Question
Asked 29 October 2024
  • Independent Researhcer

Can we benefit from artificial intelligence in combating climate change?

Can we benefit from artificial intelligence in combating climate change? Can artificial intelligence contribute to the fight against climate change? If ‘no’ why, if ‘yes’ how?

Most recent answer

Dear Recep Baydemir , no worries, my pleasure to stimulate thought (i hope i achieved that!)
1 Recommendation

Popular answers (1)

Kazi Redwan
American International University-Bangladesh
Yes, artificial intelligence (AI) can significantly contribute to combating climate change by enhancing our understanding, predicting impacts, and creating more efficient solutions. Here are some ways AI is already making a difference:
1. Climate Modeling and Prediction
  • AI-driven models can process vast climate datasets to predict weather patterns, model future climate scenarios, and assess the potential impacts of different interventions. By analyzing complex data, AI improves climate modeling accuracy, helping scientists make informed predictions and plan responses.
2. Carbon Emission Reduction and Monitoring
  • AI can monitor and analyze emissions in real time, detecting excessive carbon release and providing insights on how to reduce it. In manufacturing and energy sectors, AI optimizes processes to minimize waste and carbon output, making industries greener.
3. Renewable Energy Optimization
  • AI algorithms help optimize the generation, storage, and distribution of renewable energy (like solar, wind, and hydropower). For example, AI forecasts weather conditions to optimize energy production, making renewables more efficient and reliable.
4. Agriculture and Land Management
  • AI can improve crop yields and land use, reduce waste, and track soil health, water use, and nutrient levels, leading to more sustainable agriculture. Machine learning models also monitor deforestation, track land use changes, and provide early warnings for illegal activities that contribute to climate change.
5. Disaster Response and Adaptation
  • AI models help anticipate natural disasters, such as hurricanes or wildfires, allowing communities to prepare and respond faster. These predictive insights can reduce the environmental and human impact of climate-related events.
6. Energy Efficiency in Smart Cities
  • AI can analyze and optimize energy use in buildings, reduce waste, and manage traffic to lower emissions. Cities can leverage AI to create efficient public transportation systems and reduce overall urban carbon footprints.
While AI has great potential, its effectiveness in combating climate change depends on appropriate data access, ethical use, and collaboration across industries and governments.
3 Recommendations

All Answers (6)

Kazi Redwan
American International University-Bangladesh
Yes, artificial intelligence (AI) can significantly contribute to combating climate change by enhancing our understanding, predicting impacts, and creating more efficient solutions. Here are some ways AI is already making a difference:
1. Climate Modeling and Prediction
  • AI-driven models can process vast climate datasets to predict weather patterns, model future climate scenarios, and assess the potential impacts of different interventions. By analyzing complex data, AI improves climate modeling accuracy, helping scientists make informed predictions and plan responses.
2. Carbon Emission Reduction and Monitoring
  • AI can monitor and analyze emissions in real time, detecting excessive carbon release and providing insights on how to reduce it. In manufacturing and energy sectors, AI optimizes processes to minimize waste and carbon output, making industries greener.
3. Renewable Energy Optimization
  • AI algorithms help optimize the generation, storage, and distribution of renewable energy (like solar, wind, and hydropower). For example, AI forecasts weather conditions to optimize energy production, making renewables more efficient and reliable.
4. Agriculture and Land Management
  • AI can improve crop yields and land use, reduce waste, and track soil health, water use, and nutrient levels, leading to more sustainable agriculture. Machine learning models also monitor deforestation, track land use changes, and provide early warnings for illegal activities that contribute to climate change.
5. Disaster Response and Adaptation
  • AI models help anticipate natural disasters, such as hurricanes or wildfires, allowing communities to prepare and respond faster. These predictive insights can reduce the environmental and human impact of climate-related events.
6. Energy Efficiency in Smart Cities
  • AI can analyze and optimize energy use in buildings, reduce waste, and manage traffic to lower emissions. Cities can leverage AI to create efficient public transportation systems and reduce overall urban carbon footprints.
While AI has great potential, its effectiveness in combating climate change depends on appropriate data access, ethical use, and collaboration across industries and governments.
3 Recommendations
Dragan Ugrinov
University Business Academy in Novi Sad, European Faculty "Kallos" Belgrade
Yes, artificial intelligence (VI) can be of great importance in the fight against climate change in several ways: Data analysis: VI can process large amounts of data on climate conditions, gas emissions, and other factors, allowing better understanding of trends and predicting future changes.
Resource optimization: In the energy sector, VI can help optimize energy consumption, improve the efficiency of renewable energy sources and manage the grid. Climate change modeling: VI tools can simulate different climate change scenarios and assess the impacts of different policy measures.
Forecasting consequences: VI can predict the consequences of climate change on different ecosystems and human activities, which helps in planning adaptation strategies. Promotion of sustainability: In the field of agriculture, VI can optimize farming methods, minimize waste and improve resource management.
Education and Awareness: VI can help create interactive tools to raise awareness of climate change and engage the public in addressing the issue. Overall, AI can make a significant contribution to climate change efforts, but it is important to use it in an ethical and responsible manner.
1 Recommendation
Recep Baydemir
Independent Researhcer
Thank you very much for your valuable answers.
It contains really useful information. Thank you!
Dear Recep Baydemir , in general, I think that if one wants scientific data and 'scientific' (or 'objective') reasons and solutions to be compelled to do sth about climate change, then AI can certainly be of help.
However, if the issue is framed as political and depends mainly on political will, then AI won't be that useful. This latter answer may not be as silly as it sounds, as probably some answers to combat climate change may turn out to be quite simple (consume less for example, travel less, use less electricity including for data centers).
1 Recommendation
Recep Baydemir
Independent Researhcer
Thank you very much for your answer. I am grateful to you for taking the time to give your opinion.
You have touched on a different subject here and indeed we can look at it from this point of view. interesting indeed.
1 Recommendation
Dear Recep Baydemir , no worries, my pleasure to stimulate thought (i hope i achieved that!)
1 Recommendation

