The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches
Bibliographic record
Abstract
Geopolitical tensions, including the Russia-Ukraine conflict, ongoing Middle-Eastern wars, and the post-Cold War dynamics between the USA and Russia, have contributed to significant global political instability. These risks disrupt economic growth, destabilize energy supply chains, and foster economic uncertainty, often prioritizing energy security over environmental sustainability. Existing literature inadequately addresses how geopolitical risks interact with environmental sustainability, particularly within developed economies like Canada. To bridge this gap, this study examines the role of per capita income on environmental outcomes under the Environmental Kuznets Curve (EKC) framework, explicitly incorporating geopolitical risks as a critical determinant. Using Canadian time series data spanning from 1980 to 2022, this research employs the autoregressive distributed lag (ARDL) estimation technique to explore short- and long-term cointegrating relationships among key variables, including economic growth, energy consumption, trade openness, foreign direct investment (FDI), ICT development, and financial development. The findings confirm the inverted U-shaped EKC hypothesis for Canada, indicating that economic growth initially exacerbates carbon emissions (CO 2 ) before leading to environmental improvements at higher income levels. Geopolitical risks are found to positively contribute to CO 2 emissions, emphasizing their role as a barrier to achieving environmental sustainability. To validate robustness, the Kernel Regularized Least Squares (KRLS) machine learning approach is employed, confirming the consistency of results. Additionally, the Toda-Yamamoto causality test identifies directional causal relationships among the variables. Policy recommendations emphasize the need for Canada to implement targeted strategies that mitigate the impact of geopolitical risks on environmental outcomes. Specifically, the study advocates for: (1) diversifying energy sources to reduce reliance on geopolitically sensitive regions, (2) investing in renewable energy technologies to ensure sustainable economic growth, and (3) enhancing trade policies to prioritize low-carbon technologies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".