Climate Change and AI

Tauhid Nur Azhar (Korika)

Muhammad bin Zayed (MBZUAI)

Institute for Malaria and Climate Solutions

Exactly one year has passed since a train accident occurred involving two prestigious Argo trains on a double-track line between Kutoarjo-Jogja, in the Kalimenur area. The incident involving the Argo Semeru train sparked a series of consequences, including a collision with the Argo Wilis train traveling in the opposite direction, although it did not result in any fatalities, but caused significant damage and raised concerns about transportation safety.

The summary of the KNKT investigation report is as follows:

The National Transportation Safety Committee (KNKT) has completed its investigation into the Argo Semeru train accident. The incident occurred at Km.520 + 4 on the Sentolo-Wates track, on a curve with a radius of 397 meters and a length of 845 meters. Based on the findings on the ground, KNKT concluded that the contributing factor to the accident was the failure to identify hazards that could increase the risk of rail buckling by the track and bridge units.

In addition, there were differences in knowledge and understanding within the track and bridge organization in determining the rail gap distance at the rail connection. There was no reduction in the operational speed of the train when the geometric problem of the track was found and repairs were being made. (KNKT, Investigation of the Argo Semeru Train Accident at the Sentolo-Wates Track, DAOP 6 Yogyakarta, 2024)

Recently, in mid-October 2024, we heard news about a high tide wave in the southern coast of West Java, particularly in several districts in Sukabumi Regency. The extreme wave conditions have affected the activities of fishermen and tourism, which have been restricted due to safety concerns.

On the other hand, there is a report from the Ministry of Health on the impact of climate change on the pattern of vector-borne diseases or diseases transmitted by living agents, related to regional and local temperature changes. According to the report, climate change can cause vector-borne diseases to spread because it is related to temperature, humidity, and rainfall profiles.

Global, regional, and local warming can also cause the accumulation of allergens, air pollution, and the potential for hazardous gases to be trapped in the Earth’s atmosphere. The escalation of these factors can increase the risk of respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, as well as other non-communicable diseases, in addition to infectious diseases (vector-based), and mental health disorders.

For example, in the case of vector-borne diseases, climate change affects the life cycle of mosquitoes and the intensity of mosquito bites. This is because mosquitoes are ectothermic agents, whose body temperature depends on the ambient temperature. The vulnerable stage of the life cycle is the larva to adult stage.

Increased temperature will accelerate the development of mosquito larvae into adults. Climate change will also accelerate the digestion of blood by adult female mosquitoes, resulting in higher biting intensity. This will lead to an increase in the frequency of disease transmission.

Types of mosquitoes that can be affected by climate change include Anopheles gambiae, A. funestus, A. darlingi, Culex quinquefasciatus, and Aedes aegypti. Culex sp, for example, is one of the vectors of filariasis and is an anthropophilic mosquito (preferring to bite humans).

Changes in the life cycle and daily activity patterns of mosquitoes, as well as the presence of pathogens such as viruses in certain ecosystems that are human habitats, will undoubtedly have the potential to affect the dynamics of vector-borne disease transmission, which is highly dependent on environmental conditions and elements within the complex system.

What is the connection between the Argo Semeru train accident, the high tide wave in Sukabumi, and the dynamics of vector-borne diseases?

The train accident caused by rail buckling, the high tide wave, and the dynamics of vector-borne diseases all have a common thread that leads to weather and climate conditions, including sudden and drastic temperature changes.

Climate change is one of the biggest global challenges facing the world today. Its impact is not only felt in the environmental sector, but also in public health, and various other aspects of public services that have a wide impact. This situation is exacerbated by the fact that the impact of climate change tends to be more significant in developing countries, where healthcare infrastructure and mitigation capabilities are often limited.

This WA story will discuss how climate change affects public health, public services, and socio-economic aspects that are critical, supported by statistical data and relevant theories.

Climate change is characterized by global warming, extreme weather patterns, and rising sea levels, which are partly caused by human activities (anthropogenic), such as the use of fossil fuels and deforestation.

According to the Intergovernmental Panel on Climate Change (IPCC), the global temperature has risen by about 1.1°C since the pre-industrial era and is expected to continue to rise if greenhouse gas emissions do not decrease significantly.

Meanwhile, data from the World Meteorological Organization (WMO) shows that the decade 2011–2020 was the hottest on record, with 2020 being one of the three hottest years in history. Additionally, the World Bank estimates that more than 143 million people in sub-Saharan Africa, South Asia, and Latin America could become climate migrants by 2050 if action is not taken to address climate change.

