Nonlinear Dynamic Impact of Electricity Consumption on Economic Growth in Odisha: A Disaggregated Causality Analysis
Bibliographic record
Abstract
The paper examines the causal link between electricity consumption and Odisha’s economic growth using linear and nonlinear causality tests in annual data from 1981 to 2020. The study uses both linear and non-linear causality on aggregate and sectoral data. Based on the empirical analysis, the study finds that electricity consumption strongly granger causes state’s economic growth. Further, sectoral-level analysis shows that electricity consumption exhibits a strong causal relationship with the primary, secondary, and tertiary sectors. This finding is consistent for both linear and non-linear granger causality tests. Moreover, the estimation of long-run elasticity reveals that both secondary and tertiary sectors have greater than unity elasticity whereas the primary sector has less than unity elasticity. The rolling elasticity shows that elasticity is increasing over time and across the sectors, barring the tertiary sector. More mechanized activities in the primary sector will increase the consumption of electricity and more value addition to the economic growth of the state. The policy intervention would be to reduce electricity losses (leakages) as well as increase the production of electricity to increase economic growth. Considering the greater role of electricity in the state’s economic progress, intervention from both the state government and the Odisha Electricity Regulatory Commission, the regulatory body of the state, is very much essential.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".