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This paper aims to identify industry drivers and priorities that influence decision-making towards adopting environmentally sustainable (ES) features in stadiums. An “Environmentally Sustainable Stadium (ESS) Process Model” is proposed to track ES features throughout their life cycle, guiding planning, designing, construction, operation and management.

Data were collected through 20 online semi-structured interviews with experts from sustainability, built environment, stadium management, mega-event planning and management and researchers. The experts’ project experiences spanned across Oceania, North America, South America, Asia and Europe. The data were recorded and transcribed through Teams and analysed using NVivo 11 application software.

Stadium’s ES features predominantly focus on energy, waste and materials management with some emphasis on carbon emission reductions and renewable energy sources. Emerging focus areas include flexible designs, audience (or fans) and community engagement, circular economy principles and integration with nature. Key drivers for adopting ES features include legislation and other sector-based requirements, competitive advantages and stakeholder pressures. ES feature success depends on owner support, budget, partnerships, expertise and opportunities. Major barriers include stakeholder diversity, infrastructure limitations and limited stadium-specific requirements.

This paper proposes a six-step “ESS Process Model” to support the stadium industry to holistically integrate ES features, from the initial decision-making to the implementation, ongoing improvement and stakeholder engagement. The model helps stakeholders to identify opportunities, navigate challenges and achieve continual improvement.

The ESS process model is a novel approach to integrate ES features in stadiums, through enhancing collaboration among stakeholders and overcoming challenges in choosing, implementing and maintaining ES features.

The paper aims to explore the correlation between the agglomeration of regional innovation elements and the attraction of talent.

This paper uses the factor analysis method to measure the innovation elements index (IEI). The proportion of the regional resident population and registered population is used to measure the attractiveness of talents. The PVAR model is used to analyze the interaction between innovation element agglomeration and talent attraction.

(1) According to the annual increase rate of IEI, the order is eastern region > central region > western region. (2) Panel vector autoregressive (PVAR) research shows that the agglomeration of innovation factors has a short-term thrust on the attraction of regional talents. (3) The agglomeration of innovative elements is the Granger cause of talent attraction; talent attraction is not the Granger reason for the agglomeration of innovative elements. (4) Pulse analysis and variance decomposition show that the agglomeration of innovative elements has a one-way positive effect on talent attraction.

This study takes China’s provincial panel data as a sample without considering the differences between cities. There may be significant differences in innovation factor agglomeration and talent attraction in different cities.

The findings of this study provide valuable insights into innovation ecosystem practices. Policymakers should pay close attention to promoting the agglomeration of innovation factors by optimizing the innovation ecosystem in order to increase the attractiveness of talents.

(1) This study uses the proportion of regional resident population and household registration population to measure the attractiveness of talents, which is more realistic. (2) This paper is one of the few that examines the relationship between innovation factor agglomeration and talent attraction.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.