions from agricultural production. This indicates that economic growth can promote the development of agricultural low-carbon production technologies, thereby reducing carbon emissions from agricultural activities. Chinese scholars have mapped interprovincial rice flows and quantified the external costs of interprovincial rice production, using rice as an example, which has certain reference value for the development of cross-regional ecological compensation strategies to promote sustainable agricultural policies and regional equity (Yang et al., 2025 ). The government should pay more attention to the comparison of carbon emissions from planting crops, livestock and poultry farming, and should enhance the refinement of the formulation of ALPP to reward and incentivize farmers for low-carbon production (Cai et al., 2025 ). Different models can be constructed and different methods utilized to enhance the analysis of the effects, mechanisms and heterogeneity of the impact of different farmers on green and low-carbon production (Du et al., 2025). At the same time, attention should be focused on how to equitably and rationally distribute government subsidies and tax incentives to provide a basis for farmers to actively practice low-carbon production behaviors (Hamidoğlu and Weber, 2024 ). In China, some local governments have specifically identified implementation and monitoring units when formulating policies, and have refined implementation details and monitoring standards, thereby promoting the effectiveness of policy interventions (Fiala et al., 2024 ). Table 4 Results of regression estimation of TP of carbon intensity from agricultural production Variate Test statistic Para-errorism P-Value Ln ITP -0.050 0.076 0.008*** Ln CTP -0.082 0.029 0.023** Ln DTP -0.033 0.025 0.211 Ln VTP -0.027 0.025 0.081* Ln Population working in agriculture 0.598 0.334 0.112 Ln Percentage of primary sector structure -0.206 0.428 0.643 _cons 5.523 2.133 0.032** R-squared 0.752 Note: ***, **, and * indicate significant at the 0.01, 0.05, and 0.1 significance levels, respectively. TP = Cumulative policy power, AP = Average policy power, I represents incentive policy, V represents voluntary policy, D represents directive policy, C represents coercive policy. 4.4 Scenario prediction. Model training and scene setting. The focus of this section is to predict the effectiveness of policy interventions in curbing agricultural carbon emissions, exploring the intervention efficacy of four policy types through TP. Based on the release of ALPP, the actual scenarios of low, medium, and high growth in the policy evolution process were fitted (in Fig. 5 ). Seven scenarios with different modes were set up in the scenario prediction section. Models 1–7 are defined as a low growth scenario, a medium growth scenario, a high growth scenario, an incentive policy-led scenario, a voluntary policy-led scenario, a directive policy-led scenario and a coercive policy-led scenario. Model predictions and scenario analysis. The data are standardized so that they have the same order of magnitude, the training samples and test samples are randomly generated, the kernel function is selected as Gaussian radial function, the nonlinear support vector machine model is constructed for the four categories of different TP and agricultural carbon emissions respectively, and the reasonable model parameters are determined. According to the simulation and prediction results of the SVMR model, it can be found that the model has small error and high prediction accuracy. Finally, according to the simulation results, the TP values of 4 policy tool types and the carbon emission release under 7 scenarios are predicted for 2023–2031, and the output results are processed, and the prediction results of the TP of the four policy tool types (in Table 5 ). Table 5 The TP values of 4 policy tool types under different scenarios, 2023–2031 Different policy tool types Growth scenario 2024 2025 2026 2027 2028 2029 2030 2031 ITP Low growth rate 3278 3489 3752 3981 4238 4529 4801 5121 Medium growth rate 3743 4273 4832 5239 5834 6231 6783 7231 High growth rate 4815 5931 6123 7341 8219 9412 10781 12328 VTP Low growth rate 3643 3789 3923 4189 4347 4523 4721 4868 Medium growth rate 3943 4372 4765 5231 5632 5721 6147 6654 High growth rate 4321 4892 5283 5857 6239 6743 7231 7798 DTP Low growth rate 4123 4258 4376 4498 4602 4743 4867 5039 Medium growth rate 4398 4534 4735 4978 5189 5345 5578 5739 High growth rate 4983 5387 5723 6345 6835 7234 7781 8321 CTP Low growth rate 6680 6873 7156 6870 6965 7101 7212 7307 Medium growth rate 6894 7028 7331 7012 7172 7293 7401 7528 High growth rate 7921 8829 9732 11382 12831 13763 14876 15943 Note: ITP: cumulative incentive policy power, VTP: cumulative voluntary policy power, DTP: cumulative directive policy power, CTP: cumulative coercive policy power. Under the low and medium scenarios, carbon emissions from agricultural production increase with the TP of policies. Only under the high scenario does carbon emissions from agricultural production show a downward trend, which suggests that TP of policies has not yet reached the “tipping point”, and that maintaining a high rate of growth will only begin to have a dampening effect on carbon emissions from agricultural production after 2029 (in Fig. 6 ). This suggests that the TP of policies has not yet reached a “tipping point” and that maintaining high growth will only begin to have a dampening effect on agricultural carbon emissions after 2029. This also reflects the fact that agricultural carbon emissions may drive the formulation of ALPP. Ii is because policies are usually introduced to address an emerging or now very serious problem, which is gradually gaining attention as carbon emissions from agricultural production increase year on year, so that the higher the carbon emissions from agriculture over a short period of time, the more policies on low carbon production will be introduced, and to some extent this is a reciprocal relationship. Then, different types of policies should be based on the country’s situation, and the types of policy tools should be based on the current actual development of agriculture (Dama et al., 2024 ). In formulating intervention policies, governments should focus on multi-objective policy synergies, taking into account factors such as the prioritization of policy objectives and the degree of achievability (Reihaneh Bandari et al., 2022 ). Comparing the scenarios led by the 4 policy tool types, it is analyzed that in 2023–2031, under the incentive policy-led scenario, the carbon emissions from agricultural production continue to increase slowly, the incentive policy-led scenario is more effective in slowing down the carbon emissions from the agricultural production process. Secondly, under the voluntary policy-led scenario and coercive policy-led scenario, the effectiveness of suppressing carbon emissions from agricultural production is more obvious, and under the directive policy-led scenario, the effect of suppressing carbon emissions from agricultural production is not obvious. Overall, based on rapid growth, the policy system paired with an incentive-led policy scenario is the most effective. At the same time, as Zhang et al. ( 2025 ) show that we should increase the incentive and subsidy for low-carbon agricultural production, formulate incentive and subsidy standards, and scientifically and reasonably provide subsidies and incentives for farmers or enterprises that implement low-carbon production. Strengthening of sectoral coalitions for policy implementation, and joint promotion of the standardization of sectoral implementation policies to enhance the participation of all sectors of society in low-carbon agricultural production (Bartzas et al., 2024 ). 