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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.

Keywords

Agrochemicals Pesticide Nitrogen fertilizer GHG emissions Southeast Asia Hierarchical clustering analysis

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Copyright (c) 2025 Qalbin Salim Fazli, Ghalieb Mutig Idroes, Iin Shabrina Hilal, Iffah Hafizah, Irsan Hardi, Teuku Rizky Noviandy

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Fazli, Q. S., Idroes, G. M., Hilal, I. S., Hafizah, I., Hardi, I., & Noviandy, T. R. (2025). Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering. Grimsa Journal of Business and Economics Studies, 2(2), 140–151. https://doi.org/10.61975/gjbes.v2i2.93
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References

  1. Bhatti UA, Bhatti MA, Tang H, Syam MS, Awwad EM, Sharaf M, et al. Global Production Patterns: Understanding the Relationship between Greenhouse Gas Emissions, Agriculture Greening and Climate Variability. Environmental Research 2024;245:118049. https://doi.org/10.1016/j.envres.2023.118049.
  2. Hardi I, Ray S, Attari MUQ, Ali N, Idroes GM. Innovation and Economic Growth in the Top Five Southeast Asian Economies: A Decomposition Analysis. Ekonomikalia Journal of Economics 2024;2:1–14. https://doi.org/10.60084/eje.v2i1.145.
  3. Modisa, Jaikaew T. Sustainability in Food Supply Chains: Case of Southeast Asia. In: Mor RS, Kamble SS, Sangwan KS, editors. Oper. Supply Chain Manag. Food Ind., Singapore: Springer Nature Singapore; 2022, p. 53–72.
  4. Hardi I, Idroes GM, Hamaguchi Y, Can M, Noviandy TR, Idroes R. Business Confidence in the Shift to Renewable Energy: A Country-Specific Assessment in Major Asian Economies. Journal of Economy and Technology 2025;3:44–68. https://doi.org/10.1016/j.ject.2024.08.002.
  5. Blesh J, Mehrabi Z, Wittman H, Kerr RB, James D, Madsen S, et al. Against the Odds: Network and Institutional Pathways Enabling Agricultural Diversification. One Earth 2023;6:479–91. https://doi.org/10.1016/j.oneear.2023.03.004.
  6. Mouratiadou I, Wezel A, Kamilia K, Marchetti A, Paracchini ML, Bàrberi P. The Socio-Economic Performance of Agroecology. A Review. Agronomy for Sustainable Development 2024;44:19. https://doi.org/10.1007/s13593-024-00945-9.
  7. Surna MI, Fazli QS, Chamzurni T, Susanna S, Idroes GM. Influence of Elevational and Environmental Factors on Parasitic Nematode Distribution in Arabica Coffee in the Gayo Highlands, Indonesia. Leuser Journal of Environmental Studies 2025;3:56–66. https://doi.org/10.60084/ljes.v3i2.293.
  8. Chandio AA, Gokmenoglu KK, Dash DP, Khan I, Ahmad F, Jiang Y. Exploring the Energy-Climate-Agriculture (ECA) Nexus: A Roadmap toward Agricultural Sustainability in Asian Countries. Environment, Development and Sustainability 2024. https://doi.org/10.1007/s10668-023-04418-z.
  9. Qian H, Zhu X, Huang S, Linquist B, Kuzyakov Y, Wassmann R, et al. Greenhouse Gas Emissions and Mitigation in Rice Agriculture. Nature Reviews Earth & Environment 2023;4:716–32. https://doi.org/10.1038/s43017-023-00482-1.
  10. Xing Y, Wang X. Impact of Agricultural Activities on Climate Change: A Review of Greenhouse Gas Emission Patterns in Field Crop Systems. Plants 2024;13:2285. https://doi.org/10.3390/plants13162285.
  11. Fazli QS, Pramayudi N, Sayuthi M. Uji Keefektifan Ekstrak Daun Gegarang (Mentha sp.) untuk Mengendalikan Hama Plutella xylostella L. di Laboratorium. Jurnal Ilmiah Mahasiswa Pertanian 2023;8:1004–14.
  12. Wijerathna-Yapa A, Pathirana R. Sustainable Agro-Food Systems for Addressing Climate Change and Food Security. Agriculture 2022;12:1554. https://doi.org/10.3390/agriculture12101554.
  13. Lin H-I, Yu Y-Y, Wen F-I, Liu P-T. Status of Food Security in East and Southeast Asia and Challenges of Climate Change. Climate 2022;10:40. https://doi.org/10.3390/cli10030040.
  14. Ren C, He L, Ma Y, Reis S, Van Grinsven H, Lam SK, et al. Trade-Offs in Agricultural Outcomes across Farm Sizes. Earth Critical Zone 2024;1:100007. https://doi.org/10.1016/j.ecz.2024.100007.
