How Does Climate Finance Affect the Ease of Doing Business in Recipient Countries?
Bibliographic record
Abstract
Developing countries face a disproportionate degree of threat from climate change. As such, they require and receive significant financial support to address the menace. However, little is known about the potential externalities of this form of external liquidity for the business sector. This paper evaluates the impact of climate finance on the ease of doing business (EODB). On the one hand, climate finance might lead to an improved business environment as the funds facilitate infrastructure provision, technological innovation, and international collaboration for recipient countries. On the other hand, however, the business environment might be negatively impacted by complex new regulations, disruptive technological transitions, market distortions, and resource diversions. Countries receiving climate funds may also introduce new environmental and business regulations, implement new technologies, and divert resources to new programs to justify the receipt of aid or demonstrate a commitment to balancing economic development with environmental objectives. We theorize that given the expected disruptions to business, climate finance should negatively impact the EODB. We also argue that this negative impact will be more severe for resource-rich countries than for their resource-poor peers. Countries rich in natural resources might experience higher disruptions to business operations as they attempt to balance resource-dependent economic operations with environmental objectives mandated by climate finance. Utilizing panel data for 86 recipient countries for the 2002–2021 period, we test our hypotheses using the Generalized Methods of Moments (GMM) technique. The baseline results suggest that climate finance has a weak positive impact on the EODB. However, as argued, resource-dependence heterogeneity analysis reveals that climate finance significantly negatively disrupts the EODB in resource-rich countries. Furthermore, a sectoral comparative analysis shows that while climate finance has a significant positive impact on the growth of the service sector, it significantly slows the growth of the resource sector, affirming the argument that climate finance might attract higher disruptions to resource-dependent business operations. By implication, lowly diversified economies might realize more negative than positive effects of climate finance, and investors should consider providing support to ease the pains of transitioning from resource-intensive growth to clean energy-driven development strategies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".