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Climate Change Solutions: From Carbon Capture to Renewable Energy

Climate Change Solutions: From Carbon Capture to Renewable Energy

Ibrahim Ismail Elnafat

Climate Change is a pressing global issue, driven by increasing greenhouse gas emissions, particularly carbon dioxide (CO2), resulting from human activities. This demands urgent action without fundamentally altering how we generate and utilize energy, and safeguarding the environment.

The world population is predicted to reach about 9.9 billion by 2050, (IISD, 2020), with over 80% increase in energy demand (IEA, 2020). Countries worldwide are increasingly adopting alternate energy sources, transitioning to Renewable Energy such as solar, wind, hydroelectric, and geothermal power, by storing or repurposing captured carbon, to reduce reliance on fossil fuels. (Hassan et al., 2024).

Carbon Capture and Storage (CCS), Potentials, and Challenges.

Carbon Capture and Storage (CCS) is a technology designed to reduce CO2 emissions from industrial processes and power plants by capturing CO2 at the source and storing it underground. The captured CO2 is then transported to storage sites in geological formations where it can be securely sequestered. This process provides a pragmatic solution for transitioning to low-carbon economies without entirely dismantling current energy systems. The CCS technology has demonstrated considerable potential in reducing global CO2 emissions by at least 19%, by 2050. (IEA, 2020).

Despite its potential, CCS faces several challenges, especially of high cost of capturing and storing the CO2, which is estimated at $50 to $100 per ton. The process also requires extensive infrastructure for transportation and storage, posing logistical challenges. (Global CCS Institute, 2021). These included concerns over the long-term security of the stored CO2, with the risk of leakage potentially undermining its overall benefits.

Transitioning Of Renewable Energy Sources And Its Impact On Climate Change.

Renewable energy sources, such as solar, wind, and hydropower, harness natural processes to generate electricity without producing greenhouse gases. The effectiveness of renewable energy in mitigating climate change is well-documented, and these sources are pivotal to transitioning towards a sustainable and low-carbon energy system.

For instance, a study by Jacobson et al. (2017) demonstrated that transitioning to 100% renewable energy could significantly reduce global CO2 emissions, decrease air pollution, and improve public health. 

There is also exponential growth in the scalability of renewable energy, driven by declining costs and technological advancements. Although the cost of solar photovoltaics (PV) has declined by 89% since 2010, while the onshore wind energy costs have fallen by 69% during the same period (LCOE, 2023), the intermittent nature of solar and wind energy is still discussed as a major concern, raised by reliability issues.

In contrast, energy storage solutions, such as batteries have been presented to address this challenge, but somewhat add up to the overall cost and complexity of the renewable energy systems. The environmental impact of manufacturing and disposing of renewable energy infrastructure, such as solar panels and wind turbines, cannot also be overlooked.

Integration of Carbon Capture & Storage (CCS) and Renewable Energy.

Carbon Capture & Storage (CCS) provides a reliable backup for intermittent renewable energy sources, ensuring a steady supply of low-carbon electricity. Conversely, the growth of renewable energy can reduce the overall demand for CCS, making it more feasible and cost-effective to deploy in sectors where emissions are hardest to abate, such as heavy industry and aviation.

Artificial Intelligence in Energy Efficiency.

The global energy sector often faced with increasing challenges and rising demands, as well as efficiency concerns and lack of necessary analytics for effective management.

AI-driven solutions can transform energy operations across numerous industries and assist in addressing complex challenges in various sectors, including education, agriculture, healthcare, energy, and manufacturing (EMRC, 2023).

In smart grids, for instance, AI-driven Sensors and Real-time data analysis can be used to integrate renewable energy sources into existing energy infrastructure to balance supply and demand. This allows consumers to adjust their energy usage based on real-time pricing or grid conditions.

Use Of AI To Harness and Optimize Clean Energy Sources.

1. Machine Learning Algorithms: AI can analyze vast amount of data with speed and precision using weather patterns, generation, consumption, and equipment performance to make real-time decisions based on its predictive capabilities. For instance, AI models can be used to maximize energy capture by automating and adjusting the solar panels or the pitch of the wind turbine blades.

2. Weather Forecasting: AI learning models can forecast solar and wind energy generation accurately based on historical data and meteorological factors to enable grid operators to anticipate fluctuations in the energy supply. This is essential in efficient energy transition and stability without much energy wastage.

3. Energy Production and Storage Optimization: AI in renewable energy empower systems to learn from data, adapt, and improve over time to coordinate energy generation, storage, and distribution. AI enhances the storage of excess energy generated during peak periods to be used when the generation is extremely low, enhancing resilience and reliability. For example, In microgrid management, AI can be used to analyze energy consumption patterns to identify areas for energy savings.

Policy, Investment, and Economic Considerations.

Effective policy frameworks and economic incentives are crucial to the successful deployment of both AI and CCS. Governments play a vital role in creating an enabling environment through regulations, subsidies, and carbon pricing mechanisms. The European Union's Emissions Trading System (EU ETS) is an example of a successful policy initiative that has incentivized the adoption of low-carbon technologies, including CCS and renewable energy (European Commission, 2020).

There is also need for international cooperation to bridge the gap between regions with varying levels of technological advancement and economic development. Developed countries need to support developing nations by providing financial aid, technology transfer, and capacity-building initiatives.

Summary 

Carbon capture and renewable energy are two critical solutions with the potential to significantly reduce global greenhouse gas emissions. As the world embraces renewable energy to combat climate change, AI emerges as a significant vehicle to achieve a greener and more sustainable energy future.

While the potential benefits of AI in the energy sector are undeniably compelling, if supported by effective policies and international cooperation; their integration to anticipate ecological consequences will enable stakeholders to make informed decisions that minimize harm to ecosystems and wildlife.

The integration of Artificial Intelligence (AI) into the renewable energy sector can empower energy companies with unprecedented precision and efficiency. This, not only benefits energy providers by enhancing productivity, but also makes a substantial contribution to global efforts to combat climate change.

 

References:

  • Attanayake K, Wickramage I, Samarasinghe U, Ranmini Y, Ehalapitiya S, et al. (2024) Renewable energy as a solution to climate change: Insights from a comprehensive study across nations. PLOS ONE 19(6): e0299807. https://doi.org/10.1371/journal.pone.0299807

  • Jacobson, M. Z., et al. (2017). 100% Clean and Renewable Wind, Water, and Sunlight All-Sector Energy Roadmaps for 139 Countries of the World. *Joule*, *1*(1), 108-121. https://doi.org/10.1016/j.joule.2017.07.005

  • Qusay Hassan, Patrik Viktor, Tariq J. Al-Musawi, Bashar Mahmood Ali, Sameer Algburi, Haitham M. Alzoubi, Ali Khudhair Al-Jiboory, Aws Zuhair Sameen, Hayder M. Salman, Marek Jaszczur. (2024).

Ibrahim Ismail Elnafat
Ibrahim Ismail Elnafat

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