A Hybrid Model with Applying Machine Learning Algorithms and Optimization Model to Forecast Greenhouse Gas Emissions with Energy Market Data
Bibliographic Data
| ID | 21233857 |
|---|---|
| Authors | Majid Emami Javanmard (0000-0001-7786-5190, University of Tehran), S F Ghaderi (University of Tehran, corresponding author) |
| Year | 2022 |
| Volume | 82 |
| Pages | 103886 |
| Publication date | 2022-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2022.103886 |
| OpenAlex | W4223570837 |
| Language | EN |
| Citations received | 8 |
| References cited | 63 |
Algorithm · Autoregressive integrated moving average · Greenhouse gas · Machine learning · Time series · Air Quality Monitoring and Forecasting · Computer Science · Energy Load and Power Forecasting · Energy, Environment, Economic Growth · Engineering
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Unveiling causal dynamics and forecasting of urban carbon emissions in major emitting economies through multisource interaction
Searching for economic synergy and sustainability in Tehran metropolitan region
| Citations per year | 2.67 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |