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Introduction
Carbon emissions from industrial and energy sectors present a pressing challenge to environmental sustainability, human health, and economic stability [1]. The rapid expansion of industries and urbanization has heightened the urgency for effective pollution control, comprehensive analysis, and precise predictive modeling to develop strategic mitigation policies [2]. The electricity and service sectors contribute significantly to environmental degradation by releasing large amounts of greenhouse gases and other pollutants, accelerating climate change and global warming [3]. Several studies have examined contamination trends and their environmental impact. However, most focus on specific regions or sectors, leaving a gap in the global analysis and predictive modeling [4]. This study aims to fill this gap by analyzing a massive pollution data entity, including countries and sectors of industry, to identify ample sources of pollution and predict its future trends [5]. A thorough appraisal of temporal variations in pollution between nations and industries is provided through advanced statistical tools coupled with automatic learning models [6]. It has the dual objectives of identifying the countries and sectors with the highest annual contamination implicatures and projecting contamination trends for the ensuing 3 years using automatic learning techniques [7]. The famous applications of FNN networks in the field have been due to their effective modeling of very complex data sets along with learning long-term patterns [8]. Models provide accuracy in forecasting pollution levels, an area that one might use to make policies beneficial for formulating sound environmental policies and regulatory frameworks [9]. This study uses a variety of statistical evaluation methods for predictive analysis, including the mean absolute error (MAE) and the mean square error (RMSE), for model precision and reliability [9]. A classification model categorizes pollution levels into low, medium, and high based on metrics such as precision, precision-recall, and F1 score [10]. By coupling automated learning with the temporal-series analysis, this research widens the scope of environmental analysis while highlighting the significance of decision-based interventions toward climate resilience and sustainable development [11]. The findings of this research will enhance the understanding of global pollution patterns, thus creating a resource for well-planned monitoring and evaluation strategies. The above ideas would be useful for supporting national and international efforts for pollution control as well as achieving long-term environmental sustainability [12, 13, 14, 15–16].