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AI, Innovation, and Resilience for the Environment Journal
Published by Sinar Mentari Sundara
ISSN : 31632637     EISSN : 31632629     DOI : https://doi.org/10.68012/air
Core Subject :
The atmosphere is the most fluid and universal of all planetary systems, transcending borders and connecting every living organism. In this spirit of essentiality and purity, AI, Innovation, and Resilience for the Environment (AIR) Journal is established as a premier international forum. By harnessing the invisible yet pervasive power of artificial intelligence, AIR seeks to breathe new life into environmental conservation, resource management, and climate adaptation strategies for the modern era. The mission of AIR Journal is to catalyze the transition from reactive environmental protection to proactive, data-driven resilience. The journal’s name and acronym, AIR Journal, represent one of the four classical elements symbolizing clarity, life-giving purity, and the interconnectedness of global ecosystems. We believe that technology, when applied with wisdom, can act like a fresh breeze, clearing the "smog" of ecological degradation and providing the transparency needed for sustainable governance. As we move toward the 2026 climate milestones, AIR envisions an "Autonomous Ecology" where AI-driven systems monitor, predict, and mitigate environmental threats in real-time. The journal serves as a sanctuary for interdisciplinary research that merges high-tech innovation with "Orange Technologies"creative, human-centric solutions that foster emotional harmony between technological progress and the natural world.
Arjuna Subject : -
Articles 20 Documents
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Smart Governance Framework for Automated Urban Air Quality Decision Support Luiz Rubio; Muhammad Bukhori Dalimunthe; Fazli Rachman; Wildansyah Lubis
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.203

Abstract

Urban centers in tropical developing nations face severe air pollution crises, yet a critical policy inertia gap persists between real-time sensor data acquisition and dynamic municipal enforcement. This study aims to develop and evaluate the Smart Air Quality Governance (SAQG) framework, an automated, artificial intelligence (AI)-enhanced Environmental Decision-Support System (EDSS) designed to bridge passive monitoring with legally binding administrative action. Employing a qualitative policy gap analysis and semi-structured key informant interviews (n = 12) with municipal environmental, transportation, and public health authorities in a tropical metropolitan area (Jakarta, Indonesia), this paper examines the institutional bottlenecks delaying emergency responses. Unlike traditional passive monitoring platforms, the novelty of the SAQG framework lies in its active execution engine, which directly links real-time PM2.5 sensor networks to a three-tiered automated policy matrix, enforcing immediate interventions such as adaptive signal timing, industrial emission caps, and mandatory work-from-home orders. The results demonstrate that embedding AI-driven predictive triggers into local government regulatory architectures significantly reduces administrative response latency during atmospheric crises. This study provides local authorities with a practical blueprint to transition from reactive observation to proactive urban resilience, directly advancing UN Sustainable Development Goals (SDG 3: Good Health and Well-Being, SDG 11: Sustainable Cities and Communities, and SDG 13: Climate Action).
Machine Learning Applications for Predicting Environmental Risks and Resilience Maman Sulaeman; Suhaila Samsuri; Gabriel Fransiso
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.207

Abstract

Machine learning has emerged as a promising technological approach for addressing increasingly complex environmental challenges, particularly in the identification and prediction of environmental risks that threaten ecological sustainability and community resilience. Despite the growing availability of environmental data, accurately forecasting potential risks and evaluating resilience capacity remain significant challenges for policymakers and environmental managers. Therefore, this study aims to investigate the application of machine learning techniques for predicting environmental risks and assessing resilience factors that support sustainable environmental management and disaster preparedness. To achieve this objective, a quantitative research approach was employed through the development and evaluation of machine learning models using environmental datasets derived from multiple indicators, including climate conditions, land use patterns, ecological variables, and historical environmental events. The proposed framework integrates data preprocessing, feature selection, model training, and predictive analysis to identify patterns associated with environmental vulnerability and resilience. The findings demonstrate that machine learning models are capable of effectively detecting environmental risk patterns and generating reliable predictions that support proactive decision-making. Furthermore, the analysis reveals that resilience related indicators play a critical role in improving predictive performance and enhancing the understanding of environmental adaptation mechanisms. The integration of predictive analytics and resilience assessment provides a more comprehensive perspective on environmental risk management. In conclusion, machine learning offers substantial potential for advancing environmental risk prediction and resilience evaluation by enabling data-driven strategies for sustainable environmental governance. These findings contribute to the growing body of knowledge on intelligent environmental management systems and support the development of more adaptive and resilient environmental policies.
Generative Artificial Intelligence for Sustainable Digital Business Transformation Nor Saadah; Nurhafizah Mahri; Kayshifa Audira Audira; Noor Azura Zakaria
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.210

