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.

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.

Occupational Health and Safety (OHS) risk management is an ongoing challenge in resource-scarce chemical industries, especially in developing nations like Tunisia. The current research recommends a hybrid socio-techno-environmental model based on Fuzzy Logic and the Analytic Hierarchy Process (AHP) for occupational health and safety risk assessment and prioritization of occupational health and safety (OHS) risks. The model, based on a backup of expert judgment from pre-specified questionnaires and real data of Tunisian chemical industries, quantifies the risk factors based on criticality and probability of occurrence. Fuzzy–AHP not only evaluates qualitative estimates but also provides a systematic framework for evaluating and prioritizing occupational health and safety risks. Findings are consistent with the fact that socio-organizational factors such as occupational safety culture and compliance among workers play a pivotal role in determining total levels of risk, and in certain cases dominate over technical considerations alone. In offering a scalable and context-sensitive solution, this research has an additional contribution with a decision-support framework that assists organizations in evaluating and prioritizing occupational health and safety risks under uncertain conditions. Its use for policy makers and business interests that seek to maximise OHS performance in given constraints, and its use in actual implementation and compatibility with sustainability and worker health goals, speaks to its utility.

Ecosystem carbon storage is highly sensitive to land-use/land-cover change (LUCC). However, retrospective carbon assessment, spatial factor analysis, and future land-use optimization are often conducted separately, limiting their ability to support territorial planning involving competing economic, ecological, and carbon-storage objectives. Taking Jiangsu Province, China, as a rapidly urbanizing region, this study developed an integrated framework combining multi-temporal remote-sensing-derived land-use data with the InVEST carbon storage model, Geodetector, multi-objective programming (MOP), and the intPLUS model. Land-use data from 2000, 2010, and 2020 were used to quantify changes in land use and ecosystem carbon storage and to examine how natural, socioeconomic, and accessibility conditions corresponded spatially to the observed land-use configuration. Pareto optimization was then applied to identify non-dominated land-use allocations, which were spatialized to simulate five land-use scenarios for 2030. From 2000 to 2020, cropland decreased by 7475 km², while construction land increased by 6974 km². Correspondingly, ecosystem carbon storage declined from 1431.85 Mt to 1402.55 Mt, representing a cumulative loss of 29.30 Mt. This decline was primarily associated with cropland loss and construction land expansion, particularly the conversion of cropland into construction land. Although the overall spatial pattern of carbon storage remained relatively stable, high-value areas gradually contracted, whereas low-value areas expanded in intensively urbanized regions. Geodetector results showed that pairwise factor combinations generally corresponded more closely to the spatial differentiation of land-use patterns than individual topographic factors, and that associations involving socioeconomic activity, transport accessibility, and vegetation cover became more pronounced over time. The 2030 scenario analysis revealed clear trade-offs: the carbon storage maximization scenario produced the highest carbon storage (1497.47 Mt), the economic development scenario generated the highest economic benefit but the lowest ecosystem service value, and the natural development scenario produced the lowest carbon storage (1384.46 Mt). The Pareto-based comprehensive optimization scenario maintained 1487.12 Mt of carbon while achieving a comparatively balanced combination of economic benefit and ecosystem service value. These findings demonstrate that integrating multi-temporal remote sensing, ecosystem-service modeling, and Pareto optimization can translate historical land-use information into flexible spatial planning alternatives for rapidly urbanizing regions.

Conventional steam power plants reject a substantial fraction of their thermal energy through condenser systems, while this low-grade waste heat remains largely unutilized. This study proposes and evaluates a novel integrated concept in which buried condenser pipelines simultaneously function as a condenser cooling system and a seasonal thermal energy storage (STES) medium by storing rejected heat within the surrounding soil for subsequent winter recovery. A transient three-dimensional numerical model based on the finite-volume method was developed and implemented in ANSYS Fluent to simulate coupled heat transfer between condenser discharge pipes and the surrounding ground under realistic operating conditions of the 370 MW Tabriz steam power plant. The model incorporates actual plant operating data, local soil thermophysical properties, and practical engineering constraints, including burial depth, pipe diameter, and pipe spacing. Numerical predictions were validated against established analytical solutions and published benchmark studies, demonstrating good agreement with relative errors below 4.2%. The simulations indicate that a burial depth of 1 m, a pipe diameter of 250 mm, and a minimum pipe spacing of 3 m provide the most favorable compromise between heat transfer efficiency and thermal interference. The surrounding soil progressively accumulates thermal energy during prolonged operation, creating a stable underground thermal reservoir capable of supplying useful heat during colder seasons. The results further demonstrate that appropriate geometric optimization significantly enhances heat recovery potential while maintaining practical installation requirements. The principal novelty of this work lies in introducing buried condenser pipelines as a dual-function infrastructure that combines condenser heat rejection with large-scale seasonal thermal energy storage, rather than treating underground piping solely as a cooling mechanism. Unlike previous investigations that focused primarily on underground heat exchangers or geothermal applications, the present study develops a practical engineering framework specifically for recovering waste heat from steam power plant condensers and integrating it with future district-heating and agricultural heating systems. The proposed methodology provides a scalable design framework for improving overall power-plant energy utilization, reducing thermal pollution, and facilitating the integration of waste-heat recovery technologies into sustainable energy systems. The findings establish a foundation for future techno-economic optimization, hybrid renewable energy integration, and large-scale seasonal heat storage applications in thermal power plants operating under similar climatic conditions.