Spatial dependence and determinants of conservation easement adoptions in the United States

A conservation easement is a market-based instrument for environmental protection. It has achieved rapid growth in the United States over the past few decades. As of 2015, 1.75% of the country's total land was placed under the restriction of conservation easements. In this study, spatial dependence in adopting conservation easements in the United States and the underlying determinants are examined through a spatial econometric model. The spatial panel data covers 50 individual states and six five-year intervals from 1990 to 2015. The findings reveal that spatial correlation in adopting conservation easements across individual states has become stronger over the study period, and the indirect spillover effect for most covariates is as high as one-third of the total effect. In addition, conservation easements have been utilized to protect threatened or strained natural resources. Populations with higher income or better education generally have helped the development of conservation easements. Government programs and policies favoring conservation easements also have positive impacts on easement adoption. These results can aid policymakers, landowners, and easement holders to efficiently allocate resources in acquiring conservation easements and managing currently eased land.

Empirical likelihood for spatial dynamic panel data models

Spatial dynamic panel data (SDPD) models have received great attention in economics in recent 10 years. Existing approaches for the estimation and test of SDPD models are quasi-maximum likelihood (QML) approach and generalized method of moments (GMM). In this article, we introduce the empirical likelihood (EL) method to the statistical inference for SDPD models. The EL ratio statistics are constructed for the parameters of spatial dynamic panel data models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models. Simulation results show that the EL based confidence regions outperform the normal approximation based confidence regions.

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM_2.5) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM_2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.