New Quality Productivity (NQP) emphasizes enhancing agricultural production efficiency through technological innovation, digitalization, and green transformation while mitigating ecological impacts. Its carbon reduction effects primarily manifest in optimizing resource allocation efficiency and reducing carbon emissions across production processes. From a political economy perspective, (Luo,2024) argues that NQP restructures agricultural production models by integrating data elements with traditional agriculture, thereby reducing reliance on high-carbon inputs such as fertilizers and pesticides (Luo and Geng, 2024). Ma and Yang (2024), analyzing traditional and emerging factor allocation, demonstrate that NQP fosters innovative resource configurations, drives integrated development, and elevates production capabilities to higher value chain levels, enabling precision, intelligence, and green transformation in agriculture. Chen et al. (2025) construct an NQP evaluation framework for agriculture, revealing that its carbon reduction effects are mediated by improvements in labor and land productivity (Chen et al., 2025). Collectively, these studies underscore NQP’s inherent alignment with green productivity principles.

As a critical component of NQP, digital productivity inherently embodies low-carbon characteristics. Its technical diffusion and application spillovers facilitate effective emission reductions in agricultural practices. (Huang et al., 2024) empirically confirms that rural digitalization strengthens the inhibitory effect on agricultural carbon emissions (Huang et al., 2024). Han and Gong (2025) further reveal that digital rural construction significantly reduces agricultural carbon emissions through direct negative effects and spatial spillover mechanisms. These findings suggest that advancing NQP accelerates digital innovation and dissemination, driving profound transformations in agricultural production paradigms.

Nevertheless, international research presents nuanced perspectives on technology’s role in agricultural decarbonization. The IPCC Fifth Assessment Report cautions that while agricultural technological advancements enhance crop yields and efficiency, they may concurrently increase emissions through intensified fertilizer use and mechanization. Chen et al. (2020) distinguishes between environmental technologies (reducing emissions) and production technologies (potentially increasing emissions). Guan et al. (2023) identifies that agricultural mechanization, despite boosting productivity, may elevate energy consumption and associated emissions, corroborating the energy rebound effect hypothesis - efficiency gains paradoxically increasing total energy use. Regional disparities in technology adoption further complicate emission patterns, with developed regions adopting low-carbon technologies faster than carbon-intensive models persist in underdeveloped areas, exacerbating interregional emission differentials (He and Ding, 2022; Milindi and Inglesi-Lotz, 2022).

However, NQP’s endogenous green attributes may counterbalance these risks through three mechanisms: (1) substituting traditional factors with clean alternatives in production configurations, (2) institutional coordination to regulate high-emission agricultural processes, and (3) reshaping energy flow pathways within agricultural ecosystems. From the above research, it can be observed that existing studies have not directly explored the specific impact of the core concept of New Quality Productivity (NQP) on agricultural carbon emissions, lacking targeted analysis of its inherent green attributes. Meanwhile, the adopted holistic measurement methods obscure the carbon emission boundaries of subsystems such as pesticides, chemical fertilizers, and mechanical diesel, and fail to deconstruct carbon emissions across different agricultural production links. This makes it difficult to reveal the differentiated effects of NQP in various production links. Therefore, this study will conduct a heterogeneous discussion on carbon emissions from different agricultural production links to explain the carbon reduction impact of NQP on each production processes, and thus propose the first hypothesis of this paper.

Facility agriculture, characterized by high-efficiency agricultural production within controlled environments, demonstrates strong risk resistance, intensive resource inputs, and technology-intensive operations (Wang et al., 2025b; Zhang et al., 2024a). Empirical studies reveal its complex carbon dynamics: Tao (2017) identified that excessive fertilizer application in facility vegetable cultivation significantly increases short-term carbon emissions, while Yin et al. (2021) emphasized the critical role of technological investments and resource utilization efficiency in modulating emission patterns. Contrastingly, Tong et al. (2024) demonstrated that facility agriculture development substantially enhances agricultural carbon emission efficiency, with accelerated growth rates correlating to improved decarbonization performance.

