Index Data Research Articles

Do transportation infrastructure contracts enhance spending efficiency? Examining state highway construction programs

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.

Effectiveness of modified temporomandibular joint disc reduction and suture with tragus incision assisted by arthroscopy

To investigate the effectiveness of modified temporomandibular joint disc reduction and suture with tragus incision assisted by arthroscopy for temporomandibular joint anterior disc displacement (ADD). A clinical data of 30 patients (45 sides) with temporomandibular joint ADD, who met selective criteria and were admitted between September 2022 and February 2024, was retrospectively analyzed. Among them, 15 patients (23 sides) were treated with temporomandibular joint disc reduction and suture via small incision (open operation group), and 15 patients (22 sides) with modified temporomandibular joint disc reduction and suture with tragus incision assisted by arthroscopy (arthroscopy group). There was no significant difference in gender, age, disease duration, affected side, Wilkes-Bronstein stage, preoperative visual analogue scale (VAS) score, maximal interincisal opening (MIO), and temporomandibular joint dysfunction index (DI), craniomandibular index (CMI), palpation index (PI), and other baseline data between groups ( P>0.05). VAS score, MIO, and temporomandibular joint function indicators (PI, DI, CMI) of patients were recorded at 3 months after operation, and the difference (change value) of the above indicators between pre- and post-operation was calculated. At 1 week after operation, MRI was performed to evaluate the reduction of the articular disc compared to the preoperative image. The results were classified as excellent, good, and poor, with excellent and good being considered effective reduction. The condition of condyle process repair was observed by cone beam CT (CBCT) at 3 months after operation. All incisions healed by first intention in the two groups. All patients were followed up 3-18 months (mean, 8.2 months). Facial nerve injury occurred in 3 cases in the open operation group and 1 case in the arthroscopy group, all of which returned to normal after physiotherapy and drug treatment. At 3 months after operation, MIO and VAS scores of both groups significantly improved when compared with those before operation ( P<0.05), and temporomandibular joint function indicators (PI, DI, CMI) significantly decreased ( P<0.05). The change values of MIO and temporomandibular joint function indicators in arthroscopy group were significantly higher than those in open operation group ( P<0.05). There was no significant difference in the change value of VAS score between groups ( P>0.05). There was no recurrence during follow-up. Postoperative MRI review showed that the effective reduction rate of joint disc was 95.65% (22/23) in the open operation group and 95.45% (21/22) in the arthroscopy group, with no significant difference between groups ( P>0.05). Postoperative CBCT found that early and timely effective reduction of joint disc was conducive to condyle process repair and reconstruction. Modified temporomandibular joint disc reduction and suture with tragus incision assisted by arthroscopy has a clear effect in the treatment of ADD, with less trauma, fewer postoperative complications, and good early effectiveness.

Determining the optimal time window to detect emerald ash borer damage for effective management.

The emerald ash borer (EAB) is an invasive pest of global concern. Accurate detection of EAB is crucial for effective management. Traditional field surveys fail to meet large-scale monitoring requirements. Remote sensing methods offer a potential solution, but the phenological decline of ash trees may obscure the remote sensing features for detecting EAB. Therefore, determining the timing of leaf abscission caused by EAB before phenology is crucial for effective detection. We collected time-series data of Leaf Area Index (LAI), leaf sizes, and hyperspectral images of damaged ash trees throughout the growing season to determine the optimal detecting time window for EAB detection using field surveys or remote sensing techniques. Significant differences in LAI and leaf size were observed throughout the growing season among ash trees with different EAB infestation degrees, providing a basis for small-scale field surveys. However, in May and June, the hyperspectral reflectance showed no variation. The difference began to appear in July and became apparent from August to October. By October, severely EAB-infested ash trees had almost completely defoliated. Machine learning classification results showed that accuracies after July were higher than before July. After July, the highest classification accuracy reached 100%, while the highest accuracy before July was only 88.57%. Selecting the optimal monitoring time significantly enhanced detection accuracy. The optimal period for field surveys is from May to November, whereas for remote sensing it is from August to October. Identifying the optimal months enables us to achieve more efficient decision-making and management. © 2024 Society of Chemical Industry.

Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400nm) and plant defense hormone-mediated responses (750nm) rather than changes in leaf pigments or internal structure (800-900nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.

