Enabling more effective and informed decision-making for businesses and stakeholders in aligning their strategies with sustainability goals and regulatory compliance using Machine Learning. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

The complexity and volume of climate-related data pose significant challenges in conducting accurate Environmental, Social, and Governance (ESG) risk assessments. The impact of the challenges in accurately assessing Environmental, Social, and Governance (ESG) risks due to the complexity and volume of climate-related data is profound and multifaceted. Firstly, it significantly affects business strategy and compliance, leading to potential misalignments with sustainability goals and risks of regulatory non-compliance. This misalignment can result in missed market opportunities and legal penalties. In the realm of environmental and social governance, inaccurate ESG assessments can lead to poor risk management and investment efficiencies. This impacts not only the environment but also the financial sector, as investors rely on these assessments for informed decision-making.

Furthermore, the implications for policy development are considerable. Policymakers depend on reliable data to formulate effective environmental regulations. Inaccuracies can result in ineffective policies that fail to address key issues, hindering progress toward global sustainability goals like the United Nations Sustainable Development Goals (SDGs). Public awareness and engagement are also impacted. Misinformation due to inaccurate data can lead to public apathy or misguided actions, while stakeholder mistrust can grow if environmental risks are perceived to be inaccurately reported.

The technological and scientific research sectors are equally affected. Data fragmentation and lack of comprehensive information impede innovation and lead to gaps in scientific understanding. This hinders the development of new environmental technologies and sustainable practices. Economically, the repercussions include market volatility and the high cost of inaction, as the failure to effectively address environmental risks can lead to substantial economic losses.

Lastly, the global and regional impacts are significant. The lack of accurate data can disproportionately affect regions more vulnerable to climate change, contributing to global environmental degradation. This exacerbates issues like climate change, biodiversity loss, and pollution, affecting the global community and leading to a pressing need for a comprehensive and accurate ESG risk assessment platform. This platform is a technological necessity and a crucial step towards a sustainable and resilient future.

This project seeks to overcome these challenges by developing a platform focused on the collection, structuring, aggregation, and processing of such data. The visioned platform aims to streamline the process by providing a centralized, structured, and efficient approach to data handling. The goal is to enhance the quality trand accessibility of information, thereby supporting better decision-making in environmental sustainability and governance.

The project goals

The primary goal is to create a reliable and efficient GIS-referenced data platform that enhances the accuracy and accessibility of ESG risk assessments across Europe. The platform will focus on the European region, primarily in English, and will involve the identification and integration of both existing and new data sources. The ultimate aim is to replicate the outcome of this iteration for other regions. 

The objectives of this Omdena-Valutus Challenge are:

• The project will focus on the collection and processing of climate-related data pertinent to ESG risks.
• The geographical scope will primarily cover European regions, with content predominantly in English.
• The project will involve the identification and integration of both existing and emerging data sources.
• Key activities will include data cleaning, processing, georeferencing, and structuring.
• The platform will gather a variety of climate change-driven data variables, including temperature changes, wet bulb temperature, wildfire risk, precipitation changes, river flooding, sea level rise, storm surge, and wind risk.
• Development of a robust database containing structured, climate-related data.
• Implementation of a georeferenced system within the database for enhanced spatial analysis.
• Creation of an automated pipeline for continuous data collection, processing, and structuring.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works