"Pro Environment" Behavior Towards a Green Economy: An Analysis of Household Energy Consumption
Bibliographic record
Abstract
The environmental degradation and global warming resulting from unsustainable economic development pose a threat to the future economy.Households represent the smallest environmental unit, and household activities can involve pro-environmental practices such as reducing electricity usage, conserving water, or switching to more efficient cooking fuels.Moreover, households constitute the largest group of energy consumers.By examining households, we can identify the factors influencing pro-environmental behavior.The aim of this research is to examine the effect of energy-saving behavior on reducing energy consumption expenditure at the household level in Indonesia.This research is quantitative research using data from IFLS (Indonesian Family Life Survey).Testing was carried out using OLS regression to find the correlation between household pro-environment behavior and electricity consumption, controlling the household characteristics to prevent biased results.The results show interesting findings which states that the so-called "pro-environment" behavior somehow lead to higher electricity spending, though different result shows on rural cohort.Further research needs to be carried out using data on electricity use, not just energy expenditure.The government needs to think about long-term alternative environmentally friendly sources of electrical energy, because electricity demand tends to continue to increase along with the use of various electrical equipment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".