State Power for Low-Carbon Development: A Comparative Investigation into the Effectiveness of Carbon Finance Projects in Tanzania, Uganda and Moldova
Notice bibliographique
Résumé
Empirical investigation into afforestation and bioenergy carbon finance projects in Tanzania, Uganda and Moldova demonstrates that effective projects—both in terms of sustainable development and the generation of genuine carbon credits—are more likely to result when the state is able to bring carbon finance initiatives into alignment with national development objectives. Amongst the countries investigated, the most important factor in such alignment was, paradoxically, commitment liberal economic reforms. Contrary to the expectation that the performance of projects under the Kyoto Protocol’s Clean Development Mechanism (CDM) would be the same in states with similar administrative capacities, carbon finance projects were more effective in Uganda and Moldova than Tanzania. Commitment to liberal economic reforms in Uganda functions as an animating set of ideas that allows the state apparatus to work in a more purposeful manner and establish institutions and organizations which allow it to generate state power for low-carbon development. For CDM forest and bioenergy projects, the risk of unsustainability is mitigated by a land tenure system and investment regime that (i) offer opportunities for individual smallholders to engage directly with the carbon market and create incentives for domestic investors while (ii) also accommodating historical land governance practices. Genuine carbon credits were associated with project developers who possessed a latent organizational capacity for implementation and were motivated to pursue market opportunities—state forest agencies in Uganda and Moldova. However, the ability of the state to retain latent organizational capacity was restricted to sectors such as forestry that are less sophisticated technically; in the energy sector, such capacity was ceded to the private sector in Uganda and Moldova during structural adjustment. More skeptical of liberal economic policy, Tanzania has retained capacity in the energy sector; however, for the same reasons, it has not treated the CDM as a genuine opportunity. At current carbon prices, CDM projects investigated were effective when the state was able to play a developmental role in the economy. Whether commitment to liberal economic reforms can have similar developmental effects in other parts of the developing world is questionable—a different animating set of ideas may be important.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».