The development of artificial intelligence (AI) presents significant opportunities for economic and social flourishing. The release of foundational models such as the large language model (LLM) ChatGPT4 in early 2023 captured the world’s attention, heralding a transformation in our approach to work, communication, scientific research, and diplomacy. According to Goldman Sachs, LLMs could raise global GDP by 7 percent and lift productivity growth by 1.5 percent over 10 years. McKinsey found that generative AI such as ChatGPT4 could add $2.6-$4.4 trillion each year over 60 use cases, spanning customer operations, marketing, and sales, software engineering, and R&D. AI is also impacting international trade in various ways, and LLMs bolster this trend. The upsides of AI are significant and achieving them will require developing responsible and trustworthy AI. At the same time, it is critical to address the potential risk of harm not only from conventional AI but also from foundational AI models, which in many cases can either magnify existing AI risks or introduce new ones.

For example, LLMs are trained on data that encodes existing social norms, with all their biases and discrimination. LLMs create risks of information hazards by providing information that is true and can be used to create harm to others, such as how to build a bomb or commit fraud. A related challenge is preventing LLMs from revealing personal information about an individual that is a risk to privacy. Another higher risk from the misuse of LLMs is an increase in the incidence and effectiveness of crime. In other cases, LLMs will increase existing risks of harm, such as from misinformation which is already a problem with online platforms or increase the incidence and effectiveness of crime. LLMs may also introduce new risks, such as risks of exclusion where LLMs are unavailable in some languages.

International cooperation on AI is already happening in trade agreement and international economic forums

Many governments are either regulating AI or planning to do so, and the pace of regulation has increased since the release of ChatGPT4. However, regulating AI to maximize the upsides and minimize the risks of harm without stifling innovation will be challenging, particularly for a rapidly evolving technology that is still in its relative infancy. Making AI work for economies and societies will require getting AI governance right. Deeper and more extensive forms of international cooperation can support domestic AI governance efforts in a number of ways. This includes by facilitating the exchange of AI governance experiences which can inform approaches to domestic AI governance; addressing externalities and extraterritorial impacts of domestic AI governance which can otherwise stifle innovation and reduce opportunities for uptake and use of AI; and finding ways to broaden access globally to the computing power and data needed to develop and train AI models.

Free trade agreements (FTAs), and more recently, digital economy agreements (DEAs) already include commitments that increase access to AI and bolster its governance. These include commitments to cross-border data flows, avoiding data localization requirements, and not requiring access to source code as a condition of market access, all subject to exception provisions that give government the policy space to also pursue other legitimate regulatory goals such as consumer protection and guarding privacy. Some FTAs and DEAs such as the New Zealand-U.K. FTA and the Digital Economy Partnership Agreement include AI-specific commitments focused on developing cooperation and alignment, including in areas such as AI standards and mutual recognition agreements.

With AI being a focus of discussions, international economic forums such as the G7 and the U.S.-EU Trade and Technology Council (TTC), the Organization for Economic Cooperation and Development (OECD), as well as the Forum for Cooperation on Artificial Intelligence (FCAI) jointly led by Brookings and the Center for European Policy Studies as a track-1.5 dialogue among government, industry, and civil society, are important for developing international cooperation in AI. Initiatives to establish international AI standards in global standards development organizations (SDOs) such as the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) are also pivotal in developing international cooperation on AI.

But more is needed—where new trade commitments can support AI governance

These developments in FTAs, DEAs, and in international economic forums, while an important foundation, need to be developed further in order to fully address the opportunities and risks from foundational AI models such as LLMs. International economic policy for foundational AI models can use commitments in FTAs and DEAs and outcomes from international economic forums such as the G7 and TTC as mutually reinforcing opportunities for developing international cooperation on AI governance. This can happen as FTAs and DEAs elevate the output from AI-focused forums and standard-setting bodies into trade commitments and develop new commitments as well. FCAI is another forum to explore cutting-edge AI issues.

Joshua P. Meltzer is a senior fellow in the Global Economy and Development program at the Brookings Institution.

To read the executive summary, click here.

To access the full paper PDF, click here.

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But that does not mean the end of globalization. Rather, it reflects growing regionalization of global supply chains. Companies are deviating from a traditional, cost-driven approach to supply-chain management by making strategic and operational decisions increasingly in light of geopolitical considerations. A strategy known as “friend-shoring” has gained traction among many political and business leaders. Companies use that approach to develop supply-chain resilience by sourcing goods from ideologically compatible countries and regions. Alternative trade strategies include reshoring, which brings manufacturing back from other countries, and nearshoring, which involves sourcing from nearby countries — e.g., Canada and Mexico from the perspective of companies based in the United States.

