Artificial Intelligence Is Shaping Sustainable Climate Change Adaptation Strategies in Egypt: Experts’ Opinions

AI and Sustainable Climate Change Mitigation Strategies in Egypt

AI is any algorithm that permits a computer to execute several difficult jobs within a shorter period. Recently, there have been advances in a subarea of AI called ML, or a set of tools and systems for frequently and repeatedly obtaining some relationships from the collected or corrected data systematically. Especially some of the popular areas of AI incorporate computer vision, which is also called interpreting the content of images, natural language management (parsing words and text), and time-series analysis (Hassani, Silva, Unger, TajMazinani, & Mac Feely, 2020; McCarthy, Koeling, Weeds, & Carroll, 2004).

AI/ML is increasingly employed throughout society. There has been an increase in interest in recognizing ML’s effects on climate action to account for ML explicitly and constantly in long-term weather and energy forecasts and design appropriate and proper policies to counter the harmful effects of climate change through AI, whereas AI is the sole tool to help control these issues in a short time. According to a global survey conducted by the Boston Consulting Group (BCG) (2022), approximately 87 percent of the public and private sector climate and AI respondents viewed AI as a useful tool for combating opposition to climate change-related issues (Antonopoulos et al., 2020). Furthermore, 43 percent said they can imagine using AI for their organization’s climate change energies, documenting the high level of worries among leaders about the technology’s capability to create progressive change in society to counter climate change issues (Cowls, Tsamados, Taddeo, & Floridi, 2023; Jobin, Ienca, & Vayena, 2019).

Egypt is committed to contributing its fair share of climate action as part of its global endeavor to acclimate to climate change through technology. From this vantage point, Egypt drafted its first National Strategy for Climate Change Adaptation and Disaster Risk Reduction in 2011, and a Low Emission Development Strategy was released in 2018, both of which were anticipated to be in line with the Sustainable Development Strategy—Egypt Vision 2030 (Bank, 2022). According to the revised Egypt Vision 2030 plan, “Objective 3.1: Meeting the challenges of climate change” is a top priority. This technique, which follows a low-emission strategy, enables Egypt to project and control climate change on multiple levels, facilitating the nation’s vital economic and development goals. The major accomplishments were to grant field training on planting wheat, sugar cane, and medicinal plants with more than 600 beneficiaries with the help of scientific applications. In addition, approximately 1,573 people will be helped by improved economic conditions brought forth by increased livestock and poultry production (Bank, 2022).

The broad pertinence of ML/AI algorithms implies that they can be used in submissions and management that relieve holdups in aiming at climate change and in applications that may decrease climate action (Kaack et al., 2022). On the contrary, AI may authorize environmental change mitigation and adaptation techniques in various domains, including the energy sector, production and manufacturing, agriculture enhancement, forestry expansion, and disaster risk management. Conversely, AI can inadvertently increase greenhouse gas (GHG) emissions by promoting high-emitting sectors, stimulating consumer demand, and consuming significant power and energy (Kaack et al., 2022).

AI can play a crucial role in addressing climate change by providing valuable insights for research and development, engineering, modification and adaptation programs, and monitoring and evaluation (Rutenberg, Gwagwa, & Omino, 2021). It can offer a range of effective approaches; for example, in situations where policy-relevant information is lacking, AI can help fill the gaps by analyzing vast amounts of raw data such as geospatial imagery, text documents, or sensor data. By doing so, it can provide assessments and estimations, such as identifying sources of GHG emissions using satellite images and gathering information about building characteristics or monitoring deforestation. Additionally, AI can be utilized for prediction and forecasting purposes, aiding in areas such as wind power production systems, transportation demand, and the anticipation of hazardous events (Cheong, Sankaran, & Bastani, 2022; Kaack, Donti, Strubell, & Rolnick, 2020; Leal Filho et al., 2022).

