Bibliographic record
Abstract
The main subject of this thesis is the relationship between economic growth and environmental effects when the interaction between firms' behaviour and regulations are taken into account. In the first three papers I discuss different aspects of the relationship between regulations and environmental effects. In the last paper I perform a factor demand analysis within a multiproduct framework. \nIn Chapter 2, I focus on the frequently discussed question about the relation between liberalisation of trade and its effects on the environment. I study the North American Free Trade Agreement (NAFTA) between Mexico, United States and Canada, signed 1994, and its effects on the emission of carbon dioxide from the manufacturing industry in Mexico. I apply a dynamic factor demand model, for the Mexican manufacturing industry, to examine the changes in economic development, factor demands and the development of carbon dioxide emission following trade liberalisation. My results indicate a technological shift in the manufacturing industry after 1994, when Mexico joined NAFTA. This led to a more factor intensive use of energy, and less emission of carbon dioxide, than with a regime without NAFTA. \nIn Chapter 3, the focus is on the concern that environmental regulations hamper competitiveness and economic growth. The empirical relationship between environmental regulations and productivity growth is studied. The overall effect of the regulatory stringency faced by plants on plants' productivity growth is statistically insignificant when productivity growth is measured without environmental detrimental factors. However, when these factors are included, the effect is positive and statistically significant. This indicates that not accounting for emission reductions when measuring productivity growth can result in too pessimistic conclusions regarding the effect of regulatory stringency on productivity growth.\nIn Chapter 4, the focus is on one particular environmental regulation. The Integrated Pollution and Prevention Control (IPPC) directive from the European Union implies that regulatory emission caps should be set in accordance with each industry’s Best Available Techniques (BAT). Data Envelopment Analysis (DEA) is used to construct a frontier of all efficient plants. This provides us with an interpretation of BAT. We assume that all plants emit in accordance with the best practice technology, represented by the frontier, by reducing all inputs proportionally. The interpretation reveals a strong potential for emission reductions. Further, abatement cost estimates indicate that considerable emission reductions can be achieved with low or no social costs, but that the implementation of BAT for all plants involves substantial costs.\nIn Chapter 5, the aim is to test a multiproduct specification up against a single homogeneous output approach. Although most of the production activities involve multiple outputs, econometric models of production or cost functions normally involve only one single homogeneous output. The aim of this paper is to test the hypothesis that a multiproduct specification for Norwegian primary aluminium production is superior to a model with a single homogeneous product. To do this, I use a Multiproduct Symmetric Generalized McFadden (MSGM) cost function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".