A Survey of Environmental Performance Enhancement Strategies and Building Data Capturing Techniques in the Nigerian Context

The need to improve the performance of Nigeria’s office buildings is due to, energy challenges, increasing population, changing user needs, and climate change. With the expansion of several Nigerian cities, existing buildings constitute a significant portion of the building stock, and improving thei...

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Bibliographic Details
Main Authors: Osamudiamen Meek Omoragbon, Sura Al-Maiyah, Paul Coates
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Buildings
Subjects:
Online Access:https://www.mdpi.com/2075-5309/13/2/452
Description
Summary:The need to improve the performance of Nigeria’s office buildings is due to, energy challenges, increasing population, changing user needs, and climate change. With the expansion of several Nigerian cities, existing buildings constitute a significant portion of the building stock, and improving their environmental performance could be more cost-effective than reconstruction. The use of simulation packages to assess alternative retrofitting enhancement scenarios is a straightforward approach. However, in Nigeria it is often challenging to get appropriate information to facilitate this type of evaluation; many buildings were not built to their original specifications, and when available, the records are often in a poor state due to deterioration. Studies that aimed at enhancing a building’s performance hardly stated the acquisition of the required building information. This paper investigates current practices and future possibilities of improvement measures and data capturing of existing buildings using a questionnaire survey of 133 building professionals in Benin City. The inter-relationship between energy efficiency, the environment, and building design with a high potential for meaningful retrofit to mitigate energy inefficiencies is known but not fully utilized. The collected thought on current practices signifies the need for developing a more economical and reliable methodology for data capturing and evaluation.
ISSN:2075-5309