Agricultural Carbon Emissions Research Articles

Exploring the carbon rebound effect of agriculture and policy response: Lessons from zero growth of fertiliser action.

Clarifying the spatiotemporal characteristics of agricultural carbon emissions and influencing factors in China is crucial. A system for measuring agricultural carbon emissions was established, thus evaluating the level of carbon emissions in China and its provinces. Moreover, the dynamic evolution of agricultural carbon emissions in China and the regions on both sides of the Hu Line was analyzed, then investigated factors affecting agricultural carbon emissions by the LMDI model. The results indicate that the total amount and intensity of agricultural carbon emissions showed an upward and then a downward trend in China from 2001 to 2021. The peaks were 330.72 million tons and 1.98 tons\\ha, respectively. Agricultural carbon intensity in provinces was mostly Low-Low Cluster and the range of High-High Cluster has decreased. Inter-provincial disparities in agricultural carbon emissions were also gradually narrowing. These show that the effect of agricultural carbon emissions reduction was obvious in China. It is important to note that carbon emissions from energy consumption in agriculture and agricultural material inputs were substantial, accounting for about 95% of the total. Agricultural carbon emissions were restricted by the agricultural production efficiency, changes in industrial structure, rural population size, and agricultural industrial structure, but were promoted by the level of economy and urbanization. Therefore, we recommend enhancing inter-provincial synergistic collaboration to create agricultural carbon emissions reduction pathways with unique features. It is also essential to maximize agricultural production efficiency and grasp the direction of green and low-carbon. We also suggest that the Chinese government should accelerate the in-depth adjustment and transformation and upgrading of the industrial structure, thereby reducing agricultural carbon emissions at source.

The development of digital economy not only directly affects agricultural carbon emissions through digital technology input, but also indirectly affects inter-regional agricultural carbon emissions through spatial spillover effect. Based on the panel data of 30 provinces, autonomous regions and municipalities in China from 2012 to 2022 (excluding Tibet, Hong Kong, Macao and Taiwan), this paper uses the entropy method to measure the development level of digital economy, and constructs a spatial econometric model to test the spatial spillover effect and heterogeneity of digital economy on agricultural carbon emissions. The results show that the development of digital economy has a significant inhibitory effect on agricultural carbon emissions. Digital economy has a spatial spillover effect on agricultural carbon emissions. The higher the development level of digital economy in neighboring regions, the less agricultural carbon emissions in the region. In the northeast and central regions, the development level of the digital economy has a significant positive impact on agricultural carbon emissions. In the western region, the impact is significantly negative, while in the eastern region, it is not significant. In addition, there is a spatial spillover effect in the northeast region. This paper puts forward policy suggestions from four aspects: promoting the construction of digital infrastructure, narrowing the development gap among provinces, strengthening the digital literacy of agricultural operators, and promoting financial innovation, so as to further play the role of carbon emission reduction in the digital economy.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21487-4.

Introduction Controlling carbon dioxide emissions and pursuing green development are imperative for global sustainable development. Accurately predicting agricultural carbon emissions is crucial for accelerating emission reduction efforts and guiding green technology innovation. This study focuses on forecasting agricultural carbon emissions in Henan Province to provide data-driven support for green agricultural development. Methods This research utilizes six key influencing factors—chemical fertilizer, pesticide, and agricultural film usage, among others—to predict total carbon emissions. Two primary analytical approaches were employed: a neural network model (comparing Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models) and a nonlinear surface fitting method (specifically, Gaussian multi-modal fitting) for regression and prediction. Results The analysis yielded three main findings: 1) In carbon emission regression, the MLP model demonstrated superior performance with a smaller absolute residual error and significantly higher accuracy (R 2 = 0.998) compared to the RBF model (R 2 = 0.933), establishing it as more suitable for this forecasting task. 2) The Gaussian multi-modal fitting method effectively predicted the time-varying values of the influencing factors (all R 2 > 0.9), enabling reliable further prediction of carbon emissions. 3) Both methods indicate that agricultural carbon emissions in Henan Province follow a quadratic trend over time. The forecast for 2001-2030 reveals a pattern of rapid growth, followed by stable growth, and finally a phase of fluctuating decline. Discussion The high-precision prediction results offer a theoretical reference for advancing green agricultural development in Henan Province. Furthermore, they provide empirical, data-based support for promoting the "green production" concept and disseminating low-carbon policies, thereby enhancing the persuasiveness of ecological education. This contributes to establishing a positive ecological governance cycle of "consciousness - voluntary action - effect translation," ultimately aiding the synergistic enhancement of ecological and social benefits.

