Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China

• A BP network is combined with IPSO to study influential factors of CO2 emissions.
• PSO is improved by non-inertial weight coefficient and selective mutation strategy.
• 14 pre-selected factors are screened to 11 via correlation and significance test.
• Coal, thermal powers, vehicles and steel production should be paid more attention.
• BP-IPSO excels two compared methods in 5 evaluation indexes through empirical test.

The emission of carbon dioxide is the primary cause of the greenhouse effect, therefore a precise study of the influential factors of carbon dioxide emissions is of great significance to control the growth from the source. In this paper, non-inertial weight coefficients and selective mutation strategies are used in a particle swarm optimization algorithm, and the improved particle swarm was used to optimize the initial connection weights and thresholds of a traditional back propagation (BP) neural network. Consequently, a new BP model based on an improved particle swarm (IPSO) is established: improved particle swarm optimization-back propagation algorithm (IPSO-BP). In order to verify the overall performance and effectiveness of the proposed method, an empirical analysis of carbon dioxide emissions and influential factors was carried out in Hebei Province, China during the period 1978–2012. The results were compared with those of two other methods to prove that the proposed IPSO-BP algorithm could take full advantage of IPSO's global search capability and BP's local search capability, as well as overcome the problems of BP of random initial values and premature solutions. In addition, the precision of the fit and prediction of carbon dioxide emissions are improved notably.