Agriculture has the nature of serving as both a carbon source and carbon sink. Exploring the net carbon effect of agriculture in Guizhou Province and its coupling effects with economic development， as well as analyzing the historical changes and future trends of factors affecting net carbon amount， is of great value for promoting Guizhou Province's agriculture sector to achieve the "dual carbon" goals. In this study， we calculated the net carbon effect of agriculture in Guizhou Province from 2005 to 2022， explored the coupling state changes between the net carbon sink of agriculture and agricultural output value in Guizhou Province using the Tapio decoupling model， decomposed the driving factors of the net carbon sink of agriculture in Guizhou Province based on the logarithmic mean Divisia index （LMDI） model， and further analyzed the dynamic relationship between influencing factors and agricultural net carbon sink using the vector autoregressive （VAR） model to predict the agricultural net carbon sink. The results show that： ① During the study period， Guizhou Province's agriculture exhibited net carbon sink characteristics， with corn and rice being the main carbon sinks， while livestock and poultry farming were the main carbon emission sources. ② The coupling effect between Guizhou Province's agricultural net carbon sink and agricultural output value exhibited two states： economic/ecological dominant coupling and ecological weakening coupling. ③ The level of agricultural economy promoted the agricultural net carbon sink， while the intensity of agricultural net carbon sink， the scale of agricultural labor force， and the agricultural industrial structure all inhibited the agricultural net carbon sink， with decreasing effects in order. ④ From a dynamic perspective， after being impacted by four influencing factors， the agricultural net carbon sink exhibited repeated fluctuations of positive and negative effects over a certain period. From 2023 to 2030， Guizhou Province's agricultural net carbon sink will basically remain around 400×104 t， showing a characteristic of "low-level stabilization." Based on this， it is proposed that the development of low-carbon agriculture in Guizhou Province should be facilitated by realizing the economic value transformation of agricultural carbon sinks， reasonably adjusting the agricultural economic development model， and optimizing labor resources.

Intelligent prediction of agricultural net carbon sink and the mechanistic analysis of its driving factors are of great significance for promoting carbon reduction and sequestration policies in China's agricultural and rural sectors under the "dual carbon" goals. Based on the calculated data of agricultural net carbon sinks and panel data of 31 provincial regions in China from 2000 to 2022， multiple machine learning algorithms were used to construct a prediction model for agricultural net carbon sinks. The SHAP values and PDP plots were employed to reveal the response characteristics of the driving factors of the prediction model to agricultural net carbon sinks. The results show that：①The GWO-RF model constructed in this study demonstrated high prediction accuracy and stability for the agricultural net carbon sink （MSE = 0.04%， MAPE = 7%， R2 = 0.984）. ② The importance ranking of the model's driving factors was as follows： effective irrigated area > cultivated land area > provincial characteristics > fertilizer application intensity > regional education level > urbanization level > agricultural mechanization level. ③The 2D PDP results of pairwise interactions among the three important driving factors， namely effective irrigated area， cultivated land area， fertilizer application intensity， on agricultural net carbon sinks showed that： First， the interaction between the effective irrigated area and cultivated land area was weak when the effective irrigated area was less than 1 600×103 hm2 and strong when it was greater than this value； second， the interaction between the effective irrigated area and fertilizer application intensity was weak， and the influence of the effective irrigated area was dominant； and third， the interaction between cultivated land area and fertilizer intensity was weak when the cultivated land area was less than 1 600×103 hm2 and strong when it was greater than this value. ④ The 3D PDP visualization of the above three driving factors on agricultural net carbon sinks showed that the effective irrigated area had the most significant impact on agricultural net carbon sinks， almost dominating the entire process. The underlying reason is that the intensity and method of irrigation can significantly affect the potential of crop carbon absorption and soil carbon sinks. The research results provide a new method and novel approach for the prediction of agricultural net carbon sinks， also providing decision-making references for the government and relevant departments in formulating agricultural carbon emission reduction and sequestration plans and policies.

Under the "dual-carbon" strategy， examining the spatial correlation patterns and regional disparities in interprovincial agricultural carbon productivity （ACP） is essential for climate change mitigation and sustainable agricultural development. Using panel data from 30 Chinese provinces from 2013 to 2023 and employing Dagum's Gini coefficient， a modified gravity model， and social network analysis， we systematically investigate ACP's regional variations and spatial network characteristics. Key findings reveal that： ① China's ACP exhibited distinct spatial network correlations， with provinces like Heilongjiang， Shandong， Hubei， and Henan functioning as network hubs. The overall network demonstrated strengthening connectivity and stability， forming a typical "core-periphery" structure. ② The spatial network of China's agricultural carbon productivity can be divided into two-way spillover， broker， and main beneficiary segments， but the synergistic effect between segments still needs to be further released. ③ Regional disparities show fluctuating growth trends， with particularly pronounced intra-regional imbalances， highlighting inter-regional gap reduction as the key to addressing spatial inequality.

