映射人工智能经济复杂性
Mapping AI Economic Complexity
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中文总结 AI 辅助
本研究将绿色经济复杂性框架应用于人工智能赋能商品，利用2007-2023年BACI出口数据测算复杂性加权专门化（AECI）和邻近机会（AIAP、AECP），发现日本和中国分别领先，为贸易视角下的人工智能生产能力评估提供初步补充。
中文摘要 AI 辅助
绿色经济复杂性为考察各国在特定产品集中的生产能力提供了一个可推广的框架。我们将该框架应用于全产品空间内的人工智能赋能商品，将当前的专门化与邻近的多样化机会联系起来。利用2007年至2023年的BACI出口数据以及103种人工智能赋能商品，我们测算了复杂性加权专门化指数（AECI）、产品层面的邻近机会指数（AIAP）以及剩余候选产品的平均复杂性加权关联度指数（AECP）。2023年，日本在AECI方面领先，而中国在AECP方面领先；产品组合的广度解释了原始AECI的大部分变异。初始原始潜力与随后人工智能赋能出口份额的变化呈正相关，但其与AECI变化及专门化数量的关联在5%显著性水平下并不具有统计显著性。我们的贡献在于提供了一种基于贸易的人工智能赋能生产能力及相关机会的评估。研究结果和公共仪表板为出版物和专利指标提供了初步补充，而非国家人工智能综合表现或多样化有效预测的全面衡量标准。
英文摘要
Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.
发表机构
- Seoul National University（首尔大学）
- University of Chicago（芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。
