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 China’s traditional agricultural province, the rural areas of Henan have experienced phenomenal economic growth while also consuming too much energy. However, few studies have focused on the analysis of rural energy economic development in Henan. For the first time, this paper used the combination of symbolic regression and tapio decoupling analysis, and introduce six factors that best reflect the characteristics of rural energy consumption (E) in Henan Province, such as effective irrigated area (EIA), per capita living space (PLS), total power of agricultural machinery (PAM), Population (POP), per capita income (PI) and total value of agricultural (TVA). This paper collected data from 2000 to 2015 to fit the formula for rural energy consumption in Henan Province based on the Eureqa-formulize software. The conclusion shows that: (1) The most critical factors are affecting the energy consumption in rural is effective irrigated area, total power of agricultural machinery, rural per capita income, per capita living space and total value of agricultural, but the population is not the main factor. (2) The E-EIA decoupling and the E-PLS decoupling have two decoupling status: weak decoupling and expansive negative decoupling. The E-PAM decoupling has three decoupling status: weak decoupling, expansive coupling, and expansive negative decoupling. The E-PI decoupling and the E-TVA decoupling have one decoupling status: weak decoupling. (3) According to the incidence of each influencing factors, from strong to weak is EIA> PLS> PAM> TVA> PI. The five influencing factors have all shown a positive decoupling effect on energy consumptions.

Based on the perspective of proximity, in the context of the Economic Circle of Chengdu-Chongqing of China, we use invention patent data provided by the China National Intellectual Property Administration and conduct negative binomial models to empirically test the impact of multidimensional proximity on enterprise collaborative innovation performance. We further examine the moderating effect of enterprise innovation capacity on the relationship between multidimensional proximity and enterprise collaborative innovation performance. The results show that technological proximity, geographical proximity, and institutional proximity all positively affect enterprise collaborative innovation performance. Enterprise innovation capability has a negative moderating effect on the relationship between institutional proximity and enterprise collaborative innovation performance, but it cannot negatively moderate the positive effects of technological proximity and geographical proximity on enterprise collaborative innovation performance.