Are there ecological problems that technology cannot solve? Water scarcity and dams, climate change and biofuels
Bibliographic record
Abstract
This paper shows through a comparative case study that many contemporary engineers working on a technological response to climate change—biofuel production—continue to be guided by traditional ethical and historical principles of efficiency and growth in spite of the uniqueness of climate change as a problem unbounded globally in space and time. The comparative case study reveals that in the past environmental issues like water scarcity were viewed as deficiencies of nature. In contrast, the development of biofuels as an engineering response to climate change shows that environmental and ecological issues today are viewed as deficiencies of technologies. Yet, just like large dams on rivers had (and continue to have) negative socioecological outcomes, political economy and political ecology research show biofuel development has socially unjust and ecologically degrading outcomes. Many engineers continue to separate the “technical” from the “political” aspects of engineering work, resulting in lost opportunities to reshape the technological development paradigm. While every technology has some negative impacts, engineers, as socioecological experimentalists, must account for these outcomes in their work to mitigate them. Encouragingly, the engineers interviewed for this paper (along the authors of this paper, who are all engineers) believe that problems like climate change are too narrowly defined, and that the problem-solving capabilities of engineers would lead to more favorable outcomes if problems were more broadly defined to incorporate concerns of social justice and ecological holism, and if we are given legitimacy and agency in proposing alternative, radical, and paradigm-changing solutions to problems like climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".