Sustainability

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans.

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping.

In the field of the road transportation industry, quantitative research on the relationship between artificial intelligence (AI) technology and corporate sustainable development is relatively scarce. This disparity has led to discussions about whether artificial intelligence technology can truly promote the sustainable development level of the highway maintenance industry. Therefore, this study aims to quantify the relationship between artificial intelligence technology and the sustainable development of the highway maintenance industry, and to analyze the reasons behind the current controversies. The research results show: (1) Each exogenous variable has an impact on sustainable development, although the degree of influence varies, especially the economic development level (ED) has the strongest direct effect on sustainable development, followed by the level of market demand (MD), the level of policy support (PS), and the level of enterprise capital (EC); (2) Moderating variables can enhance this direct impact, among which the moderating effect of ED on the relationship between ED and sustainable development is the strongest; (3) Artificial intelligence technology has different impacts on enterprises at different positions in the industrial chain, thereby explaining the controversy over whether to adopt it or not. These conclusions highlight the value of artificial intelligence technology and provide a reasonable explanation for the existing controversies in the industry and research field.