Can low-carbon cities increase urban housing prices? Evidence from China's low-carbon city pilot.

The low-carbon transformation is a broad and profound systemic change that will inevitably impact many areas of the urban environment, economy, and social system. We evaluated the impact of China's ongoing "low-carbon pilot policy" on urban housing prices as a "quasi-natural experiment." Existing research findings and hedonic theory predictions suggest that the low-carbon city pilot (LCCP) policy is increasing urban housing prices through air quality improvements. However, this study contradicts these speculations based on the situation in China. This study employed the analytical framework of staggered difference-in-differences (DID), which revealed that the LCCP policy had generally reduced housing prices; the implementation of the policy had led to an average decrease of 6.2% in housing prices in pilot cities, compared to non-pilot cities. The LCCP policy affected housing prices by influencing both the "demand side" and "supply side" of housing. In terms of the demand side, the policy significantly reduced the level of urban air pollution but did not impact housing prices. Instead, it negatively affected housing prices by lowering labor wages. In terms of the supply side, housing prices were negatively affected, mainly by increasing the cost of emission reduction of real estate enterprises, which impacted the original longstanding housing production process. Additionally, there were significant differences in the impact of the LCCP policy on housing prices in cities across different geographical locations and different tiers of cities. This study suggests important policy insights for achieving stable market housing prices and promoting high-quality urban development in the process of low-carbon transformation.

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China.

COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an "S"-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a "multi-center agglomeration distribution" around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government's policy-making and measures to face public health emergencies.

Nonadherence with Employer-Mandated Sleep Apnea Treatment and Increased Risk of Serious Truck Crashes.

STUDY OBJECTIVES: To evaluate the effect of an employer-mandated obstructive sleep apnea (OSA) program on the risk of serious preventable truck crashes. METHODS: Data are from the first large-scale, employer-mandated program to screen, diagnose, and monitor OSA treatment adherence in the US trucking industry. A retrospective analysis of cohorts was constructed: polysomnogram-diagnosed drivers (OSA positive n = 1,613, OSA negative n = 403) were matched to control drivers unlikely to have OSA (n = 2,016) on two factors affecting crash risk, experience-at-hire and length of job tenure; tenure was matched on the date of each diagnosed driver's polysomnogram. Auto-adjusting positive airway pressure (APAP) treatment was provided to all cases (i.e. OSA positive drivers); treatment adherence was objectively monitored. Cases were grouped by treatment adherence: "Full Adherence" (n = 682), "Partial Adherence" (n = 571), or "No Adherence" (n = 360). Preventable Department-of-Transportation-reportable crashes/100,000 miles were compared across study subgroups. Robustness was assessed. RESULTS: After the matching date, "No Adherence" cases had a preventable Department of Transportation-reportable crash rate that was fivefold greater (incidence rate ratio = 4.97, 95% confidence interval: 2.09, 10.63) than that of matched controls (0.070 versus 0.014 per 100,000 miles). The crash rate of "Full Adherence" cases was statistically similar to controls (incidence rate ratio = 1.02, 95% confidence interval: 0.48, 2.04; 0.014 per 100,000 miles). CONCLUSIONS: Nontreatment-adherent OSA-positive drivers had a fivefold greater risk of serious preventable crashes, but were discharged or quit rapidly, being retained only one-third as long as other subjects. Thus, the mandated program removed risky nontreatment-adherent drivers and retained adherent drivers at the study firm. Current regulations allow nonadherent OSA cases to drive at another firm by keeping their diagnosis private. COMMENTARY: A commentary on this article appears in this issue on page 961.