Can data visualization storytelling in energy communication campaigns ingrain farmers’ intentions to use agrivoltaic system? Evidence from global south
Bibliographic record
Abstract
Purpose Innovative technologies pave the way to address alarming global climate issues. Among these technologies is the expansion of renewable and clean energy in farming, which aims to meet the global cheap energy demand and, at the same time, replace fossil fuels. In pursuant to this, agrivoltaic technology is an innovation that provides sustainable and low-cost production solutions to diminish the adversities associated with climate change and global warming. However, farmers from developing nations remain unacquainted or unenthusiastic about adopting such sustainable technologies. Therefore, in response to these key challenges related to climate change, this study aims to provide the utility of communication resources to inspire climate-friendly behaviors among farmers. Design/methodology/approach This study used a cross-sectional field survey method for data collection from 992 farmers. Findings The results verified that using data visualization storytelling in communication campaigns could significantly enhance farmers’ public understanding of adopting renewable technologies. Research limitations/implications Theoretically, results highlighted the importance of communication strategies in a downward spiral of ongoing challenges of optimal climate protection, counteracting rebound effects and reducing carbon emissions. Practical implications The novel contribution of this research by examining the data visualization storytelling in climate and energy communication campaigns paved the way for social marketers to develop a straightforward and user-friendly platform for implementing innovative renewable technologies. Originality/value This research underpinned a novel approach that remains understudied to understand how data visualization storytelling supports renewable technology adoption. Furthermore, it addressed the timely call for research on how data visualization storytelling can assist in achieving UNSD goals 12 and 13 by promoting renewable technologies among the farmers from the neglected area of the Global South.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".