What can Europe learn from China’s critical-tech innovation push? – Bruegel

Report on Global Innovation in Critical Technologies and Sustainable Development

Executive Summary

This report analyzes the competitive landscape of frontier innovation in Artificial Intelligence (AI), semiconductors, and quantum computing, assessing the progress of the United States (US), China, and the European Union (EU). The findings indicate that leadership in these technologies is fundamental to achieving key Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth). While the US currently leads in overall innovation, China is rapidly closing the gap, demonstrating notable strengths in semiconductor fabrication and specific AI applications. The EU lags significantly in generating novel patents but shows relative strength in quantum technologies. A critical finding is the disparity in knowledge diffusion; Chinese and US innovators replicate foreign breakthroughs far more rapidly than their European counterparts. This innovation gap poses a direct challenge to the EU’s long-term economic resilience and its capacity to leverage technology for sustainable development. The report also highlights the distinct innovation ecosystems: the US model is concentrated in large technology firms, China’s is a diverse mix of public and private entities across various sectors, and the EU relies more heavily on public research institutions.

Comparative Analysis of Innovation Ecosystems and SDG Alignment

United States: Concentrated Innovation Driving Economic Growth

The US innovation ecosystem is characterized by a high concentration of activity within a few dominant technology corporations. This structure facilitates rapid commercialization and vertical integration, directly contributing to SDG 8 by creating high-value industries. However, this concentration may also limit industrial diversity and presents challenges for inclusive innovation aligned with SDG 10 (Reduced Inequalities).

• Dominant Players: Breakthroughs are heavily concentrated in companies like Microsoft, IBM, Google, and Intel.
• Strengths: The ecosystem excels at integrating research, engineering, and commercialization, particularly in high-value areas like chip design and machine learning algorithms.
• SDG Impact: While a powerful engine for economic growth, its narrow focus on digital and algorithmic technologies may overlook applications in other sectors crucial for broader sustainable development, such as sustainable manufacturing or agriculture.

China: A Diverse, State-Guided Model for Industrialization

China’s approach is distinguished by a diverse array of innovators from multiple sectors, guided by industrial policy. This model effectively blends market competition with state-directed goals, accelerating progress toward SDG 9 by building a robust and self-reliant industrial base. The application of these technologies in areas like public health and urban management directly supports other goals.

• Diverse Innovators: Leading entities include technology giants (Huawei, Tencent), industrial firms, and even financial services companies (Ping An), which is pioneering AI for predictive health analytics, contributing to SDG 3 (Good Health and Well-being).
• Synergies: The ecosystem fosters synergies between digital technologies and manufacturing, with applications in smart cities (SDG 11 – Sustainable Cities and Communities) and logistics.
• State Support: Industrial policies like ‘Made in China 2025’ and the ‘Little Giants’ program strategically channel resources to build national capabilities in critical technologies.

European Union: Fragmented Excellence and Public Sector Reliance

The EU’s innovation landscape is more fragmented and reliant on public research centers, particularly in quantum computing. While possessing pockets of world-class excellence, the ecosystem struggles with scale and commercialization, hindering its ability to drive progress on the SDGs. The slow replication of innovations indicates a weakness in fostering the cross-border collaboration essential for SDG 17 (Partnerships for the Goals).

• Key Institutions: Public research centers like France’s CEA and various universities are major contributors, especially in foundational research.
• Niche Strengths: Europe holds advantages in specific subfields, such as power electronics and lithography, which are critical for SDG 7 (Affordable and Clean Energy), and in explainable AI, which aligns with SDG 16 (Peace, Justice, and Strong Institutions) by promoting trustworthy technology.
• Challenges: A lack of market integration and venture capital, compared to the US and China, constrains the scaling of innovations from the lab to the market.

Technological Capabilities and Contribution to Global Goals

Artificial Intelligence

AI is a transformative technology with applications across nearly all SDGs. China has established a lead in computer vision and aerial vehicle AI, leveraging these for smart city infrastructure (SDG 11). The US dominates in foundational areas like machine learning, while the EU’s focus on AI ethics provides a potential pathway for setting global standards for responsible innovation.

Semiconductors

As the bedrock of the digital economy, semiconductors are indispensable for sustainable development, enabling energy-efficient computing and renewable energy systems (SDG 7). China’s strategic focus on hardware-intensive fabrication reflects a push for industrial self-sufficiency (SDG 9). The US leads in high-value chip design, while the EU’s strength in specialized areas like lithography is a critical component of the global value chain.

Quantum Computing

Quantum computing holds immense potential for solving complex problems related to global challenges. Its applications are crucial for advancing SDG 13 (Climate Action) through improved climate modeling and SDG 3 through accelerated drug discovery. The US holds a commanding lead in this field, with the EU and China trailing but actively investing in niche capabilities.

Knowledge Diffusion and the Innovation Gap

Analysis of Cross-Regional Spillovers

The ability to rapidly replicate and build upon global innovations is a key determinant of technological leadership. Analysis shows a stark divide in the speed of knowledge diffusion.

• Rapid Replicators: China and the US exhibit fast spillover effects, with Chinese firms replicating US or EU novelties in approximately six months. This dynamic, while competitive, accelerates the global innovation cycle.
• European Lag: The EU takes 18-24 months to replicate innovations from the US or China. Critically, knowledge transfer within the EU is equally slow, highlighting significant internal fragmentation that undermines SDG 17 and the goal of an integrated European Research Area.