Artificial intelligence (AI) plays an important role in initiating global supply-chain strategies. Although AI is commonly associated with data analytics, it also has a remarkable ability to handle large volumes of unstructured data in real-time. Instances of unstructured data, such as social-media posts, emails, audiovisual files, and news reports, pose difficulties for traditional data collection and interpretation. Organizations miss out on valuable insights that can improve their supply-chain resilience and efficiency when they ignore such information. This is where AI models come into play.

AI-Infused Supply Chains
Modern AI models are based on deep neural networks that are adept at detecting patterns in vast amounts of unstructured data. By analyzing disparate data sources — e.g., social-media posts indicating unexpected spikes in demand for specific goods and news reports covering local political unrest and natural disasters — AI models can predict potential supply-chain disruptions. As the models adapt and learn, their predictions gain accuracy, enabling businesses to respond quickly and effectively.

In recent years, environmental, social, and governance (ESG) issues have come to the forefront of business operations because regulators, investors, researchers, and customers increasingly demand quantitative measures of sustainability performance from companies. However, ESG measures are meaningless unless they extend beyond a company’s boundaries to include performance across a supply chain. AI can help monitor a company’s carbon footprint throughout its entire supply chain and help ensure labor compliance throughout. AI also can evaluate potential supply partners based on their ESG ratings.

How to interact with an AI-infused supply-chain landscape is an increasingly important consideration. The pandemic catalyzed AI use in many ways by pushing businesses to their breaking points and forcing them to adapt. However, the use of AI in global supply remains in its infancy. Accelerating adoption may require a new catalyst, such as another major disruption or increased awareness of AI’s potential to improve supply-chain resilience and ESG compliance. Growing interest in generative AI (e.g., DALL.E-2 and ChatGPT) also could catalyze further adoption.

Companies need to realize that potential uses for AI go beyond data analysis. AI can help to usher in a new era of resilient, ethical, and sustainable supply-chain management. Integrating AI into supply-chain management is about more than just business survival. It ensures long-term competitiveness for companies in a world where ESG issues and geopolitical instability are at the forefront of both strategic and operational decision-making. Companies that leverage AI will be prepared for the next phase of global supply-chain transitions, gaining a competitive advantage in a world permanently changed by the COVID-19 pandemic.

Tinglong Dai is a professor of operations management and business analytics at Johns Hopkins University.

To read the full blog post, please click here.

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Generative AI is vital to national interest, regional prosperity, and tackling shared global challenges.

It can help to grow economies, quickly and fairly, by identifying the risks entailed in a long-chain transaction or a complex supply chain. So far, so good — but there is no system in place to monitor and pinpoint suspicious global trade patterns. Nor is there any mapping of complex international trade flows, or overall analysis of trading patterns.

Every data point, each statistical analysis and prediction model, must be spot on. Over-reliance on unverified data, or information that is inaccurate or misleading, can have dire consequences. A simple misunderstanding of context can result in AI’s notorious “technological hallucinations”. Errors can multiply through a supply chain, posing risks that can have far-reaching effects on the economy — such as covering up dumping, counterfeiting, or sanctions avoidance.

AI can play a vital role in monitoring compliance, analysing trends, and assessing the impact of policies. It provides transparency and engenders trust and accountability. AI-driven decisions and recommendations produce credible, far-reaching results. It can tell us where to seek proof of reliability, raise red flags, and shed light on previously invisible interconnections of the global economy. It assists in furthering our understanding of the complexities of trade dynamics.

But it’s crucial to see AI for what it is: a tool for augmenting human capabilities, not replacing them. Take this example. Over 200 million bills of lading, crucial papers in international trade, were recently reviewed by the International Centre for Trade Transparency (ICTTM). It found that 13.6 percent contained at least one error. The OECD decrees that 2.5 percent of global trades, and up to 5.8 percent of EU imports, are counterfeit. The documents provide particulars about country-of-origin, product codes and descriptions, quantities, and costs. Certifications, health and safety requirements, regulatory controls, anti-dumping measures, and taxation are all set by the data collected.

Again, any mistake can have dire results.

ICTTM research shows that goods produced with slave labour still appear on international markets; companies are bypassing safety standards by intentionally mislabelling products as requiring no certification. There have been exports of semiconductors pushed through in this way by dubious actors. Traceability becomes more muddied with each step of the transaction.

