Exnovations in Energy and Mobility in Europe: Impacts on and Engagement of Vulnerable Groups
Bibliographic record
Abstract
Abstract Exnovation refers to processes of destabilization, decline, and phase-out of carbon-intensive industries, technologies, business models, and practices, as well as those that create other systemic sustainability challenges. Implementing successful low-carbon transitions across Europe that are socially fair, just, and effective is challenging. While transition policies are usually promoting innovation and diffusion of technological advancements, less focus is dedicated to systemic decarbonization and phasing-out of non-sustainable technologies, materials and practices. Phase-out policies and their implementation supporting a just-low-carbon transition are fundamental to achieve current climate change goals. Nevertheless, the engagement of and the impacts on citizens exposed to most severe effects of transition policies remain underexplored, but are crucial to avoid reinforcement of existing injustices, such as inequitable distribution of costs, or non-inclusive decision-making processes. Therefore, in this paper we explore 27 past and present initiatives related to transition policies in the mobility and energy sector across EU, Canada and Australia to understand their undesired (negative) impacts, affected vulnerable groups and their participation in the decision-making process. This study stems from TANDEM, a Horizon Europe project, that is utilising an innovative transdisciplinary approach to assess and mitigate negative impacts on citizens at risk of vulnerability due to implementation of low-carbon transition policies. Our analysis shows that although it depends on the type, location, and scale of the transition initiatives, vulnerability factors are often related to level of education, level of income, age, gender, house ownership, ethnicity, job sector, geographic location, migration background, and disability. Unfortunately, there is a lack of understanding, awareness or recognition among policy and decision makers when it comes to vulnerability factors and undesired impacts associated with transition policies and initiatives. One-third of the analysed initiatives did not even identify any vulnerable groups and only less than half of the initiatives undertook some effort to engage citizens or account for concerns of vulnerable groups, while only two (out of 27 initiatives analysed) allowed vulnerable groups to take part in the decision-making process. This lack of analysis of inequalities and vulnerabilities prior to implementing low-carbon transition initiatives and policies can not only lead to lower acceptability of these initiatives, but also can cause serious negative consequences, such as deepening inequalities and increasing energy and mobility poverty of certain societal groups. Effective addressal of issues related to fairness and justice are imperative for successful implementation of transition policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".