Green versus non-green technology innovation and environmental quality: Cointegration and counterfactual analysis
Bibliographic record
Abstract
Despite growing interest in green innovation and green investment as solutions to climate change, existing literature largely examines these elements in isolation, overlooking their combined effects and the contrasting role of non-green technologies. Addressing this gap, the present study investigates the impact of both green and non-green technological innovations on carbon emissions in Canada, while incorporating the roles of green investment, institutional quality, and geopolitical risks. Using annual time series data from 1990 to 2023, the study employs a novel dynamic autoregressive distributed lag (DARDL) approach to explore the cointegrating and counterfactual relationships among these variables. The findings indicate that green innovation and green investment significantly enhance environmental quality by reducing carbon emissions. In contrast, non-green innovation, along with institutional quality and geopolitical risks, is associated with increased emissions and environmental degradation in the long run. Furthermore, the interactions among green and non-green innovations, as well as between green innovation and green investment, and non-green innovation and green investment, show negative links with carbon emissions. Additionally, a counterfactual analysis is conducted to assess the impact of ±1% and ±5% shocks from the independent variables on the dependent variable. To further validate the robustness of these findings, the study utilizes the Kernel-based Regularized Least Squares (KRLS) machine learning algorithm. Finally, the study discusses key policy implications based on the results.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".