Against the backdrop of global climate change and the “dual carbon” goals, the issue of agricultural greenhouse gas emissions has garnered increasing attention. As a major grain and oilseed crop in China, carbon emissions from soybean production have a significant impact on the green and low-carbon development of agriculture. Although research on agricultural carbon footprints has grown in recent years, existing studies have largely focused on single regions or specific stages of crop production, and analyses of the carbon footprint of production systems in China’s major soybean-producing regions remain relatively limited. This study employs the Life Cycle Assessment (LCA) methodology to calculate and analyze the carbon footprint of soybean production systems across China’s 10 major soybean-producing provinces, utilizing agricultural input data from 2014 to 2023. The study establishes a carbon footprint accounting system based on two key aspects: carbon emissions from agricultural inputs and soil N2O emissions. It further analyzes the temporal trends, regional variations, and contribution characteristics of each component within the carbon footprint. The results indicate that the average carbon footprint of soybean production in China is approximately 528 kg CO2eq/ha (ranging from 273 to 855) and 0.25 CO2eq/kg of soybean (ranging from 0.13 to 0.46). Specifically, the carbon footprint per unit of area and yield declined simultaneously, indicating a continuous improvement in the low-carbon efficiency of soybean production. Spatially, there are significant regional differences in the carbon footprint of soybean production. Henan, Anhui, and Inner Mongolia have relatively low carbon footprints, while Shaanxi and Shanxi have relatively high levels. In terms of composition, chemical fertilizer inputs and soil N2O emissions are the primary sources of the carbon footprint in soybean production, with chemical fertilizer inputs being the largest source, accounting for approximately 40–60%, and soil N2O emissions being the second major source. Overall, differences among regions in natural conditions, agricultural input structures, and production methods result in distinct regional characteristics in the carbon footprint composition. The findings of this study provide a scientific basis for the low-carbon transition of China’s soybean production system and serve as a reference for the formulation of policies related to green agricultural development.

Market-based instruments such as agricultural carbon credit programs are being encouraged as a way of mitigating climate change, and at the same time, encouraging farm-level sustainability. The review paper consolidates the literature presented throughout the world in agricultural carbon credit initiatives by analysing the expansion of research, theoretical and methodological frameworks, and determining factors that define the participation, result and limitations of the farmers. The systematic literature review was conducted in line with PRISMA, using the Scopus and WoS databases. A multi-stage screening method was used to select seventy-nine peer-reviewed articles published between 2005 and 2025. The Theory-Context-Characteristics-Methodology (TCCM) framework and the Antecedents-Decision-Outcomes (ADO) framework were used together with bibliometric analysis to assess the trends in research, drivers of participation and system-level outcomes. The existing literature is dominated by behavioural and economic theories as compared to institutional and systems-based views that are relatively undeveloped. The involvement of the farmers depends on a mixture of the economic incentives, the institutional trust, policy stability, transaction costs, and social and informational factors and not necessarily on the carbon prices themselves. Even though carbon credit initiatives can create supportive economic, social and environmental impacts, these impacts are extremely contextual and unevenly distributed, hence, skewed against smallholder farmers. Carbon credits can promote climate mitigation in agricultural contexts when entrenched in consistent policy systems and reliable institutional structures. • Since 2020, the research on agricultural carbon credits has increased rapidly. • The further involvement is conditional upon the trust and the policy stability and the cost of the transactions. • Existing studies on carbon credit are dominated by behavioural and economic theories. • Carbon credits have unequal advantages that occur on a local scale. • Inclusive and credible carbon markets require designs at the system level.

Non-agricultural feedstocks for next-generation biomanufacturing with yeasts.

Taking various prefecture-level cities in Anhui Province as the subject of this study, this research draws on data from the ‘Anhui Statistical Yearbook’ to analyze crop straw resources’ potential full utilization, and spatial distribution characteristics, in Anhui Province for the year 2023. The carbon neutralizing effect of straw full utilization was also evaluated using life cycle assessment. Results indicate that the total theoretical straw resources from major crops in Anhui Province in 2023 amounted to 5.213 × 107 tons (t), dominated by wheat, rice, and corn straw; which collectively accounted for 89.72% of the total. The carbon emission reductions from straw utilization through fertilization, animal feed, energy generation, substrate application, and raw material processing were approximately 4 × 106, 1.07 × 106, 5.8 × 105, 9.8 × 104, and 1.67 × 105 t of CO₂, respectively. Clarifying the total amount, types, potential utilization, and spatial distribution of straw resources at the city level is essential for promoting rational resource allocation and facilitating logicall regional planning for the utilization of those resources. These findings are of paramount importance towards efforts to achieve the goals of “Carbon peaking and carbon neutrality” (Dual-carbon) and fostering coordinated economic and social development in China. Under the framework of “dual carbon” national strategy and the overall layout of agricultural carbon emission reduction, the data analysis results of straw resource utilization in Anhui Province can serve as a reference for other regions to carry out relevant work.

