Recent advances in AI create possibilities for delegating legal decision-making to machines or enhancing human adjudication through AI assistance. Using classic normative conflicts - the trolley problem and similar moral dilemmas - as a proof of concept, we examine the alignment between AI legal...

This study utilizes machine learning methods to forecast long-term CO2 emission trends and employs interpretable machine learning techniques to reexamine the Environmental Kuznets Curve (EKC) hypothesis. All methods we experimented with, including Random Forest, XGBoost, Support Vector...

With the rise of environmental awareness, stakeholders are increasingly insisting on a sustainable corporate strategy. This study examines the impact of supply chain concentration on green innovation in Chinese manufacturing firms listed on the A-share market from 2011 to 2022. The results show...

This study utilizes 3,682 full-time workers to examine perceptions of AI-induced job displacement risk and evaluate AI's potential complementary effects on labor. By distinguishing between these variables, the research identifies which worker characteristics and industries are most affected by...

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as...