The Gross Domestic Product (GDP) in China expanded 4.80 percent in the third quarter of 2025 over the same quarter of the previous year. This dataset provides - China GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

The Gross Domestic Product (GDP) in China was worth 18743.80 billion US dollars in 2024, according to official data from the World Bank. The GDP value of China represents 17.65 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data set compiles measures taken by the U.S. and China to escalate the trade war between 01-01-2018 to 15-11-2023. To collect these measures, we have utilised lists and timelines, and articles on the U.S.-China trade war from several academic sources, credible news outlets, primary sources, and reports from established research institutes. A measure in the trade war is defined as an action by China or the U.S. targeting only - or primarily - the other party to disrupt trade or other flows of money and/or commercial goods between the countries to further a political goal. The variables are of categorical nature, as we assess the specific type of action undertaken, be it a tariff, sanction, or ban.

Description

This dataset provides monthly economic indicators examining the relationship between US protectionist trade policies and Chinese economic growth from May 2022 to May 2025. The dataset can be used for academic research, statistical analysis, and educational purposes in international economics and trade policy studies.

Context

The dataset captures the economic dynamics during a period of heightened trade tensions between the United States and China. It includes comprehensive indicators of US protectionist measures and their potential impact on various dimensions of Chinese economic performance.

Dataset Structure

Time Period: May 2022 - May 2025 Frequency: Monthly Total Observations: 1127 Total Variables: 14

Column Descriptions

Date

• Type: Categorical (YYYY-MM format)
• Description: Month and year of observation

US Protectionism Indicators

US_Tariff_Index

• Type: Continuous
• Range: 100-120
• Description: Tariff rate index where 100 represents the baseline. Higher values indicate increased protectionist measures.

US_Trade_Restrictions

• Type: Discrete
• Range: 5-15
• Description: Number of new trade restrictions implemented per month, including tariffs, quotas, and non-tariff barriers.

US_AntiDumping_Cases

• Type: Discrete
• Range: 0-12
• Description: Number of anti-dumping cases filed against Chinese products per month.

US_Trade_Policy_Uncertainty

-Type: Continuous - Range: 90-160 - Description: Index measuring uncertainty in trade policy (0-200 scale). Higher values indicate greater uncertainty.

Chinese Growth Indicators

China_GDP_Growth_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 3.5-5.5%
• Description: Year-over-year GDP growth rate for China.

China_Industrial_Production_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 2.5-5.0%
• Description: Year-over-year change in industrial production output.

China_Exports_Growth_YoY

• Type: Continuous
• Unit: Percentage (%)
• Range: 0-15%
• Description: Year-over-year growth rate of Chinese exports.

China_Manufacturing_PMI

• Type: Continuous
• Range: 47-52
• Description: Purchasing Managers' Index for manufacturing sector. Values >50 indicate expansion, <50 indicate contraction.

China_FDI_Inflow_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: 7-15
• Description: Monthly foreign direct investment inflows into China.

Bilateral Trade Indicators

US_China_Trade_Volume_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: 40-60
• Description: Total monthly bilateral trade volume between US and China.

US_Trade_Deficit_China_USD_Billion

• Type: Continuous
• Unit: Billion USD
• Range: -32 to -24
• Description: US trade deficit with China. Negative values indicate deficit.

Additional Economic Indicators

USD_CNY_Exchange_Rate

• Type: Continuous
• Range: 6.7-7.3
• Description: Exchange rate expressing yuan per US dollar.

China_Stock_Index

• Type: Continuous
• Range: 2,800-3,500
• Description: Shanghai Composite Stock Index (normalized).

The stocks of goods held by firms in China increased by 10002.90 CNY Hundred Million in 2024. This dataset provides - China Changes in Inventories - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking. Here, we propose a map point-of-interest classification method based on machine learning technology and build a dataset of the distribution of Chinese manufacturing firms called the Gridded Manufacturing Dataset. This dataset includes the number and type of manufacturing firms at a 0.01° latitude by 0.01° longitude scale. It includes all manufacturing firms (classified into seven categories) in China in 2015 (4.40 million) and 2019 (6.01 million). This dataset can be used to characterize temporal and spatial patterns in the distribution of manufacturing firms as well as reveal the mechanisms underlying the development of the manufacturing industry and changes in regional economic policies.

Full Year GDP Growth in China decreased to 5 percent in 2024 from 5.40 percent in 2023. This dataset includes a chart with historical data for China Full Year GDP Growth.

Producer Prices in China decreased 2.10 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - China Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in China expanded 1.10 percent in the third quarter of 2025 over the previous quarter. This dataset provides - China GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data package includes the underlying data files to replicate the data and charts presented in How trade cooperation by the United States, the European Union, and China can fight climate change, PIIE Working Paper 23-8.

