## 2 Literature Review
In terms of research methods, numerous scholars have measured land use efficiency according primarily to the multi-attribute evaluation method, such as the entropy method, the TOPSIS evaluation method, and the comprehensive evaluation method. However, these evaluation methods are greatly influenced by experts' subjective judgment. Evaluation results may be insufficient because they ignore the effects of resources and the environment. Therefore, scholars have increasingly employed data envelopment analysis (DEA) [19; 20], whose significant advantage over the multi-attribute evaluation method is that it can consider comprehensive indicators, such as unexpected outputs, and effectively avoid subjectivity in setting indicator weights. Scholars use a variety of models to measure land use efficiency, including stochastic frontier analysis (SFA), data envelopment analysis (DEA), and the SBM model. The super-SBM-DEA method can be directly calculated without setting weights according to the index system of input and output production. Studies adopting the super-SBM-DEA model include indices of the pollution emissions generated via land production in the efficiency evaluation system, and their evaluation results are relatively consistent with the actual development situation. For example, [PERSON] et al. calculated the industry sectors' supply chain green use efficiency in China using the directional slack-based inefficiency model [21]. [PERSON] et al. reported that urban land use efficiency is relatively low, applying the SFA method in China via stochastic frontier analysis [22]. [PERSON] and [PERSON] et al. studied the convergence of urban green land use efficiency in China [23; 24]. In addition, numerous studies have used mathematical models to evaluate UBLGUE and contributed valuable insights into the driving mechanisms that cause urban built-up land to develop in an intensive and green manner [25]. For example, [PERSON] et al. verified that industrial relocation positively drives improved use efficiency for industrial land via the Tobit method [26]. [PERSON] et al. verified that a remarkably negative connection exists between the industrial diffusion scale and industrial land prices [27]. [PERSON] et al. and [PERSON] et al. reported that several factors are closely affected by urban built-up land use efficiency in resource-based cities in China [28; 29]. In addition, environment carrying capacity is related to urban land use efficiency [30]. [PERSON] et al. and [PERSON] et al. researched the coupling and coordinated development between green manufacturing systems and built-up land efficiency in developed urban agglomerations, respectively [31; 32]. The local influencing factors include access to public services, urban land misallocation, land financing, land price, the establishment of development zones, land ownership, the land market, government intervention, city size, and the digital economy [33; 34; 35; 36; 37; 38; 39; 40]. Generally, research on the efficiency evaluation of UBLGUE has been quite rich, which provides effective policy support for enhancing intensive land use. Many empirical studies have focused on the spatial influence of UBLGUE, and studies have explored spatial-temporal patterns and simulated urban built-up land development. However, there is still some room for improvement in the scientific measurement of UBLGUE in the existing literature. Studies on how the socioeconomic factors of land resources affect UBLGUE in underdeveloped urban agglomerations are lacking. Many recent studies have targeted the urban built-up land use in developed areas, where resource allocation has contributed greatly to urban development and construction. However, less attention has been given to the dynamic characteristics of UBLGUE in underdeveloped urban agglomerations, and little empirical research has placed particular emphasis on the Central China Rise policy, although it is a significant indicator of urban construction and is strongly related to a city's ecological civilization.