  15. Xiang T, Malik TH, Hou JW, Ma J. The Impact of Climate Change on Agricultural Total Factor Productivity: A Cross-Country Panel Data Analysis, 1961–2013. Agriculture 2022;12:2123. https://doi.org/10.3390/agriculture12122123.
  16. Ma B, Karimi MS, Mohammed KS, Shahzadi I, Dai J. Nexus between Climate Change, Agricultural Output, Fertilizer Use, Agriculture Soil Emissions: Novel Implications in the Context of Environmental Management. Journal of Cleaner Production 2024;450:141801. https://doi.org/10.1016/j.jclepro.2024.141801.
  17. Gerssen‐Gondelach SJ, Wicke B, Faaij APC. GHG Emissions and Other Environmental Impacts of Indirect Land Use Change Mitigation. GCB Bioenergy 2017;9:725–42. https://doi.org/10.1111/gcbb.12394.
  18. Idroes GM, Hardi I, Hilal IS, Utami RT, Noviandy TR, Idroes R. Economic Growth and Environmental Impact: Assessing the Role of Geothermal Energy in Developing and Developed Countries. Innovation and Green Development 2024;3:100144. https://doi.org/10.1016/j.igd.2024.100144.
  19. Coderoni S, Vanino S. The Farm-by-Farm Relationship among Carbon Productivity and Economic Performance of Agriculture. Science of The Total Environment 2022;819:153103. https://doi.org/10.1016/j.scitotenv.2022.153103.
  20. Kamyab H, SaberiKamarposhti M, Hashim H, Yusuf M. Carbon Dynamics in Agricultural Greenhouse Gas Emissions and Removals: A Comprehensive Review. Carbon Letters 2024;34:265–89. https://doi.org/10.1007/s42823-023-00647-4.
  21. Hardi I, Idroes GM, Zulham T, Suriani S, Saputra J. Economic Growth, Agriculture, Capital Formation and Greenhouse Gas Emissions in Indonesia: FMOLS, DOLS and CCR Applications. Ekonomikalia Journal of Economics 2023;1:82–91. https://doi.org/10.60084/eje.v1i2.109.
  22. Maulidar P, Fitriyani F, Sasmita NR, Hardi I, Idroes GM. Exploring Indonesia’s CO2 Emissions: The Impact of Agriculture, Economic Growth, Capital and Labor. Grimsa Journal of Business and Economics Studies 2024;1:43–55. https://doi.org/10.61975/gjbes.v1i1.22.
  23. Noviandy TR, Maulana A, Emran TB, Idroes GM, Idroes R. QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine Learning Algorithms. Heca Journal of Applied Sciences 2023;1:1–7. https://doi.org/10.60084/hjas.v1i1.12.
  24. Noviandy TR, Maulana A, Idroes GM, Maulydia NB, Patwekar M, Suhendra R, et al. Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery. Malacca Pharmaceutics 2023;1:48–54. https://doi.org/10.60084/mp.v1i2.60.
  25. Noviandy TR, Idroes GM, Hardi I. Integrating Explainable Artificial Intelligence and Light Gradient Boosting Machine for Glioma Grading. Informatics and Health 2025;2:1–8. https://doi.org/10.1016/j.infoh.2024.12.001.
  26. Noviandy TR, Maulana A, Irvanizam I, Idroes GM, Maulydia NB, Tallei TE, et al. Interpretable Machine Learning Approach to Predict Hepatitis C Virus NS5B Inhibitor Activity Using Voting-Based LightGBM and SHAP. Intelligent Systems with Applications 2025;25:200481. https://doi.org/10.1016/j.iswa.2025.200481.
  27. Noviandy TR, Maulana A, Idroes GM, Suhendra R, Afidh RPF, Idroes R. An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates. Sci 2024;6:81. https://doi.org/10.3390/sci6040081.
  28. Zang L, Xue Q, Wang X, Tian B, Zhu S. An Optimized Product Model Based on the K-Means++ Algorithm. CAIBDA 2024 2024 4th Int. Conf. Artif. Intell. Big Data Algorithms, ACM; 2024, p. 677–84. https://doi.org/10.1145/3690407.3690522.
  29. Tarnutzer AA, Weber KP. Pattern Analysis of Peripheral-Vestibular Deficits with Machine Learning Using Hierarchical Clustering. Journal of the Neurological Sciences 2022;434:120159. https://doi.org/10.1016/j.jns.2022.120159.
  30. Boegl L, Kounadi O. Introducing Spatial Heterogeneity via Regionalization Methods in Machine Learning Models for Geographical Prediction: A Spatially Conscious Paradigm. European Journal of Geography 2024;15:244–55. https://doi.org/10.48088/ejg.l.boe.15.4.244.255.
  31. Shui B, Cai Z, Luo X. Towards Customized Mitigation Strategy in the Transportation Sector: An Integrated Analysis Framework Combining LMDI and Hierarchical Clustering Method. Sustainable Cities and Society 2024;107:105340. https://doi.org/10.1016/j.scs.2024.105340.
