Stephen E. White

The drivers of greenhouse gas emissions: What do we learn from local case studies?

Abstract What can local case studies contribute to our understanding of the processes underlying the growth in greenhouse gas emissions? Since much abatement and mitigation are local in character, it is important that policy makers identify the mix of local, national and international processes that contribute to changes in greenhouse gas emissions. Drawing upon the results of case studies in Kansas, North Carolina and Ohio, how patterns of emissions in local areas can be connected analytically to the driving forces of environmental change is demonstrated. Emissions at the local level are empirically associated with the same set of trends found at national and international scale, namely, changes in population, affluence‐consumption and technology.

Adjustment preferences to groundwater depletion in the American high plains

Abstract This paper explores the adjustments that residents in one part of the High Plains prefer in coping with the depletion of the Ogallala aquifer. The authors identify 31 possible adjustment policies and report on public preferences, examining differences among socioeconomic groups and also the preferred governmental agencies to administer specific programs. Adjustments involving improved water use efficiency were most favored; those entailing financial incentives were least liked. The local groundwater management district was the preferred agency overall. Most significant differences in preference were between those in agribusiness and those outside agriculture.

Interstate return migration: Regional differences and implications

Abstract This study examines the regional differences in 1975–1980 return migration patterns for states within the United States based on two return migration variables; (1) the percentage of in-migrants to a state who are returnees, and (2) the number of returnees as a share of those who are “at risk” of returning. States exhibit widely disparate return characteristics for each variable. The percentage of in-migrants who were return migrants ranged from just 3.2 percent in Nevada to 33.9 percent for West Virginia, while the share of returnees from the at risk pool ranged from 31.6 per thousand in South Dakota to 116.5 per thousand in California. The percentage of return migrants is not significantly correlated with the at risk to return variable. Thus, states have very different levels of dependence on return migration when both variables are taken into account. Some states such as West Virginia have a high percentage of returnees among in-migrants but a low at risk return rate, while other states like California receive a large share of at risk returnees but exhibit a low percentage return among all in-migrants. A typology is developed to classify states according to their return characteristics and the implications of the different return patterns are discussed. Significant regional variance in the volume of return migration, and major differences in migration motives and migrant characteristics between returnees and non-returnees suggest a need to decompose interstate migration flows when modeling patterns of human movement at a national scale.

Mental map variability: A migration modeling problem

Mental map variability is important when viewed in a migration prediction context. Models that link aggregate mental maps of residential desirability to migration have limited predictive value because mental maps vary among socioeconomic groups and migrants often have very different socioeconomic characteristics than the general population within a source region. Residential desirability also varies with both the distance to and the intensity of awareness for alternative destinations. Utility can be increased by disaggregating the mental maps of potential migrants on the basis of accessibility to potential destinations, socioeconomic status, and awareness levels for alternative locations.