Systems

Australia’s National Housing Accord aims to deliver 1.2 million new dwellings between mid-2024 and mid-2029, representing 240,000 annual completions—a 37% increase above the 2024 baseline of 175,000. This study employs a comprehensive system dynamics model with 79 equations (10 stocks, 69 auxiliary variables) to analyze whether this target is structurally achievable, given construction industry capacity constraints. The model integrates builder population dynamics, workforce capacity, construction cost inflation, material supply constraints, and financial market conditions across a ten-year simulation horizon (2024.5–2035). Three policy scenarios test the effectiveness of interventions, including capacity expansion (±10–15%), cost inflation management (±15–20%), planning reforms (+5–15% efficiency), and workforce development programs (+1000–4000 annual graduates). Model validation against Australian Bureau of Statistics data from 2015 to 2024 demonstrates strong empirical foundations. Results show that structural capacity constraints—driven by three simultaneous bottlenecks in material supply, workforce availability, and financing—create a supply ceiling of around 180,000–195,000 annual completions. Even under optimistic policy assumptions, the model projects cumulative completions of 880,000–920,000 dwellings over the Accord period, falling 23–27% short of the 1.2 million target. Critical findings include the following: (1) builder insolvencies exceeding entry rates by 15–25% annually under stress conditions, (2) capacity decline trends of 0.6–0.8% per year due to productivity losses, infrastructure bottlenecks, and regulatory burden, (3) system efficiency degradation from 100% to 96% over the projection period, and (4) non-linear capacity utilization, showing saturation above 82% baseline levels. The analysis reveals that demand-side policies cannot overcome supply-side structural limits, suggesting that policymakers must either substantially reduce targets or implement transformative capacity-building interventions beyond current policy contemplation.

Digitalisation is reshaping commercial systems in Europe, yet the joint evolution of national digital capabilities, e-commerce and macroeconomic performance remains imperfectly understood. This article develops a parsimonious Digital Maturity Index for the EU-27 over 2015–2023 and examines its association with the share of enterprise turnover generated through e-commerce using a systems-oriented econometric design. Two-way fixed-effects and dynamic panel models show that e-commerce turnover is strongly persistent within countries and systematically higher in more trade-open economies and in labour markets with slightly higher unemployment, after controlling for income and unobserved heterogeneity. The marginal effect of digital maturity on e-commerce intensity is small and statistically fragile, suggesting that digital capabilities act more as a slow-moving state variable than as a direct short-run driver of online sales. The marginal within-country effect of digital maturity on e-commerce intensity is small and statistically fragile once unobserved heterogeneity is controlled for, whereas trade openness and labour-market conditions remain robust correlates. The PVAR results suggest a stable system with strong persistence in e-commerce and digital maturity, limited spillovers to growth and a pronounced temporary contraction in output during the COVID-19 shock.

This study proposes a multi-level performance margin modeling and belief reliability framework for redundant systems. Starting from system performance, a “performance–margin–reliability” linkage is established by defining the performance and margin of multi-level redundant systems and deriving performance, margin, and metric equations that account for failures. For complex redundant systems, a hierarchical Behavior Interaction Priority (BIP) modeling approach is developed to explicitly represent the normal and failure states of atomic component models. The effects of redundant components on the overall system are transformed into variations of performance parameters, enabling quantitative analysis of redundancy mechanisms. This paper proposes a boundary search algorithm for pruning optimization, which breaks through the computational bottleneck of non-analytic threshold sets in high-dimensional topological spaces. A case study on a power supply system with multi-level structural redundancy is conducted. Based on the proposed method, a performance-margin model of the redundant power supply system is constructed, critical states are analyzed, and system reliability is calculated. The results verify the effectiveness of the proposed margin-equation formulation and solution algorithm, offering practical guidance for reliability design of redundant systems.