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What is IIASA

Ideally, regulations would be informed by understanding the whole of the paradigm, shown in Figure , from human activities through adverse outcomes. However, only the most important regulatory assessments, such as some of those done for federal rules that have major economic impacts, include a simulation of processes from activity to health impacts.

These are the rules that generate most of the benefits and costs of environmental regulation, and the modeling effort can be enormous. A recent example of such an analysis is the regulatory impact assessment RIA for the control of air pollutant emissions from nonroad diesel engines EPA b. Even the extensive modeling that accompanied this rule cannot quantitatively consider all aspects of the problem. For example, in discussing behavioral responses to increasing costs for nonroad diesel engines, stakeholders suggested that equipment users may substitute different equipment gasoline engines or even labor the use of a laborer and shovel instead of a backhoe for more expensive diesel engines EPA b.

Such behavioral aspects were only discussed qualitatively in the report. Incorporating behavior into environmental regulatory models is discussed more generally in Box Linkages among the different processes are not seamless. Each category often is represented by a separate model and regulatory analyses often require that inputs and outputs of one model interface with other models in separate categories. Sometimes temporal or spatial scales do not line up and results from one model may not have natural counterparts in the models with which it interfaces.

An example is from the air quality analysis in which emissions from vehicles and other sources that are estimated at the regional level must be allocated spatially and estimates of aggregated hydrocarbon emissions must be disaggregated by species for input into the air quality model. More fundamentally, the linking of these different categories means the linking of separate disciplines. To properly link different modeling categories requires the building of interdisciplinary bridges, which is an ongoing effort at EPA. Although there are software tools and integrated models that allow multiple processes to be combined into a single modeling framework as discussed in a subsequent section, such a model still faces the difficulty of needing to rely on the expertise from multiple disciplines.

The level of effort dedicated to environmental regulatory applications varies greatly. This variation is a critical consideration when. For regulatory purposes it is important to not only model natural systems but also human activities and their interactions with natural systems. These interactions, which can always be found at either end of the causal chain, shown in Figure , and often in the middle as well, require models from the social sciences, usually economics. A key modeling consideration is the extent to which such models incorporate human behavior.

The earliest models used for environmental regulatory purposes had little if any behavioral content. The effects of both regulations and environmental changes were estimated without considering the full range of responses available to economic agents—individuals, households, and firms.

Environmental Policy Models

One of the first models to demonstrate that possible behavioral responses could affect the costs or effectiveness of regulations was developed by Gruenspecht , who pointed out that the common regulatory practice of requiring more stringent and more costly abatement for new sources of pollutants than for existing sources could retard the turnover of existing equipment.

Behavioral responses are sensitive to the details of regulatory design, and numerous models appeared in the economics literature describing the unintended consequences of such real-world policies as CAFE Kwoka and vehicle inspection and maintenance Hubbard Behavioral responses also affect other outcomes of interest to EPA, including regulatory enforcement Harrington , pollution abatement subsidies Freeman ; Rubin Behavioral responses to adverse environmental consequences, such as private defensive expenditures, have also been analyzed.

For many years, EPA made frequent use of behavioral models for policy analysis and regulatory impact analysis. In cases involving economic incentives, behavioral models are essential because the behavioral response is what drives the policy outcome. For example, analysis of proposed emissions cap-and-trade policies to control airborne sulfur dioxide emissions from the electric power industry requires the agency to predict the behavior of utilities in the permit market.

For this task, EPA uses the integrated planning model, a proprietary dynamic linear programming model that determines the least-cost loading of generating capacity to meet electricity demand. The optimization simulates the expected outcome in the permit market. For example, the MOBILE model, which projects average regional or national motor vehicle-emission rates under a variety of regulatory design parameters, does not consider the effects that regulatory alternatives might have on fleet composition or vehicle use through their effects on vehicle or fuel prices.

At one end of the spectrum are applications that involve a small investment in resources and modeling effort. Fehrenbacher personal commun. At the other end of the spectrum, EPA may spend years or even a decade assessing the health and environmental consequences of other environmental pollutants, making their modeling efforts extremely complex. This requires major investments of resources and may take many years of assembling background information and performing analyses, including modeling analyses. While some TMDLs require extensive data collection and modeling, at least one state has proposed using a nonmodeling approach for catchments with little or no data George The SIP process can be a major undertaking requiring development of emissions inventories and analysis of control options.

