Graph and download economic data for Discussion About Pandemics Index for China (WPDICHN) from Q1 1996 to Q1 2025 about pandemic, China, and indexes.

In the context of energy crisis, Chinese government begins to vigorously advocate for the development of new energy sources in recent years including the geothermal energy. Though we have got a huge economic and social benefit, compared with other developed countries in the aspects of mining and using of geothermal resources, our using way and techniques are still in the low-level stage. To solve this problem, after analyzing the basic situation of geothermal resources in China, this article firstly introduces a new perspective, the Circular Economy Perspective. And then we analyze the roles which the government and enterprises should play in the process of creating a geothermal industry full of international competitiveness by Porters diamond model and build a cycle model of whole life cycle of the products of geothermal resources on the basis of industry development. This article asserts a basic development strategy of Chinese geothermal resources in the coming 20-40 years as follows: clear the functions of government, cultivate the subjects of markets, create a circular economy-oriented geothermal resources industry which is full of international competitiveness in a planned market economy and form the new energy economy, the geothermal resources economy in particular.

This is the replication package for "China, Europe and the Great Divergence: A Study in Historical national Accounting". As a result of recent advances in historical national accounting, estimates of GDP per capita are now available for a number of European economies back to the medieval period, including Britain, the Netherlands, Italy and Spain. The approach has also been extended to Asian economies, including India and Japan. So far, however, China, which has been at the center of the Great Divergence debate, has been absent from this approach. This paper adds China to the picture and shows that the Great Divergence began earlier than originally suggested by the California School, but later than implied by older Eurocentric writers.

This repository contains the appendix, the dataset, and the analysis files for the study "Unpacking the Nuances of Agenda-Setting in the Online Media Environment: An Hourly-Event Approach in the Context of Chinese Economic News."Except for the appendix, the "Data" folder contains 36 csv-format files, each for one specific news event. In each file, the first column "hour" denotes hourly intervals of the data, and the 2–6 columns denote the endogenous variables included in the VAR models (i.e., the raw volume of coverage or discussion in different groups concerning media, the neitizens, and other institutions of interest). The datasets have been aggregated by 19-hour lags each day, resulting in 266 lags for the 14-day time window."AnalysisFiles" folder contains the R code and copy results for analysis, in which:-TimeSeriesAnalysis" contains the R code for the time-series analysis of this study. Besides, this folder also contains copies of the results for VAR models.-"t-test & ANOVA" contains the results of 36 separate VAR models and the R code for the t-test and ANOVA for the event feature on the influence of agenda-setting. Besides, this folder also contains copies of the results of t-tests and ANOVA.-"Figure" contains the R code for creating Figure 1 and Figure 2 in the main text of this study and also contains copies of these two figures.

The observation of the transformation of the economic structure and development policy trends in mainland China, reflecting on the need to adjust our country's cross-strait strategy, includes five points: first, discussing the financial risks and prevention in mainland China; then summarizing the current situation of the "Belt and Road" policy in mainland China; analyzing the strategies and effectiveness of the dual-creation policy, and studying the promotion status of the third wave of free trade pilot zones. Finally, based on the above findings, suggestions for our country's economic policy responses are proposed.

