Cleaner the better: Macro-economic assessment of ambitious decarbonisation pathways across Indian states
Bibliographic record
Abstract
In first of its kind, this study evaluates socio -economic impacts of two ambitious decarbonisation pathways for India (i) aligned with India's Nationally Determined Commitments (NDC) negotiated through Paris agreement in 2015 and (ii) more ambitious NDC plus decarbonisation trajectory aligned with India's recent COP26 commitments at subnational level. The analysis uses a newly developed dynamic macro-econometric regional simulation model - E3-India to evaluate changes in key economic and emission parameter due to energy transition at both national and state level for India. Impacts on emission intensity of the economy, GDP, employment and income are assessed to highlight the larger macro-economic and regional distributive impacts of existing NDC targets for India . The results provide three key insights, (i) overall socio-economic impacts of committing to an ambitious decarbonisation trajectory primarily articulated through NDCs for India will be positive, but the transition trajectory will have unequal distributive impacts across states and sectors. (ii)The NDC trajectories will have an expansive impact on the harder to abate construction sector so along with decarbonisation of energy sector, steel and cement sector would also need focussed decarbonisation measures. (iii) In absence of policies promoting ‘Just transitions’ smaller coal bearing states will be worst off, stuck with expansion of only primary and extractive mining sectors while high renewable energy potential states will show expansion in technology focussed sectors and high skilled sectors
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".