Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external envir...Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and not in others. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper focuses on the same factors to assess the impact of the macroeconomic indicators （extemal factors） on the parameters of static models predicting a local approximation of the crisis at the plant. To accomplish the purpose, a Spark set of 100 companies was compiled, including 50 companies which officially declared bankruptcy in the period of 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive1 The author compared two data sets： （1） microeconomic indicators--money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes （EBIT） to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, and current assets to revenue; and （2） external factors--index of real gross domestic product （GDP）, industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard ＆ Poor （S＆P） index, the Russian Trading System （RTS） index, and region. The aim of the comparison results paging classes ＂insolvent＂ and ＂non-bankrupt＂ is achieved using two methods： classification and discrimination. In both methods, computational procedures are realized with the use of algorithms linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors, impedes models about two times.展开更多
With the implementation of the ＂Development of Western China＂ strategy, this region has become the fastest growing economic area in China. However, rapid economic growth has resulted in a substantial increase in car...With the implementation of the ＂Development of Western China＂ strategy, this region has become the fastest growing economic area in China. However, rapid economic growth has resulted in a substantial increase in carbon emissions and affected energy reduction goals. In order to effectively control the rapid increase in carbon emissions across western China, we need a comprehensively analyze the main factors causing these increases. Here, we analyze the relationship between economic development patterns and carbon emissions. The findings suggest that consumption upgrades and industrial transformation have a positive correlation with carbon emissions in this region. We then conducted an econometric FGLS analysis on the relationship and its transmission mechanism between economic growth and CO2 emissions with cross-province panel data from 1991 to 2009. A positive correlation was found, and the relationship is more significant after the implementation of the western development strategy. The influence coefficient of change in primary, secondary and tertiary industries is 16.4. The influence coefficient of increased share of heavy industry and extractive industry in the secondary industry is 14.3, and the influence coefficients of per-capita living expenditure and per capita traffic expenditure are 5.6 and 6.5. Traditional population size and income scale have a weak impact on carbon emissions, and the influence coefficients of population size and income scale are only 0.73 and 0.86. GDP increases have a second major impact on the carbon emissions. Energy intensity has a negative relationship with carbon emissions and urbanization level has a positive relationship （coefficients are -8.2 and 4.65）.展开更多
文摘Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and not in others. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper focuses on the same factors to assess the impact of the macroeconomic indicators （extemal factors） on the parameters of static models predicting a local approximation of the crisis at the plant. To accomplish the purpose, a Spark set of 100 companies was compiled, including 50 companies which officially declared bankruptcy in the period of 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive1 The author compared two data sets： （1） microeconomic indicators--money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes （EBIT） to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, and current assets to revenue; and （2） external factors--index of real gross domestic product （GDP）, industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard ＆ Poor （S＆P） index, the Russian Trading System （RTS） index, and region. The aim of the comparison results paging classes ＂insolvent＂ and ＂non-bankrupt＂ is achieved using two methods： classification and discrimination. In both methods, computational procedures are realized with the use of algorithms linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors, impedes models about two times.
基金Humanity and Social Science Youth foundation of Ministry of Education of China (12YJC790082)National Social Science Fund Key Project (11AJL007)
文摘With the implementation of the ＂Development of Western China＂ strategy, this region has become the fastest growing economic area in China. However, rapid economic growth has resulted in a substantial increase in carbon emissions and affected energy reduction goals. In order to effectively control the rapid increase in carbon emissions across western China, we need a comprehensively analyze the main factors causing these increases. Here, we analyze the relationship between economic development patterns and carbon emissions. The findings suggest that consumption upgrades and industrial transformation have a positive correlation with carbon emissions in this region. We then conducted an econometric FGLS analysis on the relationship and its transmission mechanism between economic growth and CO2 emissions with cross-province panel data from 1991 to 2009. A positive correlation was found, and the relationship is more significant after the implementation of the western development strategy. The influence coefficient of change in primary, secondary and tertiary industries is 16.4. The influence coefficient of increased share of heavy industry and extractive industry in the secondary industry is 14.3, and the influence coefficients of per-capita living expenditure and per capita traffic expenditure are 5.6 and 6.5. Traditional population size and income scale have a weak impact on carbon emissions, and the influence coefficients of population size and income scale are only 0.73 and 0.86. GDP increases have a second major impact on the carbon emissions. Energy intensity has a negative relationship with carbon emissions and urbanization level has a positive relationship （coefficients are -8.2 and 4.65）.
