Reconfiguring globalisation: A review of tariffs, industrial policies, and the global solar PV supply chain
Bibliographic record
Abstract
Trade barriers have become an increasingly popular policy design for protecting and nurturing domestic clean-tech manufacturing industries in major economies, including the US, EU, Canada, and India. Confronted with a surge of Chinese solar photovoltaics (PV) imports at drastically reduced prices, a consequence of China's rapid manufacturing expansion, multiple countries are poised to launch or strengthen existing trade barriers. However, despite extensive discussion within policy and industry circles, the effectiveness and broader impact of such policies remain underexplored in the existing literature. This paper attempts to address this gap by exploring the role of trade barriers as major economies grapple with the dilemma between decarbonisation and de-risking from Chinese equipment critical for the energy transition. This paper begins by analysing the trade conflicts of the early 2010s and their impact on the US, EU, and Chinese industries, as well as on the global supply chain. It then explores how recent and impending trade conflicts - along with the rise of green industrial policies aimed at promoting import substitution - are reshaping the global supply chain by assessing both government policies and corporate responses. Lastly, the paper examines policy solutions for the rest of the world (ROW), considering trade actions and industrial policies in the context of China's overcapacity in 2024.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".