Similar questions and discussions

Is Artificial Intelligence (AI) going to take the major role in the Peer review ? Do you see the - reviewer fatigue as the reason for this ?
New question
3 answers
  • Sandeep Savitaprakash SharmaSandeep Savitaprakash Sharma
Is Artificial Intelligence (AI) going to take the major role in the Peer review ? Do you see the - reviewer fatigue as the reason for this ?
In future AI will take over Human As The Peer Reviewers in the academic and scientific Freternity . What do you think? Please do put your views .
Reviewer Fatigue: A Growing Concern
Reviewer fatigue refers to the physical, emotional, and mental exhaustion experienced by reviewers, particularly in academic and professional settings. This phenomenon occurs when reviewers are overwhelmed with an excessive number of requests to review manuscripts, articles, grant proposals, or other documents.
Causes of Reviewer Fatigue:
  1. Increasing demand: The rise in submissions to academic journals and conferences has led to a surge in review requests.
  2. Limited pool of reviewers: The number of qualified reviewers has not kept pace with the growing demand, leading to a heavier burden on individual reviewers.
  3. Time-consuming process: Reviewing requires a significant investment of time and effort, often taking away from other important tasks and responsibilities.
  4. Lack of incentives: Reviewers often receive little to no compensation or recognition for their efforts, leading to a sense of undervaluation.
Consequences of Reviewer Fatigue:
  1. Decreased quality of reviews: Fatigued reviewers may provide less thorough and less accurate feedback, compromising the integrity of the review process.
  2. Delayed review times: Overwhelmed reviewers may take longer to complete reviews, causing delays in the publication process.
  3. Reviewer burnout: Prolonged fatigue can lead to reviewer burnout, causing individuals to abandon reviewing altogether.
  4. Negative impact on research: The diminished quality and timeliness of reviews can hinder the advancement of research and innovation.
Mitigating Reviewer Fatigue:
  1. Diversify reviewer pools: Expand the pool of reviewers by inviting new experts, early-career researchers, and individuals from diverse backgrounds.
  2. Implement efficient review processes: Streamline review procedures, use technology to facilitate communication, and set realistic deadlines.
  3. Recognize and reward reviewers: Offer incentives, such as discounts on publications, conference registrations, or monetary rewards, to acknowledge reviewers' contributions.
  4. Monitor and manage reviewer workload: Regularly assess reviewer workload and adjust the number of review requests accordingly to prevent overload.
By acknowledging and addressing reviewer fatigue, we can work towards maintaining the integrity and efficiency of the review process, ultimately supporting the advancement of research and innovation.
The Role of AI in Scholarly Review: Augmentation, Not Replacement
While AI has made significant strides in assessing scholarly work, it is unlikely to fully replace human reviewers in the near future. Instead, AI will likely augment the review process, enhancing its efficiency, accuracy, and fairness.
AI's Strengths in Scholarly Review:
  1. Speed and scalability: AI can process large volumes of manuscripts quickly, freeing human reviewers to focus on higher-level tasks.
  2. Consistency and accuracy: AI can identify formatting errors, grammatical mistakes, and inconsistencies in citations and references.
  3. Objectivity and fairness: AI can reduce bias in the review process by evaluating manuscripts based solely on their content and merit.
  4. Content analysis: AI can analyze manuscript content, identifying trends, patterns, and relationships that may not be immediately apparent to human reviewers.
Limitations of AI in Scholarly Review:
  1. Contextual understanding: AI may struggle to fully understand the nuances of human language, leading to misinterpretations or oversights.
  2. Domain expertise: AI may lack the specialized knowledge and expertise required to evaluate manuscripts in specific fields or disciplines.
  3. Critical thinking and evaluation: AI may not be able to replicate the complex, critical thinking and evaluation that human reviewers bring to the process.
  4. Ethical considerations: AI may not be able to identify or address ethical concerns, such as plagiarism, fabrication, or conflicts of interest.
Human-AI Collaboration in Scholarly Review:
  1. Hybrid review models: Combine human and AI evaluation to leverage the strengths of both.
  2. AI-assisted review tools: Develop tools that assist human reviewers in identifying errors, inconsistencies, and areas of concern.
  3. AI-powered review analytics: Use AI to analyze review data, identifying trends and patterns that can inform editorial decisions.
By embracing a collaborative approach, where AI augments and supports human reviewers, we can create a more efficient, accurate, and fair scholarly review process.

Related Publications

Got a technical question?
Get high-quality answers from experts.