This data collection consists of transcripts from 12 focus group discussions on themes related to social equality in Russia. The focus group discussions were conducted by the Institute of Applied Politics in Moscow, directed by Dr Kryshtanovskaya; using a discussion guide written by the Investigators. They were held in 12 cities chosen to represent different regions of the country, with an emphasis on provincial cities: Ufa, Kaliningrad, Ekaterinburg, Tiumen, Saratov, Ulyanovsk, Volgograd, Ivanovo, Irkutsk, Obolensk, Vladivostok and Protvino. The respondents included a mix of ages, genders, blue and white collar workers. The focus groups in Protvino and Ulyanovsk were held only for respondents age 18-29. The focus group discussions dealt with household and national economic change, perceptions of social fairness, and welfare values. Specifically, respondents were asked about the state of the national and local economies, their household economy, how they define rich and poor people and how they position themselves in relation to these categories. They were asked about whether they perceived differences in wealth between individuals, regions and between urban and rural areas as fair, and whether such differences are increasing or decreasing. Finally they were asked about whether the rich should take more responsibility for the welfare of the poor, about their own personal responsibility and that of the state and businesses, as well as about progressive income taxes and the degree to which the state should control the economy. The discussion guide is provided in Russian and English. Basic information about the respondents, including gender, age, and occupation are provided at the top of each focus group transcript. Each participant is identified by their given name only. The transcripts are provided in Russian. The Russian text was transcribed by the Institute of Applied Politics from audio files. A parallel set of focus groups was conducted in China and are available as the collection Social equality forum China: Focus group transcripts (see Related Resources). Taken together, Russia and China account for 41 per cent of the total territory of the BRICs and 63 per cent of their GDP/PPP. On Goldman Sachs projections China will be the world’s largest economy by 2050, and Russia its sixth largest. The project will seek to examine the following propositions: (1) that these two BRIC countries are becoming increasingly unequal; (2) that within them, political power and economic advantage are increasingly closely associated; (3) that their political systems have increasingly been employed to ensure that no effective challenge can be mounted to that combination of government position and economic advantage; (4) that set against a broader comparative perspective, an increasingly unequal society in which government is effectively immune from conventional challenge is likely to become increasingly regressive, or unstable, or both. Evidence will be drawn from official statistics, interviews with policy specialists and government officials, two dozen focus groups, and an analysis of the composition of the management boards of the largest companies in both countries. A final part of the analysis will employ crossnational evidence to test a series of hypotheses relating to the association between inequality and political instability on a more broadly comparative basis. Focus group discussions held in 12 Russian cities with 6 participants each drawn from a range of ages, both genders and different professions. Two focus groups were held for respondents age 18-29 only.

The statistic shows the distribution of the workforce across economic sectors in China from 2014 to 2024. In 2024, around 22.2 percent of the workforce were employed in the agricultural sector, 29 percent in the industrial sector and 48.8 percent in the service sector. In 2022, the share of agriculture had increased for the first time in more than two decades, which highlights the difficult situation of the labor market due to the pandemic and economic downturn at the end of the year. Distribution of the workforce in China In 2012, China became the largest exporting country worldwide with an export value of about two trillion U.S. dollars. China’s economic system is largely based on growth and export, with the manufacturing sector being a crucial contributor to the country’s export competitiveness. Economic development was accompanied by a steady rise of labor costs, as well as a significant slowdown in labor force growth. These changes present a serious threat to the era of China as the world’s factory. The share of workforce in agriculture also steadily decreased in China until 2021, while the agricultural gross production value displayed continuous growth, amounting to approximately 7.8 trillion yuan in 2021. Development of the service sector Since 2011, the largest share of China’s labor force has been employed in the service sector. However, compared with developed countries, such as Japan or the United States, where 73 and 79 percent of the work force were active in services in 2023 respectively, the proportion of people working in the tertiary sector in China has been relatively low. The Chinese government aims to continue economic reform by moving from an emphasis on investment to consumption, among other measures. This might lead to a stronger service economy. Meanwhile, the size of the urban middle class in China is growing steadily. A growing number of affluent middle class consumers could promote consumption and help China move towards a balanced economy.

Unemployment Rate in China remained unchanged at 5 percent in June. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data series refers to China Version 1. Two estimates are provided for China and their rationale is discussed in the Detailed Documentation. One estimate is based mostly on ICP 2005 and national growth statistics and is labeled China1. China1 does incorporate a productivity adjustment that has been applied to all countries in ICP 2005. China2 also adjusts for the urban character of its prices in ICP 2005 and also adjusts the growth rate.

