Big Data and Climate Change
Based on the given options, AI application in the study or prevention of climate change has been selected.
Big data has been chosen for this study as it is known as one of the most crucial technological aspects that assist in finding specific patterns of climate change based on which decisions regarding further improvements to those patterns can be made. Big data can be contemplated as a large as well as a complex set of data designed for analysing and extracting proper data elements to identify different patterns of climate change. As defined by Vinuesa et al. (8). the application of big data extends to scientific research, weather forecasting, crime prediction and prevention, traffic optimisation and finding patterns in every field to improve the existing process. In the study of Luccioni et al. (9), it has been found that AI-led big data analytics can only assist in terms of finding patterns of climate change; however, it cannot provide a way to mitigate those challenges. On the contrary, Jahankhani et al. (10) have defined that the combined effort of AI and Big Data can help find the root causes of the challenges based on which decisions can be made for further development.
Secondary findings from reports, journal articles, and news can be the most effective data source for the research as they can clearly explain AI applications. This is because these sources provide proper credibility and help find existing AI application areas in climate change.
AI in the study or prevention of climate change is a wide topic, due to narrowing down the research question is one of the most critical challenges. Additionally, identifying the position in the debate of whether AI applications can be used to study or prevent climate change is difficult as there are multiple studies in favour of both.
a. The essay question is: How AI applications can help to study the patterns of climate change?. Against this question, it is claimed that AI applications can further help find patterns of climate change.
b. The following reasons will help support the claim
i. AI-led data analytics is already helping to find patterns in weather forecasting and climate changes
ii. Event disaster response system forecasts are becoming accurate with the help of AI
- The topic sentence for the first two paragraphs will be AI significantly assists in the study of climate change
- After seeking scholarly sources, paraphrasing the contents that speak in favour of the chosen position in the debate will be used for supporting the claims
- Possible objections to the position may be regarding the accuracy of the analytics, dependence on AI and other related aspects.
Minevich. Mark. “11 Examples of AI Climate Change Solutions for Zero Carbon.” Forbes Magazine Online, 8 Oct. 2021, https://www.forbes.com/sites/markminevich/2021/10/08/11-examples-ofai-climate-change-solutions-for-zero-carbon/?sh=160acad52251.
The article depicts 11 key climate change solutions powered by AI for achieving zero carbon footprint, including BCG CO2 AI, Watershed, BrainBox, PlanA, Albo, Kayrros, Tomorrow, FoldAI. Patch and Blue Sky Analytics is considered the pioneer in the field of AI solutions for climate change in the future. Furthermore, the path forward to using these technologies has also been explained for clearer demarcation.
According to the idea of Boston Consulting Group, “we must reduce these carbon emissions by 50% by the end of this decade.”
The article effectively highlights the core concepts of technologies along with their impact on climate change which will be used to support the positive elements described. Besides, the forward path to progress with these technologies has been defined in the article, which will be used to support the positive views of the debate.
AI and Climate Change
Vinuesa, Ricardo, et al. “The role of artificial intelligence in achieving the Sustainable Development Goals.” Nature communications 11.1 (2020): 1-10.
The emergence of AI and its increasingly broad impact across various sectors necessitates a review of its impact on achieving the Sustainable Development Goals. This research discovered that AI could help achieve 134 goals across all goals via a consensus-based expert elicitation approach.
As the authors of the article define, “The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development.”
This article links SDG 13 with climate change actions concisely undertaken by AI. Information used to link these aspects will reflect on the usage of AI and address the associated reasons for using AI to detect and manage climate change which will be used in the essay for clear analysis.
Luccioni, Alexandra, et al. “Using artificial intelligence to visualize the impacts of climate change.” IEEE Computer Graphics and Applications 41.1 (2021): 8-14.
This study depicts AI usage for visualising the underlying impact of climate change that results in several detrimental activities regarding climate change. Moreover, the visualisation with AI is expected to enable accessible information on climate change which will further explain why extreme weather events are becoming frequent and exactly what changes are happening on a regional and global scale.
As per the authors, the ‘Decision Aiding’ approach is undertaken to determine unbiased parameters of growth.
This article effectively demonstrates the potential of AI for further visualising climate change activities, along with examples of the process of doing it. Thus, from this article, information on the core parameter of AI usage for different climate change activities can be found and used in the essay.
Cave, Stephen. “The problem with intelligence: its value-laden history and the future of AI.” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2020.
According to this study, the concept of intelligence is profoundly value-laden in ways that impact the field of AI and arguments regarding its hazards and prospects. This value-ladenness derives from intelligence usage as a justification for dominance hierarchies in the past.
The author has explained that while exploring the ideological usage of artificial intelligence, he will “sometimes refer to related concepts such as ‘mental ability’ or faculty of reason”.
