State Power for Low-Carbon Development: A Comparative Investigation into the Effectiveness of Carbon Finance Projects in Tanzania, Uganda and Moldova
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".