In 2020, the average height of males aged between 18 and 44 years in China figured at 169.7 centimeters, up 1.2 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.

According to a survey conducted by Ipsos in May 2019 regarding the most important attributes in a person to be considered beautiful, about 70 percent of the Chinese respondents considered a height range between 5'10 and 6'1 to be ideal amongst men. Globally, only 43 percent of respondents believed that this was the ideal height for men.

China Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 11.100 % in 2024. This records an increase from the previous number of 10.400 % for 2023. China Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 6.900 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 11.100 % in 2024 and a record low of 6.500 % in 2008. China Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

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

This dataset provides building height data for China in the year 2000 at a spatial resolution of 30 meters in a raster file format with a 2-degree grid. It is part of a series of datasets (2000, 2010, 2020) developed by our team to support urban and geospatial research.

CBHD30_2000: Independently inverted using methods developed by our team.

CBHD30_2010: Corrected for the "black stripe" issue (35°N–40°N) present in the Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010.

CBHD30_2020: Derived using our inversion methods and supplemented with the CNBH-10m dataset.

CBHD30_urban: Urban building data is extracted using the GUB dataset for enhanced precision.

According to a survey conducted by Ipsos in May 2019 regarding the most important attributes in a person to be considered beautiful, about 63 percent of the Chinese respondents considered a height range between 5'5 and 5'9 to be ideal amongst women. Globally, only 42 percent of respondents believed that this was the ideal female height.

SD, Standard deviation.*Exact height not height groups.

Based on the Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data, this study developed a 1 km × 1km resolution building height dataset across China in 2017.

Forecast: Re-Import of Swivel Seats with Variable Height Adjustment to China 2024 - 2028 Discover more data with ReportLinker!

Length based BMI-for-age 0–36 months and height based BMI 3–18 years. SD, Standard deviation.*Exact age not age groups.

This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

To use the data, please cite this paper or contact the corresponding author for more details.

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...

This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

To use the data, please cite this paper or contact the corresponding author for more details.

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...

China Steel: Import: Large Section: U Section: Height >=80mm data was reported at 1.664 Ton th in Feb 2025. This records an increase from the previous number of 1.125 Ton th for Jan 2025. China Steel: Import: Large Section: U Section: Height >=80mm data is updated monthly, averaging 1.092 Ton th from Jan 2010 (Median) to Feb 2025, with 182 observations. The data reached an all-time high of 2.723 Ton th in Apr 2024 and a record low of 0.420 Ton th in Apr 2022. China Steel: Import: Large Section: U 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 Import: Quantity: Monthly.

China Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data was reported at 4.500 % in 2024. This records a decrease from the previous number of 4.600 % for 2023. China Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 7.500 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 20.000 % in 2000 and a record low of 4.500 % in 2024. China Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

As of 2024, Shanghai Tower was the tallest completed skyscraper in China with a height of 632 meters. Among the top three existing tallest buildings in China were Ping An International Finance Center in Shenzhen, and CTF Finance Centre in Guangzhou.

China Steel: Export: Large Section: H Section: Height >200mm data was reported at 110.710 USD mn in Mar 2025. This records an increase from the previous number of 80.660 USD mn for Feb 2025. China Steel: Export: Large Section: H Section: Height >200mm data is updated monthly, averaging 9.032 USD mn from Jan 2010 (Median) to Mar 2025, with 183 observations. The data reached an all-time high of 131.747 USD mn in Jun 2022 and a record low of 1.824 USD mn in Oct 2014. China Steel: Export: Large Section: H Section: Height >200mm 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.

This dataset provides information about the number of properties, residents, and average property values for Brown Heights Lane cross streets in China Grove, NC.

This study developed a method to estimate building height for all of China based on the Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) DSM and other ancillary data including the Global Artificial Impervious Area (GAIA) dataset, the NASADEM dataset and the Global Roads Inventory Project (GRIP) dataset. The proposed method enabled us to accurately estimate building height with a special slope correction algorithm, improving the accuracy of building height estimation. The outcome of our procedure is a map of building height for China at a spatial resolution of 30 m. Compared to field-measured building height data and reference building height data from Baidu map, results indicate that the proposed method performed well (root mean square error (RMSE) of 4.26 m and 4.98 m, respectively). The new building height map of China contributes to the improved management of urban areas and further studies of urban environments.Reference: https://doi.org/10.1016/j.isprsjprs.2022.01.022.

As part of the GODAR (Global Ocean Data Archeology and Rescue) surface marine ship data were digitized in China. The data were received from NCDC on CDROM.

This dataset provides information about the number of properties, residents, and average property values for Corriher Heights Avenue cross streets in China Grove, NC.