Abstract

The rapid advancement of digital technology and environmental challenges has encouraged organizations to adopt innovative approaches to achieve sustainable business transformation. This study investigates the role of Generative Artificial Intelligence in supporting sustainable digital business transformation by examining how AI driven systems enhance operational efficiency, innovation capability, decision making quality, and environmental sustainability. The background of this research is based on the increasing need for businesses to integrate intelligent technologies with sustainable practices in order to remain competitive and resilient in the digital era. The objective of this study is to analyze the contribution of Generative Artificial Intelligence toward sustainable business development and digital transformation strategies. The method employed in this research uses a qualitative literature review approach by analyzing recent scholarly articles, industry reports, and technological studies related to artificial intelligence, sustainability, and digital business transformation. The results indicate that Generative Artificial Intelligence significantly contributes to improving business adaptability, automating operational processes, reducing resource consumption, and encouraging eco friendly innovation. In addition, AI technologies support organizations in developing data driven strategies that strengthen long term sustainability and resilience. The conclusion of this study emphasizes that Generative Artificial Intelligence has become an important driver in creating intelligent, innovative, and sustainable digital business ecosystems capable of addressing future environmental and technological challenges while maintaining organizational competitiveness in the global market.
Cyberpreneurship and Green Technology Adoption for Sustainable Environmental Innovation in MSMEs Abdul Hamid Arribathi; Harry Agustian; John Edward
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.221

Abstract

The growing development of digital entrepreneurship has provided significant opportunities for micro, small, and medium enterprises (MSMEs) to enhance competitiveness while addressing increasing environmental sustainability requirements. However, many MSMEs still face challenges in integrating cyberpreneurship strategies with green technology adoption to achieve sustainable environmental innovation. This study investigates the influence of digital innovation and green technology adoption on cyberpreneurship performance and examines their contribution to sustainable environmental innovation among MSMEs. A quantitative research approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.0. Data were collected from 150 digital-oriented MSME owners in Indonesia using an online structured questionnaire with a five-point Likert scale. The measurement model evaluation confirmed that all constructs achieved satisfactory reliability and validity levels. The results indicate that digital innovation significantly influences cyberpreneurship performance (β = 0.436, p < 0.001), while green technology adoption also positively affects cyberpreneurship performance (β = 0.371, p < 0.001). Furthermore, cyberpreneurship performance significantly enhances sustainable environmental innovation (β = 0.482, p < 0.001). Conversely, digital innovation does not have a significant direct effect on sustainable environmental innovation (β = 0.118, p = 0.113), whereas green technology adoption demonstrates a significant direct influence (β = 0.297, p < 0.001). The mediation analysis confirms that cyberpreneurship performance mediates the relationship between digital innovation, green technology adoption, and sustainable environmental innovation. These findings highlight that cyberpreneurship performance is a strategic mechanism for transforming digital capabilities and green technology initiatives into sustainable innovation outcomes among MSMEs.
Artificial Intelligence Driven Green Digital Transformation for Sustainable Business Development Mestiana Br Karo; Jessica G.P. Zandroto; Lili Suryani Tumanggor; Aulia Wellington Wellington
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.226

Abstract

Green Digital Transformation has emerged as a strategic approach that integrates digital innovation with environmental sustainability to support sustainable business development. This study aims to examine how Advanced digital technologies, including Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Internet of Things (IoT) capabilities. collectively drive Green Digital Transformation and contribute to sustainable business development. A qualitative conceptual literature review was conducted by synthesizing peer-reviewed studies published between 2022 and 2026 on digital transformation, sustainability, and related digital technologies. The findings indicate that these technologies improve operational efficiency, optimize resource utilization, reduce waste, enhance energy efficiency, and strengthen organizational competitiveness through data-driven decision-making and intelligent digital capabilities. Based on the literature synthesis, this study proposes an integrated conceptual framework that illustrates the pathway from digital technology adoption to Green Digital Transformation, leading to sustainable business outcomes while supporting SDGs 9, 12, and 13. The findings demonstrate that Green Digital Transformation serves as a strategic mechanism for creating long-term organizational value by integrating digital innovation with sustainability objectives. This study contributes to the literature by providing a clearer conceptual foundation and an integrated framework that can guide future empirical research and managerial strategies for sustainable business development.
Green Artificial Intelligence Framework for Energy Efficient Smart Agriculture in Tropical Environments Nur-Adib Maspo; David Edmond; Tri Pujiati; Meria Zakiyah Alfisuma
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.230