As the greenhouse effect and the environmental problems thus arising become increasingly prominent, great concern has been aroused for agricultural carbon emissions reduction and sustainable development. Aiming at exploring how to reduce agricultural carbon emissions from agricultural green finance perspective, this study theoretically analyzed the impact and mechanism of agricultural green finance on agricultural carbon emission intensity, and empirically examined the specific impact and the corresponding impact mechanism of agricultural green finance on agricultural carbon emission intensity. Specifically, a two-way fixed effect model was employed to conduct the study based on the panel data of 30 provinces in China from 2011 to 2022. The results show that agricultural green finance can significantly reduce agricultural carbon emission intensity, which has been verified to be valid after a series of robustness tests; and the reduction displays a heterogenous pattern. As it is, the impact of agricultural green finance is more prominent in the eastern region than in the central and western regions, and greater in non-major-grain-producing areas than in major grain-producing areas. On this basis, some policy suggestions are put forward, mainly including developing agricultural green finance, promoting agricultural and green technological innovation, and implementing differentiated agricultural green finance policies so as to reduce agricultural carbon emission intensity.

Driven by global environmental regulations that strictly limit copper content in brake pads, traditional copper-based friction materials face significant challenges due to their negative ecological impacts. Consequently, the development of sustainable, copper-free alternatives has become an inevitable trend in the braking industry. This study proposes a novel approach to developing high-performance green friction materials by utilizing a synergistic combination of agricultural wastes, specifically corn cobs, wheat straw, rice husks, and sugarcane bagasse, and carbon fibers. Research indicates that the friction coefficient of the synergistic formulation remains stable within the range of 0.35 to 0.48. Compared with the control group, this formulation achieves an average reduction in the wear rate of 19.28% and an increase in the recovery rate of 5.15%, demonstrating superior tribological performance. The synergistic interfacial regulation between carbon fibers and agricultural waste facilitates the construction of a smooth and stable friction layer, which maintains consistent performance during extended operating conditions. Among all formulations investigated, the composite reinforced by the synergy of corncob and carbon fiber exhibits the most prominent comprehensive properties, with the wear rate decreasing by 28.73% and the recovery performance improving by 4.05% relative to the specimen containing copper fibers. This work not only provides a new pathway for the sustainable development of green friction materials but also offers a theoretical basis for the high-value utilization of agricultural waste resources.

Faced with the dual challenges of intensifying global climate change and tightening food security, achieving a balance between food security and agricultural carbon sequestration and emissions reduction has become a focal point of academic inquiry. This study quantifies agricultural carbon intensity and food security levels in Hunan Province from 2002 to 2023. By employing the Tapio decoupling model, the Logarithmic Mean Divisa Index (LMDI) method, and spatial analysis techniques, it systematically examines the decoupling relationship and driving mechanisms between agricultural carbon intensity and food security in Hunan Province. The results indicate that agricultural carbon intensity exhibits a spatial pattern of “high in the east, low in the west,” while food security levels decline from the eastern plains to the western mountainous regions. The decoupling trajectory is broadly characterized by a transition from predominantly weak decoupling toward strong decoupling; since 2016, prefecture-level cities exhibiting strong decoupling have accounted for 92.9% of all cases, displaying spatial characteristics of “overall improvement, an uneven process, and regional asynchrony.” Agricultural energy intensity, energy structure, and rural labor force size serve as positive drivers of decoupling between agricultural carbon intensity and food security, whereas agricultural economic development and per capita cultivated area exert a restraining effect. Developing differentiated emissions reduction strategies to target these key factors is essential for advancing the coordinated development of low-carbon agriculture and food security.

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination () increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1 to 95.6 . These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops.