Changes in temperature and precipitation patterns affect the distribution of disease vectors, such as mosquitoes that transmit malaria and dengue fever. The World Health Organization (WHO) states that more than 3.9 billion people worldwide are at risk of contracting dengue fever due to the expansion of the Aedes aegypti and Aedes albopictus mosquito populations, which have increased due to warmer temperatures.

Other studies show that a global temperature increase of 2°C could lead to a 5% increase in malaria prevalence in sub-Saharan Africa. This is a serious concern, given that malaria is one of the leading causes of death in the region.

According to the Lancet Countdown on Health and Climate Change report, between 2000 and 2019, there was a 50% increase in the number of people exposed to heatwaves, which increased the risk of stroke, heart attacks, and death.

Climate change also increases the frequency and intensity of forest fires, which produce air pollution in the form of fine particulate matter (PM2.5). These particles can enter the lungs and cause various health problems, such as asthma, bronchitis, and even lung cancer. According to WHO, air pollution is responsible for more than 7 million premature deaths every year.

For example, the 2019–2020 bushfires in Australia not only caused ecological devastation but also contributed to poor air quality, resulting in more than 400 deaths due to uncontrolled air pollution.

Unpredictable weather patterns, such as droughts and floods, also impact food production. Rising temperatures also shorten the growing season in some areas, leading to decreased crop yields. The Food and Agriculture Organization (FAO) estimates that climate change could lead to a 10% to 25% decrease in global food production by 2050. As a result, food insecurity increases and leads to malnutrition, particularly in developing countries.

The most fundamental question that comes to mind is: Why is climate change happening? And what are the causes? Not so simple, right?

Climate change can occur due to various natural and anthropogenic factors. Natural factors include volcanic activity, solar radiation variability, and ocean circulation changes, but in recent decades, anthropogenic factors have become the primary cause. Here are some of the main causes of climate change that we have experienced, Greenhouse Gas Emissions (GHGs), where gases such as carbon dioxide (CO2), methane (CH4), and nitrogen oxide (N2O) produced from industrial, transportation, and agricultural activities are the largest contributors. CO2 concentrations have increased from around 280 ppm in the pre-industrial era to over 410 ppm today.

Deforestation has become rampant, where forest clearing reduces the Earth’s ability to absorb carbon dioxide, exacerbating the greenhouse effect. This is followed by changes in land use related to urbanization and increased industrial demand, leading to increased pollution and changes in the Earth’s surface albedo, which affects global temperatures.

The use of fossil fuels, which produces emissions of greenhouse gases, is a significant contributor. Consumption of coal, oil, and natural gas produces large amounts of GHGs.

Meanwhile, natural events such as El Niño, which increases sea surface temperature, also contribute to climate fluctuations.

Several hypotheses and theories related to climate change and its impacts have been proposed by researchers from various disciplines. Relevant theories include:

The Carbon Cycle Theory, which explains how greenhouse gases (GHGs) are trapped in the atmosphere, reducing radiation that is reflected back into space, and causing the greenhouse effect.

The Integrated Earth System Models (ESM), which are used to project future climate change by combining factors from the atmosphere, oceans, ice, and biosphere. These models have shown that a global temperature increase of 1.5°C to 2°C will lead to an exponential increase in extreme weather events.

The Ecosystem Resilience Theory, which explains how ecosystems react and adapt to climate change. Many natural ecosystems are approaching their limits of adaptability, which can lead to the loss of biodiversity.

In Indonesia, initiatives to anticipate global climate change have been undertaken by involving various sectors from across disciplines. From the aspect of research and technology, for example:

The National Research and Innovation Agency (BRIN) through the Climate and Atmospheric Research Center has developed a seasonal prediction model that serves as part of the weather strategic decision support system (DSS) called Kamajaya. Kamajaya is an application of the Mid-term Seasonal Forecast Study System based on atmospheric models. The data generated by Kamajaya is then developed to support atmospheric research and its applications.

In January 2023, the European Centre for Medium-Range Weather Forecasts (ECMWF) stated that global warming is expected to reach 1.21°C. In 30 years, this warming is expected to continue to 1.5°C by March 2023, and this will have an impact on climate change in Indonesia, as well as having different impacts and effects in each region of Indonesia.

Based on data from the BRIN research team, for the development of the model in Kamajaya, there has been a climatological change in Indonesia over the past 19 years, from 2001 to 2019. The duration of the rainy season has increased in several southern regions of Indonesia, including Region 1 in South Sumatra and Kalimantan and part of the southern island of Sulawesi for 49 days. Meanwhile, in Lampung and the western part of Java, the duration of the rainy season has increased by 12 days.