There’s clear evidence that some offenders re-incorporate in new jurisdictions as soon as they are caught — still selling to the same importers. This basic move, because of the lack of international oversight, makes these actions almost untraceable.

When error, fraud, and counterfeit percentages are multiplied over a complex supply chain many layers deep, the dangers become apparent. These “mistakes” have serious repercussions for society — and can even put lives at risk. There is an enormous, hidden, problem in our global supply chains and individual “empires” of technology have no way of solving it.

Nationally built systems, siloed in their own technological and political kingdoms, are not a suitable response to these problems. Countries inspect a small percentage of imports, and almost no exports. There is no system in place to monitor global trade patterns, no mapping of international trade flows.

And this is where AI can be of use. The international commerce ecosystem is complex, and bots have the capacity to spot macro- and micro-trends across the entire system, rather than just between two trading partners. The fact that we can exercise some control over our interactions with AI is significant. It can help us spot potential threats and zero-in on the primary papers that need closer inspection. It is a tool to identify and chart patterns and act as an early warning system, while keeping faith in the reliability of source materials. Once we know where to look, locating bad actors and verifying documents becomes simpler.

The boundaries of AI are still expanding. Once we are able to recognise global macro trends, we can use it to our advantage. It can shed light on our reliance on specific vendors and suppliers. It can help us to evaluate the economic risks associated with our suppliers, as well as learn how our products fit into global supply networks. With AI, a component that poses a security concern can be identified and rapidly removed from the supply chain. Without it, such problems may remain hidden.

Human and computer error, and intentional fraud in supply chains, can all be distinguished. AI’s potential lets us conduct comprehensive analyses down to the smallest of details, leading us straight back to the original suppliers, buyers, and documents. The goal is for a zero-trust approach in which papers and records are verified and analysed.

Applying AI to international trade provides a workable answer to the growing difficulties and risks associated with internationally integrated markets. By embracing it, we are not advocating for unquestioning faith in an unknown system. We are suggesting its use as a tool to draw focus to specific areas. If we continue to adopt and use AI with a zero-trust, verify-and-confirm methodology, the transparency, accuracy, and efficiency it can bring could become essential in navigating the global commerce system.

Right now, at the intersection of science and business, artificial intelligence presents a once-in-a-generation opportunity. Used wisely, it has the potential to help overcome some entrenched problems. Its potential extends beyond the cutting of human labour or the generation of otherwise unpredictable results. It gives us a new perspective, an analytical tool that could radically alter how we think about international trade. It could help our economies to flourish in ways that are beneficial to all involved.

There’s no tolerance for AI hallucinations here. Precision, clarity, and faith in human scrutiny are front and centre. ESG reporting is becoming the new norm. Interoperability affords legal protection and a process that safeguards SMEs and banks. Collaborative efforts such as Project Perseus bring together technology, finance, and policy to unlock sustainable access for SMEs via data-sharing. This is critical for stakeholders in the business and banking worlds.

Nationally built systems in technological and political silos must be avoided to combat these challenges. Collaborative efforts between nation states would enable a comprehensive understanding of patterns and targeted strategies. Artificial intelligence should be seen as an instrument that shows us the bigger picture of a vast chain over which no single country, or corporate, should ever have total control.

So, where do governments, regulators, and the private sector go from here? Frameworks and processes are in place to deliver success — and the time for theory is over.

Lord (JD) Waverley is Member of the House of Lords of the United Kingdom and Chairman of Capital Finance International (CFI.co).

To read the full commentary, please click here.

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Generative AI might hold enormous promise, but its risks have also been flagged up. These include (1) sophisticated disinformation (eg deep fakes or fake news) that could manipulate public opinion, (2) intentional exploitation of minorities and vulnerable groups, (3) historical and other biases in the data used to train generative AI models that replicate stereotypes and could lead to output such as hate speech, (4) encouraging the user to perform harmful or self-harming activities, (5) job losses in certain sectors where AI could replace humans, (6) ‘hallucinations’ or false replies, which generative AI can articulate very convincingly, (7) huge computing demands and high energy use, (8) misuse by organised crime or terrorist groups, and finally, (9) the use of copyrighted content as training data without payment of royalties.

To address those potential harms, it will be necessary to come to terms with the foundation models that underlie generative AI. Foundation models, or models through which machines learn from data, are typically trained on vast quantities of unlabelled data, from which they infer patterns without human supervision. This unsupervised learning enables foundation models to exhibit capabilities beyond those originally envisioned by their developers (often referred to as ‘emergent capabilities’).

