The transition from dirty to clean industries: optimal fiscal policy and the environmental Kuznets curve

This paper investigates privately and socially optimal patterns of economic development in a two-sector endogenous growth model with clean and dirty goods. We consider a second-best fiscal policy framework in which distortionary taxes jointly influence economic growth and environmental quality. In this policy setting, three conditions produce an Environmental Kuznets Curve (EKC): (i) dirty output is bounded; (ii) clean output grows endogenously; and (iii) growth in the dirty sector reduces growth in the clean sector. These conditions do not arise with a consumption externality, but can emerge with a production externality. Endogenous labor supply implications are also investigated. Although not necessary for producing an EKC, endogenous labor supply provides additional linkages that produce an EKC under circumstances in which it would otherwise not appear.     

A framework for predicting personal exposures to environmental hazards

This paper presents a general framework for constructing a predictive distribution of the exposure to an environmental hazard sustained by a randomly selected member of a designated population. The individual’s exposure is assumed to arise from random movement through the environment, resulting in a distribution of exposure that can be used for environmental risk analysis. A specialization of the general framework is that of predicting human exposure to air pollution that can be used to develop models for such things as exposure to particulate matter; practical aspects of their construction are considered. These models can help answer questions such as what fraction of the population sustained ‘high’ levels of exposure for say 5 days in a row. The immediate implementation of the above framework takes the form of a computing platform referred to as pCNEM. This provides a facility for simulating exposures to airborne pollutants and is described in detail elsewhere. This paper considers some theoretical aspects underpinning probabilistic exposure models of this type, with the ideas illustrated in developing a model for predicting human exposure to PM ₁₀.     

Do plants’ emissions respond to ambient environmental quality? Evidence from the clean water act

In this paper we show that plants respond to downstream ambient water quality after controlling for permitted levels of pollution. We find if past water quality declines by one percent, plants reduce current pollution by 0.35 percent. The magnitude of this coefficient is comparable to the coefficient on permitted discharge levels i.e. regulatory stringency itself. Results are consistent with two mechanisms. First, a decline in water quality may lead to more stringent permits that would raise the cost of abatement of a plant significantly. Second, the plant is likely to be subject to increased public pressure in response to poor water quality. Indeed, as expected, the impact of water quality becomes stronger in locations with higher median household income, higher percent carpooling to work, or lower percent of manufacturing employment but surprisingly with lower median age of residents, lower percent with bachelor׳s degree or higher percent of families with children.     

Demand for environmental policies to improve health: Evaluating community-level policy scenarios

Using a national survey and a discrete choice experiment format, we estimate demand for environmental polices to improve health. We use a richly detailed community-level approach that describes illnesses avoided, premature deaths avoided, policy duration, and the affected population size. We allow preferences for policy attributes to vary systematically with the scenario design, with the source of risk and type of health threat, and with respondent characteristics. Using a willingness to pay (WTP) framework similar to that used for studies of individual risk, we find that omission of illness information leads to an upward bias in estimates of the value of avoided premature deaths and that individuals view avoided deaths and avoided illnesses as substitutes. We also find evidence of strongly diminishing marginal utility in policy scope. Differences in marginal WTP from different sources of risk or types of illness appear very small relative to differences associated with respondent characteristics and/or perceptions. Self-interest strongly dominates altruistic considerations.     

Hierarchical methodological framework to improve Policy Environmental Assessment: Based on policy layer research

This study explores the current implementation challenges of the Policy Environmental Assessment (PEA), including the implementation of a far richer, more diverse (at macro level), and better understanding of PEA and integration with decision making. The results contribute to the analysis of PEA through different hierarchies of assessment. Stemming from the theory and practice of policy, a concept of PEA hierarchies is proposed including the uppermost PEA, upper-lower PEA, and lowermost PEA. Afterward, the differences of the three hierarchies are interpreted, in terms of aims, principles, processes, and methods. The evaluation of the policy environmental impacts from different lens helps solve the complexities of policies and identify opportunities for improvement of PEA.     

Environmental policy and human capital inequality: A matter of life and death

This paper analyzes the economic implications of an environmental policy when we account for the life expectancy of heterogeneous agents. In a framework in which everyone suffers from pollution but health status also depends on individual human capital, we find that the economy may be stuck in a trap in which inequality rises steadily, especially when the initial pollution intensity of production is too high. We emphasize that such inequality is in the long run costly for the economy in terms of health and growth. Therefore, we study whether a tax on pollution associated with an investment in pollution abatement can be used to address this situation. We show that a stricter environmental policy may allow the economy to escape from the inequality trap while enhancing the long-term growth rate when the initial inequality in human capital is not too large.     

A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data

Environmental pollution of urban areas is one of key factors that state authorities and local agencies have to consider in the decision-making process. To find a compromise among many criteria, spatial analysis extended by geostatistical methods and dynamic models has to be carried out. In this case, spatial analysis includes processing of a wide range of air, water and soil pollution data and possibly noise assessment and waste management data. Other spatial inputs consist of data from remote sensing and GPS field measurements. Integration and spatial data management are carried out within the framework of a geographic information system (GIS). From a modeling point of view, GIS is used mainly for the preprocessing and postprocessing of data to be displayed in digital map layers and visualized in 3D scenes. Moreover, for preprocessing and postprocessing, deterministic and geostatistical methods (IDW, ordinary kriging) are used for spatial interpolation; geoprocessing and raster algebra are used in multi-criteria evaluation and risk assessment methods. GIS is also used as a platform for spatio-temporal analyses or for building relationships between the GIS database and stand-alone modeling tools. A case study is presented illustrating the application of spatial analysis to the urban areas of Prague. This involved incorporating environmental data from monitoring networks and field measurements into digital map layers. Extra data inputs were used to represent the 3D concentration fields of air pollutants (ozone, NO₂) measured by differential absorption LIDAR. ArcGIS was used to provide spatial data management and analysis, extended by modeling tools developed internally in the ArcObjects environment and external modules developed with MapObjects. Ordinary kriging methods were employed to predict ozone concentrations in selected 3D locations together with estimates of variability. Higher ozone concentrations were found above crossroads with their heavy traffic than above the surrounding areas. Ozone concentrations also varied with height above the digital elevation model. Processed data, spatial analysis and models are integrated within the framework of the GIS project, providing an approach that state and local authorities can use to address environmental protection issues.     

A simulation framework for water allocation to meet the environmental requirements of urban rivers: model development and a case study for the Liming River in Daqing City,China

In this paper, we describe the development of a simulation framework for allocating water from different sources to meet the environmental flows of an urban river. The model permits the development of a rational balance in the utilization of storm water, reclaimed water from wastewater treatment plants, and freshwater from reservoirs with consideration of the limited capacities of different water resources. It is designed to permit the full utilization of unconventional water sources for the restoration of river water quality by increasing river flow and improving water quality. To demonstrate practical use of the model, a case study is presented in which the model was used to simulate the environmental water allocation for the Liming River in Daqing City, China, based on the three water sources mentioned above. The results demonstrate that the model provides an effective approach for helping managers allocate water to satisfy the river’s environmental water requirements.