The results of the BRIN research team’s study show significant temperature changes in the islands of Sumatra, Java, and Kalimantan (2021–2050 compared to 1991–2020). The minimum temperature has decreased in most coastal areas of Central Java and East Java, as well as the central region of West Java. The maximum temperature has increased in most coastal areas of Central Java and East Java. The number of dry days in Central Kalimantan and South Kalimantan is projected to increase, resulting in a significant increase in dryness, similar to South Sumatra, to Lampung.

This climate change is believed to have caused the formation of Vortex Storms and Tropical Cyclones in southern East Nusa Tenggara, resulting in increased rainfall and flooding in Madura and other regions of East Java. In addition, the warming of sea surface temperature in the Java Sea north of Jakarta has created high pressure. On the other hand, the cooling of sea surface temperature in the South China Sea has created a high-pressure area. The cooling of sea surface temperature is also known as the “cold tongue” (Webinar on Climate Change ITERA, 2024).

Specifically, the impact of climate change is now being tracked in various aspects of life. Here are some conditions that are strongly related to climate change and the decline of environmental resilience due to various systematic causes.

Studies have shown a link between air pollution and decreased cognitive function and increased risk of mental disorders, where research by Chen et al. (2017) found that increased exposure to PM2.5 (very fine particles) is associated with an increased risk of neuropsychiatric disorders such as depression and anxiety. Long-term exposure to chronic air pollution can also affect brain development in children, impair cognitive function, and increase the risk of neurological disorders.

Environmental pollutants can also trigger oxidative stress and inflammation in the brain, contributing to the development of mental health problems. For example, exposure to heavy metals such as mercury and lead from industrial pollution can disrupt neurotransmission and cause mood disorders and depression.

Meanwhile, noise pollution, particularly in densely populated urban areas, is linked to increased sleep disorders, stress, and increased risk of mental disorders. Constant loud noise can increase cortisol (stress hormone) levels in the body, which can trigger long-term anxiety and mood disorders.

Biologically, exposure to extreme environmental changes and pollution can affect mental health through the following mechanisms: the occurrence of stress hormone changes due to extreme weather conditions and air pollution, which can activate the HPA (hypothalamic-pituitary-adrenal) axis. This condition causes an increase in stress hormone production, such as cortisol. Prolonged cortisol increase is associated with depression, anxiety, and sleep disorders.

Air pollution can also damage the blood-brain barrier and cause systemic inflammation that affects brain health. Chronic inflammation in the brain is associated with various mental disorders, including depression and bipolar disorder.

On the other hand, environmental changes such as heatwaves can disrupt sleep cycles and circadian rhythms, which have a negative impact on emotional and cognitive stability.

If we talk about global data, the statistical data shows that if there is no significant mitigation action, the global temperature could rise to 3°C by the end of the 21st century. Projections for various sectors show the following trends:

According to WHO, 250,000 additional deaths per year are projected to occur between 2030 and 2050 due to climate change that worsens malnutrition, infectious diseases, and heat-related impacts.

Meanwhile, FAO predicts that global food production could decrease by 10% by 2050, with the greatest impact occurring in tropical countries.

The IPCC report estimates that the frequency of tropical storms could increase by 50% in the Atlantic and Pacific regions by 2100.

The cost of maintaining infrastructure for transportation systems, such as in the case of rail tracks in mass transit systems, is expected to increase by two times in coastal areas due to rising sea levels and extreme weather events.

Developing countries can lose 5–10% of their GDP each year due to the impacts of climate change on agriculture, infrastructure, and health. Truly a condition that we do not hope for, is it not?

The next fundamental question is: Can we manage and reduce the negative impacts of environmental changes that trigger global climate change with the rapid development of technology, such as artificial intelligence (AI)?

A study by Rolnick et al. (2019) shows that AI has helped improve the accuracy of predicting climate phenomena such as tropical storms and regional warming. AI algorithms that are continuously developed also make it possible to model granularly, which helps improve temporal and spatial resolution in predicting extreme weather events.

AI can process various types of data, from atmospheric data to ocean data, to create a holistic picture of climate change. Data from Earth sensors and satellites are abundant, but require sophisticated processing to be useful in climate analysis. AI can filter and combine this data to provide more accurate predictions about sea surface temperature, ice surface changes, and deforestation.

Even with the use of big data analytics, AI can analyze extreme weather trends in real-time, allowing for early detection and early warning of climate events that are detrimental, such as floods, droughts, and storms.

AI can also be used to manage decentralized power grids by combining data on weather, energy usage, and power plant capacity, AI helps ensure that renewable energy (such as solar and wind power) can be utilized optimally. This prevents waste and increases energy efficiency.

AI algorithms can also predict energy production from renewable sources, such as wind and solar power, based on weather data. This is crucial for maintaining the stability of the power grid, which is heavily dependent on consistent supply.

