Pathways to progress sustainability: an accurate ecological footprint analysis and prediction for Shandong in China based on integration of STIRPAT model, PLS, and BPNN.

Abstract

The world has been challenged by achieving the plausible goal of sustainable development. This study aims to evaluate the ecological footprint and ecological carrying capacity and their driving factors of Shandong province in China from 1994 to 2017. Back propagation neural network method is adopted to predict the ecological footprint from 2018 to 2030. The findings are as follows: (1) The growth of ecological footprint has caused the ecological deficit in Shandong. (2) With regards to population, the increase of total population and the urbanization rate will incur the expansion of ecological footprint. (3) In terms of affluence, the elasticity coefficients of GDP per capita, the production value of industrial sectors, and the proportion of output value of the secondary industry in GDP are 0.068, 0.064, and 0.130 respectively. (4) In terms of technology, the elasticity coefficients of internal expenditure on R&D in GDP and patent number are 0.096 and 0.047 respectively, indicating that technological progress can promote ecological footprint in a short term. (6) The results of the prediction show that the ecological footprint of Shandong from 2018 to 2030 in the policy-regulation scenario is far less than that of the business-as-usual scenario. The policy recommendations are suggested to tackle the sustainable development challenges.