Time Lag Effect Research Articles

Improvements in source apportionment of multiple time-resolved PM2.5 inorganic and organic speciation measurements using constrained Positive MatrixFactorization.

The equation of Positive MatrixFactorization (PMF) has been modified to resolve multiple time resolution inputs and applied in numerous field studies. The refined modeling results provide a solution with an increased number of factors and enriched profile features. However, the incorporation of low time-resolved data may retrieve unfavorable mixed factor profiles, introducing high uncertainties into the PMF solution computations. To address this issue, a dual-stage PMF modeling procedure with predefined constraints was proposed. Multiple time-resolved PM2.5 inorganic and organic speciation measurements were collected from autumn of 2022 to summer of 2023 in Taipei, Taiwan. Without using the proposed approach, a mixed factor of vehicle/biomass burning and an unphysically meaningful factor of sodium ion- and ammonium ion-rich were identified. After implementing the proposed approach, a refined number of eight factors with separated and reasonable profiles were retrieved. Over the sampling period, the largest contributor to PM2.5 and organic carbon was vehicle (contribution = 26% and 47%, respectively), while those for secondary inorganic aerosols of SO42-, NO3-, and NH4+ were industry (27%, 25%, and 31%, respectively), highlighting the importance of regulating these two sources. The low vehicle contribution to NO3- may be due to time-lag effects from gas-to-particle conversion, which led to different temporal patterns between NO3- and primary species. Addressing this issue is crucial in future studies for better apportionment of secondary aerosols.

Exploring the relationship between digital infrastructure and carbon emission efficiency: New insights from the resource curse and green technology innovation in China

Resource-rich regions can fall prey to the resource curse, leading to a decline in carbon emissions efficiency (CEE). But amid this challenge, building digital infrastructure offers an opportunity to find solutions that will pave the way for sustainable development. An indicator evaluation system was constructed to measure the level of digital infrastructure (DI) using panel data for 275 Chinese cities from 2010 to 2020. The impact of DI on CEE is validated using several econometric models. As an innovation, the time lag effect and spatial spillover effects are considered in the analysis. The study found that DI significantly improves CEE, and this effect is more effective in central and resource-based cities, but only significant in southern cities, cities with high levels of marketization, and cities with high levels of human capital. Reducing the resource curse and increasing green technological innovation are important transmission pathways, which are considered to be the key role of DI. Further, the third period of lagged DI has the most significant effect, and there is a positive spatial spillover effect of DI on the CEE of neighboring cities, which is most prominent around 600 km. Policymakers are advised to improve the level of construction of DI, focus on digital management, and utilize the role of DI at the optimal time and distance.

Precipitation controls the time-lag and cumulative effects of hydrothermal factors on the end of the growing season in a semi-arid region of China

Precipitation controls the time-lag and cumulative effects of hydrothermal factors on the end of the growing season in a semi-arid region of China

Inspect the Time Lag in Galactic Cosmic-Ray Solar Modulation

It is well known that there is a time lag between the solar activity and the galactic cosmic-ray flux. How to accurately describe this delay is the key problem in making precise predictions of cosmic-ray flux. In this work, a response function in convolution is first used to describe the relative contribution of the solar wind blowout at earlier times to the current flux (the origin of time lag), and its explicit profile is obtained by our 3D time-dependent numerical model. It is found that our response function is superior to other functions in accounting for the time lag effect, and its rigidity and physical process dependence are studied thoroughly. At last, this description is partly demonstrated by a simplified derivation based on the underling physical processes.

Path to sustainable development: The effect of China's innovative industrial clusters pilot policy on prefecture-level cities' green total factor productivity

To enhance industrial production efficiency and promote sustainable social development, the Chinese Ministry of Science and Technology announced the innovative industrial clusters pilot (IICP) policy and named three batches of pilot cities in 2013, 2014, and 2017. Whether this significant industrial policy has promoted green total factor productivity (GTFP) is a meaningful but under investigated question in the literature. This study thus explores the above question using a multiperiod spatial difference-in-differences approach and panel data from 273 prefecture-level cities covering 2007–2021. The super slacks based measure and Dagum Gini coefficient are first utilized to assess city-level GTFP and evaluate its spatiotemporal evolutionary features. The intraregional and interregional differences of GTFP reveal expanding trends, with contribution rates to the overall difference above 30%. Benchmark and robustness tests demonstrate promotional direct and spatial spillover effects of the IICP on GTFP. We also employ several additional tests for deeper exploration, determining that a) the direct effect of IICP gradually increases over time, with an evident a time-lag effect, and the positive spatial spillover effect has fluctuated since the policy was enforced; b) the heterogeneity effects of the IICP on GTFP was detected; c) green scientific and technological innovation is an important mechanism of how IICP implementation affects cities' GTFP; and d) the promotional effect of the IICP on GTFP significantly increases when industrial structure upgrading exceeds certain threshold values. The empirical findings of this study not only provide theoretical and practical recommendations for Chinese policymakers to enhance the IICP's policy effect, but also offer suggestions for other emerging markets to develop similar policies for sustainable social development.