5. Conclusion This study analyzes the relationship between the governance effects of incentive policy, voluntary policy, directive policy, coercive policy tools and carbon emissions from agricultural production. Based on the results of the study, the conclusions are as follows: Firstly, from 1993 to 2022, the TP of China’s ALPP showed an upward trend year by year. From 1994 to 2006, the number of such policies increased, but the AP exhibited a declining trend with periodic fluctuations. Additionally, there was a lag effect in the implementation of ALPP. Secondly, among the ALPP formulated in China, the number of coercive policy tool type is the highest, accounting for 40.72%, the number of directive and voluntary policy tool types are in the middle, and the number of incentive policy tool type is the lowest, accounting for 16.06%. The TP and AP of coercive policies were the best, while the AP of voluntary policy was the smallest. Thirdly, the TP of ALPP inhibits agricultural production carbon emissions more significantly than the AP. The regression results reveal that the incentive policy tool has the best inhibiting effect on carbon emissions from agricultural production, and GDP, population working in agriculture, and percentage of primary sector structure all have an important impact on the effectiveness of policy interventions. Among them, the growth of GDP and the increase of population working in agriculture will promote the carbon emission of agricultural production process, while the increase of tpercentage of primary sector structure will inhibit the effect. Fourthly, among the seven prediction scenarios, the high-growth scenario is more effective in curbing carbon emissions from agricultural production in the short term than the low-growth and medium-growth scenarios, and is more effective in curbing carbon emissions from agricultural production under the incentive-led policy mix model. 6. Policy implications Firstly, Integrate both “hard and soft” governance approaches to reinforce the effectiveness of policy tools. In this study, the coercive policy and directive policy tools are “hard management”, the incentive and voluntary policy tools are “soft management”, both hard and soft should be more in line with the concept of national governance and the application of policy tools effective model. Coercive policy tools are the core means to reduce emissions from agricultural production, but ALPP tools should be developed into a comprehensive application model in the future. Under the premise of ensuring that coercive policy tool occupy a certain position, the implementation of incentive policy tool should be further improved. The implementation of voluntary policy tool should be expanded to ensure that social forces can also participate in agricultural carbon reduction strategies. Secondly, enhance policy interventions to underscore the essential role of policy tools. Policymakers must leverage existing regulatory frameworks to enhance incentive policy tool, while improving funding mechanisms and fiscal governance to support low-carbon agricultural development. During policy design and implementation, robust planning, monitoring, and evaluation of policy tools are essential to identify and address weaknesses in emission reduction strategies. Concurrently, expanding training and public awareness programs on low-carbon agricultural technologies will foster broader societal engagement and enable effective multi-stakeholder governance. Thirdly, encourage multi-stakeholder participation to continuously enhance the feasibility of policy tools. Establishing a robust performance assessment framework will incentivize more proactive engagement from policy implementers. To bolster ALPP, increased fiscal support should be prioritized, alongside mechanisms to diversify investments from social organizations. Promising models-such as community-based agricultural food supermarkets and food banks, already operational in the UK (Papargyropoulou et al., 2024 ). A mix of positive incentives and disincentives should be employed to strengthen the economic viability of agricultural decarbonization efforts. Finally, policymakers must foster collaboration with research institutions, universities, and industry stakeholders to accelerate the adoption and innovation of sustainable agricultural technology. Declarations Competing interests The authors declare no competing interests. Ethical approval Ethical approval was not required as the study did not involve human participants. Informed consent Informed consent was not required as the study did not involve human participants. Funding This study was supported by an award from the National Social Science and Humanities Research Planning Fund Project of the Ministry of Education (24YJA790009) and Social Science Research Project of Education Department of Jilin Province (JJKH20241360SK). Author Contribution Yunpeng Liu conceived the study, drafted the manuscript, and analysed and interpreted the data. Chulin Pan drafted and revised the manuscript critically for important intellectual content and contributed to data analysis. Shuang Xu focused on revising the manuscript critically for key intellectual content. Hongpeng Guo provided the initial information and data essential for the study’s conceptualisation. All authors have approved the final version of the manuscript and agree with its submission to Humanities & Social Sciences Communications. Acknowledgement First of all, I would like to express my sincere thanks to my mentor Professor Guo, whose guidance and insight not only enabled me to make a breakthrough in this research field, but also taught me the attitude and method of learning. At the same time, I also want to deeply thank Miss Xu and Miss Pan, whose wisdom and efforts have made our team a dynamic and creative. Finally, I would like to express my deepest gratitude to the project that supported this research, without whose trust and funding none of this would have been possible. 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J Clean Prod 487:144603 Zhang Z, Shi K, Tang L, Su K, Zhu Z, Yang Q (2022) Exploring the spatiotemporal evolution and coordination of agricultural green efficiency and food security in China using ESTDA and CCD models. J Clean Prod 374:133967 Zuo C, Wen C, Clarke G, Turner A, Ke X, You L, Tang L (2023) Cropland displacement contributed 60% of the increase in carbon emissions of grain transport in China over 1990–2015. Nat Food 4(3):223–235 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 28 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 23 Dec, 2025 Editor invited by journal 27 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 19 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7897961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":564941714,"identity":"168f2a66-5de4-4590-acc2-adaaf3378f89","order_by":0,"name":"Yunpeng Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunpeng","middleName":"","lastName":"Liu","suffix":""},{"id":564941715,"identity":"18d31a17-843f-4dae-8d59-1a624615d265","order_by":1,"name":"Chulin 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05:54:20","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129640,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/19e399f0b69bf8bfefbda534.html"},{"id":98978725,"identity":"33a4783a-edd7-4a29-b5c4-e79fa5365218","added_by":"auto","created_at":"2025-12-25 05:54:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107139,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the power of ALPP in China\u003c/p\u003e\n\u003cp\u003eNote: Pi represents the total power of the policy in year i, TPi represents the cumulative power of the policy in year i, and APi represents the average power of the policy in year i.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/e8a883968093edeea2284c42.png"},{"id":98978732,"identity":"5f735c27-409c-4342-bd22-0e0a6cde717e","added_by":"auto","created_at":"2025-12-25 05:54:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294501,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon emissions of 3 agricultural production modes in China, 1993-2022\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/060d154020d054ffcca10281.png"},{"id":99311973,"identity":"8595b29f-d702-4056-9322-d1dfcd2280ec","added_by":"auto","created_at":"2025-12-31 16:17:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":849295,"visible":true,"origin":"","legend":"\u003cp\u003eRegional map of (a) ALPP (b) carbon emissions from agricultural production in China, 1993-2022\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/5a34e6c9f95f66cd473a1b54.png"},{"id":99311848,"identity":"0f1df250-f1f0-41b1-b618-b3a48f761bf3","added_by":"auto","created_at":"2025-12-31 16:17:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":305329,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the evolution of the number of ALPP tools in China, 1993-2022\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/b322409a3cd1134586af50b6.png"},{"id":98978729,"identity":"4c9de619-d77a-4412-825d-9ed3a5277d13","added_by":"auto","created_at":"2025-12-25 05:54:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44518,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of ALPP in China, 1993-2022\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/e5b16ddf8c41f3326e9dc07f.png"},{"id":98978722,"identity":"41ce37ff-2d3d-4e16-8eb0-f54b9c67525c","added_by":"auto","created_at":"2025-12-25 05:54:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22494,"visible":true,"origin":"","legend":"\u003cp\u003eProjected carbon emissions from agricultural production under different scenarios in China, 2023-2031\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/9438e608fa78596cb0b19efa.png"},{"id":99322844,"identity":"37216c95-d399-4262-ba43-a40c93dcae18","added_by":"auto","created_at":"2025-12-31 16:44:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2505224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7897961/v1/db947378-50aa-4039-9f72-2ddc9983c5ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effectiveness and prediction analysis of China’s agricultural low-carbon production policy from the perspective of policy tools","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgainst the backdrop of global climate change and increasing pressure on resources and the environment, how to effectively reduce carbon emissions and realize green development has become a focus of attention for governments. Chins\u0026rsquo;s agricultural carbon emissions (ACE) account for up to 17% of total carbon emissions (Li et al., 2011), and there is room to reduce agricultural carbon emissions by about 31% (Tang and Ma, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Reducing carbon emissions from agricultural production is not only a key part of sustainable agricultural development, but also an important issue for China in realizing the \u0026ldquo;double carbon target\u0026rdquo; and transforming the agricultural food system (Yang and Luo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). China\u0026rsquo;s agricultural production activities emit huge amounts of carbon, of which carbon emissions from grain production account for 50% of total agricultural greenhouse gas emissions (Zuo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, due to inefficiencies in resource utilization and production methods, the level of suppression of carbon emissions from agriculture is relatively limited, and there is still a long way to go to reduce carbon emissions from agricultural production (Huang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, constraining carbon emissions from the formulation of Agricultural Low-carbon Production Policy (ALPP) is a necessary way to realize carbon reduction and emission reduction and sustainable development in agriculture.\u003c/p\u003e \u003cp\u003eIn order to actively respond to global climate change and reduce carbon emissions from agricultural production, China\u0026rsquo;s Ministry of Agriculture and Rural Development released the Zero Growth Action Plan for Fertilizer Use by 2020, the National Plan for Sustainable Agricultural Development (2015\u0026ndash;2030). In 2017, China\u0026rsquo;s Ministry of Agriculture and Rural Development issued the Notice on Launching the Construction of the First Batch of National Experimental Demonstration Zones for Sustainable Agricultural Development Carrying out Early and Pilot Work on Agricultural Green Development and emphasized that it would take three years to make the concept of agricultural green development deeply rooted in people\u0026rsquo;s hearts and minds, refine and promote a number of agricultural green development systems, and strive to form an agricultural green production methods and green lifestyles. In 2021, China\u0026rsquo;s Ministry of Agriculture and Rural Development led the release of the National Green Agricultural Development Plan for the 14th Five-Year Plan and emphasized that by 2025, the intensity of greenhouse gas emissions from major agricultural products would be significantly reduced (Du et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Chinese government is increasingly developing low-carbon green development in agriculture and has elevated the issue of low-carbon green development and agricultural sustainability to a very important national strategic level.\u003c/p\u003e \u003cp\u003eAs a result, China\u0026rsquo;s carbon emissions from agricultural production still face challenges, and new solutions are urgently needed to address the negative impacts of agricultural high carbon emissions (Lei et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).From the current research landscape, the majority of scholars have focused on examining the challenges in agricultural emissions governance through the lenses of ecological sustainability and social governance. However, there remains a notable gap in the exploration of policy tool perspectives, which may hinder the accurate identification of effective measures and tools necessary to achieve overarching policy objectives (Abbasnezhad and Abrams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). China\u0026rsquo;s application of policy tools to conduct an in-depth analysis of the specific mechanisms and evolving trends in ALPP is not only critical for understanding the national government\u0026rsquo;s top-level design and ensuring the effective implementation of carbon reduction governance in agricultural production, but also serves as a vital platform for sharing China\u0026rsquo;s low-carbon agricultural governance experience globally, drawing upon the distinctive features of its existing policy framework.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Definitions and classifications of policy tools.\u003c/h2\u003e \u003cp\u003ePolicy tools are the means or measures governments adopt to pursue specific social development goals or address particular social and economic issues (Sha et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it also serves as a key tool for the government to address market failures and enhance social governance (Kern and Howlett, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The theoretical framework demonstrates particular analytical utility in public affairs governance, especially concerning environmental security regimes. Moreover, ALPP tool types function as indispensable leverage points for achieving sustainability transitions in contemporary agri-food systems (Wen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study is categorized into four types: incentive, coercive, directive and voluntary (Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Application of ALPP tools.\u003c/h2\u003e \u003cp\u003eExisting research demonstrates that although current studies on policy tools have enhanced our comprehension of their capacity to tackle complex challenges and operate in heterogeneous contexts, significant deficiencies persist in the analytical framework and integrated application of ALPP tools (Chai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Academic research perspectives on ALPP tools can be categorized into 4 aspects. Firstly, on the environmental impact: scholars believe that ALPP tools play an important role in promoting environmental innovation, and attempt to solve the problem of externalities in environmental innovation by establishing a framework for analyzing the dimensions of policy tool types and policy objectives at two stages of environmental innovation (Liao, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Secondly, on the social drivers, scholars have extended their analytical perspectives to the societal level, for example, by exploring the determinants of the preference of policy implementation targets for ALPP tools (Cho and Moon, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pedersen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thirdly, on the economic development: scholars have shifted their analytical perspective to the link between economic development and environmental policy (Ye et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or categorize environmental regulation into command-and-control and economic incentive policy tools and explore the relationship between environmental regulation, farmers\u0026rsquo; willingness and economic development using mediated effects models (Ke and Huang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fourthly, on the environmental sustainability: scholars have proposed the use of hybrid environmental policy tools to improve ecological governance and socio-economic development, research perspectives have continued to shift, which has also shown that scholars have improved the combination and synergy of policy tools through improved (Olbrich et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the organic combination of policy tool types and policy phases to solve practical problems (Yu et al., 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Evaluating the effectiveness of ALPP tools.