The Analysis on WTO's Impact to Present Situation of Taiwanese Petrochemical Industry
Bibliographic record
Abstract
[[abstract]]本文先就台灣地區石化製品的種類、進出口與國際競爭力等實際情況加以說明,再利用GTAP模型進行台灣地區加入世界貿易組織的模擬與分析。 石化製品依提煉與裂解之不同大致可製爲化學製品、塑膠製品與橡膠製品等三大類,在經歷東南亞金融危機後,各國經濟邁向複甦,台灣進、出口同步成長,石化製品的貿易出超約成長104百萬美元;主要進口地區爲德國、瑞士、美國、中國大陸地區、日本、法國、韓國、英國與加拿大等國家,主要出口地區爲中國大陸地區、日本、美國、香港、泰國與新加坡等國家。 我國石化製品除化學製品外,塑膠製品與橡膠製品已進入爲産品成熟期,近年來,勞工意識抬頭,成本大幅提高,競爭力逐年減弱等因素,致使廠商將産能移往中國大陸或東南亞地區,利用當地低廉的勞力成本,以提高産業競爭力,産業規模與因而逐年萎縮。 根據本文的模擬結果,石化製品在對外貿易除歐盟大幅提升及日本小幅成長外,大部份國家的貿易差皆爲負值,例如中國大陸地區約爲-4330百萬美元、南韓-15百萬美元、東協-2618百萬美元、紐澳地區-399百萬美元、美加地區-3812百萬美元、其他已開發國家地區-1655百萬美元、其他開發中國家地區-5345百萬美元與低度開發國家地區-650百萬美元、而歐盟成長約14931百萬美元、日本爲2240百萬美元。 Firstly this paper describes the actual conditions on Taiwan’s Petrochemical Industry, including products lines, imports, exports and international competitiveness, then utilize GTAP model to carry on the simulation analysis to understand the influence about Taiwan's joining into WTO. One can classify those Petrochemical industry products into 3 large catogries--chemicals, plastic products and rubber products. The economy of various countries become prosperous after Southeast Asia's financial crisis, in Taiwan the trading of petrochemical industry grows simultaneously, up to about more than 104 million dollars. The mainly importing areas of petrochemical industry are German, Switzerland U.S.A., Mainland China, Japan, France, S. Korean, British, Canada, etc., and those mainly exporting areas are Mainland China, Japan, U.S.A., Hong Kong, Thailand and Singapore. The plastic and rubber products of our petrochemical industry have already been in a mature condition except for chemical products; in recent years, for the raising of laborers' consciousness, cost of labor improves substantially, which gradually leads to weakened competitiveness of our industry--manufacturers move their possessions and capabilities toward Mainland China and Southeast Asia, utilize locally cheaper labors to improve their competitiveness-and this shrinks our domestic industry. According to the simulation of our article, on international trading of the petrochemical products most of the countries had an negative balance, only the EU and Japan improves respectively in the case, for example the negative balance of Mainland China is about-4,330 million dollars, South Korea-15 million dollars, ASEAN-2,618 million dollars, the area of Australia & New Zealand-399 million dollars, U.S. & Canada-3,812 million dollars, other developed countries-1,655 million, other developing countries-5,345 million , low-level developed countries-650 million dollars, as for EU and Japan, the positive balance respectively are 14,93 1 and 2,240 million.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".