Machine learning-based time series models for effective CO2 emission prediction in India Article Swipe
Surbhi Kumari
,
Sunil Kumar Singh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11356-022-21723-8
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11356-022-21723-8
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11356-022-21723-8
- https://link.springer.com/content/pdf/10.1007/s11356-022-21723-8.pdf
- OA Status
- bronze
- Cited By
- 153
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283771000
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283771000Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11356-022-21723-8Digital Object Identifier
- Title
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Machine learning-based time series models for effective CO2 emission prediction in IndiaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-02Full publication date if available
- Authors
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Surbhi Kumari, Sunil Kumar SinghList of authors in order
- Landing page
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https://doi.org/10.1007/s11356-022-21723-8Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s11356-022-21723-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11356-022-21723-8.pdfDirect OA link when available
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Series (stratigraphy), Ecotoxicology, Time series, Machine learning, Environmental science, Econometrics, Computer science, Artificial intelligence, Mathematics, Ecology, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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153Total citation count in OpenAlex
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2025: 54, 2024: 62, 2023: 32, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1777742902, https://openalex.org/W3204655310, https://openalex.org/W3026907991, https://openalex.org/W3075674906, https://openalex.org/W3093632325, https://openalex.org/W2931695972, https://openalex.org/W2040395995, https://openalex.org/W2973803227, https://openalex.org/W4240958693, https://openalex.org/W3107146702, https://openalex.org/W2090457102, https://openalex.org/W3157043261, https://openalex.org/W4206303280, https://openalex.org/W3012354890, https://openalex.org/W2165466912, https://openalex.org/W2026473779, https://openalex.org/W3165533197, https://openalex.org/W4200090231, https://openalex.org/W2971724044, https://openalex.org/W3029951939, https://openalex.org/W3016542904, https://openalex.org/W3205387235, https://openalex.org/W4247374902, https://openalex.org/W2618966890, https://openalex.org/W3005127547, https://openalex.org/W3102461524, https://openalex.org/W3191040420, https://openalex.org/W3181231283, https://openalex.org/W3046458074, https://openalex.org/W3082021424, https://openalex.org/W3015613606, https://openalex.org/W3101383241, https://openalex.org/W3212565279, https://openalex.org/W2567511014, https://openalex.org/W3001019604, https://openalex.org/W3138239077, https://openalex.org/W3008915080, https://openalex.org/W3112644111, https://openalex.org/W3112118571 |
| referenced_works_count | 39 |
| abstract_inverted_index | |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.99682188 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |