In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.

To achieve the Paris Agreement’s temperature goal, greenhouse gas emissions should be reduced as soon as, and by as much, as possible. By mid-century, CO₂ emissions would need to be cut to zero, and total greenhouse gases would need to be net zero just after mid-century. Achieving carbon neutrality is impossible without carbon dioxide removal from the atmosphere through afforestation/reforestation. It is necessary to ensure carbon storage for a period of 100 years or more. The study focuses on the theoretical feasibility of an integrated climate project involving carbon storage, emissions reduction and sequestration through the systemic implementation of plantation forestry of fast-growing eucalyptus species in Brazil, the production of long-life wood building materials and their deposition. The project defines two performance indicators: a) emission reduction units; and b) financial costs. We identified the baseline scenarios for each stage of the potential climate project and developed different trajectory options for the project scenario. Possible negative environmental and reputational effects as well as leakages outside of the project design were considered. Over 7 years of the plantation life cycle, the total CO₂ sequestration is expected to reach 403 tCO₂∙ha⁻¹. As a part of the project, we proposed to recycle or deposit for a long term the most part of the unused wood residues that account for 30% of total phytomass. The full project cycle can ensure that up to 95% of the carbon emissions from the grown wood will be sustainably avoided.