Ecological footprint analysis as a tool for advancing sustainable development goals (SDGs): Evidence from China

The growing environmental pressures linked to resource consumption and economic expansion in China necessitate a more integrated approach to sustainable development. This study explores the critical role of Ecological Footprint Analysis in aligning China’s development trajectory with Sustainable Development Goals (SDGs) by investigating the dynamic relationships between renewable energy consumption (REC), CO2 emissions, ecological footprint growth (EFG), governance effectiveness (GE), urbanization (UP), population growth (PG), and GDP per capita (GDPPC). Using time series data from 1995 to 2023, the study applies a Vector Error Correction Model (VECM), Autoregressive Distributed Lag (ARDL) analysis, and Fully Modified Ordinary Least Squares (FMOLS) to capture both long-term equilibrium and short-run dynamics. The findings reveal that ecological footprint growth significantly drives renewable energy adoption as environmental stress increases; governance effectiveness positively influences renewable energy use but with limited strength in mitigating emissions; GDP per capita initially raises CO2 emissions, validating the Environmental Kuznets Curve hypothesis; and urbanization, while contributing to short-term emissions, gradually promotes renewable energy transitions through infrastructure modernization. Policy implications underscore the urgent need for China to integrate circular economy practices, green urban planning, and stronger governance frameworks to effectively decouple economic growth from ecological degradation and achieve long-term sustainability goals.