In the context of green development and agricultural modernization, New Quality Productivity (NQP) aligns with facility agriculture’s technical demands through its dual emphasis on technological innovation and production paradigm shifts. NQP addresses inherent limitations in facility agriculture, including suboptimal decarbonization efficiency and transient carbon surge effects, by providing systematic technological frameworks and theoretical guidance (Lin et al., 2024; Luo et al., 2025). Gao et al. (2022) further argue that conventional agricultural practices, often reliant on empirical farmer knowledge, lack precision in input allocation. NQP-driven digital transformation enables precise input management in facility agriculture, fostering intensive production models that reduce carbon intensity.

Collectively, these findings suggest that facility agriculture’s carbon emissions exhibit dynamic evolutionary patterns. NQP facilitates fundamental technological transitions through breakthroughs in precision control systems and energy infrastructure reconfiguration, thereby extending operational cycles and optimizing technical frameworks. This synergy amplifies the green potential of facility agriculture, progressively unlocking its decarbonization capacity. However, Existing studies have not clarified how NQP transforms the technical foundation of facility agriculture through breakthroughs in precision control technologies and the restructuring of energy systems. Nor have they empirically tested facility agriculture as an intermediary pathway for carbon reduction driven by NQP, which leads to an unclear explanation of the synergistic logic between the carbon reduction potential of facility agriculture and NQP. Consequently, we propose the second hypothesis:

In the past, agricultural mechanization usually depended on fossil fuel-driven machinery, such as tractors and harvesters. The promotion of agricultural mechanization itself is a process that increases carbon emissions. Liu and Zhou (2018) found that in regions where the energy structure is dominated by fossil fuels, the energy consumption of agricultural machinery leads to increased carbon emissions, and the carbon footprint of mechanization is highly dependent on the degree of energy cleanliness and the improvement of mechanical efficiency. Agricultural mechanization and large-scale operations promote increased agricultural carbon emissions, and there is a mutual interaction between them. The carbon emissions generated by agricultural mechanization itself would, in turn, promote mechanization technology progress (Guan et al., 2023). Technological innovation can optimize the utilisation of agricultural machinery, increase crop yields, reduce carbon emissions, and promote the integration of food security and low-carbon economic development (Li and Zhang, 2021). Ma et al. (2022) found that promoting advanced seeding techniques and machinery suitable for mountainous areas can help reduce reliance on chemical fertilizers and subsequently reduce carbon emissions. Zhang et al. (2024b) discovered that in Belt and Road Initiative countries, agricultural mechanization can significantly reduce carbon emissions, and technological progress can enhance this effect. Yang et al. (2022) conducted research on green total factor productivity (GTFP) and found that an increase in the level of agricultural mechanization is positively correlated with GTFP growth, thereby indirectly reducing carbon emissions. However, the negative impact of agricultural mechanization on agricultural carbon reduction currently outweighs the positive impact of environmental regulation on agricultural carbon reduction (Jia and Xu, 2024). Gao et al. (2024) and Guo et al. (2020) identified the dual impact of agricultural mechanization on carbon reduction through electrification and intelligence. Empirical research shows that the carbon emissions of electric farm machinery over its entire life cycle are 62% lower than those of diesel machines.78%, with the electrification of the power system contributing 58% and the precision of the work achieved through the reduction of the use of fertilizers and pesticides contributing 24%.

In summary, existing research reveals that there is a complex interplay between agricultural mechanization and carbon emissions. Most studies indicate that the high carbon emissions associated with mechanization primarily stem from inefficiencies in the energy conversion of agricultural machinery. However, the transition to clean energy sources and technological efficiency improvements driven by new quality productivity can transform the energy sources of agricultural machinery. The continuous development of electrified and intelligent agricultural machinery is breaking through the limitations of traditional energy sources and reshaping the carbon emission trajectory of agricultural mechanization. Therefore, the third hypothesis is proposed.

In conclusion, this study constructs a theoretical framework that presents the hypothesis and pathway analysis of how New Quality Productivity (NQP) reduces agricultural carbon emissions, as illustrated in Figure 1.