\u003c/h2\u003e \u003cp\u003eCurrent research focuses on assessing the effectiveness of public policies in combating environmental pollution (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In agricultural economics, especially in assessing the effectiveness of ALPP tools, academic research is limited and mainly focuses on qualitative studies. In quantitative studies, some scholars have used the Difference-in-Differences (DID) method to evaluate the effectiveness of policy tools (Levy et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, DID requires the experimental and control groups to meet the parallel trends assumption before policy implementation and that no other relevant policies are enacted during the policy evaluation period. This implies that DID can only evaluate the effectiveness of one single policy tool, which is often inconsistent with reality. Policy Modelling Consistency Index (PMC-Index) model has been applied to evaluate policy tool effectiveness, which can break free from the constraints of the parallel trends assumption and simultaneously evaluate the effectiveness of a set of policy tools (Kuang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the determination of policy scores depends heavily on expert reviews, indicating that the evaluation process is likely to be influenced by subjective factor (Xiong et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study situates its analysis within China\u0026rsquo;s ALPP, a focus justified by three critical considerations: Firstly, agricultural carbon mitigation constitutes a pressing global challenge, and China\u0026rsquo;s status as one of the world\u0026rsquo;s leading agricultural producers lends unique empirical significance to this inquiry. Secondly, the Chinese government has implemented a comprehensive policy framework to curb agricultural production emissions, including legal system enhancements, diversification of regulatory tools, and technological innovations in monitoring mechanisms. These governance practices epitomize global efforts toward sustainable agricultural transformation. Thirdly, systematic examination of China\u0026rsquo;s policy evolution not only informs evidence-based development of its domestic agricultural decarbonization strategies but also provides theoretical foundations for strategic policy design in other nations-particularly developing economies confronting analogous sustainability dilemmas.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Methods","content":"\u003cp\u003eThis study employes Latent Dirichlet Allocation (LDA) model to achieve objective and efficient classification of central and local-level policy texts (Davidson et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This methodology is designed to overcome the inherent subjectivity and arbitrariness in traditional policy tool classification studies, thereby addressing a critical research gap in this field. Also introduces Support Vector Machine Regression (SVMR) model and establish seven distinct scenarios to evaluate the effectiveness of ALPP. This analytical framework ultimately enables the identification of optimal policy tool combinations.\u003c/p\u003e \u003cp\u003eThe study is structured analytical approach comprising 5 components: policy document collection and classification, agricultural carbon emission accounting, policy power measurement, policy effectiveness evaluation, and policy scenario simulation. The specific process is as follows:\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Collection and categorization of policy texts.\u003c/h2\u003e \u003cp\u003eThe policy texts were obtained from the Peking University Law Database (pkulaw), which includes laws, regulations and rules promulgated by China\u0026rsquo;s central and local governments, and represents a well-established system for retrieving laws and regulations. The government websites of 31 provinces, autonomous regions and municipalities (excluding Hong Kong, Macao and Taiwan) were manually collected, and relevant data was checked and supplemented to ensure comprehensiveness and prevent omissions. When collecting policy text data on the basis of existing research, the keywords \u0026ldquo;agricultural low-carbon production\u0026rdquo; \u0026ldquo;agricultural carbon emission reduction\u0026rdquo; \u0026ldquo;low-carbon agriculture\u0026rdquo; and \u0026ldquo;green agriculture\u0026rdquo; were used to accurately search the data. The policy texts were searched from January 1, 1993 to December 31, 2022, covering both the central and local levels. After systematic organization, scientific identification and elimination of duplicated policy texts, 884 valid policy texts were found. Finally, 884 valid policy texts were taken as research objects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Calculation of carbon emission.\u003c/h2\u003e \u003cp\u003eIn order to ensure that the data of agricultural carbon emissions are comprehensive and accurate, this study refers to the calculation method of Tian et al (Tian and Yin, 2022). Therefore, the carbon emissions generated by 3 types of agricultural production behaviors: crop cultivation, livestock and poultry breeding, agricultural land utilization. Crop cultivation mainly includes 8 types of planted crops, namely, corn, rice (including early rice, middle rice and late rice), wheat (including spring wheat and winter wheat), legumes, yams, oilseeds, vegetables, and cotton; Livestock and poultry breeding mainly includes 8 types of pigs, cows, sheep, horses, donkeys, mules, camels, and poultry breeding livestock and poultry; Agricultural land utilization mainly includes 5 types of agricultural production methods: fertilizer, agricultural film, diesel fuel, irrigation and ploughing (source: China\u0026rsquo;s Statistical Yearbook 1993\u0026ndash;2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Calculation of policy power.\u003c/h2\u003e \u003cp\u003eThe power of ALPP were calculated by combining the results of policy texts categorization by LDA model. The calculation of policy power includes annual policy power (P), cumulative policy power (TP) and average policy power (AP). P denotes the total power of policies enacted each year, TP denotes the cumulative power of policies enacted up to a given year, and AP denotes the average policy power of each policy each year. Based on existing research on policy power, this study establishes Eq.\u0026nbsp;(1), which shows that policy power is positively related to the level of legal effect and the level of the enacting authority. Considering the long-term and lagged nature of policy effectiveness, policies that are currently legally effective are expected to have a sustained impact on agricultural low-carbon production. Eq.\u0026nbsp;(2) is used to measure the cumulative number of ALPP per year. Equations\u0026nbsp;(3) and (4) are used to measure the TP and AP of ALPP per year, respectively. This quantitative approach provides a structured and objective way to assess the impact and evolution of ALPP over time.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}=\\sum\\:_{j=1}^{N}{SP}_{j}\\times\\:{SD}_{j}\\)\u003c/span\u003e \u003c/span\u003e i ϵ (1993,2022) (1)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{TN}_{i}={TN}_{i-1}+{N}_{i}\\)\u003c/span\u003e \u003c/span\u003e i ϵ (1993,2022) (2)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{TP}_{i}={P}_{i-1}+{N}_{i}\\)\u003c/span\u003e \u003c/span\u003e i ϵ (1993,2022) (3)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{AP}_{i}={TP}_{i}/{TN}_{i}\\)\u003c/span\u003e \u003c/span\u003e i ϵ (1993,2022) (4)\u003c/p\u003e \u003cp\u003eIn the above equation, i denotes the year, j denotes the jth policy, SPj denotes the jth policy legal rating score, SDj denotes the jth policy issuer rating score, Pi denotes the total power of all policies in year i, Ni denotes the number of policies issued in year i, TNi denotes the cumulative number of policies issued in year i, TPi denotes the cumulative policy power in year i, and APi denotes the average policy power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Assessment of policy effectiveness.