Hazardous waste from metal processing industries increases heavy metal contamination in ecosystems, threatening environmental health and regional sustainability. This study suggests a resilient and human-centered environmental monitoring approach that incorporates machine learning and decision analytics to address these challenges in line with Industry 5.0's goals. By utilising a PRINCIPAL COMPONENT REGRESSION (PCR)-based predictive model, the approach addresses variability in environmental data, predicting levels of heavy metals like lead, zinc, nickel, arsenic, and cadmium, frequently beyond regulatory thresholds. The suggested PCR-based model outperforms conventional models by lowering mean absolute error (MAE) to 2.9339, mean absolute percentage error (MAPE) to 0.0358, and nearly the same mean square error (MSE). This study introduces a more interpretable and computationally efficient alternative to existing predictive models by introducing a novel integration of PCR with machine learning for environmental monitoring. By predicting and optimising environmental outcomes, validation against test datasets confirmed its ability to optimise impurity control. After process adjustments, the average concentrations of lead, nickel, and cadmium were reduced from 13.23 to 11.26 mg/L, 2.83 to 2.70 mg/L, and 2.15 to 1.88 mg/L, respectively. This research supports sustainability, resilience, and decisionmaking aligned with Industry 5.0, offering scalable solutions and insights for global industries.HighlightsChemical plants' environmental risk is evaluated using a machine learning algorithmFor better monitoring, the PCR method forecasts process variables and interactionsIt identifies the key factors that affect the environmental risks in soil and waterAs a result, the local ecosystem's levels of toxic metals have notably decreasedInsights for managing environmental risks aligned with Industry 5.0 principles

Society could sustain the impact of climate change by adapting to the change and mitigating risks from adverse effects of increasing changes, so that it can continue maintaining its prospect and improving wellbeing. Nevertheless, climate change is more or less affecting society's functions at different scales, including both individuals and communities. In this review, we discuss the relationship between society and climate change in China from the aspects of the needs at different socioeconomic developing stages. The relationship as well as the current spatial pattern and future risks of the climate change impacts on societies are summarized. The complexity of social and climatic systems leads to the spatial heterogeneity of climate impacts and risks in China. To more effectively leverage increasing knowledge about the past, we advocate greater cross-disciplinary collaboration between climate adaption, poverty alleviation and Nature-based Solutions (Nbs). That could provide decision makers with more comprehensive train of thoughts for climate policy making.