Find in Library

Decomposing main drivers of CO₂ emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO₂ emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO₂ emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO₂ emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO₂ emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO₂ emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO₂ emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.