The evolving AI Act

The proposed AI Act (European Commission, 2021), which at time of writing is still to be finalised between the EU institutions, is a poor fit for foundation models. It is structured around the idea that each AI application can be allocated to a risk category based on its intended use. This structure largely reflects traditional EU product liability legislation, in which a product has a single, well-defined purpose. Foundation models however can easily be customised to a great many potential uses, each of which has its own risk characteristics.

In the ongoing legislative work to amend the text, the European Parliament has proposed that providers of foundation models perform basic due diligence on their offerings. In particular, this should include:

• Risk identification. Even though it is not possible to identify in advance all potential use cases of a foundation model, providers are typically aware of certain vectors of risk. OpenAI knew, for instance, that the training dataset for GPT-4 featured certain language biases because over 60 percent of all websites are in English. The European Parliament would make it mandatory to identify and mitigate reasonably foreseeable risks, in this case inaccuracy and discrimination, with the support of independent experts.
• Testing. Providers should seek to ensure that foundation models achieve appropriate levels of performance, predictability, interpretability, safety and cybersecurity. Since the foundation model functions as a building block for many downstream AI systems, it should meet certain minimum standards.
• Documentation. Providers of foundation models would be required to provide substantial documentation and intelligible usage instructions. This is essential not only to help downstream AI system providers better understand what exactly they are refining or fine-tuning, but also to enable them to comply with any regulatory requirements.

Room for improvement

These new obligations, if adopted in the final AI Act, would be positive steps, but lack detail and clarity, and would consequently rely heavily on harmonised standards, benchmarking and guidelines from the European Commission. They also risk being excessively burdensome. A number of further modifications could be put in place.

Risk-based approach

Applying all obligations to the full extent to every foundation model provider, both large and small, is unnecessary. It might impede innovation and would consolidate the market dominance of firms that already have a considerable lead in FMs, including OpenAI, Anthropic and Google Deepmind. Even without additional regulatory burdens, it might be very hard for any companies outside of this group to match the resources and catch up with the FM market leaders.

A distinction could therefore be made between systemically important and non-systemically important FMs, with significantly lower burdens for the latter. This would be in line with the approach taken by the EU Digital Services Act (DSA), which notes that “it is important that the due diligence obligations are adapted to the type, size and nature of the … service concerned.” The DSA imposes much more stringent obligations on certain service providers than on others, notably by singling out very large online platforms (VLOPs) and very large online search engines (VLOEs).

There are two reasons for differentiating between systemic and non-systemic foundation models and only imposing the full weight of mandatory obligations on the former. First, the firms developing systemic foundation models (SFMs) will tend to be larger, and better able to afford the cost of intense regulatory compliance. Second, the damage caused by any deviation by a small firm with a small number of customers will tend to be far less than that potentially caused by an SFM.

There are useful hints in the literature (Bommasani et al, 2023; Zenner, 2023) as to criteria that might be used to identify SFMs, such as the data sources used, or the computing resources required to initially train the model. These will be known in advance, as will the amount of money invested in the FM. These pre-market parameters presumably correlate somewhat with the future systemic importance of a particular FM and will likely also correlate with the ability of the provider to invest in regulatory compliance. The degree to which an FM provider employs techniques that facilitate third-party access to their foundation models and thus independent verification, such as the use of open APIs, or open source, or (especially for firms that do not publish their source code) review of the code by independent, vetted experts, might also be taken into account. Other, post-deployment parameters, including the number of downloads, or use in downstream services or revenues, can only be identified after the product has established itself in the market.

Lesser burdens

Notwithstanding the arguments for a risk-based approach, even small firms might produce FMs that work their way into applications and products that reflect high-risk uses of AI. The principles of risk identification, testing and documentation should therefore apply to all FM providers, including non-systemic foundation models, but the rigour of testing and verification should be different.

Guidance, perhaps from the European Commission, could identify what these reduced testing and verification procedures should be for firms that develop non-systemic foundation models. Obligations for testing, analysis, review and independent verification could be much less burdensome and intensive (but not less than reasonably stringent) for providers of non-systemic FMs.

This kind of differentiation would allow for a more gradual and dynamic regulatory approach to foundation models. The list of SFMs could be adjusted as the market develops. The Commission could also remove models from the list if they no longer qualify as SFMs.

