national key research and development plan of china

国家高技术研究发展计划(863计划): National High-tech R&D Program of China (863 Program);

国家重点基础研究发展规划(973计划): National Program on Key Basic Research Project (973 Program)

国家985重点建设项目: Key Construction Program of the National “985” Project

“九五”攻关项目: National Key Technologies R & D Program of China during the 9th Five-Year Plan Period

国家基础研究计划: National Basic Research Priorities Program of China;

国家科技攻关计划: National Key Technologies R & D Program of China;

国家攀登计划—B课题资助: Supported by National Climb—B Plan

国家重大科学工程二期工程基金资助: National Important Project on Science-Phase Ⅱ of NSRL

国家教育部科学基金资助: Science Foundation of Ministry of Education of China

教育部科学技术研究重点(重大)项目资助: Key (Key grant) Project of Chinese Ministry of Education

国家教育部博士点基金资助项目: Ph.D. Programs Foundation of Ministry of Education of China

高等学校博士学科点专项科研基金: Research Fund for the Doctoral Program of Higher Education of China (缩写: RFDP)

国家教育部博士点专项基金资助: Doctoral Fund of Ministry of Education of China

中国博士后科学基金: Supported by China Postdoctoral Science Foundation

国家教育部回国人员科研启动基金资助: Scientific Research Foundation for Returned Scholars, Ministry of Education of China

国家教育部留学回国人员科研启动金: Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (SRF for ROCS, SEM)

跨世纪优秀人才计划 国家教委《跨世纪优秀人才计划》基金: Trans-Century Training Programme Foundation for the Talents by the State Education Commission

国家教育部优秀青年教师基金资助: Science Foundation for The Excellent Youth Scholars of Ministry of Education of China

高等学校骨干教师资助计划: Foundation for University Key Teacher by the Ministry of Education of China

中国科学院基金资助: Science Foundation of the Chinese Academy of Sciences

中国科学院重点资助项目: Key Program of the Chinese Academy of Sciences

中国科学院知识创新项目: Knowledge Innovation Program of the Chinese Academy of Sciences;

中国科学院“九五”重大项目: Major Programs of the Chinese Academy of Sciences during the 9th Five-Year Plan Period;

中国科学院百人计划经费资助: One Hundred Person Project of the Chinese Academy of Sciences

中国科学院院长基金特别资助: Special Foundation of President of the Chinese Academy of Sciences

中国科学院西部之光基金: West Light Foundation of The Chinese Academy of Sciences

中国科学院国际合作局重点项目资助: Supported by Bureau of International Cooperation, Chinese Academy of Sciences

中国科学院上海分院择优资助项目: Advanced Programs of Shanghai Branch, the Chinese Academy of Sciences;

国家自然科学基金(面上项目; 重点项目; 重大项目): National Natural Science Foundation of China (General Program; Key Program; Major Program);

国家杰出青年科学基金: National Science Fund for Distinguished Young Scholars;

国家自然科学基金国际合作与交流项目: Supported by Projects of International Cooperation and Exchanges NSFC

海外及香港、澳门青年学者合作研究基金: Joint Research Fund for Overseas Chinese, Hong Kong and Macao Young Scholars

日本科学技术厅科学家交流项目: Japan STA Scientist Exchange Program

海峡两岸自然科学基金共同资助: Science Foundation of Two sides of Strait

“九五”国家医学科技攻关基金资助项目: National Medical Science and Technique Foundation during the 9th Five-Year Plan Period;

核工业科学基金资助: Science Foundation of Chinese Nuclear Industry

北京正负电子对撞机国家实验室重点课题资助: BEPC National Laboratory

兰州重离子加速器国家实验室原子核理论中心基金资助: Supported by Center of Theoretical Nuclear Physics, National Laboratory of Heavy Ion Accelerator of Lanzhou

北京市自然科学基金资助: Beijing Municipal Natural Science Foundation

河南省教育厅基金资助: Foundation of He’nan Educational Committee

河南省杰出青年基金（9911）资助: Excellent Youth Foundation of He’nan Scientific Committee

黑龙江省自然科学基金资助: Natural Science Foundation of Heilongjiang Province of China

湖北省教育厅重点项目资助: Educational Commission of Hubei Province of China

江苏省科委应用基础基金资助项目: Applied Basic Research Programs of Science and Technology Commission Foundation of Jiangsu Province.

山西省归国人员基金资助: Shanxi Province Foundation for Returness

山西省青年科学基金资助: Shanxi Province Science Foundation for Youths

上海市科技启明星计划资助: Shanghai Science and Technology Development Funds

东南大学基金资助: Foundation of Southeast of University

华北电力大学青年科研基金资助: Youth Foundation of North-China Electric Power University

华中师范大学自然科学基金资助: Natural Science Foundation of Central China Normal University

西南交通大学基础学科研究基金资助: Foundation Sciences Southwest Jiaotong University

1 国家高技术研究发展计划资助项目（863计划）（No. ）

This work was supported by a grant from the National High Technology Research and Development Program of China (863 Program) （No. ）

2 国家自然科学基金资助项目（No. ）

General Program(面上项目), Key Program(重点项目), Major Program（重大项目）

This work was supported by a grant from the National Natural Science Foundation of China （No. ）

3 国家“九五”攻关项目(No. )

This work was supported by a grant from the National Key Technologies R & D Program of China during the 9th Five-Year Plan Period （No. ）

4 中国科学院“九五”重大项目(No. )

This work was supported by a grant from the Major Programs of the Chinese Academy of Sciences during the 9th Five-Year Plan Period （No. ）

5 中国科学院重点资助项目(No. )

This work was supported by a grant from the Key Programs of the Chinese Academy of Sciences (No. )

6 “九五”国家医学科技攻关基金资助项目(No. )

This work was supported by a grant from the National Medical Science and Technique Foundation during the 9th Five-Year Plan Period （No. ）

7 江苏省科委应用基础基金资助项目 (No. )

This work was supported by a grant from the Applied Basic Research Programs of Science and Technology Commission Foundation of Jiangsu Province (No. )

8 国家教育部博士点基金资助项目(No. )

This work was supported by a grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. )

9 中国科学院上海分院择优资助项目(No. )

This work was supported by a grant from Advanced Programs of Shanghai Branch, the Chinese Academy of Sciences (No. )

10 国家重点基础研究发展规划项目(973计划)(No. )

This work was supported by a grant from the Major State Basic Research Development Program of China (973 Program) (No. )

11 国家杰出青年科学基金(No. )

This work was supported by a grant from National Science Fund for Distinguished Young Scholars （No. ）

12 海外香港青年学者合作研究基金(No. )

This work was supported by a grant from Joint Research Fund for Young Scholars in Hong Kong and Abroad （No. ）

各项基金资助书写格式（中英文对照）

Supported by Science Foundation of The Chinese Academy of Sciences

中国科学院九五重大项目（项目编号： ）资助

Supported by Major Subject of The Chinese Academy of Sciences（项目编号： ）

中国科学院院长基金特别资助

Supported by Special Foundation of President of The Chinese Academy of Sciences

中国科学院国际合作局重点项目资助

Supported by Bureau of International Cooperation, The Chinese Academy of Sciences

中国科学院百人计划经费资助

Supported by 100 Talents Program of The Chinese Academy of Sciences

Supported by One Hundred Person Project of The Chinese Academy of Sciences中国科学院知识创新工程重大项目资助

Supported by Knowledge Innovation Project of The Chinese Academy of Sciences

Supported by Knowledge Innovation Program of The Chinese Academy of Sciences

中国科学院西部之光基金（项目编号： ）资助

Supported by West Light Foundation of The Chinese Academy of Sciences（项目编号： ）

北京正负电子对撞机国家实验室重点课题资助

Supported by BEPC National Laboratory

兰州重离子加速器国家实验室原子核理论中心基金资助

Supported by Center of Theoretical Nuclear Physics, National Laboratory of Heavy Ion Accelerator of Lanzhou

国家自然科学基金（项目编号： ）资助

Supported by National Natural Science Foundation of China（项目编号： ）

[Supported by NSFC（项目编号： ）]

国家自然科学基金重大项目资助

Supported by Major Program of National Natural Science Foundation of China (1991483)

国家自然科学基金国际合作与交流项目（项目编号： ）资助

Supported by Projects of International Cooperation and Exchanges NSFC（项目编号： ）

国家重点基础研究发展规划项目（项目编号： ）资助 (973计划项目)

Supported by Major State Basic Research Development Program（项目编号： ）

Supported by China Ministry of Science and Technology under Contract（项目编号： ）

Supported by State Key Development Program of (for) Basic Research of China（项目编号： ）

国家高技术研究发展计划(863计划)资助

Supported by National High Technology Research and Development Program of China

国家重大科学工程二期工程基金资助

Supported by National Important Project on Science-Phase Ⅱ of NSRL

国家攀登计划—B课题资助

Supported by National Climb—B Plan

国家杰出青年科学基金资助

Supported by National Natural Science Funds for Distinguished Young Scholar

Supported by State Commission of Science Technology of China（科委）

Supported by Ministry of Science and Technology of China

Supported by China Postdoctoral Science Foundation

海峡两岸自然科学基金（项目编号： ）共同资助

Supported by Science Foundation of Two sides of Strait（项目编号： ）

Supported by Science Foundation of Chinese Nuclear Industry

国家教育部科学基金资助

Supported by Science Foundation of The Chinese Education Commission (教委)

Supported by Science Foundation of Ministry of Education of China

国家教育部博士点专项基金资助

Supported by Doctoral Fund of Ministry of Education of China

国家教育部回国人员科研启动基金资助

Supported by Scientific Research Foundation for Returned Scholars, Ministry of Education of China

国家教育部优秀青年教师基金资助

Supported by Science Foundation for The Excellent Youth Scholars of Ministry of Education of China

高等学校博士学科点专项科研基金资助

Supported by Research Fund for the Doctoral Program of Higher Education of China

Supported by Doctoral Program Foundation of Institutions of Higher Education of China

霍英东教育基金会青年教师基金资助

黑龙江省自然科学基金资助

Supported by Natural Science Foundation of Heilongjiang Province of China

湖北省教育厅重点项目资助

Supported by Educational Commission of Hubei Province of China

河南省杰出青年基金（9911）资助

Supported by Excellent Youth Foundation of He’nan Scientific Committee（项目编号： ）

Supported by Foundation of He’nan Educational Committee

山西省青年科学基金（项目编号： ）资助

Supported by Shanxi Province Science Foundation for Youths（项目编号： ）

山西省归国人员基金资助

Supported by Shanxi Province Foundation for Returness

北京市自然科学基金资助

Supported by Beijing Municipal Natural Science Foundation

上海市科技启明星计划（项目编号： ）资助

Supported by Shanghai Science and Technology Development Funds（项目编号： ）

华北电力大学青年科研基金资助

Supported by Youth Foundation of North-China Electric Power University

华中师范大学自然科学基金资助

Supported by Natural Science Foundation of Central China Normal University

东南大学基金（项目编号： ）资助

Supported by Foundation of Southeast of University（项目编号： ）

西南交通大学基础学科研究基金（项目编号： ）资助

Supported by Foundation Sciences Southwest Jiaotong University（项目编号： ）

日本科学技术厅科学家交流项目（项目编号： ）

Supported by Japan STA Scientist Exchange Program （项目编号： ）

中央高校基本科研业务费专项资金资助

Supported by the Fundamental Research Funds for the Central Universities

International

China Inaugurates National R&D Plan

China began a national key research & development (R&D) plan on Tuesday, to streamline numerous state-funded scientific and technological programs.

The plan focuses on research in fields vital to the country's development and people's well-being, such as agriculture, energy, the environment and health, as well as strategic fields key to industrial competitiveness, innovation and national security, said Hou Jianguo, vice minister of science and technology.

The plan now covers 59 specific projects, Hou told a Ministry of Science and Technology press conference.

The plan merges several prominent state sci-tech programs, including the program 863 and program 973, focused on key fields such as biotechnology, space, information, automation, energy, new materials, telecommunications and marine technology.

Breakthroughs of the program 863 include supercomputer Tianhe-1, manned deep-sea research submersible Jiaolong, and super hybrid rice.

To address low efficiency resulting from redundant programs, over 100 programs will be merged into five plans: natural science, major sci-tech, key R&D plan, technical innovation and the sci-tech human resources.

The national key R&D plan is the first to be started. (Xinhua)

German Scientist Sees Unprecedented Opportunities in China

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More support for science projects and young scientists

China plans to widen the scope of, and give more support to, science projects led by young scientists. Earlier this month, the Ministry of Science and Technology released the 2021 guideline for applications for national key R&D programs, covering a wide range of areas, from new strategic electronic materials and rare earth new materials to high-end smart materials, and Earth observation and navigation.

Zheng Jianjian, an official from the ministry's Department of Resource Allocation and Management, said the aim is to dedicate about 80 percent of the initiative to setting up special projects for young scientists, and supporting more than 230 teams of young scientists this year.

According to the ministry's data, the government allocated 874 million yuan ($136.15 million) for 235 projects for young scientists during the 13th Five-Year Plan (2016-20) period, and under the national key R&D programs, projects in eight key areas were established for young scientists, including nanotechnology, synthetic biology and digital diagnosis equipment development.

"The plan aims to establish more projects, with a wider scope, for young scientists to further expand the fields of study for young researchers and to train more outstanding talents through a national-level platform," Zheng said.

Young researchers can participate in different fields of studies by choosing different research topics under separate projects, or in independent projects with no subordinate research topics, and without any budget evaluation, according to Zheng.

Unlike basic research projects funded by the National Natural Science Foundation, the key national R&D programs for young scientists will focus on major strategic tasks of the country and will be more demand-driven and goal-oriented, he said.

However, there is an age limit for the applicants. For frontier research areas, male applicants should be below 35 years of age and females below 38. For other fields, the age limit is below 38 for men and below 40 for women.

Addressing a news briefing while introducing China's next plan for innovation-driven development in February, Xie Xin, head of the ministry's Department of Resource Allocation and Management, said: "Young people will be the main force advancing science and technology during the 14th Five-Year Plan (2021-25) period. We should give them a higher and bigger platform, let them undertake independent tasks, take the lead in organizing national projects, and be bold and innovative in the process."

The guideline was issued as part of the 14th Five-Year Plan, which was adopted in March at the annual session of the National People's Congress. It is aimed at building a high-level talent pool of young scientists with international competitiveness.

Some academic institutions have already started inviting young talents to apply for the programs. For example, East China Normal University held a meeting in April, asking young researchers at the university to make full use of the preferential policy and build strong teams to apply and contribute to the country's scientific and technological development.

Some local governments, too, have issued policies to encourage youths to engage in innovation-oriented research. In April, Chongqing issued 17 concrete measures to attract and cultivate talents aged below 40. The Chongqing local government plans to select at least 100 young talents each year and give them incentives and funds of up to 400,000 yuan for research. As for young entrepreneurs, they can apply for interest-free loans of up to 2 million yuan loans, and high-quality entrepreneur projects can get an extra 500,000 yuan.

The other advantages include allowances, housing, vocational training and entrepreneurial guidance, which are aimed creating a more favorable environment for young talents to engage in research and innovation.

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Full Translation: China’s ‘New Generation Artificial Intelligence Development Plan’ (2017)

August 1, 2017

Graham Webster

Rogier Creemers

Paul Triolo

[The translation below was originally published as a PDF document alongside DigiChina analysis by the translators . It has not been changed except to render the name of the document as “New Generation Artificial Intelligence Development Plan” (AIDP) rather than “Next Generation,” matching other DigiChina analysis on the topic. The feedback e-mail has also been updated in the original introductory text. The original, unedited PDF version is here . –Graham Webster, October 2018]

On July 20[, 2017,] China’s State Council issued a seminal document , entitled A [New] Generation Artificial Intelligence Development Plan. This important aspirational document sets out a top-level design blueprint charting the country’s approach to developing artificial intelligence (AI) technology and applications, setting broad goals up to 2030.

Please find the full text of the document below. The translators produced analysis on the new document and Chinese AI ambitions for New America here .

The document has been translated into English by a group of experienced Chinese linguists with deep backgrounds on the subject matter and on China’s S&T establishment and current AI capabilities. They are: Rogier Creemers, Leiden Asia Centre; Graham Webster, Yale Law School Paul Tsai China Center; Paul Triolo, Eurasia Group; and Elsa Kania. The group is grateful to New America Cybersecurity Initiative Fellow John Costello for comments that helped to improve the translation.

Any errors in translation are the responsibility of the translators, and we welcome comments, which can be directed to the collaborators at this address: [email protected].

