UK food sustainability and global food supply chains: a sustainability impact study of Ghana's fresh vegetable exports to the UK
Bibliographic record
Abstract
The purpose of this research is to explore the opportunities for reducing sustainability implications associated with the UK's global food supply chains by analysing Ghana's fresh vegetables exports. Existing literature assesses sustainability implications focusing on the traditional sustainability dimensions; namely, the environmental, social, and economic dimensions. Further, studies on the assessment of the UK food sustainability are yet to consider sustainability concerns generated by global food sources. To facilitate a holistic evaluation of the UK's global food supply chains and propagate its vision of global leadership in food sustainability, there is a need to consider all other relevant sustainability dimensions and their impacts associated with the activities and operations of global food suppliers. Case study data involving interviews and focus groups, together with survey data, are obtained from producers of Ghanaian fresh vegetables, such as smallholder farmers, outgrowers, local farmers, and exporters. The interviews and focus groups are first analysed using NVivo 11 software, following a thematic approach. Multilinear Regression (MLR) is performed using the Statistical Package for the Social Sciences (SPSS) to analyse the survey, in order to examine the relationship between sustainable food supply chains (sustainable FSC) and sustainability dimensions identified from the thematic analysis of the interviews and focus groups. \n \nThese findings indicate that six sustainability dimensions and their associated impacts are important in analysing Ghana's fresh vegetable exports to the UK. These are environmental, social, and economic dimensions, regulatory frameworks, collaboration, and producers' complexities in developing sustainable food supply chains (sustainable FSC). Interestingly, the survey results suggest that four of these dimensions are statistically significant; these are environmental, social, regulatory frameworks, and collaboration. The survey further revealed that an increase in regulatory frameworks and mechanisms can reduce sustainable FSC; whereas an increase in the practices and activities of the environmental, social, and collaborative dimensions increases sustainable FSC, thus improving overall sustainability. Revelations and findings from both the thematic and survey analysis were utilised to develop, test and validate the Sustainability Impact Assessment (SIA) model (thus, a conceptual framework of the study). \n \nThis study contributes to the body of knowledge in several ways. To theory, an SIA model is suggested, demonstrating the capture of all important sustainability dimensions; namely, environmental, economic, social, regulatory, collaboration, and complexities of food supply chains. It extends the discussion on sustainability impact assessments and sustainability development and encourages research in sustainability assessment. In practice, this SIA model can facilitate easy capture, examination, and evaluation of all relevant sustainability implications and allow new insights into the development and assessment of the stream of sustainability development. \n \nAmong many other implications such as promoting collaboration, policymakers need to encourage FairTrade for producers in developing countries, and regulatory mechanisms should be re-designed to enhance profitability by using simple conformity and economic incentives. Further, food trade partners and FSC professionals should encourage smart strategies and technologies to enhance logistics that minimise food waste and energy consumption, while boosting producers' welfare. Moreover, governments and policymakers should ensure that the sustainability concerns of overseas countries are captured in food policies and strategies to help facilitate global leadership in food sustainability.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".