Write a Python function that receives a list of dictionaries, each containing an energy consumption value in kWh and its source type (e.g., "grid", "renewable"), and returns the total CO₂ emissions in metric tons using the appropriate emission factor for each source.

CIRCLES framework: Clarify inputs, Identify scope, Review data types, Calculate emissions, Communicate results, Evaluate edge cases, Summarize solution. Step‑by‑step: 1) Define emission factors dictionary; 2) Validate list and dict keys; 3) Iterate or vectorize to multiply kWh by factor; 4) Sum results; 5) Convert kWh to metric tons; 6) Return float.

To solve this, I first defined a dictionary mapping source types to emission factors (kg CO₂/kWh). I validated the input list to ensure each element is a dictionary with required keys. Using a list comprehension, I multiplied each consumption value by its corresponding factor, handling missing or unknown sources by logging a warning and defaulting to a neutral factor. I summed the weighted emissions, converted the total from kg to metric tons, and returned the result as a float. I included unit tests covering typical, edge, and error cases, and documented the function with type hints and docstrings for maintainability. This approach ensures accurate, scalable, and auditable emissions calculations.