Non–tariff Barriers Facing Trade in Selected Environmental Goods and Associated Services
Bibliographic record
Abstract
Surveying 136 exporting firms from ten Organization for Economic Cooperation and Development (OECD) and non–OECD countries, this article documents the incidence, and impact of, non–tariff measures that are perceived to act as barriers to trade in seven sectors of environmental goods and associated services. Although the DDA has a mandate to address, inter alia, such trade barriers, information shedding light on the specific problems that firms encounter in their export activities has been scarce. Accounts by exporting firms in Austria, Brazil, Canada, Chile, France, Germany, India, Japan, Korea and the United States suggest that environmental goods indeed face a variety of obstacles when traded abroad. Firms participating in the survey mentioned relatively often problems associated with product testing and certification requirements, customs procedures, regulations on payment, problems with intellectual property protection, government procurement procedures and technical regulations and standards. Certain types of reported barriers appear to be more prevalent in certain markets. For example, customs procedures reportedly pose a problem predominantly in developing and transition economies and problems with intellectual property rights are associated especially with China. The non–tariff barriers reported by the firms appear to be generic and not specific to the environmental sector. The article shows that in many countries the environmental industry consists mostly of SMEs, for whom cost–raising barriers pose disproportionately greater problems due to their limited resources. The survey helps to better understand the effects that NTBs have at the firm level, and what firms do when they encounter barriers of various types. It appears that the firms participating in this study mostly seek to devise ways of coping with the difficulties that they encounter, rather than seeking help from governments. Since these measures are ad hoc and do not address problems at their source, they cannot substitute for governments taking action. The study points out that many of the concerns voiced by firms in the environment sector can be addressed at the WTO but that more can be done also at the bilateral and regional levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".