The swift advancement of the digital economy presents new pathways toward achieving common prosperity in China. Based on microdata derived from the China Family Panel Studies (2010–2022), this study employs the “Broadband China” pilot policy as a quasi-natural experiment to explore how digital economy development influences multidimensional relative poverty. We develop a multidimensional relative poverty index encompassing economic, health, education, and living condition aspects utilizing the Alkire–Foster dual cutoff method and employ a staggered Difference-in-Differences design for empirical analysis. Results show that the policy leads to an average decrease of 1.8 percentage points in the probability of multidimensional relative poverty across households. The effect is more pronounced in central and western regions, rural households, and those with a high proportion of non-labor force, particularly in the dimensions of economic, health, and living conditions dimensions. Mechanism analysis via interaction term regression indicates that increased population mobility and improved informal employment are key channels. These findings suggest that enhancing digital infrastructure and tailoring mobility and employment policies to fit regional and urban–rural contexts can effectively alleviate multidimensional relative poverty. This study contributes empirical evidence connecting the advancement of the digital economy to poverty alleviation and aligns with the United Nations Sustainable Development Goal 1 (No Poverty).

Artificial Intelligence (AI) is emerging as a critical enabler of climate mitigation. However, its spatial and temporal impacts remain insufficiently understood. Using balanced panel data from 279 Chinese cities (2006–2019), this study examines how AI (measured by robot adoption) affects carbon emission intensity. We apply the geographically and temporally weighted regression (GTWR) model to uncover heterogeneous impacts. The results indicate that AI significantly reduces carbon emission intensity, thus contributing to climate mitigation. Nonetheless, the magnitude of this effect varies substantially across cities. The mitigation benefits are more pronounced in cities with limited natural resources, those located within the five major economic zones, and non-traditional industrial centers. These patterns reflect the presence of a ‘natural resource curse’ in resource-rich cities and a ‘social resource blessing’ in socially advantaged regions. The GTWR results further reveal pronounced spatial disparities, with eastern cities experiencing greater reductions in carbon intensity than those in the west. Over time, this spatial imbalance has been narrowing, indicating a gradual convergence in AI’s climate mitigation effects. These findings underscore the importance for regionally differentiated AI development strategies and policy interventions to reduce spatial inequities in mitigation capacity. The study provides robust empirical evidence from China, offering new insights into AI’s potential to support equitable and effective climate action on a global scale.

In green credit markets, information asymmetry and corporate greenwashing increasingly undermine the efficiency of resource allocation, while traditional assessment models relying on static, self-reported environmental data fail to impose effective constraints. To address this limitation, this paper develops a dynamic corporate carbon credit evaluation framework by integrating multiple sources of physical (hard) signals and embeds it into commercial banks’ credit management systems. Anchored in multi-source power-carbon signals (e.g., carbon intensity and compliance records), the framework integrates verifiable physical metrics with ESG disclosures via a Bayesian AHP–CRITIC weighting scheme to construct a dual-dimensional classification scheme (“Credit Rating–Green Label”). It further embeds carbon credit scores into dynamic adjustments to credit limits and differentiated interest rate pricing, forming an integrated risk management mechanism. Empirically, a stratified validation strategy is adopted. Analysis based on a sample of 3327 firms shows that the proposed framework achieves a classification consistency of 81.3%, significantly outperforming both a financial-only baseline model (46.8%) and models based on voluntary carbon disclosure (61.4%). Ablation studies further confirm that physical (hard) signal indicators contribute substantially to ranking stability. Moreover, panel regression analysis, based on 36,185 firm-year observations from 3327 firms over the period 2000–2023, demonstrates that carbon credit scores have robust predictive power for future financial distress. Overall, the proposed framework offers a sustainable, data-driven approach to green credit risk management.