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All Journal Grimsa Journal of Business and Economics Studies
Fazli, Qalbin Salim
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Environmental Science Social Sciences

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Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering Fazli, Qalbin Salim; Idroes, Ghalieb Mutig; Hilal, Iin Shabrina; Hafizah, Iffah; Hardi, Irsan; Noviandy, Teuku Rizky
Grimsa Journal of Business and Economics Studies Vol. 2 No. 2 (2025): July 2025
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjbes.v2i2.93

Abstract

Agrochemical use, GHG emissions, and gross domestic product (GDP) vary widely across Southeast Asia, making the region suitable for cluster-based sustainability analysis. This study applies hierarchical clustering analysis (HCA) to classify nine Southeast Asian countries using four standardized indicators: pesticide use, nitrogen fertilizer use, GHG emissions, and GDP. Exploratory data analysis reveals significant disparities, with Brunei and Indonesia emerging as outliers due to exceptionally high input intensity and emissions, respectively. HCA identifies four distinct clusters: (1) low-input, low-emission economies (Cambodia, Laos, Myanmar); (2) moderately intensive systems (Malaysia, Thailand, the Philippines, Vietnam); (3) a high-pesticide profile (Brunei); and (4) a high-emission, high-output outlier (Indonesia). Principal Component Analysis confirms the cluster structure and highlights variation in emission efficiency. The findings show that similar agroecological contexts can yield divergent environmental outcomes, emphasizing the role of policy and technology. This study provides the first region-wide, data-driven typology of agricultural sustainability in Southeast Asia using HCA.
Co-Authors Hafizah, Iffah Idroes, Ghalieb Mutig Iin Shabrina Hilal Irsan Hardi Sugeng Santoso