Relationship between Tibetan Plateau Surface Heat Fluxes and Daily Heavy Precipitation in the Middle and Lower Yangtze River Basins (1980–2022)

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR.

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series

Representational learning of time series plays a crucial role in various fields. However, existing time-series models do not perform well in representation learning. These models usually focus only on the relationship between variables at the same timestamp or only consider the change of individual variables in time, while failing to effectively integrate the two, which limits their ability to capture complex time dependencies and multivariate interactions. We propose a Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series (Bi-FI) to address these issues. Specifically, we elaborated a frequency domain analysis branch to address the complex associations between variables that are difficult to visualize in the time domain. In addition, to eliminate the time lag effect, another branch employs the mechanism of variable tokenization for attention to learning intra-variable representations. Ultimately, we interactively fuse the features learned from the two branches to obtain a more comprehensive time series representation. Bi-FI performs well in three time series analysis tasks: long sequence prediction, classification, and anomaly detection. Our code and dataset will be available at https://github.com/wwy8/Bi_FI.

Dynamic Analysis and Temporal Governance of Safety Risks: Evidence from Underground Construction Accident Reports

Due to the complexity and dynamics of underground construction projects, safety risk management has experienced significant challenges restricting the sustainable development of underground space. The research on risk causal chains and risk coupling has yet to reveal the dynamic interactive characteristics of these risk factors and their temporal relationships over time. This study utilized a complex system view for safety risk analysis, using 37 accident investigation reports of underground construction projects. Combined with two novel and emerging analytical methods, temporal qualitative comparative analysis and crisp-set qualitative comparative analysis, this study discusses the temporal relationship of risk factors to the cause of accidents and explores the multi-actor coupling characteristics of management risk. The findings indicate that (1) compared with general construction projects, underground construction should pay more attention to management safety risks because they have an obvious time lag effect expressed in all accident causation paths, namely, preceding management risk, management risk, and machine/material risk cross-concurrently, and management risk initiation and (2) underground construction project management risks have three key main paths, namely, single-actor-dominated management deficiency (supervisors, owners, and subcontractors that cause management risks as a single-core actor) and dual-actor-dominated management deficiency (owner and subcontractor as dual core actors of management risk). Multi-actor-dominated management deficiency (owners, subcontractors, and supervisors are the multiple core actors of management risk). This study thus developed a temporal governance framework of underground construction safety risks based on the synergy of multi-actors and proposed risk governance strategies, such as synergistic multi-actor governance, to consider the temporal relationship of safety risk. This study further reveals the temporal and coupling characteristics of safety risks to enrich the risk casual chain theory and risk coupling theory and establish a systematic risk analysis framework for new guidance for safety and risk management for underground construction projects.

Spatiotemporal analysis of different vegetation indices and relation to meteorological parameters over a tropical urban location and its surroundings

The paper investigates the long-term spatiotemporal characteristics of various satellite-derived vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) over the Kolkata conurbation and its surrounding areas from 2003 to 2016. Additionally, it analyzes the correlation between these vegetation indices and atmospheric parameters like rainfall, soil moisture (SM), evapotranspiration (ET), and land surface temperature (LST). Monthly variations of these parameters are observed, and inter-annual variability is examined using linear regression techniques. The study also observes the time average spatial correlation between vegetation indices and weather parameters. Moreover, it investigates the time-lag effect (0, 1, 2, and 3 months) using Pearson correlation coefficient analysis between VI and other meteorological parameters. NDVI and EVI exhibit maximum correlation with rainfall, SM, ET, and LST within specific lag periods. NDVI and EVI show a slow response rate to rainfall, and their sensitivity depends on SM and ET. A positive correlation is observed between NDVI and ET, indicating that NDVI increases with vaporized water in the atmosphere. A negative correlation is noted between NDVI and LST in the region studied. The study’s insights are valuable for predicting future vegetation index characteristics based on meteorological parameters in tropical urban areas like Kolkata and its surroundings. This predictive capability can aid in mitigating adverse weather effects on vegetation.

Effects of supply chain innovation and application policy on firm performance: evidence from China

To accelerate the innovation and application of supply chains, China issued the Supply Chain Innovation and Application (SCIA) policy in 2017. We empirically explored the net and dynamic effects of SCIA policy on firm profitability by applying difference in differences and propensity score matching methods. Results show that SCIA policy significantly promoted firm profitability and it had a dynamic and time-lag effect. For firms in developed regions, SCIA played a more obvious role in promoting firm profitability. Notably, SCIA policy has been more supportive for non-state firms’ profitability than state ones. The results enrich the understanding about the net and dynamic effects of supply chain innovation policies on firm profitability. Accordingly, they not only guide firms on how to perform supply chain innovation and application activities under the new policy, but also provide insights to improve the existing SICA policy.

