Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Analysis of Determinant Factors of Carbon Efficiency in Indonesia Based on Domestic Waste Management using Causal Machine Learning

JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) · Vol. 17 No. 1 (2025) · hlm. 31–44

DOI
10.20473/jkl.v17i1.2025.31-44
ISSN
2540-881X, 1829-7285
Terbit
2025-01-31
Penerbit
Universitas Airlangga
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc-sa/4.0

Abstrak

Introduction: Domestic waste management, which is a provincial-level program, is expected to reduce greenhouse gas (GHG) emissions for the sustainability of climate control efforts in Indonesia. Given this context, it is necessary to conduct a carbon efficiency analysis in Indonesia based on domestic waste management efforts. Methods: This study used an observational research design with a cross-sectional time approach. This study predicted the reduction in carbon emissions based on domestic waste management using causal machine learning by analyzing data on GHG emissions and domestic waste management from all provinces in Indonesia. An advantage of causal machine learning is its ability to assess the impact of treatment (domestic waste management) on the results (GHG emissions), as well as mitigating the effects of confounding variables. Results and Discussion: Despite improvements in waste management, several provinces experienced increased waste production, particularly from domestic waste and plastic waste. Analysis using the R programming language revealed that waste management is a significant variable (p = 0.011). However, data limitations posed challenges to comprehensive analysis. Conclusion: Achieving carbon efficiency requires serious waste management efforts. All provinces and cities/regencies must actively participate in program implementation. Routine reporting is essential to monitor the progress toward reducing GHG emissions.

Kata kunci

Jaringan kolaborasi para penulis

23 penulis · 63 tautan kepenulisan bersama · 26 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universitas Nahdlatul Ulama Surabaya
  • Universitas Airlangga
  • University of Indonesia
  • City St George's, University of London
  • STIKES Hang Tuah Surabaya
  • Ministry of Population and Family Planning
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Indah LutfiyaUniversitas Airlangga3
Shofi Futri PratiwiUniversitas Nahdlatul Ulama Surabaya3
Shofiyah Ajeng Sekar ArumUniversitas Nahdlatul Ulama Surabaya3
Farhan Qudrad AnugerahUniversitas Nahdlatul Ulama Surabaya1
Maisya Icha MahyayaUniversitas Nahdlatul Ulama Surabaya1
Atik Qurrota A’Yunin Al IsyrofiUniversitas Nahdlatul Ulama Surabaya1
Hario MegatsariUniversitas Airlangga1
Siti Rahayu NadhirohUniversitas Airlangga1
Erni AstutikUniversitas Airlangga1
Rita DamayantiUniversity of Indonesia1
Dian KusumaCity St George's, University of London1
Susy Katikana SebayangUniversitas Airlangga1
Diyah HerowatiMinistry of Population and Family Planning1
Afif KurniawanAfiliasi tidak diketahui1
Nuke AmaliaSTIKES Hang Tuah Surabaya1
Haning FirdausiUniversitas Nahdlatul Ulama Surabaya1
Tofan Agung Eka PrasetyaUniversitas Airlangga1
Nur Anisah RahmawatiUniversitas Airlangga1
Ratih DamayantiUniversitas Airlangga1
Ibrahim Al KhowwasUniversitas Airlangga1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JKL/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Analysis of Determinant Factors of Carbon Efficiency in Indonesia Based on Domestic Waste Management using Causal Machine Learning</dc:title>
	<dc:creator>Fasya, Abdul Hakim Zakkiy</dc:creator>
	<dc:creator>Ibad, Mursyidul </dc:creator>
	<dc:creator>El Muna, Kuuni Ulfah Naila </dc:creator>
	<dc:subject xml:lang="en-US">Carbon efficiency</dc:subject>
	<dc:subject xml:lang="en-US">Causal machine learning</dc:subject>
	<dc:subject xml:lang="en-US">Domestic waste management</dc:subject>
	<dc:description xml:lang="en-US">Introduction: Domestic waste management, which is a provincial-level program, is expected to reduce greenhouse gas (GHG) emissions for the sustainability of climate control efforts in Indonesia. Given this context, it is necessary to conduct a carbon efficiency analysis in Indonesia based on domestic waste management efforts. Methods: This study used an observational research design with a cross-sectional time approach. This study predicted the reduction in carbon emissions based on domestic waste management using causal machine learning by analyzing data on GHG emissions and domestic waste management from all provinces in Indonesia. An advantage of causal machine learning is its ability to assess the impact of treatment (domestic waste management) on the results (GHG emissions), as well as mitigating the effects of confounding variables. Results and Discussion: Despite improvements in waste management, several provinces experienced increased waste production, particularly from domestic waste and plastic waste. Analysis using the R programming language revealed that waste management is a significant variable (p = 0.011). However, data limitations posed challenges to comprehensive analysis. Conclusion: Achieving carbon efficiency requires serious waste management efforts. All provinces and cities/regencies must actively participate in program implementation. Routine reporting is essential to monitor the progress toward reducing GHG emissions.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2025-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>text/xml</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JKL/article/view/63433</dc:identifier>
	<dc:identifier>10.20473/jkl.v17i1.2025.31-44</dc:identifier>
	<dc:source xml:lang="en-US">JURNAL KESEHATAN LINGKUNGAN ; Vol. 17 No. 1 (2025): JURNAL KESEHATAN LINGKUNGAN; 31-44</dc:source>
	<dc:source>2540-881X</dc:source>
	<dc:source>1829-7285</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JKL/article/view/63433/31637</dc:relation>
	<dc:relation>https://e-journal.unair.ac.id/JKL/article/view/63433/31638</dc:relation>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
</oai_dc:dc>