\u003c/h2\u003e \u003cp\u003ePolicy effectiveness refers to the direct and indirect impacts on target groups, the economy or society as a result of policy implementation (Wen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The effectiveness of ALPP is evaluated by the calculated policy power. Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e5\u003c/span\u003e) is introduced to quantitatively analyze the effectiveness of policy interventions, aiming to reveal the impact boundaries of different policy tools.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{n}{\\text{A}\\text{C}\\text{E}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+{{\\beta\\:}}_{1}{\\text{X}}_{\\text{t}}+{{\\beta\\:}}_{2}\\text{l}\\text{n}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the equation, i represents the type of crop cultivation, livestock and poultry breeding and agricultural land utilization, ACEit is the explained variable representing the agricultural carbon emissions in year t for i way, Xt is the explanatory variable representing N, TP, and AP in year t. β\u003csub\u003e0\u003c/sub\u003e represents the constant, ε\u003csub\u003eit\u003c/sub\u003e represents the error term, Control\u003csub\u003eit\u003c/sub\u003e represents a set of control variables. In order to control the influence of factors other than policy factors on carbon emissions, based on the Conway et al (1973), this study selected the Gross Domestic Product (GDP), the size of the number of population working in agriculture and the percentage of primary sector structure to measure (Source: China Statistical Yearbook 2008\u0026ndash;2022). The data analysis using Stata in this section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Prediction of policy scenario.\u003c/h2\u003e \u003cp\u003eThe policy scenarios are projected with 2022 as the base year, the natural year as the cycle node, and the end year of the projections is 2031 to explore the evolution of carbon emissions from agricultural production under different policy scenarios. In order to screen the optimal policy combination, this study uses Matlab software to construct a support vector machine model design and forecasting part in the scenario. It is suitable for solving medium and long term problems learning methods with few parameters and small samples for the prediction problem. This this study mainly uses SVMR has the advantages of good fit and high prediction accuracy (Xi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The empirical analysis reveals that the quantitative growth of ALPP between 1993 and 2022 can be classified into three distinct patterns: low-speed, medium-speed, and high-speed growth modes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Evolution of policy power and effectiveness.\u003c/h2\u003e \u003cp\u003eAmong the 884 policy texts analyzed, 817 policies scored more than 4 points (out of 8), accounting for 92.42% of the total, and 768 policies scored more than 4 points (out of 5), accounting for 86.76% of the total, demonstrating that the Chinese government attaches great importance to the work of agricultural carbon emission reduction (in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, laws exhibit the highest level of authority; however, only 8 policy tools fall under this category. In contrast, Provincial and ministerial government regulations and normative documents are the most numerous, totaling 484 and accounting for 54.75% of the texts. Local policy documents at the district and county levels are relatively scarce, comprising only 3.62%. This means that at the district and county levels, government departments are mostly policy implementation agencies rather than policy-making institutions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of China\u0026rsquo;s ALPP of different effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffectiveness hierarchy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy power\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicy quantity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e8-mark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLegal and judicial interpretations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative regulations and State Council normative documents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvincial and ministerial government regulations and normative documents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther normative documents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilitary regulations and rules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParty regulations and systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulations on social organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e5-mark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvincial (autonomous region, municipality directly under the Central Government)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe number of ALPP in China shows a fluctuating growth trend from 1993\u0026ndash;2022. The AP and TP of ALPP increases year by year (in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, from 1994 to 2006, as the number of policies increased, but the AP showed a decreasing trend, analyzing the main reason is caused by the decline of the policy issuing agency and the legal level of the policy. The policy power in 2011 and 2017 was higher in that year, but there were cyclical fluctuations, with the first trough occurring in 2013, and the second trough occurring in 2019, which indicates that the policy is formulated in the After a certain time of release, the relevant government departments need to reformulate the emission reduction policy in the light of the reality of agricultural development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003ePi represents the total power of the policy in year i, TPi represents the cumulative power of the policy in year i, and APi represents the average power of the policy in year i.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the China\u0026rsquo;s agricultural production carbon emissions has a rising trend, but after 2004 between 1993 and 2003, the Chinese government gradually emphasized the issue of agricultural carbon emission reduction, and introduced and formulated ALPP, due to the fact that the number of ALPP and the TP have increased year by year, resulting in a decreasing trend of China\u0026rsquo;s agricultural production carbon emissions from 2004 to 2022, which indicates that agricultural carbon emissions are affected by ALPP and the evolution is consistent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the period 1993\u0026ndash;2022, the overall carbon emissions from China\u0026rsquo;s agricultural production show a fluctuating state, which is closely related to policy formulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The northern region of China has promulgated a substantial number of ALPP, primarily due to Northeast China\u0026rsquo;s status as the country\u0026rsquo;s primary agricultural and grain production base, coupled with Inner Mongolia\u0026rsquo;s well-developed livestock industry. These regional characteristics have necessitated the formulation of a comparatively larger volume of ALPP (Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The higher carbon emissions in the central and eastern regions are mainly related to the region\u0026rsquo;s higher economic level and the development of smart agricultural practices, in which farmers improve crop production efficiency through improved rice varieties, farmyard fertilizers, water-saving technologies and bio-pesticides, which lead to higher carbon emissions while promoting growth in crop yields (Vatsa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Policy power of different policy tools.\u003c/h2\u003e \u003cp\u003eThrough computational determination of topic categories, the analysis encompasses various keywords reflected in ALPP (Kim et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Drawing on Howlett\u0026rsquo;s categorization method, this study classified the different policy tools categories into four types: incentive, coercive, directive and voluntary policy tools.