Crude oil price (COP) data are time-series data that are assessed as having both volatility and heteroscedasticity variance. One of the best models that can be applied to address the heteroscedasticity problem is GARCH (generalized autoregressive conditional heteroscedasticity) model. The purpose of this study is to construct the best-fitted model to forecast daily COP as well as to discuss the prepared recommendation for reducing the impact of daily COP movement. Daily COP data are observed for the last decade, i.e., from 2009 to 2018. The finding with the error of less than 0.0001 is AR (1) -GARCH (1,1). The implementation of the model is applicable for both predicting the next 90 days for the COP and its anticipated impact in the future. Because of the increasing prediction, it is recommended that policymakers convert energy use to renewable energy to reduce the cost of oil use.

This research article analyzes the relationship between carbon dioxide gas emission, economic development, energy consumption and FDI in Asian states. The Autoregressive Distribution of Lag bounds tests has been applied for co-integration from 1970 to 2014. According to the results, there is a direct relationship between energy consumption and carbon dioxide gas emission. Moreover, there is no existence of a significant relationship between FDI and gas emission of carbon dioxide. In the long run, the coefficient value of FDI is insignificant and hence it shows confusion about the decision whether FDI will be a cause to increase the carbon dioxide gas emission or not. These results are suitable for the Asian countries that they should focus on the energy consumption that produce the carbon emission and damaged the environment severely and also put their intension on the growth part of this aspect. This study also provided the insight to the regulation making authorities while formulating policies on environmental condition of the country.

This study attempts to investigate the impact of urbanization on environmental degradation in the presence of economic growth, trade, and use of energy for Indonesia. For this purpose, this study uses CO 2 emission as endogenous indicators and GDP per capita, the use of energy, urbanization, and trade liberalization as exogenous indicators. Annual time series data are taken from World Development Indicators (WDI) for the period of 1970 to 2018. First of all, in order to check the characteristics of the indicator ADF and PP unit root tests are applied. Results indicate that Trade and Urbanization are stationary at a level while rests of all are at first difference. Further, the study uses the ARDL-bound test to check the co-integration in the model and verifies the existence of co-integration. The long run results are estimated by ARDL methodology. Results confirmed that there does not exit the EKC hypothesis in Indonesia because economic growth boosts the carbon production level in Indonesia. Energy consumption also creates environmental degradation while trade decreases the carbon emission level. Urbanization has not significantly influenced the level of the environment. It is just because of the country's high urban development, energy use is still less due to the less income of the majority of population, and this may be one of the explanations why urbanization is not affecting the country's carbon dioxide pollution.

Future natural gas (FNG) price is a collected data over the years and is a volatile movement in the market. In other words, FNG price is categorised as a time series data with volatility in both variance and mean, as well as most likely in some cases having heteroscedasticity problem. To come up with an estimated prediction model, some analysis tools, such as autoregressive integrated moving average (ARIMA) and generalised autoregressive conditional heteroscedasticity (GARCH), are introduced to find the best-fitted model having the smallest error value with high significance of probability value. This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value. Thus, it is required to check its heteroscedasticity by conducting an ARCH effect test. It is concluded that a data set has an effect of ARCH, so AR (p)-GARCH (p,q) model is then tested. AR (1)-GARCH (1,1) model is believed to be a best-fitted model having a significant P < 0.0001 with significantly small mean squared error and root mean squared error values, indicating that it has a very accurate prediction model. The forecasting model is to adjust the offered recommendation of policy for the government regarding the issue of high volatility of daily FNG prices in the future. We then offer a best-suited policy for some certain governments like Indonesia to give subsidy for targeted users in order to keep increasing their use of FNG that will expectedly affect their marketable product innovation and expansion, so economic growth in Indonesia is maintained.

Latar Belakang : Pertumbuhan dan pembangunan perekonomian suatu wilayah membutuhkan berbagai aspek-aspek penting untuk menjaga stabilitas dan kemajuan perekonomian dalam mengahadapi globalisasi dunia. Tujuan : Tujuan dari penelitian ini adalah untuk melihat pengaruh dari pertumbuhan ekonomi, pertumbuhan penduduk, serta tingkat kemiskinan terhadap Indeks Kualitas Lingkungan Hidup di Pulau Sumatera tahun 2011 – 2019. Metode : Dalam penelitian ini menggunakan jenis data deskriptif melalui pendekatan kuantitatif. Hasil : hasil dari penelitian ini dijelaskan melalui angka atau nilai yang telah diolah. Data yang digunakan dalam penelitian ini adalah data panel, data panel yaitu gabungan antara data time series dan cross section. Data time series dalam penelitian ini dapat dilihat dari sembilan tahun terakhir yaitu tahun 2011 hingga tahun 2019. Kesimpulan : Pertumbuhan Ekonomi bepengaruh negatif dan tidak signifikan terhadap IKLH di Pulau Sumatera tahun 2011 – 2019. Artinya yaitu, jika terjadi kenaikan pertumbuhan ekonomi, maka akan diringi dengan penurunan nilai indeks kualitas lingkungan hidup di Pulau Sumatera.