中美贸易摩擦经济影响量化分析--以粤港澳大湾区为例 (Quantifying the Impact of the China-US Trade Friction on Regional Economic Growth: The Case of the Guangdong-Hong Kong-Macao Greater Bay Area)
Bibliographic record
Abstract
Chinese Abstract: 中文摘要 中美贸易摩擦不断升级对中美两国及区域经济产生深远影响。文章采用全球、区域可计算一般均衡模型（CGE）对中美贸易摩擦进行量化模型。其中文章创新之处在于采用粤港澳大湾区（主要是大湾区内地9市）TERM模型，对中美贸易摩擦的区域经济影响进行政策模拟。模拟结果表明，中美贸易摩擦对粤港澳大湾区外贸部门带来较大冲击，其中美国贸易打击的主要行业部门制造业部门受损严重，相关行业部门产量、贸易量均有下跌，对该区域出口导向型经济带来较大负面影响，也对粤港澳大湾区未来经济发展模式提出挑战。另一方面，服务贸易出口形势向好，对区域贸易经济结构调整提出新思路。面对国际经贸环境的不稳定性，粤港澳大湾区应在区域经济一体化发展规划下，调整产业结构，优化对外贸易结构和方式，同时大力发展创新型经济，实现区域经济可持续发展。 English Abstract: The escalation of China-US trade friction has generated profound impact on both countries. Based on the global and regional computable general equilibrium model (CGE), this article uses the Guangdong-Hong Kong-Macao Greater Bay Area TERM model to quantify the economic impact of China-US trade frictions on the Greater Bay Area. The simulation result shows that US new tariffs will lead to negative growth of the output and trade of the local manufacturing sector which is the pillar industry of reginal economies. Moreover, US trade attack will bring detrimental impact to this region’s export-driven mode of economic development as well as its future growth. But on the other hand, the result also shows some positive signs of the service trade in this regions, which will potentially lead to the restructuring of the regional trade structure. When facing the uncertainty of international trade and business, the Greater Bay area should reconfigure the industrial structure, optimize the structure and mode of trade, and insist on the innovation-driven mode of economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".