In 2020, the average height of males aged between 18 and 44 years in China figured at ***** centimeters, up *** centimeters compared to that in 2015. On the other side, obesity and overweight conditions have seen a gradual increase across the country mainly related to an unhealthy diet and a less active urban lifestyle.

Descriptive statistics for height components by quintiles in men:Height (m): Quintile 1: mean = 1.62, range = 1.15–1.65, std = 0.03; Quintile 2: mean = 1.67, range = 1.65–1.69, std = 0.009; Quintile 3: mean = 1.70, range = 1.69–1.72, std = 0.008; Quintile 4: mean = 1.73, range = 1.72–1.75, std = 0.008; Quintile 5: mean = 1.78, range = 1.75–1.96, std = 0.03. Leg Length (cm): Quintile 1: mean = 74.1, range = 37–76, std = 2.02; Quintile 2: mean = 77.5, range = 76.1–78.9, std = 0.61; Quintile 3: mean = 79.5, range = 79–80, std = 0.47; Quintile 4: mean = 81.4, range = 80–82.5, std = 0.61; Quintile 5: mean = 84.9, range = 82.5–101, std = 2.06. Sitting height (cm): Quintile 1: mean = 85.6, range = 56.087.9, std = 1.84; Quintile 2: mean = 88.6, range = 88.0–90.0, std = 0.53; Quintile 3: mean = 90.5, range = 90.0–91.5, std = 0.53; Quintile 4: mean = 92.4, range = 91.6–93.1, std = 0.47; Quintile 5: mean = 95.2, range = 93.1–108.0, std = 1.51.*Expressed as per standard deviation change. Model 1: univariate analyses. Model 2 controlled for birth cohort, education, and income. Model 3 controlled for birth cohort, education, income, and BMI at baseline. Model 4 controlled for birth cohort, education, income, smoking before age 20, BMI at age 20, BMI at baseline, and participation in team sports during adolescence.

The unprecedented urbanization in China has driven rapid urban and rural development in recent decades. While existing studies have extensively focused on horizontal urban expansion, research on vertical urban expansion patterns in China remains limited. To address this gap, we proposed a Multi-Temporal Building Height estimation network (MTBH-Net) to estimate building heights at a 30 m spatial resolution in China for 2005, 2010, 2015, and 2020 by integrating Global Ecosystem Dynamics Investigation (GEDI), Landsat, and PALSAR data. Specifically, we introduced sample migration to generate reference building height data and utilized the Continuous Change Detection and Classification (CCDC) disturbance feature to ensure consistency in unchanged built-up areas. Validation with GEDI L2A V2 data demonstrated that MTBH-Net achieved RMSEs of 5.38 m, 5.73 m, 6.26 m, and 6.36 m for the respective years. Further validation with field-measured data and GF-7 building height data yielded RMSEs of 9.13 m and 10.99 m, respectively. The proposed 30-m China Multi-Temporal Building Height (CMTBH-30) dataset reveals an increase in average building heights in China from 10.48 m in 2005 to 11.37 m in 2020, reflecting an upward trend in urban development. Additionally, the standard deviation of building heights rose from 3.87 m in 2005 to 6.35 m in 2020, indicating increased height variation nationwide. Regional analysis from 2005 to 2020 shows notable vertical growth on newly expanded impervious surfaces in Macau (+14.9 m), Hong Kong (+13.9 m), and Guangdong (+13.5 m), while Chongqing (+3.6 m), Guizhou (+3.0 m), and Qinghai (+3.0 m) also experienced significant growth on stable impervious surfaces. Minimal growth was observed in Jilin, Heilongjiang, and Xinjiang. CMTBH-30 offers a more refined and accurate depiction of building heights, effectively capturing height variations and mitigating the underestimation of high-rise buildings. It fills the gap in multi-temporal building height estimation. Overall, this study provides a new dime

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.

