Almotasem Al Huniti

Type of the article: Research Article

Abstract
Artificial intelligence is diffusing across Gulf Cooperation Council insurance markets, yet disclosure-based evidence remains fragmented on whether adoption is associated with organizational change or localized automation. This study examines a purposive disclosure-based sample of 120 insurers from Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain. Because inclusion required sufficient disclosure of artificial intelligence practices, the sample is not intended to represent the insurance market. The study examines whether disclosed artificial intelligence adoption is associated with organizational change through financial transparency and operational efficiency. The dataset is constructed from annual reports, audited financial statements, governance reports, environmental, social, and governance reports, investor materials, and regulatory documents. Documents from 2017 to 2023 are treated as an observation window, coded at the item level, and aggregated into one firm-level score per insurer for cross-sectional structural equation modeling. Results indicate positive associations from artificial intelligence adoption to financial transparency (β = 0.52, p < 0.001) and operational efficiency (β = 0.49, p < 0.001). Financial transparency (β = 0.41, p = 0.003) and operational efficiency (β = 0.38, p = 0.012) are associated with organizational change. The indirect paths through transparency and efficiency are statistically distinguishable from zero within the model. Because all variables are derived from similar disclosure evidence, the pattern is interpreted as disclosure co-patterning rather than proof of a mechanism. The findings are associational, not causal, representative, or longitudinal.

Type of the article: Research Article

Abstract
Municipal waste-to-energy is often assessed through technical efficiency, energy output, or regulatory compliance. Yet, these indicators do not show whether plants create positive social welfare after operating costs and environmental damages are monetized. This study examines how operational performance, emissions intensity, and stricter environmental valuation shape the net social benefit of German municipal waste-to-energy plants from 2017 to 2023. Germany serves as a benchmark case because its mature waste-to-energy sector, European emissions regulation, and public environmental and energy-market reporting enable transparent public-record welfare assessment. The study constructs a plant-year analytical dataset for 70 facilities from publicly accessible administrative, environmental, market, and technical records, with net social benefit treated as a constructed welfare-accounting measure based on observed records, documented public proxies, and explicit valuation assumptions. It uses plant- and year-fixed-effects models, valuation sensitivity tests, and an optimization-based decision-support layer. The descriptive evidence shows substantial welfare heterogeneity, with a mean traceable-baseline net social benefit of 17.9 euros per metric ton under the central valuation case and lower mean welfare under higher shadow prices. The fixed-effects results do not provide statistical support for the hypothesized operational drivers in the public-data panel: energy recovery, emissions intensity, availability, oxygen instability, and the interaction between emissions intensity and the shadow price index are not statistically significant. The study contributes by integrating cost accounting, externality valuation, and operational performance into a transparent public-record welfare metric for policy appraisal.