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Enregistrement W4318448105 · doi:10.1111/add.16131

The virtues of bans on high‐THC content cannabis products?

2023· letter· en· W4318448105 sur OpenAlexaboutno aff
Jonathan P. Caulkins

Notice bibliographique

RevueAddiction · 2023
Typeletter
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLegalizationCannabisPotencyConsumption (sociology)Product (mathematics)TetrahydrocannabinolBusinessAdvertisingEconomicsLawCannabinoidMedicineChemistryPolitical science

Résumé

récupéré en direct d'OpenAlex

Product bans may have some advantages over tetrahydrocannabinol (THC)-based taxes as a response to high THC content cannabis products, but another driver of higher levels of THC consumption may be legalization-induced price declines, not just legalization-induced increases in potency. Hall et al. [1] observe that (1) legalizing commercial cannabis supply brought greater availability and use of high-tetrahydrocannabinol (THC) products (both higher potency flower and forms such as vapes and dabs), and (2) the (imperfect) literature suggests that although consumers use less when potency rises (i.e. they ‘titrate’) the reduction is less than proportionate, so higher potency brings greater THC consumption. As greater THC consumption might increase health harms, Hall et al. suggest considering policy responses including better labeling, product bans and progressive THC-based taxes (e.g. taxing 20% flower more per gram than 15% flower). I will comment upon these observations through the lens of economics but—perhaps surprisingly—conclude that product bans may be more promising than THC taxes. Hall et al. note that many who supported legalization failed to anticipate the trend towards higher-THC products. However, it is naive to imagine that legalizing supply will change little besides arrest rates. Legalization is like a radical technological innovation that upends the structure, conduct and performance of the industry [2]. Among its myriad effects are distribution methods that permit greater product variety (a store can stock hundreds of products; a classic ‘drug dealer’ can only carry a few) and the ability to own and operate machinery that can extract and concentrate cannabinoids from parts of the plant that used to be discarded. Regarding potential policy responses, few could argue with providing consumers with accurate labels. Efficient markets depend upon good information. When products create harms, economists often eschew crude product bans and prefer manipulating tax rates to create socially optimal incentives (e.g. Pigouvian taxes that internalize externalities [3]). However, taxing cannabis potency is complicated. Whereas alcohol is just one molecule (ethanol) that appears in varying concentrations, cannabinoids, like opioids, are a family of related molecules with varying strengths. Taxing Δ9-THC might induce producers to sell products with Δ8-THC. Taxing all forms of THC equally might induce producers to sell Δ9-THCP products, as Δ9-THCP appears to be much more potent per unit weight [4]. One day there may be cannabinoid equivalency ratios analogous to the morphine equivalent dose (MED) system that compares different opioids’ analgesic effects, but the requisite science base does not yet exist. By contrast, bans on specific types of products are simple, common and—contrary to some claims—do not always produce illegal markets large enough to undermine their benefits. Uruguay restricted cannabis potency, Canada limited legalization to flower products in the first year and Quebec still restricts the forms and flavors of edible cannabis products, all without collapsing the system. Banning certain alcohol and tobacco products is also common. For example, the United States banned flavored cigarettes in 2009 and caffeinated alcohol drinks in 2010. Not all bans are models of science-based reasoning. Many countries banned absinthe for almost a century based on absinthe-specific fears that now seem dubious or manageable by regulating thujone content [5]. My point is not that all product bans are good. They have costs and benefits that must be weighed, but concerns regarding illegal markets should not automatically silence their consideration. The viability of banning specific products within a broader class is even more clear when looking at safety regulations more widely. For example, in 1982, the United States banned walk-behind rotary lawn mowers that lacked blade control systems [6]. That did not spawn a massive illegal market for lawn mowers lacking a ‘dead man’s switch’. Safer (albeit somewhat more expensive) lawn mowers are good substitutes for the older type, so that product ban achieves its end. In short, product-specific bans are not like broad prohibitions, so legally supplied low- and medium-THC products may be good enough substitutes for illegally produced high-THC products that banning high-THC forms will not create large illegal markets. However, I wonder whether the main driver of potentially problematic levels of THC consumption is the proliferation of high-potency products or the sharp declines in cannabis prices. The Western States Information Network’s 2008 Drug Price and Purity Guide describes pound prices for sinsemilla-type marijuana in California as averaging approximately $4000 in 2008, and Cannabis Benchmarks reports California pound prices of $702 in November 2022 [7]. Adjusting for inflation, that is a real price decline of 87%. As THC content is now approximately 50% higher (e.g. 21% versus 14% THC), the decline in price per unit of THC is more like 92%. Therefore, observed increases in THC consumption during the period of policy liberalization that ushered in high-potency products may be due, in part, to legalization-induced declines in price, not just legalization-induced increases in potency. None. None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,817
Score d'incertitude au seuil0,698

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,280
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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