Accurate prediction of exhaust noise is increasingly important for integrating acoustic constraints into model-based development of large-bore marine engines. However, one-dimensional acoustic modeling practices established primarily for automotive engines may not transfer directly to large-bore systems. This study investigates the sensitivity of predicted exhaust-pressure spectra to numerical resolution, downstream exhaust-stack representation, turbocharger acoustic modeling, and local duct geometry using a one-dimensional model of a Wärtsilä 4L20 medium-speed engine. A configuration combining the selected refinements is then evaluated against measured in-duct pressure spectra. Numerical resolution produced the strongest sensitivity, with coupled spatial–temporal refinement reducing the spectral RMSE relative to the finest-mesh reference from 10.7 to 2.3 dB below 600 Hz. Explicit exhaust-stack representation substantially redistributed spectral energy, while an acoustic turbine representation introduced frequency-dependent transmission loss rising to 15–17 dB near 1 kHz. The combined refined configuration produced closer frequency-resolved agreement immediately downstream of the turbine, where the narrowband RMSE decreased from 11.42 to 9.88 dB and the mean absolute error from 9.66 to 7.57 dB, although the mean-level deviation increased from 2.42 to 3.05 dB. Within the plane-wave-valid range, one-third-octave errors also decreased, and a separate comparison at a farther-downstream station gave a mean absolute error of 7.30 dB, although residual narrowband discrepancies of 9–10 dB remained. The results demonstrate that reliable one-dimensional exhaust-acoustic prediction for the investigated large-bore engine requires adequate numerical wave resolution together with representation of the dominant propagation paths and passive turbine acoustics. However, the remaining spectral errors indicate that further model development and validation are required. The quantitative resolution thresholds and relative importance of individual model components remain platform-specific and require validation across additional engines and operating conditions.
Accelerating the transition of China’s energy consumption structure toward low-carbon development is essential for achieving global carbon neutrality goals. As the country with the world’s largest share of energy-related emissions, China provides a critical case in which substantial provincial disparities remain. Using panel data from 30 provinces from 2012 to 2022, this study develops an integrated framework combining Geographically and Temporally Weighted Regression (GTWR), eXtreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to examine the driving mechanisms of low-carbon energy transition from a regional inequality perspective. The results reveal persistent east–west disparities, significant spatial clustering, and clear temporal shifts in the effects of key drivers. Results reveal pronounced spatiotemporal heterogeneity. Green technology innovation consistently showed the strongest positive effect, while industrialization and urban–rural income gaps exerted stronger negative impacts in central and western regions. Government intervention shifted from a negative factor in early years to a positive driver in later years, which reflects the evolving role of policy in steering decarbonization. Moreover, nonlinear threshold effects were identified, such as U-shaped impacts of government intervention and scale-sensitive effects of afforestation. Findings show that China’s low-carbon transition is evolving from regional heterogeneity toward policy convergence, yet inequalities remain significant. These results underscore the need for targeted strategies for reducing disparities, including technology diffusion and financial support in less-developed provinces, to ensure a more balanced and equitable energy transition. This study contributes new empirical insights to understanding low-carbon drivers and designing decarbonization policies for ensuring an equitable and coordinated national transition.
PurposeThis study examines the interrelationships among AI-enabled digital ecosystems, digital maturity, organizational resilience, sustainable value co-creation, and sustainable performance within Small and Medium-Sized Enterprises (SMEs) in Pakistan's manufacturing sector. By doing so, it addresses critical gaps in the literature on how these constructs collectively shape resilience and long-term sustainability in information-intensive business environments.Design/methodology/approachA quantitative research design was employed, with data collected from 476 SMEs using structured questionnaires. Structural Equation Modeling using SmartPLS was applied to test the hypothesized relationships and assess the mediating and moderating roles of organizational resilience, sustainable value co-creation, and digital maturity.FindingsThe results show that AI-driven digital ecosystems have a significant positive effect on both organizational resilience and sustainable value co-creation, which in turn drives sustainable performance. Digital maturity acts as both an enabler and a moderator, amplifying the benefits of AI ecosystems. Among the predictors, sustainable value co-creation emerges as the strongest driver of sustainable performance, while the reciprocal relationship between resilience and co-creation highlights their self-reinforcing nature.Practical implicationsThe study extends the Resource-Based View, Dynamic Capability Theory, and Stakeholder Theory by integrating AI-driven ecosystems and sustainability into a unified framework. Digital maturity is reconceptualized as a multi-dimensional construct encompassing technological, behavioral, and strategic readiness, offering a more nuanced understanding of its role in enabling resilience and Value Co-Creation. Practically, the findings provide guidance for SMEs to pursue phased digital transformation, build collaborative stakeholder networks, and embed sustainability-oriented practices. Policy recommendations include targeted programs to enhance SMEs' digital readiness and resilience.Originality/valueThis research advances knowledge by redefining SME performance to include economic, social, and environmental dimensions. It offers a holistic pathway for how AI ecosystems and digital maturity can foster resilience and sustainability, enabling SMEs in resource-constrained contexts to remain competitive while contributing to global sustainability goals such as the SDGs and ESG metrics.
This study evaluates the environmental and economic impacts of bio-based anode material and its implications for policy researchers and businesses. This study is based on upscaling laboratory work to develop and electrochemically test bio-based alternatives for anode materials. The study uses biochar synthesised from bark waste via pyrolysis. Electrochemical testing is conducted using biochar as the anode in lithium-ion batteries (LIB) and sodium-ion batteries (NIB). Technical economic analysis (TEA) and life cycle assessment (LCA) are employed to assess the economic and environmental viability of producing the materials. The findings suggest a trade-off between economic and environmental aspects compared with a conventional anode material. Both aspects are sensitive to the chemicals used during impregnation and acid washing, ZnCl2 and HCl. It shows the importance of securing the chemical supply chain and experimenting with alternative chemicals to reduce impacts without compromising quality. This work also provides a foundation in green energy storage systems and in evidence-based decision-making for related stakeholders.
