Building Carbon Emission Scenario Prediction Using STIRPAT and GA-BP Neural Network Model

As a major province of energy consumption and carbon emission, Jiangsu Province is also the construction industry, which key region potential area for emission reduction in China. The research prediction industry great significance development low-carbon policies other cities. purpose this paper to study influencing factors whole life cycle emissions buildings Province, predict based on main factors. This uses balance sheet splitting method, STIRPAT model, gray correlation method GA-BP neural network model Province. results show that resident population, urbanization rate, steel production, average distance road transportation, labor productivity enterprises have catalytic effect emissions; GDP per capita added value tertiary suppressive effect; reached historical peak 2012; future generally decreasing trend. provide possibility refine basis guidance subsequent emission.