In response to the international appeal of developing low-carbon economy and realizing carbon peaking and neutrality goals, China has been exploring the construction of the carbon emissions trading market for years. Without the guidance of advanced technology, there would not be a low-carbon economy in the first place. Based on panel data of 30 provinces and cities in China from 2005 to 2020, this paper takes Beijing—which is the first pilot city in China—as the only treated group and uses the Synthetic Control Method to study the impact of the pilot policy on low-carbon technology innovation. The results show that, firstly, the number of low-carbon technology patents in Beijing increases significantly after the implementation of the pilot policy, proving a positive influence on technology innovation. Secondly, the policy effect has a certain time lag and is sensitive to the shock from both domestic and foreign carbon market, but this effect is gradually stable over time. This paper confirms that technological innovation is the key means of promoting the development of the low-carbon economy and calls on various carbon trading markets to pay attention to the internal mechanism of promoting low-carbon technology innovation to stimulate the vitality of market entities.

With the accelerated urbanization in China, the irrational utilization of land resources has triggered a series of ecological challenges. In this context, exploring the decoupling relationship between population and construction land is crucial for achieving land sustainable development. This paper applied Tapio and Geodetector models to Shandong Province, analyzing population–land decoupling evolution and mechanism. The results show the following: (1) Significant spatiotemporal differences exist, with a total of eight decoupling types identified; the main decoupling types of Shandong include strong negative decoupling, expansive negative decoupling, weak decoupling, and strong decoupling. (2) A strong negative decoupling type characterized by “population decline and land expansion” was predominant, though coordination trends are emerging. (3) Weak decoupling townships were primarily influenced by resource factors and transportation; strong decoupling townships were mainly affected by economic activities and transportation; strong negative decoupling townships were closely related to resource factors and economic activities. (4) Multi-factor interactions have a considerable impact on the formation of the population–land decoupling relationship, with natural constraints and economic transformation drivers jointly contributing to diverse decoupling patterns.