AI-Based Carbon Emission Monitoring And Penalty System For Industrial Pollution Control

Industrial growth has been accompanied by a steady rise in carbon dioxide and other hazardous emissions, with the industrial sector directly responsible for roughly a fifth to a quarter of global greenhouse gas (GHG) emissions. Current emission accounting largely depends on voluntary self-reporting by companies, which is difficult to verify and easy to under-report. This paper proposes a comprehensive framework in which Artificial Intelligence (AI) is used to independently estimate and cross-verify the carbon emissions of individual companies by fusing satellite/remote-sensing data, Internet of Things (IoT)-based continuous emission monitors, and company activity data, following the GHG Protocol's Scope 1/2/3 classification. Based on the verified emission intensity of each company relative to a sector-specific benchmark, a tiered financial penalty is proposed, modelled loosely on existing mechanisms such as the European Union Emissions Trading System and India's Carbon Credit Trading Scheme. The paper further proposes that the computed emission data be compiled into a public annual report released by the government, so that regulators, investors and citizens can hold polluting industries accountable. Experimental simulation indicates a coefficient-of-determination (R²) cross-verification fidelity, demonstrating strong agreement with the reference data across the evaluated industrial sectors. The framework is intended to make industrial pollution measurement more transparent, harder to falsify, and to create a financial incentive for industries to adopt cleaner technology.