Comparison of combination therapy with tamsulosin and dutasteride or finasteride in patients with benign prostatic hyperplasia: a randomized clinical trial

BackgroundBased on our observations at the largest outpatient urology clinic in Iran, patients for whom finasteride is prescribed as a secondary drug to tamsulosin tend to experience earlier and more severe sexual side effects without any difference in the amelioration of symptoms. This study aimed to compare the time lag, efficacy, and side effects of combination therapy with varying doses of dutasteride or finasteride added to tamsulosin for benign prostatic hyperplasia (BPH) treatment.MethodsIn this study 165 were randomized into 5 groups (each N = 33); receiving tamsulosin 0.4mg plus either of A: finasteride 3mg, B: placebo, C: dutasteride 0.25mg, D: finasteride 5mg or E: dutasteride 0.5mg. During the 6-month period of the study, International Prostate Symptom Score (IPSS), post-void residual urine (PVR), International Index of Erectile Function (IIEF-5), prostate volume (PV), prostate specific antigen (PSA) and maximum urinary flow rate (Qmax) were evaluated at baseline and at the 1st, 3rd and 6th month. The differences between each time point and baseline were then compared between groups.ResultsAt 3-month follow-up, group E exhibited a higher decrease in PSA but a greater increase in Qmax compared to group A (p = 0.047 and 0.006, respectively). Group C showed higher Qmax increase compared to group A at 3 and 6 months (p = 0.003 and 0.014) and concurrently a more pronounced PV decrease at 1 and 3 months (p = 0.047 and 0.003, respectively). Group D had a significantly more decrease in their IIEF-5 compared to group A at one-month visit (p = 0.006).ConclusionsIn summary, at the sixth-month follow-up, dutasteride demonstrated superiority over finasteride solely in enhancing Qmax. Therefore, dutasteride may be marginally more beneficial as a secondary component of combination therapy in BPH.Trial registration IRCT, IRCT20120516009772N2. Registered 18 January 2021 Retrospectively registered, https://irct.behdasht.gov.ir/search/result?query=IRCT20120516009772N2.

Can ecological zoning act as an environmental management tool for protecting regional habitat quality: Causal evidence from the national key ecological function zone in China

Habitat degradation is one of the most serious environmental problems worldwide, which threatens human wellbeing and sustainable development. Exploring how to protect natural habitats has become a research hotspot in the environmental management field. Ecological zoning has been widely used in habitat protection, but its actual effectiveness has been rarely revealed, especially with the robust and reliable causal inference method. Therefore, taking the Yangtze River Economic Belt (YREB) in China as the study area, we first quantified habitat quality and changes at the district/county scale during 2005–2019. Then, we used the National Key Ecological Function Zone (NKEFZ) as a representative ecological zone scheme, and explored the effectiveness of the NKEFZ in protecting habitat quality with the Propensity Score Matching and Difference-in-Difference combined method, which is a representative causal inference method. We found that habitat quality showed an uneven distribution during the study period, and habitat quality of districts/counties within the NKEFZ was higher than that outside the NKEFZ with statistical significance. Most districts/counties experienced a habitat quality decrease, but the decrease within the NKEFZ is less than that outside the NKEFZ. We observed that the NKEFZ can protect habitat quality from the causal relationship perspective, but there was the spatio-temporal heterogeneity of the NKEFZ effectiveness. In terms of the spatial dimension, the NKEFZ was the most effective in the Lower Reach of the YREB. In terms of the temporal dimension, there was a 1-year time-lag effect of the NKEFZ effectiveness, and NKEFZ effectiveness experienced a sharp increase from 2016. The NKEFZ contributed to protecting habitat quality through multiple management strategies such as urban land quota, high pollution industry prohibition, and ecological compensation. Overall, this study verified that the ecological zone scheme can contribute to habitat quality protection, and emphasizes that the effectiveness of ecological zoning may vary across time and space. This work can support ecological zoning implementation with robust evidence, and provides a novel perspective for understanding the effectiveness of ecological zoning.

Research on Spatiotemporal Continuous Information Perception of Overburden Compression-Tensile Strain Transition Zone during Mining and Integrated Safety Guarantee System.

