Impact Of Forest Based Industries on Indian Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Forest products are a significant part of regional, national, and local economies. Understanding their economic importance is therefore essential. By using the IMPLAN model, Ram Prasad Dahal determined the value of forest products as a sector of the economy in 13 southern states, individually and regionally. For three primary forest products industries (wood furniture, paper products, and wood and wood products), the direct impacts of the three sectors are estimated along with their associated multipliers. Impacts direct to the economy are illustrated by the direct impacts, while multipliers represent the indirect effects. As a source of employment and income, the forest products industry performed well in 2009. There are new wood-based products being developed with great potential and attractive markets, such as textiles, liquid biofuels, platform chemicals, plastics, and packaging. There is a discussion of the extent to which these emerging wood-based products could compensate for anticipated declines in the graphic paper market in four major forest industry countries: the United States, Canada, Sweden, and Finland. In the four countries selected, a 1–2% percent market share would lead to an increase in revenues of 18–75 billion euros per year. As a result, 10-43% of forest industries' production value was lost in 2016. By 2030, the graphic paper industry is expected to lose 5.5 billion euros in revenue. As many of the products utilize byproducts as feedstock, the respective impacts on wood usage are numerous. This increase in primary wood use, which is largely due to the construction and to a lesser extent the textile markets, would amount to as much as 15–133 million m3, or 2–21% of the current structural roundwood supply in the countries selected.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it