Forest load capacity and carbon emissions in the world's largest forest nations: An EKC-based assessment for sustainable management

Abstract

The 2030 Sustainable Development Goals (SDGs) emphasize the crucial role of forests in regulating the global climate. This study investigates the relationship between the forest load capacity factor (F-LCF), which measures the biocapacity of a country's forests relative to human demand, alongside per capita income, urbanization, and CO2 emissions. A panel of the 10 largest forest nations from 1992 to 2021 is analyzed using a cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model, and the Environmental Kuznets Curve (EKC) hypothesis (an inverted-U trajectory of environmental impacts as income grows) is tested by comparing short- and long-term income elasticities. The EKC pattern is confirmed for the whole sample and for Russia, Brazil, Canada, the United States, and Australia, but not for China, India, Indonesia, Peru, and the Democratic Republic of Congo. The results show that higher F-LCF levels reduce CO2 emissions, while rising GDP and urbanization amplify them. These findings underscore the importance of sustainable forest management for achieving the climate target (SDG-13) and protecting terrestrial ecosystems (SDG-15) and call for tailored policies that reflect the forest dynamics of individual countries.