Environmental performance indicators for energy sector industry : a thesis presented in fulfilment of the requirements for the degree of Masters in Applied Science in Natural Resource Management at Massey University
Bibliographic record
Abstract
Mounting evidence regarding the degradation of our environment and a growing sense of shared responsibility has provided the impetus to develop multilateral environmental agreements to address global environmental problems. Concerns regarding unsustainable energy consumption and production patterns have also underscored the need to improve environmental monitoring. This research provides an analysis of the role and status of environmental performance indicators for energy sector industry in New Zealand. The environmental indicators considered are those that are directly aligned to energy consumption and production patterns. In order to be able to identify and isolate the range of issues associated with energy consumption and production patterns, it is necessary to understand both the factors that influence energy use and the effects that arise. Factors that can be utilised as environmental indicators include, energy efficiency, energy intensity, energy fuel mixes and energy prices, and the carbon dioxide emissions associated with energy use. Much progress has been made at a national and international level in the development and use of environmental indicators for energy sector industry. The UN, OECD, and Natural Resources Canada all utilise the above-described environmental indicators to assess energy consumption and production patterns. This progress provides useful insight for the MfE in the development of their national energy indicators. The MfE's energy indicators when introduced will prove a fundamental monitoring tool for policy makers in New Zealand. Environmental indicators will enable policy makers at either a local, national or international level to be able to accurately monitor and evaluate the environmental consequences associated with energy consumption and production patterns (including those of energy sector industry). From this monitoring, policy makers will be able to assess the effectiveness of their environmental policy frameworks. In doing so, policy makers will avoid misinterpreting or inappropriately responding to their environmental policy frameworks or obligations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".