State Council Notice on the Issuance of the New Generation Artificial Intelligence Development Plan

Completed: July 8, 2017

Released: July 20, 2017

A New Generation Artificial Intelligence Development Plan

The rapid development of artificial intelligence (AI) will profoundly change human society and life and change the world. To seize the major strategic opportunity for the development of AI, to build China’s first-mover advantage in the development of AI, to accelerate the construction of an innovative nation and global power in science and technology, in accordance with the requirements of the CCP Central Committee and the State Council, this plan has been formulated.

I. The Strategic Situation

The development of AI has entered a new stage. After sixty years of evolution, especially in mobile Internet, big data, supercomputing, sensor networks, brain science, and other new theories and new technologies, under the joint impetus of powerful demands of economic and social development, AI’s development has accelerated, displaying deep learning, cross-domain integration, man-machine collaboration, the opening of swarm intelligence, autonomous control, and other new characteristics. Big data-driven cognitive learning, cross-media collaborative processing, and man-machine collaboration–strengthened intelligence, swarm integrated intelligence, and autonomous intelligent systems have become the focus of the development of AI. The results of brain science research inspired human-like intelligence that awaits action; the trends involving the chips, hardware, and platform have become apparent; the development of AI has entered into a new stage. At present, the development a new generation of AI and related disciplines, theoretical modeling, technological innovation, hardware and software upgrades, etc., all advance, provoking chain-style breakthroughs, promoting the acceleration of the elevation of economic and social domains from digitization and networkization to intelligentization.

AI has become a new focus of international competition. AI is a strategic technology that will lead in the future; the world’s major developed countries are taking the development of AI as a major strategy to enhance national competitiveness and protect national security; intensifying the introduction of plans and strategies for this core technology, top talent, standards and regulations, etc.; and trying to seize the initiative in the new round of international science and technology competition. At present, China’s situation in national security and international competition is more complex, and [China] must, looking at the world, take the development of AI to the national strategic level with systemic layout, take the initiative in planning, firmly seize the strategic initiative in the new stage of international competition in AI development, to create new competitive advantage, opening up the development of new space, and effectively protecting national security.

AI has become a new engine of economic development. AI has become the core driving force for a new round of industrial transformation, [which] will advance the release of the huge energy stored from the previous scientific and technological revolution and industrial transformation, and create a new powerful engine, reconstructing production, distribution, exchange, consumption, etc., links in economic activities; with new demands taking shape from the macro to the micro within each domain of intelligentization; with the birth of new technologies, new products, new industries, new formats, new models; triggering significant changes in economic structure, profound changes in human modes of production, lifestyle, and thinking; and a whole leap of achieving social productivity. China’s economic development enters a new normal, deepening the supply side of structural reform task is very arduous, [and China] must accelerate the rapid application of AI, cultivating and expanding AI industries to inject new kinetic energy into China’s economic development.

AI brings new opportunities for social construction. China is currently in the decisive stage of comprehensively constructing a moderately prosperous society. The challenges of population aging, environmental constraints, etc., remain serious. The widespread use of AI in education, medical care, pensions, environmental protection, urban operations, judicial services, and other fields will greatly improve the level of precision in public services, comprehensively enhancing the people’s quality of life. AI technologies can accurately sense, forecast, and provide early warning of major situations for infrastructure facilities and social security operations; grasp group cognition and psychological changes in a timely manner; and take the initiative in decision-making and reactions—which will significantly elevate the capability and level of social governance, playing an irreplaceable role in effectively maintaining social stability.

The uncertainties in the development of AI create new challenges. AI is a disruptive technology with widespread influence that may cause: transformation of employment structures; impact on legal and social theories; violations of personal privacy; challenges in international relations and norms; and other problems. It will have far-reaching effects on the management of government, economic security, and social stability, as well as global governance. While vigorously developing AI, we must attach great importance to the potential safety risks and challenges, strengthen the forward-looking prevention and guidance on restraint, minimize risk, and ensure the safe, reliable, and controllable development of AI.

China possesses a favorable foundation for the development of AI. The nation has: deployed the National Key Research and Development Plan’s key special projects, such as intelligent manufacturing; issued and implemented the “Internet +” and AI Three-Year Activities and Implementation Program, releasing a series of measures from science and technology research and development; and promoted applications and industrial development, and other aspects. As a result of many years of continuous accumulation, China has achieved important progress in the field of AI, with the number of international scientific and technology papers published and the number of inventions patented ranked second in the world, while achieving important breakthroughs in certain domains of core crucial technologies. Leading the world in voice recognition and visual recognition technologies; initially possessing the capability for leapfrog development in adaptive autonomous learning, intuitive sensing, comprehensive reasoning, hybrid intelligence, and swarm intelligence, etc.; with Chinese information processing, intelligent monitoring, biometric identification, industrial robots, service robots, and unmanned driving gradually entering practical application; AI innovation and entrepreneurship have become increasingly active, and a number of leading enterprises have accelerated their growth, receiving widespread concern and recognition internationally. Accelerate the accumulation of technological capabilities and massive data resources, the organization integration of both the huge demand for applications and an open market environment, which together constitute China’s unique advantage in AI development.

At the same time, we must also clearly see that there is still a gap between China’s overall level of development of AI relative to that of developed countries—lacking major original results in the basic theory, core algorithms, key equipment, high-end chips, major products and systems, foundational materials, components, software and interfaces, etc. Scientific research institutions and enterprises do not yet possess international influence upon ecological cycles and supply chain, lacking a systematic research and development layout; cutting-edge talent for AI is far from meeting demand. Adapting to the development of AI requires the urgent improvement of basic infrastructure, policies and regulations, and standards systems.

Facing a new situation and new demands, we must take the initiative to pursue and adapt to change, firmly seize the major historic opportunity for the development of AI, stick closely to development, study and evaluate the general trends, take the initiative to plan, grasp the direction, seize the opportunity, lead the world in new trends in the development of AI, serve economic and social development, and support national security, promoting the overall elevation of the nation’s competitiveness and leapfrog development.

II. The Overall Requirements (1) Guiding Ideology

Comprehensively implement the spirit of the 18th Party Congress and 18th Central Committee’s Third, Fourth, Fifth, and Sixth Plenary Sessions. Thoroughly study and implement the spirit of General Secretary Xi Jinping’s series of important sayings and new concepts, new ideas, and new strategy for governing the country; according to the “five in one” overall layout and “four comprehensives” strategic layout, conscientiously implement the CPC Central Committee and State Council decision-making arrangements, deeply implement the innovation-driven development strategy to accelerate the deep integration of AI with the economy, society and national defense as a primary line, to enhance: scientific and technological innovation capacity for a new generation of AI as the main direction of attack; intelligent economy development; smart society construction; protecting national security; building of knowledge clusters, technology clusters, and industry clusters mutually integrated with talent, system, and culture, for a mutually supporting ecosystem, advancing intelligentization as the center of humanity’s sustainable development. Comprehensively enhance society’s productive forces, comprehensive national power, and national competitiveness, in order to provide strong support to accelerate the construction of an innovative new-type nation and global science and technology power, to achieve the two centennial goals and the great rejuvenation of the Chinese nation.

(2) The Basic Principles

Technology-Led. Grasp the global development trend of AI, highlight the deployment of forward-looking research and development, explore the layout in key frontier domains, long-term support, and strive to achieve transformational and disruptive breakthroughs in theory, methods, tools, and systems; comprehensively enhance original innovation capability in AI, accelerate the construction of a first-mover advantage, to achieve high-end leading development.

Systems Layout. According to the different characteristics of foundational research, technological research and development, industrial development, and commercial applications, formulate a targeted systems development strategy. Fully give play to the advantages of the socialist system to concentrate forces to do major undertakings, promote the planning and layout of projects, bases, and a talent pool, organically link already-deployed major projects and new missions, continue current urgent needs and long-term development echelons, construct innovation capacity, create a collaborative force for institutional reforms and the policy environment.

Market-Dominant. Follow the rules of the market, remain oriented toward application, highlight companies’ choices on the technological line and primary role in the development of commercial product standards, accelerate the commercialization of AI technology and results, and create a competitive advantage. Grasp well the division of labor between government and the market, better take advantage of the government in planning and guidance, policy support, security and guarding, market regulation, environmental construction, the formulation of ethical regulations, etc.

Open-Source and Open. Advocate the concept of open-source sharing, and promote the concept of industry, academia, research, and production units each innovating and in principal pursuing joint innovation and sharing. Follow the coordinated development law for economic and national defense construction; promote two-way conversion and application for military and civilian scientific and technological achievements and co-construction and sharing of military and civilian innovation resources; form an all-element, multi-domain, highly efficient new pattern of civil-military integration. Actively participate in global research and development and management of AI, and optimize the allocation of innovative resources on a global scale.

(3) Strategic Objectives

These are divided into the following three steps:

First, by 2020, the overall technology and application of AI will be in step with globally advanced levels, the AI industry will have become a new important economic growth point, and AI technology applications will have become a new way to improve people’s livelihoods, strongly supporting [China’s] entrance into the ranks of innovative nations and comprehensively achieving the struggle toward the goal of a moderately prosperous society.

• By 2020 China will have achieved important progress in a new generation of AI theories and technologies. It will have actualized important progress in big data intelligence, cross-medium intelligence, swarm intelligence, hybrid enhanced intelligence, and autonomous intelligence systems, and will have achieved important progress in other foundational theories and core technologies; the country will have achieved iconic advances in AI models and methods, core devices, high-end equipment, and foundational software.
• The AI industry’s competitiveness will have entered the first echelon internationally. China will have established initial AI technology standards, service systems, and industrial ecological system chains. It will have cultivated a number of the world’s leading AI backbone enterprises, with the scale of AI’s core industry exceeding 150 billion RMB, and exceeding 1 trillion RMB as driven by the scale of related industries.
• The AI development environment will be further optimized, opening up new applications in important domains, gathering a number of high-level personnel and innovation teams, and initially establishing AI ethical norms, policies, and regulations in some areas.

Second, by 2025, China will achieve major breakthroughs in basic theories for AI, such that some technologies and applications achieve a world-leading level and AI becomes the main driving force for China’s industrial upgrading and economic transformation, while intelligent social construction has made positive progress.

• By 2025, a new generation of AI theory and technology system will be initially established, as AI with autonomous learning ability achieves breakthroughs in many areas to obtain leading research results.
• The AI industry will enter into the global high-end value chain. This new-generation AI will be widely used in intelligent manufacturing, intelligent medicine, intelligent city, intelligent agriculture, national defense construction, and other fields, while the scale of AI’s core industry will be more than 400 billion RMB, and the scale of related industries will exceed 5 trillion RMB.
• By 2025 China will have seen the initial establishment of AI laws and regulations, ethical norms and policy systems, and the formation of AI security assessment and control capabilities.

Third, by 2030, China’s AI theories, technologies, and applications should achieve world-leading levels, making China the world’s primary AI innovation center, achieving visible results in intelligent economy and intelligent society applications, and laying an important foundation for becoming a leading innovation-style nation and an economic power.

• China will have formed a more mature new-generation AI theory and technology system. The country will achieve major breakthroughs in brain-inspired intelligence, autonomous intelligence, hybrid intelligence, swarm intelligence, and other areas, having important impact in the domain of international AI research and occupying the commanding heights of AI technology.
• AI industry competitiveness will reach the world-leading level. AI should be expansively deepened and greatly expanded into production and livelihood, social governance, national defense construction, and in all aspects of applications, will become an expansive core technology for key systems, support platforms, and the intelligent application of a complete industrial chain and high-end industrial clusters, with AI core industry scale exceeding 1 trillion RMB, and with the scale of related industries exceeding 10 trillion RMB.
• China will have established a number of world-leading AI technology innovation and personnel training centers (or bases), and will have constructed more comprehensive AI laws and regulations, and an ethical norms and policy system.

(4) Overall Deployment

The development of AI is a complex systemic project related to the overall situation, that must be arranged in accordance with “build one system, grasp the two attributes, adhere to the trinity, and strengthen the four supports” to form a strategic path for the healthy and sustainable development of AI.

Construct an open and cooperative AI technology innovation system. Target the weak foundation in original theories, and the key difficulties and deficiencies in major products and systems. Establish foundational theories and a common technology system for a new generation of AI, laying out the construction of a major scientific and technological innovation base. Strengthen the high-end talent team in AI to promote innovation and cooperative interactions. Form a continuous innovation capability for AI.

Grasp AI’s characteristic high degree of integration of technological attributes and social attributes. It is necessary not only to increase efforts in the research and development and applications of AI, maximizing the potential of AI, but also to predict AI’s challenges, coordinate industrial policies, innovate in policies and social policies, achieve the coordination of encouraging development and reasonable regulation, and maximize risk prevention.

Adhere to the promotion of the trinity of breakthroughs in AI research and development, product applications, and fostering industry development. Adapt to the characteristics and trends of AI development. Strengthen the deep integration of the innovation chain and industrial chain, the interactive evolution of technology supply and market demand. Take technological breakthroughs to promote domain applications and industrial upgrading. Through application demonstrations, promote the optimization of technologies and systems. At the same time as greatly promoting technology applications and industrial development, strengthen long-term R&D layout and research. Achieve rolling development and continuous improvement. Ensure that theory is in the front, the technological commanding heights are occupied, and applications are secure and controllable.

Fully support science and technology, the economy, social development, and national security . Drive comprehensive elevation on national innovative capability with AI technological breakthroughs. Lead in the process of constructing a global science and technology power. Through strengthening intelligent industry and cultivating the intelligent economy, create a new growth cycle for China’s next decade or even decades of economic prosperity. Through building an intelligent society, promote the improvement of people’s livelihoods and welfare and implement people-centric development thinking. Through AI, elevate national defense strength and assure and protect national security.

III. Focus Tasks

Based on the overall picture of national development, accurately grasp the global development trends of AI, find the correct openings for breakthroughs and directions for the main thrust, comprehensively strengthen basic science and technology innovation capabilities, comprehensively expand the depth and breadth of application in focus areas, and comprehensively enhance the built-in intelligence levels of applications in economic and social development, as well as in national defence.

(1) Build open and coordinated AI science and technology innovation systems

Focus on increasing the supply of AI innovation sources; strengthen deployments in areas such as advanced basic theory, key general technologies, basic platforms, talent teams, etc.; stimulate open-source sharing; systematically enhance sustained innovation capabilities; ensure that our country’s AI science and technology levels ascend to the leading global ranks; and make ever more contributions to the development of global AI.

1. Establish basic theory systems for a new generation of AI

Focus on major advanced scientific AI questions; concurrently deal with present needs and long-term developments; make breakthroughs in basic AI application theory bottlenecks; give priority to deploying basic research that may trigger paradigmatic change in AI; stimulate the intersection and convergence of disciplines; and provide powerful scientific reserves for the sustained development and profound application of AI.

Make breakthroughs in basic application theory bottlenecks. Aim at basic theoretical orientations with clear applied objectives, which promise to trigger an upgrade of AI technology, strengthen basic theoretical research on big data intelligence, cross-media sensing and computing, human-machine blended intelligence, mass intelligence, autonomous cooperation and decision-making, etc. Focus on breakthroughs in big data intelligence, unsupervised learning, comprehensive deep reasoning and other such difficult issues. Establish data-driven cognitive computing models with natural language understanding at the core, and shape capabilities to go from big data to knowledge, and from knowledge to decision-making. Focus on breakthroughs in cross-media sensing and computing theory, including theories and methods for: low-cost and low-energy smart sensing, active sensing in complex landscapes, listening comprehension in the natural environment as well as language sensing, autonomous multimedia learning, etc. Realize superhuman sensing and highly-dynamic, high-dimensional, and multi-model distributed large-landscape sensing. The focuses on breakthroughs in blended and enhanced intelligence theory are: theories on human-machine cooperative and blended environmental understanding, decision-making, and learning; intuitive reasoning and causal models, recall and knowledge evolution, etc.; realizing blended and enhanced intelligence where learning and reflection approach or exceed human intelligence levels. The focuses for breakthroughs in collective intelligence theory are: theories and methods for the organization, emergence and learning of collective intelligence; establishment of expressible and computable mass intelligence incentive algorithms and models; and shaping Internet-based collective intelligence theory systems. The focuses for breakthroughs in autonomous coordination, control and optimized decision-making theory are: theories concerning coordination sensing and interaction aimed at autonomous unmanned systems; autonomous coordination control and optimized decision-making; knowledge-driven human-machine-object triangular coordination and interoperation, etc.; and shaping novel theoretical systems and frameworks for innovation in autonomous intelligence and unmanned systems.