Each local area out of attainment must submit a plan for each pollutant. For example, there are currently counties out of attainment with the current hour PM 2. The elements that are included in modeling may change over time for a given type of assessment, typically adding complexity to the modeling process. This is a result of changes to regulatory requirements, scientific understandings, and modeling capabilities.

A potential example is in the health risk assessment paradigm. A major modeling component is the development of dose-response relationships through analysis of epidemiological or toxicological studies Setzer The NRC reports on toxicological effects of arsenic from drinking water provide a prime example of many of the issues associated with developing dose-response modeling for a contaminant NRC a, b.

Box described this case study in more detail. Source: NRC Establishing a U. From the perspective of environmental regulation, the arsenic story is an interesting one for a number of reasons. First, exposures arise from natural sources and some have even argued that at very low doses it is an essential element for human health. Second, arsenic is not directly carcinogenic in animals; hence, all evidence for human health effects arises from epidemiological studies.

Two National Research Council committees NRC a, b convened to advise EPA on this matter suggested that regulation be based on data from 42 villages in southwestern Taiwan, which showed increased rates of bladder and lung cancer as a function of arsenic levels measured in village wells. Instead, the committee recommended reliance on more empirically based statistical models. Although the dose-response modeling was based on human data, which removed the uncertainty associated with extrapolation of results from animals to humans, the inherent variability associated with human data introduced other sources of uncertainty.

There were many concerns expressed about the appropriateness of relying on the Taiwanese data for the purpose of setting regulations in the U. Some cited differences in dietary patterns between the United States and Taiwan, particularly in this relatively poor rural area of Taiwan. Others were concerned that the Taiwanese study used cancer incidence data extracted from population records and exposure crudely assessed based on the median levels of arsenic measured in villages wells. Indeed, Morales et al. As shown in Figure , these various models differed substantially in their fitted values, especially in the critical low-dose area that is so important for establishing the benchmark dose used to set a reference dose RfD.

However, research and practice has enabled major changes since in how the risk components are developed. As the black box between exposure and effect gathered light, improvements in risk assessment practice Reddy et al. For example, analysts understand that even if humans and rats received the same external exposure, they did not receive the same dose of active chemical to the target tissue.

To understand these events, data on basic physiological and pharmacokinetic processes and resultant physiologically based pharmacokinetic PBPK modeling can integrate specific properties of chemicals with age- and organ-specific physiological processes in different species to relate an effective experimental exposure to an effective environmental exposure Clewell PBPK models offer the possibility that doses of chemicals delivered to target cells of a rat could be quantitatively extrapolated to target cells of humans.

These models also offer the possibility that differences in age or sensitivity for example, polymorphisms in metabolism in the human population could be incorporated in models. However, the use of a PBPK model in a risk assessment can be a time- and cost-intensive undertaking requiring expertise Clewell It must be accompanied by a thorough evaluation that includes the following:.

These models may also bring with them many technical and science policy challenges. One outcome of more mechanistic approaches to health risk assessment modeling is that regulatory end points might be based on an upstream biochemical precursor event instead of observed adverse health outcomes. The challenge and controversy then becomes selecting the appropriate point between an innocuous molecular change and frank disease to use in the assessment.

The risk assessment of perchlorate NRC b offers an example of how this can be addressed by selecting a nonadverse effect the inhibition of iodide uptake by the. This figure illustrates the fundamental elements of assessment and the models that link the elements. Source: EPA b. More fundamentally, as understanding increases, so do options and questions about the most appropriate approaches to assess risks. For example, in a particular scenario, judgments may be needed as to whether EPA should give preference to empirical models using human epidemiology or mechanistic rodent-based models Preuss Some models tend to fit into a single category, while other regulatory models represent multiple categories of processes, such as modeling emissions and fate and transport together.

For example, the integrated planning model produces estimates of electricity sector activity, including fuel demands, prices, and emission-control decisions for given levels of emissions Napolitano and Lieberman Models that represent. More recently, the movement toward integration has utilized advances in software to develop modeling frameworks that allow user flexibility to use a combination of compatible models, facilitate multiple simulations, and facilitate output analysis.

