Time Lag Effect Research Articles

The impact of R&D investment on the new orders received by the shipbuilding enterprises under the background of innovation-driven development

ABSTRACT Using the panel data of listed shipbuilding enterprises from 2010 to 2021 all over the world, this paper empirically studies the impact of Research & Development investment on new orders received by shipbuilding enterprises using the panel fixed effect model and the threshold regression model. The results show that R&D investment has a positive impact on the new order of high-tech and high-value-added ships in shipbuilding enterprises. R&D investment has a time lag effect on the new orders and a non-linear relationship with the newly received orders. The 1-year lagged R&D investment has a single threshold effect on the newly received orders of liquid tankers. When the R&D investment intensity exceeds the threshold, the positive impact on the newly received orders is weakened. To be specific, when the excessive R&D investment intensity does not match the scientific research capacity of shipbuilding enterprises, enterprise resources will be mismatched. Therefore, shipbuilding enterprises should increase their investment in R&D if they want to build more high-tech and high-value-added ships. At the same time, enterprises should also pay attention not to blindly strengthen the R&D investment, but to keep it within a reasonable range, so that resources can be allocated appropriately.

Can the establishment of an innovative city improve the level of technological entrepreneurship?

Based on the data of 278 prefecture-level city panels in China from 2008 to 2020, this paper presents the policy of innovative pilot city as a quasi-natural experiment. It is found that (1) the implementation of innovative urban policy can significantly improve the level of science and technology entrepreneurship, but the pilot policy has a time lag effect and has a continuous promoting effect since the third year. (2) For large cities, areas with high levels of economic development, central and eastern regions and cities with high new infrastructure, innovative cities can improve the level of technological entrepreneurship; (3) Innovative cities improve the level of technological entrepreneurship by improving the incubator of technological enterprises and strengthening the flow of human capital; (4) The effect of the establishment of innovative cities on the level of technological entrepreneurship mainly shows that the siphon effect does not show radiation effect, and this siphon effect mainly improves the level of technological entrepreneurship by increasing the number of incubators of technological enterprises.

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

BackgroundMara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.ResultsThe results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons.ConclusionsThe findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

How does the environmental protection tax law affect firm ESG? Evidence from the Chinese stock markets

The serious environmental pollution problem has been proven to harm people's health and economic development in China. Chinese authorities implemented the Environmental Protection Tax Law (EPTL) in 2018—the first environmental tax law in this country—to solve the problem. Since firms are main objects of this policy, its effect on firms' sustainable development capacity is a worthwhile but not yet discussed question. Therefore, this study adopts the Environmental, Social, and Governance (ESG) to quantify firms' sustainable development capacity and employs the difference-in-differences method to investigate the above research question using a sample of A shares listed in Chinese stock markets from 2014 to 2021. Benchmark regression and robustness tests demonstrated that the EPTL could promote heavily polluting firms' ESG, thus, confirming the Porter hypothesis' existence in China from a micro perspective. We conducted a series of additional tests to provide insight into the effects of the EPTL under different circumstances, and achieved the following important findings: a) although a time-lag effect exists, the policy effect increases over time; b) the boosting effect of this policy is stronger on higher ESG firms than on lower ESG firms; c) innovation compensation effect and government financial subsidies can exponentially increase the EPTL's effect on firm ESG; and d) this policy has an inhibiting effect on politically connected firm's ESG. Based on these findings, we provide theoretical and practical suggestions for academia and policymakers. The Chinese authorities should insist on enforcing the EPTL in the long run, encourage firms on research and development investments, and provide them more financial subsidies, so as to enhance the policy's effect. Moreover, the government should implement some supporting policies to improve the sustainable development capacity of politically connected firms.

A novel multivariate time-lag discrete grey model based on action time and intensities for predicting the productions in food industry

The food industry is a pillar industry and plays an important role in the development of China's national economy. In view of the complex environments and the characteristics of fluctuating development in which the Chinese food industry are located, this paper delves into the mechanism of action of relevant factors on the system behavior and proposes a grey prediction model based on action time and intensities considering the widespread existence of time-lag effects that is applicable to the prediction of the food industry. Firstly, considering the action time and intensity of the factors, this paper introduces the action duration, the parameters of time lag and intensity coefficient to expand the multivariate discrete grey prediction model. Secondly, the bidirectional grey A-S (Area-Slope) comprehensive relational model is used to calculate the degree of correlation between relevant factors acting on time-effect sub-sequence and the system behavior sequence under different action durations and time lag, to identify the driving factors and estimate their action durations and time lag. Then, the driving factors are incorporated into the grey prediction model based on action time and intensities, and the structural parameters and intensity coefficients of the model are estimated by using the least square and particle swarm algorithm. Finally, by fitting and predicting two examples in the food industry, the results of the proposed novel model are compared with those of six other prediction models to verify effectiveness. The results demonstrates that the prediction accuracy of the proposed model is better than those of other grey models in time-lag data sequences.