Abstract

The increasing demand for sustainable agricultural production in tropical environments has encouraged the integration of environmentally responsible digital technologies to address energy consumption and resource inefficiency in smart farming systems. This study proposes a Green Artificial Intelligence framework designed to enhance energy efficiency, optimize agricultural resource utilization, and support sustainable farming practices in tropical regions characterized by high climate variability and ecological sensitivity. The research aims to develop an intelligent and adaptive agricultural model capable of reducing computational energy usage while improving environmental monitoring and decision making processes in precision agriculture. The proposed framework integrates Internet of Things sensors, machine learning algorithms, and energyaware data processing techniques to monitor soil moisture, temperature, humidity, and crop health in real time. A quantitative experimental approach was employed using simulated agricultural datasets and smart sensor data collected from tropical farming environments. The results demonstrate that the proposed Green AI framework significantly reduces energy consumption in data processing operations while maintaining high predictive accuracy and operational efficiency in smart irrigation and crop management systems. Furthermore, the framework improves environmental sustainability by minimizing excessive water and energy usage in agricultural activities. In conclusion, the study high lights the potential of Green Artificial Intelligence as an innovative solution for developing sustainable, resilient, and energyefficient smart agriculture systems that support longterm environmental conservation and food security in tropical regions.
Artificial Intelligence Research Trends for Environmental Resilience in the Digital Era Fajar Muttaqi; Henry Newell; Algiyant Rezki Tri Utama; Umi Rusilowati
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.238

Abstract

The rapid development of Artificial Intelligence (AI) has created significant opportunities to address environmental challenges in the digital era. Increasing issues such as climate change, pollution, waste management problems, and natural resource depletion highlight the need for innovative and sustainable solutions. The main problem addressed in this study is how Artificial Intelligence can enhance environmental resilience in the digital era. Therefore, the objective of this study is to analyze the role of AI in supporting environmental resilience and sustainable environmental management. This study employs a bibliometric analysis approach using scientific publications indexed in the Scopus database. The data were analyzed to identify publication trends, keyword relationships, research networks, thematic clusters, and emerging research directions related to Artificial Intelligence and Environmental Resilience. The results show that Artificial Intelligence contributes significantly to environmental monitoring, climate prediction, renewable energy optimization, waste management, and natural resource conservation. The findings reveal that the integration of AI with digital technologies improves decision-making accuracy, increases operational efficiency, reduces environmental risks, and supports data-driven sustainability initiatives. In conclusion, Artificial Intelligence plays a strategic role in enhancing environmental resilience in the digital era by optimizing resource management, improving energy efficiency, and supporting sustainable environmental policies. The adoption of AI should therefore be encouraged while maintaining principles of sustainability, ethics, and environmental responsibility.
Artificial Intelligence for Environmental Resilience and Sustainable Innovation Sri Poedji Lestari; Rosa Lesmana; Sabil Maulana Fauzi; Alexander Johnson Johnson
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.239

Abstract

Environmental challenges such as climate change, resource depletion, biodiversity loss, and increasing pollution require innovative and adaptive solutions to strengthen environmental resilience. Artificial intelligence (AI) has emerged as a transformative technology capable of improving environmental monitoring, prediction, and decision-making processes. This study examines the role of artificial intelligence in enhancing environmental resilience and promoting sustainable innovation across various sectors, including natural resource management, climate adaptation, waste management, environmental conservation, and smart environmental systems. This study adopts a Systematic Literature Review (SLR) methodology integrated with bibliometric analysis and thematic content analysis to identify research trends, conceptual developments, and emerging themes related to AI applications in environmental sustainability. The selected studies were analyzed using VOSviewer, Tableau, and Microsoft Excel to visualize knowledge structures, thematic relationships, and research patterns. The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making. Furthermore, AI-driven innovations support sustainable practices through renewable energy optimization, precision agriculture, waste management improvement, water resource monitoring, and smart city development. However, challenges remain regarding data quality, technological accessibility, computational requirements, ethical considerations, and responsible governance. The study concludes that AI has substantial potential to accelerate environmental resilience and sustainable innovation when supported by appropriate policies, stakeholder collaboration, and long-term sustainability strategies. By integrating intelligent technologies with responsible environmental management approaches, AI can serve as a strategic enabler in addressing complex ecological challenges and supporting a more adaptive, resilient, and sustainable future.
Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data Dirvi Surya Abbas; Asep Sutarman; Ryan Davis; Maulana Abbas
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.252

Abstract

Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.
Artificial Intelligence for Socio-Ecological Resilience and Sustainable Resource Governance Ayu Rimanda; Prima Wira Nanda; Zakia Hary Nisa; Lilik Susilowati
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.256

Abstract

Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15

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