The research scope of this study encompasses 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan) from 2010 to 2022. Data were primarily sourced from authoritative publications including the China Rural Statistical Yearbook, China Statistical Yearbook on Culture and Related Industries, China Agricultural Yearbook, provincial statistical yearbooks, the National Bureau of Statistics (NBS) official website, and the EPS database.

To ensure data comprehensiveness and accuracy, systematic processing was implemented for missing values and outliers in specific years. For unavailable data points in provincial statistical yearbooks, interpolation methods based on adjacent years were employed for data imputation. Additional methodological details regarding data processing procedures are explicitly outlined in subsequent sections of this paper.

The explanatory variable in this study is New Quality Productivity (NQP). Drawing on the measurement framework proposed by Lu et al. (2024), we employ the entropy method to construct a comprehensive evaluation system encompassing three dimensions: technological productivity, green productivity, and digital productivity. Within the secondary indicators, resource-saving and environmentally friendly productivity exhibits a close association with agricultural carbon emissions, while also being influenced by technological advancement and digitalization levels. The enhancement of NQP optimizes agricultural production patterns, improves resource utilization efficiency, and reduces carbon emissions, thereby fostering sustainable agricultural development. This measurement framework aligns with the intrinsic attributes of NQP, enabling a systematic exploration of its impact mechanisms on agricultural carbon emissions. Specific secondary indicators and their definitions are detailed in Table 1.

The dependent variable in this study is agricultural carbon emissions. The measurement of carbon emissions primarily follows the methodology established by Fang et al. (2024). On this basis, draw on the life cycle carbon emission assessment method of Nian et al. (2025), and the agricultural carbon emission was divided into different agricultural production processes for calculation. The specific calculation formula and reference indicators are as follows (Equation 7): (7) TotalCarbonEmissions = ∑ i = 1 n ( w i × e i ) wherein: wi is the weight of the i-th activity, ei is the carbon emission of the i-th activity, and n is the number of different agricultural activities, as shown in Table 2.

To enhance the scientific rigor and accuracy of the research findings, this study incorporates multidimensional control variables that align with the comprehensive nature of New Quality Productivity (NQP). The selected controls are categorized into three hierarchical dimensions to mitigate estimation bias:

1. Rural-level Controls

The inclusion of these multidimensional controls enhances the model’s capacity to isolate NQP’s independent effect on agricultural carbon emissions by mitigating confounding factors. Furthermore, the hierarchical integration of controls across governance levels provides actionable insights for multi-tiered policymaking.

Existing studies on the mediating mechanisms of agricultural carbon emissions have mostly focused on the reconstruction of traditional factors, such as agricultural industrial structure, generalized technological progress (Yu et al., 2025), and rural labor productivity (Chen et al., 2025). These variables suffer from vague conceptual boundaries and high measurement heterogeneity. Meanwhile, the “black-box” treatment makes it difficult to decompose the mediating mechanisms. This study focuses on the concrete carriers of new productive forces, taking agricultural mechanization (measured by per capita total power of agricultural machinery) and protected agriculture (measured by the level of protected agriculture) as mechanism variables.

From the perspective of actual production, the level of protected agriculture is directly related to resource use efficiency. For instance, precision agricultural facilities can reduce excessive fertilizer application, thereby lowering carbon emissions. The level of agricultural mechanization directly reflects the low-carbon upgrading of production tools; for example, the application of electric agricultural machinery can reduce carbon emissions caused by diesel consumption during production. As mediating variables, both can transform the green attributes of new productive forces into observable and quantifiable production practices, making the mediating transmission chain more specific and verifiable.

This design not only avoids the “generalization” defect of traditional mediating variables but also converts the abstract concept of new productive forces into observable and intervenable real-world carriers, enhancing the academic increment and practical value of the research. At the data processing level, to address the issue of missing observations for some mediating variables in 2021–2022, the study adopts linear interpolation and moving average methods for scientific extrapolation. This ensures the temporal continuity of the data while minimizing the interference of estimation bias on causal inference to the greatest extent possible.