Arrange advanced basic theoretical research. Aim for a direction that may trigger a paradigmatic change in AI, far-sightedly arrange research on high-level machine learning, brain-inspired intelligence computing, quantum smart computing, and other such cross-domain basic theories. The focuses for breakthroughs in high-level machine learning theory are theories and methods concerning self-adaptive learning, autonomous learning, etc., and realizing AI with high interpretative and strong generalization capabilities. The focuses for breakthroughs in brain-inspired intelligence computing theory are: theories concerning brain-inspired information encoding, processing, recall, learning and reasoning; the creation of brain-inspired complex systems and brain-inspired control theories and methods; and establishment of new large-scale brain-inspired intelligence computing models and brain-inspired understanding computing models. The focuses for breakthroughs in quantum computing theory are: methods for quantum-accelerated machine learning; establishment of high-performance computing and quantum computing convergence models; and shaping high-efficiency, accurate, and autonomous quantum AI system setups.

Launch cross-disciplinary exploratory research. Promote the intersection and convergence of AI with neurology, cognitive science, quantum science, psychology, mathematics, economics, sociology and other such related basic disciplines; strengthen basic theoretical mathematical research to guide the development of AI algorithms and models; focus on researching the basic theoretical questions of AI legal principles; support exploratory research that is strongly original, and where there is no consensus; encourage scientists to explore freely; dare to overcome front-line scientific difficulties in AI; create ever more original theory; and make ever more original discoveries.

Box 1: Basic Theories

• Big data intelligence theory. Research new data-driven and knowledge-driven AI methods, theories and methods for sensing computing theory with natural language understanding, images and figures at the core, comprehensive deep reasoning and creative AI theories and methods, basic theories and frameworks on smart decision-making with incomplete information, data-driven common AI data models and theories, etc.
• Cross-media sensing and computing theory. Research sensing that exceeds human visual abilities, active visual sensing and computing aimed at the real world, auditory sensing and computing of natural acoustic scenes, language sensing and computing in an environment of natural interaction, human sensing and computing aimed at asynchronous orders, autonomous learning aimed at smart media sensing, and urban omnidimensional smart sensing and reasoning engines.
• Hybrid and enhanced intelligence theory. Research hybridization and convergence where “the human is in the loop,” behavioral strengthening through human-machine smart symbiosis and brain-machine coordination, intuitive machine reasoning and causal models, associative recall models and knowledge evolution methods, complex data and task blended and enhanced intelligence learning methods, cloud robotics coordination computing methods, and situational comprehension and human-machine group coordination in real-world environments.
• Swarm intelligence theory. Research swarm intelligence structural theory and organizational methods, swarm intelligence incentive mechanisms and emergence mechanisms, swarm intelligence learning theories and methods, common swarm intelligence computing paradigms and models.
• Autonomous coordination and control, and optimized decision-making theory. Research coordination sensing and interaction aimed at autonomous unmanned systems, coordination, control and optimized decision-making aimed at autonomous and unmanned systems, knowledge-driven human-machine-object triangular coordination and interoperability theories.
• High-level machine learning theory. Research basic statistical learning theories, reasoning and decision-making under uncertainty, distributed learning and interaction, learning while protecting privacy, small-sample learning, deep intensive learning, unsupervised learning, semi-supervised learning, active learning and other such learning theories and efficient models.
• Brain-inspired intelligence computing theory. Research theories and methods on rain-inspired sensing, brain-inspired learning, and brain-inspired recall mechanisms and computing blends, brain-inspired complex systems, brain-inspired control, etc.
• Quantum intelligent computing theory. Explore cognitive quantum models and intrinsic mechanisms, research efficient quantum intelligence models and algorithms, high-performance and high-bitrate quantum AI processors, real-time quantum AI systems that can exchange information with the outside world, etc.

2. Build a next-generation AI key general technology system

Focusing on the urgent need to raise China’s international competitiveness in AI, next-generation AI key general technology R&D and deployment should make algorithms the core; data and hardware the foundation; and upping capabilities in sensing and recognition, knowledge computing, cognitive reasoning, executing motion, and human-machine interface the emphasis; in order to form openly compatible, stable and mature technological systems.

Knowledge computing engine and knowledge service technology. Key breakthroughs in knowledge processing, deep search, and visual interactive core technology; realization of automatic acquisition of incrementally growing knowledge; possession of concept discernment, object discovery, attribute prediction, evolutionary knowledge modeling, and relationship discovery capabilities; the formation of multi-billion-scale, multi-source, multi-disciplinary, multi-data type, and cross-medium knowledge maps.

Cross-medium analytical reasoning technology. Key breakthroughs in cross-medium unified indicators; relational understanding and knowledge mining; knowledge map structure and learning; knowledge evolution and reasoning; intelligent description and generation, etc., technology. Realization of cross-medium knowledge indicators, analysis, mining, reasoning, evolution, and utilization. Construct analytic reasoning engines.

Key swarm intelligence technology. Key breakthroughs on the basis of the popularization of the internet, mass collaboration, knowledge resource management, and open sharing, etc., technologies. Building frameworks to display swarm intelligence knowledge. Realize the integration and strengthening of swarm intelligence-based knowledge acquisition and swarm intelligence under open development conditions. Support swarm perception, cooperation, and evolution at a national, tens-of-millions scale.

New architecture and new technology for hybrid and enhanced intelligence. Key breakthroughs in human-machine interaction for perception and execution integration models, new types of intelligent computing-fronted sensors, common use hybrid architecture, etc., core technologies. Build autonomous, environmentally adaptable hybrid enhanced intelligent systems, human-machine hybrid enhanced intelligent systems and support environments.

Intelligent technologies of autonomous unmanned systems. Key breakthroughs in autonomous unmanned system computing architecture, complex situational environment perception and understanding, real-time accurate positioning, adaptable, intelligent navigation in complex environments, etc., general technologies. Unmanned and autonomously controlled systems including automobiles, ships, automatic driving in traffic, etc., intelligent technologies. Develop service robots, special-purpose robots, etc., core technologies and support unmanned system application and manufacturing development.

Intelligent virtual reality modeling technology. Key breakthroughs in intelligent modeling technology for virtual counterparts. Increasing the sociality, diversity, and lifelike quality of virtual reality intelligent counterpart behavior. Realize the organic integration, high efficiency, and interactivity of virtual reality and augmented reality, etc., technologies.

Intelligent computing chips and systems. Key breakthroughs in high energy efficiency, reconfigurable brain-inspired computing chips and brain-inspired visual sensor systems with computational imaging capabilities. Research and develop high-efficiency brain-inspired neural network architectures and hardware systems with autonomous learning capabilities. Realize brain-inspired intelligent systems with multimedia sensory information understanding, intelligence growth, and common sense reasoning capabilities.

Natural language processing technology. Key breakthroughs in natural language grammar logic, word-concept symbols, and deep semantic analysis core technologies. Advance effective human-machine communication and free interaction. Realize multi-style, multi-language, multi-domain natural language intelligent understanding and automated [results] generation.

Box 2: Key General Technologies

• Knowledge computing engines and knowledge service technology. Researching knowledge computing and visual interaction engines; researching innovative design, digital creation, and commercial intelligence with visual media at the core; developing large-scale organic data knowledge discovery.
• Cross-medium analytic reasoning technology. Researching cross-medium unified indicators, connected understanding and knowledge mining, knowledge map building and learning, knowledge evolution and inference, intelligent description and generation, etc., technology; developing cross-medium analytic reasoning engine and verification systems.
• Key swarm intelligence technology. Developing swarm intelligence’s active perception and discovery, knowledge gain and generation, cooperation and sharing, evaluation and evolution, human-machine integration and enhancement, self-preservation and mutual security, etc., key technology studies; building service system architecture for the crowd intelligence space; researching mobile crowd intelligent coordinated decision making and control technologies.
• Hybrid enhanced intelligent new architectures and technologies. Researching hybrid enhanced intelligent core technology and cognitive computing frameworks; new-model hybrid computing architectures, human-machine collective driving, online intelligent learning technology, and hybrid enhanced frameworks for simultaneous management and control.
• Autonomous unmanned systems intelligent technology. Researching unmanned autonomous control intelligent technology for automobiles, ships, traffic, automatic driving, etc.; service, space, maritime, and polar robot technology; unmanned workshop/intelligent factory intelligent technology; high-end intelligent control technology and autonomous unmanned operating systems. Researching positioning, navigation, recognition, etc., robotic and mechanical arm autonomous control technology for visual sensing in complex environments.
• Virtual reality intelligent modeling technology. Researching mathematical expression and modeling methods for virtual counterpart intelligent behavior; problems such as natural, persistent, and deep exchange between users and virtual counterparts and virtual environments; intelligent counterpart modeling technology and method systems.
• Intelligent computing chips and systems. Researching neural network processors, as well as high-energy efficiency, reconfigurable brain-inspired computing chips, etc.; new-model perception chips and systems, intelligent computing system structure and systems, and AI operating systems. Researching architectures suitable for AI hybrid architectures, etc.
• Natural language processing technology. Researching short text computing and analysis technology, cross-language text mining technology and turning toward semantic comprehension technology for machine cognitive intelligence, and human-machine interaction systems for multimedia information comprehension.

3. Coordinate the layout of AI innovation platforms

Construct AI innovation platforms. Strengthen the foundational support for AI research and development and applications. AI open-source hardware and software infrastructure platforms should focus on building and supporting unified computing frameworks for knowledge reasoning, probability statistics, depth learning, and other AI paradigms. Form and promote an ecological chain of platforms for interaction and synergies among AI software, hardware, and intelligent clouds. The group intelligent service platform should focus on the construction of knowledge resource management and the open sharing tools based on the large-scale cooperation on the Internet. Create a platform and service environment for the innovation of the industry and university. The hybrid enhanced intelligent support platforms should focus on the construction of a heterogeneous real-time computing engine supporting large-scale training and a new computing clusters, providing a service-oriented, systematic platform and solution for complex intelligent computing. Autonomous unmanned system support platform focuses on the construction of autonomous system environmental awareness, autonomous collaborative control, intelligent decision-making and other AI common core technology support systems. Create development and test environments for open, modular, reconfigurable autonomous unmanned systems. AI basic data and security detection platforms should focus on the construction of AI for the public data resource library, the standard test data set, cloud service platform, the formation of AI algorithms and platform security test evaluation methods, techniques, norms and tools, promoting the open sourcing and openness of all kinds of common software and technology platform. Promote military-civilian sharing and joint use for all kinds of platforms in accordance with the requirements of deep military-civil integration related provisions.

Box 3: Basic Support Platforms

• AI Open-Source Hardware and Software Infrastructure and Platforms. Establish big data and AI open-source software platforms, terminal, and cloud collaborative AI cloud service platforms, new multi-intelligent sensor and integrated platforms, new product design platforms based on AI hardware, and future network, big data intelligent service platforms.
• Group Intelligent Service Platforms. Establish group knowledge-based computing and support platforms, science and technology public service systems, group intelligent software development and verification automation systems, group intelligent software learning and innovation systems, open environment cluster decision-making systems, and group-sharing economic service systems.
• Hybrid Enhanced Intelligent Support Platforms. Establish AI supercomputing centers, large-scale super intelligent computing support environments, online intelligent education platforms, “human-in-the-loop” driving brains, intelligent platforms for complexity analyses and risk assessment in industrial development, intelligent security platforms to support nuclear power security operations, and research and development and testing platforms for human-machine joint driving technology.
• Autonomous Unmanned System Support Platforms. Establish common core technology and support platforms, independent unmanned systems, independent control of unmanned aerial vehicles, and automatic driving support platforms for auto, ship and rail traffic, service robots, space robots, marine robots, polar robot support platforms, technical support platforms for intelligent factory and intelligent control equipment, etc.
• AI Basic Data and Security Detection Platforms. Construct artificial data-oriented public data resource libraries, standard test data sets, and cloud service platforms. Establish test models and evaluation models for the security of AI algorithms and platforms. Research and develop security evaluation tools for AI algorithms and platforms.

4. Accelerate the training and gathering of high-end AI talent

Make the construction of a high-end talent team of the utmost importance in the development of AI. Adhere to the combination of training and introduction. Improve the AI education system, strengthen the construction of a talent pool and echelons, especially accelerate the introduction of the world’s top talent and young talent, forming China’s AI top talent base.

Cultivate high-level of AI innovative talents and teams. Support and cultivate the development potential of leading AI talent. Strengthen professional and technical personnel training for basic research, applied research, operations and maintenance aspects of AI. Pay attention to the training of compound talents, focusing on cultivating vertical composite talents for AI theory, methods, technology, products, and application, and compound talents who master the “AI +” economy, society, management, standards, law, and other horizontal areas. Through major research and development tasks and base and platform construction, converge high-end talents in AI. Create high-level innovation teams in a number of AI key domains. Encourage and guide domestic innovative talents and the teams to strengthen cooperation with the world’s top AI research institutions.

Increase the introduction of high-end AI talent. Open up specialized channels and implement special policies to achieve the precise introduction of peak AI talent. Focus on the introduction of international top scientists and high-level innovation teams in neural awareness, machine learning, automatic driving, intelligent robots, and other areas. Encourage the use of flexible introduction of AI talent through project cooperation, technical advice, etc. Coordinate the use of the “Thousands Talents” plan and other existing talent plans to strengthen the field of AI talents, especially through the introduction of outstanding young talent. Improve enterprise human capital cost accounting and related policies. Encourage enterprises and scientific research institutions to introduce AI talent.

Construct an AI academic discipline. Improve the disciplinary layout of the AI domain. Establish AI majors. Promote the construction of a discipline in the domain of AI. Establish AI institutes as soon as possible in pilot institutions. Increase the enrollment places for masters and PhDs in working in AI and related disciplines. Encourage colleges and universities to broaden the content of AI professional education on an original basis. Create a new model of “AI + X” compound professional training, attaching importance to cross-integration of professional education for AI and mathematics, computer science, physics, biology, psychology, sociology, law, and other disciplines. Strengthen cooperation in production and research. Encourage universities, research institutes, enterprises and other institutions to carry out the construction of an AI discipline.

(2) Fostering a high-end, highly efficient smart economy

Accelerate the fostering of an AI industry with a major leading and driving effect, stimulate the profound convergence of AI and all industrial areas, and create data-driven smart economic patterns with human-machine coordination, cross-sectoral convergence, and joint creation and sharing. Data and knowledge will become the first factor for economic growth; human-machine coordination will become the mainstream method of production and service; cross-sectoral convergence will become an important economic model; joint creation and sharing will become basic characteristics of the economic ecology; individualized demands and made-to-order will become new consumption trends; and productivity will increase substantially, drive industries to migrate towards the high end of value chains, powerfully support the development of the real economy, and comprehensively increase the quality and efficiency of economic development.

1. Forcefully develop new AI industries

Accelerate the transformation and application of key AI technologies, stimulate the integration of technologies with commercial model innovation, promote the innovation of smart products in focus areas, vigorously foster new AI business models, compose high-end industry chains, and forge AI industry groups with international competitiveness.

Smart software and hardware. Develop operating systems, databases, intermediary devices, development tools, and other such key software and hardware aimed at AI; make breakthroughs in graphic processing and other such core hardware; research solution plans for smart systems in pattern recognition, voice understanding, machine translation, smart interaction, knowledge processing, control and decision-making, etc.; and foster and expand basic software and hardware industries aimed at AI.

Smart robots. Tackle core components and special sensors for smart robots, perfect hardware interface standards, software interface standards, and safe usage standards for smart robots. Research and develop smart industrial robots and smart service robots, realize large-scale application, and enter into global markets. Research, produce, and popularize space robots, maritime robots, polar robots, and other such special kinds of smart robots. Establish smart robot standard systems and security norms.

Smart delivery tools. Develop self-driving vehicles and rail traffic systems; strengthen the integration and coordination of vehicle load sensing, automatic driving, the Internet of cars, the Internet of Things, and other such technologies; develop smart traffic sensing systems, create national indigenous automatic driving platform technology systems and industrial assembly capabilities; and explore self-driving vehicle sharing models. Develop consumer and commercial unmanned aircraft and unmanned ships, and establish and trial specialized service systems for authentication, monitoring, technology competition, etc., perfect management measures for the space and maritime areas.

Virtual reality and augmented reality. Make breakthroughs in key technologies such as high-performance software modelling, content capturing and generation, augmented reality and human-machine interaction, integrated environments and tools, etc. Research and create virtual display devices, optical devices, high-performance three-dimensional display devices, development engines, and other such products. Establish standards and evaluation systems for virtual reality and augmented reality technologies, products, and services, and promote their converged application in focus sectors.