Descriptive statistics for height components by quintiles in women: Height (m): Quintile 1: mean = 1.50, range = 1.19–1.54, std = 0.03; Quintile 2: mean = 1.55, range = 1.54–1.57, std = 0.008; Quintile 3: mean = 1.58, range = 1.57–1.60, std = 0.008; Quintile 4: mean = 1.61, range = 1.60–1.63, std = 0.009; Quintile 5: mean = 1.66, range = 1.63–1.86, std = 0.02. Leg Length (cm): Quintile 1: mean = 68.3, range = 0.40–0.70, std = 1.86; Quintile 2: mean = 71.4, range = 70–72.3, std = 0.59; Quintile 3: mean = 73.4, range = 72.3–74.0, std = 0.54; Quintile 4: mean = 75.4, range = 74.05–76.5, std = 0.60; Quintile 5: mean = 78.9, range = 76.5–105.0, std = 2.18. Sitting height (cm): Quintile 1: mean = 80.3, range = 52.0–82.0, std = 2.13; Quintile 2: mean = 83.5, range = 82.1–84.0, std = 0.53; Quintile 3: mean = 85.0, range = 84.1–85.9, std = 0.30; Quintile 4: mean = 86.5, range = 86.0–87.0, std = 0.47; Quintile 5: mean = 89.2, range = 87.1–105.0, std = 1.48.*Expressed as per standard deviation change. Model 1: univariate analyses. Model 2 controlled for birth cohort, education, and income. Model 3 controlled for birth cohort, education, income, and BMI at baseline. Model 4 controlled for birth cohort, education, income, smoking before age 20, BMI at age 20, BMI at baseline, and participation in team sports during adolescence.

Thanks to the substantial economic development in the country, obesity is replacing malnutrition and growth delay in becoming a new prominent health issue among China's youth. In December 2020, China's National Health Commission reported that while the average height of youngsters between *** and 17 years old increased between 2015 and 2020, the obesity rate also rose continuously, with almost one in **** children and adolescents aged between *** and 17 years being obese or overweight.

Supplementary information files for article Socio-economic disparities in child-to-adolescent growth trajectories in China: Findings from the China Health and Nutrition Survey 1991-2015

Backgrounds: Socio-economic disparities in growth trajectories of children from low-/middle-income countries are poorly understood, especially those experiencing rapid economic growth. We investigated socio-economic disparities in child growth in recent decades in China. Methods: Using longitudinal data on 5,095 children/adolescents (7-18y) from the China Health and Nutrition Survey (1991-2015), we estimated mean height and BMI trajectories by socio-economic position (SEP) and sex for cohorts born in 1981-85, 1986-90, 1991-95, 1996-2000, using random-effects models. We estimated differences between high (urbanization index ≥median, household income per capita ≥median, parental education ≥high school, or occupational classes I-IV) and low SEP groups. Findings: Mean height and BMI trajectories have shifted upwards across cohorts. In all cohorts, growth trajectories for high SEP groups were above those for low SEP groups across SEP indicators. For height, socio-economic differences persisted across cohorts (e.g. 3.8cm and 2.9cm in earliest and latest cohorts by urbanization index for boys at 10y, and 3.6cm and 3.1cm respectively by household income). For BMI, trends were greater in high than low SEP groups, thus socio-economic differences increased across cohorts (e.g. 0.5 to 0.8kg/m2 by urbanization index, 0.4 to 1.1kg/m2 by household income for boys at 10y). Similar trends were found for stunting and overweight/obesity by SEP. There was no association between SEP indicators and thinness. Interpretation: Socio-economic disparities in physical growth persist among Chinese youth. Short stature was associated with lower SEP, but high BMI with higher SEP. Public health interventions should be tailored by SEP, in order to improve children’s growth while reducing overweight/obesity.

We have developed a tree-based approach to create spatially continuous forest stand mean height maps across China through integrating high-point density, high-precision close-range LiDAR data and multisource remote sensing data. The accuracy analysis of the arithmetic mean height (Ha) and the weighted mean height (Hw) demonstrates the feasibility of the proposed method. A practical framework for forestry investigation based on close-range LiDAR was proposed. The mean values of Ha and Hw are 13.3 ± 3.3 m 11.3 ± 2.9 m on pixel level, respectively. Validation based on LiDAR and field sample data shows that the RMSE values, range from 2.6 to 4.1 m for Ha and 2.9 to 4.3 m for Hw, respectively, indicating that our approach outperforms existing forest canopy height maps derived from area-based approaches. Hopefully, our methods and maps will serve as a foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability.

China Steel: Export: Medium & Small Section: I Section: Height <80mm data was reported at 0.597 USD mn in Mar 2025. This records an increase from the previous number of 0.369 USD mn for Feb 2025. China Steel: Export: Medium & Small Section: I Section: Height <80mm data is updated monthly, averaging 0.314 USD mn from Jan 2010 (Median) to Mar 2025, with 183 observations. The data reached an all-time high of 2.949 USD mn in Nov 2023 and a record low of 0.001 USD mn in Mar 2010. China Steel: Export: Medium & Small Section: I Section: Height <80mm data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s Metal and Steel Sector – Table CN.WAG: Steel Export: Monthly.