Drawing a Long Shadow: Analyzing Spatial Segregation of Afghan Immigrants in Tehran

Although recent dramatic political changes in Afghanistan have brought that country to global attention, migration from Afghanistan to Iran has a long history. Nearly three quarters of Afghan immigrants in Iran are located in cities, particularly in Tehran’s metropolitan area. However, despite the long-term presence of Afghan immigrants in Iran, research on patterns and drivers of spatial segregation of immigrants has been very limited. The research method involves a secondary analysis of census data. Therefore, this article utilizes 2006 Iran census tract data to examine patterns of spatial segregation of Afghan immigrants in the Tehran metropolis. The required data for two-group segregation indices, Getis–Ord statistics, and Geographically Weighted Regression, were analyzed as a map using ArcMap and Geo-Segregation Analyzer. The results reveal that the spatial segregation of Afghans is high and that most live in lower-SES census tracts. Multivariable analyses indicate that the extent of segregation can be explained by education, job class, and generation status. It can be concluded that generational transition and access to human capital have reduced various indicators of spatial segregation of Afghan immigrants in Tehran.

Integrating Copernicus LMS with ground measurements data for leaf area index and biomass assessment for grasslands in Poland and Norway

ABSTRACT The integration of satellite data from the Copernicus Land Monitoring Service (CLMS) with ground-based measurements represents a pioneering approach to the assessment of leaf area index (LAI) and in situ biomass at grasslands in Poland and Norway. The aim of this study was to develop a method for assessing grass growth conditions and predicting biomass yield based on Sentinel-2 data and CLMS products. LAI values derived from CLMS were compared with in-situ measurements on grassland plots in the two regions of Podlaskie (PL84) and Wielkopolskie (PL41). Small random statistical errors observed in the differences represent a significant opportunity to use Copernicus data to develop a relationship model for biomass prediction. The NDII index calculated using Sentinel2 was considered important for biomass assessment in the humid areas of Norway. This relational biomass prediction model could provide valuable information to farmers, improving their ability to manage grasslands effectively. As a result of the research, relational models were developed has been developed to predict grassland fresh biomass yield with an R 2 accuracy of 0.72 for the first cut, 0.81 for the second cut and 0.91 for the third cut. This allows farmers to effectively manage and monitor grassland throughout the growing season.

Prediction of fire danger index using a new machine learning based method to enhance power system resiliency against wildfires

AbstractWildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

Novelty detection for long-term diagnostic data with Gaussian and non-Gaussian disturbances using a support vector machine

Abstract In condition monitoring lack of properly balanced data sets with faulty and healthy cases makes proper condition recognition very challenging. In many cases, one may have good condition data only as the machine is unique and there is no other example. This issue is addressed by proposing a support vector machine (SVM) for novelty detection applied to health index (HI) data. In this scheme, the moving window approach has been utilised in which the simple statistical parameterisation of the data is carried out. Then the model in the multidimensional (mD) space is constructed whose shape is defined by an estimated hypersphere border. If the data lies inside the border then it can be used to re-train the model. Whereas if it is outside the border then it cannot be recognized as a healthy case. The size of the mD hypersphere (for m=2) describes the location of the good-condition data cloud as a potential feature. If the size of the data cloud is growing, it means more dispersion of the data. The efficiency of the method is tested on simulated and well-known real data sets having Gaussian and non-Gaussian disturbances.&amp;#xD;

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

Analyzing financial interdependencies: The cointegration of Jordan and BRIC stock markets

This study examines the financial integration between Jordan and the BRIC economies (Brazil, Russia, India, and China) to determine whether long-term equilibrium relationships exist and to assess implications for portfolio diversification and policy. Drawing on daily stock index data from 01 January 2014, to 31 August 2024, the study employs econometric techniques, including Granger Causality tests, Johansen Cointegration, and Vector Autoregression (VAR). The stationarity of stock indices at the first difference level is confirmed through unit root testing. Results indicate minimal long-term cointegration between Jordan and BRIC markets, pointing to low integration and potential diversification benefits for institutional investors. However, short-term causal links—particularly between Jordan and the Russian and Indian markets—highlight these countries’ influence on Jordan’s stock fluctuations. The findings suggest that, in the absence of long-term cointegration, investors may mitigate risk by investing in less correlated markets, such as Jordan, while leveraging short-term partnerships with Russia and India. Additionally, the study provides valuable insights for business leaders considering strategic alliances with BRIC counterparts in sectors like technology, agriculture, and energy, and calls for future research into factors like regulatory frameworks and geopolitical stability that may limit long-term financial integration. These results have significant implications for institutional investors, business executives, and policymakers, suggesting targeted strategies for financial stability, risk mitigation, and economic collaboration.