If you use the data, please cite as: Bown, Chad P., and Kimberly A. Clausing. 2023. How trade cooperation by the United States, the European Union, and China can fight climate change. PIIE Working Paper 23-8. Washington, DC: Peterson Institute for International Economics.

Time series data for the statistic Foreign_Direct_Investment_Net_Inflows_$ and country China. Indicator Definition:Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars.The statistic "Foreign Direct Investment Net Inflows $" stands at 18,556,141,172.84 Chinese Yuans as of 12/31/2024, the lowest value since 12/31/1993. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -63.86 percent compared to the value the year prior.The 1 year change in percent is -63.86.The 3 year change in percent is -94.61.The 5 year change in percent is -90.09.The 10 year change in percent is -93.08.The Serie's long term average value is 96,650,683,811.72 Chinese Yuans. It's latest available value, on 12/31/2024, is 80.80 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1979, to it's latest available value, on 12/31/2024, is +23,195,076.47%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2024, is -94.61%.

Time series data for the statistic Gross_Domestic_Product_Current_USD and country China. Indicator Definition:GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.The statistic "Gross Domestic Product Current USD" stands at 18,743,803,170,827.20 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.59 percent compared to the value the year prior.The 1 year change in percent is 2.59.The 3 year change in percent is 2.98.The 5 year change in percent is 28.73.The 10 year change in percent is 75.59.The Serie's long term average value is 3,590,131,888,959.60 United States Dollars. It's latest available value, on 12/31/2024, is 422.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1962, to it's latest available value, on 12/31/2024, is +39,518.50%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

China’s unprecedented change offers a unique opportunity for uncovering relationships between economic growth and environmental pressure. Here we show the trajectories of China’s environmental pressure and reveal underlying socioeconomic drivers during 1992–2010. Mining and manufacturing industries are the main contributors to increasing environmental pressure from the producer perspective. Changes in urban household consumption, fixed capital formation, and exports are the main drivers from the consumer perspective. While absolute decoupling is not realized, China has in general achieved relative decoupling between economic growth and environmental pressure. China’s decoupling performance has four distinguishable periods, closely aligning with nation-wide major policy adjustments, which indicates significant impact of China’s national socioeconomic policies on its environmental pressure. Material intensity change is the main contributor to the mitigation of environmental pressure, except for ammonia nitrogen, solid wastes, aquatic Cu, and aquatic Zn. Production structure change is the largest contributor to mitigate ammonia nitrogen emissions, and final demand structure change is the largest contributor to mitigate emissions of solid wastes, aquatic Cu, and aquatic Zn. We observe materialization trends for China’s production structure and final demand structure during 2002–2007. Environmental sustainability can only be achieved by timely technology innovation and changes of production structure and consumption pattern.

This data set is aimed at the Chinese scenario, and constructs sub national population forecast data with detailed demographic attributes such as age, gender and education level, and the corresponding grid data set. This data is based on the five SSP scenario storylines. On the basis of considering the national fertility policy and the population ceiling policy of super cities, it estimates the information of population factors such as population size, age, sex and education level of 31 provinces in China from 2010 to 2100 under the SSPs scenario year by year, and establishes a 1km spatial resolution grid population dataset, which makes up for the vacancy of China's provinces and grid data, To meet the research needs of high spatial resolution.

As the world's second-largest economy, information about China is in high demand. In addition, its prospect has increased due to the opening of A-share markets to foreign investors. China is different from western economies when it comes to the generation of data, as Chinese consumers do not generate data through traditional providers such as Google. Instead, this data is generated by Chinese proxies.

The power of SpaceKnow Nowcasting data lies in its standardization. You can safely compare all our Chinese data with each other or to other datasets for other countries. SpaceKnow obtains data from radar satellites which consistently deliver data down to earth. SpaceKnow monitors over 10,000 locations in China.

About data: SpaceKnow data has a history since January 2017 SpaceKnow data is updated on a weekly and daily basis SpaceKnow data provides the latest data point to customers instantly SpaceKnow data is transparent about locations from which it collects data SpaceKnow data is not affected by weather conditions

Available datasets: China Country Nowcasting Weekly updated change data Indices focused on the macroeconomic sector: manufacturing, mining with traditional benchmark predictions Indices focused on sectors: mining, automotive, chemical, transport, etc. Indices focused on regional and country pollution Industry indices provide information in z-score and percentages for low, normal and high activity Pollution indices provide information in mol/m2 and parts per billion for methane

China Nowcasting Summary:

