Inspiration The idea for this project emerged from a desire to combine sustainability with actionable insights for renewable energy projects. With ESG (Environmental, Social, and Governance) factors becoming a cornerstone of modern decision-making, we aimed to build a tool that bridges the gap between environmental goals and local community needs. The growing demand for renewable energy and the challenges associated with finding optimal project locations inspired us to create a robust and dynamic scoring framework.
What We Learned Throughout this journey, we gained valuable insights into data-driven decision-making, particularly in geospatial analysis, sentiment analysis, and economic evaluation. We delved into the complexities of integrating diverse datasets like soil properties, biodiversity information, and social media trends. Beyond technical learning, we developed a deeper understanding of the interconnectedness of ESG factors and how local engagement shapes project success.
How We Built It We built this project using a modular approach: 1. Data Gathering: Tools like GoogleScrapper.py and get_bio_diversity_data.py were used to extract biodiversity and sentiment insights. 2. Data Analysis: Custom scripts such as LatestSoilData.py and WindSpeed.py provided environmental and location-specific data. 3. Core Calculation: The heart of the system, implemented in esg_calculator.py, dynamically computes ESG scores based on predefined metrics. 4. Summarization and Visualization: Using TextSummrizer.py, we distilled findings into actionable insights for stakeholders.
Challenges Faced The biggest challenges included harmonizing disparate data formats, ensuring real-time recalculations, and accounting for dynamic local factors like sentiment trends. We also encountered technical hurdles in integrating multiple tools and optimizing computation speed for large datasets.
Built With
- llama
- python
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