Recommendations for a Sustainable and Innovative Europe

To close the innovation gap and harness critical technologies for the 2030 Agenda, the EU must adopt a strategic, integrated approach that enhances both basic research and commercial deployment. The following actions are recommended:

1. Establish EU-Wide Regulatory Sandboxes: Create dedicated environments for technology transfer and patent licensing to reduce bureaucratic barriers and accelerate cross-border collaboration, directly supporting SDG 9 and SDG 17.
2. Focus Research Funding on Deployment: Reorient funding programs like Horizon Europe to include direct financial incentives for private firms to prototype and commercialize novelties, mirroring successful models that have propelled other ecosystems.
3. Leverage Public Procurement for Innovation: Utilize the EU’s €2 trillion public procurement market as a tool to generate demand for sustainable technologies. Mandating the inclusion of EU-sourced AI or semiconductors in public contracts would create stable markets and advance SDG 12 (Responsible Consumption and Production).
4. Create an EU Critical Technology Observatory: Establish a body to monitor global patent trends in real-time, enabling proactive “fast-follower” strategies that identify and replicate high-potential innovations, strengthening institutional capacity in line with SDG 16.
5. Integrate Dual-Use Technology Development: Coordinate increased defense spending to create demand for dual-use technologies, spurring innovation that has broad applications for building resilient infrastructure and enhancing civilian technological sovereignty (SDG 9).

SDGs Addressed in the Article

• SDG 9: Industry, Innovation and Infrastructure
• SDG 8: Decent Work and Economic Growth
• SDG 17: Partnerships for the Goals

Specific SDG Targets Identified

1. SDG Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation and substantially increasing… public and private research and development spending.

   The article is fundamentally about enhancing scientific research and upgrading technological capabilities in the critical sectors of artificial intelligence, semiconductors, and quantum computing. It directly compares the innovation ecosystems of the US, China, and the EU. The text highlights the importance of R&D spending, noting that while the “EU spends more on basic research than China,” China’s “growth in basic research expenditure is double that of the EU.” It also discusses the need for Europe to “increase research and development in critical technologies” to close the innovation gap, which aligns perfectly with the goal of this target.

2. SDG Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation…

   The article establishes a direct link between technological innovation and economic strength. It states that “Supremacy in critical technologies… has become a cornerstone of economic and strategic power” and that these technologies “underpin everything from autonomous weapons to climate modelling.” The analysis of how the US innovation ecosystem turns “theoretical breakthroughs into trillion-dollar industries” demonstrates the connection between technological upgrading in high-value sectors and achieving higher levels of economic productivity and resilience, as outlined in this target.

3. SDG Target 17.6: Enhance… regional and international cooperation on and access to science, technology and innovation and enhance knowledge sharing…

   This target is addressed through the article’s extensive analysis of “knowledge spillovers,” which it defines as “the spread of new technologies or ideas from one region to others.” The article measures the speed of this knowledge sharing, finding that “Chinese and US innovators are much faster than their European counterparts at replicating novel patents from other countries.” It also points to a failure in regional cooperation within the EU, stating that “the average time for a breakthrough from one EU country to be replicated by an innovator in another EU country is as long, if not longer” than replicating a Chinese patent. This highlights the challenges and importance of cooperation in technology and innovation.

4. SDG Target 9.b: Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment…

   The article provides a detailed case study of China’s strategy to foster domestic technology development and “homegrown innovation.” It describes how China’s industrial policy, including programs like “Made in China 2025” and the “Little Giants,” creates a “conducive policy environment” that “strategically aligns long-term objectives… with flexible implementation mechanisms.” This state-led approach, which has allowed China to rapidly move “up the innovation ladder,” exemplifies the principles of supporting domestic research and innovation described in this target.

Indicators for Measuring Progress

1. Indicator: Research and development expenditure.

   The article directly uses R&D spending as a key metric to compare innovation efforts. It provides specific figures: “the EU spends more on basic research than China – $47.5 billion in 2024, compared to China’s $34.7 billion (OECD, 2025).” It also mentions the growth rate of this spending, noting that “China’s growth in basic research expenditure is double that of the EU (over 10 percent versus 5 percent).” This aligns with the official SDG indicator 9.5.1 (Research and development expenditure as a proportion of GDP).

2. Indicator: Number of novel patents or ‘radical novelties’.

   A primary indicator used throughout the article is the number of “radical novelties,” which are defined as “new patents for which there are no prior similar patents and which are then repeated at least five times in subsequent patents.” The article uses this metric to compare the innovation output of the US, China, and the EU across AI, semiconductors, and quantum computing, as shown in Figures 1, 2, and 3. This serves as a direct measure of innovation output and technological breakthroughs.

3. Indicator: Time lag for cross-regional technology replication (knowledge spillover speed).

   The article introduces and measures the speed of knowledge diffusion by calculating “the time lag between the publication of an original, radically novel patent and the appearance of similar technologies in patents from other regions.” It provides concrete data for this indicator, stating that “China excels by replicating novel patents from the US or EU in only in six months,” whereas “EU countries, meanwhile, take 18-24 months.” This is a direct measure of progress towards enhancing knowledge sharing and technology transfer (Target 17.6).

4. Indicator: Diversity of innovating entities.

   The article implies an indicator related to the structure of the innovation ecosystem by analyzing the types of entities filing patents. It contrasts the US, where “breakthroughs are heavily concentrated in the big-tech companies,” with China, which has a “balanced mix of private and public entities” and a “greater variety in types of companies.” The EU is described as relying more on “public research centres.” This qualitative indicator helps measure the breadth and resilience of a national innovation system.

Summary Table: SDGs, Targets, and Indicators

Source: bruegel.org