Study On Factor Decomposition And Network Flow Analysis Of Carbon Emission In Power System

To meet the challenge of climate change which has been a major challenge facing the world, controlling carbon emissions becomes an international consensus. As the largest carbon emission emitter in the world, China is under tremendous pressure of reducing carbon emissions. To support social and economic development, China’s power sector consumes a large amount of fossil fuels, producing a huge number of carbon emissions. With the steady growth of China’s economy, carbon emissions of power sector are expected to continue growing. Therefore, controlling carbon emissions in power sector is of great importance to realizing China’s carbon emission reduction.To formulate reasonable mitigation policies, it is very important to mine the key factors affecting carbon emissions of power sector. In this paper, Log-Mean Divisa Index (LMDI) decomposition has been conducted to study the driving forces behind carbon emissions in power sector at provincial level for the period of 1997-2012. Except for economic activity and energy intensity, the contributions of electricity intensity, electricity transmission and generation structure effect varied with provinces. Based on these results, we discuss potential strategies for reducing carbon emissions in China’s power sector.The realization of carbon emission reduction requires proper reduction target. By determining reasonable responsibility of carbon emission reduction, proper reduction target can be set for all parties. The premise of reasonable responsibility is accurate carbon emission accounting. By considering the topological structure of power system and the characteristics of electricity transmission, carbon emission flow analysis has been successfully used to calculate carbon emissions associated with electricity consumption. In this paper, a new calculation model is proposed to handle the issue of carbon emissions calculation in a hybrid market. The proposed method can take the difference between bilateral transaction and pool transaction into consideration. And calculation results can provide consumers with carbon emission information corresponding to their power transactions signed with power generation enterprises. From the perspective of reducing carbon footprints, consumers may be more likely to conclude bilateral electricity transactions with low-carbon power generation enterprises, which can promote the development of low-carbon electricity, such as wind power and solar power.