Cities as leaders in EU multilevel climate governance: embedded upscaling of local experiments in Europe

ABSTRACT

The success of local climate governance in Europe depends not only on leading cities but also on the dynamics between leaders, followers, and laggards. Upscaling local experiments helps to close the gap between these actors. This process is driven by the increasing embeddedness of cities and their networks in EU multilevel governance. Embedded upscaling combines horizontal upscaling between leading cities with vertical upscaling between leaders and followers that is mediated by higher levels of government, and hierarchical upscaling that even reaches the laggards. Various types of upscaling, their combinations, and their impacts are analyzed. Networks have become denser and networking has intensified. City networks and their member cities have become embedded in national and EU governance, lost authority and depend more and more on regional, national, and European authorities.     

Confidence limits on one-stage model parameters in benchmark risk assessment

In modern environmental risk analysis, inferences are often desired on those low dose levels at which a fixed benchmark risk is achieved. In this paper, we study the use of confidence limits on parameters from a simple one-stage model of risk historically popular in benchmark analysis with quantal data. Based on these confidence bounds, we present methods for deriving upper confidence limits on extra risk and lower bounds on the benchmark dose. The methods are seen to extend automatically to the case where simultaneous inferences are desired at multiple doses. Monte Carlo evaluations explore characteristics of the parameter estimates and the confidence limits under this setting.

Bootstrap methods for simultaneous benchmark analysis with quantal response data

A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers on the “benchmark dose” at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence. For the case of quantal data, simultaneous methods have been constructed that appeal to the large sample normality of parameter estimates. The suitability of these methods for use with small sample sizes will be considered. A new bootstrap technique is proposed as an alternative to the large sample methodology. This technique is evaluated via a simulation study and examples from environmental toxicology.

Benchmark calculations from summarized data: an example

Benchmark calculations often are made from data extracted from publications. Such data may not be in a form most appropriate for benchmark analysis, and, as a result, suboptimal and/or non-standard benchmark analyses are often applied. This problem can be mitigated in some cases using Monte Carlo computational methods that allow the likelihood of the published data to be calculated while still using an appropriate benchmark dose (BMD) definition. Such an approach is illustrated herein using data from a study of workers exposed to styrene, in which a hybrid BMD calculation is implemented from dose response data reported only as means and standard deviations of ratios of scores on neuropsychological tests from exposed subjects to corresponding scores from matched controls. The likelihood of the data is computed using a combination of analytic and Monte Carlo integration methods.

Multiplicity-adjusted Inferences in Risk Assessment: Benchmark Analysis with Continuous Response Data

We develop and study multiplicity adjustments for low-dose inferences in environmental risk assessment. Application is intended for risk analysis studies where human, animal, or ecological data are used to set safe levels of a hazardous environmental agent. A modern method for making inferences in this setting is known as benchmark analysis, where attention centers on the dose at which a fixed benchmark level of risk is achieved. Both upper confidence limits on the risk and lower confidence limits on the “benchmark dose” are of interest. In practice, a number of possible benchmark risks may be under study; if so, corrections must be applied to adjust the limits for multiplicity. In this note, we discuss approaches for doing so with continuous, nonquantal response data.     

荷兰长期能效协议和能效标杆合约及启示

提高能效是减排的重要途径,荷兰长期能效协议和能效标杆合约在这方面取得了很大成效,其成功经验值得研究和学习。本文首先系统介绍了荷兰长期能效协议和能效标杆合约的构建、实施及监督过程,分析了其取得的成效和存在的不足。然后描述了自愿协议式方法在中国的应用情况和我国面临的节能减排压力。最后总结出长期能效协议和能效标杆合约对我国应用和推广自愿协议式方法的启示。     

Beta-normal distribution in dose–response modeling and risk assessment for quantitative responses

To establish allowable daily intakes for humans from animal bioassay experiments, benchmark doses corresponding to low levels of risk have been proposed to replace the no-observed-adverse-effect level for non-cancer endpoints. When the experimental outcomes are quantal, each animal can be classified with or without the disease. The proportion of affected animals is observed as a function of dose and calculation of the benchmark dose is relatively simple. For quantitative responses, on the other hand, one method is to convert the continuous data to quantal data and proceed with benchmark dose estimation. Another method which has found more popularity (Crump, Risk Anal 15:79–89; 1995) is to fit an appropriate dose–response model to the continuous data, and directly estimate the risk and benchmark doses. The normal distribution has often been used in the past as a dose–response model. However, for non-symmetric data, the normal distribution can lead to erroneous results. Here, we propose the use of the class of beta-normal distribution and demonstrate its application in risk assessment for quantitative responses. The most important feature of this class of distributions is its generality, encompassing a wide range of distributional shapes including the normal distribution as a special case. The properties of the model are briefly discussed and risk estimates are derived based on the asymptotic properties of the maximum likelihood estimates. An example is used for illustration.

An approach to comparative assessments of potential health risks from exposure to radionuclides and hazardous chemicals

The need to compare potential health risks to the public associated with different activities that can result in releases of hazardous substances to the environment is becoming increasingly important in decision-making. In making such comparisons, it is desirable to use equivalent indicators of potential health risks for radionuclides, chemical carcinogens, and noncarcinogenic hazardous chemicals. Current approaches to risk assessment that were developed for purposes of protecting human health do not provide equivalent indicators of potential risks from exposure to radionuclides and hazardous chemicals. Comparisons of environmental concentrations or calculated exposures or risks with standards for protection of public health also do not provide equivalent indicators of potential risks. We propose a simple approach to comparative risk assessments in which calculated exposures to any hazardous substances are expressed relative to no-observed-effect levels (NOELs) or, preferably, lower confidence limits of benchmark doses (BMDLs) in humans. This approach provides an equivalent, science-based indicator of the relative risks posed by different exposures to any hazardous substances.     

Replace the NOAEL and LOAEL with the BMDL01 and BMDL10

Although benchmark-dose methodology has existed for more than 20 years, benchmark doses (BMDs) still have not fully supplanted the no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) as points of departure from the experimental dose–response range for setting acceptable exposure levels of toxic substances. Among the issues involved in replacing the NOAEL (LOAEL) with a BMD are (1) which added risk level(s) above background risk should be targeted as benchmark responses (BMRs), (2) whether to apply the BMD methodology to both carcinogenic and noncarcinogenic toxic effects, and (3) how to model continuous health effects that aren’t observed in a natural risk-based context like dichotomous health effects. This paper addresses these issues and recommends specific BMDs to replace the NOAEL and LOAEL.

Development of an Evaluation Methodology for Triple Bottom Line Reports Using International Standards on Reporting

The purpose of this article is twofold. First, evaluation scoring systems for triple bottom line (TBL) reports to date are examined and potential methodological weaknesses and problems are highlighted. In this context, a new assessment methodology is presented based explicitly on the most widely acknowledged standard on non-financial reporting worldwide, the Global Reporting Initiative (GRI) guidelines. The set of GRI topics and performance indicators was converted into scoring criteria while the generic scoring devise was set from 0 to 4 points. Secondly, the proposed benchmark tool was applied to the TBL reports published by Greek companies. Results reveal major gaps in reporting practices, stressing the need for the further development of internal systems and processes in order to collect essential non-financial performance data. A critical overview of the structure and rationale of the evaluation tool in conjunction with the Greek case study is discussed while recommendations for future research on the field of this relatively new form of reporting are suggested.