Evaluation Method of Water Injection Development Effects in Fractured Vuggy Carbonate Reservoirs: Case Study of FI7 Strike-Slip Fault Zone

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

Hydration behavior of L-proline in the presence of mono, bis, tris-(2-hydroxyethyl) ammonium acetate protic ionic liquids: Thermophysical properties

The hydration behavior of amino acids, essential for biological macromolecules, is influenced by ammonium biomaterials. The protic ionic liquids (PILs) are gaining attention in the food and pharmaceutical industries due to their nontoxicity and adjustable properties. Thus, study of the amino acids, such as L-proline, in the presence of PILs is crucial for understanding their hydration behavior. In this work, the effect of PILs, including mono, bis, tris (2-hydroxyethyl)ammonium acetate protic ionic liquids that might be naturally produced in human body, on L-proline hydration behavior was studied using COSMO calculations and thermophysical measurements. Measurements were the density, speed of sound, viscosity, and refractive index data of the solutions (L-proline + PILs + water) at various PIL concentrations at temperatures (298.15 to 318.15) K and under atmospheric pressure. The study indicates L-proline has weaker interactions with water compared to PILs ([2-HEA][Ac], [bis-2-HEA][Ac], and [tris-2-HEA][Ac]) due to its compact structure and lower negative dielectric energy. PILs interact more strongly with water through hydrogen bonding. Increasing temperature affects L-proline’s hydration layer, releasing more water molecules compared to PIL solutions. This effect is more pronounced with [tris-2-HEA][Ac], likely due to its larger size and complex structure. While L-proline promotes an ordered water structure, PILs can disrupt this by rearranging water molecules and forming their own hydrogen bonds.

HyProCure for Flatfoot Deformity: A Clinical Characteristics Analysis in China.

Flatfoot is a common foot disorder involving progressive foot deformity of the three-dimensional structures of the forefoot, midfoot, and hindfoot. Currently, Chinese surgeons and patients favor subtalar arthroereisis (SA) due to its minimally invasive and low-damage characteristics. HyProCure device is widely used. However, there is limited analysis of large sample sizes. This study utilized statistical evaluation with a large sample size to analyze clinical characteristics trends of SA for flatfoot, including baseline indicators, selection of HyProCure size and surgical strategy, complications, and implant removal. This study will enhance the understanding of SA in China. A retrospective analysis of 732 patients (1008 ft) who underwent SA from June 2015 to June 2023, with 509 pediatric and adolescent patients (772 ft) and 223 adult patients (236 ft). Based on the patient's age, patients aged ≤ 18 were included in the children and adolescent group, while adult acquired flatfoot deformity (AAFD) patients aged > 18 were included in the adult group. General data was collected, including patient sex, age, side, body mass index (BMI), surgery date, HyproCure size, and surgical data, and trends were analyzed. Postoperative complications and HyProCure removal were collected as outcome measures during follow-up. The age of patients treated with SA was gradually getting younger, with male patients predominating, mainly concentrated in the 11-14 age group. HyProCure 7 has the highest usage rate. In the children and adolescent group, 288 ft (37.31%) only underwent SA. In the adult group, 18 ft (7.63%) only underwent SA. Complications include sinus tarsi pain, peroneal spasms, achilles tendon tension, and muscle strength decline. The complication rate in the children and adolescent group was 5.05%, while in the adult group it was 28.81%. Overall, it was 10.62%. The removal rate of HyProCure in the children and adolescent group is 1.04%, in the adult group is 15.25%, and overall is 4.37%. The trend in flatfoot treated with SA was towards children and adolescent male patients, and sinus tarsi pain was the most common complication after SA. The complication rate and removal rate in the children and adolescent group were lower than those in the adult group. HyProCure can be removed without additional adverse effects.

