As one of the world’s largest and fastest growing industries, tourism is facing the challenge of balancing growth and eco-environmental protection. Taking tourism CO2 emissions as undesirable outputs, this research employs the bootstrapping data envelopment analysis (DEA) approach to measure the eco-efficiency of China’s hotel industry. Using a dataset consisting of 31 provinces in the period 2016–2019, the bootstrapping-based test validates that the technology exhibits variable returns to scale. The partitioning around medoids (PAM) algorithm, based on the bootstrap samples of eco-efficiency, clusters China’s hotel industry into two groups: Cluster 1 with Shandong as the representative medoid consists of half of the superior coastal provinces and half of the competitive inland provinces, while Cluster 2 is less efficient with Jiangsu as the representative medoid. Therefore, it is suggested that the China government conduct a survey of only Shandong and Jiangsu to approximately capture the key characteristics of the domestic hotel industry’s eco-efficiency in order to formulate appropriate sustainable development policies. Lastly, biased upward eco-efficiencies may provide incorrect information and misguide managerial and/or policy implications.

Total-factor energy efficiency (TFEE) is widely used to measure energy efficiency under the data envelopment analysis (DEA) framework, but the efficiencies obtained from DEA are structurally biased upward, and thus TFEE tends to overestimate energy efficiency. This research thus applies the bootstrapped DEA approach to correct the bias of TFEE. Using a dataset consisting of 30 provinces of China in the period 2016–2019, the bootstrapped-based test supports technology with variable returns to scale. The biased-corrected TFEE also indicates that energy consumption on average can be scaled down by 42.36%, rather than the biased value of 19.4%. The bootstrapped clustering partitions provinces into three groups: Cluster 1, with Guizhou as the representative medoid, includes half of the superior coastal provinces in terms of actual energy consumption and TFEE and half of the competitive inland provinces, whereas Cluster 3 outperforms Cluster 2 in terms of TFEE, but the actual energy consumption is higher, with Shandong and Hebei as the representative medoids, respectively. Lastly, empirical results imply that the northeast and central regions need more government attention and resources to practice sustainable development and improve TFEE.

China currently adopts voluntary principles to disclose sustainable development information, and so considerable numbers of listed companies have chosen not to disclose such information. Since disclosure and non-disclosure groups face different production opportunities, this research uses the meta-frontier framework to completely analyze sustainable development practices of China’s artificial intelligence (AI) industry. Empirical results show that the disclosure group outperforms the non-disclosure group in operating scales, efficiencies, and technologies, while the superior efficiency of state-owned enterprises (SOEs) comes entirely from the non-disclosure group. Hence, the government should mandate or actively encourage capable corporations, especially SOEs, to disclose sustainable development information, as doing so improves the overall sustainable development of society and also enhances these firms’ performance. Finally, the authority can formulate a nationwide disclosure policy regardless of the existing differences in regional development.