Making Environmental Policy
openalex(2023)
Abstract
Who speaks for the trees, the water, the soil, and the air in American government today? Which agencies confront environmental problems, and how do they set priorities? How are the opposing claims of interest groups evaluated? Why do certain issues capture the public's attention? In Making Environmental Policy, Daniel Fiorino combines the hands-on experience of an insider with the analytic rigor of a scholar to provide the fullest, most readable introduction to federal environmental policymaking yet published. A committed environmental advocate, he takes readers from theory to practice, demonstrating how laws and institutions address environmental needs and balance them against other political pressures. Drawing on the academic literature and his own familiarity with current trends and controversies, Fiorino offers a lucid view of the institutional and analytic aspects of environmental policymaking. A chapter on analytic methods describes policymakers' attempts to apply objective standards to complex environmental decisions. The book also examines how the law, the courts, political tensions, and international environmental agencies have shaped environmental issues. Fiorino grounds his discussion with references to numerous specific cases, including radon, global warming, lead, and hazardous wastes. Timely and necessary, this is an invaluable handbook for students, activists, and anyone wanting to unravel contemporary American environmental politics.
MoreTranslated text
Key words
Public Policy
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined