This study discusses the analysis of the impact of infrastructure development,human capital and trade openness on regional economic growth in Indonesia using the paneldata method. The model was built based on the Solow growth model using road infrastructure,electricity infrastructure, health infrastructure, life expectancy, mean years of schooling and tradeopenness in 34 provinces in Indonesia. Estimation results obtained from this study using the fixedeffects model indicate that regional economic growth in Indonesia is influenced by electricityinfrastructure, health infrastructure, mean years of schooling, life expectancy, and trade openness.Whereas road infrastructure has a negative and not significant effect on regional economic growthin Indonesia. Life expectancy has the biggest impact on regional economic growth followed bymean years of schooling, health infrastructure, electricity infrastructure, and trade openness.

This study discusses the analysis of the impact of infrastructure development, human capital and trade openness on regional economic growth in Indonesia using the panel data method. The model was built based on the Solow growth model using road infrastructure, electricity infrastructure, health infrastructure, life expectancy, mean years of schooling and trade openness in 34 provinces in Indonesia. Estimation results obtained from this study using the fixed effects model indicate that regional economic growth in Indonesia is influenced by electricity infrastructure, health infrastructure, mean years of schooling, life expectancy, and trade openness. Whereas road infrastructure has a negative and not significant effect on regional economic growth in Indonesia. Life expectancy has the biggest impact on regional economic growth followed by mean years of schooling, health infrastructure, electricity infrastructure, and trade openness.

Forests are unique resources and environments because, in general, they provide many benefits. Changing the function of forest areas to other functions is inseparable from economic development. As a developing country, Indonesia's economy is still dependent on natural resources to support its development. Economic integration through trade openness plays a vital role in economic growth. Policies that enhance the country's ability to trade will help the economy to develop. The more open the trade regime will make the country specialize in semi-finished input products, its competitive advantage. However, economic integration also creates negative externalities in the form of increased deforestation. This study explores the effect of trade openness on deforestation using a panel data method in 20 provinces in Indonesia from 2008-2018. Not many studies have focused on trade openness, large plantations, and social interactions as the driving forces behind deforestation in Indonesia. From the estimation results of the model, it is known that trade openness, economic growth, and activities of logging and forest conversion each contribute to changes in forest cover. If the commodity price rises, it will impact decreasing forest cover. Also, increasing population and density have decreased forest cover because land outside the forest area is limited.

Population growth and the construction of settlements and industrial estates continue to increase at an unprecedented rate that has created gains and losses on environmental quality. The trend of population growth shows a declining trend but is not directly proportional to the fluctuating water quality index over the past ten years. The study uses secondary data with the quantitative approach using the panel data Fixed Effect Model (FEM) with Generalized Least Squares (GLS) to examine socioeconomic indicators in 34 provinces on water quality in Indonesia. Through analysis in this study shows that explanatory variables of the number of population and population density have a negative and significant effect on water quality in Indonesia of 4.69 and 1.95—ceteris paribus. The control variables of the number of establishments of micro and small scale manufacturing industry, and a group of workers, GRDP per capita, and realization of foreign direct investment show negative and significant results on water quality in Indonesia. It indicates that environmental management in Indonesia experiences a higher pressure from the utilization of ecological resources compared to efforts to improve the environment itself. Whereas household control variables of households and improve sanitation, the volume of water distributed by water supply establishment and the squared of GRDP per capita show positive and significant results on water quality in Indonesia, which shows that this is evidence of the government's success in managing the environment better.

