Critical Issues in Environmental Taxation: Volume VII: International and Comparative Perspectives
Bibliographic record
Abstract
Volume 4 in the Critical Issues in Environmental Taxation series provides a peer-reviewed selection of papers on environmental taxation written by experts from around the world. Selected from papers delivered at the Annual Global Conference on Environmental Taxation, they cover the theory of environmental taxation, countries' experiences of specific environmental taxes, proposed environmental taxes, and evaluations of the role of taxation compared with other environmental instruments. The book provides an interdisciplinary approach to environmental taxation, drawing on the fields of economics, law, political science, and accounting. Each volume in the series reflects the theme of the conference from which the papers are drawn, as well as other broader themes. Volume 4 will focus on the role of taxation in promoting renewable energy, but also includes a number of papers on other topics related to environmental taxation. Written predominantly by academics, the papers provide in-depth analysis that will provide a valuable resource to people interested in environmental taxation. Contributors to this volume - Mikael Skou Andersen, National Environmental Research Institute, Denmark Abdul Haseeb Ansari, IIU, Malaysia Stefan Bach, German Institute for Economic Research, Berlin, Germany Ronnie Belmans, University of Leuven, The Netherlands Matthew Bramley, Pembina Institute for Appropriate Development, Canada J. Bruha, Charles University Environment Centre, Prague, Czech Republic Bill Butcher, University of New South Wales, Sydney, Australia Rowena Cantley-Smith, Monash University, Australia Pablo Chico, Rey Juan Carlos University of Madrid, Spain Anna Christiernsson, Lulea University of Technology, Sweden Jacqueline Cottrell, Green Budget Germany, Germany David G. Duff, University of Toronto, Canada Willemien du Plessis, North-West University, South Africa Kristina Ek, Lulea University of Technology, Sweden Hana Foltynova, Charles University Environment Centre, Prague, Czech Republic Denise D. Fort, University of New Mexico School of Law, USA Jose Juan Gonzalez M., Universidad Autonoma Metropolitana, Mexico Anselm Gorres, Green Budget Germany, Germany Amparo Grau, Complutense University of Madrid, Spain Andrew J. Green, University of Toronto, Canada Pedro M. Herrera, Complutense University of Madrid, Spain Andrew H. Kelly, University of Wollongong, New South Wales, Australia Michael Kohlhaas, German Institute for Economic Research, Berlin, Germany Larry Kreiser, Cleveland State University, Ohio, USA Paul Lee, Cleveland State University, Ohio, USA Julie A. Lockhart, Western Washington University, USA Vojtech Maca, Charles University Environment Centre, Prague, Czech Republic Leonardo Meeus, University of Leuven, The Netherlands Janet E. Milne, Vermont Law School, USA Anna Mortimore, Griffith University, Australia Kenneth R. Richards, School of Public and Environmental Affairs, Indiana University, USA Kai Schlegelmilch, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, Berlin, Germany Julsuchada Sirisom, Mahasarakham University, Thailand Claudia Dias Soares, Portuguese Catholic University Patrik Soderholm, Lulea University of Technology, Sweden Hans Sprohge, Wright State University, Ohio, USA Natalie P. Stoianoff, University of Wollongong, New South Wales, Australia Rahmat Tavallali, Walsh University, Ohio, USA Amy Taylor, Pembina Institute for Appropriate Development, Canada Karolien Verhaegen, University of Leuven, Belgium Stefan Weishaar, University of Maastricht, The Netherlands Mark Winfield, Pembina Institute for Appropriate Development, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".