The CNBH10m national product is a China 2020 building height map generated based on high spatiotemporal resolution Earth observation data (including radar, optical, and night light images), with a resolution of 10 meters. It uses a random forest model to estimate building height, and the results have a strong correlation with the actual observed height (RMSE of 6.1 meters, MAE of 5.2 meters, R of 0.77). The main contributing factor of this product is the Combined Shadow Index (CSI), which reveals the differences in building volume and average height among cities in China. CNBH10m is an open access building height dataset that provides strong support for urban research, regional climate analysis, and human environment interaction research, especially in helping to gain a deeper understanding of the three-dimensional morphological characteristics of cities.

aThe value of site index is equal to the average dominant height of actual stand of Chinese fir plantation at the reference age of 20. Dbh means diameter at breast height.

This dataset contains detailed information on large old trees from Hainan Island, China, collected for the study "Diversity and abundance of large old trees in Hainan Island: Spatial analysis and environmental correlations." The dataset includes tree location, family, species, estimated age, height (in meters), diameter at breast height (DBH in centimeters), and average crown diameter (in meters). These data were gathered to analyze the spatial distribution and abundance of large old trees and explore their correlations with various environmental factors. The dataset provides a valuable resource for ecological studies focused on tree conservation, biodiversity, and the role of large old trees in tropical ecosystems. Methods This study combined field surveys with government data to analyze LOTs in Hainan. We consulted the "Announcement on the List of Old and Valuable Trees in Hainan Province" issued by The People's Government of Hainan Province. Hereinafter referred to as "the LOT-list", the document provides essential tree data of trees over 300 years old, such as serial number, geographical location, elevation, age, height, diameter at breast height (DBH), and crown size.

China EVI: MoM: HS4: Tall Oil, Whether or not Refined. data was reported at 186.900 Average 12 Mths PY=100 in Feb 2025. This records an increase from the previous number of 44.900 Average 12 Mths PY=100 for Jan 2025. China EVI: MoM: HS4: Tall Oil, Whether or not Refined. data is updated monthly, averaging 114.200 Average 12 Mths PY=100 from Feb 2018 (Median) to Feb 2025, with 55 observations. The data reached an all-time high of 2,927.700 Average 12 Mths PY=100 in Nov 2020 and a record low of 1.028 Average 12 Mths PY=100 in Nov 2018. China EVI: MoM: HS4: Tall Oil, Whether or not Refined. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Trade Value Index: MoM: HS4 Classification.

China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data was reported at 199.900 Average 12 Mths PY=100 in Mar 2025. This records a decrease from the previous number of 242.800 Average 12 Mths PY=100 for Feb 2025. China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data is updated monthly, averaging 107.650 Average 12 Mths PY=100 from Feb 2018 (Median) to Mar 2025, with 56 observations. The data reached an all-time high of 4,421.100 Average 12 Mths PY=100 in Nov 2020 and a record low of 0.842 Average 12 Mths PY=100 in Nov 2018. China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

Descriptive statistics for the leg-length-to-sitting-height ratio by quintiles of the leg-length-to-sitting-height in women are as follows: Quintile 1: mean = 0.80, range = 0.43–0.82, std = 0.02; Quintile 2: mean = 0.84, range = 0.82–0.85, std = 0.008; Quintile 3: mean = 0.86, range = 0.85–0.88, std = 0.007; Quintile 4: mean = 0.89, range = 0.88–0.91, std = 0.009; Quintile 5: mean = 0.95, range = 0.91–2.02, std = 0.05. Descriptive statistics for the leg-length-to-sitting-height ratio by quintiles of the leg-length-to-sitting-height in men are as follows: Quintile 1: mean = 0.82, range = 0.43–0.84, std = 0.02; Quintile 2: mean = 0.85, range = 0.84–0.87, std = 0.007; Quintile 3: mean = 0.88, range = 0.87–0.89, std = 0.006; Quintile 4: mean = 0.90, range = 0.89–0.91, std = 0.008; Quintile 5: mean = 0.94, range = 0.91–1.46, std = 0.03.*Expressed as per standard deviation change.Model 1: univariate analyses. Model 2 controlled for birth cohort, education, and income. Model 3 controlled for birth cohort, education, income, and BMI at baseline. Model 4 controlled for birth cohort, education, income, smoking before age 20, BMI at age 20, BMI at baseline, and participation in team sports during adolescence.

In February 2025, the average tall oil import price amounted to $1,425 per ton, rising by 4.8% against the previous month.