A precise understanding of the carbon dynamics of agricultural land use is essential for advancing China’s “dual carbon” goals and promoting sustainable rural development. Drawing on panel datasets for 31 Chinese provinces over the period 1997–2022, this study comprehensively analyzes the spatiotemporal evolution, regional disparities, and transition dynamics of agricultural carbon capture and emissions. Using a combination of the emission factor method, the Dagum Gini coefficient, kernel density estimation, and Markov chain models, the study finds that China’s total agricultural carbon capture has continued to increase, yet regional disparities are widening, with the central region leading and the northeastern region lagging. Meanwhile, agricultural carbon emissions exhibit a “strong west, weak east” spatial pattern and demonstrate a high degree of club convergence. Club convergence refers to the phenomenon where regions with similar initial levels converge to the same steady-state over the long run, while remaining persistently different from other regions. The net carbon effect exhibits a dual structure of carbon surplus zones and carbon deficit zones: 23 provinces act as carbon surplus zones, while 8 provinces are carbon deficit zones, primarily located in ecologically fragile or special-function regions. These findings highlight the spatial heterogeneity, path dependence, and policy sensitivity of carbon effects from agricultural land use. Accordingly, the study proposes differentiated policy recommendations, including region-specific carbon management strategies, the establishment of a unified agricultural carbon trading system, and the integration of technological and institutional innovations to achieve a balanced and low-carbon agricultural transformation.

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm2. (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale.

A study on the mechanism and spatial effect of rural digital economy on agricultural carbon emissions - evidence from China’s provinces

The development of digital rural areas has important theoretical and practical implications for promoting agricultural carbon emission reduction in China. Based on the panel data of 29 provinces， cities， and autonomous regions in China （with data for Hong Kong， Macau， Taiwan， Tibet， and Hainan temporarily unavailable） from 2011 to 2021， the spatial spillover effect of digital rural development on agricultural carbon emission reduction was tested using the Spatial Durbin Model. The study produced several interesting results： ① The process of digital rural development and the changes in agricultural carbon emissions are highly correlated in space. ② Digital rural development has significant spatial spillover characteristics in suppressing agricultural carbon emissions. The development of digital rural areas not only effectively suppresses carbon emissions in the region but also has a positive inhibitory effect on the carbon emissions of nearby regions. ③ Digital rural development can lower the level of agricultural carbon emissions through the pathway of promoting green technology innovation. ④ The heterogeneity test results show that the inhibitory effect of digital rural development on agricultural carbon emissions in the central region is more significant than the effect in the eastern and western regions. ⑤ The spatial spillover effect of digital rural development first increases and then decreases as the geographic distance increases and reaches its maximum value at a distance of 500 km.

In the context of China's goal of achieving carbon neutrality by 2060， an accurate assessment of the degree and spatial spillovers of provincial agricultural carbon neutrality has great significance for formulating effective mitigation strategies and promoting regional and national carbon emission management. This study precisely defined the systematic boundary of agricultural carbon emission and carbon sink and then constructed an agricultural carbon neutral evaluation model from the two dimensions of carbon source and carbon sink. The entropy weight TOPSIS method was adopted to evaluate the degree of agricultural carbon neutrality at the provincial level in China empirically and explore the spatial correlation characteristics. The results showed that the agricultural carbon neutrality is highest in the northeast， followed by the west， central， and east regions. The agricultural carbon neutrality exhibits clear and consistent spatial dependence， as well as local spatial agglomeration. Rural income， agricultural technology， financial investment， and industrial structure can significantly promote agricultural carbon neutrality. However， these factors have the potential to exert a negative spatial spillover effect. Consequently， it is imperative to employ a multitude of strategies in these regions to promote agricultural carbon neutrality.

