Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method—A case study in Henan Province, China

Abstract

Taking Henan Province of China as an example, we computed and analyzed the ecological footprint (EF) in . The results showed that the EF in Henan Province quadrupled in the 23 years, but its ecological carrying capacity (EC) was rather low and was in a state of slow decline, indicating that Henan's ecological deficit (ED) had become a remarkable social problem. Therefore, the major drivers of the EF's change were analyzed. According to the simulations with STIRPAT model, the major drivers of Henan's EF were human population (P), GDP per capita (A 1 ), quadratic term of GDP per capita (A 2 ), percent of economy excluded in the service sector (Ta 1 ) and percent of urban population (Tb 1 ). However, these drivers themselves had strong collinearity, which might produce some uncertain impact to the final results. In order to avoid the impact of collinearity, the method of partial least squares (PLS) was used. The results showed that the major drivers of EF were P, A 1 , A 2 and Tb 1 . Ta 1 was excluded. Compared with the results by the STIRPAT model, which showed that P is the most dominant driver and the effect of the other drivers could almost be ignored, the results by PLS method were considered as more reasonable and acceptable because the impacts of the A (Affluence) and T (Technology) conditions to the regional EF were still too important to be ignored. In addition, the results acquired by both methods showed that the curvilinear relationship between economic development and ecological impact (EF) or the classical EKC hypothesis didn't exist in Henan Province.

Analysis of major driving forces of ecological footprint based on the STRIPAT model and RR method: A case of Sichuan Province, Southwest China

“…Additional factors can be added to the basic STIRTPAT model (York et al 2003;Dietz et al 2007;Jia et al 2009). The specific and measurable factors which may have influenced the EF include: total population (P), GDP per capita (A 1 ) and the quadratic term of GDP per capita (A 2 ), percent of GDP from industry (T 1 ), urbanization rate (T 2 ) and the quadratic term of T 1 (T 1 ' ) and of T 2 (T 2 ' ), fixed assets investment, energy intensity and others.…”

“…They showed that population size and affluence are the principal drivers of anthropogenic environmental stressors. Jia et al (2009) andWang et al (2010) used STIRPAT to identify the major drivers of EF in Henan Province and Jilin Province respectively and showed that the model is an effective way to assess sustainability of anthropic systems (Wang et al 2010). They modified the STIRPAT model to a logarithmic regression model (York et al 2003), using OLS (ordinary least square) regression method to calculate the coefficients of independent variables.…”

Analysis on major drivers of cement consumption during the urbanization process in China

“…For these reasons, a quantitative exploration of drivers related to cement consumption is urgent that can facilitate national decision makers and local urban planners to track cement consumption trend, to formulate management strategies and policies, and then to promote the development of resource-saving and environment-friendly city. Recently, the STIRPAT model has been extensively harnessed by researchers in economic environmental analysis for its extendibility (Fan et al, 2006;Ji and Chen, 2015;Jia et al, 2009;Lin et al, 2009;Liu et al, 2015;Shahbaz et al, 2016;Wang et al, 2013a). These studies, however, rarely concern about which factors are driving or reducing regional cement consumption in China.…”

County-level patterns of cropland and their relationships with socio-economic factors in northwestern China

“…This method combines the features of principal component analysis and multiple linear regression (Abdi, 2010) and can produce regression models, provide a correlation analysis and simplify data structure in one algorithm (Wang, 1999). This approach is particularly useful when independent variables display strong collinearity (Wang, 1999(Wang, , 2006Jia et al, 2009). Ordinary multiple regression models are often limited by the sample size.…”