28 August 2026
Moongyung Lee (Blue Dot Network), Daragh McQuaid (Blue Dot Network), Ely Sandler (Harvard University)
Global infrastructure investment needs are estimated at around USD 106 trillion through 2040 (McKinsey Global Institute, 2025), with EMDEs alone facing an annual financing gap of approximately USD 3 trillion (OECD, 2025). Private capital will be essential to closing this gap, yet investment remains concentrated in a relatively narrow group of lower-risk, middle-income markets. Mobilisation is constrained in part by high perceived risks, limited project-level information and significant due-diligence costs. The Blue Dot Network (BDN) seeks to address these barriers by certifying infrastructure projects against internationally recognised standards of quality, sustainability, transparency and governance. In support of the newly established ECG Private Capital Working Group, this forthcoming paper, developed jointly with Harvard University, examines whether the characteristics recognised through BDN certification can contribute to improved financing terms and a lower cost of capital, while recognising that certification alone does not make a project investable.
A key input is the proprietary, anonymised transaction-level dataset developed by Sandler et al. (2026), which contains actual financing terms for 744 infrastructure projects across 61 countries. This provides a rare empirical basis for comparison in a market where loan pricing, tenors and other deal terms are seldom disclosed. The analysis indicates that more than one-third of the observed variation in EMDE infrastructure financing costs is associated with project-specific factors rather than country or sector alone. These factors include preparation quality, risk allocation, procurement credibility and revenue structure—all areas covered by BDN certification. When such information is difficult to verify or compare, investors may rely more heavily on broad country and sector assessments, leaving genuine differences in project quality unrecognised in financing terms.
Because the dataset is anonymised, individual projects cannot be identified or used as direct comparators. Instead, the paper uses the wider dataset to estimate the financing terms expected for a project with similar characteristics and assesses whether each BDN project’s financing costs fall above, within or below the model-implied range. Case studies complement this quantitative analysis by examining how the preparation, governance, risk allocation and financing structures of individual BDN projects may help explain the observed outcomes. Together, these approaches move the discussion beyond the theoretical value of certification towards an initial market-based assessment of whether greater transparency and independent verification can support more differentiated project-level risk assessment.
Only a small number of BDN-certified projects currently have sufficiently detailed financing data available. The comparisons are therefore indicative and cannot establish a general or causal “BDN effect” on financing costs. The paper’s immediate contribution is to establish a working methodology and market reference point that can be strengthened as the BDN portfolio grows and more consistent financial data become available. The methodology will be applied to additional projects and refined through engagement with private investors. Over time, it could help funds compare opportunities across countries and sectors, distinguish project-specific pricing from broader market effects, and make differences in project quality more visible in a market where they have often remained difficult to assess.