The Facility Layout Problem (FLP) is a challenging task with significant sustainability implications. Due to its multifaceted nature, it is classified as a non-deterministic polynomial-time (NP)-hard problem, further compounded by increasing computational demands. The FLP aims to optimise the allocation of factory facilities to maximise production efficiency while minimising material handling costs. To tackle these challenges, researchers have developed a two-phase heuristic algorithm called The Origin (TO), which draws inspiration from the experiential process of the bin-packing problem. The TO algorithm considers various factors, such as operating and capacity limitations, as well as material handling costs, to optimize facility layout designs. Extensive evaluations of the TO algorithm using benchmark sizes ranging from 14 to 125 have shown that it outperforms alternative metaheuristic approaches. The TO algorithm yields more sustainable outcomes by minimising material handling costs while improving production efficiency. These results highlight the importance of addressing various constraints, especially industrial constraints, in facility layout designs and demonstrates the potential of heuristic algorithms to support these efforts.

In this study, we examine the impact of government green subsidies on corporate ESG performance. We employ the method of double machine learning for causal inference. We use all A-share listed companies in China from 2013 to 2023 as the research sample. After excluding financial and insurance companies, those in ST/*ST/PT status, and those with missing key indicators, we ultimately obtain 2337 sample observations. Our baseline results based on double machine learning reveal government green subsidies significantly enhance corporate ESG performance. The findings suggest that this enhancement occurs notably through the mediating variables of digital technology innovation and technology conversion efficiency. We also introduce heterogeneous dimensions such as the level of digital inclusive finance, the intensity of environmental regulations, and the scale of enterprises. Meanwhile, we adopt multiple robustness test methods, including changing the dependent variable, excluding data from special years, controlling for exogenous policy shocks, using instrumental variable methods, and resetting the double machine learning model—adjusting the sample partition ratio from the original 1:4 to 1:9 and replacing the prediction algorithm from random forest to gradient boosting, lasso regression, and ensemble machine learning methods—to ensure the reliability and scientific nature of the research conclusions. Additional tests indicate that the regression coefficient remains positive and is significant, indicating the robustness of our conclusions. This research offers implications for further optimizing the design of government green subsidy policies, and to promote the improvement of enterprises’ ESG performance and economic green transformation.

The world population is aging, which along with recent shifts in the labor force participation (LFP), is having a significant longevity influence on state pension programs across the board, including Canada, Finland, Japan, and Germany. Except Japan, these countries have set their statutory re-tirement age at 65, but the impact of aging workforce and declined fertility rates create wonders on the estimate future trends in the LFP, as well as the length of retirement. In this study, we fit the LFP rates of these countries, representing continents from Asia, Europe and North America among the OECD countries, using the Lee-Carter and Cairns-Blake-Dowd (CBD) stochastic models. The es-timates are then used for the projection of future LFP rates (1989-2066), and by combining the mortality forecasts from the United Nations, we project the expected length of retirement (1989-2066). This study provided a novel comparison between the Lee-Carter and the CBD LFP models by fitting and forecasting the LFP rates of senior employees between 50- to 74-year-olds. The results revealed disparities between models that provided proof of the presence of model risk for longer retirement durations. The study findings emphasized the importance for decision-makers in pension industry to have awareness of potential risks and limits associated with the LFP models. The Lee-Carter model outperformed the CBD model even though the CBD model is known for its ac-curate prediction in higher ages. Population aging should be considered in any analysis of the long-term viability of pensions, together with the participation rate trends for a sustainable labor market future.

In this study, we examine the impact of data asset elements on corporate ESG risks. Our baseline results based on double machine learning reveal data asset elements significantly reduce corporate ESG risks. Additional tests indicate that the regression coefficient remains positive and is significant at the 1% level, indicating the robustness of our conclusions. The findings suggest that this reduction occurs notably through the mediating variables of digital technology innovation and technology conversion efficiency. Heterogeneity analysis indicates the risk-mitigating effect is stronger in firms facing low financing constraints, possessing high fintech digitalization, operating under strong environmental regulations, and having high asset support levels. This study enriches relevant theories and provides implications for firms, regulators, and investors.