How effective is government spending on environmental protection in a developing country?
Bibliographic record
Abstract
Purpose The objective of this paper is twofold as follows: first, it explores the relationship between economic growth and the environment in the context of the environmental Kuznets curve (EKC) in Iran, as a semi-industrialized and largest developing economy in the Middle East. Second, it investigates the effectiveness of government spending on environmental protection. Design/methodology/approach The paper uses the ecological footprint data and an ARDL model to gauge the income and government spending effects on environmental improvement. This method avoids the problems associated with using the regression including a squared income. Findings The results find no evidence for a turning point in the income–pollution relationship and no significant impact of government spending on reducing footprint. We conjecture that the structure of the economy and the weak institutional quality may explain the results. Research limitations/implications This includes limited time series data on institutional quality indices and their small variations over time. Practical implications Creating an environmental fund using the oil windfall and applying environmental tax/subsidies policies will help address increasing environmental challenges in energy-rich developing countries. Education and public awareness about environmental problems and their impacts on the standard of living are also nonexpensive but effective ways to increase citizen's engagement towards improving environment. Social implications The EKC may take different forms in various countries depending on their economic structure and institution qualities. Originality/value The paper uses the ARDL method rather than a commonly used regression with a squared income to estimate the EKC. It also uses ecological footprint as a measure of environmental damage. Exploring government effectiveness in managing public good is also novel in the empirical literature.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".