Abstract Construction projects worldwide suffer from misaligned risk allocation, poorly chosen payment schemes, and opaque claims and dispute processes, leading to chronic cost overruns and litigation. Building on comparative analyses of widely used international and national standard forms of contract, empirical public works dispute case studies, and principal–agent theory of payment methods, this research develops a data-driven framework for designing incentive-compatible construction contracts. First, we code and compare more than 60 distinct risk events and 40 claims and dispute procedures across several representative standard contract regimes for large infrastructure projects. Second, we construct a dataset of approximately 80 real dispute or major change-order cases (including highway, bridge, and building projects) and classify them by triggering risk, procurement mode, and payment scheme. Third, we calibrate a structural principal–agent model of contractor effort, cost uncertainty, and risk aversion to this dataset, comparing fixed-price, unit-price, cost-plus, and target-price contracts. The calibrated model is then used to simulate how alternative combinations of risk allocation clauses and payment mechanisms affect expected owner cost, contractor risk exposure, and the probability of disputes. Simulation results are used to quantify potential performance gains from better-aligned contract design; illustrative scenarios target a 15%–25% reduction in expected total project cost, a 30%–40% reduction in formal disputes, and a 40% reduction in claims processing time when more balanced risk-sharing clauses and incentive-compatible payment schemes are adopted. The research is useful as a guideline for international contractors and public agencies involved in mega infrastructure projects.
This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561-1739 rpm), primary air flow (98.87-351.13 m(3)/h), and secondary air flow (6-74 m(3)/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15-18 mu m and 18-21 mu m. The Pareto results revealed a clear trade-off: CAI decreased from similar to 2.30 to similar to 1.65 as CS increased slightly in the fine separation regime and stabilized at similar to 1.58-1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate-high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
Amid intensifying climate change and escalating environmental degradation, achieving environmental sustainability has become a pressing global challenge, particularly for highly industrialized and energy-intensive economies in the OECD region. Despite growing scholarly attention, prior empirical studies have not sufficiently examined how financial stability, environmental policy stringency, and renewable energy collectively influence environmental sustainability. Addressing this gap, the present study investigates the heterogeneous effects of economic growth, renewable energy consumption, non-renewable energy consumption, hydro energy consumption, foreign direct investment (FDI), environmental policy stringency, and financial stability on environmental sustainability. Using balanced panel data for OECD countries from 2000 to 2020, the study employs the innovative Method of Moments Quantile Regression (MMQR) estimator to capture variation across the conditional distribution of environmental sustainability. The empirical results reveal that renewable energy consumption, hydro energy consumption, stringent environmental policies, and financial stability enhance environmental sustainability across heterogeneous quantiles. Conversely, economic growth, non-renewable energy consumption, and FDI undermine environmental sustainability across heterogeneous quantiles. The study recommends that OECD economies should expand the share of renewable and hydro energy through clean energy investments and subsidies, strengthen the enforcement of environmental regulations, incentivize green FDI through environmental screening and tax benefits, and integrate climate-related risks into financial supervision. Furthermore, implementing carbon pricing mechanisms and allocating the resulting revenues toward renewable energy R D and green innovation are crucial for achieving long-term sustainable development.
This study examines the relationship between sustainability expenditures and Environmental, Social, and Governance performance, emphasizing the moderating role of Financial Reporting Quality, proxied by value relevance and income smoothing. Using a panel of 1770 firm-year observations from 177 industrial, energy, and basic materials firms in the United Kingdom over the period 2014-2023, the study employs Fixed Effects regressions as the main estimation strategy, complemented by Heckman correction for sample selection bias and GMM to address potential endogeneity. The results indicate that Environmental Expenditure, Environmental Expenditure Investments, and Research and Development expenditures have a positive and significant impact on ESG performance. This effect is significantly moderated by FRQ; value relevance amplifies the impact of sustainability expenditures, while income smoothing supports long-term strategic consistency. These findings highlight the importance of transparent, informative reporting to enhance the effectiveness of sustainability investments. The study contributes to the ESG literature by showing that sustainability expenditures function as strategic investments rather than mere costs. From a practical perspective, the results suggest that managers should align sustainability strategies with high-quality financial reporting practices, while policymakers should promote clearer disclosure standards to improve ESG outcomes.
The present study investigates the relationship between corporate environmental disclosure and dividend policy. The study also examines the moderating role of environmental controversies in the relationship between corporate environmental disclosure and dividend policy. A sample of 2579 listed firms from European countries was selected, and fixed-effect, GMM, and quantile regression methodologies were employed. The result suggests that corporate environmental disclosure reflects the importance of environmental practices in shaping payout policies. The result also suggests that firms become more effective at enhancing dividend outcomes when they actively manage controversies. The findings are robust with GMM and quantile regression. This study is the first to combine various measures of corporate environmental disclosure, including CSR strategy scores, Environmental Pillar Scores, ESG Scores, Green Building Scores, and Emissions Scores. It also incorporates financial metrics such as cash dividends paid, net common stock buybacks, dividend payout ratio, dividend yield, and common stock dividends. The aim is to thoroughly examine the relationship between these factors and dividend policy in European countries.