  32. Yang X, Liu Y, Bezama A, Thrän D. Agricultural Carbon Emission Efficiency and Agricultural Practices: Implications for Balancing Carbon Emissions Reduction and Agricultural Productivity Increment. Environmental Development 2024;50:101004. https://doi.org/10.1016/j.envdev.2024.101004.
  33. Liu B, Guan Y, Shan Y, Cui C, Hubacek K. Emission Growth and Drivers in Mainland Southeast Asian Countries. Journal of Environmental Management 2023;329:117034. https://doi.org/10.1016/j.jenvman.2022.117034.
  34. Qayyum M, Zhang Y, Wang M, Yu Y, Li S, Ahmad W, et al. Advancements in Technology and Innovation for Sustainable Agriculture: Understanding and Mitigating Greenhouse Gas Emissions from Agricultural Soils. Journal of Environmental Management 2023;347:119147. https://doi.org/10.1016/j.jenvman.2023.119147.
  35. D’Orazio P. Mapping the Emergence and Diffusion of Climate-Related Financial Policies: Evidence from a Cluster Analysis on G20 Countries. International Economics 2022;169:135–47. https://doi.org/10.1016/j.inteco.2021.11.005.
  36. Grillitsch M, Sotarauta M. Trinity of Change Agency, Regional Development Paths and Opportunity Spaces. Progress in Human Geography 2020;44:704–23. https://doi.org/10.1177/0309132519853870.
  37. Islam MS, Kieu E. Tackling Regional Climate Change Impacts and Food Security Issues: A Critical Analysis across ASEAN, PIF, and SAARC. Sustainability 2020;12:883. https://doi.org/10.3390/su12030883.
  38. Our World in Data (OWID). Pesticides 2023. https://ourworldindata.org/pesticides.
  39. Our World in Data (OWID). Fertilizers 2023. https://ourworldindata.org/fertilizers.
  40. Our World in Data (OWID). CO₂ and Greenhouse Gas Emissions 2024. https://ourworldindata.org/#entries.
  41. World Bank. WorldBank Development Indicator Databased 2023. https://databank.worldbank.org/source/world-development-indicators.
  42. Idroes GM, Syahnur S, Majid SA, Sasmita NR, Idroes R. Provincial Economic Level Analysis in Indonesia Based on the Geothermal Energy Potential and Growth Regional Domestic Products Using Cluster Analysis. IOP Conference Series: Materials Science and Engineering 2021;1087:012079. https://doi.org/10.1088/1757-899X/1087/1/012079.
  43. Noviandy TR, Nisa K, Idroes GM, Hardi I, Sasmita NR. Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM. Journal of Computing Theories and Applications 2024;1:358–67. https://doi.org/10.62411/jcta.10129.
  44. Sasmita NR, Phonna RA, Fikri MK, Khairul M, Apriliansyah F, Idroes GM, et al. Statistical Assessment of Human Development Index Variations and Their Correlates: A Case Study of Aceh Province, Indonesia. Grimsa Journal of Business and Economics Studies 2023;1:12–24. https://doi.org/10.61975/gjbes.v1i1.14.
  45. Noviandy TR, Hardi I, Zahriah Z, Sofyan R, Sasmita NR, Hilal IS, et al. Environmental and Economic Clustering of Indonesian Provinces: Insights from K-Means Analysis. Leuser Journal of Environmental Studies 2024;2:41–51. https://doi.org/10.60084/ljes.v2i1.181.
  46. Gil-Garcia RJ, Badia-Contelles JM, Pons-Porrata A. A General Framework for Agglomerative Hierarchical Clustering Algorithms. 18th Int. Conf. Pattern Recognit., IEEE; 2006, p. 569–72. https://doi.org/10.1109/ICPR.2006.69.
  47. Sasmita NR, Khairul M, Sofyan H, Kruba R, Mardalena S, Dahlawy A, et al. Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia. Infolitika Journal of Data Science 2023;1:63–72. https://doi.org/10.60084/ijds.v1i2.130.
  48. Vijaya, Sharma S, Batra N. Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering. 2019 Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput., IEEE; 2019, p. 568–73. https://doi.org/10.1109/COMITCon.2019.8862232.
  49. Granato D, Santos JS, Escher GB, Ferreira BL, Maggio RM. Use of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for Multivariate Association between Bioactive Compounds and Functional Properties in Foods: A Critical Perspective. Trends in Food Science & Technology 2018;72:83–90. https://doi.org/10.1016/j.tifs.2017.12.006.