Table 3 and Figure 2 depict the dynamic evolution trends of new-quality productivity and agricultural carbon emissions during 2010–2026. New-quality productivity increased continuously from an initial value of 0.16308 to a projected final value of 0.23341, with an average annual growth rate of approximately 3.5%, showing an overall upward trajectory despite periodic fluctuations: it grew steadily from 0.16308 to 0.21362 during 2010–2016, reflecting the effects of technological accumulation or initial investment; experienced a slight correction to 0.20662 in 2017–2018, possibly associated with structural adjustments or short-term policy realignments; and accelerated growth in the projection period (2022–2026), surging from 0.20504 to 0.23341, which may signal technological breakthroughs and large-scale application.

Agricultural carbon emissions exhibited an inverted U-shaped curve of “first increasing, then decreasing”: rising from 3.1596 million tons in the early stage to a peak of 3.4489 million tons in 2016, likely linked to agricultural production expansion and technological path dependence; and declining continuously after 2017, with the projected value dropping to 2.3710 million tons by 2026, representing an average annual decline of approximately 3.2%. This decline is hypothesized to stem from green agricultural policies (such as emission reduction subsidies and carbon trading mechanisms), clean technology substitutions (e.g., bioenergy, precision fertilization), and the promotion of eco-agricultural models.

The relationship between the two variables shows that in the early stage (2010–2016), productivity improvements were accompanied by increased carbon emissions, reflecting the high-resource-consumption characteristics of initial technological upgrades. In the later stage (2017–2026), productivity growth decoupled from carbon emissions, demonstrating a “technology-environment” synergistic effect consistent with the Environmental Kuznets Curve hypothesis—i.e., after reaching a certain stage of economic development, green innovation and policy intervention offset environmental pressures. The accelerated decline in carbon emissions during the projection period may benefit from the implementation of carbon taxes, the popularization of digital agriculture, and the deepening of circular economy models.

Overall, the transition from a “contradictory” to a “synergistic” relationship between new quality productivity and agricultural carbon emissions highlights the core role of technological innovation and policy guidance in driving agricultural low-carbon transformation.

For panel data, due to differences in data characteristics, non-stationary data may exhibit unit root discrepancies, which can easily lead to issues like spurious regression. Therefore, we conducted panel unit root tests. The results showed that in the LLC test, most variables were stationary; however, some variables such as social consumption level displayed instability. Based on the current data characteristics and the LLC test results, we applied differencing to non-stationary variables to generate new variables: first-order differencing was performed on tax burden level, human capital level, and government intervention level, while second-order differencing was applied to social consumption level. After data processing, the panel data became stationary, indicating that it is suitable for further panel data benchmark regression analysis. The specific descriptive statistics of the processed data and the unit root test results of the processed data are provided in the Supplementary materials.

Before conducting baseline regression, we first perform the variance inflation factor (VIF) test to evaluate multicollinearity among variables. The results show that the average variance inflation factor is 2.61, while the variance inflation factor of the degree of openness to the outside world reaches 7.66. The addition of this variable has brought certain collinearity risks to the model. Theoretically, the degree of openness to the outside world may have a certain intrinsic correlation with other control variables. At the same time, there may be a certain degree of variable overlap when included. Therefore, the variable of the degree of openness to the outside world was excluded in the benchmark regression. In the variance inflation factor test after exclusion, the average vif of the model was 1.74. This indicates that the corrected model does not have significant multicollinearity problems, has good stability and explanatory power, and can provide stability for subsequent regressions.

The regression results (Table 4) reveal that in the baseline Model I, New Quality Productivity (NQP) significantly reduces agricultural carbon emissions at the 5% significance level, with a one unit increase in NQP corresponding to a 0.164 unit decrease in the logarithm of agricultural carbon emissions. Upon incorporating rural level control variables—the growth rate of rural per capita income and the rural Engel coefficient—the coefficient of NQP in Model II strengthened to −0.296, indicating an enhanced carbon reduction effect. This suggests that controlling for rural income growth and consumption structure further amplifies NQP’s emission-reducing impact. Rural per capita income growth exert statistically significant negative effects on agricultural carbon emissions at the 1% level. The research by Chang et al. and Li et al. indicates that the increase in per capita income in rural areas can prompt them to adopt innovative low-carbon technologies and promote the transformation of household energy structure, thereby reducing agricultural carbon emissions (Li and Jiang, 2024; Chang et al., 2022).

