Forecasting and Cross-Correlation of Series on the Waste Generation, Population, GDP and Household Consumption Expenditures in Jordan
Bibliographic record
Abstract
The household waste is the biggest contributor to the total municipal solid waste generation. The main objective of the research is to discover the Jordanian economy in four dimensions: the household waste generation, the number of population, the gross domestic product (GDP) at purchasers’ prices, and the household final consumption expenditures. In the focus of interest are (1) finding adequate time series models for forecasting of the number of population, the GDP at purchasers’ prices, the household final consumption expenditures, and the household waste generation; (2) the cross-correlation between the household waste generation variable and other variables – population, GDP and household final consumption expenditures. The analysis of cross-correlation functions has revealed that pairs of series (waste/gdp, waste/consumption, waste/population) move in one direction with no shift in time of one series to the other. The relationship in every pair of series is significant. It is expected that in the short-run, ceteris paribus, with the growth of the population numbers, GDP and/or household consumption expenditures, the level of household waste generation in the country will also grow in a close relationship. Appropriate ARIMA models have been proposed to use for the short-run forecastsing of time series. The research outcomes are useful for policy makers to realize the scale of the household waste problem and to optimize capital expenditures into the waste management system of Jordan.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".