Considering Climatic Factors, Time Lag, and Cumulative Effects of Climate Change and Human Activities on Vegetation NDVI in Yinshanbeilu, China.

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.

Interconnectedness between land resource misallocation and environmental pollution: Exploring the sustainable development potential in China

The link between land resource misallocation and environmental quality is a compelling motivation for addressing the challenges posed by rapid urbanization. As cities expand and populations grow, the demand for land resources intensifies, leading to a misallocation between available land resources and urban development requirements. Therefore, using a panel fixed-effects model, this study explores the impact of land resources misallocation (LRM) on environmental pollution (EP) using 267 prefecture-level city data from 2011 to 2020. Our findings show that LRM significantly increases EP with a time lag effect. LRM increases the number of urban industrial enterprises and thus increases EP. Implementing the broadband China strategy negatively moderates the positive relationship between LRM and EP. There is a threshold effect of LRM on EP, which is more evident in cities with high levels of EP. The promotion effect of LRM on EP will be more evident in southern cities, cities with smaller population sizes and cities with lower digitalization levels. This study clarifies the mechanism between LRM in EP and the active role of broadband China strategy, which has important practical significance for promoting government personnel in optimizing land resource allocation to achieve green development.

Grassland cover dynamics and their relationship with climatic factors in China from 1982 to 2021

China possesses abundant grassland resources, making it imperative to comprehend the influence of climate change on Chinese grassland ecosystems. Nonetheless, the impact pathways and lag effects of climate factors on various grassland types in this region at multiple temporal scales are still to be investigated in long-term sequences. This study investigated the dynamics of grassland FVC (fractional vegetation cover), temperature, precipitation, and drought from 1982 to 2021 using trend analysis, multiple linear regression, path analysis, and correlation analysis and explored the dominant, direct, indirect, and time-lag effects of climate factors on different grassland types at multiple time scales. Precipitation-grassland correlation pathways dominated the annual-scale grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated spring grassland FVC. The correlation path of drought to grassland FVC and the direct path of drought dominated summer grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated autumn and winter grassland FVC. The effects of temperature and precipitation on alpine and subalpine meadows, desert grasslands, and alpine and subalpine plains grasslands had a 1-month lag. The response to drought exhibited a 1-month lag in desert grasslands, a 2-month lag in alpine and subalpine meadows, plains grasslands, meadows, and alpine and subalpine plains grasslands, and a 3-month lag in sloped grasslands. This study seeks to provide a scientific reference to reveal the impact of climate change on grasslands and to protect grassland ecosystems.

Thermal and energy performance evaluation of a dry floor heating system based on hybrid heat storage aggregates for field application

The application of thermal energy storage (TES) systems in buildings has increased with growing interest in renewable energy technology. TES systems that use phase change materials (PCMs) are preferred over other energy storage methods. This study produced an activated carbon (AC)-based shape-stabilized PCM (ACS-PCM) using AC and n-eicosane, which was injected into a polyvinyl chloride (PVC) container to manufacture a heat storage container. The thermal properties of the ACS-PCM were analyzed to evaluate its performance when the manufactured heat storage container was applied to a dry floor heating system. Because of its high thermal conductivity, the ACS-PCM was superior to n-eicosane in heat transfer when heated. Moreover, the ACS-PCM exhibited a time-lag effect owing to heat storage during heating and free cooling. The PCM dry floor heating system consumed 77.3 % of the energy of the wet floor heating system. Finally, to increase the heat transfer efficiency, the ACS-PCM and pure n-eicosane were simultaneously injected into the PCM container to fill the gap between them; however, there was a temperature difference, highlighting the need for improved thermal conductivity of PCM container materials.

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

Drought monitoring ensures the Yangtze River Basin’s social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts.

