Kingdom University

This study presents a localized sustainability assessment of autonomous battery electric vehicles (BEVs) in Saudi Arabian urban environments using a monetized external cost framework that covers accidents, congestion, emissions, urban land use, and noise exposure. The study contributes theoretically by extending AV externality modeling to a Gulf-specific context and practically by providing a policy-ready framework aligned with Saudi Vision 2030. Using 2023-2024 national datasets from the Ministry of Transport, the General Authority for Statistics, and the Saudi Energy Efficiency Center within a three-pillar evaluation model (environmental, economic, and social), we find that autonomous BEVs reduce total external costs by 1.138 SAR per vehicle-kilometer (39.4%) compared with human-driven BEVs, with reductions of 55% in accident costs, 40% in land-use costs, and 25% in congestion costs. In the baseline BEV-to-BEV comparison, fuel-combustion emissions are zero and grid-electricity emissions are held constant, so emissions show no change; environmental gains increase under cleaner-grid scenarios. A dynamic microscopic simulation on Prince Mohammed Bin Salman Road in Riyadh corroborates the reductions through lower delays and more stable flow. The framework is parameterized by automation levels and market penetration and supports policy recommendations including data-sharing frameworks and parking and curb management reforms for high-density Saudi cities.

Autonomous battery electric vehicles (BEVs) have the potential to reshape urban mobility systems, yet their sustainability impacts remain underexplored in Gulf-region cities where traffic dynamics, land-use structures, and environmental conditions differ substantially from Western contexts. This study introduces a Saudi-specific assessment framework that integrates monetised externalities with empirically calibrated traffic dynamics to evaluate how automation influences safety, congestion, land use, emissions, and noise. To the best of our knowledge, this is the first Riyadh-calibrated monetised external-cost evaluation of autonomous BEVs that couples externality valuation with simulation-validated time-varying traffic dynamics (SAR per vkm and SAR per pkm), enabling realistic peak-period sustainability assessment. The framework's key contribution is linking external-cost modelling with spatiotemporal traffic behaviour derived from Riyadh's 2023 mobility patterns, providing a more realistic basis for sustainability evaluation. Using national datasets from transport, energy, and statistical authorities, the model estimates substantial reductions in external costs when transitioning from human-driven to autonomous BEVs, driven primarily by lower crash exposure and smoother traffic flow. To validate these findings under real operating conditions, a dynamic analysis incorporating hourly and seasonal traffic variability was developed, revealing that automation delivers its strongest improvements during peak-demand periods where congestion externalities are highest. The integrated results demonstrate the relevance of autonomous BEVs for dense rapidly growing Saudi cities and provide actionable insights for future mobility planning. The study highlights the policy importance of coordinated transport, land-use, and energy strategies to ensure that automation contributes meaningfully to national sustainability goals under Vision 2030.

In light of rising ecological challenges, the debate surrounding the digital economy and globalization in climate change has become prominent in formulating stringent environmental and sustainable policies. The current research debates environmental outcomes by analyzing the nexus between climate change, digital economy, globalization, industrial value addition, and urbanization in the top-10 digital economies. We use extensive empirical and econometric analysis to confirm the inverted U-shaped EKC hypothesis for gross domestic product and further report that the digital economy is integral to combating climate change. However, globalization, industrial value addition, and urbanization degrade ecological sustainability. Our strong theoretical and empirical analysis allows the current study to report novel policy suggestions aimed at promoting digital economic development and environmental sustainability.

It is commonly accepted that vegetation plays an important role in climatic studies conducted at local, national, and international scales. The aim of this study is to examine the cooling effects of tree species in the cities and to reveal how they affect the microclimate in & Idot;zzetpa & scedil;a Neighborhood of Elaz & imath;& gbreve; province of Turkiye. This study, which was conducted by purchasing ENVI-met 5.6.1 microclimate software, aimed to create the most appropriate microclimate scenarios in order to mitigate the urban heat island (UHI). Among the nine scenarios in which different tree species were used; the greatest cooling effect was obtained from the scenario where Acer platanoides L. was used. It was determined that the air temperature dropped by 0.8 degrees C compared to the base scenario and by 3.0 degrees C compared to the scenario in which a tree cover was not used. The lowest cooling effect was detected in the scenarios where Pinus sylvestris L. and Abies cilicica Carr. were used. In general, it was observed that there was no significant temperature decrease in the scenarios where coniferous trees were used. In scenarios where deciduous trees were used, more temperature decreases were detected compared to the coniferous trees. According to the winter simulation results of these scenarios, the daily average air temperature values vary between -0.6 and +0.1 degrees C compared to the base scenario. In the scenario where Acer platanoides L. was used, where the highest cooling effect was observed, the highest relative humidity rate and the lowest Tmrt value were determined. Evaluating the cooling effect of high vegetation on a species basis in reducing the UHI effect as a basis for planning in urban areas will constitute a key strategy in improving the UHI effect. It is envisaged that this study may provide a solution to help reduce the UHI in studies to be carried out in urban areas.

This paper evaluates the macroeconomic and financial determinants of the money demand of India from 1996: Q1 to 2024: Q4. The paper utilizes the Autoregressive Distributed Lags (ARDL) bounds testing framework and an Error Correction Model (ECM) to estimate the long-run equilibrium and the short-run dynamics of monetary aggregates, narrow money (M1) and broad money (M3). The empirical findings confirm a stable, singularly cointegrated relationship between real money balances (M1, M3), real income (GDP), opportunity cost (91-Day Treasury Bill Rate), and equity wealth (BSE Sensex). Since M1’s income elasticity is 0.53 and M3’s is 0.98, the traditional transaction motives dominate both M1 and M3. The interest rate exerts a negative substitution effect on M1; however, M3 remains structurally safeguarded against short-term fluctuations. Equity market valuations exhibit statistical insignificance across all variable specifications, indicating that the equity market fluctuations do not systematically destabilize long-run money demand. The ECM results reveal a short-term adjustment speed of 17.92% and 8.37% per quarter for M1 and M3, respectively. These findings establish that M3 acts as a robust and stable measure of the Reserve Bank of India’s long-term monetary targeting, driven predominantly by the fluctuations in real fundamental macro variables.