Use of data subject to copyright

Even though the 2019 EU Copyright Directive provides an exception from copyright for text and data mining (Article 4(1) of Directive 2019/790), which would appear in principle to permit the use of copyrighted material for training of FMs, this provision does not appear in practice to have resolved the issue. The AI Act should amend the Copyright Directive to clarify the permitted uses of copyrighted content for training FMs, and the conditions under which royalties must be paid.

Third-party oversight

The question of third-party oversight is tricky for the regulation of FMs. Is an internal quality management system sufficient? Or do increasingly capable foundation models pose such a great systemic risk that pre-market auditing and post-deployment evaluations by external experts are necessary (with protection for trade secrets)?

Given the scarcity of experts, it will be important to leverage the work of researchers and civil society to identify risks and ensure conformity. A mandatory SFM incident reporting procedure that could draw on an AI incident reporting framework under development at the Organisation for Economic Co-operation and Development might be a good alternative.

Internationally agreed frameworks

Internationally agreed frameworks, technical standards and benchmarks will be needed to identify SFMs. They could also help document their environmental impacts.

Until now, the development of large-scale FMs has demanded enormous amounts of electricity and has the potential to create a large carbon footprint (depending on how the energy is sourced). Common indicators would allow for comparability, helping improve energy efficiency throughout the lifecycle of an SFM.

Safety and security

Providers of SFMs should be obliged to invest heavily in safety and security. Cyberattacks on cutting-edge AI research laboratories pose a major risk; nonetheless, and despite rapidly growing investments in SFMs, the funding for research in AI guardrails and AI alignment is still rather low. The internal safety of SFMs is crucial to prevent harmful outputs. External security is essential, but it alone will not be sufficient – the possibility of bribes in return for access to models should be reduced as much as possible.

Conclusion

The EU is likely to be a major deployer of generative AI. This market power may help ensure that the technology evolves in ways that accord with EU values.

The AI Act is potentially ground-breaking but more precision is needed to manage the risks of FMs while not impeding innovation by smaller competitors, especially those in the EU. Unless these issues are taken into account in the finalisation of the AI Act, there is a risk of significantly handicapping the EU’s own AI developers while failing to install the adequate safeguards.

To read the full analysis by Bruegel, please click here.

J. Scott Marcus is a Senior Fellow at Bruegel, a Brussels-based economics think tank, and also works as an independent consultant dealing with policy and regulatory policy regarding electronic communications. His work is interdisciplinary and entails economics, political science / public administration, policy analysis, and engineering.

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On Thursday, June 29 panelists looked at the role of critical minerals in the green energy transition, acceleration of advanced technologies and enhanced defense applications. Discussants provided a multinational perspective on the scramble to pivot away from dependencies on insecure sources and the defense against potential supply disruptions. 

Featured Speakers:

Lachlan Carey, Manager, Rocky Mountain Institute

Andrew Jory, Minister-Counsellor (Trade), Embassy of Australia

Helaina Matza, Deputy Special Coordinator for the Partnership on Global Infrastructure Investment, U.S. Department of State

David Schwietert, Chief Policy Officer, Alliance for Automotive Innovation

Moderator: Adina Renee Adler, Deputy Executive Director, Silverado Policy Accelerator

Speaker Biographies:

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1. In the first quarter of 2023, China was the world’s top exporter of semiconductors, shipping out $49.4 billion, down 9.7% from the same period in 2022, followed by Taiwan, Singapore, Malaysia and South Korea.
2. The U.S. was only sixth in global export rankings, selling $12.9 billion worth, down 14.1%. If U.S. companies do take advantage of the Inflation Reduction Act, which includes $39 billion for domestic manufacturing, and boost production, it will likely be for domestic markets.
3. Overall, as countries become more protectionist, there’s been a retrenchment from global trade. Five of the top six exporters in the world reported declines in exports.
4. As a bloc, the European Union was China’s top export market for semiconductors, buying $7.8 billion, up 36% from the same period in 2022. The next biggest importers from China were South Korea, Taiwan, Netherlands, Vietnam, Malaysia, India, Singapore, Brazil and Japan. Meanwhile, the U.S., in ninth place, bought $842.4 million worth. That was up 31.6%, a less meaningful increase because of the smaller amount.
5. U.S. semiconductor exports fell 14.1% to $12.9 billion in the first quarter. Its top customer was Mexico, which bought $3.4 billion, 0.7% higher than in 2022. Shipments to China, its second biggest buyer, fell 43.4% to $1.49 billion.
6. U.S. exports to the EU increased 6.6% to $1.46 billion. It’s the EU that is poised to benefit the most from the current shakeup in global trade. The third biggest importer from the U.S. is Taiwan, followed by South Korea and the Philippines.
7. German exports of semiconductors increased 14.9% to $7.9 billion. Another new upstart player is Thailand, which hiked exports 16.2% in the first quarter.
8. China is also the world’s number one importer of semiconductors, buying $85.1 billion worth in the first quarter. However, that number fell 26.2% compared to the same period in 2022.
9. The U.S. was one of the only major economies that increased imports of semiconductors in the first quarter of 2023. It imported $15.4 billion worth, up 13.1% from the same period in 2022.
10. The U.S.’s top supplier of semiconductors was Malaysia. It imported $3.3 billion worth, down 32.3% from the same period in 2022. The next biggest exporter to the U.S. was Taiwan, which shipped $2.2 billion, up 8.1%, in the first quarter of 2022. The biggest increases came from Vietnam, up 62.7% to $1.7 billion, and Thailand, up 91% to $1.5 billion.

