Challenges for the United States in Negotiating a BIT with China: Reconciling Reciprocal Investment Protection with Policy Concerns
Bibliographic record
Abstract
The United States has been engaged in periodic discussions with China since 2008 toward the conclusion of a bilateral investment treaty (BIT), with both governments and business interests recognizing the mutual benefits of a detailed series of rules to facilitate and protect investment in each other’s territory. However, the United States faces unprecedented challenges in completing these negotiations. A BIT with China holds potentially far greater potential benefits and complexities for the United States than any BIT or FTA investment chapter since the conclusion of NAFTA’s Chapter 11, and like NAFTA will inevitably result in claims lodged against the United States. For a successful negotiation the United States must convince China to be far more forthcoming in protecting foreign investors than in the past. While China in recent years has agreed in BITs to investor-state arbitration, it has balked at extensive national treatment requirements, limitations on performance requirements and various efforts to rein in privileges for state-owned enterprises, let alone including labor rights and environmental protection. Even if the U.S. negotiators are successful with the text, they will have to convince U.S. stakeholders, the Congress and civil society that facilitating Chinese investment in the United States is in the national interest. Apart from the BIT itself, issues unrelated to investment such as Chinese (and now U.S.) hacking of the others’ computer systems; frequent “unfair trade” actions; China’s open conflicts with U.S. allies over islands in the South and East China Seas; reciprocal accusations of currency manipulation; and discomfort in the United States of being eclipsed in a few decades as the world’s dominant economy, all further complicate the negotiating climate. This article briefly discusses the history of BIT and FTA investment chapter experience in the United States and the evolution of the U.S. approach post-NAFTA; China’s experience with BITs, including the recent and instructive “Foreign Investment Protection Agreement” with Canada; the key substantive challenges to reaching agreement with China; and the government/congressional and civil society challenges to bringing such an agreement into force.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".