Urwawuska Ladini

Generalized Structured Component Analysis pada Hubungan antara Pengelolaan Lingkungan, Faktor Sosial, dan Kualitas Lingkungan

Introduction

Generalized structured component analysis pada hubungan antara pengelolaan lingkungan, faktor sosial, dan kualitas lingkungan . Teliti hubungan pengelolaan lingkungan, faktor sosial, dan kualitas lingkungan di 34 provinsi Indonesia (2016-2020) menggunakan Generalized Structured Component Analysis (GSCA). Temukan pengaruh signifikan antar variabel.

Abstract

Salah satu dampak pemanasan global yang masih dirasakan saat ini adalah terkait masalah lingkungan. Beberapa faktor mempunyai hubungan dengan kualitas lingkungan, baik dari segi sosial maupun ekonomi. Penelitian ini bertujuan untuk mengetahui bagaimana hubungan antara partisipasi dan pengelolaan lingkungan, faktor sosial, dan kualitas lingkungan menggunakan analisis SEM yaitu metode generalized structured component analysis (GSCA). Data yang digunakan dalam penelitian ini menggunakan data dari 34 provinsi di Indonesia dari tahun 2016-2020 yang memberikan kesimpulan bahwa pada model pengukuran, semua indikator pembentuk peubah laten valid dan masing-masing peubah laten mempunyai validitas konvergen yang baik yang dapat menjelaskan masing-masing indikator. Begitupun pada model struktural bahwa seluruh jalur pengaruh antara ketiga peubah mempunyai pengaruh yang signifikan. Hasil analisis menggunakan GSCA ini memberikan kebaikan model sebesar 54.4% yang dapat dijelaskan oleh seluruh peubah laten dan indikator yang ada didalam model.

Review

The study, "Generalized Structured Component Analysis pada Hubungan antara Pengelolaan Lingkungan, Faktor Sosial, dan Kualitas Lingkungan," addresses a critically important and timely issue concerning environmental quality, especially in the context of ongoing global warming impacts. The research aims to comprehensively understand the interrelationships between environmental management (including participation), social factors, and environmental quality in Indonesia. The selection of Generalized Structured Component Analysis (GSCA), a sophisticated Structural Equation Modeling (SEM) technique, highlights a rigorous methodological approach to dissecting these complex, multi-faceted interactions and provides a robust quantitative framework for analyzing sustainability challenges. Methodologically, the research utilizes GSCA to analyze a substantial dataset covering all 34 provinces in Indonesia from 2016 to 2020. This extensive temporal and spatial scope offers a strong empirical foundation for the study's conclusions. The abstract reports highly promising results, indicating that the measurement model successfully validates all indicators forming the latent variables, demonstrating good convergent validity and reliable measurement of the constructs. Crucially, the structural model reveals significant pathways of influence among environmental management, social factors, and environmental quality, emphasizing their interconnectedness. The model's explanatory power, indicated by 54.4% of the variance explained by the latent variables and indicators, suggests a substantial contribution to understanding these complex dynamics. This study's strengths lie in its application of an advanced statistical method (GSCA) and its use of a comprehensive, national-level panel dataset, which enhances the generalizability and relevance of its findings for Indonesia. It effectively highlights the intricate connections between human actions and environmental outcomes. However, the abstract could be improved by providing more explicit details on the specific indicators used for "social factors," "environmental management," and "environmental quality," which would allow for a deeper appreciation of the study's scope and implications. While significant pathways are identified, the abstract does not elaborate on the direction or relative magnitude of these influences, information vital for actionable policy recommendations. Future research could build upon these findings by exploring specific causal mechanisms, investigating regional variations within Indonesia, and detailing the policy implications derived from the significant relationships uncovered.

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