The threat of TB continues to occur in the world. In 2018, 10 million people suffered from TB, and 1.5 million people die from this infectious disease. Referring to target 3 of Sustainable Development Goals (SDGs) goals 03 regarding good health and well-being, by 2030, end the epidemic of AIDS, TB, malaria, and neglected tropical diseases and combat hepatitis, water-borne diseases, and other communicable diseases. Based on data from the WHO, Indonesia ranks 3rd for TB cases globally. The estimated population suffering from TB is 845,000 cases; only 68 percent of cases were found and treated in 2018. The high number of TB cases in Indonesia could threaten the golden generation's opportunity in the next 2025 demographic bonus, where the number of productive age population is higher than the population non-productive age. This study found that population factors such as population, population density, and the number of poor people had a positive and significant effect on TB cases. In contrast, the GRDP per capita, the number of health workers, and literacy rates negatively affected the TB cases.Furthermore,environmental factors from the availability of proper sanitation and toilet facilities show a negative but insignificant effect on TB cases.

On March 11, 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic due to its Case Fatality Rate (CFR) and numerous determining factors. Therefore, this study aims to identify the sociodemographic, environmental, community mobility, and health indicators associated with the pandemic and CFR. The design applied is an ecological study with data collected from 34 provinces in Indonesia and analyzed using the Spearman correlation. The results showed that independent variables such as population, density, the number of workers engaged in micro and small enterprises, hotel rooms, stroke, diabetes, general practitioners, specialists, nurses, PHC per district, PHC plus, and COVID-19 referral hospitals were positive and significant to the COVID-19 cases (p<0.01). Poverty in rural areas, elderly in rural and urban areas, sanitation, and hypertension were positive and significant to COVID-19 cases (p<0.05). Retail and recreation, grocery and pharmacy, transit stations, and residential areas were negative and significant to COVID-19 cases (p<0.01). Population growth rate, workplaces, and poverty in rural areas were negative and significant to COVID-19 cases (p<0.05). Elderly in rural and urban areas, urban slum households, immunization, and hypertension were positive and significant to CFR (p<0.05). The government needs to prevent the spread of the virus in provinces, especially in areas with high population and density, increased elderly population, low immunization rate, poor sanitation, and a significant number of residents living with comorbidities, such as stroke, hypertension, and diabetes. Furthermore, beds, tents, emergency buildings, oxygen cylinders, and multilevel referral systems between health facilities need to be provided. The government also needs to limit the inflow of people abroad, optimize the Work from Home (WFH) policy, and limit community mobility outside the home, especially in Bali.

Population growth and urban development continue to increase at an unprecedented rate and create pressure on the quality of clean water. Previous empirical studies have shown that uncontrolled population growth has a negative and significant impact on the quality of clean water. In the case of Indonesia, the population growth trend has decreased every year, but not followed by an index of water quality that should have increased. This study examines population growth in the water quality index in 33 provinces in Indonesia during 2013-2017 using the panel method of fixed-effect models. This study found that population growth has a negative and significant effect on the water quality index in Indonesia. Every 1000 population in-crease will reduce the water quality index by 0.04 (ceteris paribus), which indicates that there is a need for control of the population growth rate to be more aware of the preservation of a sustainable environment.

Population growth and the construction of settlements and industrial estates continue to increase at an unprecedented rate that has created gains and losses on environmental quality. The trend of population growth shows a declining trend but is not directly proportional to the fluctuating water quality index over the past ten years. The study uses secondary data with the quantitative approach using the panel data Fixed Effect Model (FEM) with Generalized Least Squares (GLS) to examine socioeconomic indicators in 34 provinces on water quality in Indonesia. Through analysis in this study shows that explanatory variables of the number of population and population density have a negative and significant effect on water quality in Indonesia of 4.69 and 1.95—ceteris paribus. The control variables of the number of establishments of micro and small scale manufacturing industry, and a group of workers, GRDP per capita, and realization of foreign direct investment show negative and significant results on water quality in Indonesia. It indicates that environmental management in Indonesia experiences a higher pressure from the utilization of ecological resources compared to efforts to improve the environment itself. Whereas household control variables of households and improve sanitation, the volume of water distributed by water supply establishment and the squared of GRDP per capita show positive and significant results on water quality in Indonesia, which shows that this is evidence of the government's success in managing the environment better.