Determinants of the load capacity factor in China: A novel dynamic ARDL approach for ecological footprint accounting

The load capacity factor tracks a certain ecological threshold by comparing biocapacity and ecological footprint, thus enabling a comprehensive research on environmental degradation. It can be observed that the environmental degradation rises with decreasing the load capacity factor. However, until now, researchers have empirically considered environmental issues using ecological footprint, carbon dioxide, sulfur dioxide, nitrogen oxide emissions, and similar indicators. The use of these indicators can lead to the neglect of the supply side of environmental issues. To compensate for this shortcoming, this study aims to investigate the influence of human capital, natural resource rent, per capita income, and energy intensity on the load capacity factor, which focuses on environmental concerns on both the supply and demand sides. In this regard, the study utilizes a recently developed dynamic autoregressive distributed lag (ARDL) simulation model for China from 1981 to 2017. The results of dynamic ARDL demonstrates that an increase in income, energy intensity, and resource rent leads to a decline in the load capacity factor, while human capital improves environmental quality in the long-run. Moreover, according to Narayan and narayan (2010) approach, the environmental Kuznets curve hypothesis is valid for China because the short-run income elasticity is lower than the long-run elasticity (-0.644 was compared with -0.460). Based on the results, policy recommendations for China's sustainable development are presented.