For example, the Better Assessment Science Integrating Point and Nonpoint Sources BASINS is a multipurpose environmental analysis system that integrates a geographical information system, national watershed data, and state-of-the-science environmental assessment and modeling tools into one modeling package EPA e. The model integrates individual stand-alone models that simulate pollutant loadings from point and nonpoint sources and instream water quality models for performing watershed- and water-quality-based studies.

In this section, we describe the use of models in six phases of the regulatory process. Strategic planning identifies environmental problems of present and future importance and assembles data and constructs modeling tools to permit analysis. Rule-making translates congressional directives into specific regulations. Delegation has states and localities given responsibilities for developing plans to achieve environmental goals locally and writing regulations to achieve those goals. Permitting, licensing, registration is where these rules are applied to govern the behavior of polluting individuals, firms, or other entities.

The last two phases are enforcement and ex post facto analysis. The first element in the regulatory sequence above involves the strategic use of models to inform Congress and decision makers within. EPA in deciding whether or how to legislate or regulate. Because strategies are inherently predictive, models are crucial. They can inform the identification of goals that are important to achieve for example, whether a certain air pollutant already regulated is still an important public health risk requiring additional legislation or regulations , and they can characterize approaches to achieving them for example, whether the predominant source of this air pollutant is stationary, mobile, or personal identifies optimal regulatory targets.

The use of modeling in strategic planning can become part of the debate between Congress and EPA over environmental policy. An example of this is a May 13, , letter from Congressman Thomas Allen to EPA Administrator Michael Leavitt concerning delays of model runs assessing control options for electric power-plant emissions of mercury Allen One of the broadest uses of models in environmental strategic planning was by the congressionally mandated National Acid Precipitation Assessment Program NAPAP , which was directed to perform research to inform decisions on regulations of acid rain.

The interagency program EPA and 11 other federal agencies was funded for 10 years in the s and produced 27 state-of-the-science and -technology reports on all aspects of the acid rain issue. One of the primary products was the air quality models that are precursors to the models used at EPA today.

Information developed by NAPAP was useful for changing the understanding of the scientific-information related to acid rain and informing Congress in its development of the parts of the CAA Amendments of that dealt with acid rain. However, this legislation was enacted before NAPAP completed its integrated assessment report of its activities. Global warming modeling provides a contemporary example of the strategic use of models. The U.


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Rule-making encompasses the tasks of regulatory design and promulgation. The goal of regulatory design is to produce a proposed rule that complies with the legislative requirements set down by Congress and that provides sufficient support and analysis of the rule. For example, the non-road diesel RIA included the use of activity models, emissions models, air quality models, engineering cost models, energy forecasting models, petroleum refinery models, and human health and agricultural impacts models to assess the benefits and costs of the proposed regulation EPA b.

Other rules incorporate less modeling. Many environmental statutes, including the CAA and CWA, delegate important roles for compliance, which includes implementation and enforcement, to states. States may further delegate some responsibilities to local agencies. Delegation of authority for implementation and enforcement is also given to tribal governments. Modeling analysis is part of the delegated responsibility. The roles of EPA and the state and local agencies vary by the statutes and within statutes. Under the SIP process, states or local governments must prepare a plan for each area that does not meet NAAQS, describing how that area will be brought into attainment.

This process includes the modeling analysis described in Table For large urban areas, typically a metropolitan planning or other local air quality agency prepares the SIP that must be then approved by the state and eventually by EPA. For the TMDL program, states are primarily responsible for carrying out the program, including the modeling described in Table However, EPA will carry.

Thirty-five states run their own programs for dealing with leaking underground storage tanks, including assessments of subsurface containment transport and risk assessment modeling. As mentioned, tribal governments have the option of running their own environmental programs, and some tribes have received authorization to run air quality and water quality programs. Private consultants often are engaged to perform part of the modeling analysis required under state delegated programs. State-generated source-specific regulations, required by both SIP and TMDL, are based on the effects of air and water pollutants on environmental quality.

