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All Journal Emerging Science Journal
Elena B. Sparrow
Department of Natural Resources and Environment, University of Alaska Fairbanks, AK
Environmental Science

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Journal : emerging science journal

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Multi-Country GHG Emissions Forecasting by Sector Using a GCN-LSTM Model Babey Dimla Tonny; Krisanadej Jaroensutasinee; Mullica Jaroensutasinee; Elena B. Sparrow
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-03

Abstract

This study developed a novel hybrid Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model to forecast greenhouse gas (GHG) emissions across multiple country sectors, aiming to enhance climate policy. We analyzed 52 years (1970–2022) of GHG emissions data (CO₂, CH₄, N₂O, F-Gases) from 163 countries and eight sectors (Agriculture, Buildings, Fuel Exploitation, Industrial Combustion, Power Industry, Processes, Transport, Waste), sourced from the EDGAR v8 database. The GCN adjacency matrix captures spatial relationships on a weighted sum of Haversine distance and cosine similarity, while the LSTM models temporal dynamics. Data preprocessing includes min-max scaling and outlier handling with Interquartile Range capping. The model was trained on 70% of the data, validated on 15%, and tested on 15%, using Mean Squared Error (MSE) loss and the Adam optimizer. The performance was evaluated with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The GCN–LSTM model outperformed baseline models (ARIMA, Simple LSTM, Stacked LSTM), achieving the lowest MAE (0.0207 in Waste) and highest R² (0.9756 in Waste). Model interpretability highlighted strong regional connections, such as Thailand–Cambodia in the Waste sector, suggesting that spatial and temporal dependencies offer superior forecasting accuracy, informing targeted climate action.
Synergizing IoT Precision Irrigation and Community Capacity Building for Enhanced Mangosteen Quality Krisanadej Jaroensutasinee; Mullica Jaroensutasinee; Chacha Sattharat; Elena B. Sparrow
Emerging Science Journal Vol. 10 No. 5 (2026): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-05-022

Abstract

This study evaluated how IoT-driven precision agriculture and community capacity building affect mangosteen yield and quality. We set up a high-density NB-IoT sensor network on 50 smart farms and 10 conventional rainfed farms, and we confirmed the statistical reliability of the sample imbalance using Welch's t-tests. The IoT system used the Magellan cloud platform to keep a consistent 480 cb root-zone soil water matric potential, and precision irrigation notably increased fruit weight and total yield while reducing serious physiological defects like translucent flesh and gumming by more than 80%. Empirical validation showed uniform microclimates across subdistricts, which supports the centralized weather-station approach, and this research uniquely combined cyber-physical systems with organized human capacity development to address the flowering challenge in mangosteen. This study offers a scalable framework for tropical perennials, ensuring high export-grade fruit quality and improved socioeconomic resilience in agricultural communities vulnerable to climate change.
Co-Authors Babey Dimla Tonny Chacha Sattharat Krisanadej Jaroensutasinee Krisanadej Jaroensutasinee Mullica Jaroensutasinee Mullica Jaroensutasinee