Drought-Related Spatiotemporal Cumulative and Time-Lag Effects on Terrestrial Vegetation across China

Vegetation is one of the most important indicators of climate change, as it can show regional change in the environment. Vegetation health is affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, the cumulative and time-lag effects of drought on different terrestrial vegetation in China are still unclear. To address this issue, this study examined the cumulative and time-lag effects of drought on vegetation from 2001 to 2020 using the Standardized Precipitation Evapotranspiration Index (SPEI) in the Global SPEI database and the Normalized Difference Vegetation Index (NDVI) in MOD13A3. Based on Sen-Median trend analysis and the Mann–Kendall test, the change trend and significance of the NDVI from 2001 to 2020 were explored. The Pearson correlation coefficient was used to analyze the correlation between the SPEI and NDVI at each cumulative scale and time-lag scale and to further analyze the cumulative and time-lag effects of drought on vegetation. The results show the following: (1) The NDVI value increased at a rate of 0.019/10 years, and the increased area of the NDVI accounted for 80.53% of mainland China, with a spatial trend of low values in the west and high values in the east. (2) The average SPEI cumulative time scale most relevant to the NDVI was 7.3 months, and the cumulative effect demonstrated a high correlation at the scale of 9–12 months and revealed different distributions in different areas. The cumulative effect was widely distributed at the 9-month scale, followed by the 12-month scale. The correlation coefficients of cumulative effects between the SPEI and NDVI for cropland, woodland and grassland peaked at 9 months. (3) The average SPEI time-lag scale for the NDVI was 6.9 months, and the time-lag effect had the highest correlation coefficient at the 7-month scale. The strongest time-lag effect for cropland and grassland was seen at 7 months, while the strongest time-lag effect for woodland was seen at 6 months. Woodland had a lower time-lag effect than grassland at different scales. The research results are significant for their use in aiding the scientific response to drought disasters and making decisions for climate change precautions.

Global distinct variations of surface urban heat islands in inter- and intra-cities revealed by local climate zones and seamless daily land surface temperature data

The continuous dynamic of surface urban heat island (SUHI) effect is highly needed in urban climate studies. However, temporal variations of SUHI intensity (SUHII) have been understudied in previous studies owing to the lack of spatially seamless and temporally continuous land surface temperature (LST) data, particularly in urban domains. Also, the relationship between SUHII variations and heterogeneous landscapes, reflecting various thermal and biophysical properties, has not been comprehensively explored globally. Here, we investigated the annual dynamic of nighttime SUHII and the inter- (i.e., city level) and intra-city (i.e., local climate zones level) SUHII variations of 1,028 worldwide cities, using the annual temperature cycle (ATC) model and seamless daily LST data and local climate zones (LCZ) data. The results reveal that the annual mean SUHII (SUHIIM) in urban areas is 0.69 °C for nighttime with an increasing trend towards high latitudes, while the annual SUHII amplitude (SUHIIA) is 0.32 °C and is relatively stable along latitudes. Global SUHIIM and SUHIIA indicate a robust pattern that decreases with decreasing buildings’ height (i.e., from LCZ1 to LCZ3 and from LCZ4 to LCZ6) and increasing openness of building layout (i.e., from compact to open types). In contrast, the annual pattern of LST is mainly regulated by the background climate. The phase shift (i.e., day of year when the LST reaches its maximum value) of annual SUHII shows a distinct time lag effect along the LCZ gradient in cold climate zone. These findings are valuable for scientific planning of resilient cities and can mitigate the impact of heat islands on urban residents by optimizing the composition and configuration of urban landscapes, e.g., changing the height and morphology of buildings.

Hazard Model with Random Treatment Time-Lag Effect

Hazard Model with Random Treatment Time-Lag Effect

Revisiting the main driving factors influencing the dynamics of gross primary productivity in the Mongolian Plateau

By contributing to what is considered as the world's largest carbon flux, global climate change has been significantly altering terrestrial vegetation gross primary production (GPP) at a global level. Thus, a robust understanding of the main driving factors influencing GPP change is neccessary for increasing terrestrial carbon sequestration potential of a region. In this study, we conduct an analysis of the main driving factors that have influenced GPP change in the Mongolian Plateau in the 1982–2018 period. The results of our analysis indicate that the GPP of the plateau's total vegetated area has witnessed both growth and decline (of 51.16% and 48.48%, respectively) over the past 37 years, with the latter occurring mainly across northern Mongolia and northeastern Inner Mongolia. Water availability, temperature, and solar radiation are deemed the most crucial climatic factors behind GPP change in 51.21%, 15.51%, and 18.63%, respectively, of the plateau's total vegetated area, according to a nonlinear Granger causality test. These three factors also explain up to 31%, 25.20%, and 17.21% of GPP change in 82.78%, 64.23%, and 69.96%, respectively, of the plateau's total vegetated area, as 34.52%, 31.57%, and 48.62% of the total vegetated area affected by the abovementioned driving factors concomitantly exhibit time-lag effects. The average intensity and extent of the effects of extreme climate indices on GPP change are lesser than the effect of general climate change; up to 21.31% and 22.35% of the GPP change can be explained by the extreme precipitation and temperature in 47.41% and 34.33%, respectively, of the plateau's total vegetated area. Moreover, the standardized total effects of this study's structural equation model (SEM) show that precipitation has the greatest impact on the GPP change (0.42), followed by snow water equivalent (SWE) (–0.11), solar radiation (0.08), and soil moisture or near surface temperature (0.07). Overall, our study provides new insights into the effects of the aforementioned driving factors on GPP change in the Mongolian Plateau, which will be helpful in mitigating the adverse effects of future climate change in the region.

