Middle-income economies contribute significantly to global CO₂ emissions as they pursue economic development, creating an urgent need to understand emission drivers. This article investigates the impact of renewable energy use, financial development, and industrialization on CO₂ emissions in 71 middle-income countries (32 upper-middle income, 39 lower-middle income) between 2002 and 2020. We used the advanced Generalized Method of Moments Panel Vector Autoregression (GMM-PVAR) approach to address endogeneity and reveal complex relationships among the variables. Our findings revealed that renewable energy utilization had no substantial influence on emissions reduction in either upper- or lower-middle-income countries, challenging conventional policy assumptions. Financial development consistently reduces emissions across both income groups (−0.08% and −0.06%, respectively). Industrialization has heterogeneous effects, increasing emissions by 2.03 percent in upper-middle-income countries and with no effect in lower-middle-income countries. Granger causality tests illustrated a bidirectional relationship connecting CO₂ emissions and financial development, whereas no causal link was found between CO₂ emissions and renewable energy use. These findings prove the importance of coordinated policies that strengthen financial systems and sustainable industrial practices.

Land subsidence from coal mining has shaped new artificial aquatic ecosystems, these subsidence lakes are known for their restricted ecological system, water pollution, and extreme habitat conditions. However, knowledge concerning the community structure of plankton in these types of water bodies is still limited. Therefore, both phytoplankton and zooplankton communities’ abundance, distribution, and diversity, as well as relations of these communities to physicochemical water quality variables were analyzed, alongside the interaction between phytoplankton and zooplankton groups. The results indicate zooplankton abundance was 842.375 to 186,355.0 ind./L. Biomass ranged from 0.3408 to 10.0842 mg/L. Phytoplankton abundance varied between 0.541 × 10⁶ cell/L and 52.340 × 10⁶ cell/L while phytoplankton wet biomass ranged from 0.5123 to 5.6532 mg/L. Pearson correlation analysis revealed that both the zooplankton and phytoplankton total densities were significantly correlated with nutrients (TN, TP, PO₄³⁻) and COD_cr; zooplankton abundance was significantly correlated with phytoplankton abundance. According to the biodiversity index of Shannon–Wiener, both phytoplankton and zooplankton revealed less biodiversity in the subsidence water region than in the Huihe river system and Xiangshun canal, with values ranging from 0.20 to 2.60 for phytoplankton and 1.18 to 2.45 for zooplankton; however, the phytoplankton community showed lower biodiversity index values compared to the zooplankton community. Overall, the knowledge gleaned from the study of plankton community structure and diversity represents a valuable approach for the evaluation of the ecological conditions within the subsidence lakes, which has significant repercussions for the management and protection of aquatic environments in mining areas.

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study.