Note: Over GDP, 1 US dollar (US$) = 1 international dollar (I$). Purchasing power parity is the number of currency units required to buy goods equivalent to what can be bought with one unit of the base country. We calculated our PPP over GDP. That is, our PPP is the national currency value of GDP divided by the real value of GDP in international dollars. International dollar has the same purchasing power over total U.S. GDP as the U.S. dollar in a given base year. For more information and proper citation see http://www.rug.nl/research/ggdc/data/pwt/pwt-7.1

Source Indicator: ppp

Figure 3 depicts China-Africa trade from 2000 to 2013. It shows that China-Africa trade consistently grew since the formation of the FOCAC in 2000. As can be seen in the figure, the US trade with Africa declined after the 2008 global financial crisis, allowing China to take the lead as Africa's largest trading partner. Figure 7 shows trade between China and Africa from 2003 to 2021. Although with fluctuations, trade between the two sides has been increasing since the establishment of the FOCAC mechanism. It reached a first high of US$203 billion in 2015 and then declined significantly the following year. However, the trade increased again from 2017 and surged to US$254 billion in 2021, up by 35% from the previous year. The high trade volume in 2021 has been attributed to the additional Chinese exports of Personal Protective Equipment (PPEs), such as masks and hazmat suits, as well as pharmaceutical products and testing equipment for the COVID-19 pandemic to Africa. However, Gu et al (2022: 11) indicated that the strong increase in China-Africa trade volume in 2021 is remarkable as data from China's customs agency shows that it is "made up of an increase in both Chinese exports to Africa (29.9% year-on-year) and African exports to China (43.7% year-on-year)". Figure 4 shows the number of countries around the world that have joined China's Belt and Road Initiatiative (BRI). As can be seen in the figure, China's BRI has attracted more than 140 countries. In Africa, the first countries that signed up for the BRI project were East and North African countries such as Kenya, Djibouti, Tanzania and Egypt. In Figure 5, the map shows the number of African countries that have signed up for the BRI since 2015. As can be seen in the figure, 52 countries in Africa had signed some BRI-related Memorandum of Understanding (MoU) with China by 2022.

Table 1 shows that studies that analysed the China-Africa relationship focusing on their 'strategic partnership' are very few, given the voluminous literature on China and Africa. A search of Sino-Africa studies conducted in English with the term 'strategic partnership' in their titles produced only ten papers (see table). Furthermore, as the table shows, studies investigating the increased security cooperation in China-Africa relations conducted in English are rare, although this part of the debate has also produced numerous research publications. The column titled 'Focus of study' in Table 1 above shows that majority of these studies concentrated on analysing economic cooperation, while a few also included political relations between China and Africa. Also, the column titled 'Definition of strategic partnership' shows that, all these studies, except Akpan and Onya (2018), made no attempts to define the concept of strategic partnership. Figure 8 shows the countries around the world in which the United Nations (UN) has deployed its peacekeepers. As shown in the figure, the UN has deployed several peacekeeping missions around the world since the late 1940s, with most of these operations taking place in the African continent. Figure 9 focuses on the UN’s peacekeeping operations in Africa. As can be seen in the figure, Chinese peacekeeping troops were deployed in five out of the seven UN-led missions on the African continent as of 2019. Figure 12 shows the foreign military bases that currently exist in African countries. As the figure shows, the African Continent is a host to 47 known foreign military bases, of which 34 are United States (US) bases. Figure 13 shows the foreign military bases in Djibouti. As seen in the figure, Djibouti hosts the US' Camp Lemonnier military base, just 13.4 kilometres away from the Chinese PLA's new navy facility, along with military bases of other major powers such as France, Germany and Japan in close proximity. Djibouti thus found itself in the middle of diplomatic tensions between China and the US over fears of a Chinese takeover of the Doraleh Container Terminal, Djibouti's main container port, in 2018, as China financed the development of the port. Figure 6 shows China's Forum on China-Africa Cooperation (FOCAC) commitments from 2006 to 2021. As can be seen in the figure, China's financial pledges to assist Africa increased from US$5 billion to US$60 in 2015. However, they dropped to US$40 billion in 2021. Further, drops in the number of activities, such as official development assistance (ODAs) and capacity building, including reductions in security collaborations, were also noted. However, a new development was China's reallocation of US$10 billion of its Special Drawing Rights (SDRs) towards Africa from the US$40 billion that it received from the International Monetary Fund (IMF).

