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In recent years, environmental concerns have emerged as significant obstacles to economic advancement, leading to a critical examination of the complex relationship between environmental sustainability and economic expansion. With the economy expanding swiftly and environmental hazards intensifying, immediate action must be taken to address these problems. This necessitates innovative policies and practices that foster sustainable economic growth while minimizing environmental impact. To solve these issues, this study proposed an extensive analytical framework employing machine learning techniques, particularly artificial neural networks (ANNs), to investigate the Environmental Kuznets Curve (EKC) hypothesis. According to the EKC hypothesis, environmental degradation worsens before economic growth occurs. This study examines GDP per capita and key environmental indicators to measure economic development, using data from Anhui Province, China, from 2012 to 2019. The analysis provides empirical support for the theory and establishes a modeling framework that seamlessly integrates ML methodologies with EKC analysis, systematically examining and assessing the EKC's reliability for Anhui Province. The intricate interplay between Anhui Province's environment and economy, utilizing industrial waste gas, wastewater, and solid waste as critical indicators, evaluates environmental quality. At the same time, per capita GDP is the economic growth parameter. The findings indicate that the EKC does not adhere to an unchanging law but instead displays variations in distribution characteristics. The experimental results of the proposed ANN model demonstrate an impressive accuracy rate of 87%, outperforming five alternative models, including RF, KNN, DT, and LR. This superior model performance underscores the effectiveness of the ANN-based approach in capturing the intricate dynamics linking economic development and environmental pollution. [ABSTRACT FROM AUTHOR]

A debt-based economy requires the accumulation of more and more debt to finance economic growth, while future economic growth is needed to repay the debt, and so the cycle continues. Despite global debt reaching unprecedented levels, little research has been done to understand the impacts of debt dynamics on environmental sustainability. Here, we explore the environmental impacts of the debt-growth cycle in Indonesia, the world's largest debt-based producer of palm oil. Our empirical Agent-Based Model analyses the future effects (2018–2050) of power (im)balance scenarios between debt-driven economic forces (i.e. banks, firms), and conservation forces, on two ecosystem services (food production, climate regulation) and biodiversity. The model shows the trade-offs and synergies among these indicators for Business As Usual as compared to alternative scenarios. Results show that debt-driven economic forces can partially support environmental conservation, provided the state's role in protecting the environment is reinforced. Our analysis provides a lesson for developing countries that are highly dependent on debt-based production systems: sustainable development pathways can be achievable in the short and medium terms; however, reaching long-term sustainability requires reduced dependency on external financial powers, as well as further government intervention to protect the environment from the rough edges of the market economy. [ABSTRACT FROM AUTHOR]