Level of Urbanization significantly increases agricultural carbon emissions at the 1% level. In Model III, a one unit rise in urbanization elevates emissions by 2.303 units. From a labor substitution perspective, urbanization-driven rural to urban migration shifts labor-intensive farming toward mechanized production, escalating energy use and emissions. Tang and Chen (2022) substantiate this mechanism, noting that below a certain threshold, rural population transfer heightens reliance on labor- and land-saving technologies, directly driving energy consumption and emissions. Tax Burden also exhibit positive and statistically significant coefficients (1.365 units in Model III), implying that higher tax intensifies agricultural carbon emissions.

Model IV indicates that the coefficient of NQP to the logarithm of agricultural carbon emissions is −0.202, which is significant at the 1% level, demonstrating the robustness of NQP in suppressing agricultural carbon emissions. Meanwhile, although the coefficient of government intervention level to agricultural carbon emissions is negative, it has no significant meaning.

Potential bidirectional causality between New Quality Productivity (NQP) and carbon emission intensity—where changes in emissions may inversely affect NQP—raises endogeneity concerns. To address this issue, we employ an Instrumental Variable (IV) approach, utilizing lagged terms of NQP as instruments. Both Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) regressions are conducted, with results presented in Table 5.

The endogeneity test results demonstrate that the first-order lag of New Quality Productivity (Lag1_NQP) effectively predicts contemporaneous productivity levels. In the first-stage regression, the coefficient estimates for the lagged term are 1.007 and 0.920, both statistically significant at the 1% level, indicating strong correlation between the instrument and the endogenous variable. The first-stage F-statistics (7321.26 and 2,249) substantially exceed the conventional threshold for instrument relevance (F > 10), confirming the validity of the lagged term as a robust instrumental variable.

The second-stage regression further validates the significant inhibitory effect of NQP on agricultural carbon emissions. Without controlling for covariates, a one-unit increase in NQP reduces carbon emissions by 38.2%. When control variables are included, the emission reduction effect attenuates but remains statistically significant at the 5% level, with a coefficient of −0.169. These results statistically confirm that NQP enhancement effectively drives agricultural decarbonization, and the conclusion holds robustly across alternative model specifications.

In the robustness tests, this paper employs three approaches to assess robustness. First, the regression analysis is performed by reducing the sample size and excluding observations from developed and underdeveloped regions, Such as Beijing, Shanghai, Xizang. Second, informatization level is added as an additional control variable to mitigate omitted variable bias. Theoretically, informatization level may concurrently influence both NQP and agricultural carbon emissions. Regions with higher informatization levels are more likely to adopt NQP technologies (e.g., smart agricultural machinery, precision agriculture), thereby reducing carbon emissions through optimizing resource utilization efficiency. Controlling for informatization level thus enables the isolation of the independent effect of NQP, enhancing the accuracy of causal inference. Third, due to the high-dimensional nature of the TWFE model, which may give rise to heteroscedasticity and autocorrelation that distort regression results, the GLS (Generalized Least Squares) model is substituted for the TWFE model in the robustness check. The specific robustness check results are presented in Table 6.

Table 6 presents the results of the robustness tests. Using three methods—excluding samples from economically extreme provinces, adding the omitted variable of informatization level, and replacing the estimation model with GLS, all results show that new quality productivity (NQP) has a significant negative impact on agricultural carbon emissions (LnCO₂). After excluding economically extreme provinces, the coefficient of NQP is −0.210, significant at the 1% level, indicating that carbon emission patterns in economically extreme provinces are heterogeneous, and excluding them allows for a clearer capture of the universal emission reduction effect of NQP. When informatization level is added, the NQP coefficient is −0.201, significant at the 1% level. Although the absolute value of the coefficient decreases slightly compared to the original model, the significance remains substantially unchanged, suggesting that NQP’s emission reduction effect is not driven by informatization level and retains independent explanatory power. After controlling for panel heteroscedasticity and common AR(1) autocorrelation using generalized least squares (GLS), the NQP coefficient is −0.165, significant at the 5% level. All three tests support the theoretical hypothesis that NQP achieves agricultural low-carbon transformation through technological innovation and factor restructuring, enhancing the credibility of causal inference. In summary, the robustness tests further validate the reliability of the research conclusions and provide empirical evidence for agricultural carbon neutrality pathways.

In the mechanism analysis, this study employs causal mediation analysis approach to test mediating effects, while utilizing a parallel mediation analysis to systematically examine the intermediary roles of facility agriculture and agricultural mechanization in the relationship between new quality productivity and agricultural carbon emissions. The initial phase focused on verifying the mediating effect of facility agriculture as a key variable, with detailed test outcomes presented in Table 7 and Figure 3.

Table 7 and Figure 3 show that the coefficient of NQP for facility agriculture is 0.826, and it is significant at the 5% level, indicating that the improvement of new quality productivity will significantly promote the development of facility agriculture. For every 1 unit increase in new quality productivity, facility agriculture increases by an average of 0.826 units. Meanwhile, the regression coefficient of facility agriculture on agricultural carbon emissions is −0.034, which is significant at the 1% level. This indicates that the development of facility agriculture can significantly curb agricultural carbon emissions. For every 1 unit increase in facility agriculture, the logarithmic average reduction of agricultural carbon emissions is 0.034 units, reflecting the positive role of facility agriculture in carbon reduction. It conforms to its resource-intensive utilization model (such as precise environmental control), but the direct effect coefficient of NQP on agricultural carbon emissions is −0.118, which fails the significance test, indicating that the emission reduction effect of NQP is more dependent on the intermediary transmission of specific technical paths such as facility agriculture. It reveals that apart from facility agriculture, there are also independent emission reduction approaches such as clean energy substitution or digital management systems. Further calculation of the mediating effect indicates that the indirect effect of NQP on agricultural carbon emissions is −0.028. In conclusion, the research results confirm the “technology-institutional synergy” theory, that is, NQP achieves emission reduction through the technological substitution for the scale expansion of facility agriculture and the complementary institutional optimization required to release greater synergy effects. This provides empirical evidence for the multi-path advancement of carbon neutrality in agriculture. Therefore, Hypothesis H2 is validated.

As shown in Table 8. The results of the mediating effect test show that NQP has multiple pathways of influence on agricultural carbon emissions. The direct impact coefficient of NQP on agricultural mechanization is 1.005, which is significant at the 1% level. That is, for every 1 unit increase in NQP, the level of agricultural mechanization increases by 1.005 units. The impact coefficient of agricultural mechanization on carbon emissions is 0.192 (significant at the 1% level), which means that for every 1 unit increase in mechanization, agricultural carbon emissions will increase by 0.192 units. This positive connection may stem from the carbon-intensive nature of agricultural machinery that relies on fossil fuels.

Meanwhile, as can be seen from Figure 4, the direct impact coefficient of NQP on agricultural carbon emissions is −0.395 (p < 0.01), indicating that the reduction effect is significant through non-mechanized approaches such as adopting clean technologies or improving management efficiency. Further calculations show that the indirect effect of NQP caused by agricultural mechanization is 0.193, and the pure direct effect is −0.588. This indicates that mechanization partially offsets the emission reduction potential of NQP as a mediating variable, verifying the “technology-carbon lock-in paradox,” that is, the improvement of technological efficiency is restricted by the rigid energy structure, thereby limiting the outcome of decarbonization. Suppose H3 is thus rejected.

