Ping Wang

By 2021, approximately 2.3 billion people worldwide still relied on polluting fuels and technologies for cooking. Such harmful fuels pose serious risks to both health and the environment, contributing to health issues and constraining economic activities. Based on the China Health and Retirement Longitudinal Study data, this study investigates whether and how the renovation of tap water and sanitary toilets affects clean energy adoption among rural households in China by utilizing a Probit model. The results demonstrates that the probability of rural households using clean energy for cooking significantly increased by 8.0 percentage points after undergoing the renovation of tap water and sanitary toilets. These effects are particularly pronounced in households with male heads and low-income households. Mechanism analysis suggests that the renovation of tap water and sanitary toilets significantly elevates educational levels, strengthens cognitive abilities, and fosters the accumulation of human capital, while also raising environmental awareness, thereby driving the utilization of clean energy sources. These findings underscore the significance of sanitation improvements in driving energy transition in rural areas of China. For policymakers focusing on sanitation facilities and socioeconomic development in developing countries, such as China, investments in sanitation infrastructure can yield broad benefits beyond public health, fostering human capital accumulation, environmental awareness, and energy transition. These outcomes have significant implications for long-term sustainable development goals.

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.