Leveraging Green Technology Systems for Green Accounting and Environmental Auditing with AI moderating
Annotatsiya
With such accelerating ecological degradation and potential cross-sectoral impact, environmental accountability is a significant compliance issue in sustainable development frameworks. The purpose of this paper is to present and validate composite indicators used for environmental auditing in green accounting systems, which are associated with relatively high relevance to the policy environment and possible investment trajectories. Here, we estimate, based on AI-assisted regression and AHP-derived diagnostic measures, the time-specific and spatially estimated patterns of the green performance index [moderated effects] of AI integration – between short- and medium-term depth – at unique enterprise nodes along this accountability continuum (regulatory thresholds). We also analyzed real-time metrics collected from green technology systems, which suggest significant transitional variance. Data from four key operational sectors of different infrastructure capacities (urban, semi-urban, industrial, and agro-regional) were used for the development of the forecasting model and for the comparison of index outputs generated using AI-driven techniques to outputs generated by different conventional benchmarking tools (metrics from manual audit trails, values from baseline regression, and scores from AHP matrices). By fitting the regression models with time-series performance data for the organizations in each sector, the normalized index number (NIG) was estimated to be 7.43, 8.16, and 9.05 which are greater than industry baseline thresholds. The major impact of this research is that those dimensions of environmental resilience defined by non-digital audit schemes will have limited capability to maintain their data quality and to keep pace with current standards of green accountability, thus posing a barrier to compliance, potentially leading to investment withdrawal.