Synergies for sustainability: Renewable energy, urban planning, and green industry in carbon emission reduction
Bibliographic record
Abstract
This study addresses global carbon emission reduction by integrating renewable energy, urban sustainability, and green industry practices. It highlights the necessity of a holistic approach to tackling carbon footprints, emphasizing renewable alternatives like wind and solar energy alongside sustainable urban planning strategies, such as green roofs, solar energy, and electric vehicle use. Industrial transitions focusing on carbon capture and storage (CCS) and circular economies are essential for reducing emissions. The research underscores the interconnectedness of these strategies, advocating for cross-sectoral collaboration to drive sustainable development. Through data-driven analysis, the study advocates for aligning economic growth with environmental sustainability, promoting a low-carbon economy. The study also examines the significance of integrating renewable energy, urban planning, and industrial transformations to establish a comprehensive emission reduction system. Practical recommendations are provided for policymakers, urging the implementation of comprehensive, integrated strategies that balance ecological responsibility with economic growth. Additionally, the study utilizes predictive modeling, using Long Short-Term Memory (LSTM) neural networks to forecast CO₂ emissions trends, ensuring a robust tool for future decision-making. This research aims to provide actionable insights for reducing global carbon footprints, contributing to sustainable urban development, the adoption of renewable energy and green industry practices.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".