Geographic analyses of Japanese children have shown a paradoxical trend: effective daylength – the duration of sunlight above a given illumination threshold – is negatively associated with height and positively with weight adjusted for height. These patterns suggest photoperiodic influences, possibly resembling thyroid hormone effects. To investigate whether similar associations exist in Han Chinese children, we analyzed province-level data from 2019 on average height and weight, using annual mean global solar radiation at each provincial capital as a proxy for effective daylength. We applied the regression model: Height = a₀ + a₁ × Weight − a₂ × Solar Radiation. Under normal physiological conditions, height and weight are typically proportional; thus, support for this model would imply solar radiation is linked to reduced height and increased weight. To assess regional variation, we used geographically weighted regression (GWR), which estimates location-specific coefficients. The results showed spatial heterogeneity: the weight coefficient was greater in western provinces, while the solar radiation coefficient tended to be smaller at higher latitudes. A global regression for provinces north of 30°N revealed statistically significant associations for 9-year-old boys. These findings suggest that the height – daylength and weight – daylength relationships observed in Japan may also exist in northern China, though weaker and more variable.

Data: "data_updated_0618-empty rows removed-3-2.xlsx" contains 968 records of total annual litterfall production (Mg/ha/yr) collected at 314 forest sites covering the full geographical range of Chinese forests. The sites were distributed across various climatic zones, spanning latitudes from 18.26° to 51.50° N, longitudes from 82.25° to 129.53° E, altitudes from 0 to 4115 m above sea level, mean annual temperatures (MAT) from −5.4 to 25.4 °C, and mean annual precipitation (MAP) levels from 370 to 2800 mm. In addition to the geographical location and climate conditions, associated stand information is also included, such as forest type, stand origin, stand age, mean diameter at breast height (DBH), mean tree height, and stand density. Trap size (i.e., the surface area of the litter traps) is also included as it is a potentially important factor affecting litter collection. Code: "Litterfall v1.2_[total]_public release.ipynb" contains a complete pipeline of data parsing, cleaning, preprocessing, model training, and prediction.

ObjectiveTo examine the influence of childhood obesity on the early onset of puberty and sex hormones in girls.MethodsHealthy girls with different percentages of body fat at baseline (40 obese, 40 normal, and 40 lean) were recruited from three elementary schools in Shenyang, China. These girls (mean age 8.5 years) were also matched by height, school grade, Tanner stage, and family economic status at baseline. Anthropometry, puberty characteristics, and sex hormone concentrations were measured at baseline and at each follow-up visit. The generalized estimating equation model and analysis of variance for repeated measures using a generalized linear model were used to determine the differences in puberty characteristics and sex hormones among three groups.ResultsOver 4 years, mean age of breast II onset was earlier among obese girls (8.8 years) than normal girls (9.2 years) and lean girls (9.3 years). The prevalence (%) of early-maturation in the obese, normal, and lean groups was 25.9%, 11.1%, and 7.4%, respectively. Obesity was associated with an increased risk for breast stage II (year 2: RR, 6.3; 95% CI, 1.9–21.1 and year 3: RR, 6.9; 95% CI, 0.8–60.1). None of the girls experienced menarche in the first year; however, by the fourth year 50.0% of obese girls had menarche onset, which was higher than normal weight (27.5%) and lean girls (8.1%). The mean estradiol level increased with age in the obese, normal, and lean groups. The mean estradiol concentration was higher in obese girls than in normal and lean girls throughout the 4-year period (P<0.05).ConclusionsChildhood obesity contributes to early onset of puberty and elevated levels of estradiol in girls.

The related studies on soil respiration (Rs) are increasing year by year in China, amounts of Rs data were published, especially in the form of monthly dynamics figures. Here, we compiled a comprehensive and uniform Rs database in China's forests from 568 literatures published up to 2018, including Rs and the concurrently measured soil temperature (N=8317), mean monthly Rs (N=5003), and annual Rs (N=634). Besides the Rs data directly given in the original papers, the monthly patterns of Rs and the concurrently measured soil temperature at 5 cm and/or 10 cm depth in the figures were digitized. These Rs data derived from the undisturbed forest ecosystems. The common measurement methods were selected, i.e. infrared gas analyzers (model Li-6400, Li-8100, Li-8150 (LI-COR Inc., Lincoln, Nebraska, USA)) and gas chromatography. Meanwhile, the associated information was recorded, e.g. geographical location (province, study site, latitude, longitude and elevation), climate factors (mean annual temperature and mean annual precipitation), stand description (forest type, origin, age, density, mean tree height and diameter at breast height), measurement regime (method, time, frequency, collar area, height and numbers). We hope the database will be used by the science community to provide a better understanding of carbon cycle in China's forests and reduce the uncertainty in evaluating of carbon budget at the large scale.