Data and code for JCP

Abstract
10 The G7 countries, as global leaders in adopting artificial intelligence (AI), have pledged to enhance ecological quality
11 (EQ) through sustainable AI integration by 2040. This commitment underscores the need to transition from traditional
12 industrial practices to AI-driven solutions that support ecological systems. The purpose of this study is to investigate
13 the asymmetric effects of AI adoption on EQ within the G7 economies over the period 2000m1–2019m12, using an
14 innovative quantile-on-quantile regression (QQR) approach to capture variations in the AI-EQ relationship across
15 different levels of AI adoption. The findings reveal that at the initial stages of AI adoption (low quantiles), the impact
16 on EQ is modestly positive in most G7 countries. This effect increases stronger in the transition phase and becomes
17 significantly beneficial at higher quantiles of AI adoption. Robustness checks using kernel regularized least squares
18 (KRLS), quantile regression (QR), and alternative measures of EQ confirm these results, ensuring the reliability of
19 the conclusions. The study also highlights substantial cross-country differences in the AI-EQ relationship, indicating
20 that tailored policy measures are necessary to maximize the ecological benefits of AI adoption. This research provides
21 insights into how AI can be leveraged for sustainable development in major economies