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e report that among the 884 ALPP analyzed, coercive policies constitute the largest proportion at 40.72%, followed by directive policies and incentive policies, while voluntary policies account for the smallest share at 20.25%. Statistical analysis reveals that over 20% of voluntary policies originate from municipal-level or lower administrative agencies, with virtually no policies being formulated by societal actors. Nevertheless, the potential contribution of social forces to agricultural emission reduction merits serious consideration. As Fabi et al (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have argued, social organizations can leverage their technical expertise to provide assistance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification and quantity distribution of China\u0026rsquo;s ALPP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncentive policy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoercive policy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirective poliy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoluntary policy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain document type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartmental normative documents; local normative documents; provincial local regulations, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaws; departmental regulations and normative documents; local government regulations; provincial local regulations, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepartmental normative documents; local normative documents; provincial local regulations, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProvincial local regulations; local normative documents, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of policies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the local normative documents are the main source of ALPP, with the highest number of coercive policy tools and the most diverse issuing organizations. As shown in equations (3) and (4), there are statistically significant differences between the three types of policy tools when calculating the TP and AP of coercive, directive, incentive and voluntary policy tools. Combining TP, AP and number of policies, the order from highest to lowest is coercive, incentive, directive and voluntary policy tools.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple regression descriptive statistics of N, TP, and AP of ALPP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTool type of policy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategorization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd.Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6580.467\u003c/p\u003e \u003cp\u003e21.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4958.515\u003c/p\u003e \u003cp\u003e.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1496\u003c/p\u003e \u003cp\u003e19.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17503\u003c/p\u003e \u003cp\u003e22.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoercive policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003cp\u003eCTP\u003c/p\u003e \u003cp\u003eCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.600\u003c/p\u003e \u003cp\u003e1884.800\u003c/p\u003e \u003cp\u003e19.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.058\u003c/p\u003e \u003cp\u003e2019.706\u003c/p\u003e \u003cp\u003e.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003cp\u003e6430\u003c/p\u003e \u003cp\u003e20.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncentive policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN\u003c/p\u003e \u003cp\u003eITP\u003c/p\u003e \u003cp\u003eIAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.200\u003c/p\u003e \u003cp\u003e865.467\u003c/p\u003e \u003cp\u003e21.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.405\u003c/p\u003e \u003cp\u003e815.562\u003c/p\u003e \u003cp\u003e1.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e2775\u003c/p\u003e \u003cp\u003e24.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirective poliy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDN\u003c/p\u003e \u003cp\u003eDTP\u003c/p\u003e \u003cp\u003eDAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.133\u003c/p\u003e \u003cp\u003e1389.533\u003c/p\u003e \u003cp\u003e20.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.526\u003c/p\u003e \u003cp\u003e1196.259\u003c/p\u003e \u003cp\u003e3.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e10.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e3820\u003c/p\u003e \u003cp\u003e24.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoluntary policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVN\u003c/p\u003e \u003cp\u003eVTP\u003c/p\u003e \u003cp\u003eVAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003cp\u003e1156.667\u003c/p\u003e \u003cp\u003e21.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.872\u003c/p\u003e \u003cp\u003e971.109\u003c/p\u003e \u003cp\u003e2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e19.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e3224\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote:N\u0026thinsp;=\u0026thinsp;numbers of year, TP\u0026thinsp;=\u0026thinsp;Cumulative policy power, AP\u0026thinsp;=\u0026thinsp;Average policy power, I represents incentive policy, V represents voluntary policy, D represents directive policy, C represents coercive policy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Analysis of policy interventions effectiveness.\u003c/h2\u003e \u003cp\u003eSince the number of ALPP in China from 1993 to 2007 was relatively small, with only 57, and the regression results were not significant, this part adopts 827 policies from the 15 years from 2008 to 2022 for analysis, the TP is analyzed in multiple linear regressions for four different policy power types.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the cumulative coercive policy power (CTP) has the most significant effect on carbon emission suppression (-0.082), followed by cumulative incentive policy power (ITP) (-0.050), cumulative directive policy power (DTP) (-0.033), and cumulative voluntary policy power (VTP) (-0.027). This is primarily because incentive policies mostly focus on areas such as pesticide and fertilizer reduction, while coercive policies are more concentrated in aspects like mechanization in crop production. By controlling variables to analyze the impact on regression results, it was found that the population working in agriculture can increase carbon emissions from agricultural activities, whereas an increase in the percentage of primary sector structure can suppress carbon emissions from agricultural production. This indicates that economic growth can promote the development of agricultural low-carbon production technologies, thereby reducing carbon emissions from agricultural activities.\u003c/p\u003e \u003cp\u003eChinese scholars have mapped interprovincial rice flows and quantified the external costs of interprovincial rice production, using rice as an example, which has certain reference value for the development of cross-regional ecological compensation strategies to promote sustainable agricultural policies and regional equity (Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The government should pay more attention to the comparison of carbon emissions from planting crops, livestock and poultry farming, and should enhance the refinement of the formulation of ALPP to reward and incentivize farmers for low-carbon production (Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Different models can be constructed and different methods utilized to enhance the analysis of the effects, mechanisms and heterogeneity of the impact of different farmers on green and low-carbon production (Du et al., 2025). At the same time, attention should be focused on how to equitably and rationally distribute government subsidies and tax incentives to provide a basis for farmers to actively practice low-carbon production behaviors (Hamidoğlu and Weber, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In China, some local governments have specifically identified implementation and monitoring units when formulating policies, and have refined implementation details and monitoring standards, thereby promoting the effectiveness of policy interventions (Fiala et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of regression estimation of TP of carbon intensity from agricultural production\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePara-errorism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn ITP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn CTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn DTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn VTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn Population working in agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn Percentage of primary sector structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, and * indicate significant at the 0.01, 0.05, and 0.1 significance levels, respectively. TP\u0026thinsp;=\u0026thinsp;Cumulative policy power, AP\u0026thinsp;=\u0026thinsp;Average policy power, I represents incentive policy, V represents voluntary policy, D represents directive policy, C represents coercive policy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Scenario prediction.