Examining the heterogeneity by employment status in dynamics between BMI and cognition: a longitudinal cohort study of the Korean aging population

PurposeThis study investigated the potential heterogeneity by employment status in the relationship between body mass index (BMI) trajectory and cognitive function among an aging population. MethodsWe analyzed 2010–2018 cohort data from the Korean Longitudinal Study of Aging involving 4889 individuals aged over 45. We used statistical techniques of quadratic terms, interaction terms, time-invariant unobserved fixed effects, and time-lag effects to estimate the dynamic and interactive relationships among study variables. ResultsThe effect of BMI on cognitive function was heterogeneous based on employment status. For the nonemployed group, the impact of BMI on cognitive function showed an inverted U-shape with a turning point (BMI 25); a higher BMI (up to 25) was associated with higher cognitive function, but further increases beyond this threshold led to decreased cognitive function. For the employed group, however, the impact of BMI on cognitive function was nonsignificant. The nonlinear effect of BMI on cognitive function for the nonemployed group was robust across various subgroups and specifications. ConclusionsThe findings highlight the risk of obesity (BMI ≥ 25) on cognitive decline, particularly among nonemployed individuals. This illuminates the critical role of labor activity in regulating the impact of BMI on cognitive function among an aging population.

Time-lag effects of flood stimulation on methane emissions in the Dongting Lake floodplain, China

Natural wetlands are the primary sources of CH4 emissions in the natural environment. However, the understanding of CH4 fluxes in floodplain wetlands remains limited. This study employed eddy covariance methods to observe CH4 fluxes over a three-year period in a subtropical wetland floodplain, specifically the Miscanthus sacchariflorus (M. sacchariflorus) ecosystem in Dongting Lake wetland. Our analysis focused on exploring the impact of flooding frequency on CH4 emissions, flood stimulation effect, time-lag effects, and the environmental factors influencing CH4 fluxes. The M. sacchariflorus ecosystem exhibited an annual CH4 emission rate of 14.54 g CH4C m−2 y−1. During the flood period, the average daily CH4 emissions reached 0.155 g CH4C m−2 d−1, contrasting with the pre-flood period's average of 0.014 g CH4C m−2 d−1. Moreover, the time-lag effect of flooding on CH4 emissions was found to be 10 days, representing the period between inundation and a substantial increase in CH4 emissions. Comparatively, in 2021, following three fluctuations in floodwaters, the average CH4 emission intensity during the flood period decreased by 46.2% and 48.9% when compared to the years 2019 and 2020 which both following one fluctuation, respectively. CH4 emissions during flooding are predominantly influenced by water depth (WD), wherein shallow WD corresponds to higher CH4 emissions. This correlation can be attributed to factors such as vegetation type, water-column pressure, and soil oxygen content. Therefore, increasing frequency of inundation and a higher WD hold promise as effective measures for mitigating CH4 emissions in floodplain wetlands.

The non-uniform time-lag and cumulative responses of terrestrial ecosystem water use efficiency to climate change in Lake Victoria Basin, East Africa

Revealing the response mechanisms of carbon–water cycle to maximum and minimum temperatures and daytime and nighttime warming is important for managing fragile ecosystems exposed to severe climate warming and extreme weathers. Taking water use efficiency (WUE) characterizing the coupled carbon–water cycle as the research object, and maximum and minimum temperatures as the indicators of temperature change, this study explored the response of ecosystem WUE to temperature change in Lake Victoria Basin during distinct rainy and dry seasons. The results revealed the time-lag and cumulative multiple effects of antecedent maximum and minimum temperatures on current ecosystem WUE, which marked differences between the rainy and dry seasons. The monthly average ecosystem WUE in the rainy seasons (0.99 g C kg−1 H2O) were higher than those in the dry seasons (0.83 g C kg−1 H2O) during 2001–2018. WUE were more sensitive to temperature change in the dry seasons, with significant negative correlation with minimum temperature and positive correlation with cumulative maximum temperature. In rainy seasons, WUE showed more significant time-lag and cumulative effects, with response to maximum temperature for lagging approximate 3 months, to minimum temperature for lagging 0–2 months, to cumulative maximum temperature for lagging 1 month, and to cumulative minimum temperature lagging 3–4 months, respectively. The results add new evidence for differential non-instantaneous responses of WUE to maximum and minimum temperatures under dry and wet conditions.

