Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering - Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering

Dataset description:

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...

Nguồn: Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering

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Miền	Giá trị
Data last updated	3 tháng 10, 2025
Metadata last updated	5 tháng 10, 2025
Được tạo ra	3 tháng 10, 2025
Định dạng	Website
Giấy phép	Creative Commons Attribution Non Commercial 4.0
Datastore active	False
Datastore contains all records of source file	False
Has views	False
Id	108560b4-aebf-4f08-89f8-5ac2d7696b8f
Name translated	{'en': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'km': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'lo': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'my_MM': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'vi': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering'}
Package id	f3c3c11e-b8b6-4f31-8e29-70571c488d6d
Position	0
Resource description	{'en': '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.', 'km': '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.', 'lo': '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.', 'my_MM': '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.', 'vi': '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.'}
State	active
Tên	Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering
Sự miêu tả	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.

Miền

Giá trị

Data last updated

3 tháng 10, 2025

Metadata last updated

5 tháng 10, 2025

Được tạo ra

3 tháng 10, 2025

Định dạng

Website

Giấy phép

Creative Commons Attribution Non Commercial 4.0

Datastore active

False

Datastore contains all records of source file

False

Has views

False

108560b4-aebf-4f08-89f8-5ac2d7696b8f

Name translated

{'en': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'km': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'lo': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'my_MM': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering', 'vi': 'Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering'}

Package id

f3c3c11e-b8b6-4f31-8e29-70571c488d6d

Position

Resource description

{'en': '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.', 'km': '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.', 'lo': '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.', 'my_MM': '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.', 'vi': '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.'}

State

active

Tên

Section Articles Agrochemicals, GHG Emissions, and GDP in Southeast Asia: A Machine Learning Approach with Hierarchical Clustering

Sự miêu tả

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.

Section Articles Agrochemicals, GHG Emissions,...

Dataset description:

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