Carbon tax policies (CTP) are globally acknowledged for curbing energy consumption and emissions. Despite global adoption, China's reluctance to embrace a carbon tax, possibly due to effectiveness and public acceptance concerns, is notable. This study employs Choice-Based Conjoint Analysis to gauge Shanghai residents' preferences on four CTP attributes. Among Shanghai's populace, the annual cost of the carbon tax policy ranks highest (48.87%), followed by implementation transparency (24.72%), revenue utilization (16.68%), and policy implementers (9.73%). Notably, Shanghai residents exhibit an average annual willingness to pay of 1435.28 CNY, indicating increased acceptance. Enhanced public awareness of carbon tax policies significantly boosts willingness to pay, augmenting carbon tax revenues and fostering low-carbon technology development. Additionally, transparency improvements in carbon tax policy yield notable increases in willingness to pay, underscoring the link between understanding CTP and its perceived importance. These findings highlight the potential of carbon tax policies in advancing sustainability and transitioning to low-carbon economies.

The United States housing market has historically exhibited regional imbalances in housing supply and demand, which have contributed to reduced housing affordability and market volatility. The application of big data technology enables the utilisation of data to enhance comprehension of these imbalances, thereby informing the formulation of policy. We put forth a model for analyzing the housing supply and demand based on big data, which employs a comprehensive approach to examine both the demand and supply sides. With regard to the demand side, the model incorporates a multitude of data sources to ascertain and delineate the pivotal elements influencing housing demand. These include population growth rate, household income level, employment opportunity distribution, migration and flow trends, and cost of living. By constructing cubes, the model is capable of capturing the characteristics of dynamic demand changes in different regions. With regard to the supply side, the model assesses land use, building materials and labor costs, the timeliness of building permitting and approval processes, and the impact of regional policies and regulations. By means of a quantitative analysis of the aforementioned factors, the model is able to identify housing supply bottlenecks in different regions. The model's efficacy in identifying significant imbalances between supply and demand in the United States housing market was validated through experimental analysis of historical data.