Over the last decade, business corporations and UN/Yale University have made various levels of commitments and investments for sustainable development and environmental tracking and reporting on the progress towards their goals. At the global level, the Global Reporting Initiative (GRI) tracks corporations. UN tracks its progress towards its country 2030 Sustainable Development Goals (SDGs). Yale University, through its Environmental Performance Index (EPI), tracks some 180 countries’ environmental issues.

This research proposes a combined model for utilizing modern technological tools, Blockchain and Artificial Intelligence (AI), to better track the accuracy of the collected data and the ability to predict the goals for the future. This new process will significantly help towards the achievement of sustainability goals at a lower cost.

Organizations such as GRI can implement a reporting framework for annual Corporate Social Responsibility Reports (CSR) with built-in Blockchain technology, where the data is immutable and traceable. This process and outcome are key to investors’ confidence in sustainability index funds (e.g., ESG funds). Countries can use blockchain technology for SDGs and EPI data to increase reliability. This step could also benefit the integration of corporate and country-level data to achieve corporate and country goals.

The proposed model includes IBM Blockchain technology, IBM’s LLM offering ‘watosonx.ai,’ and IBM’s ‘Learning AI Agent.’ This combined IBM architecture can collaborate well with individual corporations’ data center applications and country-level data centers to ensure accurate data tracking, immutable data storage, and predictive data analysis. This data analytics model helps with a more qualitative and quantitative outcome for all. Data Analysis and prediction can be continuous rather than annual. The reporting and analysis will be more effective (doing it right) and efficient (more cost-effective).

Many aspects of AI Agents are under research, and their real application will expand. AI agents will focus on a task execution, choosing the best outcome given the goals. With the noted above approach, AI Agents are expected to be more cost-effective since corporations or non-profits do not have to invest in large computer infrastructure, including LLM models.

Index terms: Sustainability, Blockchain, Artificial Intelligence, Architecture, AI Agent, Data Center