Public opinion on the expenditure of adult-use cannabis tax revenue: Evidence from New Jersey.

OBJECTIVES: To describe New Jersey residents' relative priorities for the allocation of tax revenue generated by recreational cannabis sales. We aim to assess preferences for public health initiatives, including drug treatment, compared to a range of alternatives, including traditional policing, especially within the social and demographic groupings of people generally most impacted by punitive drug enforcement policies. METHODS: We collected population-representative survey data four months post-implementation of recreational cannabis sales in New Jersey (N = 1,006). We gauge respondents' top preferences for the allocation of new revenue generated by the legal cannabis market. Using multinomial logistic regression, we assess how various demographic and political factors shape public support for devoting revenue toward public health initiatives. RESULTS: While priorities are mixed within the sample, we find more general support for funding community-based initiatives in public health, housing, and education than for funding police, courts, and prisons. Among Black residents, the largest proportion chose investments in affordable housing. Regression analysis reveals political orientation as having the most consistent association with expressed preferences, with Republicans favoring investments in traditional law enforcement priorities over other potential funding domains. CONCLUSIONS: Recreational cannabis legalization is occurring at a rapid pace, yet important context, including how the tax revenue could be invested in communities, remains unclear. Insight into current public opinion on funding priorities suggests a desire for investment in fundamental societal institutions, including education and public health, rather than the punitive enforcement mechanisms that have defined cannabis policy for many decades.

A New Neighborhood Every Fall: Aging in Place in a College Town.

Older adults who live in residential neighborhoods adjacent to college and university campuses have a unique experience that makes them vulnerable to marginalization and displacement. As these neighborhoods become increasingly dominated by college students living in rental properties, older adults find themselves in the minority in a neighborhood where they have lived for many years. In addition, these neighborhoods are attractive to universities, city governments, and private companies for their development potential, which can result in gentrification. A year-long ethnographic study of a campus-adjacent neighborhood in a small US college town that is home to a medium-sized public university sheds light on the relationships between members of 5 stakeholder groups that have a vested interest in the neighborhood. The study highlights the need for additional research on different types of neighborhoods and their effects on aging in place in addition to outlining social work interventions in campus-adjacent neighborhoods that are designed to enhance these intergenerational spaces.

Estimating random errors due to shot noise in backscatter lidar observations.

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson- distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment.