Light rail use is more likely on “walkable” blocks: Further support for using micro-level environmental audit measures

The primary purpose of the study was to address the claim that people are more likely to walk to a transit stop if they live on a “walkable” block. An additional purpose was to evaluate the Irvine Minnesota Inventory (IMI) as an environmental audit tool. The IMI was used to measure walkability features of 19 blocks in a revitalizing neighborhood. We hypothesized that residents who walked to a light rail TRAX stop (n = 22) would live on blocks with higher walkability ratings compared to residents who did not walk to TRAX (nonriders, n = 15), or who walked only after a closer TRAX stop was built (new users, n = 11). A MANCOVA indicated the IMI scales differentiated the blocks; the strongest differences were obtained in subscales that measured the block's diversity, safety from crime and density (marginally significant). Participants' perceptions of their complete walk to the transit stop were consistent with the home block IMI scores, with the continuing riders having more positive views than the other two groups. The results show the ability of the IMI to distinguish among blocks in walkability, and support further use of this audit tool in environmental analyses and active living research. We end with a discussion of conceptual and methodological issues in the use of audit tools and provide recommendations for using these measures for local, immediate interests, as well as for building a broader science of environmental measurement.     

Cross-Hauling of Polluting Factors

This paper analyzes the investment/environment overlap using a two stage model of two jurisdictions. Capital exports are modeled as choice variables for capital owners who weigh income considerations against the effects of capital exports on the quality of their local environments and on environmental policy. This framework reveals that capital owners have an incentive to cross-haul polluting factors across jurisdictions when direct controls are used to regulate emissions and that this cross-hauling induces policymakers to ratchet up environmental policies in both locations. The importance of instrument type and commitment on environmental policy is also analyzed.     

Predictors of restraint use among child occupants

Objectives: The objective of this study was to identify factors that predict restraint use and optimal restraint use among children aged 0 to 13 years.

Methods: The data set is a national sample of police-reported crashes for years 2010–2014 in which type of child restraint is recorded. The data set was supplemented with demographic census data linked by driver ZIP code, as well as a score for the state child restraint law during the year of the crash relative to best practice recommendations for protecting child occupants. Analysis used linear regression techniques.

Results: The main predictor of unrestrained child occupants was the presence of an unrestrained driver. Among restrained children, children had 1.66 (95% confidence interval, 1.27, 2.17) times higher odds of using the recommended type of restraint system if the state law at the time of the crash included requirements based on best practice recommendations.

Conclusions: Children are more likely to ride in the recommended type of child restraint when their state's child restraint law includes wording that follows best practice recommendations for child occupant protection. However, state child restraint law requirements do not influence when caregivers fail to use an occupant restraint for their child passengers.     

Environmentally stratified sampling design for the development of Great Lakes environmental indicators

Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for a population of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Sustainable Remediation and Decision Analysis Practices at an Onshore Gas Well Site

Strategies for remediation of drilling mud wastes at a typical deep sour gas well site in the foothills of Alberta were assessed in terms of financial and social costs and benefits, in alignment with established sustainable remediation and decision analysis principles. Managers of contaminated sites containing historical drilling wastes are challenged with managing liability through several regulatory changes over time. Excavation and disposal of the contaminated soil from the site was the only means of securing regulatory release, with the nearest landfill located 150 km away. A perception exists that in many cases excavation and disposal inflicts unnecessary levels of site intrusiveness and public disturbance when other options achieving a similar risk end point may do so for lower social cost. The study tested this hypothesis to ascertain whether the currently accepted solution is the best option when the wider costs and benefits to society and the environment are included. Eight remedial strategies were assessed using cost–benefit analysis, including using environmental economics techniques to quantify social and environmental impacts. The economic model showed that methods such as capping in‐place or engineered encapsulation were superior to full excavation and disposal from financial and sustainability perspectives. Quantified external costs and benefits such as road damage, greenhouse gas emissions, public nuisance and safety, and community amenity value were influential in identifying superior options. It was demonstrated that $0.2 million of societal costs could be avoided by choosing capping over landfill disposal. This represents substantial implications when viewed in the context of this and other operators’ portfolios of hundreds of abandoned wells in the area. ©2016 Wiley Periodicals, Inc.     

Understanding Human–Landscape Interactions in the “Anthropocene”

This article summarizes the primary outcomes of an interdisciplinary workshop in 2010, sponsored by the U.S. National Science Foundation, focused on developing key questions and integrative themes for advancing the science of human–landscape systems. The workshop was a response to a grand challenge identified recently by the U.S. National Research Council (2010a)—“How will Earth’s surface evolve in the “Anthropocene?”—suggesting that new theories and methodological approaches are needed to tackle increasingly complex human–landscape interactions in the new era. A new science of human–landscape systems recognizes the interdependence of hydro-geomorphological, ecological, and human processes and functions. Advances within a range of disciplines spanning the physical, biological, and social sciences are therefore needed to contribute toward interdisciplinary research that lies at the heart of the science. Four integrative research themes were identified—thresholds/tipping points, time scales and time lags, spatial scales and boundaries, and feedback loops—serving as potential focal points around which theory can be built for human–landscape systems. Implementing the integrative themes requires that the research communities: (1) establish common metrics to describe and quantify human, biological, and geomorphological systems; (2) develop new ways to integrate diverse data and methods; and (3) focus on synthesis, generalization, and meta-analyses, as individual case studies continue to accumulate. Challenges to meeting these needs center on effective communication and collaboration across diverse disciplines spanning the natural and social scientific divide. Creating venues and mechanisms for sustained focused interdisciplinary collaborations, such as synthesis centers, becomes extraordinarily important for advancing the science.