Osia Uchenna, Cutts Bethany B, Pullen Fedinick Kristi, Boone Kofi
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA.
Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA.
Int J Environ Res Public Health. 2025 Aug 7;22(8):1232. doi: 10.3390/ijerph22081232.
This study evaluates the 2022 rollout of the Clean School Bus Rebate Program (CSBRP) to understand how eligibility rules and data practices shape funding distribution across communities with varying needs. We ask whether more accurate maps can improve environmental funding outcomes or whether challenges stem from how agencies define and apply eligibility criteria. Using logistic regression and dasymetric mapping, we find that prioritization criteria helped direct funds to underserved areas, but reliance on school district boundaries introduced inconsistencies that affected program reach. Including charter schools as independent applicants increased competition and sometimes diverted funds from larger public systems serving more. Our geospatial analysis shows that while refined mapping approaches improve resource targeting and reduce goal-outcome mismatches, agency discretion and administrative rules remain key factors in ensuring equitable outcomes.
本研究评估了2022年清洁校车回扣计划(CSBRP)的推出情况,以了解资格规则和数据实践如何影响不同需求社区的资金分配。我们探讨更精确的地图是否能改善环境资金的分配结果,或者挑战是否源于各机构定义和应用资格标准的方式。通过逻辑回归和密度制图,我们发现优先排序标准有助于将资金导向服务不足的地区,但对学区边界的依赖引入了不一致性,影响了项目覆盖范围。将特许学校作为独立申请者纳入增加了竞争,有时还会使为更多学生服务的大型公共系统的资金被挪用。我们的地理空间分析表明,虽然精细的制图方法能改善资源定位并减少目标与结果的不匹配,但机构的自由裁量权和行政规则仍是确保公平结果的关键因素。