Moreover, by analyzing models based on historical data, AI can aid in forecasting quantities such as wind power production systems, transportation demand, and dangerous occurrences. Considering these projections, the optimization of power systems, the development of infrastructure, and crisis management may all benefit from a healthy dose of caution. All of these contribute to the system operating more effortlessly and efficiently. One way it can do this is by enhancing real-world systems with fewer financial resources through greater efficacy. Examples include the consolidation of freight shipments, the reduction of food waste, and the regulation of industrial heating and cooling systems throughout manufacturing. In addition, it facilitates preventive maintenance by disclosing errors early on (Behara & Saha, 2022; Kosovic et al., 2020; Zhao, Ma, Chen, Shang, & Song, 2022).

Similarly, AI may aid in enhancing security, reducing expenses, and enhancing energy efficiency across the board in infrastructure. The acceleration of scientific discovery can be aided by the use of AI, which has already been put to work in a variety of contexts, including the identification of leaks in natural gas pipelines, the description of discrepancies in solar panel outputs, the prediction of liabilities in infrastructure or industrial equipment, and the facilitation of accelerated scientific research. Therefore, AI may accelerate the creation of environmentally friendly products such as batteries and next-generation solar cells (Agrawala et al., 2004). The computationally complex simulations required to study climate physics or engineering systems and processes may be sped up with the help of these estimation-intensive imitations. For instance, by accelerating city planning tools to assist in real-time decision-making, AI can help estimate the tasks of climate and power system optimization models (Fouda, 2020).

Concluding Comments

Climate change has a global impact on the world’s environmental, social, political, and economic systems (Agrawala et al., 2004). Climate change mitigation, adaptation, and flexibility are thus vital issues, including efforts to achieve net-zero emissions by 2050, prepare for the effects of climate change, and decrease the consequent harm. This is observable in the present needs of Egypt. Advanced analytics and AI are useful in this regard (a pairing that we will portray in this report simply as “AI”) and are tools separately arranged to help oversee these complex border issues.

Three major general areas of application of AI/ML are specifically useful in mitigation. AI can help mitigate the climate issue by measuring macro and micro emissions, reducing emissions and GHG properties, and removing present pollution from the atmosphere. In BCG’s experience, for example, AI can be used to assist in reducing GHG emissions by 5–10 percent of an organization’s carbon footprint or 2.6–5.3 gigatons of CO₂ equivalent, if implemented globally (Degot, Duranton, Frédeau, & Hutchinson, 2021). On top of that, adaptation and resilience are two other significant areas where AI may tackle climate-related concerns (Lane, 2019).

AI can be harnessed in raising adaptation and resilience capability, partly through improved hazard estimations of devolved long-term impacts (such as sea-level rise) or utmost events (such as hurricanes or droughts). The macro-level measurement encompassed assessing outside carbon natural stock, and the micro-level measurement, for example, computed the carbon footprint of a personal product. Furthermore, the reduction lowers GHG emissions. It also increases energy productivity via the promotion of behavioral change among the broad communities (Cheong et al., 2022; Huntingford et al., 2019; Walsh et al., 2020). Moreover, it is also beneficial in lowering greenhouse influences, such as speeding up aerosol and chemistry research and development processes. It also helps eliminate environmental waste by observing intrusion on forests and other natural reserves. Hazard forecasting is also a use of AI in climate-related matters where it facilitates in predicting specified long-term trends, such as devolved modeling of sea-level rise or intense incidents such as wildfires and floods building early alarm systems, for example, near-term extrapolation of dangerous occurrences such as cyclones (Casper, 2010; National Academies of Sciences, Engineering, and Medicine, 2019).

One of the most important uses of AI in climate change approaches is in the measurement side, reduction process, and elimination of emissions and GHG effects. Increased energy in these areas is important to ensure that they meet the whole goals of the Paris Agreement on climate change and its effects (Cowls et al., 2023). Furthermore, measuring and recognizing the size of the problem are very serious to confronting it essentially and in sustainable ways. Measuring emissions, both in the overall environment and at the level of products and actions, allows for the ability to take stock of the current situation and forecast future trends. It also allows the practitioners to select the reduction efforts with the highest possibility to reduce emissions at both the macro and micro levels. The reduction of rapid exertions to cut the level of continuing releases and the following GHG effects from adjusting transportation networks to promising research on additional equipment is required to change the consequences of the climate (Milfont et al., 2012; Ostrom, 2009).