Measuring challenges in adoption of sustainable environmental technologies in Indian cement industry
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
The Indian cement industry has adopted various environmental protection technologies, but adoption of these new environmental technologies and development of working model could not resolve many issues related to environmental concern among Stakeholders. This paper examined the current technologies used by the cement companies and the challenges they are facing in adoption of these technologies. This research describes the effects of cement manufacturing on global warming, water, coal and other pollution emissions during cement production process and involves environmental manufacturing technologies. The study measures the challenges of introducing environmental technologies by creating a model of challenges. including challenges in perceived usability of technology, challenges in perceived utility, challenges in user engagement, and challenges in intent to use behaviour. The study examines the challenges of introducing environmental technologies into the Indian cement industry to mitigate air, water, and energy pollution and to highlight the new environmental technologies and development of the model. The data from 1540 professionals responsible for using the environmental technology were gathered and analysed with t test and regression analysis. The final outcome of the research is the model expressing the challenges in adoption of environmental technologies in India.
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 | 0.000 | 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 it