Big Data-Driven Approaches for Predicting and Optimizing the Costs of Environmental Degradation

Abstract

This study presents a mathematical framework to evaluate the value of ecological services in areas targeted for land use projects. It first employs a composite coefficient set to establish the baseline ecological status of the land, taking into account services across air, water, and soil. This set provides a quantitative basis to assess the land's initial ecological value. The framework then uses a logistic growth model to predict how the project's implementation may impact the land's ecological services over time. The model captures the non-linear nature of ecological changes and identifies a “tipping point” where the project either maximizes ecological benefits or risks causing degradation. This information helps planners make more informed decisions on project scale and timing. The framework was applied to three cases: Kubuqi Desert greening, China Baowu Steel Group's steel production, and Wolong National Nature Reserve. In the Kubuqi Desert, greening efforts shifted the land's ecological value from negative to positive, with the model identifying a tipping point for maximizing benefits. For the Baowu Steel Group, the model indicated that increased industrial activity progressively worsened ecological conditions due to pollution, highlighting the need for mitigation strategies. In the Wolong Nature Reserve, the model showed minimal impact on the original ecological value, aligning with the reserve's conservation goals. Overall, this framework provides a versatile tool for measuring and predicting ecological impacts across different land use projects. By combining a composite coefficient set and a logistic growth model, it offers valuable insights into optimizing land use strategies, balancing development with environmental sustainability, and guiding policy decisions based on ecological service value.