Iteration, Evidence, and Accountability: Advancing a Sustainable Approach to HRDD Research

As the project progresses, my understanding of the research domain has become more systematic, and I am increasingly able to identify the key analytical pathways through complex information. At the beginning, my focus was on collecting materials and understanding concepts. Now, I am more focused on integrating these pieces into a framework that can be executed. When team members have questions about the project scope, priorities, or execution logic, I am able to provide clarity and share the tables and models I have completed as reference templates. This made me realize that my role in the team is both driving progress and anchoring stability.

I also came to recognize that this form of stability and iteration reflects the underlying logic of sustainable investing. A sustainable system maintains continuity through consistent inputs and disciplined refinement, and the HRDD worker-voice tool we are analyzing contributes to this logic by enhancing long-term accountability and supporting continuous improvement based on verifiable evidence rather than one-time assessments. With this understanding, I view my contribution as part of the same sustainability mindset. I have consistently produced research outputs, and I continue refining the deliverables after every professor meeting or client meeting, especially the scoring model and the comparison table. I see this ongoing revision as part of the process rather than a sign of uncertainty, because the final deliverable should be supported by evidence and able to withstand scrutiny. The discipline of iteration itself reflects a sustainable approach to analysis and decision making.

The team dynamic is now much more stable compared to the beginning of the project, and stability requires consistent effort. Being patient and understanding toward teammates requires continuous input, because unexpected situations always arise. I have already adapted to the team’s rhythm, and I am gradually finding where I should contribute. I also hope to continue bringing structure when the team needs direction, while giving everyone space to complete their work independently.

Throughout the process of developing the deliverables, two readings helped refine my analytical approach.

The first reading is Transparency in ESG and the Circular Economy: Capturing Opportunities Through Data. It states that the core difficulty of ESG does not lie in the concept itself but in data. What truly matters is establishing a 'journey of data' that can be traced, verified, and used for oversight, meaning a complete data chain from the source of information to corrective action.¹ The reading explains that transparency depends on consistent data flows supported by digital infrastructure rather than narrative descriptions after the fact.¹ I believe this closely aligns with the advantage of the digital due diligence tool we are working on, because it connects information from different stages into one traceable data journey. This allows indicators to remain consistent, verifiable, and usable for oversight, and it links the data to the closed loop that the circular economy relies on. For me, this reading not only strengthened my understanding of what an evidence chain should look like but also refined my approach to organizing deliverables.

When building the scoring model, my focus shifted to evaluating whether companies are under regulatory or buyer pressure to produce verifiable HRDD evidence and whether their existing systems allow them to adopt a more cost-effective solution.¹ In the comparison table, I differentiate platforms based on whether they generate traceable remediation evidence rather than survey snapshots or grievance intake alone, which ensures my recommendations are grounded in evidence readiness rather than stated intentions.¹

The second reading, Do Commercial Ties Influence ESG Ratings?, states that when a company has commercial ties with rating agencies, its ESG score tends to increase.² Due to this upward bias, ESG ratings become less informative in predicting future ESG events.² This finding forced me to reassess how I evaluate companies. When ESG scores can improve through relationship-driven incentives, some companies have limited motivation to invest in substantiated due diligence systems, because an elevated rating already fulfils their objective.²

This helped me recognize that the ranked implementation lead list should avoid companies whose disclosure practices remain primarily narrative and instead focus on companies that must maintain business or regulatory compliance through verified evidence, such as those subject to market access requirements or required by buyers or financial institutions to submit continuous evidence.

This means our goal is not to target companies that reference ESG commitments in reports, but companies that must demonstrate actions through verifiable data. In other words, companies that rely on ratings as a proxy for performance have limited incentive to invest in evidence-based due diligence. Our target should be companies that operate under proof obligations.

These two readings helped me understand that continuous iteration does not indicate that the deliverables are insufficient. Instead, it reflects that my grasp of what constitutes substantiated evidence is becoming more rigorous. As I worked through traceable data flows, the implications of biased ESG ratings, and the construction of the ranked implementation lead list, my model and analysis became progressively more structured and precise. I have become increasingly certain that my work is grounded in evidence rather than narrative, and I can confidently take on the dual role of driving progress and ensuring stability. This project has strengthened my analytical discipline and made my approach to problem-solving more methodical and deliberate. Beyond that, it has helped me understand that sustainability is not only an investment principle. It is also a working logic that relies on consistency, iteration, verification, and long-term accountability.

References

¹ Dolan, R., & Barrero Zalles, M. Transparency in ESG and the Circular Economy: Capturing Opportunities Through Data (2021).

² Li, S., Lou, Y., & Zhang, H. Do Commercial Ties Influence ESG Ratings? (2024).