Sustainable Development Goals Synergies/Trade-offs: Exploring Long- and Short-Run Impacts of Economic Growth, Income Inequality, Energy Consumption and Unemployment on Carbon Dioxide Emissions in South Africa
Bibliographic record
Abstract
This study examines the short- and long-run impacts of economic growth and unemployment (SDG 8), energy consumption (SDG 7) and income inequality (SDG 10) on CO2 emissions in South Africa over the period 1980-2012 to establish potential synergy/trade-off to achieve these goals. We use the autoregressive distributed lag (ARDL) approach to cointegration technique. Our results show that the promotion of economic growth (SDG 8) as well as the reduction in energy consumption/increase in energy efficiency (SDG 7) can be achieved in synergy with the reduction of CO2 emissions (SDG 13). On the other hand, our results suggest a trade-off between reducing unemployment rate or achieving productive employment and decent work (SGD 8) and mitigating CO2 emissions (SDG 13). Neither a synergy nor trade-off (neutral relationship) is found between the reduction of income inequality (SDG 10) and the reduction of CO2 emissions (SDG 13). Our findings highlight the importance of mainstreaming SDG’s policy interactions to achieve sustainable development goals, while also stressing the potential for South Africa to achieve low-carbon development should economic growth be decoupled from carbon-intensive energy use through policies that promote access and effective use of clean energy. The transition to clean energy is therefore emphasized and relevant factors underpinning such as transition should be explored further. Highlights This study examines the short- and long-run impacts of economic growth and unemployment (SDG 8), energy consumption (SDG 7) and income inequality (SDG 10) on CO2 emissions in South Africa The autoregressive distributed lag approach to cointegration is employed A synergy is established between the promotion of economic growth (SDG 8) and the reduction of CO2 emissions (SDG 13), and between the reduction in energy consumption/increase in energy efficiency (SDG 7) and the reduction of CO2 emissions (SDG 13) A trade-off is confirmed between reducing unemployment rate or achieving productive employment and decent work (SDG 8) and mitigating CO2 emissions (SDG 13) A neutral relationship is found between the reduction of income inequality (SDG 10) and the reduction of CO2 emissions (SDG 13) Policymakers need to mainstream SDG’s policy interactions to achieve sustainable development goals South Africa can achieve low-carbon development should economic growth be decoupled from carbon-intensive energy use through policies that promote access and effective use of clean energy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".