This requirement raises a host of technical, economic, and political issues that are sometimes not sufficiently covered in the writing of federal standards. The issues include the following:. The environmental effects of emissions from any one source depend on the emissions from numerous other sources. Nonpoint sources. Emissions from sources that are difficult to monitor and regulate at the individual level either because the sources are numerous and diffuse or because the emissions are episodic and dependent on natural processes.

Distributional asymmetry. The sources responsible for pollutant discharges are located in a different area from where the environmental damages are suffered. For example, states and cities may have no control over air pollutants that have blown in from afar. This activity might be required for the construction and operation of a point emissions source or the introduction and continued use of a chemical in the market.

The statues vary in what role modeling plays and which entities perform the modeling. For licensing new pesticides, manufacturers supply a substantial amount of modeling of environmental and human health risks to EPA that might be supplemented by additional agency analysis. For the premanufacturing determination that must be made before new chemicals can enter the market under the Toxic Substances Control Act, EPA is responsible for assessing risks. The initial screening is done using structure-activity models, and the results of such modeling determine whether a more thorough assessment is needed and whether manufacturers will be required to submit more test data.

Programs that permit discharges into water, controlled under the National Pollution Discharge Elimination System NPDES , are primarily run by the states, although some states have only partial authority. Although many of the requirements under the NPDES program are still driven by technology-based standards, increasingly state and federal permit writers must take into account water quality standards and watershed considerations, which increases modeling needs.

The CAA mandates that the states implement and that EPA oversee permit programs to control and regulate pollutant emissions from major stationary sources. Under these programs, each new major stationary source of air pollutants must apply for a permit before construction and provide modeling to help to demonstrate that the new facility will meet appropriate emission-control standards. Permittee modeling is subsequently reviewed by state regulators. Models are used in compliance and enforcement in several ways.

For enforcing some regulations, EPA uses models to estimate the benefit to the regulated party—usually cost savings—from delaying or avoiding pollution control expenditures. The damage estimates are generally based on contingent valuation surveys, as well as models that attempt to estimate the costs of restoring or replacing the damaged resources.


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Like strategic planning, assessment of the performance and costs of regulations after they have been implemented is relatively rare within EPA, although it is often carried out by other parties. The Office of Management and Budget OMB reviewed recent ex post facto analyses of regulations, including environmental regulations. EPA has also received periodic requests from Congress to report on the aggregate costs and benefits of its regulations.

Census Bureau on a sample of manufacturing establishments. EPA uses the survey results to estimate expenditures for all manufacturing plants and adds information on expenditures in other sectors to produce the report. These analyses are of limited use for policy assessment because they report only on the aggregate costs of regulation rather than the costs of specific regulations.

Similarly, EPA also occasionally conducts ex post facto studies of benefits. The most prominent example is the ongoing study of the benefits and costs of the CAA—a study required by section of the CAA. A second prospective study is in progress for the period from to It illustrates the difficulty of estimating benefits and costs of massive, aggregate programs such as the CAA. For very large changes, determining an appropriate without-regulation scenario be-.

Modelers outside of federal agencies also contribute post hoc analysis of environmental regulatory activities. The literature is vast.

Some particular examples include the assessments of compliance costs and other impacts of the sulfur dioxide emissions trading programs e. There are some particular influences and constraints on the regulatory process resulting from the enabling statutes passed by Congress and from a series of executive orders that over time have given OMB oversight responsibility over regulations and imposed specific requirements on how regulatory decisions are supported through modeling.

It is essential to understand these influences. Federal environmental statutes, such as the CAA and CWA, usually contain statements of health and welfare goals, schedules and deadlines for meeting them, and, often, criteria for determining whether the goal has been met. Table contains a sample of some of the general and specific directives found in several important environmental statutes. To write regulations to meet these requirements, EPA produces much analysis to justify its decisions and show how its actions meet the congressional directives, which can sometimes require the agency to do the following:.

Explain quantitatively the magnitudes as well as the spatial and temporal patterns of present and projected contamination. Trace the contaminant back to the human activities that contribute to the contamination and trace the contaminant forward to its health impacts. Project patterns of contamination and their impacts under various regulatory proposals including no regulation and, in some cases, deregulation. To produce the kind of regulations authorized by such health- or welfare-oriented legislation, therefore, requires the use of the types of models discussed in the preceding section and displayed in Figure The figure, to be sure, suggests a degree of simplicity that EPA does not necessarily enjoy in its regulatory activities.

