Success.ai’s Manufacturing Company Data for Chemicals & Manufacturing Executives in Asia provides a robust dataset tailored to businesses seeking to connect with decision-makers in the chemical and manufacturing industries across Asia. Covering executives, operations managers, and procurement leaders, this dataset offers verified email addresses, phone numbers, and detailed company insights.

With access to over 700 million verified global profiles and data from 170 million professional datasets, Success.ai ensures your outreach, market research, and partnership development efforts are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution is designed to help businesses thrive in Asia’s fast-evolving manufacturing sector.

Why Choose Success.ai’s Manufacturing Company Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, direct phone numbers, and LinkedIn profiles of manufacturing executives, chemical engineers, and operations leaders.
   • AI-driven validation ensures 99% accuracy, optimizing campaign efficiency and reducing communication errors.

2. Comprehensive Coverage Across Asia’s Manufacturing Sector

   • Includes profiles of companies from manufacturing hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional trends, supply chain dynamics, and market opportunities in Asia’s diverse manufacturing landscape.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, company expansions, and market activities.
   • Stay aligned with the fast-paced nature of the manufacturing and chemical industries to seize opportunities effectively.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with executives, engineers, and operational leaders in Asia’s manufacturing and chemical industries.
• 170M Professional Datasets: Access verified contact details and actionable insights for strategic outreach and business growth.
• Company Insights: Gain visibility into company structures, production capacities, and market positioning.
• Decision-Maker Contacts: Connect directly with CEOs, production managers, and procurement officers driving industry innovation.

Key Features of the Dataset:

1. Leadership Profiles in Chemicals & Manufacturing

   • Identify and connect with professionals responsible for operations, supply chain management, and research and development in the chemical and manufacturing sectors.
   • Target decision-makers overseeing material procurement, technology integration, and compliance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (chemical manufacturing, industrial machinery, consumer goods), geographic location, or revenue size.
   • Align campaigns to address specific industry challenges, such as sustainability, cost management, or operational efficiency.

3. Firmographic Insights and Company Data

   • Access detailed firmographic data, including company hierarchies, operational scopes, and market presence.
   • Use these insights to identify high-value prospects and tailor your approach effectively.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with manufacturing professionals.

Strategic Use Cases:

1. Sales and Vendor Development

   • Present products, services, or equipment tailored to the needs of chemical manufacturers and industrial production companies.
   • Build relationships with procurement teams and operations managers seeking innovative solutions to streamline processes.

2. Market Research and Competitive Analysis

   • Analyze trends in Asia’s manufacturing and chemical sectors to guide product innovation and strategic planning.
   • Benchmark against competitors to identify growth opportunities, market gaps, and emerging technologies.

3. Supply Chain Optimization and Partnership Development

   • Engage with manufacturers seeking reliable suppliers, logistics partners, or co-manufacturers to support their operations.
   • Foster alliances that enhance efficiency, scalability, and quality in supply chain networks.

4. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and risk managers ensuring adherence to regional and global manufacturing standards.
   • Offer solutions that streamline compliance reporting, quality assurance, and risk management.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality manufacturing data at competitive prices, ensuring strong ROI for your marketing, sales, and partnership initiatives.

2. Seamless I...

The Supplementary Materials for ariticle Potential Health and Economic Impacts of Shifting Manufacturing from China to Indonesia or India.

Success.ai’s Import Export Data for Import, Export & Trade Professionals in Asia delivers a comprehensive dataset tailored for businesses aiming to connect with key players in Asia’s dynamic trade industry. Covering professionals involved in import/export operations, international logistics, and supply chain management, this dataset provides verified contact details, firmographic insights, and actionable professional data.

With access to over 700 million verified global profiles and 70 million business datasets, Success.ai ensures your outreach, market research, and trade strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the complexities of global trade in Asia.

Why Choose Success.ai’s Import Export Data?

1. Verified Contact Data for Effective Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of trade professionals, logistics experts, and supply chain managers.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication efficiency.

2. Comprehensive Coverage of Asian Trade Markets

   • Includes profiles of professionals from key Asian markets such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional trade trends, import/export regulations, and supply chain dynamics.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, trade activities, and market expansions.
   • Stay aligned with evolving market conditions and emerging trade opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with import/export professionals, logistics managers, and trade consultants across Asia.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Contact Details: Gain verified work emails, phone numbers, and business locations for precise targeting.
• Industry Trends: Understand key import/export opportunities, supply chain challenges, and market dynamics in Asia.

Key Features of the Dataset:

1. Professional Profiles in Import/Export and Logistics

   • Identify and engage with trade professionals managing cross-border operations, customs compliance, and supply chain efficiency.
   • Target individuals responsible for vendor selection, international partnerships, and trade negotiations.

2. Firmographic and Geographic Insights

   • Access data on company structures, trade volumes, and operational hubs in key Asian markets.
   • Pinpoint high-value prospects in established and emerging trade routes for strategic engagement.

3. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (manufacturing, wholesale, retail), geographic location, or revenue size.
   • Tailor campaigns to address specific trade needs such as market entry, cost optimization, or regulatory compliance.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with trade professionals.

Strategic Use Cases:

1. Sales and Business Development

   • Present trade services, logistics solutions, or supply chain optimization tools to import/export managers and trade consultants.
   • Build relationships with procurement teams and logistics managers seeking reliable partners and innovative solutions.

2. Market Research and Competitive Analysis

   • Analyze trends in Asia’s import/export landscape, including key trade routes, regulatory changes, and logistics challenges.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and emerging needs.

3. Partnership Development and Trade Collaboration

   • Engage with businesses seeking partnerships for supply chain management, customs compliance, or international expansion.
   • Foster alliances that enhance efficiency, reduce costs, and drive growth in the import/export sector.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in international trade, logistics, or operations.
   • Provide workforce optimization platforms or training solutions tailored to the trade and logistics industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality import/export data at competitive prices, ensuring maximum ROI for your marketing, sales, and trade initiatives.

2. Seamless Integration

   • Integrate verified trade data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and ...

This is an update of the scientific dataset on process CO₂ emissions from cement production documented in:

Andrew, R.M., 2019. Global CO₂ emissions from cement production, 1928–2018. Earth System Science Data 11, 1675–1710. https://doi.org/10.5194/essd-11-1675-2019.

Data in this release cover the period 1880–2019.

Note that emissions from use of fossil fuels in cement production are not included in this dataset since they are usually included elsewhere in global datasets of fossil CO₂ emissions. The process emissions in this dataset, which result from the decomposition of carbonates in the production of cement clinker, amounted to ~1.6 Gt CO₂ in 2019, while emissions from combustion of fossil fuels to produce the heat required amounted to an additional ~0.9 Gt CO₂ in 2019.

May 2021 release (210505): Major changes

• Updated to latest data from USGS.
• Included all new Biennial Update Reports (BURs) and National Communications (NCs) from UNFCCC non-Annex I countries.
• All UNFCCC Annex I countries updated to 2021 submissions of national inventory reports (NIRs) (1990-2019).
• US now includes Puerto Rico to align with its NIR, and is extended back to 1880 using cement production data from USGS publications.
• Viet Nam now follows the method in its NIR, using cement production from Statistical Yearbooks and USGS with clinker ratio from NIR2016, adjusted for clinker trade from COMTRADE.
• Egyptian cement production data obtained from cementdivision.com.
• Iranian cement production obtained from www.mimt.gov.ir.
• Taiwan's emissions taken from its 2020 NIR (https://unfccc.saveoursky.org.tw/nir/tw_nir_2020.php).
• Consolidated most primary data into combined_cement_data.xlsx.

The Cement Production dataset

Cement production data by country are primarily derived from USGS statistics. The construction of this dataset begins with production back-calculated from CDIAC's 2019 edition cement emissions data, which are a direct function of cement production (from the 2020 edition CDIAC has changed its methodology). Then using available data for some former Soviet states before the dissolution of the Soviet Union, Soviet states are disaggregated for all years before dissolution. Data obtained directly from USGS are used to overwrite from 1990 onwards, with a small number of additional corrections. Countries for which cement production is not available in the most recent years are extrapolated simply. Finally, country-specific cement production data are overwritten for the following countries: USA, China, India, Norway, Sweden, Iran, Saudi Arabia, South Korea, Jamaica, Moldova, Mexico, Namibia, Afghanistan, Argentina, Egypt. Note that many zeros in the cement production dataset are propagated from CDIAC and should probably be NODATA. The approach used for each country is summarised in the file "6. cement_production_method.csv".

Emissions calculation

• Emissions for all UNFCCC Annex I countries ("developed" countries) are derived from their official submissions to the UNFCCC in Common Reporting Format (structured Excel files), for which data are available from 1990 (slightly earlier for some Economies in Transition).
• For non-Annex I countries clinker ratios derived from the Getting the Numbers Right (GNR) cement sustainability initiative are applied to the cement production dataset to derive approximate clinker production by country, from which emissions are calculated using IPCC default factors.
• Country-specific methods are used for China, India, Japan, Turkey, USA.
• The combined_cement_data.xlsx file is used to overwrite emissions with superior data, in most cases as reported in official reporting to the UNFCCC, e.g. Biennial Update Reports, National Communications, and National Inventory Reports.
• Some countries do not report time-series of emissions, but do supply some isolated estimates in their official reporting to the UNFCCC, and these are used in some cases to constrain estimates.
• A number of countries state in their official reporting to the UNFCCC that they have never produced clinker, so emissions are set to zero for all years for these countries. In other cases, statements are made that no clinker was produced before a certain year, and this information is also incorporated.
• The information available usually covers a number of years, up to 3 decades. These are then extrapolated by combining available data and assumptions about historical developments in clinker ratios to produce longer time series of emissions based on the longer cement production dataset. More detail on this method are given in the accompanying journal paper.