John W. Miller is Trade Data Monitor’s Chief Economic Analyst, in charge of writing TDM Insights, a newsletter analyzing key issues through trade statistics. John is an award-winning journalist who’s reported from 45 countries for the Wall Street Journal, Time Magazine, and NPR.

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The agreed non-agreement between the three states pertains to some of the most complex machinery and most miniscule components humankind has ever produced. With their accord, the countries effectively restricted the export to China of the most advanced microchips and the tools to produce them. These items have become a focal point in international power politics because of their use in developing artificial intelligence and their centrality to many of the 21st century’s most important technologies.

As news on the matter emerged, the Dutch prime minister confined his remarks to saying: “Those talks have been going on for a long time and we’re not saying anything about it.” The reason for reticence was clear; in response to their decision, China threatened retaliation against the Netherlands and Japan.

The move followed on from measures unilaterally implemented by the US in October 2022 to restrict the trade of advanced semiconductor technologies with China for reasons of international security. And it now appears that the Dutch national measures could soon be followed by a decision by the German government to restrict the export to China of chemicals needed for chip production.

As these sorts of incidents mount amid the escalating US-China strategic technology competition, the EU and its member states will find themselves increasingly caught in the crossfire. Washington will maintain pressure on its allies to align with its China policy. China’s military build-up will continue to change the balance of power. And Beijing’s willingness and ability to weaponise trade will likely continue to grow – it will no longer be possible for the EU to keep its pursuit of free trade separate from these powerful currents. If a rules-based order is to remain, the rules will need to change to take account of the ways in which economic security forms part of this wider competition.

To steer a course according to its own interests in this new era of strategic trade controls, the EU must urgently develop its own strategy and upgrade its tools to deliver on it. If it is to promote and defend its own sovereignty, it must start to draw its own red lines in technology engagement with China and upgrade its export control policy.

Tobias Gehrke is a senior policy fellow at the European Council on Foreign Relations, based in the Berlin office. He leads ECFR’s Geoeconomics Initiative. His area of focus includes economic security, European economic strategy, and great power competition in the global economy.

Julian Ringhof is a policy fellow with the European Power programme at the European Council on Foreign Relations. His research focuses on the implications of digital and emerging technologies for international affairs, including the topics of EU digital diplomacy and EU technological sovereignty.

To read the full policy brief, please click here.

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As digitalisation changes the way that international trade is conducted, it is important to adopt trade rules to govern and support such trade. Increasingly, economies are including provisions aimed at enabling digital trade into trade agreements, and some economies, including APEC members, have entered agreements focused on strengthening digital collaboration. While there is broad recognition that growing digital trade could bring economic benefits to economies and the region, research on the effect of adopting digital trade rules, including e-commerce rules (hereafter referred to as “digital trade rules” or “digital trade provisions”), on economic growth has been more limited to-date. This study seeks to contribute to this body of research, including by taking a novel approach to estimate bilateral digital trade flows for APEC member economies and its largest trading partners.

The report is structured into three sections:

Section 1 defines digital trade and provides observations about the trends in digital trade flows over the last two decades in APEC economies. It explores the relationship between increased digital trade and economic growth for economies through an economic contribution model and systematic analysis.

Section 2 considers recent developments on digital trade rules in the APEC region and explores the potential impact of adopting such rules on digital trade flows and economic growth. It uses a “Digital Trade Openness Index” to approximate the extent of trade liberalisation and seeks to identify the digital trade provisions with the most observable effects on digital trade flows through a study of APEC economies.

Section 3 provides recommendations on how APEC policymakers can unlock the benefits of digital trade.

To read the full report, please click here.

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