Data are the core element of digital transformation. Data factor marketization (DFM) is critical in the process of digital transformation, which promotes urban green development. This paper analyzes the role of digitization in urban environmental sustainability. We investigate the effects of DFM on environmental pollution (EP) using a difference-in-differences approach and data from 283 cities in China from 2006 to 2019. The findings reveal that cities implementing DFM demonstrate an average reduction in EP of 2.67%. The mechanism behind DFM lowering EP involves fostering green innovation, increasing public awareness of environmental issues, attracting IT professionals, optimizing the industrial structure, and enhancing digital finance capabilities. Large cities, cities in the south, and those not primarily dependent on natural resources exhibit a more pronounced reduction in EP through DFM. Implementing policies related to digital infrastructure and enhancing the protection of urban intellectual property rights further amplifies the effect of DFM in reducing pollution. Additionally, this effect exhibits spatial spillover effects. This study contributes to the existing literature by (1) demonstrating DFM’s role in improving urban environmental quality in China through digital technology and market mechanisms, thereby aligning economic growth with ecological sustainability; (2) emphasizing the importance of public engagement in environmental stewardship through increased awareness and community participation in policymaking, as well as fostering social inclusion and ecological conservation; (3) emphasizing spatial spillover effects, the importance of inter-city collaboration in environmental policies, and advocating for comprehensive strategies to achieve broader environmental improvements across urban areas.

The green and low-carbon transformation of land use (GLTLU) is a pressing global issue that requires urgent attention. The digital economy has emerged as a new driver for the GLTLU. However, current research mainly focuses on the measurement and environmental effects of the digital economy, with less exploration of how the digital economy influences the spatial effects and regulatory mechanisms of GLTLU, particularly regarding the differential impacts and specific mechanisms at the regional level. This study uses panel data from 283 cities in China from 2011 to 2019, employing the spatial Durbin model (SDM) and the panel threshold model to examine the spatial and regulatory mechanisms of the digital economy’s impact on GLTLU. The findings reveal that digital economy promotes GLTLU not only within cities but also in surrounding regions. Robustness analyses support this conclusion. Notably, the digital economy’s positive impact on GLTLU in surrounding areas is confined to the central region of China. In contrast, the Yangtze River Delta urban agglomeration experiences a significant negative impact on GLTLU in nearby regions due to the digital economy. The study also identifies that the positive spatial spillover effect of the digital economy on GLTLU reaches its peak at a distance of 450 km. Additionally, the digital economy’s ability to promote GLTLU is contingent upon financial agglomeration levels exceeding 9.1728. Moreover, the local government’s emphasis on the digital economy and intellectual property protection enhances the digital economy’s impact on GLTLU. The promotion effect is maximized when these factors surpass the thresholds of 27.8054 and 3.5189, respectively. Overall, this study contributes to the understanding of how the digital economy influences sustainable land development, highlighting the critical role of regional factors and regulatory mechanisms in amplifying the digital economy’s positive effects on GLTLU.

Existing research lacks a systematic and comprehensive analysis of the digital economy (DE)’s impact on the low-carbon transformation of resource-based cities. This study utilizes panel data from 114 of these cities in China from 2006 to 2019 to construct a DE measurement system. Based on the global SBM directional distance function and the Malmquist–Luenberger index (SBM-DDF-GML), we calculated the total factor carbon productivity (TFCP), decomposed the carbon inefficiency value (CIV), and examined DE’s impact, mechanism, and heterogeneity on low-carbon transition development (LCTD) during distinct growth phases of resource-based cities. Based on this examination, we found the following: (1) The DE effectively reduced carbon intensity and inefficiency and improved the total factor carbon productivity in resource-based cities. These findings remained robust after a series of robustness tests. (2) The DE empowered LCTD by improving energy efficiency, upgrading industrial structure, and optimizing innovation factor allocation. Finally, (3) this effect varied across the different city stages, being most significant in mature cities and weakest in declining ones. The research findings provide empirical evidence for the LCTD of resource-based cities.

Traditional survey methods make it difficult to collect sensitive data, as respondents provide false information or deliberately refuse to answer. The current investigation presents innovative methods utilizing multi-stage randomized response models (MRDRMs) to tackle challenges in precisely quantifying sensitive numerical variables while safeguarding respondent anonymity. The MRDRMs framework, comprising two as well as three-stage mathematical models. To employ two randomized response mechanisms designed to improve privacy protection and estimation efficiency to address perceptions like social desirability and over-estimation, which are prevalent in sensitive public health data collection. The main problem addressed in this study is the difficulty of obtaining precise estimates of sensitive quantitative variables because respondents often hesitate to provide truthful answers due to privacy concerns, fear, and social desirability bias. The incorporation of substantial randomization stages enables the models to provide unbiased and precise estimates of means and sensitivity levels, thereby ensuring the reliability of the data while reducing the psychological strain on participants. Theoretical analysis, particularly simulations conducted using Monte Carlo methods, suggests that MRDRMs, in both two-stage and three-stage formats, significantly improve the precision and relative efficiency of estimates compared to conventional randomized response strategies. The empirical validation carried out via a cross-sectional survey in the districts of Faisalabad, Lahore, Multan, and Rawalpindi in Punjab, Pakistan, examined not sufficiently reported COVID-19 cases along with vaccine hesitancy, thereby affirming the practical significance of the MRDRMs methods. The findings demonstrate a substantial improvement in estimation accuracy accompanied by reduced variance compared to conventional approaches. The results highlighting the effectiveness of MRDRM-I and MRDRM-II as robust methods for sensitive data collection. The findings from this research highlight the potential of MRDRMs for enhancing the acquisition and evaluation regarding sensitive data, strengthening consistent, ethical, and tangible results in research related to public health. These improvements strengthen privacy protection and enhance the validity of responses in sensitive surveys, offering meaningful contributions to public health monitoring and evidence-based policy development at the global level. The proposed models can be applied in public health surveillance, epidemiological studies, social science research, and policy-sensitive domains requiring accurate and privacy-preserving data collection.