Analysis And Prediction On Energy Consumption And CO₂ Emissions Of Industrial Sector In China

With the living standard of our people being continually improved and industrialization and urbanization progress being speedily developed, the energy consumption of industrial sector in China will increased year by year. The environmental problem, especially energy-related industrial carbon dioxide continually increasing, will also be prominent. Therefore, a reasonable overall energy-use plan is of great significance for energy problem, environmental problem and sustainable development. This paper aims to identify the impacts of the main factors on China's industrial energy consumption and energy-related carbon dioxide emissions, and makes its long-term forecasts for the government and relevant agencies in addressing energy and environmental issues.Firstly, we used path analysis method to find the impacts of various factors on industrial energy consumption, choosing industrial GDP, industrial structure, energy consumption structure and the proportion of urban population as influencing factors. We got the conclusion that industrial GDP was main decisive factor, however industrial energy consumption structure played negative impact. We also combined gray forecasting methods with partial least squares regression to predict industrial energy consumption of China in 2015 and 2020.Secondly, potential factors influencing the energy-related CO2 emissions in China's industrial sector was analyzed based on logarithmic mean Divisa decomposition approach. This paper mainly decomposed the differences of industrial energy-related CO2 emissions among seven regions in 2007 in China, as well as the changes of industrial CO2 emissions over the period 1999-2007, taking carbon emission coefficient, fuel mix, industrial energy intensity, industrial share of GDP, economic growth and population factor as the influencing factors. Results showed that economic growth and population were two dominant factors driving industrial CO2 emissions increase in time-series and cross-region decomposition analysis. It was also found that economic growth, industrial energy intensity and population were the factors which make differences among regions. Furthermore, carbon emission coefficient and fuel mix were found to contribute a little to the changes of CO2 emissions.Finally, an energy-related industrial CO2 emissions predication model was established, according to LMDI decomposition method. Meanwhile three scenarios were assumed and described, including baseline scenario, energy-saving scenario and low-carbon scenario. Under these scenarios, China's energy-related industrial CO2 emissions were predicted in 2015 and 2020.