This paper draws a different conclusion on the context of AI usage for the study of climate change as it highlights the factor of accuracy challenges in the discussing context. Therefore, while defining the challenges or contrasting viewpoints of AI’s advantages in climate change study, this information can contribute to a greater extent.
AI-led big data has become an inevitable aspect of consideration for processing large scale data regarding climate change and finding patterns to further take actions for improvement (Dorau 2). The advantages of AI-led big data in the domain range from efficiency increment and focus on local analytics preferences. Besides that, better decision making and increased agility can be considered other advantages of using AI-led big data for climate change studies (Schwartz et al. 55). On the contrary, increased level of errors, finding short-term analytics, and increasing stratification of data that increase complexity may appear as some critical challenges of big data.
Secondary Findings
Thesis statement: The government can adopt AI-led big data analytics for studying climate change action as it helps in efficient analytics behind the reasons for the change and helps in decision making
As Duan et al. (65) defined, AI-led data analytics helps identify patterns for reducing carbon footprint. Furthermore, Vinuesa et al. (1) have supported that in the case of SDG 13 on climate action, there is evidence that improvements in artificial intelligence will aid in knowing climate change and modelling its potential consequences. Tools like BCG, CO2 AI, and PlanA already contribute to the same (Minevich 1). In addition, AI will aid low-carbon energy systems with a high level of renewable energy and energy efficiency, all of which are necessary to combat climate change.
Due to its potential multifunctional roles, such as in measuring and lowering emissions, enabling creative business models to improve the climate, and boosting resilience to climate disasters, AI-led analytics is regarded as one of the most important components of studying climate change (Sebestyén et al. 70). Furthermore, multiple studies have supported the fact of AI can contribute to developing creative solutions with its existing potential (Luccioni et al. 11). For example, Albo, Keyrros, Tomorrow, and FoldAI are some core examples stating AI’s contribution.
In contrast, Cave (2) has defined that accuracy issues in ai-led analytics make researchers sceptical regarding its usage in climate change studies. Moreover, Fawzy et al. (2071) have supported that the concept of intelligence is fundamentally value-laden in ways that impact the area of AI and debates about its risks and potential. Thus, the use of intelligence as a justification for dominance hierarchies in the past has given rise to this value-ladenness. Nevertheless, with improved AI, machine learning and analytics, accuracy is increasing and becoming unmatchable.
Conclusion
The primary intent of the essay was to answer the proposed question by proving supporting details and counter-arguments against them. Likewise, the essay defined the advantages and disadvantages of using AI applications to study climate change in the initial stage, followed by supporting evidence on each. Moreover, the positive elements stated in the essay’s findings demonstrate that AI-led analutics are already supporting the study of climate change, and in the future, it will also help prevent the same. Nevertheless, the accuracy of using AI-led analytics to predict climate change has raised questionability. However, with the current progress level of analytics due to AI and its effective prediction, the negative foresight of the issue has been nullified. Thus, the thesis statement is duly met.
Cave, Stephen. “The problem with intelligence: its value-laden history and the future of AI.” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2020.
Dorau, Bethany Groff. “Artificial Intelligence: Overview.” Canadian Points of View: Artificial Intelligence, Mar. 2020, pp. 1-3. Canadian Points of View Reference Centre, https://search.ebscohost.com/login.aspx?direct=true&db=p3h&AN=143004699&site=eds-live.
Duan, Yanqing, John S. Edwards, and Yogesh K. Dwivedi. “Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda.” International Journal of Information Management 48 (2019): 63-71.
Fawzy, Samer, et al. “Strategies for mitigation of climate change: a review.” Environmental Chemistry Letters 18.6 (2020): 2069-2094.
Jahankhani, Hamid, et al., eds. Cyber defence in the age of AI, smart societies and augmented humanity. Springer Nature, 2020.
Luccioni, Alexandra, et al. “Using artificial intelligence to visualize the impacts of climate change.” IEEE Computer Graphics and Applications 41.1 (2021): 8-14.
Minevich. Mark. “11 Examples of AI Climate Change Solutions for Zero Carbon.” Forbes Magazine Online, 8 Oct. 2021, https://www.forbes.com/sites/markminevich/2021/10/08/11-examples-ofai-climate-change-solutions-for-zero-carbon/?sh=160acad52251.
Schwartz, Roy, et al. “Green ai.” Communications of the ACM 63.12 (2020): 54-63.
Sebestyén, Viktor, Tímea Czvetkó, and János Abonyi. “The applicability of Big Data in climate change research: the importance of system of systems thinking.” Frontiers in Environmental Science 9 (2021): 70.
Vinuesa, Ricardo, et al. “The role of artificial intelligence in achieving the Sustainable Development Goals.” Nature communications 11.1 (2020): 1-10.
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