The above analysis of the mediating effect reflects the inherent contradiction of NQP in reducing carbon emissions in agriculture. At the level of synergy, NQP itself has the underlying logic to directly or indirectly reduce emissions through technological innovation and management optimization. However, at the level of conflict, different production methods such as facility agriculture or mechanization have extremely different impacts on carbon emissions. This demonstrates the significance of providing targeted guidance for the development of intermediaries at the specific practical level.

Given the significant regional disparities in the development of New Quality Productivity (NQP) across China, shaped by distinct geographical characteristics and resource endowments that influence technological pathways and factor allocation efficiency, this study conducts subgroup regression analyses based on China’s four major geographical divisions (eastern, central, western, and northeastern regions). As shown in Table 9, the impact of NQP on agricultural carbon emissions (lnCO₂) exhibits marked regional heterogeneity:

Eastern Region: The NQP coefficient for agricultural carbon emissions is 0.595***, which is significant at the 1% level. This indicates that the development of NQP has instead increased agricultural carbon emissions, reflecting the characteristics of high input and high emissions in agricultural development. This phenomenon is consistent with the technological carbon lock-in effect of highly intensive agricultural mechanization and digitalization. In the development of NQP, the popularization of intensive agricultural production has increased productivity, but it has also strengthened the reliance on chemical fertilizers, pesticides and fossil fuels, leading to excessive consumption of resources.

Apart from regional heterogeneity, technological advancements driven by New Quality Productivity (NQP) exhibit varying carbon mitigation potentials across different agricultural production sectors. Conducting heterogeneity analyses by disaggregating agricultural processes enables the formulation of targeted and adaptive decarbonization strategies. This study categorizes agricultural production into six sectors—fertilizers, pesticides, irrigation, diesel, tillage, and plastic film—based on data availability and sector-specific emission profiles. Empirical results, detailed in Table 10, reveal significant differences in NQP’s inhibitory effects on carbon emissions from distinct inputs:

Fertilizers, Pesticides, and Irrigation: Among these three production processes, NQP has the strongest carbon reduction effect, with coefficients and significance of −55.41***, −28.50***, and −18.89***, respectively. It indicates that the increase in NQP can significantly reduce carbon emissions in these three production processes. This might be because the increase in NQP has led to more precise fertilization techniques, reducing carbon emissions caused by excessive use of chemical fertilizers. At the same time, efficient fertilizer technology has also lowered the carbon emission intensity per unit of chemical fertilizer. These results verify the intensive substitution hypothesis, that is, the potential for optimizing resource utilization efficiency contained in NQP significantly reduces redundant high-carbon inputs.

These findings highlight that the mitigation efficacy of NQP hinges critically on the technological adaptability specific to each sector. Standardized sectors, such as fertilizers and pesticides, can achieve rapid decarbonization. In contrast, systemic transitions like energy substitution in machinery call for longer-term policy coordination.

To elaborate the dynamic changes in the annual carbon reduction effects across production processes under the role of new quality productivity, this study categorizes production processes into groups, conducts yearly regression analyses, and visualizes the results as a stacked area chart, as illustrated in Figure 4.

As can be seen from the Figure 5, from 2010 to 2022, the impact coefficient of NQP on carbon emissions in each production processes of agriculture showed a significant dynamic change trend, reflecting the synergy effect between technological progress and the transformation of production methods. The coefficient of the traditional high-carbon fertilizer industry has gradually declined and transitioned to a negative value, indicating that green fertilization techniques and clean energy substitution have effectively reduced its carbon emission intensity. This reflects a breakthrough in technological innovation in resource utilization efficiency.

The continuous negative deepening of pesticide and irrigation coefficients may stem from the widespread adoption of biopesticides and water-saving technologies, which have significantly reduced the carbon footprint associated with chemical inputs and water consumption. The expanded negative value of the tillage coefficient reveals the emission reduction contribution of conservation agriculture practices such as no-tillage or minimum tillage systems, which is consistent with the production concepts of resource conservation and environmental friendliness.

However, even though the coefficient of NQP on diesel carbon emissions has been decreasing year by year, the coefficient of NQP on diesel carbon emissions remains positive, indicating that the development of new quality productivity has not yet played a role in this traditional production process of diesel.

