This paper aimed to highlight the effects of conflict in Mongolia on trade policy and openness, by estimating the trade flows with neighbor countries （China and Russia）. Fourteen years＇ （2000-2013） data of Mongol...This paper aimed to highlight the effects of conflict in Mongolia on trade policy and openness, by estimating the trade flows with neighbor countries （China and Russia）. Fourteen years＇ （2000-2013） data of Mongolian imports and exports were collected and gone through principal component analysis （PCA） and empirical analysis for grouping various trades with China and Russia. The empirical analysis identified the determining factors of Mongolian trade flow and openness with China and Russia. Empirical analysis evidenced that Mongolian trade and openness policy raised bilateral trade between China and Russia, leaving a great influence on economic size. Two main questions represented as empirically tested by each sample country. How did Mongolian trade policy and openness influence trade flows between China and Russia and economic growth of Mongolia？ Did Mongolian trade policy and the bilateral trade with China and Russia increase on trade openness？ Finally, the study focused on the forecasts from 2016 to 2018 to examine Mongolian trade flows with China and Russia using ordinary least squares method and autoregressive-moving-average （ARMA） model. China-Mongolia-Russia trade flows will continue to dominate during the forecasted period. As shown by the structure of export and import, goods with China and Russia influenced the mutual trade amount. Moreover, China and Russia traded to continue with Mongolia for goods in long run. Trade policy and openness, the major contributor in Mongolian economy, are significantly playing roles in trade and economy.展开更多
With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantita...With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.展开更多
文摘This paper aimed to highlight the effects of conflict in Mongolia on trade policy and openness, by estimating the trade flows with neighbor countries （China and Russia）. Fourteen years＇ （2000-2013） data of Mongolian imports and exports were collected and gone through principal component analysis （PCA） and empirical analysis for grouping various trades with China and Russia. The empirical analysis identified the determining factors of Mongolian trade flow and openness with China and Russia. Empirical analysis evidenced that Mongolian trade and openness policy raised bilateral trade between China and Russia, leaving a great influence on economic size. Two main questions represented as empirically tested by each sample country. How did Mongolian trade policy and openness influence trade flows between China and Russia and economic growth of Mongolia？ Did Mongolian trade policy and the bilateral trade with China and Russia increase on trade openness？ Finally, the study focused on the forecasts from 2016 to 2018 to examine Mongolian trade flows with China and Russia using ordinary least squares method and autoregressive-moving-average （ARMA） model. China-Mongolia-Russia trade flows will continue to dominate during the forecasted period. As shown by the structure of export and import, goods with China and Russia influenced the mutual trade amount. Moreover, China and Russia traded to continue with Mongolia for goods in long run. Trade policy and openness, the major contributor in Mongolian economy, are significantly playing roles in trade and economy.
文摘With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.