The AI model can process geospatial data to detect patterns of carbon emissions that are difficult for humans to see. With the help of satellites and thermal imaging technology, AI can provide real-time monitoring of industrial areas, cities, and even ships that produce high emissions.

AI technology can be used to optimize carbon capture and storage (CCS) technology. Using machine learning models, this system can maximize the efficiency of CO₂ capture processes in high-emission manufacturing industries.

In the context of environmental preservation and conservation, AI models can be used to monitor the health of marine ecosystems, which are directly affected by rising sea temperatures and ocean acidification. AI predicts changes in coral reef populations, fish, and other marine species that are crucial to global ecosystems.

Even with the use of drone technology and satellite imagery supported by AI analysis, forest management and wildlife conservation can be improved. AI image recognition systems can detect changes in wildlife populations and large-scale forest loss caused by climate change and human activities.

In the context of disaster mitigation, deep learning algorithms can be used to improve the accuracy of early warning systems for climate-related disasters, such as storms, floods, or droughts. This is done by analyzing historical data, real-time weather, and climate projections.

In the aftermath of a disaster, AI can be used to manage disaster relief distribution and direct resources to the most affected areas. AI can analyze impact maps and optimize transportation routes for rescue teams to speed up disaster response.

Considering various facts and analysis results above, there are several big PRs that we should take note of after reading this WA story:

1. It’s time to synergize, collaborate, and initiate strategic partnerships to raise awareness about the importance of preserving and protecting the environment, by improving our consumptive, exploitative, and manipulative patterns of living.
2. Start corrective initiatives as a follow-up to the emergence of awareness about the impacts of climate change and environmental degradation, which with strategic collaboration and the implementation of technology in a timely and effective manner, we will be able to improve the current condition that is now entering the catastrophic destruction phase.
3. Focus all resources and competencies on continuous global environmental improvement efforts; regulations to govern behavior, equality and justice in access to well-being, and optimization of the use of cutting-edge technologies like AI to help accelerate innovation in the context of restoration and reactivation of environmental resilience.

Additional Reading Materials

1. IPCC. (2023). Sixth Assessment Report. Geneva: Intergovernmental Panel on Climate Change.
2. WHO. (2020). Climate Change and Health. Geneva: World Health Organization.
3. FAO. (2021). The Impact of Climate Change on Food Security. Rome: Food and Agriculture Organization.
4. World Bank. (2022). Climate Risk and Adaptation. Washington, DC: World Bank.
5. NOAA. (2021). State of the Climate: Global Climate Report. National Oceanic and Atmospheric Administration.
6. World Health Organization (2020). Climate Change and Health. Geneva: WHO.
7. Chen, H., Kwong, J. C., Copes, R., Tu, K., Villeneuve, P. J., van Donkelaar, A.,… & Hystad, P. (2017). Living near major roads and the incidence of dementia, Parkinson’s disease, and multiple sclerosis: a population-based cohort study. The Lancet, 389(10070), 718–726.
8. Norris, F. H., Friedman, M. J., Watson, P. J., Byrne, C. M., Diaz, E., & Kaniasty, K. (2002). 60,000 disaster victims speak: Part I. An empirical review of the empirical literature, 1981–2001. Psychiatry: Interpersonal and Biological Processes, 65(3), 207–239.
9. IPCC. (2023). Sixth Assessment Report. Geneva: Intergovernmental Panel on Climate Change.
10. Atwoli, L., Baqui, A. H., Benbow, N., Bhutta, Z. A., Boerma, T., Bush, A., & Cobham, V. E. (2021). Mental health during the COVID-19 pandemic: implications for global health governance. The Lancet, 398(10312), 940–942.
11. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K.,… & Bengio, Y. (2019). Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433.
12. IPCC. (2023). Sixth Assessment Report. Geneva: Intergovernmental Panel on Climate Change.
13. Chen, Y., Liu, L., Gao, Y., Zhang, Z., & Ren, S. (2020). AI and Climate Change: The Synergy between Artificial Intelligence and Climate Science. Climate Dynamics, 55(4), 1329–1345.
14. World Economic Forum (2021). Harnessing Artificial Intelligence for the Earth. Geneva: World Economic Forum.
15. U.N. Environment Programme. (2021). Artificial Intelligence in the Context of Climate Change. United Nations.
16. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Rao, K. S., et al. (2020). AI for Climate Action: Mitigating Climate Change through Artificial Intelligence and Big Data. International Journal of Environmental Research and Public Health, 17(9), 3375.

15. U.N. Environment Programme. (2021). Artificial Intelligence in the Context of Climate Change. United Nations.

16. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 7. K. S. Rao et al. (2020). AI for Climate Action: Mitigating Climate Change through Artificial Intelligence and Big Data. International Journal of Environmental Research and Public Health, 17(9), 3375.