There is a growing concern over environmental degradation and climate change in rapidly developing Asian nations. However, little research has been conducted on the impact of agricultural carbon emissions and renewable energy use on sustainable development outcomes in Asia. This research looks at the relationship between agricultural carbon footprints (ACF), renewable energy consumption (RE), and sustainable development (SD) in nine Asian nations from 2000 to 2022. The study employed the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL), Method of Moments Quantile Regression (MM-QR), and Dumitrescu-Hurlin (DH) panel causality techniques to examine the connection. The findings reveal that the relationships vary significantly across the SD distribution. The findings of DH causality indicate bidirectional causality between agricultural carbon footprints and SD, with economic growth primarily driving agricultural emission patterns rather than the reverse. MMQR results demonstrate that agricultural carbon footprints positively impact SD, with effects strongest at lower sustainability levels but diminishing at higher quantiles, suggesting a nonlinear relationship. Trade openness consistently demonstrates negative relationships with SD across all quantiles, while renewable energy shows positive but statistically insignificant effects. Significant country-level heterogeneity emerges, with China and India demonstrating strong Granger causality from agricultural carbon footprints to SD, while other sampled countries show weaker or insignificant relationships. These findings accentuate the need for contextually appropriate policies that recognize the stage-specific relationship between agricultural practices, renewable energy adoption, and sustainable development outcomes in diverse Asian economies.

Exploring the impact of rural human capital (RHC) on agricultural carbon emissions: Evidence from China

The criticality of environmental sustainability in agriculture: The decarbonization role of green finance in China.

IntroductionIn the context of global climate warming and agricultural carbon emission management, corn, as the widely cultivated and cereal crop in China, plays a crucial role in ensuring food security and supporting the development of the livestock industry. Its production process generates carbon emissions and can affect the nitrogen cycle in the environment. To contribute to a more comprehensive understanding of regional characteristics and policy landscape, this study aims to calculate the carbon and nitrogen footprints of corn production in China and explore relevant emission reduction strategies, thereby providing a more comprehensive nationwide systematic analysis and offering a more nuanced depiction of regional differentiation.MethodsThis study uses agricultural statistical data from 2014 to 2023 to analyze corn production trends. Major corn-producing regions are categorized based on government’s corn regional planning and agroecological humidity zones. This study employs life cycle assessment and area-weighted methods to estimate the carbon and nitrogen footprints across major corn-producing regions. It further examines the impact of key factors—including planting area expansion, fertilizer reduction, and pesticide reduction—on carbon and nitrogen footprints through scenario-based simulations.ResultsThe results indicate that: (1) Both the Northwestern Irrigated Corn Region and Arid to Semi-arid Zone have elevated carbon footprints, while nitrogen footprints peak in the Northwestern Region and Arid Zone (2) Scenario simulations show that planting area expansion serves as a baseline for the carbon and nitrogen footprints. The impact of fertilizer and pesticide reduction on the carbon footprint varies depending on regional characteristics, while fertilizer reduction has a notably greater effect on reducing the nitrogen footprint.DiscussionThis study provides quantitative evidence and policy recommendations for balancing food security with low-carbon transformation and nitrogen management in major corn-producing regions, thereby contributing to carbon neutrality and agricultural sustainability in China.

Geographical Indications for Agricultural Products (GIAP), as an intellectual property protection system, hold potential for driving agricultural green transformation. However, their impact mechanisms on agricultural carbon emissions (ACE) remain unclear. This study empirically investigates their impact using provincial panel data from China spanning 2004 to 2022 and employs a difference-in-differences (DID) model. The results indicate that the implementation of GIAP systems has a significant positive effect on reducing agricultural carbon emissions, each additional GIAP certification reduces ACE intensity by 0.3679 units on average. The conclusion that remains robust across a variety of specification tests. Mechanism analysis demonstrates that GIAP effectively lower agricultural carbon emissions primarily by facilitating farmland transfer, strengthening agricultural socialized services, increasing the proportion of grain crops, and improving technical efficiency. Furthermore, the empirical findings reveal significant spatial spillover effects associated with GIAP. Additional heterogeneity analysis shows that the carbon emission reduction effects of GIAP vary substantially across different regions, economic zones, functional areas, and “hot” and “cold” spatial clusters. Based on these findings, it is recommended that greater emphasis be placed on the development and refinement of the GIAP system, and that regionally differentiated strategies be adopted to further integrate GIAP policy with agricultural carbon reduction initiatives, thereby laying a solid institutional foundation for the green and low-carbon transformation of agriculture.

IntroductionFood security is essential for national development, and agricultural insurance is a crucial tool for managing agricultural risks. It plays a key role in enhancing grain production capacity, but its impact across various dimensions has not been thoroughly examined.MethodsThis study develops a multidimensional model of comprehensive grain production capacity based on production function theory. The model incorporates labor productivity, land use efficiency, agricultural technological innovation, and agricultural carbon emissions. We use panel data from 27 Chinese provinces spanning from 2009 to 2021 and apply a fixed-effects model to assess the effects of agricultural insurance on these dimensions. Robustness and endogeneity tests are conducted to ensure the validity of the results.ResultsOur findings demonstrate that agricultural insurance significantly improves overall grain production capacity. The positive effects are particularly notable in labor productivity, land use efficiency, technological innovation, and a reduction in agricultural carbon emissions. Moreover, the impacts are more pronounced in non-major grain-producing areas compared to major ones.DiscussionThese results suggest that agricultural insurance has a vital role in enhancing sustainable grain production. We recommend that policymakers and insurers focus on strengthening the role of agricultural insurance, especially in regions that are less involved in major grain production.

ABSTRACTIdentifying the characteristics and driving factors of the carbon balance within major food‐producing areas is important for achieving low‐carbon and green production in cultivated land. In the middle reaches of the Yangtze River (MRYR), a global rice production hub, it is challenging to ensure food security while mitigating agricultural carbon emissions. However, the designs, indicator systems, and approaches used in past studies have limitations. To overcome these limitations, we established a process framework to evaluate internal and external carbon cycle flows in cultivated land systems. The net carbon sink of cultivated land (NCSCL) in the MRYR was measured from the dual perspective of cultivated land as both a source and a sink from 2006 to 2022. The spatial and temporal changes and driving factors were explored via spatial autocorrelation analysis, kernel density estimation, and GeoDetector modeling. Moreover, a pathway for carbon sequestration and emission reduction was proposed. The results indicated that the NCSCL in the study area increased from 26.26 million tons in 2006 to 37.01 million tons in 2022, with an average annual increase of 1.93%. The carbon sink function was consistently highlighted. In addition, the NCSCL in each city exhibited a spatial distribution pattern of low‐value dispersion and high‐value agglomeration. There was a spatial correlation trend involving alternating changes in the NCSCL, namely, positive agglomeration–negative agglomeration–positive agglomeration. NCSCL diffusion throughout the province and within local regions occurred, and both the NCSCL levels and regional differences continuously increased. The results revealed multiple interaction effects of the driving factors on the spatial pattern of the NCSCL. The spatiotemporal pattern of the NCSCL was affected mainly by cultivated land utilization and agricultural economic factors, with the cultivated land area and agricultural mechanization level as the main influencing factors. Moreover, there was regional heterogeneity in the influence of the driving factors. These findings could be used to optimize the systematic measurement of the NCSCL to provide a decision‐making reference for carbon sequestration in cultivated land.

Improving agricultural eco-efficiency (AEE) plays a critical role in fulfilling agriculture sustainable development goals (SDGs). China’s agriculture-led Rural Industrial Integration (RII) seeks to synergize rural industrialization with agricultural sustainability, yet its impact on AEE remains underexplored. Using a 2008–2022 panel of 285 prefecture-level cities in China, this study uses a series of econometric methods to empirically verify the impact of RII on AEE. The coefficient of RII under the fixed effect model is 0.366, indicating that it has a significant positive impact on AEE, which remains valid after robustness tests such as the instrumental variable method and the use of the “Rural Industrial Integration Development Demonstration County” pilot as a quasi-natural experiment. Mechanism tests show that rural labor transfer, agricultural technology innovation, and agricultural carbon emissions play an important role in mediating the impact of RII on AEE. RII has a negative spatial spillover effect on AEE, with a coefficient of −2.280. In addition, the impact of RII on AEE also varies under the heterogeneity of regions and development models. This study provides new evidence that China’s RII practices can promote sustainable agricultural development, deepens theoretical understanding of the impact of RII on AEE, and provides a reference for future policy implementation.

In the context of the "dual-carbon" goal and shaping of new advantages in green agricultural development, empirical references and policy recommendations are provided for agricultural agglomeration regions, mainly in grain-producing areas such as Henan Province, to explore the green, low-carbon transformation and high-quality sustainable development of agriculture. Based on the coupled coordination model, the GTWR model was used to explore the influence mechanism and spatiotemporal heterogeneity of the two synergies and their drivers, with a view to promoting the green, low-carbon and sustainable development of agriculture in Henan Province by taking pollution reduction and carbon reduction as a common goal. The results showed that： Between 2010 and 2022, agricultural carbon emissions and agricultural surface pollution emissions in Henan Province showed a slow increase, followed by a slow decline and a gradual stabilization trend, with obvious spatial and temporal heterogeneity. Between 2010 and 2022, the synergistic degree of agricultural carbon emissions and agricultural surface pollution in Henan Province had a trend of slow decrease and weakening of the polarization phenomenon, and the synergistic degree of differentiation in various regions decreased. The level of agricultural concentration and the degree of scale of cultivated land showed a negative regulatory effect, the urbanization rate showed a positive regulatory effect, and the positive and negative effects were distributed. The level of agricultural agglomeration and the degree of scale of arable land showed a negative regulatory effect； the urbanization rate showed a positive regulatory effect； the agricultural industry structure, crop planting structure, and effective irrigation rate of the regulatory effect of the spatial difference was obvious； and the positive and negative impacts were distributed. For the grain growing regions in Henan Province, a more positive driving effect was observed. In addition, the negative value of the regression coefficient of financial support for agriculture was concentrated in the central part of Henan Province, which had a significant negative driving effect on agricultural pollution reduction and carbon reduction in the central part of Henan Province. Agricultural carbon emissions and agricultural surface pollution in Henan Province had a high degree of synergy and strong spatial heterogeneity, and the regional differences in the impact of the driving factors on the synergistic effect were obvious. Thus, we should carry out the reuse of waste resources, strengthen agricultural technological innovation, and formulate differentiated pollution reduction and carbon reduction policies according to the local conditions, so as to promote the green and high-quality development of agriculture and to help achieve the "dual-carbon" goal.

Low-carbon agriculture is crucial for China's agricultural green transformation and the development of an ecological civilization. The net carbon sink of agriculture plays a vital role in this process. Here, we take China's 31 provinces （municipalities and autonomous regions） as the research object, select the data from 2000 to 2022, and discuss them from multiple perspectives around the three dimensions of time series, space, and coupling. Additionally, we constructed an environment-economy coupling index and refined it by phases to analyze the relationship between stages and regions. The study revealed the following： ① China's overall agricultural carbon emissions fluctuated and decreased, while the agricultural carbon sink continued to expand, showing steady growth. ② The net agricultural carbon sink was distributed among provinces, and the gap between provinces in terms of net carbon sink tended to widen. Agricultural net carbon sinks exhibited regional aggregation characteristics, forming two distinct growth areas. The traditional growth area comprised Shandong and Henan as the core and Hebei, Anhui, and Jiangsu as the neighboring radiation areas. The other emerging growth areas in Northeast China included Heilongjiang, Jilin, and Liaoning. ③ The net agricultural carbon sink demonstrated a clear positive spatial correlation. However, a tendency was observed for the spatial correlation to weaken and an increase in the spatial type of low-low form of aggregation over the years. ④ From 2000 to 2022, the coupling relationship between net agricultural carbon sinks and agricultural economic growth improved, with most provinces shifting from weak or strong decoupling to expanding negative decoupling. Six provinces, namely, Zhejiang, Fujian, Yunnan, Gansu, Xinjiang, and Inner Mongolia, have shown the most significant shifts. Overall, the net agricultural carbon sinks and agricultural economic growth are expected to be in a state of negative expansion or weak decoupling for a prolonged period in the future. While the contribution of agricultural carbon sinks to the resource reserve will be substantial, the sustainable growth of the agricultural economy will face challenges.

Objectives: This paper aims to assess the agricultural ecological and economic efficiency of the Yangtze River Economic Belt by using the data envelopment analysis (DEA) model to evaluate the regional agricultural level. Methods: Relevant data from 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2020 was collected from statistical yearbooks. Then, the agricultural eco-efficiency and economic efficiency were evaluated using the slack-based measure (SBM) model in the DEA model. Findings: The evaluation result of agricultural eco-efficiency was consistently higher than that of ecological efficiency. From a regional perspective, the eco-efficiency of the downstream area was higher than that of the middle and upper reaches. From the perspective of group division, only Guizhou and Chongqing had a high eco-efficiency. Improvement: The findings suggest that the overall agricultural eco-efficiency in the Yangtze River Economic Belt is low, and there is still a large space for development. It is necessary to further reduce agricultural carbon emissions and non-point source pollution and improve agriculture through technological innovation and other means.