Geospatial Data Analysis for Mapping Carbon Sequestration Hotspots DOI

Ayush Tripathi -,

Prashant Upadhyay, Pawan Kumar Goel

и другие.

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 193 - 218

Опубликована: Апрель 11, 2025

Geospatial measurement of carbon is required for hotspot identification and precise quantification sinks across various ecosystems. The evolution GIS, remote sensing, LiDAR, spatial modeling using AI has significantly improved the precision extent monitoring. chapter describes techniques examining forest biomass, soil sequestration, ocean through satellite data, geospatial computation, machine learning models. Integration big data enhances flux estimation land-use impact assessment on sequestration capacity. Significant challenges such as resolution, model uncertainty, computational complexity are addressed, along with new solutions. analysis augmented by at core activities maximization, enabling climate change mitigation, sustainable land management, transparent credit systems.

Язык: Английский

Rationally Designed High-Temperature Polymer Dielectrics for Capacitive Energy Storage: An Experimental and Computational Alliance DOI

Pritish S. Aklujkar,

Rishi Gurnani,

Pragati Rout

и другие.

Progress in Polymer Science, Год журнала: 2025, Номер unknown, С. 101931 - 101931

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study DOI Creative Commons
Petros Chavula, Fredrick Kayusi,

Gilbert Lungu

и другие.

LatIA, Год журнала: 2025, Номер 3, С. 330 - 330

Опубликована: Март 25, 2025

Climate change poses significant challenges to global agriculture, necessitating innovative solutions enhance sustainability and productivity. Artificial intelligence (AI) has emerged as a key enabler in climate-smart agricultural technologies (CSAT), offering data-driven approaches optimize resource use, mitigate climate risks, improve decision-making. This study aims evaluate AI's integration into CSAT, focusing on its applications, benefits, adoption challenges, particularly climate-vulnerable regions. A bibliographic review employing machine learning (ML) natural language processing (NLP) techniques was conducted analyze over 40,000 scientific articles from academic databases. Topic modeling classification algorithms were applied identify trends, barriers, implementation pathways for AI-driven CSAT. The also incorporated expert validation through the Delphi method refine AI-generated insights ensure their alignment with real-world challenges. Findings indicate that AI enhances decision-making conservation precision farming, water management, market intelligence. AI-powered tools facilitate early pest detection, irrigation schedules, provide real-time advisory services, significantly improving resilience food security. However, major barriers include high costs, limited digital literacy, inadequate infrastructure, low-income Despite these CSAT presents potential transform especially climate-affected areas. Strategic investments infrastructure development, supportive policy frameworks are essential adoption. Strengthening interdisciplinary collaboration among researchers, policymakers, farmers will be crucial advancing sustainable practices ensuring long-term

Язык: Английский

Процитировано

1

Climate Sustainability through AI-Crypto Synergies and Energy Transition in the Digital Landscape to Cut 0.7 GtCO2e by 2030 DOI
Apoorv Lal, Fengqi You

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

The rapid expansion of artificial intelligence (AI)-enabled systems and cryptocurrency mining poses significant challenges to climate sustainability due energy-intensive operations relying on fossil-powered grids. This work investigates the strategic coupling AI data centers through shared energy infrastructure including colocated renewable power installations, battery storage, green hydrogen infrastructure, carbon offsetting measures achieve cost-effective climate-neutral operations. Employing a novel modeling framework, it explores synergistic AI-crypto with detailed scenario design along an optimization framework assess decarbonization potential economic implications, enabling transformative shift in digital landscape. results indicate that synergizing while achieving net-zero targets can avoid up 0.7 Gt CO2-equiv 2030. Moreover, reaching these strategies globally requires 90.7 GW solar 119.3 wind capacity. findings advocate for robust policy facilitate credit schemes tailored sector, incentives efficiency improvements, international collaborations bridge disparities. Future research should focus refining interventions across different geopolitical contexts enhance global applicability.

Язык: Английский

Процитировано

0

Smart Technologies in Enhanced Oil Recovery: Integrating AI, Nanotechnology, and Sustainable Practices DOI Creative Commons
Nouratan Singh,

Poonam Rani,

Neeraj Tandan

и другие.

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Enhanced oil recovery (EOR) is a critical method for extracting additional from mature reservoirs, but it faces increasing pressure to become more efficient and environmentally sustainable. This chapter explores the integration of smart technologies such as artificial intelligence (AI), nanotechnology, sustainable practices into EOR. AI revolutionizing EOR operations by optimizing reservoir management, improving real-time monitoring, reducing operational costs. Nanotechnology enhances through use functionalized nanoparticles fluids, which improve mobility reduce chemical consumption. Additionally, practices, including CO2-EOR, water-efficient techniques, biodegradable chemicals, are being adopted lower environmental impact EOR, especially in terms carbon emissions water use. While challenges remain—such high cost technology fluctuating prices—the future holds promise continuous technological innovation growing emphasis on sustainability.

Язык: Английский

Процитировано

0

AI‐Powered Sustainable Tourism: Unlocking Circular Economies and Overcoming Resistance to Change DOI Open Access

Hwang Bang‐Ning,

Siriprapha Jitanugoon, Pittinun Puntha

и другие.

Business Strategy and the Environment, Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

ABSTRACT This study examines the integration of artificial intelligence (AI) with circular economy (CE) principles in Thailand's tourism industry. It explores interactions between AI‐Enhanced Predictive Waste Analytics (AI‐PWA), Regenerative Resource Integration (RRI), Dynamic Material Flow Optimization (DMFO), and AI‐Induced Resistance to Change (AIRC). Using a mixed‐methods approach, qualitative insights from industry stakeholders are combined quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS‐SEM). Findings reveal that AI‐PWA improves real‐time resource management, driving DMFO supporting regenerative practices through RRI. However, AIRC moderates AI's effectiveness sustainability transitions, concerns such as job displacement, mistrust, complexity hindering adoption. provides actionable strategies mitigate resistance, enhance stakeholder collaboration, scale AI adoption resource‐constrained settings, contributing SDG 12 13. The findings offer practical for aligning innovations sustainable development high‐variability industries.

Язык: Английский

Процитировано

0

Transformative Approaches in Photocatalytic CO2 Conversion: The Impact of AI and Computational Chemistry DOI
Nur Umisyuhada Mohd Nor,

Khaireddin Boukayouht,

Samir El Hankari

и другие.

Current Opinion in Green and Sustainable Chemistry, Год журнала: 2025, Номер unknown, С. 101027 - 101027

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Geospatial Data Analysis for Mapping Carbon Sequestration Hotspots DOI

Ayush Tripathi -,

Prashant Upadhyay, Pawan Kumar Goel

и другие.

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 193 - 218

Опубликована: Апрель 11, 2025

Geospatial measurement of carbon is required for hotspot identification and precise quantification sinks across various ecosystems. The evolution GIS, remote sensing, LiDAR, spatial modeling using AI has significantly improved the precision extent monitoring. chapter describes techniques examining forest biomass, soil sequestration, ocean through satellite data, geospatial computation, machine learning models. Integration big data enhances flux estimation land-use impact assessment on sequestration capacity. Significant challenges such as resolution, model uncertainty, computational complexity are addressed, along with new solutions. analysis augmented by at core activities maximization, enabling climate change mitigation, sustainable land management, transparent credit systems.

Язык: Английский

Процитировано

0