The impact mechanism of digital economy on agricultural pollution reduction and carbon mitigation synergy was investigated to inform coordinated environmental governance and climate response. Based on panel data from 30 Chinese provinces from 2013 to 2022, the spatiotemporal evolution characteristics were analyzed using the coupling coordination model and spatial econometric methods, with fixed-effects models and instrumental variable approaches employed for empirical verification. Key findings include: ① Both digital economy development and pollution-carbon synergy demonstrated an "eastern-high, western-low" regional disparity, though interprovincial gaps gradually narrowed. ② Digital economy significantly enhanced synergistic effects, with robustness confirmed through exogenous policy shocks and endogeneity controls. ③ Agricultural industrial structure upgrading and machinery technology advancement served as effective pathways. ④ Heterogeneity analysis revealed stronger effects in major grain-producing regions and economically developed provinces. These findings provide empirical evidence for digital economy-enabled agricultural green transition.

Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China's major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects-including carbon emissions, carbon absorption, and net carbon sequestration-during 2011-2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu's farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province's agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.

Based on the panel data of 30 provinces and cities (excluding Tibet, Hong Kong, Macao and Taiwan) from 2012 to 2023, this paper empirically tests the spatial spillover effect and heterogeneity of agricultural carbon emissions by using entropy method, kernel density estimation, spatial autocorrelation analysis, two-way fixed effect model and spatial econometric model. The results showed that: from 2012 to 2023, the overall level of agricultural new quality productivity showed a trend of continuous improvement, the nuclear density map maintained a single peak shape, the peak value first decreased, then increased, then decreased, and finally increased steadily; The regional distribution of agricultural carbon emissions showed significant heterogeneity. The nuclear density map maintained a single peak shape, and the peak value experienced a process of first rising, then falling, and then rising until stable. The local Moran index of agricultural new quality productivity is always positive, showing a fluctuating trend of "rise decline rise again", and most provinces, regions and cities are distributed in the first, second and third quadrants; The local Moran index of agricultural carbon emissions is also positive, but it shows a downward trend year by year, and most provinces, regions and cities are distributed in the first, second and fourth quadrants. Agricultural new quality productivity will significantly inhibit agricultural carbon emissions in this region and adjacent areas. Agricultural new qualitative productivity can inhibit agricultural carbon emissions by improving land productivity. In terms of regional heterogeneity, the agricultural new quality productivity in the main grain producing areas and the production and marketing balance areas will inhibit the agricultural carbon emissions in the region, but the spillover effect in the main grain producing areas is significantly positive, while the spillover effect in the production and marketing balance areas is not significant; The agricultural new quality productivity in the main grain sales areas can inhibit the agricultural carbon emissions in the adjacent areas, but the direct effect is not significant.

ABSTRACT Climate change is urgent and has multifaceted impacts on agricultural production and the national food security system, especially in climate‐vulnerable agricultural economies. The present study assesses the role of climate shocks and agricultural emissions in agricultural productivity and food dependency in China and the European Union (EU). Using the time series data, it adopted the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods in empirical estimation. The results show that climate shocks increase (decrease) food dependency (agricultural productivity) in China, whereas climate change decreases agricultural output and food dependency in the EU. On the one hand, agricultural carbon emissions boost (decline) agricultural productivity (food dependency) in China and stimulate it in the EU. In the robust analysis, the canonical cointegrating regression (CCR) confirms the results of the regression. Overall, the study provides comprehensive and cohesive climate policies for sustainable food supplies and agricultural production. From a policy suggestion, China and the EU need to accelerate the adoption of smart agriculture technologies—such as precision farming, digital monitoring systems, climate forecasting tools, and data‐driven irrigation—to enhance input efficiency, reduce environmental externalities, and stabilize production under climate uncertainty.

Understanding the drivers and mechanisms underlying variations in autotrophic (Ra), heterotrophic (Rh), and total soil respiration (Rs) is essential for improving carbon management in croplands. In this four-year field study (2018–2021) conducted in a maize system, we compared practices without straw (NS) and with straw mulch (SM). Random Forest analysis identified bulk density, soil temperature, soil inorganic carbon, and urease activity as the primary determinants of Ra, Rh, and Rs. Straw mulch improved soil biodiversity, species richness, α-diversity, and evenness. Piecewise structural equation modeling further showed that soil microenvironmental changes regulate substrate availability by altering soil biological properties, which subsequently shape bacterial communities and microbial metabolism, ultimately controlling Rs. Compared with NS, on average over the four-year study period, SM substantially enhanced Ra and Rh by 96.9% and 82.2%, respectively, and increased maize grain yield and aboveground biomass by 9.7% and 6.2%. Notably, SM shifted the net ecosystem carbon budget from negative to positive, indicating a pronounced carbon sink effect. Overall, our findings highlight the pivotal role of straw amendments in regulating Rs and its components and elucidating the pathways through which these effects occur. These results provide mechanistic evidence supporting SM as a climate-smart agricultural practice that can simultaneously enhance crop productivity and contribute to agricultural carbon sequestration strategies and carbon neutrality targets. Effects and driving mechanisms of straw additions on soil respiration and net ecosystem carbon budget. The outer brown dashed line represents the indirect effect of soil properties and bacterial structure affecting soil respiration. Rh: soil heterotrophic respiration; Rs: soil respiration; NPP: net primary productivity; NEP: net ecosystem production; NECB: net ecosystem carbon budget. NS: no straw; SM: straw mulch. * Represents the level of significance (* P < 0.05; ** P < 0.01) and ns represents non statistically significant. • Straw mulch had a bidirectional promotion effect on Ra (+96.9%) and Rh (+82.2%). • Straw mulch had a significant C sink effect. • Straw mulch increased the species richness, α-diversity, evenness, and modularity degree. • Main factors affecting Rs was soil substrate availability, followed by soil microenvironment.

