mlsanityData to 7 Oct 2026

Towards Climate Awareness in NLP Research

arXiv 2205.05071 ↗First posted 10 May 2022cs.CL cs.CYcited by 19

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Abstract (arXiv)

The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 26 of its 68 references that are in the map. First posted on arXiv 10 May 2022.

Builds on

26 of 68 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  4. 2018Model Cards for Model Reportingcited by 1,068
  5. 2019Green AIcited by 620
  6. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  7. 2019Tackling Climate Change with Machine Learningcited by 264
  8. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  9. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  10. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
  11. 2021Block Pruning For Faster Transformerscited by 176
  12. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
14 more
  1. 2021Primer: Searching for Efficient Transformers for Language Modelingcited by 162
  2. 2022Compute Trends Across Three Eras of Machine Learningcited by 161
  3. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  4. 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 89
  5. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  6. 2019Energy Usage Reports: Environmental awareness as part of algorithmic accountabilitycited by 43
  7. 2020Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?cited by 27
  8. 2020Analyzing Sustainability Reports Using Natural Language Processingcited by 18
  9. 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impactcited by 18
  10. 2022Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysiscited by 18
  11. 2021A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021cited by 9
  12. 2020Natural language processing for achieving sustainable development: the case of neural labelling to enhance community profilingcited by 4
  13. 2020Social and Governance Implications of Improved Data Efficiencycited by 3
  14. 2021NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Actioncited by 3

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Citing papers per year: 2022: 2; 2023: 7; 2024: 6; 2025: 4.

19 papers.

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  1. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  2. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 32
  3. 2023Text Rendering Strategies for Pixel Language Modelscited by 22
  4. 2023Enhancing Large Language Models with Climate Resourcescited by 18
  5. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  6. 2022Environmental Claim Detectioncited by 11
  7. 2023On Evaluating Multilingual Compositional Generalization with Translated Datasetscited by 6
  8. 2024What the Weight?! A Unified Framework for Zero-Shot Knowledge Compositioncited by 4
  9. 2025EvalCards: A Framework for Standardized Evaluation Reportingcited by 4
  10. 2023Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial riskscited by 3
  11. 2024EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detectioncited by 3
  12. 2024Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domaincited by 3
7 more
  1. 2024Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statementscited by 3
  2. 2025The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Reboundcited by 2
  3. 2023Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Modelscited by 1
  4. 2024Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Studycited by 1
  5. 2024Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processingcited by 0
  6. 2025Preface to the Special Issue of the TAL Journal on Scholarly Document Processingcited by 0
  7. 2025Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languagescited by 0