Fossil fuels have helped our civilization get to where it is today, we’ve used them to power our homes, factories, and vehicles. Fossil fuels are plant and animal matter that died millions of years ago and have then been subjected to heat and pressure over millions of years.

Fossil fuels come in three major groups: Coal – is mined and fuels 1/3 of the world’s power (the largest consumers are China, India, and the U.S.) Crude oil – pumped up through the earth and split through refining to produce different oils we use for fuel (like gasoline, diesel, kerosene, etc.) Natural gas – this is mainly methane found near oil deposits and caused the development of the controversial fracking process.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

The statistic shows the unemployment rate in India from 1999 to 2023. In 2023, the unemployment rate in India was estimated to be 4.17 percent. India's economy in comparison to other BRIC states India possesses one of the fastest-growing economies in the world and as a result, India is recognized as one of the G-20 major economies as well as a member of the BRIC countries, an association that is made up of rapidly growing economies. As well as India, three other countries, namely Brazil, Russia and China, are BRIC members. India’s manufacturing industry plays a large part in the development of its economy; however its services industry is the most significant economical factor. The majority of the population of India works in this sector. India’s notable economic boost can be attributed to significant gains over the past decade in regards to the efficiency of the production of goods as well as maintaining relatively low debt, particularly when compared to the total amount earned from goods and services produced throughout the years. When considering individual development as a country, India progressed significantly over the years. However, in comparison to the other emerging countries in the BRIC group, India’s progress was rather minimal. While China experienced the most apparent growth, India’s efficiency and productivity remained somewhat stagnant over the course of 3 or 4 years. India also reported a rather large trade deficit over the past decade, implying that its total imports exceeded its total amount of exports, essentially forcing the country to borrow money in order to finance the nation. Most economists consider trade deficits a negative factor, especially in the long run and for developing or emerging countries.

Machine Learning Chips Market Outlook

The global machine learning chips market size was valued at $8.5 billion in 2023 and is projected to reach $50.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 21.9% during the forecast period. The growth of the machine learning chips market is primarily driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries, advancements in semiconductor technology, and the rising demand for high-performance computing applications.

One of the primary growth factors for the machine learning chips market is the proliferation of AI and ML technologies across diverse sectors such as healthcare, automotive, consumer electronics, and robotics. These technologies require advanced hardware capable of processing complex algorithms and large datasets efficiently, leading to higher demand for specialized chips. The development of more sophisticated and efficient machine learning models has necessitated the creation of optimized hardware solutions, further propelling market growth. Additionally, the trend towards edge computing, where data processing is performed closer to the data source, has accentuated the need for powerful and efficient chips, significantly contributing to market expansion.

Another major growth factor is the continuous advancements in semiconductor technology, which have led to the development of machine learning chips with enhanced processing capabilities, lower power consumption, and reduced latency. Innovations such as heterogeneous computing, where different types of processors are combined on a single chip, and neuromorphic computing, which mimics the human brain's neural network, are paving the way for more efficient and powerful machine learning chips. These technological advancements are expected to drive the adoption of machine learning chips in various applications, boosting market growth.

The growing demand for high-performance computing applications, particularly in data centers and cloud computing, is also fueling the growth of the machine learning chips market. As organizations continue to generate and process vast amounts of data, there is an increasing need for powerful hardware solutions that can handle complex computations and large datasets efficiently. Machine learning chips, with their ability to accelerate data processing and improve computational efficiency, are becoming essential components in modern data centers, further driving market growth.

Regionally, North America dominates the machine learning chips market, followed by Asia Pacific, Europe, Latin America, and the Middle East & Africa. The high adoption of advanced technologies, significant investments in AI and ML research, and the presence of major semiconductor companies in North America are key factors contributing to the region's market dominance. Asia Pacific, on the other hand, is expected to witness the fastest growth during the forecast period, driven by the rapid adoption of AI and ML technologies in emerging economies such as China and India, coupled with increasing investments in semiconductor manufacturing capabilities.

Chip Type Analysis

The machine learning chips market is segmented by chip type into Graphics Processing Units (GPUs), Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), Central Processing Units (CPUs), and others. GPUs are widely recognized for their ability to handle parallel processing tasks efficiently, making them ideal for machine learning applications. The high computational power and flexibility of GPUs have made them the preferred choice for training and inference in deep learning models. As a result, GPUs hold a significant share in the machine learning chips market and are expected to continue dominating the segment during the forecast period.

ASICs, on the other hand, are custom-designed chips optimized for specific machine learning tasks. These chips offer higher performance and energy efficiency compared to general-purpose processors, making them suitable for applications that require high-speed processing and low power consumption. The increasing demand for specialized hardware solutions in AI applications is driving the adoption of ASICs, contributing to the growth of this market segment. Companies such as Google and Intel have been at the forefront of developing ASICs, further propelling market growth.

FPGAs are another key segment in the machine learning chips market, known for their reconfigurability and adaptability to different wo

This dataset provides values for MANUFACTURING PMI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States Imports from China was US$462.62 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China - data, historical chart and statistics - was last updated on June of 2025.