Overall, the correlation coefficients of various departments tend to be negative, indicating the internal mechanism by which new high-quality productive forces drive the low-carbon transformation of agricultural production through technological innovation, policy incentives, and market orientation. This trajectory not only confirms the theoretical expectations of sustainability research regarding technology spillover effects and the environmental Kuznets curve (EKC), but also highlights the structural adjustment potential of agricultural systems in mitigating climate change. This empirical model emphasizes the crucial role of production factor reconstruction and system optimization in achieving the dual goals of emission control and agricultural modernization under the evolving technological paradigm.

Based on China’s provincial panel data, this study empirically finds that new quality productivity has a significant inhibitory effect on agricultural carbon emissions with its impacts exhibiting multi-dimensional and complex characteristics, where benchmark regression and endogeneity tests show that a 1-unit increase in new-quality productivity reduces agricultural carbon emissions by 16.4 to 38.2% and the reliability of this conclusion is validated through instrumental variable methods and multiple robustness tests; mechanism analysis reveals that new quality productivity achieves a 2.8% indirect emission reduction effect through the promotion of protected agriculture technologies while the improvement in mechanization generates a masking effect due to fossil energy dependence, partially offsetting the emission reduction effects and forming a technology-carbon lock-in paradox; regional heterogeneity tests show that the western region has the strongest emission reduction effect with a coefficient of −0.916, the eastern region exhibits an anomalous promoting effect (coefficient = 0.595) due to energy path dependence in intensive production, and the central and northeastern regions show no significant effects; heterogeneity analysis across production stages further reveals that new quality productivity has the most prominent emission reduction effects on high-carbon input stages such as chemical fertilizers and pesticides (coefficients of −55.41 and −28.50, respectively) while its effects on stages involving diesel and agricultural films are weaker (coefficients of 5.740 and −6.139, respectively) and its impact on the carbon emissions of the film is negligible(coefficients is −0.458); the study demonstrates that the carbon reduction effect of new quality productivity is constrained by technological pathways, regional resource endowments, and production stage characteristics with its low-carbon transformation potential requiring the synergistic release of technological innovation and systemic reconstruction, thus providing theoretical and empirical evidence for achieving agricultural carbon neutrality goals.

First, enhance domestic technological innovation and energy system restructuring. Accelerate the integration of facility agriculture with photovoltaic and biomass energy systems, address technical bottlenecks in battery endurance and lightweight design for electric agricultural machinery, and develop a digital management system for carbon footprint monitoring and input optimization. This will drive the low-carbon transformation of agricultural machinery power systems and green technological upgrading in facility agriculture.

Second, adopt region-specific low-carbon pathways. In western regions, promote “pasture-solar hybrid” and “forest-grain intercropping” models with carbon sink revenue compensation mechanisms. In eastern regions, establish dual performance evaluation systems for both production growth and carbon reduction, and pilot “smart farm-carbon trading” integration models. In central and northeastern regions, prioritize smart agricultural machinery adoption to replace chemical inputs, implement protective tillage for black soil conservation, and advance electrification of agricultural machinery.

Third, establish a global collaborative governance framework for carbon emissions. Strengthen international R&D cooperation on agricultural carbon neutrality with partners such as the EU and Brazil, sharing precision control technologies for facility agriculture and promoting the export of electric agricultural machinery technologies. Deploy photovoltaic greenhouse models along the Belt and Road, participate in international mutual recognition of agricultural carbon sink certification systems, and integrate domestic carbon sequestration metrics into global carbon market accounting frameworks.

Finally, deepen international policy coordination and global governance platform development. Domestically, establish an international cooperation fund for new-quality agricultural productivity to support low-carbon technology training and capacity-building in developing countries. Under frameworks like the G20, launch a Global Alliance for Agricultural Carbon Neutrality to facilitate technology transfer of electric agricultural machinery and smart agriculture from developed to developing nations, and institute a performance evaluation system for agricultural emission reduction outcomes.