China Logistic Centres Daily updated data aggregated by country and segregated by the 17 Chinese provinces Dataset provides three types of indices with different information: A level index that captures the long-term trends in the level of domestic trade A change index that captures the total flow of activity entering and exiting the monitored locations An activity index that captures different types of activity across time Indices are level in squared meters, change in z-score and activity in percentage

China Retail Indices Daily updated level data in squared meters Indices capture retail-related activity across China over parking areas that belong to shopping centres and metro stations Indices estimate the current state of the retail market in China Retail Parking Retail Metro Parking

China Automotive Companies [Released] Daily updated level data in squared meters Indices cover the production of assembled cars, movement at employee parking areas Covered companies: SAIC, BBAC, Changan, Dongfeng, Geely, GAC Group, Tesla Shanghai and more

China Coal [Coming Soon] Daily updated level data in squared meters Focus on mines, storage, processing and distribution centres Indices cover country and also region levels for Xinjiang, Shaanxi, Shanxi, Inner Mongolia China Truck Stops [Coming Soon]

System of indices for evaluating China’s economic institutional change.

We analyzed the formation mechanism of digital economic gap (DEG), measured the DEGs at four levels (the gaps in information and communication technology accessibility, application skill, digital economic outcome, and efficiency), and explored its spatiotemporal evolution in China by using DEA–Malmquist index method, Gini Coefficent method, Kernel density, and Geodetector. Data from 263 cities in China between 2011 and 2019 were collected. The results demonstrated that (1) The four levels of DEGs showed different trends. The first-, second- and third- level DEGs showed ceiling effects, and the fourth-level DEG oscillated upward. (2) The distribution location of the four levels of DEGs varied. The first- and second-level DEGs shifted at a stable low degree. The third-level DEG increased steadily and polarized. The fourth-level DEG increased steadily and formed a multi-polarization trend, with one strong polar. (3) The long-term transfer trend of the DEGs at four levels changed little, an..., A total of 263 cities in 30 provinces (cities or regions) in China were selected as the study subjects. Data were obtained from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, Provincial and City Statistical Yearbooks, White Paper on China City DE Index, and the Mark Data website (https://www.macrodatas.cn/). The expedition period for this study was from 2011 to 2019. , , The dataset -- data.dta (city = 263, year = 8) -- was compiled from the peer-reviewed literature. This was from study sites in 263 cities, China.

The dataset was compiled by co-authors Shujuan Wu (jane333444@126.com), Jinting Li (1311028217@qq.com), Daqian Huang (1953836900@qq.com), Jianhua Xiao (1312655857@qq.com) of Wuyi University.

For any questions regarding the dataset, please send an email to Shujuan Wu (jane333444@126.com)Â and Jinting Li (1311028217@qq.com).

Filename: data.dta

â—ˆyear: The year of the data

â—ˆcity: City No.

â—ˆregion: The No. Of the region

◈rndexp: R&D expenditure (10000 Yuan)

◈exgebudget:Total financial expenditure (10000 Yuan)

â—ˆfixass: Â Fixed asset investment (10000 Yuan)

◈fstdeg: First-level of digital economy (/)

◈library: The collection of books in public libraries per capi...

Land use data: In 2010 and 2020, collected from second and third national land resource surveys as well as land use change surveys conducted by the State Council. The prefecture-level farmland area data was obtained by the China Land Survey Results Sharing Application Service Platform (https://gtdc.mnr.gov.cn/Share#/), while prefectures that underwent major adjustments of administrative divisions between 2010 and 2020 or lacked planning data were excluded. Additionally, we calculated farmland area using the China Land Use/cover Dataset with 30m spatial resolution from the Resource and Environment Science and Data Center (RESDC) of the Chinese Academy of Sciences.Planning quotas: The prefecture-level farmland protection quotas and the reported farmland area at base period were collected from provincial land use master plans for 2006-2020. Socio-economic factors: All data were collected from the China Statistical Yearbook, the China Land and Resources Statistical Yearbook or provincial statistical yearbooks, with missing data obtained from the population census yearbooks or prefectural statistical bulletins.

Change-In-Cash Time Series for Tianjin Economic-technological Development Area Co Ltd. Tianjin TEDA Resources Recycling Group Co., Ltd. provides services for ecological environmental protection, regional development, energy trade wholesale, real estate sales, textile, clothing, and other industrial fields in China. It also engages in domestic waste incineration power generation and sanitary landfill, kitchen, sludge treatment and biomass power generation and other resource recycling related services. The company also offers wholesale, retail, and storage of petrochemical and its products, such as gasoline and diesel. In addition, it is involved in equity investments. The company was formerly known as Tianjin TEDA Co., Ltd. and changed its name to Tianjin TEDA Resources Recycling Group Co., Ltd. in July 2025. The company was founded in 1981 and is based in Tianjin, China.