The stability of soil organic carbon (SOC) is fundamental to the integrity of agricultural carbon credits but remains challenging to verify and predict. A persistent methodological challenge lies in isolating the specific effect of soil pH amelioration from confounding factors like organic matter inputs. Here, by applying bivariate linear mixed-effects modelling to a global synthesis of 180 field trials, we quantitatively disentangled the effects of pH amelioration on SOC components across a stability continuum from bulk soils to aggregate fractions. The results showed that pH amelioration enhanced bulk SOC stocks by 18%-20%, with mineral-associated organic carbon and microbial necromass carbon significantly increasing by 11%-15% and 12%-19%, respectively. Simultaneously, pH amelioration restructured soil architecture toward enhanced aggregate stability, preferentially enriching carbon within microaggregates (by up to 44% in alkaline soils). Structural equation modelling confirmed that this process is hierarchically driven by pH-induced shifts in microbial biomass and aggregate stability. The pH amelioration-shared increment in particulate organic carbon and mineral-associated organic carbon tended to attenuate with prolonged experimental duration, while that in microbial necromass carbon remained invariant. Across organic substitution types, pH amelioration under manure substitution significantly increased all carbon components, while straw substitution exhibited a weaker pH amelioration-shared effect on particulate organic carbon increment compared to biochar and manure. Our findings suggest that pH amelioration is a fundamental process that engineers persistent carbon sinks by directing carbon flow into mineral-stabilized and physically protected pools. This work repositions precision pH management as an essential ecological engineering strategy and provides a mechanistic foundation for transitioning carbon credit protocols from stock-based accounting to stability-centric verification.

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development.

Introduction Food production systems are a significant source of carbon emissions, and optimizing agricultural infrastructure is crucial for advancing low-carbon food systems. This study investigates how different configurations of agricultural infrastructure contribute to lower carbon intensity in 30 Chinese provinces between 2013 and 2022. Methods We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the combined effects of farmland water conservancy, rural transportation, digital infrastructure, agricultural electrification, cultivation scale, and technological progress on agricultural carbon emissions. Results Our findings reveal that no single infrastructure type is necessary for emission reduction. Instead, two dominant pathways emerge. The scale expansion pathway involves the joint presence of farmland water conservancy, rural transportation, and large-scale grain cultivation. The technological progress pathway combines digital infrastructure, farmland water conservancy, and advances in grain production technology. The technological progress pathway demonstrates slightly stronger consistency (0.9813) than the scale expansion pathway (0.9674). Discussion Regional analysis shows that scale-driven pathways are more common in southeastern coastal provinces, whereas technology-driven pathways dominate in northwestern and southwestern regions. These findings provide actionable guidance for region-specific, policy-driven low-carbon transitions in Chinese agriculture.

ABSTRACT Frameworks and metrics that reliably describe agricultural emission sources and carbon sinks are critical to the formulation of cost‐effective mitigation investment. This study identifies and compares three distinct emission reporting frameworks across metrics of total emissions and emissions intensity. The frameworks and metrics are applied to agricultural industries in Western Australia. Findings for 2005 and 2023 and the trends and uncertainties over the intervening period are reported. This study novelly considers a framework that includes emissions and sequestration from land use, land use change and forestry (LULUCF) activities at an industry scale. Most of Western Australia's agricultural emissions occur in the beef, sheep and grains industries, which also dominate agricultural land use. The emissions intensity of the grains industry in particular is highly sensitive to the inclusion of LULUCF activities. While large uncertainties surround emissions and sequestration from LULUCF activities, there are indications that the effect of LULUCF on emissions has changed considerably over time. We conclude it is preferable not to rely solely on one framework or one reporting metric. We highlight that more research is needed to lessen uncertainties surrounding LULUCF activities by agricultural industries.