An assessment of how street view imagery and remote-sensing data of green and blue spaces can explain variations in housing prices: a case study in Suzhou, China

ABSTRACT Urban blue and green spaces have significant influences on housing prices. In this study, we employed three data sources of blue and green space data sources (street view images, remote-sensing indicators, and land use data) and compared the variations of the three data sources of data in housing prices in Suzhou, China. The Spearman correlation showed street view data and remote sensing data reflect different perspectives of blue and green spaces. In terms of green spaces, street view greenery demonstrated the highest explanatory power for housing prices. This finding suggests that residents favour street greenery than Normalized Difference Vegetation Index (NDVI) and green space land use data. Regarding blue spaces, Normalized Difference Water Index (NDWI) exhibited the highest explanatory power for housing prices. This can be attributed to the fact that NDWI effectively captures water bodies around residential areas. The findings of this study have significant implications for urban planning and real estate development.

Assessing Land Use and Cover Changes arising from the 2022 water crisis in Southeast China: A comparative analysis of Remote Sensing Imagery classifications and Machine Learning algorithms

Abstract. The water crisis in the southeast region of China in 2022, caused by one of the worst heatwaves on record, was characterized by severe shortages of water resources, leading to challenges for local communities, agriculture, and industry. To analyze changes in land use and land cover (LULC) in the Jialing River region, Chongqing, China, we compared Remote Sensing (RS) imagery classifications before and after the intense heat waves of 2022. We evaluated the performance of two machine learning algorithms, KDTree KNN and Random Forest (RF), in LULC classifications. The classifications were carried out based on the RS images from the OLI/Landsat 8 system, NDWI index, and SRTM data. The model performances were similar, the classification accuracy showed that the RF algorithm was superior to KDTree KNN. The RF LULC classification and area calculation corroborate with the visual analysis, reaffirming the superiority of RF, which shows a decrease in water surface area, unlike DKTree KNN.

Mini nutritional assessment short form as a predictor of mortality among older adults with dementia residing in group homes: A 5-year longitudinal study

Many people with dementia frequently experience various health-related problems and are known to have poor nutritional status. However, very few studies have examined the nutritional status of dementia residents in group homes, which play an important role in a society-wide system of support for people with dementia, or the relationship between nutritional status and long-term prognosis. This study was conducted to determine the nutritional status of group-home residents and to examine the relationship between nutritional status and the 5-year survival rate. Participant characteristic (e.g., age, sex, body mass index, and medical history) and Mini Nutritional Assessment-Short Form (MNA-SF), Barthel index (BI), and Clinical Dementia Rating (CDR) data were collected from 79 older people (12 men/67 women; mean age: 88.3±5.5 years) residing in 5 group homes as a baseline survey. Mortality data were obtained from medical records using an endline survey. The nutritional status of the participants using the MNA-SF was normal, at risk of malnutrition, and malnourished in 15.2% (n=12), 69.6% (n=55), and 15.2% (n=12) of participants, respectively. Following the survey, mortality data were collected for 5 years, and the 5-year survival rates were 64.3% in those with normal nutritional status, 30.4% in those at risk of malnutrition, and 9.1% in the malnourished group, with a significant difference observed in the Kaplan-Meier analysis (p<0.001). The hazard ratio for the MNA-SF scores and survival rates adjusted for sex, age, BI, and CDR were significant 0.80 [0.69-0.93, p=0.003]. This study showed that 84.8% of patients with dementia residing in group homes were malnourished or at risk of malnutrition and that low MNA-SF levels were an independent determinant of the 5-year survival rate.

An improved framework for quantifying the contribution of climatic and anthropogenic factors to vegetation dynamics - A case study of China