Smart terminals. Accelerate the research and development of smart terminal core technologies and products, develop new-generation smart phones, on-board smart terminals for cars, and other such mobile smart terminal products and equipment. Encourage the research and development of smart watches, smart earpieces, smart glasses, and other such wearable terminal products, and expand product forms and application services.

Basic Internet of Things devices. Develop high-sensitivity and highly reliable smart sensors and chips supporting the new-generation Internet of Things. Make progress in core Internet of Things technologies such as RFID and short-distance machine communications, as well as key components such as low-power processors.

2. Accelerate and promote the upgrade of industrial intelligentization

Promote the converged innovation of AI in all sectors. Launch AI application demonstrations and trials in focus sectors and areas such as manufacturing, agriculture, logistics, finance, commerce, household goods, etc. Promote the application of AI at scale, and comprehensively upgrade the smartness level of industrial development.

Smart manufacturing. Focus on the major demands for building a strong manufacturing country, move forward the integrated application of systems such as key technologies and equipment for smart manufacturing, core supporting software, the industrial internet, etc. Research and develop smart products and smart connected products, tools and systems that can be used in smart manufacturing, and smart manufacturing cloud service platforms. Popularize smart manufacturing processes, distributed smart manufacturing, networked coordinated manufacturing, long-distance diagnosis and operational services, and other such novel manufacturing models. Establish smart manufacturing standard systems, and move forward with the intelligentization of manufacturing activities across the entire lifecycle.

Smart agriculture. Research and formulate smart agricultural sensing and control systems, smart agricultural equipment, autonomous tasking systems for farming equipment across fields, etc. Establish and complete smart agriculture information remote sensing and monitoring networks integrating air, space, and land components. Establish model agriculture big data smart decision-making and analysis systems, launch trials of smart farms, smart plant factories, smart pastures, smart fisheries, smart orchards, smart farm produce processing workshops, green and smart farm product supply chains and other such integrated applications.

Smart logistics. Strengthen research, development and broad use of smart logistics equipment for smart loading, unloading, and transportation; parcel sorting, processing and delivery; etc. Establish smart deep-sensing storage systems, and enhance storage and operational management levels and efficiency. Perfect smart logistics public information platforms and command systems, product quality authentication and tracing systems, smart distribution and dispatch systems, etc.

Smart finance. Establish big data systems for finance, and enhance multimedia data processing and comprehension capabilities for finance. Innovate smart financial products and services, develop new financial business models. Encourage the financial sector to use smart customer service, smart inspection, and other such technologies and equipment. Build smart warning and prevention systems for financial risk.

Smart commerce. Encourage the application of cross-media analysis and reasoning, knowledge computing engines and knowledge services, and other such new technologies in the commercial area, and popularize AI-based novel commercial services and decision-making systems. Build cross-medium data platforms covering geographic positioning, online media, urban basic data, etc., and support enterprises’ launching smart services. Encourage the provision of made-to-order commercial smart decision-making services focusing on individual demands and enterprise management.

Smart household goods. Strengthen the converged application of AI technology and household and building systems, and enhance the smartness levels of building facilities and household goods. Research, develop, and use household connection and interactivity agreements, as well as interface standards suited for different application settings. Enhance sensing and connection capabilities of household electrical appliances, durable goods and other such household products. Support smart household enterprises in innovating new service models, and promote interactive and sharing solutions and plans.

3. Forcefully develop smart enterprises

Promote the upgrading of enterprises’ smartness levels on a large scale. Support and guide enterprises to use new AI technologies in core operational segments such as design, production, management, logistics, sales, etc. Build novel enterprise organization structures and operational models; create smart and converged business models for manufacturing, services, and finance; and develop individualized made-to-order; and broaden smart product supply. Encourage large-scale Internet enterprises to build cloud manufacturing platforms and service platforms, and provide online key industry software and model databases aimed at manufacturing enterprises. Launch outsourcing services for manufacturing capacity, and promote the development of smartness among small and mid-size enterprises.

Popularize the use of smart factories. Strengthen the application and demonstration of key technologies and system methods for smart factories. Focus on popularizing production line reconstruction and dynamic smart control, production faculty smart interconnection and cloud data collection, multi-dimensional human-machine-object coordination, interoperability, and other such technologies. Encourage and guide enterprises to build factory big data systems, networked distributed production facilities, etc. Realize the networking of production equipment, the visualization of production data, the transparency of production processes, and the automation of production sites; and enhance the smartness levels of factory operational management.

Accelerate the fostering of AI industry-leading enterprises. Accelerate the creation of global leading AI enterprises and brands in advantageous areas such as unmanned aircraft, speech recognition, pattern recognition, etc. Accelerate the fostering of a batch of key enterprises in novel areas such as smart robots, smart cars, wearable equipment, virtual reality, etc. Support AI enterprises to strengthen their patent structures, and take the lead in or participate in the formulation of international standards. Promote domestic advantageous enterprises, sectoral organizations, scientific research bodies, higher education institutes, etc., to jointly establish the AI Industry and Technology Innovation Alliance of China. Support key backbone enterprises to build open source hardware factories, open source software platforms, create innovative ecologies integrating all kinds of resources, stimulate small and mid-size AI enterprises to develop and to be used in all areas. Support all kinds of bodies and platforms to provide specialized services aimed at AI enterprises.

4. Create AI innovation heights

Combined with each locality’s foundation and advantages, according to the field of AI applications classifications, advance the layout of the relevant industries. Encourage local industry chains and innovation chains around AI. Gather high-end factors, high-end enterprises, and high-end talent. Build AI industry clusters and heights of innovation.

Launch AI innovation application pilot demonstrations. In areas where the AI foundation is favorable and its development potential bigger, organize and launch national AI innovation experiments. Explore systems and mechanisms, policy and regulation, the cultivation of talent, and other major reforms. Promote the transformation of the AI achievements, major product integrated innovation, and demonstration of applications. Form replicable, promotable experience, leading to the promotion of intelligent economy and intelligent social development.

Construct national AI industrial parks. Rely upon national independent innovation demonstration areas and the national high-tech industry development zone and other innovative vectors. Strengthen science and technology talent, finance, policy, and other elements of the optimal allocation and combination. Accelerate the construction of AI industry innovation cluster.

Construct national AI mass innovation bases. Relying on colleges and universities and scientific research institutes concentrated in localities, build AI field professionalized innovation platforms and other new entrepreneurial service agencies. Construct a number of low-cost, convenient, all-factor, open-style AI ‘hackerspaces.’ Improve incubation services system, promote the transformation of AI scientific and technological achievements, and support AI innovation and entrepreneurship.

(3) Construct a safe and convenient intelligent society

Based on the goal of improving people’s living standards and quality, speed up and deepen the applications of AI, increase the level of intelligentization of the whole society to form an all-encompassing and ubiquitous intelligent environment. Increasingly, repetitive, dangerous tasks will be completed by AI, while individual creativity will play a greater role. Form more high-quality and high comfort jobs; make precision intelligent services more diverse, such that people can maximize their enjoyment of high quality services and convenient life. Through a substantial increase in the level of intelligentization of social governance, make social operations more safe and efficient.

1. Develop convenient and efficient intelligent services

Accelerate the application of innovative AI throughout education, health care, pension and other urgent needs involving people’s livelihood, to provide for the public personalized, diversified, high-quality services.

Intelligent Education. Utilize intelligent technology to accelerate and promote a personnel training model and reform to teaching methods; establish new-type education systems, including intelligent learning and interactive learning. Launch the construction of intelligent campuses; promote AI in teaching, management, resource construction, and other full-scale applications. Develop three-dimensional integrated teaching field, based on big data intelligent online learning and education platforms. Develop intelligent educational assistants; establish intelligent, fast and comprehensive education analysis system. Establish a learner-centered educational environment, and provide precision-deployed education services, achieve daily education and lifelong education.

Intelligent Medical Care. Promote the use of new models and new methods of AI treatment, establish a rapid, accurate intelligent medical system. Explore intelligent hospital construction, develop human-machine coordinated surgical robots and intelligent clinic assistants. Pursue research and development on flexible wearable, biologically compatible physiological monitoring systems, research and development of human-computer collaboration intelligent clinical diagnosis and treatment programs. Achieve intelligent image recognition, pathology classification, and intelligent multi-disciplinary consultation. Carry out large-scale genome recognition, proteomics, metabolomics, and other research and development of new drugs based on AI, promote intelligent pharmaceutical regulation. Strengthen epidemic intelligence monitoring, prevention, and control.

Intelligent Health and Elder Care Systems. Strengthen community intelligent health management, achieve breakthroughs in big data analysis, Internet of Things, and other key technologies. Research and develop health management wearable equipment and home intelligent health testing and monitoring equipment. Promote changes in health management from point-like monitoring to continuous monitoring, from short process management to long process management. Construct intelligent elder care communities and institutions; build a safe and convenient intelligent pension infrastructure system. Strengthen the intelligentization of products for elderly persons and intelligent products suitable for the aged. Develop audio-visual aid equipment, physical auxiliary equipment, and other intelligent home care equipment, expanding the elderly’s activity space. Develop mobile social and service platform for the elderly and emotional escort assistant to enhance the quality of life of the elderly.

2. Promote the intelligentization of social governance

Promote the application of AI technology for administrative management, judicial management, urban management, environmental protection, and other hot and difficult issues in social governance, to promote the modernization of social governance.

Intelligent Government. Develop an AI platform for government services and decision-making. Develop a decision-making engine for the open environment. Promote applications in research on complex social problems, policy assessment, risk warning, emergency response, and other major matters of strategic decision-making. Strengthen the integration of government information resources and accurate forecasting of public demands, and smooth communication channels between the government and the public.

Smart Courts. Construct a set of trial, personnel, data applications, judicial disclosure, and dynamic monitoring into an integrated court data platform. Promote AI applications for applications including evidence collection, case analysis, and legal document reading and analysis. Achieve the intelligentization of courts and trial systems and trial capacity.

Smart Cities. Build an intelligentized city infrastructure, develop intelligent buildings, and promote the intelligentization, transformation, and upgrading of underground corridors and other municipal infrastructure. Construct urban big data platforms to build a heterogeneous, integrated data system for urban operations and management. Achieve comprehensive perception and deep understanding of the operation of complex urban systems for urban infrastructure and urban green space, wetlands, and other important ecological elements. Research and develop to build community public service information systems. Promote community service system and residents’ intelligent home system collaboration. Promote the intelligentization of the full lifecycle of urban planning, construction, and management.

Smart Transportation. Research, establish, and operate vehicle automatic driving and road coordination technology systems. Research and develop information and integrated data platforms for transportation under complex multi-dimensional conditions. Establish intelligentized transportation command, control, and integrated operations. Actualize intelligent transportation obstacle removal and integrated management and coordination and command. Build intelligent transportation monitoring, management, and service systems covering the ground, tracks, low altitude, and the sea.

Intelligent Environmental Protection. Establish an intelligent monitoring large data platforms and systems covering the atmosphere, water, soil, and other environmental areas. Build information-sharing and intelligent environmental monitoring networks and service platforms for coordination of land and sea, integration of atmosphere and earth, and upwards and downwards synergies. Research and develop intelligent forecasting models and method and early warning programs for energy resource consumption and environmental pollutant discharge. Strengthen the Beijing-Tianjin-Hebei, Yangtze River Economic Zone, and other major national strategic regions’ construction of intelligent prevention and control system for environmental protection and sudden environmental events.

3. Use AI to enhance public safety and security capabilities

Advance the deepening of AI applications in the field of public safety. Promote the construction of public safety and intelligent monitoring and early warning and control systems. Research and develop a variety of detection sensor technology, video image information analysis and identification technology, biometric identification technology, intelligent security and police products. Establish intelligent monitoring platform for comprehensive community management, new criminal investigations, anti-terrorism, and other urgent needs. Strengthen the upgrading and intelligentization of security equipment for key public areas. Support carrying out public security regional demonstrations based on AI according to the conditions of the community or the city. Strengthen the use of AI for food safety protection, food classification, warning level, food safety risks and assessment, and the establishment of intelligent food safety early warning system. Strengthen the effective monitoring of natural disasters, natural disasters, around the earthquake disaster, geological disasters, meteorological disasters, floods and disasters and marine disasters and other major natural disasters, to build an intelligent monitoring and early warning and comprehensive response platform.

4. Promote social interaction and mutual trust

Give full play to the role of AI technology in enhancing social interaction and promoting credible communication. Strengthen the next generation of social network research and development, accelerate innovation in augmented reality, virtual reality, and other technologies to promote the integrative use of virtual environments and physical environments to meet personal perception, analysis, judgment and decision-making real-time information needs, and to achieve the smooth transition of different scenes of work, study, life, and entertainment. In order to improve the interpersonal communication needs, develop intelligent assistant products with the ability to accurately understand the needs of emotional interaction. Promote the integration of blockchain technology and AI, establish a new social credit system, and minimize the cost and risks of interpersonal communication.

(4) Strengthen military-civilian integration in the AI domain

Deepen implementation of military-civilian integration development strategy, to promote the formation of an all-element, multi-field, high efficiency AI military-civilian integration pattern. Build new generation AI based on research and development in the common theory and critical common technology. Establish mechanisms to normalize communication and coordination among scientific research institutes, universities, enterprises and military industry units. Promote military-civilian two-way transformation of AI technology. Strengthen a new generation of AI technology as a strong support to command and decision-making, military deduction, defense equipment, and other applications. Guide defense domain AI technology toward civilian applications. Encourage and advantage people’s scientific research forces to participate in the domain of national defense for major scientific and technological innovation tasks in AI. Promote all kinds of AI technology to become quickly embedded in the field of national defense innovation. Strengthen the construction of military and civilian AI technology standard systems. Promote the overall layout and open sharing of science and technology innovation platforms and bases.

(5) Build a safe and efficient intelligent infrastructure system

Vigorously promote the construction of intelligent information infrastructure. Enhance the traditional level of intelligent infrastructure to form a smart economy, intelligent society and national defense needs of the infrastructure system. Speed ​​up the promotion of information transmission as the core of the digital, network information infrastructure. Take integration awareness, transmission, storage, computing, and processing in intelligent information infrastructure changes. Optimize network infrastructure, research and develop the layout of fifth generation mobile communication (5G) systems. Improve the Internet of Things infrastructure. Accelerate the integration of information network construction. Improve low-latency, high-throughput transmission capacity. Coordinate the use of big data infrastructure, strengthen data security and privacy protection, to provide massive data support for AI research and development and extensive applications. Build high-performance computing infrastructure, and enhance the service support capabilities of supercomputing centers for AI applications. Construct distributed and efficient energy Internet, form multi-energy support complementary, timely, and effective access to new energy networks. Promote intelligent energy storage facilities, intelligent electricity facilities, energy supply and demand information to achieve real-time matching and intelligent response.

Box 4 Intelligentized Infrastructure

• Network Infrastructure. Speed ​​up the layout of real-time collaborative AI 5G enhanced technology research and the development and application of space-oriented collaborative AI for the construction of high-precision navigation and positioning networks to strengthen the core of intelligent sensing technology research and key facilities. Develop intelligent industrial support, driving networks, etc., to study the intelligent network security architecture. Speed ​​up the construction of integrated information network for space and earth, promoting a space-based information network, the future of the Internet, mobile communication network of the full integration.
• Big Data Infrastructure. Rely on a national data sharing exchange platform, open data platform and other public infrastructure. Construct governance, public services, industrial development, technology research and development, and other fields of big data information databases Support the implementation of national governance data applications. Integrate various types of social data platforms and data center resources. Create nationwide integrated service capabilities with reasonable layout and linkages.
• High-performance computing infrastructure. Continue to strengthen the supercomputing infrastructure, distributed computing infrastructure and cloud computing center construction. Build sustainable development of high-performance computing application for the ecological environment. Promote the next generation of supercomputer research and development and applications.

(6) Plan a new generation of AI major science and technology projects

For the development of China’s AI needs and weak links, establish of a new generation of AI major scientific and technological projects. Strengthen the overall co-ordination, clear the boundaries of the tasks and the focus of research and development. Form a new generation of AI major scientific and technological projects as the core, and use existing R&D layout to support the “1 + N” AI program.

“1” refers to a new generation of AI scientific and technological mega-projects, focusing on forward-looking layout for basic theories and key common technologies, including the study of big data intelligence, cross-media perception and computing, hybrid enhanced intelligence, group intelligence, autonomous collaborative control, and decision-making theory. Research knowledge computing engines and knowledge service technologies, cross-medium analysis reasoning technology, key swarm intelligence technologies, new architecture and new technology for hybrid enhanced intelligent, autonomous unmanned control technology, and basic theory and common technology for open-source shared AI. Continue to carry out the development of AI prediction and research, strengthening the economic and social impact of and countermeasures for AI.

