Does economic complexity help in achieving environmental sustainability? New empirical evidence from N-11 countries
Bibliographic record
Abstract
In view of the SDGs argued by UNO, it is vital to address the pressing issues regarding sustainable development. The aim of current study is to investigate the impact of economic complexity (ECC) on environmental sustainability. To achieve this aim, we sampled the 25 years of data of Next-11 countries over the period 1995 to 2019. The economic complexity was measured by the economic complexity index (ECI) while environmental sustainability was measured by two proxy variables including CO 2 and greenhouse gas (GHG) emissions. The empirical analysis was established by utilizing the unit root test, cointegration test, FMOLS (fully modified OLS) and DOLS (dynamic OLS) models. The estimated coefficient values disclosed that ECC has a negative and statistically significant relationship with both CO 2 and GHG emissions in the long run, implying that ECC ensured environmental sustainability. In addition, the analysis reveals that financial development has a negative while economic growth and energy imports have a positive and statistically significant association with both CO 2 and GHG emissions. The findings of the current study suggested an important policy regarding the focus on ECC for achieving environmental sustainability in underlying economies. This study provides robustness to the existing literature in alternative data settings (N-11 countries) and by the unique objective of focusing on environmental sustainability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".