The ethics of climate change and the green new deal: a qualitative study
Bibliographic record
Abstract
Purpose This paper builds on the findings of Bridge (2021) and attempts to understand the major ethical, equity, and leadership issues that may arise when governments plan massive infrastructure and amelioration programs such as the United States’ Green New Deal (GND). The methodology developed here could be applied to the plans being created in other developed countries such as Canada and Korea. Design/methodology/approach A qualitative approach was used to analyse the ethical issues associated with the Green New Deal via semi-structured interviews with 34 published authors of academic articles dealing with the ethics of climate change. Two industry experts were also consulted for reference. Findings This paper identifies three key themes arising from the proposed implementation of the Green New Deal. Firstly, the GND has the potential to present equity, justice, and ethical issues that must be considered as part of any intended adoption. Secondly, the GND will present opportunities for economic and climate success, but some groups may suffer due to its implementation. Thirdly, those that have the capacity, wealth, leadership, and ability should lead climate change initiatives. This may require market solutions in the short-term to reach 2050 net zero targets. Originality/value This paper is the first qualitative study undertaken on the Green New Deal, contributing to the development of the scant literature on this topic and also informing the practical implementation of wholesale infrastructure plans.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".