EPA must translate general and sometimes vague statutory prescriptions into specific rules governing the behavior of individuals, firms, and state and local governments; pollutant sources must be identified and brought into compliance with the rules; and periodic assessments must be undertaken to ensure that satisfactory progress is being made to meet the statutory goals.

Such legislative mandates often require EPA to develop or use models despite substantial data gaps and minimal supporting theory. A similar modeling challenge occurs with the mandates in the Food Quality Protection Act that requires EPA to assess aggregate health risks from exposure to one chemical from multiple pathways and cumulative health risks from aggregate exposure to multiple pesticides EPA c, d.

Legislation also affects how EPA uses model assumptions. Criteria pollutants are air pollutants emitted from numerous or diverse stationary or mobile sources for which NAAQS have been set to protect human health and public welfare. The criteria pollutants are ozone, particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and lead.

New toolset evaluates economic impacts of ozone reduction policies for nine income groups.

The impact on regulatory modeling is that control costs, technological feasibility, and cost-benefit comparisons are not included in the analysis used to set NAAQS. It also causes EPA to consider a variety of sources of modeling evidence as shown in Figure While being overseen by the White House, Congress, and others, EPA exercises substantial discretionary authority to implement and enforce environmental laws. With respect to models, EPA makes the vast majority of decisions about whether a model is needed to implement or enforce a legislative mandate, how to select and review models to carry out its authorities, and when it is time to replace one model with another.

The executive branch has provided oversight of the regulatory process through analytical requirements for the review of the costs, benefits, and effects of all major regulations. This factor has produced extensive modeling requirements for major regulatory actions overseen primarily by the Office of Information and Regulatory Affairs within OMB. One requirement is for an assessment of benefits and costs for major regulations through an RIA. Box discusses the history of the RIA requirement. Lead Indus. EPA , F. Source: Murphy and Richmond The requirement for an RIA came about as Presidents sought to have more influence over the agendas of executive agencies by requiring a review of the costs, benefits, and effects of all major regulations.

The key event was Executive Order EO , issued on February 17, , announcing new rules governing the issuance of regulations by federal agencies. EO introduced two important innovations into federal rule-making. Each President since has either issued his own executive order affirming the RIA requirement and the OMB review or accepted that of his predecessor. See Magat et al. For the most part, recent Presidents of both parties have retained support for regulatory review requirements, including the RIA.

The implication of the RIAs for EPA modeling is that where possible, all the effects of a proposed regulation, positive and negative, must be expressed in monetary terms. However, that did not prevent a very extensive and thorough RIA from being prepared to support the revision of the ambient standards for ozone and fine particulates EPA g.

The RIA found very large positive net benefits for both standards, so there was no actual conflict between the RIA requirement that the costs be justified by the benefits and the legislative conflict that costs not be considered. As discussed in Chapter 4 , uncertainty analysis adds considerably to the analytical burden of producing and comparing alternative regulations with unclear benefits. In addition, the executive branch has been interested in the quality of information and peer review practices used by federal agencies, including EPA. Recognizing the critical roles that models have in developing information, EPA has developed its own guidelines for data use to ensure that the models used in regulatory proceedings are objective, transparent, and reproducible EPA a.

After Congress or EPA has decided to use a model for one or more regulation-relevant purposes, the model normally goes through some internal and external oversight to ensure that it meets scientific, stakeholder, and public approval. Although these oversight processes are not perfect and run the risk of introducing their own sources of error or complication, they nevertheless exert an important and independent pressure on regulatory models that is generally not present when models are developed and used in nonregulatory settings. Figure shows a schematic of the regulatory requirements placed on the regulatory design and promulgation phase.

Models used for other regulatory purposes—outside of rule-makings—are generally not subjected to these extensive internal and external review requirements. Models used at the enforcement stage, for example, are generally not required to go through peer review or even notice and comment, but they are required to at least gain judicial acceptance before a court will enter penalties against a violator based on the model.

Models used in environmental regulatory programs delegated to the states, such as models used to develop SIPs and TMDLs, can be subjected to public comments and debate, but independent peer reviews of individual model applications are not required. The Science Advisory Board and Science Advisory Panel, described in a subsequent section, are two sources for peer reviews.

