A Case Study of Technological Switching and Technological Lock‐In in the French Fisheries Sector: Why is Sustainable Change so Difficult?
Bibliographic record
Abstract
Many sectors such as the fishery show classic examples of technological lock‐in and path dependence, even though some economists might predict smooth switching toward technologies that are more cost effective and sustainable. We use ideas from the evolutionary economics and public choice literatures to explain why trajectories of technological change, especially in fisheries, may not be smooth at all, but rather punctuated. The interest of technological change and switching behavior for fisheries economists and managers stems from the fact that control of effective effort, often necessary for sustainable management of the resource, remains a central management problem for that sector worldwide, even in developed countries. However, various policies put in place by governments to support the fishing sector, and often put in place to “correct” for certain market failures, may inadvertently produce other “nonmarket” failures, which result in technological lock‐ins which are unsustainable. For example, the trawling technique was widely promoted in France in the 1970s and 1980s. Path dependency developed in such a way that the preferred choice of new entrants into the fishery was this technology. Technological lock‐in occurred on the trawling technique as the trawling sector also became more politically active, making it ultimately the most widely used technique in the French fisheries sector in the Atlantic. Switching away from this technology has not taken place even with poorer economic performance of that technology. This paper also discusses the influence of state subsidies on the adoption of trawling. Even if trawling was a major innovation in fisheries in the past, its potential for technological adaptations or minor innovations is limited now. These limitations are more obvious during periods of increasing energy prices, especially in the absence of state aid. However, due to collective choice phenomena, switches to more sustainable technologies will occur more slowly. Plusieurs secteurs, tel que celui des pêcheries, offrent des exemples de verrouillage technologique et de dépendances au sentier, alors même que les économistes s’attendent à un changement régulier vers une technologie plus efficace en termes de coûts et plus soutenable. Nous nous appuyons sur la littérature évolutionniste et des choix publics afin d’expliquer pourquoi les trajectoires du changement technologique, en particulier dans les pêcheries, peuvent ne pas être régulières, mais au contraire discontinues. L’intérêt des économistes et des gestionnaires pour le changement technologique et le comportement face au retour des techniques est lié au contrôle de l’effort réel, souvent nécessaire pour une gestion durable de la ressource. Le contrôle de l’effort reste le problème essentiel de la gestion des pêches à l’échelle mondiale, y compris dans les pays développés. Toutefois, de nombreuses mesures publiques mises en place par les gouvernements pour soutenir le secteur des pêcheries, afin de «contrecarrer» certaines défaillances du marché, peuvent déboucher involontairement sur d’autres défaillances «non marchandes», se traduisant par des verrouillages technologiques non‐soutenables. Par exemple, la technique du chalutage fut largement diffusé en France dans les années soixante‐dix et quatre‐vingt. Un sentier de dépendance s’est développé tel que les nouveaux entrants dans la pêcherie optaient pour cette technologie. Un verrouillage technologique s’est produit sur la technique du chalutage rendant ce secteur politiquement important, et finalement la technique la plus répandue parmi les pêcheries françaises de l’Atlantique. Un changement de technologie n’a donc pu survenir, même en présence de faibles performances économiques. L’article traite de l’influence des subventions étatiques liées à l’adoption du chalutage. Même si cette technologie fut une innovation majeure dans les pêcheries par le passé, les adaptations techniques potentielles ou les innovations mineures sont désormais limitées. Ces limites apparaissent clairement dans les périodes de coûts énergétiques croissants, notamment en l’absence d’aides étatiques. Toutefois, en raison du phénomène de comportements collectifs, les changements vers des technologies plus soutenables se déroulent avec lenteur.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".