Impact of Climate Change on the Major Eco-Hydrological Parameters in the Dongting Lake Basin

Quantifying the impacts of climate change on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin is essential for assessing water scarcity and implementing sustainable development strategies. Premised on actual measurements and remote sensing data from 47 stations, the impact of climate change on eco-hydrological parameters in the Dongting Lake Basin was analyzed in the present study using the BESS model (The Breathing Earth System Simulator), ridge regression analysis, stepwise regression model and time-lag analysis. The results reveal that: (1) the Dongting Lake Basin has been warm and arid over the last four decades, with the frequent occurrence of extreme climate events. Vegetation carbon sequestration capacity exhibited a slightly upward trend with 0.0081 g C m−2 d−1/year from 2000–2017. ET changed with rates of −3.309 mm/year, with possible risk conflicts between water demand and supply in the future. (2) The increasing temperature was the main driver of ET enhancement in the Dongting Lake Basin. Meanwhile, both temperature and precipitation were found to be the dominant drivers of GPP enhancement. The effect of temperature on GPP was found to be greater in the areas covered by crops and tree vegetation, and natural vegetation was more strongly influenced by precipitation than radiation. (3) Extreme temperature events have had a significant impact on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin. Specifically, the cold index in extreme temperature events was found to significantly affect ET, while the heat index in extreme temperature events significantly affected GPP. Additionally, both ET and GPP were found to respond to extreme precipitation events in the region. The results of the study established that vegetation is highly sensitive to temperature, especially temperature extremes, and that precipitation also has a stressful effect. Increasing temperatures and precipitation within a range benefit vegetation productivity. (4) In the Dongting Lake Basin, we found that different climatic factors produced different time lag effects on GPP and ET by time lag analysis. This study highlights the lag effects of climate factors and extreme climate events on eco-hydrological parameters. We suggest incorporating the effects into simulation models of eco-hydrological parameters. This will lead to a better understanding of the variation of eco-hydrological parameters under climate change.

Unveiling the time-lag effects of digital transformation on financing constraints in Chinese listed enterprises: a study of automation and network technology

Unveiling the time-lag effects of digital transformation on financing constraints in Chinese listed enterprises: a study of automation and network technology

Shifts bidirectional dependency between vegetation greening and soil moisture over the past four decades in China

Soil moisture (SM) has changed significantly over the past 40 years in China, while NDVI has varied dramatically, leading to increasing regional conflict between vegetation growth and water resource use. Quantifying the bidirectional dependency between SM and NDVI is essential for understanding the balance between land vegetation and water resources. However, few studies have reported their mutual feedback and spatiotemporal bidirectional dependency. This paper aims to reveal the bidirectional dependency between SM and NDVI using Granger causality test to show spatiotemporal tendency coupling patterns through trend coupling analysis, wavelet transform, and lag correlation. The Results indicated that a coupling relationship existed between SM and NDVI over most of China. The unidirectional Granger effect between SM on NDVI was 58 %, the unidirectional Granger effect of NDVI on SM was 26 %, and the bidirectional Granger relationship between SM and NDVI was 16 %. The Granger relationship is different for different soil layers or land cover types. SM and NDVI increased together in 36 % of the land cover areas, but SM increased and NDVI decreased in 12 %, and the SM decreased and NDVI increased in 27 %. The trend coupling between SM and NDVI has spatial heterogeneity. There is no change rule of coupling relationship with drought variation, but SM and NDVI increased together with more overlapping ecological restoration projects. SM decreased with the increase of NDVI from 1982 to 2010 but has reversed since 2011. NDVI and SM co-increased significantly with the implementation of ecological restoration projects during 2011–2022. The coupling relationship has a time lag effect of 1–3 months, and the time lag of NDVI to SM of deep soil layers mainly occurred in Southern China. This study illustrated the coupling framework and feedback analysis between SM and vegetation greening, which is helpful for the scientific implementing ecological restoration projects and the management of ecosystem carbon and water cycles.