\u003c/h2\u003e \u003cp\u003e \u003cem\u003eModel training and scene setting.\u003c/em\u003e The focus of this section is to predict the effectiveness of policy interventions in curbing agricultural carbon emissions, exploring the intervention efficacy of four policy types through TP. Based on the release of ALPP, the actual scenarios of low, medium, and high growth in the policy evolution process were fitted (in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Seven scenarios with different modes were set up in the scenario prediction section. Models 1\u0026ndash;7 are defined as a low growth scenario, a medium growth scenario, a high growth scenario, an incentive policy-led scenario, a voluntary policy-led scenario, a directive policy-led scenario and a coercive policy-led scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eModel predictions and scenario analysis.\u003c/em\u003e The data are standardized so that they have the same order of magnitude, the training samples and test samples are randomly generated, the kernel function is selected as Gaussian radial function, the nonlinear support vector machine model is constructed for the four categories of different TP and agricultural carbon emissions respectively, and the reasonable model parameters are determined. According to the simulation and prediction results of the SVMR model, it can be found that the model has small error and high prediction accuracy. Finally, according to the simulation results, the TP values of 4 policy tool types and the carbon emission release under 7 scenarios are predicted for 2023\u0026ndash;2031, and the output results are processed, and the prediction results of the TP of the four policy tool types (in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe TP values of 4 policy tool types under different scenarios, 2023\u0026ndash;2031\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferent policy tool types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2029\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2031\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eITP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: ITP: cumulative incentive policy power, VTP: cumulative voluntary policy power, DTP: cumulative directive policy power, CTP: cumulative coercive policy power.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnder the low and medium scenarios, carbon emissions from agricultural production increase with the TP of policies. Only under the high scenario does carbon emissions from agricultural production show a downward trend, which suggests that TP of policies has not yet reached the \u0026ldquo;tipping point\u0026rdquo;, and that maintaining a high rate of growth will only begin to have a dampening effect on carbon emissions from agricultural production after 2029 (in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This suggests that the TP of policies has not yet reached a \u0026ldquo;tipping point\u0026rdquo; and that maintaining high growth will only begin to have a dampening effect on agricultural carbon emissions after 2029. This also reflects the fact that agricultural carbon emissions may drive the formulation of ALPP. Ii is because policies are usually introduced to address an emerging or now very serious problem, which is gradually gaining attention as carbon emissions from agricultural production increase year on year, so that the higher the carbon emissions from agriculture over a short period of time, the more policies on low carbon production will be introduced, and to some extent this is a reciprocal relationship. Then, different types of policies should be based on the country\u0026rsquo;s situation, and the types of policy tools should be based on the current actual development of agriculture (Dama et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In formulating intervention policies, governments should focus on multi-objective policy synergies, taking into account factors such as the prioritization of policy objectives and the degree of achievability (Reihaneh Bandari et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the scenarios led by the 4 policy tool types, it is analyzed that in 2023\u0026ndash;2031, under the incentive policy-led scenario, the carbon emissions from agricultural production continue to increase slowly, the incentive policy-led scenario is more effective in slowing down the carbon emissions from the agricultural production process. Secondly, under the voluntary policy-led scenario and coercive policy-led scenario, the effectiveness of suppressing carbon emissions from agricultural production is more obvious, and under the directive policy-led scenario, the effect of suppressing carbon emissions from agricultural production is not obvious. Overall, based on rapid growth, the policy system paired with an incentive-led policy scenario is the most effective. At the same time, as Zhang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that we should increase the incentive and subsidy for low-carbon agricultural production, formulate incentive and subsidy standards, and scientifically and reasonably provide subsidies and incentives for farmers or enterprises that implement low-carbon production. Strengthening of sectoral coalitions for policy implementation, and joint promotion of the standardization of sectoral implementation policies to enhance the participation of all sectors of society in low-carbon agricultural production (Bartzas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study analyzes the relationship between the governance effects of incentive policy, voluntary policy, directive policy, coercive policy tools and carbon emissions from agricultural production. Based on the results of the study, the conclusions are as follows:\u003c/p\u003e \u003cp\u003eFirstly, from 1993 to 2022, the TP of China\u0026rsquo;s ALPP showed an upward trend year by year. From 1994 to 2006, the number of such policies increased, but the AP exhibited a declining trend with periodic fluctuations. Additionally, there was a lag effect in the implementation of ALPP.\u003c/p\u003e \u003cp\u003eSecondly, among the ALPP formulated in China, the number of coercive policy tool type is the highest, accounting for 40.72%, the number of directive and voluntary policy tool types are in the middle, and the number of incentive policy tool type is the lowest, accounting for 16.06%. The TP and AP of coercive policies were the best, while the AP of voluntary policy was the smallest.\u003c/p\u003e \u003cp\u003eThirdly, the TP of ALPP inhibits agricultural production carbon emissions more significantly than the AP. The regression results reveal that the incentive policy tool has the best inhibiting effect on carbon emissions from agricultural production, and GDP, population working in agriculture, and percentage of primary sector structure all have an important impact on the effectiveness of policy interventions. Among them, the growth of GDP and the increase of population working in agriculture will promote the carbon emission of agricultural production process, while the increase of tpercentage of primary sector structure will inhibit the effect.\u003c/p\u003e \u003cp\u003eFourthly, among the seven prediction scenarios, the high-growth scenario is more effective in curbing carbon emissions from agricultural production in the short term than the low-growth and medium-growth scenarios, and is more effective in curbing carbon emissions from agricultural production under the incentive-led policy mix model.