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This is the publisher's version, also available electronically from http://www.jstor.org/stable/10.1086/467377. ; Lax enforcement of environmental protection laws in the formerly communist countries of Eastern and Central Europe is offered as one contributing factor to the large‐scale environmental degradation that these countries have experienced. This article empirically examines enforcement responses to water‐damaging "accidents" (for example, an oil spill) in the Czech Republic for the year 1988–92, a time period that spans both the communist political regime and the democratic political regime. In particular, it focuses on ex post penalties: required remediation (for example, cleanup after an oil spill) and monetary fines. Empirical analysis reveals the factors driving enforcement strategies in each political period and contrasts their influence under the two regimes. In particular, it identifies the operative liability rules guiding remediation and monetary fine decisions.

This is the publisher's version, also available electronically from http://www.mitpressjournals.org/doi/abs/10.1162/003465304323023895#.U1q6GIUvDGI. ; This paper analyzes the regulatory factors shaping environmental performance at individual polluting facilities. In particular, it examines the influence of actual government interventions, namely, inspections and enforcement actions performed at specific facilities. This influence represents specific deterrence. This paper also examines general deterrence, that is, the threat of receiving an intervention. As important, it controls for differences in certain regulatory features of facility-specific pollution control permits. Unlike previous attempts to examine regulatory factors, this analysis uses panel data techniques to capture the heterogeneity across individual facilities, while exploring the dynamics of each facility; the analysis also captures heterogeneity across individual time periods.

The 2018 World Health Organization (WHO) global ambient air quality database is an impressive compilation that includes PM10 (particulate matter [PM] with an aerodynamic diameter ≤ 10 µm) monitoring data for 3,570 cities in 97 countries and PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) data for 2,628 cities in 81 countries. The database collects measurements and estimates of these fractions, which contain pollutants such as sulphates, nitrates, and black carbon, from established public air quality monitoring systems. These pollutants can penetrate deep into the lungs and the cardiovascular system, posing the greatest risk to human health. Unsurprisingly, the WHO database reports relatively low levels of urban PM pollution in high-income (HI) countries in Western Europe, the Americas, the Western Pacific, and Oceania but high levels in low-and middle-income (LMI) countries in Africa, Southeast Asia, and Latin America—where lack of funding and inadequate staffing are key barriers to effectively reducing the air pollution—and even in high-income countries in the third region. Unfortunately, politicians, organizations, and the media have used the database to draw inaccurate and misleading conclusions based on comparisons between cities, such as occurred with the 2016 version. In this paper, we investigate the strengths and weaknesses of the 2018 database with respect to several criteria (e.g., the selection of pollutants, completeness, spatial and temporal representativeness, and quality assurance and quality control) and offer recommendations for improvement.

Objective: Our aim was to review empirical studies of complacency and bias in human interaction with automated and decision support systems and provide an integrated theoretical model for their explanation. Background: Automation-related complacency and automation bias have typically been considered separately and independently. Methods: Studies on complacency and automation bias were analyzed with respect to the cognitive processes involved. Results: Automation complacency occurs under conditions of multiple-task load, when manual tasks compete with the automated task for the operator's attention. Automation complacency is found in both naive and expert participants and cannot be overcome with simple practice. Automation bias results in making both omission and commission errors when decision aids are imperfect.Automation bias occurs in both naive and expert participants, cannot be prevented by training or instructions, and can affect decision making in individuals as well as in teams.While automation bias has been conceived of as a special case of decision bias, our analysis suggests that it also depends on attentional processes similar to those involved in automation-related complacency. Conclusion: Complacency and automation bias represent different manifestations of overlapping automation-induced phenomena, with attention playing a central role. An integrated model of complacency and automation bias shows that they result from the dynamic interaction of personal, situational, and automation-related characteristics. Application: The integrated model and attentional synthesis provides a heuristic framework for further research on complacency and automation bias and design options for mitigating such effects in automated and decision support systems.