China recorded a trade surplus of 114.77 USD Billion in June of 2025. This dataset provides - China Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Discuss Trump's domestic and foreign economic and trade policies, focusing on tax cuts, immigration, energy, infrastructure, and other policies in the domestic policy aspect; foreign policies include economic and trade tools, multilateral and bilateral trade strategies, manufacturing relocations, and related policies that Trump may adopt. Second, observe the interactions and economic and trade situations between the United States and countries such as China, Russia, Japan, and South Korea, and select important issues for analysis. Third, comprehensively analyze the current situation and future development trends of regional economic integration such as TPP and AEC based on the content of the above chapters. Finally, evaluate the overall situation of the Asia-Pacific economic and trade situation under Trump's new policy, as well as the observation of the interactions between the United States and various countries in the Asia-Pacific region, and assess the possible impact on our country.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

With the continuous promotion of China’s innovation-driven development strategy, the role of technological innovation on economic development has become increasingly important. In this context, the support of R&D capital investment for technological innovation also becomes non-negligible. This leads to the question of whether the allocation of R&D capital is reasonable and whether there is room for further improvement. This paper is based on inter-provincial panel data from 2009 to 2020, which are classified based on China’s National Bureau of Statistics for R&D funding sources in high-tech industries and incorporated into an overall discussion framework. Using STATA16 statistical software, the R&D innovation output of high-tech industries is inves-tigated by building a PVAR model with the perspective of funding sources of R&D input intensity. The study results show that (1) the increase in the intensity of enterprises’ own capital investment has a positive impact on innovation output because it can generate a financial "reservoir" effect to support technological innovation. (2) the increase in the intensity of government capital invest-ment has a positive impact on innovation output because it can alleviate the loss of income of en-terprises due to "R&D spillover" and will send a positive signal to the market. (3) the foreign in-vestment intensity has a positive impact on the innovation output of enterprises due to the com-bined effect of "spillover effect" and "crowding out effect". (4) the increase of other capital in-vestment intensity also has a neutral effect on the increase of innovation output under the current financial market environment. Finally, based on the above findings, corresponding policy impli-cations are drawn. This study will help to improve the understanding of R&D capital allocation imbalance and R&D input and output issues in developing countries and provide a reference for policy makers.

GDP: Sichuan: Chengdu data was reported at 2,351.130 RMB bn in 2024. This records an increase from the previous number of 2,233.660 RMB bn for 2023. GDP: Sichuan: Chengdu data is updated yearly, averaging 10.538 RMB bn from Dec 1949 (Median) to 2024, with 76 observations. The data reached an all-time high of 2,351.130 RMB bn in 2024 and a record low of 0.400 RMB bn in 1949. GDP: Sichuan: Chengdu data remains active status in CEIC and is reported by Chengdu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City.

With the continuous promotion of China’s innovation-driven development strategy, the role of technological innovation on economic development has become increasingly important. In this context, the support of R&D capital investment for technological innovation also becomes non-negligible. This leads to the question of whether the allocation of R&D capital is reasonable and whether there is room for further improvement. This paper is based on inter-provincial panel data from 2009 to 2020, which are classified based on China’s National Bureau of Statistics for R&D funding sources in high-tech industries and incorporated into an overall discussion framework. Using STATA16 statistical software, the R&D innovation output of high-tech industries is inves-tigated by building a PVAR model with the perspective of funding sources of R&D input intensity. The study results show that (1) the increase in the intensity of enterprises’ own capital investment has a positive impact on innovation output because it can generate a financial "reservoir" effect to support technological innovation. (2) the increase in the intensity of government capital invest-ment has a positive impact on innovation output because it can alleviate the loss of income of en-terprises due to "R&D spillover" and will send a positive signal to the market. (3) the foreign in-vestment intensity has a positive impact on the innovation output of enterprises due to the com-bined effect of "spillover effect" and "crowding out effect". (4) the increase of other capital in-vestment intensity also has a neutral effect on the increase of innovation output under the current financial market environment. Finally, based on the above findings, corresponding policy impli-cations are drawn. This study will help to improve the understanding of R&D capital allocation imbalance and R&D input and output issues in developing countries and provide a reference for policy makers.