Accurately quantifying the impact of climatic and anthropogenic factors on vegetation change is critical for developing and evaluating ecological management strategies. However, most presented studies typically ignore the climate temporal effects and assume that all pixels are affected by both climate change (CC) and human activities (HA), which often introduce uncertainties. In this study, Leaf area index (LAI), temperature, precipitation, solar radiation, and land cover data from 1982 to 2020 were used to detect and attribute vegetation dynamics in China. We used partial correlation analysis, generalize linear model, trend analysis, and improved RESTREND to analyze the spatiotemporal variation characteristics of vegetation LAI from 1982 to 2020 and to quantify the effects of CC and HA on vegetation dynamics since the implementation of the Grain for Green Project (GGP). The results indicate that significant vegetation greening appeared in most areas (66.2%) between 2000 and 2020. Both CC and HA have positive effects on vegetation greening. In arid and semi-arid regions, precipitation was the primary driver of vegetation change, while in high-latitude areas of southern and southwestern China, temperature was the primary determinant. After considering the temporal effects, the explanatory power of climate variables for vegetation dynamics increased by 4.0% compared to ignoring the temporal effects, accounting for 72.81%. After dividing the pixels into those affected by CC and those affected by both CC and HA, the contribution of HA was decreased from 31.19% to 25.96%. Although the contribution of HA is lower than that of CC, ecological engineering has effectively promoted vegetation greening. These research findings provide scientific data and theoretical basis for ecological environment protection and natural resource management.

Decoding the stock market dynamics in the banking sector: Short versus long-term insights

The severity of extreme fluctuations and crises within the global banking sector is escalating. Conventional models, operating on a single time scale, may misinterpret any shift as a change in the long-term trend, distorting market insights. To address this issue, the present paper introduces a hierarchical structure into the standard hidden Markov model, enabling the differentiation of short and long-term trends within the U.S. banking industry. Using NASDAQ Bank stock market index data from January 1, 2007, to July 31, 2023 at two different frequencies, we construct and evaluate different calibrations of the hierarchical hidden Markov model. Results reveal two long-term regimes: turbulent periods with high volatility, instability, and negative returns, and prevalent stable markets. Within each of them, two distinct states representing short-term trends are identified, exhibiting significant differences in persistence, likelihood, expected returns, and risk profiles. The results show that an investor should carefully differentiate between regimes on both hierarchies to make informed investment decisions.

Pressing, pressure and re-aggressing as tactical movement of anterior cruciate ligament injuries in women's soccer.

A better understanding of anterior cruciate ligament (ACL) injury mechanisms in female soccer can guide better research on both prevention programs and late-stage rehabilitation for the return-to-sport process. This narrative review investigates the technical and tactical game situations in female soccer linked to ACL injury mechanisms. Through a literature search, we reviewed scientific literature to identify soccer-specific technical movements and tactical patterns that increase the risk of ACL injuries in female players. Articles were retrieved through Web of Science, SPORTDiscus, ScienceDirect, PubMed, and PubMed Central. Inclusion criteria were: 1) studies on ACL injury mechanisms in women's soccer; 2) studies examining soccer techniques/tactics to identify non-contact injury mechanisms. Gray literature was included to supplement limited indexed data, aiming to stay within author guidelines. Female athletes experience a 2-8 times higher risk of ACL injury than males, with 70% of these injuries being non-contact. Common scenarios leading to these injuries involve ball possession/non-possession phases, tactical actions (pressing, pressure, re-aggression), and technical movements (cutting, changing direction). Tactical aspects, particularly pressing and re-aggression, are key contributors to ACL injury risk regardless of ball possession. In female soccer, ACL injuries are related to non-contact injury mechanisms during technical and tactical situations. The knowledge and study of these situational patterns of play, such as pressing, re-aggression, pressure, and cutting maneuvers, are essential to target prevention strategies and return-to-sport processes objectively.

Greening‐Induced Biophysical Impacts Lead to Earlier Spring and Autumn Phenology in Temperate and Boreal Forests

AbstractTree phenology, the timing of periodic biological events in trees, is highly sensitive to climate change. Previous studies have indicated that forest greening can impact the local climate by modifying the seasonal surface energy budget. However, the understanding of tree phenological responses to forest greening at large spatial scales remains limited. Utilizing satellite‐derived phenological and leaf area index data spanning from 2001 to 2021, herein we show that forest greening led to earlier spring and autumn phenology in both temperate and boreal forests. Our findings demonstrated that forest greening during winter and spring contributed to a reduction in surface albedo, resulting in biophysical warming and consequently advancing spring leaf phenology. Conversely, forest greening in summer and autumn induced biophysical cooling through increased evapotranspiration, leading to an earlier onset of autumn leaf phenology. Our findings highlight the significant impact of forest greening‐induced local seasonal climate changes on shaping tree phenology in temperate and boreal forests. It is crucial to consider these greening‐induced alterations in microclimate conditions when modeling changes in tree phenology under future climate warming scenarios.