AI Scenario Examples
- Limited accuracy in climate change forecasting and future scenario modelling
Problem
Classical methods for predicting climate change are based on historical data, mathematical models and physical principles. Their predictions are often inaccurate. This can lead to wrong strategic decisions, globally and locally, within one country or another.
Potential Solutions
- AI can process massive climate datasets to improve forecasting, model environmental impact, and help governments and businesses plan adaptation strategies, addressing climate change proactively
- AI can improve existing General Circulation Models by identifying patterns and optimizing simulations
- Machine learning techniques, such as Deep Learning, can refine predictions by analyzing vast amount of historical and real-time data.
Example
Jupiter Intelligence provides climate risk analytics for businesses and governments, using AI to predict the impact of climate-related events like floods, fires, and storms, helping organizations make better resilience plans.
- Inefficient use of water, fertilizers, and pesticides in agriculture, leading to resource waste and environmental harm
Problem
Many countries are experiencing a shortage of water for agriculture. At the same time, the available water is contaminated by fertilizers and plant protection products.
Potential Solutions
- By analyzing soil and weather data, AI can enable precision farming practices, optimizing water and pesticide use, reducing waste, and increasing crop yields, all of which contribute to sustainable agriculture.
- Agronomists use phenocalendars to plan the life cycle activities of a particular agricultural crop. AI can help generate phenocalendars for the activities that farmers need to perform. AI can advise farmers on the right amounts of water, fertilizers and preparations and on how to treat plants.
Example
Taranis uses AI and drone imaging to identify crop diseases, pests, and nutrient deficiencies, enabling farmers to manage fields more precisely, reduce pesticide use, and increase yield.
- High energy waste in residential and commercial buildings:
Problem
Residential and commercial buildings are heated, cooled, and illuminated in an inefficient manner. This often applies to older buildings.
Potential Solutions
- AI systems can optimize energy use in buildings and grids, adaptively controlling heating, cooling, and lighting to reduce emissions and integrate renewable sources seamlessly.
- IoT платформа, свързва домашни уреди като климатици, бойлери и локално отопление и събира данни от сензорите на тези уреди. AI анализира начина, по който потребителите използват тези уреди. На тази база AI създава и внедрява индивидуализирани програми за управление на тези home appliances.
BrainBox AI: Analyzes data from building systems to predict and adjust energy use, providing smart climate control that can reduce energy costs and emissions by up to 25%.
- Real-time tracking and management of carbon emissions across industries and supply chains
Problem
The European Green Deal aims to transform the EU into a fair and prosperous society with a resource-efficient and competitive economy with minimal greenhouse gas emissions. Businesses will be required to measure, evaluate and report emissions associated with the production of their products. This includes both own emissions and emissions related to the production and delivery of the products purchased.
Potential Solutions
- Through real-time tracking and analysis of carbon footprints, AI can help companies identify high-emission areas and transition toward greener alternatives.
- Software to estimate the emissions associated with the production of specific products. This software can be an upgrade of existing ERP systems (Enterprise Resource Planning systems).
- Especially for the transport industry – expansion of the functionalities of GPS navigation and fleet management systems with emissions reporting for the delivery of products between two points.
Example
Watershed offers a platform for companies to measure, reduce, and report carbon emissions in real-time. Watershed uses AI to analyze emission sources and helps companies devise reduction strategies.
- Designing products and processes that align with circular economy principles
Problem
The circular economy requires a rethinking of product lifecycle activities
Potential Solutions
- AI can support the transition to a circular economy by analyzing product life cycles and suggesting sustainable alternatives in design, manufacturing, and reuse
- Development of Product Lifecycle Management (PLM) systems or upgrading of existing PLM-systems so that appropriate circular economy measures are implemented at each stage of the product lifecycle.
- For specific industries (verticals), development of advisory systems to implement the principles of the circular economy in the design of technology and production processes.
Example
Rheaply: Uses AI and machine learning to help organizations in sectors like healthcare, education, and tech manage and repurpose surplus inventory and resources. The platform connects companies with excess materials to those who need them, minimizing waste and supporting circular economy goals.