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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Trade policy can advance environmental objectives through the reduction or elimination of tariff barriers for environmental goods. But that goal, though obvious, has proved elusive over the 20 years since the World Trade Organization adopted it in the 2001 Doha Mandate. Since then, negotiators have made several strides toward defining the scope of environmental goods in the 2012 APEC List and during plurilateral negotiations at the World Trade Organization (WTO) that collapsed at the end of 2016. However, in addition to a mismatch in like-mindedness, environmental goods negotiations have also been plagued by a failure to effectively define the impact that trade in environmental goods will have on environmental outcomes.

In the past, negotiators in APEC and the WTO have combined typical analyses of the probable economic effects of trade liberalization with national assessments of products’ suitability to create a list of environmental goods. Driven by consistent participation and technical input from environmental ministries, these efforts to define the scope of environmental goods have focused on products’ suitability based on alleged: environmental function (like solar panels or water filters); preferability as a substitute product derived from its composition or other intrinsic factors (bamboo flooring, for example); lifecycle factors such as durability, energy-efficiency, and end-of-life costs (such as LED lighting); and green construction — that is, the environmental soundness of products’ manufacturing process.

Although using varied national definitions of environmental value based on function, composition, construction, and lifecycle attributes is a useful framework for defining the universe of what could be considered an environmental good, it has proved to be an ineffective method to define environmental goods worthy of liberalized tariffs — principally because it fails to account for the actual environmental effects of removing or reducing tariffs on specific environmental goods.

Consider, for instance, bicycles. A reasonable person would conclude that bicycles offer many environmental benefits: they are relatively easy and inexpensive to produce with environmentally-friendly materials, last decades, provide a carbon-free and pollution-free alternative to motor vehicles (and even public transportation), and as a bonus, provide users with health benefits like exercise and a healthy dose of fresh air.

So should bicycles be included on a list of environmental goods that qualify for tariff reduction? Not necessarily.

A trade-driven assessment of bicycles’ environmental suitability would instead ask: Will cheaper bikes, driven by tariff reduction, equate to more bicycle commuters (i.e. increased demand)? The answer, based on the available evidence, is no. If lower prices for bicycles do not lead to more cyclists on the road, the net environmental impact of lower tariffs on bicycles would be marginal at best. In fact, the only known substantive effect of tariff reduction on the global bicycle market has been a shift in production to lower-cost regions — including those with less stringent environmental standards. Global free trade in bicycles? Maybe not.

The example of bicycles illustrates a broader point: that in the trade policy arena, the scope and definition of “environmental goods and technologies” should be grounded in an empirical appraisal of trade’s value as an engine of technology acquisition. By focusing on only those products for which tariff reduction translates into more widespread adoption of that technology — and, as a function of trade liberalization itself, result in positive environmental outcomes — countries can arrive at a workable and effective list of environmental goods. The recent revival of environmental goods discussions within APEC and the WTO makes this an especially timely reconsideration.

In the absence of a new analytical approach to defining environmental goods, the history of past environmental goods negotiations is likely to repeat itself. An important first step toward implementing this new approach would be for the Biden administration to use its authorities pursuant to section 332(g) of the Tariff Act of 1930 to assess the trade and environmental impacts of tariff liberalization on a variety of previously proposed environmental goods. Such an inquiry would allow the United States and others to determine where tariff liberalization would substantially increase deployment (i.e. raise international demand and consumption), and where it would simply lead to origin switching or environmentally equivalent product-switching. From there, analysts could determine the environmental impact of the increased demand and/or technology capacity that would result from tariff liberalization (e.g. carbon reduced, M3 of water treated, tons of waste recycled, etc.) among major markets. For example, how much additional SOx/NOx could be prevented from entering the atmosphere in areas where trade analysis indicates that there is a potential increase in demand for stationary air pollution control devices?

Importantly, a Section 332 investigation would neither commit the United States to joining any new negotiations towards an environmental goods agreement nor wed it to any particular position. Instead, it would help the U.S. reframe the way that environmental goods lists are defined while simultaneously providing the United States and its trading partners with an accurate picture of where potential market access opportunities for U.S. technologies exist and which products may be the most sensitive if U.S. tariffs were reduced further.

The U.S. is a leader in the production and adoption of environmental technologies. As the U.S. works to transition to a 21st-century economy at home, it will need to work with key trading partners to expand U.S. market access on priority goods and remove burdensome tariff barriers that delay the adoption of these products. A transparent and trade-specific definition of environmental good can become a powerful tool for stimulating green growth both at home and abroad.

Maureen Hinman is the Chair and Co-Founder of Silverado Policy Accelerator. She previously served as the Director for Environment and Natural Resources at the Office of the United States Trade Representative.

To read the original commentary by Silverado Policy Accelerator, please click here.

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