“N” refers to the national planning and deployment of AI research and development projects. Focusing on strengthening the new generation of AI with the convergence major scientific and technological projects, collaborative impetus for research, technological breakthroughs and product development applications. Strengthen the convergence of major national science and technology projects. Support AI hardware and software development in the “ Hegaoji ” Megaproject,[1] integrated circuit equipment and other national science and technology major projects. Strengthen mutual support for AI and other “Technological Innovation 2030 – Mega-Projects.” Accelerate the use of AI to provide support for major technical breakthroughs in brain science and brain computing, quantum information and quantum computing, intelligent manufacturing and robotics, and big data research. The National Key Research and Development Plan will continue to promote high-performance computing and other key special applications, while increasing support for AI-related technology research and development and application; the National Natural Science Foundation will strengthen cross-disciplinary research and support for free exploration in the field of AI. Focus on special deployment and strengthen the application of AI technology demonstrations to the deep sea space station, health protection, and other major projects, smart cities, intelligent agricultural equipment and other Key National R&D Projects. Support the openness and sharing of research results on basic theory of AI and common technology through other basic science and technology plans.

Innovate in the organization and implementation of models for new generation AI major scientific and technological projects. Adhere to focus on doing things, focusing on the principle of breakthrough. Give full play to the role of market mechanisms to mobilize departments, local, business and social forces to promote the implementation of all aspects. Pursue clear management responsibility, regular assessments, to strengthen the dynamic adjustments and improve management efficiency.

IV. Resource Allocation

Fully use existing finances, bases and other such stored resources, comprehensively plan the allocation of international and domestic innovation resources, give rein to the guiding role of finance administration input and policy incentives, and the dominant role of the market in allocating resources, impel enterprises and society to expand input, and create a new pattern of multi-sided support through finance administration funding, financial capital, and social capital.

(1) Establish financial support mechanisms guided by the financial administration and dominated by the market

Comprehensively plan multiple-channel financial input by government and markets, strengthen support through finance administration funding, enliven existing resources, and provide support for fundamental and advanced AI research, critical public technology breakthroughs, result transformation, base and platform construction, innovative application demonstrations, etc. Use existing policy input funds to support AI programs to meet conditions, encourage leading and backbone enterprises and industrial innovation alliances to take the lead in establishing marketized AI development bases. Use angel investment, risk investment, start-up investment funds, financial market funding and many other such channels to guide social capital to support AI development. Vigorously use governmental and social capital cooperation and other such models and guide social capital to participate in the implementation of major AI programmes and the transformation and application of scientific and technological achievements.

(2) Optimize arrangements to build AI innovation bases

According to the national-level science and technology innovation base arrangements and frameworks, comprehensively promote a few internationally advanced innovation bases in the area of AI construction. Guide existing AI-related national focus laboratories, corporate national focus laboratories, national engineering laboratories, and other such bases, and conduct research focused on an advanced direction of a new generation of AI. According to regulatory procedure, build technological and industrial innovation bases related to the AI area with enterprises in the lead, and in cooperation between industry, scholarship, and research. Give rein to the driving role of leading and backbone enterprises concerning technological innovation demonstrations. Develop specialized public maker spaces in the AI area, stimulate the precise linkage of the newest technological achievements, resources and services. Fully give rein to the role of all kinds of innovation bases in concentrating talent, finance, and other such innovation resources; make breakthroughs in basic and advanced AI theory and key common technologies; and launch application demonstrations.

(3) Comprehensively plan international and domestic innovation resources

Support domestic AI enterprises to cooperate with international leading AI schools, scientific research institutes and teams. Encourage domestic AI enterprises to “go out,” and provide conveniences and services to powerful AI enterprises conducting foreign mergers or acquisitions, share investment, start-up investment, establishing foreign research centres, etc. Encourage foreign AI enterprises and research institutes to establish research and development centers in China. With the support of the “One Belt, One Road” strategy, promote the construction of international AI science and technology cooperation bases, joint research centres, etc.; accelerate the broad application of AI technologies in countries along the “One Belt, One Road.” Promote the establishment of international AI organizations, jointly formulate related international standards. Support related sectoral associations, alliances, and service bodies to build globalized service platforms aimed at AI enterprises.

V. Guarantee measures

Aiming at the realistic requirements of promoting the healthy and rapid development of AI in China, it is necessary to deal with the possible challenges of AI, form an institutional arrangement to adapt to the development of AI, build an open and inclusive international environment, and reinforce the social foundation of AI development.

(1) Develop laws, regulations, and ethical norms that promote the development of AI

Strengthen research on legal, ethical, and social issues related to AI, and establish laws, regulations and ethical frameworks to ensure the healthy development of AI. Conduct research on legal issues such as civil and criminal responsibility confirmation, proteciton of privacy and property, and information security utilization related to AI applications. Establish a traceability and accountability system, and clarify the main body of AI and related rights, obligations, and responsibilities. Focus on autonomous driving, service robots, and other application subsectors with a comparatively good usage foundation, and speed up the study and development of relevant safety management laws and regulations, to lay a legal foundation for the rapid application of new technology. Launch research on AI behavior science and ethics and other issues, establish an ethical and moral multi-level judgment structure and human-computer collaboration ethical framework. Develop an ethical code of conduct and R&D design for AI products, strengthen the assessment of the potential hazards and benefits of AI, and build solutions for emergencies in complex AI scenarios. China will actively participate in global governance of AI, strengthen the study of major international common problems such as robot alienation and safety supervision, deepen international cooperation on AI laws and regulations, international rules and so on, and jointly cope with global challenges.

(2) Improve key policies for the support of AI development

Implement tax incentives for small and mid-sized enterprise and startup AI development, and, using high-tech enterprises, tax incentives, R&D cost deductions, and other policies, support the development of AI enterprises. Improve the implementation of open data and protection-related policies, launch open public data reform pilots to support the public and enterprises in fully tapping the commercial value of public data, and promote the application of AI innovation. China will study the policy system of education, medical care, insurance, and social assistance to adapt to AI, and effectively deal with the social problems brought by AI.

(3) Establish an AI technology standards and intellectual property system

Conduct research on strengthening the AI standards framework system. Adhere to the principles of security, availability, interoperability, and traceability; and gradually establish and improve the basic basis of AI, interoperability, industry applications, network security, privacy protection, and other technical standards. Speed ​​up the promotion of autonomous driving, service robot, and other application sector industry associations in developing relevant standards. Encourage AI enterprises to participate in or lead the development of international standards, and a technical standards “going out” approach to promote AI products and services in overseas applications. Strengthen the protection of intellectual property in the field of AI, improve the field of AI technology innovation, patent protection, and standardization of interactive support mechanisms to promote the innovation of AI intellectual property rights. Establish AI public patent pools to promote the use of AI and the spread of new technologies.

(4) Establish an AI security supervision and evaluation system

Strengthen research and evaluation of the influence of AI on national security and secrecy protection; improve the security protection system of human, technology, material, and management support; and construct an early warning mechanism of AI security monitoring. Strengthen the development of AI technology prediction, research and follow-up research, adhere to a problem-oriented, accurate grasping of technology and industry trends. Enhance the awareness of risk, pay attention to risk assessment and prevention and control, and strengthen prospective prevention and restraint guidance. In the near term focus on the impact on employment, with a long-term focus on the impact on social ethics, to ensure that the development of AI falls with the sphere of secure and controllable. Establish and improve an open and transparent AI supervision system, the implementation of design accountability, and application of the supervision of a two-tiered regulatory structure, to achieve management of the whole process of AI algorithm design, product development and results application. Promote AI industry and enterprise self-discipline, and earnestly strengthen management, increase disciplinary efforts aimed at the abuse of data, violations of personal privacy, and actions contrary to moral ethics. Strengthen AI cybersecurity technology research and development, strengthen AI products and systems cybersecurity protection. Develop dynamic AI research and development evaluation mechanisms, focus on AI design, product and system complexity, risk, uncertainty, interpretability, potential economic impact, and other issues. Develop a systematic testing methods and indicators system. Construct a cross-domain AI test platform to promote AI security certification, and assessment of AI products and systems key performance.

(5) Vigorously strengthen the training of an AI labor force

Accelerate the study of the employment structure brought on by AI, changes in employment methods, and the skills demand of new occupations and jobs, establish a lifelong learning and employment training system to meet the needs of the intelligent economy and intelligent society, and support institutions of higher learning, vocational schools and socialization training Institutions to carry out AI skills training. Substantially increase the professional skills of workers to meet the development requirements of China’s AI to bring high-quality jobs. Encourage enterprises and organizations to provide AI skills training for employees. Strengthen the re-employment training and guidance of workers to ensure the smooth transfer of simple and repetitive workers due to AI.

(6) Carry out a wide range of AI scientific activities

Support the development of a variety of AI scientific activities, encourage the broad masses of scientific and technological workers to join the promotion of AI popular science, and comprehensively improve the level of the whole society on the application of AI. Implement a universal intelligence education project. In the primary and secondary schools, set up AI-related courses, and gradually promote programming education to encourage social forces to participate in the promotion and development of educational programming software and games. Construct and improve the AI science infrastructure, give full play to all kinds of AI innovation base platforms and other popular science roles, encourage AI enterprises, and research institutions to build open source platforms for public open AI research and development, plus production facilities or exhibition halls. Support the development of AI competitions, encourage the formation of a variety of AI science creational work efforts. Encourage scientists to participate in AI science.

VI. Organization and Implementation

The development plan for a new generation of AI is a far-sighted scheme affecting the overall picture and the long term. We must strengthen organizational leadership, complete mechanisms, take aim at objectives, keep tasks closely in view, realistically grasp implementation with a spirit of hammering nails, and carry out the blueprint to the end.

(1) Organizational leadership

According to the unified deployment of the Party Center and the State Council, the National Science and Technology Structural Reform and Innovation System Construction Leading Small Group will take the lead in comprehensive planning and coordination, it will deliberate major tasks, major policies, major questions, and major work arrangements. Promote AI-related legal and regulatory construction. Guide, coordinate and supervise relevant departments in carrying out the deployment and implementation of tasks from the plan. With the support of the interministerial joint conferences for national science and technology planning (earmarks, funding, etc.) management, the Ministry of Science and Technology will, together with relevant departments, be responsible for moving forward the implementation of major science and technology programmes for a new generation of AI, and strengthen linkages and coordination with other programmatic tasks. Establish an AI Plan Implementation Office. This office will be part of the Ministry of Science and Technology and will be concretely responsible for moving the implementation of the plan forward. Establish an AI Strategy Advisory Committee, to research major far-sighted and strategic questions concerning AI and to provide advice and assessment concerning major policy decisions on AI. Move forward with the construction of an AI think tank, support all kinds of think tanks to launch research on major AI questions, and provide strong and powerful support for the development of AI.

(2) Guarantee implementation

Strengthen the deconstruction of plan tasks, clarify responsible work units, schedules and arrangements, formulate annual and phase-type implementation plans. Establish monitoring and evaluation mechanisms for the implementation situation of the plan, such as annual assessment and intermediate evaluation. Adapt to the characteristics of the rapid development of AI, and strengthen dynamic adjustment of plans and programs on the basis of the progress of tasks, the completion of intermediate objectives, new trends in technological development, etc.

(3) Trials and demonstrations

We must formulate concrete plans for major AI tasks and focus policy measures, and launch trials and demonstrations. Strengthen comprehensive guidance over trials and demonstrations in all departments and all localities, quickly summarize and disseminate replicable experiences and methods. Advance the healthy and orderly development of AI through advance trials and guiding demonstrations.

( 4) Public opinion guidance

Fully use all kinds of traditional media and new media to quickly propagate new progress and new achievements in AI, to let the healthy development of AI become a consensus in all of society, and muster the vigor of all of society to participate in and support the development of AI. Conduct timely public opinion guidance, and respond even better to social, theoretical, and legal challenges that may be brought about by the development of AI.

[1] Translator’s note: This refers to the Medium and Long-term Plan for S&T Development 2006-2020 megaproject: core ( he) electronic devices, high-end ( gao ) general-purpose chips, and basic ( ji ) software.

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China launching National Key Research and Development Programs

October 24, 2016.

Prof. Dr.  Lin   ZHEN   GLP SSC member   Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.  

Contacts:    Email:  [email protected]   Phone: 0086-10-64888155  

Led by Prof. Dr. Lin Zhen (PI) , a SSC member of GLP, and jointed  (Co-PIs)  by 5 national recognized institutions, i.e.,  Institute of Soil and Water Conservation, Chinese Academy of Sciences  (CAS)  and  Ministry of Water Resources, Northwest Agriculture & Forest Univer sity,   China Institute of Water Resources and Hydropower,  t he Monitoring Center of Soil and Water Conservation Ministry of Water Resource, Lanzhou Center for Literature and Information of  CAS ,  this program  focuses on the 3 main  degraded  ecosystems  as pilots  including soil and water erosion in the Loess Plateau, desertification in Inner Mongolia and K arst rocky desertification  in southwestern  Guizhou  province , and it  will  also include visits and surveys in other countries to explore potentials to import best management practices to China, and export China ’ s best practices to other countries in needy. The program will  produce series expected outcomes, which include  3 data base containing national and global data used for the research, atlas of distribution of ecological degradation at national and global level, 5 indicator systems and models for assessing ecological technologies, 2 national standards on evaluation of key ecological restoration technologies, 2 long list of ecological technologies and recommended best technologies, 2-4 policy briefs, 2 software and integrated application system, 15 scientific reports, more than 50 journal publications, and book publication on Assessment Report on Global Ecological Restoration Technologies.  

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• v.133(13); 2020 Jul 5

Clean air actions and health plans in China

1 Department of Environmental Health Risk Assessment, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 10001, China

Jian-Long Fang

Wan-ying shi, tian-tian li, xiao-ming shi.

2 National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing10001, China.

China has been experiencing some of the world's most serious air pollution, especially severe smog events swept China in January of 2013, leading to extensive international attention. Efforts to understand and mitigate the impacts of ambient air pollution on public health have been taken to tackle this pollution in China. Clean air actions developed nationwide have aggressively lowered fine particulate matter (PM 2.5 ) pollution in recent years. [ 1 ] National health plans and research projects on air pollution have been motivated and funded by the government. Rapidly growing epidemiological evidence has emerged to preliminarily uncover the effects of elevated levels of ambient air pollution on human health. However, China's pollution levels still exceed that of the World Health Organization (WHO) least-stringent target. [ 1 ] Ambient air pollution still poses a serious threat to human health and welfare. It is critical to elucidate targets and efforts on the improvement of air quality and the reduction of related health effects. Therefore, this paper has organized the development of clean air actions, health plans, relevant research projects, and reviewed key milestones of health evidence in China to propose suggestions to air pollution mitigation in its next stage.

China Clean Air Actions

Since 2013, the China National Environmental Monitoring Centre has expanded the air pollution monitoring network, with now more than 2000 stations across the country, recording concentrations of pollutants including inhalable particulate matter (PM 10 ), PM 2.5 , ozone (O 3 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and carbon monoxide (CO). [ 2 , 3 ] This monitoring network provides a key data source for air pollution control.

In the same year, the China Air Pollution Prevention and Control Action Plan (APPCAP) was established, stipulating targets for PM control across the country and in several key areas by 2017. Specifically, concentrations of PM 2.5 in major cities nationwide will drop by more than 10% relative to 2012 levels. For some of the most heavily polluted areas of concern, APPCAP also pointed out that the concentration of PM 2.5 in the Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) will drop by about 25%, 20%, and 15% by 2017, respectively. [ 4 , 5 ] In order to achieve the above goals, APPCAP proposes ten tasks that need to be completed, including optimizing the industrial structure, accelerating technological advancement, establishing early warning and emergency monitoring systems, and so on. [ 5 ] In line with the policies related to air quality control, relevant laws were formulated to put forward the prevention and control of air pollution requirements. The newly revised environmental protection law in 2014 clearly stated the requirements to establish and improve environmental and health monitoring, investigation and risk assessment systems, as well as encourage research on environmental health. [ 6 , 7 ]

Up until 2017, air quality has been significantly improved across the whole country as well as in key regions, with the percentage of days with good air quality reaching 72.7%. The annual average PM 2.5 concentration in 74 major cities was 47 μg/m 3 , decreasing by 33.3% compared with PM 2.5 concentrations in 2013, while the reduction of annual average PM 2.5 concentration in BTH, YRD, and PRD was 37.3%, 35.2%, and 26.1%, respectively. [ 5 ] In addition, levels of SO 2 , NO 2 , and CO across the country also showed a slight or significant decrease compared to that of 2013, decreasing by 57.5%, 9.1%, and 32.0%, respectively. [ 3 ] In 2018, the State Council released a three-year action plan to “win the defense of the blue sky,” reducing total emissions of major air pollutants and greenhouse gases by 2020, increasing the proportion of days with blue sky to 80%, and reducing heavy pollution days by 25% compared to that of 2015. [ 8 ] However, challenges remain. Air pollution in China is still much worse than that experienced on average across the globe. O 3 pollution, with a significant increasing trend recently, has not been included in the clean actions taken to date.

National Health Plans and Research Projects in China

With the promoting and deepening of clean air actions, a series of health plans and scientific research projects, which bring benefits to air quality improvement, have been issued by the government in recent years. The National Health Commission has adopted environmental pollution control as a critical step to reduce health hazards and listed it into national health plans. Health China 2030, released in 2016, calls for better management of health risks caused by pollution, in which the emphasis is on integrating health policies with all other major policies, such as reducing environmental pollution, to promote the level of people's health. Healthy China initiative 2019 to 2030, from vision to action for health care, targeted largely towards the promotion of a healthy environment and improvement of air quality. [ 9 ] One of the main tasks is that, by 2022 and 2030, residents’ environmental and health literacy levels will reach 15% and 25% or above, respectively.

The Ministry of Science and Technology of the People's Republic of China issued guidelines of the National Key Research and Development Plan “Research on the Causes and Control Techniques of Air Pollution” and granted four projects to explore the health effects of air pollution in 2016 and 2017. These projects focus on acute and chronic health effects of air pollution and the development of high-precision exposure assessment and risk source identification techniques, as well as early identification techniques for the adverse human health effects caused by air pollutants. The National Natural Science Foundation of China issued a joint research program titled “Causes, health effects and coping mechanisms of combined air pollution in China” to reveal the key chemical and physical processes of the atmosphere and explore key control technologies and principles to cope with combined pollution. As the most critical air pollution area in China, the BTH region and surrounding areas (named “2 + 26” cities) have received great attention. In 2017, 28 projects were supported by the Ministry of Ecology and Environment of the People's Republic of China to investigate the sources of heavy air pollution, advance techniques for emissions reduction, and evaluate the health impacts of air pollution in the “2 + 26” cities.

Epidemiological Evidence on Air Pollution and Human Health in China

Efforts to understand the effects of China's air pollution on human health have skyrocketed since 2013, especially focusing on PM 2.5 as the primary pollutant of interest. Early studies took smog events as a starting point and compared death counts or hospital visits for a short period before, during, and after extreme air pollution episodes. [ 10 , 11 ] Research then began to better estimate short-term pollution-mortality associations in China and found a link between heavy PM 2.5 pollution and adverse health impacts in China. [ 12 , 13 ] Chen et al [ 12 ] found that a per 10 μg/m 3 increase in the 2-day moving average of PM 2.5 concentrations at the city-level was significantly associated with increases in mortality of 0.29% in respiratory diseases and 0.27% for cardiovascular mortality. A study of 130 Chinese counties performed from 2013 to 2018 added additional causes of death at the county-level, including acute myocardial infarction (0.21%, 95% confidence interval [CI]: 0.05–0.37) and acute ischemic heart disease (0.19%, 95% CI: 0.04–0.35). [ 13 ] A new study of 104 Chinese counties found a J-shaped association of the mortality burden attributable to short-term PM 2.5 exposure and an estimated 169,862 additional deaths from PM 2.5 pollution in 2015. [ 14 ]

In terms of long-term effects of ambient PM 2.5 pollution, Li et al [ 15 ] estimated an hazard ratio (HR) of 1.08 (95% CI 1.06–1.09) of all-cause mortality for a 10 μg/m 3 increase in PM 2.5 based on the Chinese Longitudinal Healthy Longevity Survey. An HR of 1.11 (95% CI 1.05–1.17) of incident hypertension for a 10 μg/m 3 increase in PM 2.5 was estimated in the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort study. [ 16 ] Xie et al [ 17 ] reported that pre-mature deaths attributed to ambient PM 2.5 pollution have resulted in 1,255,400 pre-mature deaths in 2010, 42% greater than that of in 2000.

In addition, comparable findings suggested that clean air actions not only improved air quality but also reduced the burden of air pollution-related diseases. Huang et al [ 5 ] evaluated the effects of the China APPCAP on long-term air quality management and found that after substantial improvements in air quality, 47,240 fewer deaths and 710,020 fewer years of life lost in 2017 than in 2013. Wang et al [ 18 ] estimated health benefits associated with PM 2.5 nationwide under the air quality scenarios proposed by the 13th Five-Year Plan for Eco-Environmental Protection and reported that these scenarios could reduce the PM 2.5- related pre-mature deaths by 129,278 by 2020 and 217,988 by 2030.

The health pieces of evidence listed above have given some preliminary answers regarding how air pollution affects health in China. They consistently show that air pollution still has a large impact on public health during the implementation of clean air actions. Nevertheless, most research focused on PM 2.5 and ignored the impact of combined pollution nationwide, as well as potential new challenges, such as O 3 pollution.

Suggestions for Clean Air and Health Protection

Air quality intervention is a protracted and arduous task. To better improve air quality and health impacts, we propose some suggestions below to plan strategies against air pollution in the next stage.

To learn from our experience and lessons to date for the development of future interventions, involving a clear understanding of the effectiveness and benefits of air quality interventions, and currently unresolved pollution problems and existing barriers. We can use pollution exposure and health risk assessments to assess short- and long-term benefits of air quality improvement as well as identify where health risks still exist. These can provide massive and important information for policy-makers to implement the next steps in clean air actions.

To continue to take efforts to substantially improve our air quality. On one hand, more steps should be taken to reduce ambient PM 2.5 pollution. We should notice that PM 2.5 pollution in China still far exceeds the levels of most countries worldwide. Furthermore, we observed robust and consistent evidence of China pointing out life-threatening of PM 2.5 at this pollution level. One of the effective steps is to identify the primary targeted fractions and components of PM 2.5 for local clean-air initiatives, and then take further control against these toxic components as well as their major emission sources. On the other hand, it is important to pay more attention to ambient O 3 pollution. With respect to O 3 pollution posing new health risks with the continual increase in ground-level O 3 , which does not get enough attention in clean air actions, emission reduction actions should target relevant emission sources that largely contribute to O 3 pollution.

To improve China's current air quality standards to achieve broad population health co-benefits. Standards or guidelines have been a powerful tool in air quality management during the history of air quality improvement in the United States and Europe. However, our air quality standards implemented in 2012 lacked evidence from China-specific epidemiologic studies. They are not strict enough to protect public health effects, especially compared with developed countries’ standards and the WHO's guidelines. We need to strengthen the standards and plan appropriate goals supported by current evidence of pollutant-related health effects in China to gradually close in on a sustainable target at pollution reduction to a relatively safe level.

To motivate and initiate innovative research projects to support air pollution control development. Current research projects have concentrated on PM 2.5 pollution. However, the health effects of PM 2.5 extends far beyond mass alone, and its inhaled fractions and chemical components are suggested to be toxicologically more important. Besides, combined pollution of multi-pollutants, including O 3 and other gas pollutants, can be more harmful to public health. Therefore, priority areas for research to guide policy include a better understanding of the injury mechanism involved in PM 2.5 -related fractions and components and additional studies on adverse health outcomes from air pollution mixtures, especially characterizing the health risks from O 3 .

This work is supported by grants from the National Key Research and Development Program of China (No. 2016YFC0206500), and the National Research Program for Key Issues in Air Pollution Control (No. DQGG0401).

Conflict of Interest

How to cite this article: Chen C, Fang JL, Shi WY, Li TT, Shi XM. Clean air actions and health plans in China. Chin Med J 2020;133:1609–1611. doi: 10.1097/CM9.0000000000000888

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• Published: 07 March 2022

Evaluation and analysis of the projected population of China

• Kaixuan Dai 1 , 2 , 3 ,
• Shi Shen 1 , 2 , 3 &
• Changxiu Cheng 1 , 2 , 3 , 4  

Scientific Reports volume  12 , Article number:  3644 ( 2022 ) Cite this article

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• Climate-change policy
• Socioeconomic scenarios
• Sustainability

The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy.

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Introduction.

At present, human activities have become the dominant force in Earth's ecological processes and global climate change, which indicates Earth has entered a new epoch, Anthropocene 1 , 2 . The highly intensive human activities have caused global temperature to warm by approximately 1.09 °C since the industrial revolution in the 1700s 3 . According to China's seventh national population census, the total population was 1.41 billion at the end of 2020, accounting for about 18% of the global population 4 .

As the largest populated and most active economic development country, China's vast population provides a large consumer market with more business opportunities for enterprises 5 , 6 , 7 , 8 . However, overpopulation negatively influences natural resources, the ecological environment, and global climate change 9 , 10 . Moreover, the growing population has a critical influence on achieving the Sustainable Development Goals (SDG), such as urban expansion control (SDG 11.3.1) and education equality (SDG 4.6.1) 11 . Therefore, China's future population growth is a crucial issue that has attracted international attention.

Many international organizations have estimated China's future population without spatial properties. For instance, the World Bank has estimated national total populations and age compositions with different economic development levels until 2050 12 . The United Nations (UN) has assessed previous global population growth situations and projected future world populations in prospect reports 13 . The International Institute for Applied Systems Analysis (IIASA) has provided a country-scale projection population dataset under different shared socioeconomic pathways (SSPs) from 2010 to 2100 14 . These national-scale projections could reveal the general population growth tendency and serve as inputs for addressing natural and socioeconomic issues. For example, Scovronick et al. analyzed the impact of population growth on world climate change policies based on the UN future population projection dataset 15 . Dottori et al. explored the threat of river flooding based on IIASA population projections under different anthropogenic warming scenarios 16 . Li et al. used the IIASA's SSP population and GDP projection data to forecast worldwide urban expansion conditions 17 . However, the national data cannot reflect the spatial heterogeneity of population distribution and is insufficient to support policy decision-making at local scales.

As a result, several researchers have created spatially explicit population projections at a small scale. For example, Jones and O'Neill projected global population values from 2000 to 2100 over 5 years under 5 SSP scenarios 18 . Gao converted the global 1/8-degree grid data of Jones and O'Neill to 1-km degree grids by constructing a downscaling transform weight matrix, thereby providing more accurate and detailed data in small regions 19 . Furthermore, the Japanese National Institute for Environmental Studies (NIES) created global population projection datasets from 1980 to 2100 over 10 years with 0.5-degree grids, although these datasets included only the SSP1, SSP2, and SSP3 scenarios 20 . To accurately grasp China's future population growth tendency, the projections of NUIST (Nanjing University of Information Science and Technology) and THU (Tsinghua University) were created recently by Huang et al. 21 and Chen et al. 22 , respectively. These spatially explicit population datasets have been widely used to explore the influence of future population levels on global climate change 23 , 24 , 25 , 26 , extreme weather disaster events 27 , 28 , 29 , land-use change 24 , and ecosystem service change 30 . Although they have been applied in many research fields, we know little about their projection accuracy and poorly understand the factors that affect their projection accuracy. Moreover, uncertainties remain about these datasets, which have hindered further investigations and research on adaptations to climate change and sustainability. Consequently, it is necessary to evaluate their projection accuracy and determine their applicability in different regions.

This study compares China's population projections with actual census data from 2010 to 2020 at different scales. Then, the spatial error regression model (SEM) is applied to attribute the factors affecting projection accuracy. The contributions of this study are evaluating the gaps between actual and projection populations and analyzing the contributions of various impact factors to projection accuracy. In addition, the results provide a better understanding of China's population growth situations and the characteristics of different projection datasets. Besides, it also provides insights into the parameters adjustment of projection models to reduce the projection errors in the future 31 , 32 .

The rest of this paper is organized as follows. Second section presents the study data. Third section describes the methods of measuring population projection quality. Fourth section presents the results of this study. Fifth section provides a discussion on the study results. Sixth section summarizes this study.

Population projection datasets

In this study, we collected nine population projection datasets of China published by different scholars and organizations. The details of these population projection datasets are summarized in Table 1 . We named them according to publishers' institutions or organizations' abbreviations. Additionally, China's actual population at the country and province scale from 2010 to 2020 is derived from China's Statistical Yearbook. In general, these datasets are different in spatial, temporal, and scenarios dimensions.

For the spatial resolution, four datasets provide spatially explicit population distribution, including THU, NUIST, NIES, and SEDAC. Another five datasets only project total population change at the national scale. The most detailed spatial resolution is 30 × 30 m of THU.

For the temporal resolution, these datasets are different in the initial year, end years, and interval timespans. For example, five datasets provide the population from 2010 to 2100, including THU, NUIST, SEDAC, IIASA, CEPAM. In addition, the NIES, IHME, WCDE, and UN provide the estimated population in history years before 2010. As for the time interval, the THU, NUIST, IHME, and UN provide yearly population data. The IIASA and WCDE provide the population with 5 years intervals. The NIES, SEDAC, and CEPAM merely offer 10 years' interval population projection.

For the scenarios, except the IHME and UN, all datasets follow the narratives of the SSPs scenarios. SSP1 is a sustainability scenario, representing that the increase in educational level leads to low fertility in future population growth. SSP2 is the Business-as-Usual or moderate scenario, which keeps the traditional development tendency in future changes. SSP3 is the regional rivalry scenario, denoting a rapidly increasing population with high fertility to ensure abundant human labor resources. In the projection of CEPAM and WCDE, they extend the SSP2 scenario by assuming different international migration rates. The IHME focus on the role of female educational attainment in population growth. Therefore, they set four scenarios to represent different situations of female educational attainment improvement. The UN provides the most complex scenarios by combining different fertility, mortality, and international migrations.

Due to the mismatches in spatial, temporal, and scenarios, it is necessary to unify them into the same scale for comparison. Limited by the spatial resolution, we could merely compare the projection with the actual population at the country and province scale. Besides, only the spatial explicit datasets could be aggregated into provincial data, such as THU, NUIST, NIES, and SEDAC. However, we only compare the projection of NUIST and THU at the province scale, because the projected intervals of NIES and SEDAC are too long as 10 years. We compare them for the years from 2010 to 2020, due to 2010 is the initial projection year of most datasets, and 2020 is the latest population census year. Furthermore, we select the medium pathway scenario of each dataset to compare, such as the SSP2 and Middle scenarios. Because the projection in the medium scenario reflects the conditional population circumstances, and it is the basis of other scenarios. Additionally, the middle pathway is the most similar to the present world's future trajectory 33 , 34 .

Impact factors of projection errors

We collect thirty demographic indicators of 31 provinces of China reflecting the population information to support the regression of SEM, as Table 2 shows. The outline indicators are the most basic information to describe the population profile for a specific province, including the total population, birth rate, mortality rate, natural growth rate, and annual population growth rate. The structure information depicts the population proportion division by the age and household registration types. The sex ratio is the number of males per 100 females. Besides the total sex ratios, we obtain the sex ratios for various population groups, such as urban, rural, and births. Fertility is significant in population projection. In this class, we obtain the number of births with different types and reflect females' reproductive situations. In the migration class, the population leaving more than half a year and the population from other provinces could represent the domestic population mobility. The number of foreigners reflects the influence of transnational migration. The economic level plays a vital role in population change. In this class, we utilize the provincial average wage and unemployment rate to depict their economic standards. The governmental policy change is the crucial impact factor for population changes. We use the expenditure of maternity insurance and hospitals' quantity to reflect government attitudes to population control. In the education class, we acquire the proportion of the population with high school education or above to depict the educational level of a certain province. To eliminate the effect of data units, all impact factor values are standardized by the Z-Score transformation.

The Fig.  1 shows the research workflow of this study. In the beginning, we collect nine projection datasets from various scholars and organizations. Then, we evaluate them with the actual population data from 2010 to 2020 at the country and province scale. Besides, we utilize the mean absolute percentage error ( MAPE ), mean algebraic percentage error ( MALPE ), and R-Square to measure the quantitative differences between actual and projection populations. Finally, we employ the SEM regression models to explore the impact factors for projection accuracy.

Workflow of the research.

