<p><strong>Abstrak</strong></p><p>Tingginya tingkat kemiskinan di Jawa Tengah menunjukkan proses pembangunan ekonomi yang belum bisa meningkatkan kesejahteraan masyarakat secara merata. Dengan demikian, diperlukan adanya analisis untuk mengetahui factor-faktor yang mempengaruhi kemiskinan dalam rangka mengatasi kemiskinan. Tujuan dari penelitian ini yaitu menganalisis Produk Domestik Regional Bruto (PDRB), Tingkat Kemiskinan, Indeks Pembangunan Manusia (IPM) yang mempengaruhi kemiskinan pada 35 Kabupaten/Kota di Provinsi Jawa Tengah dari tahun 2011 hingga 2015. Penelitian ini menggunakan data sekunder dengan data cross-section terdiri dari 35 Kabupaten/Kota di Provinsi Jawa Tengah dan data time-series yaitu tahun 2011-2015. Alat analisis yang digunakan dalam dalam mengestimasi model regresi data panel yaitu <em>Fixed Effect Model (FEM)</em> atau disebut juga <em>Least Square Dummy Variable</em>. Hasil penelitian menunjukkan bahwa variabel laju pertumbuhan PDRB berpengaruh positif dan signifikan terhadap Tingkat Kemiskinan. IPM berpengaruh negatif dan signifikan terhadap Tingkat Kemiskinan. Sedangkan Tingkat Pengangguran Terbuka berpengaruh positif dan signifikan terhadap Tingkat Kemiskinan.</p><p> </p><p>Kata kunci:      Kemiskinan, Laju Pertumbuhan PDRB, Indeks Pembangunan                                 Manusia (IPM), <em>Fixed Effect Model</em>.</p><p align="center"><strong> </strong></p><p align="center"><strong> </strong></p><p><strong>Abstract</strong></p><p><em>The high level of poverty in Central Java shows unreliable development that still cannot increase prosperity equally. Hence, analysis is required to identify several factor that affect. This research’s purpose is to identify Gross Domestic Regional Product (GDRP), Unemployment Level, Human Development Index (HDI) that affect the poverty level of the poverty level of 35 Districts/Cities of Central Java Province from 2011 until 2016.</em><em> </em><em>This research uses secondary data containing 35 Districts/Cities of Central Java on cross section data and 2011 until 2016 on time series data. The analytical method of this research is Fixed Effect Model (FEM) or Least Square Dummy Variable (LSDV). The results of this research show that Growth of GDRP gives positive and significant effect for poverty level. HDI give negative and significant effect for poverty level. On the other side, Unemployment Level give positive and significant effect for poverty level.</em></p><p><em> </em></p><p><em>Keyword:        Poverty, Growth of GDRP, Human Development Index (HDI), Unemployment, Fixed Effect Model.</em></p>

Rules (formal constraints) are expected to be able to shape human behavior to act based on what should and should not do accordingly. The connection of rules in the energy sector to carbon dioxide emissions depends on how far the rules are able to shape behavior as expected. The purpose of this study is to analyze the effect of rules (formal constraints) on CO 2 emissions in Indonesia. Other variables used in this study are energy consumptions (fossil energy and renewable energy), and population growth which are in line with the previous study. The method used to help answer the research question is multiple linear regression analysis with ordinary least square approach. Using time series data in the period of 1990-2017 in Indonesia, this study found that fossil energy consumption and population growth have positive and significant impacts on CO 2 emissions in Indonesia. Meanwhile, the consumption of renewable energy and the rules (formal constraints) have negative effects on the emissions of CO 2 produced. These results show that rules (Formal Constraints) can indeed shape behavior, in this case the reduction of CO 2 emissions.

This research explores the tourism sector's role in poverty reduction in the Bangka Belitung Islands Province. Independent variables used in this study include the number of tourists, the number of business units in the tourism sector, and the number of employment in the tourism sector. We use data from the Central Bureau of Statistics consisting of 6 districts and one city in the Bangka Belitung Islands from 2013-to 2018. Ordinary least square is used in this study. The results of this study indicate that the three independent variables in the survey simultaneously affect the dependent variable. The number of tourists and the number of business units in the tourism sector has adverse and significant effects on poverty reduction. In contrast, the employment variable in the tourism sector partially can't impact poverty reduction.