Energy and Environmental Policy Modeling

Models used for strategic. FIGURE Flow chart of general regulatory requirements for models used at the regulatory design and promulgation stage. Yet in these cases, EPA still develops guidelines for internal peer review and may voluntarily subject these models to sources of external review as well. Because regulatory design models encounter the most extensive oversight requirements and also tend to be an important modeling activity at EPA, regulatory design models are the focus of the remaining discussion.

In general, these models require multiple layers of review, including formal scientific peer review, notice and comment processes, and intra-agency review. Interested parties are also provided with an opportunity to challenge the model to the agency and in court to ensure that the model is reliable. The first and perhaps most important set of requirements involves subjecting regulatory decisions, including the models underlying them, to review by three layers of outside reviewers. This external review is thus conducted independently of the authors of the model or the users for a specific application.

This section summarizes the current state of EPA. It depends on the nature of the model, its application, the needs of the model developers and users, the peer review guidance being followed, and the requirement of the specific regulatory environment statutes. For the purposes of this section, external reviews are categorized as peer review, public review, and interagency review.

This category refers to technical experts reviewing the model and its application for scientific merit. Although it is expected that key elements of models will be published in the peer reviewed literature, this discussion does not address journal reviews. Peer review is embedded in the history of science because of its value in improving the quality of a technical product and providing assurance to nonexperts that the product is of adequate quality. These values are so important that attention must be paid to the quality of the peer review itself and to whether the comments were addressed and appropriately incorporated into the final product.

All peer reviews are not equivalent. A peer review on model code, for example, will be useful, but inadequate to evaluate the utility of the model for a specific application. Thus, the charge to each peer review for a model and its application needs to be considered relative to the criteria for model evaluation and where the model is in its life cycle, as described in Chapter 4. The guidance is very comprehensive and detailed, describing such elements as matching the kind and degree of peer review to the impact of the work product product of influential scientific information, very influential scientific information, or other or rule Tier 1, 2, or 3 rule , determining resources needed for peer review, selecting peer reviewers, documenting the review, and so forth.

How to make a model - science project - how to save environment model

They also describe the need for peer reviewers to have the necessary technical expertise and to be free of con-. The EPA Science Inventory keeps a list of the different science activities and their required levels of peer review. Box lists elements of peer review described by EPA for use with regulatory models. This guidance also offers a framework for reviewing model development, model application, and environmental regulatory decision making.

It explains that policy decisions resulting from the science and other factors are required by law to be made by EPA decision makers. The policy decisions are often subject to public comment. What is the regulatory context in which the model will be used and what broad scientific question is the model intended to answer? Theoretical Basis for the Model—formulating the basis for problem solution.

How can the adequacy of the data be defined taking into account the regulatory objectives of the model? What is the basis for each key assumption and what is the range of possible alternatives? Did the data bases used in the performance evaluation provide an adequate test of the model? Does the documentation cover model applicability and limitations, data input, and interpretation of results? They are described in more detail in Box The first three organizations are convened under the Federal Advisory Committee Act and are subject to requirements of that act, including that all meetings and deliberations must be public.

Major ad hoc committees also hold open meetings. Ad hoc committees are often used for more in-depth reviews. All types of peer review are of substantial value, but the adequacy of peer review of a model must be judged in context with the need for evaluation of each major step from model conception to application. Major reviews, such as those performed by SAB, besides providing valuable input to agency scientists and managers, can become a part of the administrative record and can be used in court challenges. It is administratively housed in SAB.

Both documents rely on models. SAB traces its history to Its charge is to provide independent science and technical advice, consultation, and recommendations to the EPA administrator on the technical bases for agency positions and regulations. Most of its activities involve reviewing technical documents, including numerous model reviews e. SAP provides scientific advice, information, and recommendations to the EPA administrator on pesticides and pesticide-related issues as to the impact of regulatory actions on health and the environment.

Several SAP panels have focused on models to predict exposures to pesticides or on pesticide health assessments that were partly based on models. SAP panels summarize their discussions and issue recommendations in the minutes of the meetings e. As related to models, they might involve highly technical reviews before the SAB-level stage or might be for risk assessments that include some degree of reliance upon models. They include members of the general public.