Measurements of population projection errors

Generally, national official census data is the most reliable population criteria 39 , 40 , 41 . Smith proposed evaluating the population projection data quality by examining its projection accuracy and projection bias 42 . Projection accuracy is the absolute difference between projected and actual values, and it expresses the degree of error deviation 43 , 44 . Projection bias is the real difference between the values, and it shows the direction and magnitude of the projection error 32 , 45 . Therefore, we select the MAPE (Eq. ( 1 )) and the MALPE (Eq. ( 2 )) to indicate the population projection accuracy and projection bias, respectively 46 . Moreover, we utilize the coefficient of determination \({(R}^{2}\) , Eq. ( 3 )). In this study, we calculate these projection error indicators at the country and province scales. These indicators are calculated as follows:

In the above equations, \(t\) is a year from 2010 to 2020, \(n\) is 11 years. \(P\) is the population projection datasets, \(A\) is the actual population data. \({P}_{t}\) and \({A}_{t}\) is the projected and actual population in the \(t\) year. According to the equations, a positive MALPE indicates that the projection is greater than the actual values and a negative MALPE means that the population projection is less than the actual values. The MAPE is a nonnegative value without upper limitations. The zero MAPE indicates that the projection results are entirely correct, and a large MAPE indicates lower projection accuracy. The percentage variables are unit-free and easy to understand and interpret. Thus, they are standard measurements in the applied demography literature 31 , 47 .

Attribution analysis of projection errors

We utilize spatial error regression models to analyze the possible relationships between the projection accuracy and those impact factors. The SEM could discover the spatial autocorrelation of variables, allowing us to explore deeper spatial associations that the ordinary linear regression model cannot reveal 48 , 49 . Due to there are only eleven years intervals of validation data as samples, it could not support the attribution analysis at the country scale. Therefore, we merely analyze the impact factors of population projection at the province level. As a result, the dependent variables of the SEM models are the MAPE and MALPE of China's 31 provinces from 2010 to 2020. The explanatory variables are the provincial demographic and social indicators, as Table 2 shown.

In the SEM, mutual effects are assumed for neighboring districts' same explanatory variables, and the dependent variables have no spatial correlations. Therefore, the formulas of SEM are shown as Eq.  4 and Eq.  5 . \(Y\) is the \(n\times 1\) vector of response variables, \(X\) is an \(n\times p\) matrix of the explanatory variable, \(\beta\) is an \(p\times 1\) vector of regression coefficients, \(\varepsilon\) represent "white noise," \(u\) is the error refers to spatial variations, \({W}_{1}\) is the spatial weight matrix describing the spatial mode of residuals, and \(\lambda\) is the parameter of the spatial error term. The closer \(\lambda\) is to 1, the more similar the explanatory variables in neighboring places.

In this study, the dependent variables are the MAPE and MALPE of China's 31 provinces; thus, the SEM model's \(Y\) matrix is a \(31\times 1\) vector. We utilize the stepwise method to select the explanatory variables when constructing the SEM model to handle multicollinearity among variables. Therefore, the final models could contain different impact factors.

Country scale comparison

As Fig.  2 a shows, all dataset projection population to 2100, but four datasets provide population start before 2010, and other five start from 2010. The IHME, UN, and WCDE are higher than the actual data since the 1970s, yet the WCDE coincides with the proper condition in most historical years.

Comparison between the actual and projection population at the country scale. The vertical axis is the population number (unit: billion), and the horizontal axis represents the years.

In the evaluation years from 2010 to 2020 (Fig.  2 b), most projections show an approximatively linear growth trend, and they do not foresee the inflection point arising prematurely in 2017. Only the WCDE reveals the slowdown tendency from 2015 to 2020. In this period, the projections of UN, IHME, WCDE, and CEPAM are higher than the actual population. The NIES, IIASA, SEDAC, and NUIST are lower than the truth, but the NUIST approaches the actual value gradually from 2015. The projection of THU is the closest to the actual population curve from 2010 to 2017, but it overestimates the population after 2017 as well.

According to the long-term population projection results, China's population size is generally projected to peak and show a decreasing trend shortly. These datasets predict China's maximum population is between 1.38 billion to 1.45 billion (Supplement Table S1 ). The IHME thinks the population will reach the peak in 2024 as the fastest growth among projections. The NUIST considers it will be maximum in 2034 as the latest. The average value for the maximum population year of all projections is 2028. Furthermore, these datasets show three types of trajectories after reaching the population peak. The NUIST reveals the most slowly population decreasing trend, and it thinks the population will be 1.24 billion in 2100, which is the highest in nine datasets. The UN and THU represent the medium population reduction situations, and they project the population will maintain 1 billion above in 2100. The rest six datasets show the total population will sharply decrease under 0.9 billion in 2100.

The quantitative indicators for measuring projection accuracy and bias during the validation years are calculated in Table 3 . The THU has the lowest MAPE and MALPE , and the largest \({R}^{2}\) as 41.40%, 8.00%, and 0.90 respectively, thus it is the best projection dataset in this period. Inversely, the projection of UN is the worst among these datasets.

Furthermore, we could reveal the direction of projection bias by analyzing the relation between MAPE and MALPE . For example, THU's MALPE is lower than MAPE significantly, which reveals both overestimate and underestimate for THU, but the overestimates cause more projection accuracy loss. The WCDE takes the second high projection accuracy with equal MAPE and MALPE . Thus it overestimates the population for each year in this period. In summary, the projection accuracy loss of NUIST, NIES, IIASA, and SEDAC is caused by the negative errors, and the THU, IHME, CEPAM, WCDE, and UN are own to positive errors.

Province scale comparison

At the province scale, the THU and NUIST are validated with each actual provincial population, and the results are shown in Fig.  3 . The results reveal that NUIST and THU have various conditions in different provinces with over or underestimated projection compared to the actual population.

Comparison between actual and projection population at the province scale. The subfigures from (1) to (31) represent different provinces. The vertical axis is the population number (unit: million), and the horizontal axis represents the validation years from 2010 to 2020. The red star indicates the actual provincial population, the blue circle represents the NUIST, and the purple circle denotes the THU.

The first pattern is that the actual population turns from rapid to slow growth, such as Beijing, Tianjin, and Shanghai (Fig.  3 (1, 2, 9)). In these provinces, the THU discovers the slowdown trend of population, but NUIST keeps linearly growing without turning points in the period.

The second pattern is that the actual population keeps reducing in the period, but both projections show population increasing (Fig.  3 (4, 5, 6, 7, 8, 28)). The pattern includes six provinces as Shanxi, Neimenggu, Liaoning, Heilongjiang, Jilin, and Gansu provinces. These provinces are all located in the northeast and northwest of China, and they have experienced severe population loss in the last years. However, the two projections do not expect such a rapid population reduction in these regions.

The third pattern is that the actual population remains to increase, but both THU and NUIST overestimate the tendency. There are six provinces in this category, containing Hebei, Anhui, Jiangxi, Hunan, Yunnan, and Qinghai provinces (Fig.  3 (3, 12, 14, 18, 25, 29)).

The fourth pattern is that the THU and NUIST underestimate the actual growth population. This class includes thirteen provinces as Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Chongqing, Sichuan, Guizhou, Xizang, Shannxi, Ningxia, and Xinjiang province. (Fig.  3 (10, 11, 13, 15, 19, 21, 22, 23, 24, 26, 27, 30, 31)). These provinces are primarily located in the southwest and southeast coastal areas, revealing that the population of south China is maintaining increasement.

The fifth pattern is that the actual population is less than THU but larger than NUIST. The three provinces as Hunan, Hubei, and Guangxi belonging to the type. In this type, both the two projections are closed to the truth.

We utilize the MAPE and MALPE to measure the projection errors at the province scale quantitatively, and the results are displayed in Fig.  4 . There are differences in the two datasets' MAPE (Fig.  4 a, b). THU's MAPE distribution could be divided into three distinct portions from south to north China, the center regions have the least values, and the northeast provinces own the largest values. Moreover, the NUIST's MAPE distributions display three sections from east to west China, the northeast and southeast provinces have the highest values, and the middle region has the lowest values. As a result, both THU and NUIST have large errors in northeast China and Xizang province.

Distribution of the provincial MAPE and MALPE of NUIST and THU. The left panel ( a , c ) shows the THU results, and the right panel ( b , d ) shows the NUIST results.

For the MALPE , the THU and NUIST have similar spatial distributions as the Fig.  4 c, d shown. The red color indicates the projection overate the population, and the blue color means the projection underestimates the population growth in a certain province. Therefore, both the THU and NUIST overestimate the population development in north China, especially the northeast regions such as Heilongjiang, Jilin, and Neimenggu province. The overestimated projection may be caused by they do not consider the population outflow in these areas. Besides, the southeast coastal provinces and southwest provinces own the negative MALPE , denoting their population are underestimated.

Additionally, we compare the NUIST and THU's projection accuracies from 2010 to 2020 based on their MAPE values. We calculate the difference for the MAPE of NUIST and THU. When the difference is positive, the NUIST projection is more inaccurate than the THU projection. In contrast, if the difference is negative, the NUIST projection is more accurate than the THU projection in the individual province. The MAPE comparison results are displayed in Fig.  5 .

Projection accuracy comparison of THU and NUIST. The purple color indicates that THU has a lower MAPE than NUIST, and the blue color indicates that NUIST has a lower MAPE than THU in a particular province.

The purple color indicates that the THU projected population is more accurate than the NUIST projected population. Conversely, the blue indicates that the NUIST projected population is closer to the actual population than the THU population. According to the Fig.  5 , the purple regions are primarily distributed in the western and northern coast of China, such as Xingjiang, Xizang, Guangdong, and Shangdong province. The blue regions are mainly located in the northeastern and central of China, such as Heilongjiang, Jilin, Hubei, and Jiangxi province.

Attribution of the population projection error

We utilize the SEM model to analyze the impact factors of projection errors at the province scale. Therefore, there are four models for MAPE and MALPE of THU and NUIST, and their regression coefficients are displayed in Fig.  6 . In this figure, only the impact factors with significant influence are drawn. The square size represents the significance level of impact factors, and the color indicates the regression coefficient value.

Regression coefficients of the SEM for the MAPE and MALPE of NUIST and THU.

Three impact factors significantly influence THU's MAPE , including the total fertility rate, average wage, and the number of tertiary hospitals. Besides, all the indicators are negatively related to the THU's MAPE , which means the provinces with lower fertility rates, average wages, and more tertiary hospitals would have worse projection results. For instance, in the northeast provinces with low fertility rates, the THU's projection errors are higher than NUIST. Furthermore, four impact factors are related to NUIST's MAPE , including the mortality rate, rural sex ratio, number of the first child, and number of abortions.

When analyzing the impact factors of MALPE , it is necessary to consider the positive or negative of values. As shown in Fig.  5 , there are five provinces with the positive MALPE , including the Heilongjiang, Jinlin, Neimenggu, Shanxi, and Gansu provinces. According to the Fig.  6 , the MALPE of THU and NIUST are influenced by some common impact factors. For example, the proportion of the population aged 15 to 64, the contraceptive rate of married women, and the number of third children have a significantly positive relation with MALPE . Therefore, for the provinces with negative MALPE , the provinces with more population aged 15 to 64 would have higher projection accuracy.

On the contrary, the average population growth rate and proportion of the population leaving the province for more than a half year negatively correlate with MALPE . Therefore, their negative population growth rates expand projection errors for the provinces with positive MALPE , such as the Heilongjiang and Jilin provinces. Similarly, the provinces with negative MALPE and positive population growth rates face more significant projection errors, such as the Guangdong and Zhejiang province. Meanwhile, the proportion of the population leaving more than half a year is negatively related to the MALPE . As a result, the more significant growth rate and population migration lead to higher projection errors for the two datasets.

The deceleration of China's population growth rate

The slowing of China's total population development starts from 2017 (Fig.  2 b), yet some provinces' population reduction from 2010 already (Fig.  3 ). However, these projections datasets do not anticipate the turning point of China's population growth coming so early and population reduction so sharply for some provinces.

The overestimate of total fertility rate (TFR) in projection is a significant reason for overrating the population growth. Due to many studies deem the TFR 1.180 in the sixth national population census is severely underestimated 50 , 51 , the TFR in 2010 is rectified higher in all projections, as Table 4 shows. The UN offers the maximum TFR of 1.620, and IHME provides the minimum TFR of 1.220. The THU and NUIST up-regulate the projected TFR of 2020 and 2030, because they think the loosened governmental birth control policies will facilitate birth effectively. Nevertheless, according to the newest seventh national population census, the TFR is merely 1.300 in 2020. Therefore, the IHME, CEPAM, and WCDE are approaching the actual population because their TFR is closer to the census results. The UN and THU are higher than the actual condition in 2020 seriously.

After publishing the low TFR in the seventh census results, the worries for maintaining China's future population steadily growth are discussed again. For China, the present TFR is lower than the replacement-level fertility, which means the new generations will be seriously less than the aged population in the future. The Sub-replacement fertility probably leads to the labor shortage, economic contraction, and increased social pensions burden 52 . Therefore, China's government implemented the "Three-Child" policy allowing couples to nurse three children in 2020 after the "Two-Child" policy permitting two children in for family in 2015 53 . However, considering the actual TFR does not realize the high level as THU, NUIST, and UN projected in the validation years, the population may reach a peak earlier than these datasets projected. Besides, to avoid the population decline as the IHME, IIASA and WCDE predicted, China's fertility regulation implements may need further loosened.

On the other hand, international net migration has an import effect on China's future population change. As Table 4 shows, the net migration values are assumed unchanged for a time in some projections. For example, the UN supposes the migration invariability from 2040 to 2100, and the IIASA assumes it unchanged from 2010 to 2060. Moreover, in the projection of the THU and IIASA, they believe the net migration would gradually equal to zero in 2100, as Abel stated 54 . Nevertheless, other projections do not set the net migration as zero in 2100. Furthermore, although UN keeps a high TFR in the total periods, its projection population is not the largest, which could be attributed to its large population outflow. Similarly, the IHME supposed population inflow would be since 2070, but its low fertility hypothesis predicts the lowest population in 2100. As a result, the migration should be set based on more reasonable methods.

The imbalance of population growth in north–south China

As shown in Fig.  4 c, d, the projections cannot reflect the radical population reduction in northeast provinces and underrate the increase in southwest and southeast provinces. For example, the THU thinks their population keeps increasing in Liaoning, Jilin, and Heilongjiang provinces with linear population decrease, and THU predicts it decrease with gentle rates. The unpredictable population reduction may be ascribed to these models underrate the population outflow intensity in northeast China. Besides, the population reduction is caused by low fertility and influenced by economic and social factors. Moreover, in southeast China such as Guangdong and Zhejiang province, both projections are lower than the actual values, which may be caused by their flourishing economic activities attracting plenty of population inflow 55 .

In southwest China, the projections seriously underrate the population of Chongqing, Sichuan, and Xizang. These errors are likely because the government policies boost economic development and attract more population inflow. For instance, the "Cheng-Yu Economic Zone" policy was introduced in 2011, which accelerated the economic development and population expansion of Chongqing and Sichuan Provinces 56 , 57 . Due to China's "poverty alleviation" policies, Xizang has received generous economic assistance from the central government to support its rapid development 58 . However, the population projection models are unable to consider the policy changes.

Factors that impact the projection accuracy

According to the SEM regression results, some common factors impact the projection accuracy for THU and NUIST. The first category is the population change indicators, as the population growth rates and province-cross migration persons. The high population annually change rate extends the projection errors, and the population migration also brings excellent uncertainty to projections. Due to the siphonage phenomenon, the urban agglomeration regions constantly attract populations from other undeveloped provinces, such as the Pearl River Delta and Yangtze River Delta regions 59 , 60 . However, the population projection models of THU and NUIST oversimplified the depiction of migration internal China. As a result, their projections overestimated population outflow provinces and underestimated provinces with massive population inflow.

Moreover, the proportion of the population aged 15 to 64 also significantly impacts the projection accuracy. Based on the regression results, the more population in this group, the higher the projection accuracy. Because the group accounts for the largest in the total population, and it is also the primary fertility group. If the projection could not acquire reasonable population and fertility rates in the group, the entire total population projection may be seriously inaccurate.

Furthermore, the contraceptive rate of childbearing married women significantly influences projection accuracy, and the indicator could denote people's fertility desire. Generally, the population projection models estimate the future population change based on fertility, mortality, and migration rates. However, these general parameters are challenging to represent individual s' mentality thoughts. Besides, society, economy, and culture play an essential role in people's fertility desire. Therefore, the accurate population projection needs reasonable parameters of fertility, mortality, and migrations. However, fertility is depended on the individual's choice and very personal behavior. While building the population projection models, scholars should combine the influence of society and the environment.