The mining of deep underground coal seams induces the movement, failure, and collapse of the overlying rock-soil body, and the development of this damaging effect on the surface causes ground fissures and ground subsidence on the surface. To ensure safety throughout the life cycle of the mine, fully distributed, real-time, and continuous sensing and early warning is essential. However, due to mining being a dynamic process with time and space, the overburden movement and collapse induced by mining activities often have a time lag effect. Therefore, how to find a new way to resolve the issue of the existing discontinuous monitoring technology of overburden deformation, obtain the spatiotemporal continuous information of the overlying strata above the coal seam in real time and accurately, and clarify the whole process of deformation in the compression-tensile strain transition zone of overburden has become a key breakthrough in the investigation of overburden deformation mechanism and mining subsidence. On this basis, firstly, the advantages and disadvantages of in situ observation technology of mine rock-soil body were compared and analyzed from the five levels of survey, remote sensing, testing, exploration, and monitoring, and a deformation and failure perception technology based on spatiotemporal continuity was proposed. Secondly, the evolution characteristics and deformation failure mechanism of the compression-tensile strain transition zone of overburden were summarized from three aspects: the typical mode of deformation and collapse of overlying rock-soil body, the key controlling factors of deformation and failure in the overburden compression-tensile strain transition zone, and the stability evaluation of overburden based on reliability theory. Finally, the spatiotemporal continuous perception technology of overburden deformation based on DFOS is introduced in detail, and an integrated coal seam mining overburden safety guarantee system is proposed. The results of the research can provide an important evaluation basis for the design of mining intensity, emergency decisions, and disposal of risks, and they can also give important guidance for the assessment of ground geological and ecological restoration and management caused by underground coal mining.

Time‐Lagged Effects of Ionospheric Response to Severe Geomagnetic Storms on GNSS Kinematic Precise Point Positioning

AbstractThis paper investigates time‐lag effects of ionospheric response to two severe geomagnetic storms (Kp = 8) on the degradation of kinematic precise point positioning (PPP) solutions, utilizing over 5500 Global Navigation Satellite Systems (GNSS) stations distributed worldwide. Focusing on these two severe geomagnetic storms that occurred during solar cycle 24, the study employs an open‐source positioning software package, namely RTKLIB, to derive the PPP solutions. The findings reveal significant variations in time lags across different magnetic latitudes. These variations are driven by ionospheric responses to a southward interplanetary magnetic field and subsequent decreases in the SMY‐H index during the 2015 St. Patrick's Day Storm and the 2017 September 7–8 Storm. Specifically, at high latitudes, PPP degradation primarily manifests during the main phase of the storm, resulting in delays spanning from several minutes to 1–2 hr after the sudden onset of the storm. In contrast, mid‐ and low latitudes exhibit a wider range of delays extending up to tens of hours. Notably, rapid positioning degradation is observed predominantly at the magnetic local time noon and midnight sectors. The study discusses these time lag effects concerning the intensity of various ionospheric disturbances triggered by the interactions among the solar wind, magnetosphere, and ionosphere during geomagnetic storms. The insights obtained from this research have the potential to be integrated into physics‐based and machine‐learning models to enhance forecasting capabilities of space weather impacts.

Vegetation Dynamics and Driving Mechanisms Considering Time-Lag and Accumulation Effects: A Case Study of Hubao–Egyu Urban Agglomeration

Vegetation Dynamics and Driving Mechanisms Considering Time-Lag and Accumulation Effects: A Case Study of Hubao–Egyu Urban Agglomeration

Spatiotemporal change characteristics of NDVI and response to climate factors in the Jixi Wetland, Eastern China.

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1month, and the response to average temperature and relative humidity was longer.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.

Study on the Impact of China's Digital Economy on Agricultural Carbon Emissions

<p _msthash="770" _msttexthash="18511649104"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Under the background of China's “double carbon” goal, digital economy has become an important way to reduce carbon emissions in China. This paper utilizes the provincial panel data of China from 2012 to 2022, introduces the perspective of agricultural science and technology innovation, empirically examines the impact mechanism of regional digital economy development on agricultural carbon emission through regression analysis model, and portrays the dynamic effect and spillover effect of digital economy development on agricultural carbon emission from both time and space dimensions. The empirical results show that: digital economic development will have a significant inhibitory effect on the intensity of agricultural carbon emissions, and the inhibitory effect will be indirectly affected through the path of agricultural scientific and technological innovation; the impact of digital economic development on the intensity of agricultural carbon emissions there is a time lag effect, the current stage of the digital economic development will still have a strong inhibitory effect on the intensity of agricultural carbon emissions in the future; Digital economic development has a spatial spillover effect, i.e., the development of the regional digital economy will have an inhibitory effect on the intensity of agricultural carbon emissions in neighboring provinces. Based on this, it is proposed to strengthen the construction of digital infrastructure, promote the coordinated development of the digital economy in the region, and formulate policies to reduce carbon emissions in agriculture.</span></span></p>