Thus, many individuals and entities are stakeholders and have different interests, capabilities, and capacities to perform this role. These differences need to be understood and accommodated when fulfilling the intent and actual requirements for public review. Furthermore, EPA is required by statute to solicit comments from affected parties and the public at large on all final proposals for agency action 5 U. According to statute, EPA must also make all relevant documents in the record supporting its decision available to the public for viewing during the comment process.

Thus, there is a variety of both formal and informal processes for interagency review of regulatory models and analysis based on these models. The majority of interagency reviews involve mandatory oversight by OMB, although other agencies may also engage in more informal review and comment.

Journal Articles – The WITCH model

OMB oversees these process requirements and will work with the agencies to ensure that their regulatory analyses are satisfactory. Several of the processes of external reviews are still not transparent in regard to the disposition of the comments. In some instances, the effect of comments on the regulatory process is not clear. It is understood that not all comments are appropriate or useful, even though all need to be carefully considered. Thus, the issue is transparency—those commenting, from prominent scientists on the SAB to members of the general public, need to understand how their comments were considered.

The first and most established is the ability of interested parties to challenge agency action in court. If the model supports a regulation and has been subject to notice and comment, the courts give EPA considerable deference. Browner, F. If the model has not been subject to notice and comment but creates obligations for private parties—for example, at the permitting or enforcement stage—those af-.

In some of the cases, the agency may receive much less deference from the courts compared to the situation where the model has been subject to notice and comment for example, see United States v. Plaza Health Laboratories, Inc. Generally, a complete model history documenting the justification for various decisions related to model design and development may help the agency defend a model against formal challenges.

If EPA applies a generic air dispersion model to a large power plant located in a meteorologically unusual setting, such as the shores of Lake Erie, EPA might have to test the location to establish that the model provides some reliability in that setting, or it must be prepared to explain why its model should be accepted as is for example, State of Ohio v.

EPA, F. Consumer Product Safety Commission, F. However, this line of cases is more complex and unpredictable Pierce. A second, more recent opportunity for external challenge to model use in the regulatory process is through the Information Quality Act Treasury and General Government Appropriations Act for Fiscal Year , Pub. To date, courts have refused to review these challenges, but the challenges can be appealed inside the agency and the agency must respond to complaints that the information, including information used in models or the models themselves, is unreliable.

However, there are continued efforts to make challenges under the Information Quality Act reviewable by the judiciary Shapiro et al. In the case, CEI argued that the models were not reliable and had not been adequately peer reviewed. CEI then appealed its case to the D. District Court where CEI ultimately withdrew its case. If an agency. In , Malaysia had a total of palm oil mills with a production …. Currently, guaranteeing energy supply while energy models adapt to the impact of climate change is one of the main concerns for most governments. The European Union—EU—has put forward several strategies in order to ensure a transition towards a ….

Considering the demand and uncertain supply of fossil fuels, search for a renewable source of alternative energy is indispensable. The increasing consumption of fossil fuels such as coal, petroleum, natural gas, oil shale, bitumen, tar sands, and heavy oils in the industry, transportation and construction sectors causes a large amount of greenhouse gas such as CO 2 , CH 4 ….

The electricity generation in the United States has been on the rise over the past seven decades as shown in Fig. EIA a. The increase in electricity generation in the past 10 years, however, has not been as rapid as in the past. Cement production releases a significant amount of solid waste and gaseous substances in the environment. In addition to …. Sea cucumber Apostichopus japonicas has high nutritional and medicinal value, containing protein, calcium, potassium, zinc, iron, selenium, manganese, and amino acids, but no cholesterol Zohdi et al. In China, sea cucumbers are ….

Currently, there are no legal …. The study is aimed to enhance the productivity of microalgal culture by varying the organic and inorganic components during wastewater treatment. A model organism Chlorella pyrenoidosa C. The rapid growth of industry has contributed …. The steel industry is a significant contributor to the development of the world economy and provides raw materials for many important industries, such as machinery manufacturing and the petrochemical and construction industries.

China is a major …. Given the vast area of roofs, roads, and other urban surfaces without vegetation cover, they can rapidly absorb the thermal radiation of sunlight and act as secondary sources of radiant thermal energy.