In this study, we evaluate the projection accuracy of some population projection datasets of China. Nine datasets are compared with the actual population from 2010 to 2020 at the country scale. The projections of THU and NUIST are validated at the province scale in the same periods. Besides, we utilize the MAPE , MALPE , and R-Square to quantificationally measure the projection errors. Furthermore, we analyze the contributions of several impact factors to the projection errors based on SEM regression models. According to study results, these projections provide various population growth situations at the country and province scale, but most of them cannot show the deceleration of population growth after 2017. Moreover, the annual population change rates and the migration population significantly influence the projection accuracy. Finally, we discuss the different fertility values between the actual condition and projection set and provide suggestions for further population projection models.

Data availability

The actual demographical data are available from the National Bureau of Statistics of the People's Republic of China ( http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm ). The population projection data of Nanjing University of Information Science and Technology (NUIST) are available from the website at https://geography.nuist.edu.cn/2019/1113/c1954a147560/page.htm . The population projection data of Tsinghua University (THU) are available from the website at https://doi.org/10.6084/m9.figshare.c.4605713 . The population projection data of the International Institute for Applied Systems Analysis (IIASA) are provided on the websites of the SSP database ( https://tntcat.iiasa.ac.at ). The population projection data of the United Nations are available from the website at https://population.un.org/wpp/ . The National Institute for Environmental Studies (NIES) population projection data are available from the website at https://www.cger.nies.go.jp/gcp/population-and-gdp.html . The population of Socioeconomic Data and Applications Center (SEDAC) are derived from https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-km-downscaled-pop-base-year-projection-ssp-2000-2100-rev01 . The data of the Institute for Health Metrics and Evaluation (IHME) is downloaded from http://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100 . The data of the Centre of Expertise on Population and Migration (CEPAM) are acquired from https://core.ac.uk/display/158646554?source=2 . The data of Wittgenstein Centre Data Explorer (WCDE) are obtained from http://dataexplorer.wittgensteincentre.org/wcde-v2 .

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Acknowledgements

We want to thank the high-performance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University [ https://gda.bnu.edu.cn/ ].

This work is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK0608], the National Key Research and Development Plan of China [Grant No. 2019YFA0606901], and the State Key Laboratory of Earth Surface Processes and Resource Ecology [Grant No. 2020-KF-05].

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Home » News » Updates » National key research and development plan project holds kick-off meeting

National key research and development plan project holds kick-off meeting

From Sept 13 to 14, the "14th Five-Year Plan" national key research and development plan "natural grassland intelligent grazing and key technologies for precise management and control of grass and livestock (2021YFD1300500)" project launch and progress promotion meeting was held in Beijing. It was conducted online and offline and was chaired by Researcher Xin Xiaoping from the Institute of Agricultural Resources and Regional Planning (IARRP) of the Chinese Academy of Agricultural Sciences (CAAS) and presided over by Yi Keke, the Deputy Director General of the IARRP.

More than 70 people including executive experts, project/sub-project leaders, and key team members of the project's participating units attended the meeting.

From Sept 13 to 14, the "14th Five-Year Plan" national key research and development plan "natural grassland intelligent grazing and key technologies for precise management and control of grass and livestock (2021YFD1300500)" project launch and progress promotion meeting is held in Beijing. [Photo/IARRP]

At the meeting, Wu Wenbin, Director General of the IARRP, delivered a speech. He pointed out that the project is another national key R&D project organized and implemented by the researchers of the institute on the basis of the "13th Five-Year Plan" national key R&D plan project "Management Technology and Demonstration of Degraded Grassland in Northern Meadow" led by the Institute.

The project aims at key problems such as the diverse types of natural grassland grazing systems in China, the complex interaction mechanism between grassland and livestock, the difficulty in monitoring grassland animal husbandry, the low level of production management and control, and the lack of practical digital technology products, and comprehensively integrates the latest achievements of related modern information technology.

The project focuses on theoretical and technological innovations in the fields of accurate acquisition of natural grassland information, automatic monitoring and intelligent grazing of livestock, simulation and precise management and control of the grass and livestock growth process.

At the same time, it complements the shortcomings of digital and intelligent technology products of China's grassland animal husbandry, and conducts industrial demonstration and business applications that will provide important support for promoting the high-quality development of China's natural grassland animal husbandry.

Deng Xiaoming, Director of the China Rural Technology Development Center, made an important exposition from three aspects: national strategy, industry development and the project itself. First, from the perspective of big food, grasslands are very important in agriculture, animal husbandry, food and health. Second, from the development of the grassland animal husbandry industry, grassland degradation, protection and utilization still face many problems, and if managed properly have great potential. The third is to put forward requirements for the implementation of the project itself. The project team must enhance their sense of responsibility and mission, improve their position and understanding, and strive to treat the project as a career. To strengthen team management and organizational innovation, it is necessary to pay attention to close cooperation with local governments to ensure the smooth implementation of the project and achieve high-quality results and demonstrable effects.

The special management team introduced the "14th Five-Year" national key research and development plan "new breeds of livestock and poultry and technological innovation of modern pastures", including its key special projects, overall situation and management requirements. Researcher Xin Xiaoping reported on the project implementation and management plan, while the academicians, experts and leaders participating in the meeting exchanged views and gave comprehensive guidance on the technical plan, organization implementation and performance management of the project.

After the kick-off meeting, progress reports were made on project components and topics, while project consultants evaluated their progress and achievements and put forward valuable suggestions on how to implement the next step.

Researcher Xin Xiaoping made a summary based on the report and expert opinions, and suggested  arrangements for the project's future work and subject focuses.

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Chinese Company Under Congressional Scrutiny Makes Key U.S. Drugs

Lawmakers raising national security concerns and seeking to disconnect a major Chinese firm from U.S. pharmaceutical interests have rattled the biotech industry. The firm is deeply involved in development and manufacturing of crucial therapies for cancer, cystic fibrosis, H.I.V. and other illnesses.

A WuXi Biologics facility in Wuxi, China. WuXi AppTec and an affiliated company, WuXi Biologics, have received millions of dollars in tax incentives to build sprawling research and manufacturing sites in Massachusetts and Delaware. Credit... Imaginechina Limited, via Alamy

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By Christina Jewett

• April 15, 2024

A Chinese company targeted by members of Congress over potential ties to the Chinese government makes blockbuster drugs for the American market that have been hailed as advances in the treatment of cancers, obesity and debilitating illnesses like cystic fibrosis.

WuXi AppTec is one of several companies that lawmakers have identified as potential threats to the security of individual Americans’ genetic information and U.S. intellectual property. A Senate committee approved a bill in March that aides say is intended to push U.S. companies away from doing business with them.

But lawmakers discussing the bill in the Senate and the House have said almost nothing in hearings about the vast scope of work that WuXi does for the U.S. biotech and pharmaceutical industries — and patients. A New York Times review of hundreds of pages of records worldwide shows that WuXi is heavily embedded in the U.S. medicine chest, making some or all of the main ingredients for multibillion-dollar therapies that are highly sought to treat cancers like some types of leukemia and lymphoma as well as obesity and H.I.V.

The Congressional spotlight on the company has rattled the pharmaceutical industry, which is already struggling with widespread drug shortages now at a 20-year high . Some biotech executives have pushed back, trying to impress on Congress that a sudden decoupling could take some drugs out of the pipeline for years.

WuXi AppTec and an affiliated company, WuXi Biologics grew rapidly, offering services to major U.S. drugmakers that were seeking to shed costs and had shifted most manufacturing overseas in the last several decades.

WuXi companies developed a reputation for low-cost and reliable work by thousands of chemists who could create new molecules and operate complex equipment to make them in bulk. By one estimate, WuXi has been involved in developing one-fourth of the drugs used in the United States. WuXi AppTec reported earning about $3.6 billion in revenue for its U.S. work.

“They have become a one-stop shop to a biotech,” said Kevin Lustig, founder of Scientist.com, a clearinghouse that matches drug companies seeking research help with contractors like WuXi.

WuXi AppTec and WuXi Biologics have also received millions of dollars in tax incentives to build sprawling research and manufacturing sites in Massachusetts and Delaware that local government officials have welcomed as job and revenue generators. One WuXi site in Philadelphia was working alongside a U.S. biotech firm to give patients a cutting-edge therapy that would turbocharge their immune cells to treat advanced skin cancers.

The tension has grown since February, when four lawmakers asked the Commerce, Defense and Treasury Departments to investigate WuXi AppTec and affiliated companies, calling WuXi a “giant that threatens U.S. intellectual property and national security.”

A House bill called the Biosecure Act linked the company to the People’s Liberation Army, the military arm of the Chinese Communist Party. The bill claims WuXi AppTec sponsored military-civil events and received military-civil fusion funding.

Richard Connell, the chief operating officer of WuXi AppTec in the United States and Europe, said the company participates in community events, which do not “imply any association with or endorsement of a government institution, political party or policy such as military-civil fusion.” He also said shareholders do not have control over the company or access to nonpublic information.

Last month, after a classified briefing with intelligence staff, the Senate homeland security committee advanced a bill by a vote of 11 to 1: It would bar companies from receiving government contracts for work with Wuxi, but would allow the companies to still obtain contracts for unrelated projects. Government contracts with drugmakers are generally limited, though they were worth billions of dollars in revenue to companies that responded to the Covid-19 pandemic.

Mr. Connell defended the company’s record, saying the proposed legislation “relies on misleading allegations and inaccurate assertions against our company.”

WuXi operates in a highly regulated environment by “multiple U.S. federal agencies — none of which has placed our company on any sanctions list or designated it as posing a national security risk,” Mr. Connell said. WuXi Biologics did not respond to requests for comment.

Smaller biotech companies, which tend to rely on government grants and have fewer reserves, are among the most alarmed. Dr. Jonathan Kil, the chief executive of Seattle-based Sound Pharmaceuticals, said WuXi has worked alongside the company for 16 years to develop a treatment for hearing loss and tinnitus, or ringing in the ear. Finding another contractor to make the drug could set the company back two years, he said.

“What I don’t want to see is that we get very anti-Chinese to the point where we’re not thinking correctly,” Dr. Kil said.

It is unclear whether a bill targeting WuXi will advance at all this year. The Senate version has been amended to protect existing contracts and limit supply disruptions. Still, the scrutiny has prompted some drug and biotechnology companies to begin making backup plans.

Peter Kolchinsky, managing partner of RA Capital Management, estimated that half of the 200 biotech companies in his firm’s investment portfolio work with WuXi.

“Everyone is likely considering moving away from Wuxi and China more broadly,” he said in an email. “Even though the current versions of the bill don’t create that imperative clearly, no one wants to be caught flat-footed in China if the pullback from China accelerates.”

The chill toward China extends beyond drugmakers. U.S. companies are receiving billions of dollars in funding under the CHIPS Act, a federal law aimed at bringing semiconductor manufacturing stateside.

For the last several years, U.S. intelligence agencies have been warning about Chinese biotech companies in general and WuXi in particular. The National Counterintelligence and Security Center, the arm of the intelligence community charged with warning companies about national security issues, raised alarms about WuXi’s acquisition of NextCODE, an American genomic data company.

Though WuXi later spun off that company, a U.S. official said the government remains skeptical of WuXi’s corporate structure, noting that some independent entities have overlapping management and that there were other signs of the Chinese government’s continuing control or influence over WuXi.

Aides from the Senate homeland security committee said their core concerns are about the misuse of Americans’ genomic data, an issue that’s been more closely tied to other companies named in the bill.

Aides said the effort to discourage companies from working with WuXi and others was influenced by the U.S. government’s experience with Huawei, a Chinese telecommunications giant. By the time Congress acted on concerns about Huawei’s access to Americans’ private information, taxpayers had to pay billions of dollars to tear Huawei’s telecommunication equipment out of the ground.

Yet WuXi has far deeper involvement in American health care than has been discussed in Congress. Supply chain analytics firms QYOBO and Pharm3r, and some public records, show that WuXi and its affiliates have made the active ingredients for critical drugs.

They include Imbruvica, a leukemia treatment sold by Janssen Biotech and AbbVie that brought in $5.9 billion in worldwide revenue in 2023. WuXi subsidiary factories in Shanghai and Changzhou were listed in government records as makers of the drug’s core ingredient, ibrutinib.

Dr. Mikkael A. Sekeres, chief of hematology at the University of Miami Health System, called that treatment for chronic lymphocytic leukemia “truly revolutionary” for replacing highly toxic drugs and extending patients’ lives.

Janssen Biotech and AbbVie, partners in selling the drug, declined to comment.

WuXi Biologics also manufactures Jemperli, a GSK treatment approved by the Food and Drug Administration last year for some endometrial cancers. In combination with standard therapies, the drug improves survival in patients with advanced disease, said Dr. Amanda Nickles Fader, president of the Society of Gynecologic Oncology.

“This is particularly important because while most cancers are plateauing or decreasing in incidence and mortality, endometrial cancer is one of the only cancers globally” increasing in both, Dr. Fader said.

GSK declined to comment.

The drug that possibly captures WuXi’s most significant impact is Trikafta, manufactured by an affiliate in Shanghai and Changzhou to treat cystic fibrosis, a deadly disease that clogs the lungs with debilitating, thick mucus. The treatment is credited with clearing the lungs and extending by decades the life expectancy of about 40,000 U.S. residents. It also had manufacturers in Italy, Portugal and Spain.

The treatment has been so effective that the Make-A-Wish Foundation stopped uniformly granting wishes to children with cystic fibrosis. Trikafta costs about $320,000 a year per patient and has been a boon for Boston-based Vertex Pharmaceuticals and its shareholders, with worldwide revenue rising to $8.9 billion last year from $5.7 billion in 2021, according to a securities filing .

Trikafta “completely transformed cystic fibrosis and did it very quickly,” said Dr. Meghan McGarry, a University of California San Francisco pulmonologist who treats children with the condition. “People came off oxygen and from being hospitalized all the time to not being hospitalized and being able to get a job, go to school and start a family.”

Vertex declined to comment.

Two industry sources said WuXi plays a role in making Eli Lilly’s popular obesity drugs. Eli Lilly did not respond to requests for comment. WuXi companies also make an infusion for treatment-resistant H.I.V., a drug for advanced ovarian cancer and a therapy for adults with a rare disorder called Pompe disease.

WuXi is known for helping biotech firms from the idea stage to mass production, Dr. Kolchinsky said. For example, a start-up could hypothesize that a molecule that sticks to a certain protein might cure a disease. The company would then hire WuXi chemists to create or find the molecule and test it in petri dishes and animals to see whether the idea works — and whether it’s safe enough for humans.

“Your U.S. company has the idea and raises the money and owns the rights to the drug,” Dr. Kolchinsky said. “But they may count on WuXi or similar contractors for almost every step of the process.”

WuXi operates large bioreactors and manufactures complex peptide, immunotherapy and antibody drugs at sprawling plants in China.

WuXi AppTec said it has about 1,900 U.S. employees. Officials in Delaware gave the company $19 million in tax funds in 2021 to build a research and drug manufacturing site that is expected to employ about 1,000 people when fully operational next year, public records and company reports show.

Mayor Kenneth L. Branner Jr. of Middletown, Del., called it “one of those once-in-a-lifetime opportunities to land a company like this,” according to a news report when the deal was approved.

In 2022, the lieutenant governor of Massachusetts expressed a similar sentiment when workers placed the final steel beam on a WuXi Biologics research and manufacturing plant in Worcester. Government officials had approved roughly $11.5 million in tax breaks to support the project. The company announced this year that it would double the site’s planned manufacturing capacity in response to customer demand.

And in Philadelphia, a WuXi Advanced Therapies site next to Iovance Biotherapeutics was approved by regulators to help process individualized cell therapies for skin cancer patients. Iovance has said it is capable of meeting demand for the therapies independently.

By revenue, WuXi Biologics is one of the top five drug development and manufacturing companies worldwide, according to Statista , a data analytics company. A WuXi AppTec annual report showed that two-thirds of its revenue came from U.S. work.

Stepping away from WuXi could cause a “substantial slowdown” in drug development for a majority of the 105 biotech companies surveyed by BioCentury , a trade publication. Just over half said it would be “extremely difficult” to replace China-based drug manufacturers.

BIO, a trade group for the biotechnology industry, is also surveying its members about the impact of disconnecting from WuXi companies. John F. Crowley, BIO’s president, said the effects would be most difficult for companies that rely on WuXi to manufacture complex drugs at commercial scale. Moving such an operation could take five to seven years.

“We have to be very thoughtful about this so that we first do no harm to patients,” Mr. Crowley said. “And that we don’t slow or unnecessarily interfere with the advancement of biomedical research.”

Julian E. Barnes contributed reporting, and Susan C. Beachy contributed research.

Christina Jewett covers the Food and Drug Administration, which means keeping a close eye on drugs, medical devices, food safety and tobacco policy. More about Christina Jewett

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