Pseudo-code:

1. Define a function Fibonacci(n) that takes an integer n as input.
2. Base Cases:

   • If n is 0, return 0.
   • If n is 1, return 1.

3. Recursive Step:

   • Otherwise, return Fibonacci(n-1) + Fibonacci(n-2).

Efficiency Improvement: The recursive approach is inefficient due to repeated calculations. A more efficient approach would be to use iteration (a loop) to calculate the Fibonacci number. This avoids redundant calculations by storing previously computed values. For example, using dynamic programming (memoization) or a simple iterative loop.

Iterative Pseudo-code (example):

1. Define a function FibonacciIterative(n) that takes an integer n as input.
2. If n is 0, return 0.
3. If n is 1, return 1.
4. Create a variable a and initialize it to 0.
5. Create a variable b and initialize it to 1.
6. For i from 2 to n:
7. Create a temporary variable c and set c to a + b.
8. Set a to b.
9. Set b to c.
10. Return b.

The use of procedures can significantly improve an algorithm's efficiency in both time and space complexity. The key is to avoid redundant code and to structure the algorithm in a way that minimizes unnecessary computations.

Time Complexity: By encapsulating repetitive tasks within procedures, we reduce the number of lines of code that need to be executed. This can lead to a reduction in the overall execution time, especially for algorithms with nested loops or complex calculations. For example, if a complex calculation is performed repeatedly within a loop, putting that calculation into a procedure avoids recomputing it each time, improving the time complexity. This is particularly relevant when dealing with algorithms that have a time complexity that scales with the input size (e.g., O(n^2)).

Space Complexity: Procedures can also improve space complexity by avoiding the creation of multiple copies of data. Instead of repeatedly creating temporary variables or data structures within a loop, a procedure can operate on the data directly, minimizing memory usage. For instance, if a procedure sorts a list in place, it avoids creating a new sorted list, which would require additional memory. However, it's important to note that procedures themselves can consume space (for local variables), so the overall impact on space complexity needs to be considered.

Example: Consider an algorithm that needs to repeatedly check if a number is prime. Instead of having the primality test code repeated for each number, a procedure can be created to perform the primality test. This avoids code duplication and ensures that the primality test is only executed once for each number.

Potential Problems:

• Lack of Reusability: The code is specific to calculating the sum of the first 'n' natural numbers. If you need to calculate the sum of a different set of numbers, you'd have to rewrite the code.
• Poor Modularity: The code is not broken down into logical units. This makes it harder to understand and maintain.
• Limited Readability: While relatively simple, the code lacks a descriptive name and doesn't clearly communicate its purpose beyond the calculation itself.

How a procedure could improve it: A procedure could encapsulate the entire calculation logic, making it reusable and more modular. The procedure would accept 'n' as a parameter and perform the summation. This would improve readability and allow the code to be easily used in other parts of a program.

Example of improved code (Conceptual - language agnostic):

PROCEDURE CalculateSum(n)
sum = 0
FOR i = 1 TO n
sum = sum + i
END FOR
RETURN sum
END PROCEDURE
BEGIN
result = CalculateSum(10)  // Example: Calculate the sum of the first 10 natural numbers
PRINT result
END

This improved code defines a procedure CalculateSum that takes 'n' as input and returns the sum. The main part of the program then calls this procedure with the desired value of 'n'.