Ecological Indicators (Feb 2024)
Multi-dimensional factor coupling-driven mechanism of spatio-temporal evolution of energy ecological footprint: Evidence from China
Abstract
The energy ecological footprint measures the environmental impact of energy consumption and is an important indicator for assessing sustainable development. This study introduced the concept of embodied carbon and developed a new energy ecological footprint accounting model. The Spatial Durbin Model was used to analyze the spillover effects of various factors influencing the energy ecological footprint. The quantile regression method was applied to examine the phased impact of each influencing factor on spatio-temporal evolution of the energy ecological footprint. A nested matrix was constructed to classify the quantile response types of each influencing factor and the spatio-temporal transition types. Additionally, a governance model for regional energy ecological footprint reduction was proposed. The results showed that: First, the spatio-temporal distribution of the energy ecological footprint of China's 29 provinces was not completely random while the energy ecological footprint of each province had significant spatial correlation characteristics. Moreover, the change of energy ecological footprint affected by its neighboring provinces. Second, industrial structure, environmental regulation, and urbanization level had a critical effect on the spatio-temporal evolution of China's energy ecological footprint. Furthermore, each factor will affect the change of the energy ecological footprint in the surrounding areas through the spatial transmission mechanism of the influencing factors, and there was a significant spatio-temporal spillover effect. Third, there were driving and constraint modes of spatio-temporal transition in the process of spatial agglomeration of energy ecological footprint in each province. Quantile regression model can well explain the driving mechanism of each driving factor on the spatio-temporal transition of energy ecological footprint, and there was a strong nesting between the quantile of driving factors at different response stages and the types of spatio-temporal transition of energy ecological footprint. These findings can serve as a basis for decision-making and help formulate targeted policies, considering the “common but differentiated” approach, to reduce China's energy ecological footprint at the provincial level.
