# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 4 Discussion
Additionally, as emission reduction is a global challenge, common factors like weather patterns and energy production resources vary significantly across regions, further complicating the development of unified datasets. The identified articles come from various geographical areas, and some of the results of the research are applicable only in the original research setting. Challenges in this field vary widely based on location and energy production methods, which are often shaped by country-specific political decisions. Moreover, researchers' access to energy infrastructure for testing AI solutions differs across regions. This can lead to a situation where local research in energy production lags behind global standards. To address this issue, we recommend implementing policies that provide researchers with enhanced access to energy infrastructure for their work. Integrating AI with emerging technologies, including the Internet of Things (IoT), offers significant potential to improve energy management and reduce emissions. Implementing these solutions in real-world scenarios is crucial to uncover practical challenges and benefits. However, technologies like IoT are often closed resources, thus making real-world integration and data acquisition impractical. Additionally, further research is needed to explore the application of AI in underdeveloped areas of the energy sector, such as energy storage and distribution, to maximize its impact on reducing CO\({}_{2}\) emissions. Although many papers fitting our inclusion criteria used neural networks and deep learning models in their research, the model architectures were based on traditional multi-layer perceptron or convolutional neural network architectures. Recent advancements in natural language processing (NLP) have been achieved with the transformer neural network architecture, which is also successfully applied to other application areas in addition to NLP. Furthermore, a recent trend is to use pre-trained foundation models as a base for new AI solutions [68]. An example of this is the use of large language models for various different applications such as classification. We identified a potential gap in the existing research in this area. None of the included papers used these technologies yet. Our search phrases focused on finding the research papers that explicitly stated CO\({}_{2}\) emission reduction as the goal of the study. However, in the energy sector, CO\({}_{2}\) emission reductions can also be achieved while optimizing towards other goals, such as cost. Cost is mainly driven by energy usage, and by reducing energy usage, indirect reduction to CO\({}_{2}\) emissions can be accomplished. These types of papers did not fit our inclusion criteria. Nevertheless, this paper includes many references to related studies which cover this shortcoming. Furthermore, because the goal of this study was to provide an overview of existing studies covering the topic, we ended up conducting this study using the scoping review methodology. Scoping review is a more suitable methodology than systematic review for a broad research question such as ours. However, a scoping review is not as comprehensive as systematic reviews in the literature retrieval phase; thus, it is impossible to say if all the existing research fitting the criteria is covered. Based on the findings, some recommendations can be made to improve technologies to reduce emissions. Firstly, without access to the process, it is difficult for researchers to improve them. Having more datasets, especially open datasets, would provide more opportunities to offer AI solutions. Furthermore, hosting challenges to provide the best optimization could be a viable way of finding new solutions. Secondly, standardized interfaces and process descriptions could provide more opportunities to use AI solutions in this domain. AI has demonstrated promise in reducing CO\({}_{2}\) emissions in the energy sector through optimization, forecasting, and efficiency improvements.