China’s proposed double carbon goal makes it urgent to promote green consumption and green lifestyles. The present study selected separate economic indicators of urban and rural areas and assigned different weights in the construction of a green consumption indicator system. Based on data from 30 provinces (excluding Tibet) between 2003 and 2019, this study investigated the connections between green consumption, environmental regulations, and carbon emissions, analyzing their mechanism. This study found that green consumption will reduce anthropogenic carbon emissions in the short term but will result in low carbon emissions in the long term. Environmental rules have a definite long-term impact on green consumption, as evidenced by the “U”-shaped trend they follow. Second, this study found that the level of green consumption exhibits a rising and then falling trend on the vegetation’s capacity to sequester carbon, and the impeding force will become stronger over time. Third, this study found that green consumption innately has a degree of inertia and self-enhancement bias.

As future industries expand, a key question is whether computing resources can move beyond installed infrastructure and become a productive capacity deployable in industrial activity. This study introduces Productive Computing Capacity, a composite productive capacity centered on computing power and jointly organized through network transmission, data storage, and energy support and computing efficiency conditions. Using panel data for 30 Chinese provinces from 2013 to 2024, we construct a provincial Productive Computing Capacity Index (CPCI) with normalized entropy weights and estimate two-way fixed-effects models. Productive Computing Capacity is positively associated with future industry development. The association between the CPCI and favorable decoupling is positive but only marginally significant. Although the reported linear probability interaction coefficients are positive, they are not treated as hypothesis-confirming moderation evidence because validated nonlinear robustness estimates and simple slope confidence intervals are not available. By conceptualizing computing resources as a regional productive capacity, this study extends research on computing power and digital infrastructure while maintaining cautious inferences about sustainability and causality.

In the context of global climate change, achieving a green and low-carbon economic transition is essential for sustainable development. This study constructs a model using data from 30 provinces collected between 2006 and 2020 to investigate how green finance influences China’s carbon productivity and the transmission mechanism mediated by factor-biased technological progress. The findings reveal the following: (1) The Moran’s index test for carbon productivity across Chinese provinces demonstrates significant spatial clustering. (2) Green finance exhibits substantial spillover effects on carbon productivity in surrounding regions. (3) Capital-biased and energy-biased technological progress significantly mediate the relationship between green finance and carbon productivity, indicating that green finance enhances carbon productivity by optimizing the allocation of capital, labor, and energy factors. (4) Regional heterogeneity analysis indicates that capital-technology-biased and energy-factor-technology-biased approaches can significantly enhance carbon productivity in Central and Northeastern China. Notably, energy-factor innovation delivers far greater environmental efficiency gains in these regions than in Eastern and Western China.