4209240619223291eed161120240925061603220 beie:beie director@blueeyesintelligence.orgWEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 09 30 2024 13 3 Department of Estate Management and valuation, Akanu Ibiam Federal Polytechnic, Unwana-Afikpo, Nigeria. S. Ziiweritin https://orcid.org/0000-0003-1530-3293 I.D. Waheed Department of computer science, University of Portharcourt, Nigeria. Explainable AI(XAI) is a revolutionary concept in artificial intelligence that supports professionals in creating trust between people in the decisions of learning models. Greenhouse gases created in the atmosphere is driving our weather to become more irregular and intense. This endangers human health, affects crops and plants. XAI techniques remain popular, but they cannot disclose system behavior in a way that promotes analysis. Predicting GHG emissions and their impact on human health is an important aspect of monitoring emission rates by industries and other sectors. However, a handful of investigations have being used to examine the collective effect of industries such as construction, transportation, CO2, and others on emission patterns. This research tackles a knowledge vacuum by offering an explainable machine learning model. This framework employed a random forest classifier combined with two different explainable AI methodologies to give insights into the viability of the proposed learning model. The goal is to use XAI in determining the impact of GHG emissions on humans and its environment. A quantitative survey was carried out to investigate the possibilities of determining GHG emission rates more explainable. We created a random forest model, trained on GHG emission data using SHAP and LIME techniques. This was helpful in providing local and global explanations on model sample order by similarity, output value, and original sample ranking. The model resulted in high accuracy and enhanced interpretability with XAI, allowing decision makers comprehend what the AI system truly tells us. LIME exceeded SHAP in terms of comprehension, and satisfaction. In terms of trustworthiness, SHAP surpassed LIME. 09 30 2024 7 14 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE): https://www.ijrte.org/ Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The dataset comprising GHG emissions and the source is provided above: https://catalog.data.gov/dataset/?organization=epa-gov& res_format=EXCEL The authorship of this article is contributed equally to all participating individuals. 10.35940/ijrte.C8140.13030924 https://www.ijrte.org/portfolio-item/C814013030924/