AJIRSS: Asian Journal of Innovative Research in Social Science

Does Digital Transformation Drive Economic Growth? Evidence From Asian And European Countries

Saeed A.A. Saif — Sun, 15 Mar 2026 00:00:00 +0700

This study investigates the impact of digital transformation on economic growth using panel data from selected Asian and European countries. It examines how key digitalization indicators shape differences in economic performance across regions with distinct structural and developmental characteristics. The analysis employs a fixed-effects panel model to control for unobserved country-specific heterogeneity. Descriptive statistics, correlation analysis, and robustness tests using Driscoll–Kraay standard errors are applied to address heteroscedasticity, autocorrelation, and cross-sectional dependence. Economic growth is measured by GDP per capita. Digital transformation is captured through internet usage, mobile subscriptions, ICT service exports, and high-technology exports. The model also includes control variables for infrastructure availability and trade openness. The results show that digital transformation significantly supports economic growth, although its effects vary across regions. Internet penetration, mobile connectivity, and ICT service exports have positive and statistically significant effects on GDP per capita, particularly in Asian economies. In contrast, high-technology exports are negatively associated with growth, indicating possible structural limitations and adjustment costs in technology-intensive sectors. The regional analysis suggests that Asian countries benefit more from expanding basic digital infrastructure, while European economies gain more from advanced ICT services. Overall, the findings provide comparative regional evidence on the digital transformation–growth nexus and emphasize the importance of context-specific digital development strategies.

Analysis Of Tourism Stakeholders’ Willingness To Pay For Environmental Conservation: A Case Study Of Pulau Weh Nature Tourism Park

Amanda Ridwan, Aliasuddin Aliasuddin, Farhan Ramadhani — Sun, 15 Mar 2026 00:00:00 +0700

This study examines the willingness to pay among tourism operators for environmental conservation within Pulau Weh Nature Tourism Park and delineates the factors influencing this willingness. A quantitative methodology was employed, utilizing structured interviews via questionnaires administered to 48 tourism operators, including tour guides, dive operators, hotel and guesthouse proprietors, as well as restaurant and café owners in Iboih Village. Stratified random sampling was employed to select participants from a population of 148 tourism operators.

Green Macroprudential Banking Stability Framework : A Global Transmission Mechanism Of Sustainable Finance

Wahyu Indah Sari, Sirojuzilam Hasyim, M. Syafii — Sun, 15 Mar 2026 00:00:00 +0700

The increasing exposure of the financial system to climate-related risks has encouraged the evolution of macroprudential policy toward sustainability-oriented frameworks. This study develops the Green Macroprudential Banking Stability Framework (GMBSF) as an integrated analytical model explaining how sustainable finance instruments influence banking system resilience. Unlike conventional approaches that treat green finance as a complementary policy, this research positions it as a core macroprudential transmission channel affecting systemic stability. The study employs a panel dynamic approach using multi-institution banking data over the 2015–2024 period to examine both short-run adjustments and long-run equilibrium relationships between green financing, Environmental, Social, and Governance (ESG) performance, and banking stability. Stability is proxied by the Z-Score, while capital adequacy, profitability, credit risk, and institutional size are incorporated as control variables. The empirical findings indicate that sustainable finance exposure and stronger ESG governance significantly enhance banking stability by improving risk absorption capacity, strengthening capital buffers, and reducing credit volatility. Conversely, higher non-performing loans weaken systemic resilience. The results support the argument that green finance functions as a macroprudential shock absorber within climate-sensitive financial systems. This study contributes theoretically by introducing a globally applicable green macroprudential framework that integrates sustainability indicators into systemic risk management architecture. The proposed GMBSF provides strategic implications for central banks and financial regulators in designing climate-responsive macroprudential policies to maintain financial stability during the transition toward a low-carbon economy.

Effectiveness of Rooftop Photovoltaic Inspection Based on RGB Aerial Imagery and YOLOv11 Compared with Conventional Inspection

Evan Rega Mahendra — Sun, 15 Mar 2026 00:00:00 +0700

Conventional rooftop photovoltaic (PV) inspection is still commonly performed through direct visual assessment by technicians. However, this approach is time-consuming, exposes workers to higher safety risks, and often produces less consistent documentation. This study develops an automatic inspection approach based on RGB aerial imagery and deep learning and compares its effectiveness with conventional inspection. A case study was conducted on a 60 kWp rooftop PV system at the Smart and Green Learning Center (SGLC), Faculty of Engineering, Universitas Gadjah Mada. A total of 150 RGB images were acquired using a drone, annotated, and used to train a YOLOv11 model through a Roboflow workflow. Technical evaluation was complemented by a 24-item Likert survey involving five technicians and by investment and operational cost analysis. The model achieved an mAP@50 of 98.3%, precision of 100%, and recall of 97.0%. Automatic inspection reduced inspection time from approximately 95 minutes to 18 minutes per session, corresponding to a time saving of about 81%. Technician perception also favored the automatic method, with an average score of 4.82 compared with 3.34 for the conventional approach, and the difference was statistically significant (p = 0.000036). Although the automatic method requires higher initial investment, it yields lower annual operational costs. These findings indicate that drone-based RGB imaging and YOLOv11 provide a feasible approach for modern rooftop PV maintenance, particularly for periodic inspection and multi-installation deployment.