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"Skripsi ini bertujuan untuk menyelidiki hubungan potensial antara kemiskinan, ketimpangan pendapatan, dan perubahan iklim dalam konteks Indonesia. Meskipun terdapat konsensus yang terbukti dalam beberapa literatur mengenai potensi hubungan positif antara kemiskinan dan perubahan iklim, upaya pemerintah yang terfokus dalam mengatasi masalah kemiskinan dan iklim masih dapat dikatakan belum maksimal. Hal ini terlihat dari alokasi anggaran yang semakin berkurang untuk isu-isu terkait upaya pencegahan dan adaptasi terhadap perubahan iklim yang kini menjadi semakin relevan. Rencana awal dalam penulisan skripsi ini adalah untuk menganalisis hubungan antara kemiskinan, ketimpangan pendapatan, dan perubahan iklim menggunakan teknik regresi variabel instrumental (IV) direncanakan untuk mengatasi masalah endogenitas yang terkait dengan variabel endogen utama, yaitu kemiskinan dan ketimpangan. Namun, karena adanya keterbatasan yang teridentifikasi dalam variabel instrumental, termasuk pelanggaran asumsi monotonicity dan koreksi bias yang tidak signifikan yang diamati dari uji Oster, pendekatan alternatif seperti ordinary least squares (OLS) dan panel fixed effect model akhirnya digunakan sebagai model estimasi utama dalam penulisan skripsi ini. Hasil yang diperoleh dari model OLS dan panel fixed effect mengungkapkan hubungan yang tidak signifikan secara statistik antara kemiskinan, ketimpangan, dan perubahan iklim. Hal ini menunjukkan bahwa upaya pengentasan kemiskinan dan mitigasi perubahan iklim di Indonesia mungkin tidak selalu berjalan seiring. Korelasi yang didapat dari hasil estimasi utama pada skripsi ini dapat dipengaruhi oleh berbagai faktor, termasuk inklusi variabel mediasi seperti PDB, kemungkinan adanya reverse causality antara kemiskinan dan perubahan iklim, dan sifat yang kompleks dari kemiskinan. Secara kesimpulan, temuan ini menunjukkan bahwa pemerintah dapat fokus pada penanggulangan kemiskinan dan mengatasi perubahan iklim secara terpisah, tanpa terlalu memperhatikan korelasi potensial antara kedua masalah ini. Namun, penting juga untuk tetap mengkonsiderasikan limitasi dari studi ini untuk pengambilan kebijakan dan studi lanjutan kedepannya, termasuk ketiadaan analisis simultan tentang kemiskinan, ketimpangan, dan perubahan iklim, serta tidak tersedianya variabel instrumental yang cocok untuk regresi IV. 

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This thesis investigates the relationship between poverty, inequality, and climate changes in the context of Indonesia. Despite the existing consensus in the literature regarding the potential positive link between poverty and climate changes, there is a notable lack of focused government efforts in addressing the poverty-climate problems. This is evident from the decreasing budget allocations for climate-related issues. Initially, instrumental variable (IV) regression was planned to address potential endogeneity problems associated with the main endogenous variables, namely poverty and inequality. However, due to identified limitations in the instrumental variables, including the violation of monotonicity assumptions and insignificant bias correction observed from the Oster test, alternative approaches such as ordinary least squares (OLS) and panel fixed effect models were employed. The results obtained from the OLS and panel fixed effect models reveal a non-statistically significant relationship between poverty, inequality, and climate changes. This suggests that the pursuit of poverty alleviation and climate change mitigation in Indonesia may not necessarily go hand in hand. The observed correlation could be influenced by various factors, including the inclusion of mediation variables like GDP, potential reverse causality between poverty and climate changes, and the multifaceted nature of poverty. In conclusion, the findings indicate that the government can focus on tackling poverty and addressing climate changes separately, without overly concerning themselves with the potential correlations between these two issues. However, it is important to acknowledge the limitations of this study, including the absence of simultaneous analysis on poverty, inequality, and climate changes, as well as the unavailability of suitable instrumental variables for IV regression."

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