Americans' understanding of tariffs appears limited, with only 27 percent feeling very confident about their knowledge of the trade policy tool. This lack of awareness comes at a time when tariffs have become a significant topic in U.S. economic discussions, particularly in relation to international trade relations and domestic industry protection. Potential impact of proposed tariffs Despite the public's uncertainty, proposed tariffs could have far-reaching effects on the U.S. economy. If implemented, certain proposals could increase the average tariff rate on dutiable imports to nearly 18 percent, a substantial rise from the two percent rate in 2024. Such changes would not only affect dutiable goods but also impose taxes on previously duty-free imports, potentially leading to a sharp increase in the overall tariff burden. Estimates suggest that imposing tariffs on Mexico, Canada, and China could increase federal tax revenue by approximately 106 billion U.S. dollars, equivalent to 0.35 percent of the nation's GDP.

Techsalerator’s Import/Export Trade Data for China

Techsalerator’s Import/Export Trade Data for China provides an extensive and detailed collection of information on international trade activities involving Chinese companies. This dataset offers a thorough analysis of trade transactions, documenting and categorizing imports and exports across various industries within China.

To obtain Techsalerator’s Import/Export Trade Data for China, please contact info@techsalerator.com or visit https://www.techsalerator.com/contact-us with your specific requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for China delivers an in-depth examination of trade activities, integrating data from customs reports, trade agreements, and shipping records. This comprehensive dataset assists businesses, investors, and trade analysts in understanding China’s trade landscape in detail.

Key Data Fields

• Company Name: Lists the companies involved in trade transactions, helping identify potential partners or competitors and track industry-specific trade patterns.
• Trade Volume: Details the quantity or value of goods traded, offering insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as electronics or textiles, aiding in understanding market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, providing insights into global trade relationships and market access.
• Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in China

• Trade Balance Dynamics: China’s trade balance exhibits significant fluctuations, influenced by major partners such as the United States and the European Union. Trade policies and negotiations play a crucial role in addressing imbalances and shaping trade relationships.
• US-China Trade Relations: The trade relationship with the United States remains pivotal, influenced by agreements and tariffs. This partnership impacts various aspects of China’s trade policies and practices.
• Expansion of Global Trade Networks: China is increasingly broadening its trade partners and markets, reflecting a trend towards more extensive global trade engagement.
• Growth in Technological Exports: China continues to experience substantial trade in technology products, including electronics and machinery, which are key components of its export economy.
• Emphasis on Sustainable Trade Practices: There is a growing focus on integrating sustainability into trade policies, promoting environmentally friendly practices and technologies.

Notable Companies in Chinese Trade Data

• Huawei Technologies: A leading telecommunications company involved in exporting and importing advanced technology and electronics, playing a significant role in China’s trade dynamics.
• Lenovo: A major player in the technology sector, known for its global trade in computing products and electronics.
• China National Petroleum Corporation (CNPC): A key entity in the energy sector, involved in the import and export of oil and gas products.
• Alibaba Group: A major e-commerce company that facilitates a substantial volume of international trade through its platforms.
• BYD: A significant player in the electric vehicle and battery industry, impacting China’s trade in automotive technology and related components.

Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for China, please contact us at info@techsalerator.com with your requirements. We will provide a customized quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into China’s import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

Chinese business leaders, meeting with President Xi Jinping, express optimism about the technological future, emphasizing advancements in self-reliance and innovation.

Graph and download economic data for Discussion About Pandemics Index for Taiwan Province of China (WPDITWN) from Q1 1996 to Q2 2025 about Taiwan, pandemic, and indexes.