“Sustainable development is impossible without an inclusive AI.”

Artificial intelligence, which we feel directly or indirectly in every aspect of our lives, has become an important issue for better management of environmental impacts in our Life Cycle Assessment (LCA) reporting. LCA is a methodology that assesses the environmental impacts of a product or process throughout its entire life cycle. This process covers the stages from raw material extraction through production, use and final disposal. Artificial intelligence is being used to optimize and accelerate LCA processes and achieve more accurate results. This paper explores how LCA and AI together create a synergistic effect and contribute to sustainability.

What is Life Cycle Assessment (LCA)?

It is a comprehensive analysis method used to assess the environmental performance of products, services or processes. The aim is to determine at which stages the most improvement can be made to reduce the identified environmental footprint and resource consumption. According to ISO 14040/44 series standards, it consists of the following four stages:

• Determining the Purpose and Scope: The purpose of the analysis, the unit to which the measurement results will be declared, the boundaries of the study and data quality requirements are determined at this stage.
• Inventory Analysis: Process inputs and outputs (raw materials, energy, emissions, etc.) are quantitatively evaluated according to scientific principles to ensure mass and energy balance.
• Impact Assessment: Environmental impacts are calculated using the data collected.
• Yorumlama: Sonuçlar analiz edilir çevresel etkinin yoğun geldiği sıcak noktalar tespit edilir.

Artificial Intelligence and Life Cycle Assessment

Artificial intelligence can be used in a variety of ways to improve LDD processes:

• Data Collection and Analysis: AI algorithms can analyze large amounts of data quickly and accurately. This makes it easier to collect and process the data needed for LDD.
• Forecasting and Simulation: Artificial intelligence allows LCA to predict future scenarios. This enables more sustainable decisions to be made by predicting the environmental impacts of a particular product or process. Simulations can be used to test the effects of different materials or process changes.
• Optimization: Artificial intelligence can be used to optimize LCA results. For example, optimization algorithms can be applied to determine which improvements to a production process will minimize environmental impact. This helps to reduce the use of resources and the amount of waste.
• Decision Support Systems: AI-based decision support systems can show companies which process changes will most improve sustainability by analyzing the results of the LDD. This is especially important for companies with complex supply chains.

Application Areas of RDD and Artificial Intelligence

• Manufacturing and Industry: In manufacturing processes, LDD and AI can be used to improve material efficiency, reduce energy consumption and optimize waste management.
• Agriculture: Smart agriculture applications can optimize water use, reduce the use of fertilizers and pesticides, and minimize the environmental impact of agricultural processes through the combination of WSA and AI.
• City Planning: AI-powered LDD can help plan cities in a sustainable way. This includes energy efficiency of buildings, optimization of transport networks and planning of green spaces.

Challenges of Using AI for WDD

• Data Quality: High quality data is needed for AI to work effectively. Incomplete, inaccurate or inconsistent data can negatively affect the results of IA.
• Energy Consumption: Running AI models requires high energy consumption, especially when large data sets and complex algorithms are involved. This can limit AI’s contribution to sustainability.
• Model Bias: AI models built with biased data can lead to erroneous analyses and misguidance for sustainability. Therefore, AI applications should be carefully designed and managed.

“The intersection of AI and sustainability is a complex but critical area that demands our attention. As we continue to advance in AI and beyond, we must also prioritize the sustainability of these technologies. This dual approach positions AI as a transformative force for good, enabling a more sustainable and prosperous future for all.”

Conclusion

When used together, LDD and AI can make sustainability analysis more efficient, accurate and effective. Artificial intelligence enhances the data collection, analysis, prediction and optimization processes of LCA, allowing for better management of environmental impacts. However, it is important to consider the environmental impacts of these technologies and manage them in line with sustainability goals. The integration of LDD and AI offers a powerful opportunity for collaboration for a greener and more sustainable world in the future.

Sources:

1. https://www.forbes.com/councils/forbestechcouncil/2024/05/24/putting-the-ai-in-sustainability/
2. https://www.weforum.org/agenda/2024/01/inclusive-ai-sustainable-development/