\u003c/p\u003e"},{"header":"6. Policy implications","content":"\u003cp\u003eFirstly, Integrate both \u0026ldquo;hard and soft\u0026rdquo; governance approaches to reinforce the effectiveness of policy tools. In this study, the coercive policy and directive policy tools are \u0026ldquo;hard management\u0026rdquo;, the incentive and voluntary policy tools are \u0026ldquo;soft management\u0026rdquo;, both hard and soft should be more in line with the concept of national governance and the application of policy tools effective model. Coercive policy tools are the core means to reduce emissions from agricultural production, but ALPP tools should be developed into a comprehensive application model in the future. Under the premise of ensuring that coercive policy tool occupy a certain position, the implementation of incentive policy tool should be further improved. The implementation of voluntary policy tool should be expanded to ensure that social forces can also participate in agricultural carbon reduction strategies.\u003c/p\u003e \u003cp\u003eSecondly, enhance policy interventions to underscore the essential role of policy tools. Policymakers must leverage existing regulatory frameworks to enhance incentive policy tool, while improving funding mechanisms and fiscal governance to support low-carbon agricultural development. During policy design and implementation, robust planning, monitoring, and evaluation of policy tools are essential to identify and address weaknesses in emission reduction strategies. Concurrently, expanding training and public awareness programs on low-carbon agricultural technologies will foster broader societal engagement and enable effective multi-stakeholder governance.\u003c/p\u003e \u003cp\u003eThirdly, encourage multi-stakeholder participation to continuously enhance the feasibility of policy tools. Establishing a robust performance assessment framework will incentivize more proactive engagement from policy implementers. To bolster ALPP, increased fiscal support should be prioritized, alongside mechanisms to diversify investments from social organizations. Promising models-such as community-based agricultural food supermarkets and food banks, already operational in the UK (Papargyropoulou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A mix of positive incentives and disincentives should be employed to strengthen the economic viability of agricultural decarbonization efforts. Finally, policymakers must foster collaboration with research institutions, universities, and industry stakeholders to accelerate the adoption and innovation of sustainable agricultural technology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required as the study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was not required as the study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by an award from the National Social Science and Humanities Research Planning Fund Project of the Ministry of Education (24YJA790009) and Social Science Research Project of Education Department of Jilin Province (JJKH20241360SK).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYunpeng Liu conceived the study, drafted the manuscript, and analysed and interpreted the data. Chulin Pan drafted and revised the manuscript critically for important intellectual content and contributed to data analysis. Shuang Xu focused on revising the manuscript critically for key intellectual content. Hongpeng Guo provided the initial information and data essential for the study\u0026rsquo;s conceptualisation. All authors have approved the final version of the manuscript and agree with its submission to Humanities \u0026amp; Social Sciences Communications.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eFirst of all, I would like to express my sincere thanks to my mentor Professor Guo, whose guidance and insight not only enabled me to make a breakthrough in this research field, but also taught me the attitude and method of learning. At the same time, I also want to deeply thank Miss Xu and Miss Pan, whose wisdom and efforts have made our team a dynamic and creative. Finally, I would like to express my deepest gratitude to the project that supported this research, without whose trust and funding none of this would have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasnezhad B, Abrams JB (2025) Are prevailing policy tools effective in conserving ecosystem services under individual private tenure? Challenges and policy gaps in a rapidly urbanizing region. Trees Forests People 19:100730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartzas G, Doula M, Hliaoutakis A, Papadopoulos NS, Tsotsolas N, Komnitsas K (2024) Low carbon certification of agricultural production using field GHG measurements. Development of an integrated framework with emphasis on mediterranean products. 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J Clean Prod 374:133967\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo C, Wen C, Clarke G, Turner A, Ke X, You L, Tang L (2023) Cropland displacement contributed 60% of the increase in carbon emissions of grain transport in China over 1990\u0026ndash;2015. Nat Food 4(3):223\u0026ndash;235\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"evaluation of policy effectiveness, incentive policy, policy intervention, policy tools, policy prediction","lastPublishedDoi":"10.21203/rs.3.rs-7897961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7897961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGovernment intervention through policy measures to curb carbon emissions from agricultural production is an essential pathway toward achieving sustainable agricultural development in China. However, does the intervention effects of Agricultural Low-carbon Production Policy vary depending on the policy tool types employed? This study compiles 884 policy texts and carbon emission datasets at both national and regional levels in China (1993\u0026ndash;2022). Using the Latent Dirichlet Allocation to classify policy tools and Support Vector Machine Regression to predict the effectiveness of policy combinations. We find that (1) Agricultural low-carbon production policies exhibit a lag effect; (2) Coercive policy tools are the most prevalent, accounting for 40.72%, while incentive policy tools are the least common, making up only 16.06%; (3) Empirical results demonstrate that incentive policy tools yield the most effective intervention outcomes from agricultural low-carbon production, followed by coercive, directive, and voluntary policy tools. (4) Predictive results show that a high-growth model combined with incentive policy exerts the most significant suppression effect on agricultural carbon emissions. The findings of this study offer the following insights: Building upon existing research on policy tools, future policy formulation should be guided and informed by strengthening effectiveness, highlighting significance, and enhancing feasibility across three key dimensions.\u003c/p\u003e","manuscriptTitle":"The effectiveness and prediction analysis of China’s agricultural low-carbon production policy from the perspective of policy tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-25 05:53:50","doi":"10.21203/rs.3.rs-7897961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T18:15:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T15:26:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T13:24:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T03:36:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215009944140125859329566269535622131763","date":"2025-12-28T09:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254087970125864325140004981621769433902","date":"2025-12-25T06:36:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275600014573939551062661602910529210121","date":"2025-12-24T00:22:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-23T09:57:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-27T10:58:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T05:03:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T09:13:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-19T10:34:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4147aff1-da00-48d8-9b75-2294d5c8e1a0","owner":[],"postedDate":"December 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60148350,"name":"Business and commerce/Economics"},{"id":60148351,"name":"Social science/Economics"},{"id":60148352,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-02T13:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-25 05:53:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7897961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7897961","identity":"rs-7897961","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}