Sustainability of the Linkages Between Water–Energy–Food Resources Based on Structural Equation Modeling Under Changing Climate: A Case Study of Narok County (Kenya) and Vhembe District Municipality (South Africa)
Bibliographic record
Abstract
Due to the current and predicted increase in the global demand for water–energy–food (WEF) resources, as well as the inevitable linkages between the WEF sectors and sustainable development, the WEF nexus is rapidly being recognized as a method to effectively manage sustainable development. Many African countries still face challenges in terms of the demand for and accessibility of WEF resources. For this reason, a comparative study of two sites (Narok County and Vhembe District Municipality), which exhibit similar socio-economic, environmental, and technological circumstances, was undertaken. In the present study, we considered 218 questionnaire responses, which we analyzed using partial least squares structural equation modeling (SEM) based on the WEF nexus constructs. This study is anchored on the null hypothesis (H0), whereby no interdependencies exist between the state of the climate and WEF resources, as constrained by sustainable development options. The results show that the proposed hypothesis does not hold, but rather, an alternative hypothesis (Ha)—there exist linkages between climate change and WEF resources—holds. This is demonstrated by the descriptive statistics indicating p values < 0.05 for both the t-test and the Bartlett test. Furthermore, analysis from the multi-regression, particularly for the model where we combined the sites, showed p values < 0.05 and higher adjusted r-squared values, which denoted a better fit. The communities in both study sites agree that the regions have experienced a scarcity of WEF resources due to climate change. The results show that climate change is an intrinsic part of the developmental options for the sustainable livelihood of both study sites, which aligns with the 2030 UN agenda on sustainable development goals targets. Moreover, the sustainable management of natural resources that are people- and planet-centric is crucial to climate change adaptation and mitigation, social justice, equity, and inclusion. The SEM results showed with significant confidence that the water, energy, and food sectors are closely interconnected; however, their impact on climate and sustainability is significantly different. Food has a direct positive impact on climate and sustainability, while both water and energy have an indirect negative impact. Moreover, the climate construct indicated a significant direct link to sustainability for all the relationships explored. This is particularly true because, in most underdeveloped countries, sustainable development and societal wellbeing heavily rely on goods and services derived from natural resources and the environment. This study contributes to the nexus modeling research field by introducing SEM as an innovative methodology over a single equation modeling framework in analyzing variables that have complex interrelationships, facilitating advanced WEF nexus resource governance.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".