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Record W4321490222 · doi:10.5194/egusphere-egu23-3940

Sustainability trade-offs for equity and climate interventions in global food systems: The case of cocoa in Ghana

2023· preprint· en· W4321490222 on OpenAlexaff
Sophia Carodenuto, Marshall Alhassan Adams

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSustainabilityFood securityLivelihoodBusinessPovertyNatural resource economicsPopulationAgricultureEquity (law)Agricultural economicsGeographyEconomicsEconomic growthEcology

Abstract

fetched live from OpenAlex

Despite widespread attempts to ‘eat local,’ many of the lifestyle factors in the Global North rely on the production of agrifood commodities that can only be grown in tropical ecosystems, far from the dominant geographies of consumption. Chocolate, coffee, and palm oil represent a handful of consumer goods that are described as ‘tropical forest risk commodities,’  whose production threatens some of the last remaining biodiversity hotspots and stable carbon sinks. This research assesses the trade-offs between dominant approaches to poverty reduction in tropical forest landscapes – regions where global land use change is concentrated as forests are converted to agrifood commodity production areas to produce consumer goods that are core to global food systems. After Côte d’Ivoire, Ghana is the second largest exporter of cocoa (the main ingredient in chocolate). Ghana’s economy is highly cocoa-dependent, and cocoa provides livelihoods for about a quarter of the population, especially in rural areas where alternative incomes are limited. Although the cocoa sector contributed an estimated US$2.71 billion in government revenues in 2017, many cocoa producers live below the national poverty line.Policy responses to balance the trade-offs between global food production, climate change, and socioeconomic development have recently come to the fore in Ghana – the world’s second largest producer of cocoa. In 2019, the Government of Ghana introduced the Living Income Differential (LID), which requires buyers to pay an additional US$400 per ton of cocoa on top of the floor price. With low farmer incomes identified as a critical driver of multiple sustainability issues in Ghana’s cocoa sector, this differential is meant to be directly transferred to cocoa farmers in response to the persistent challenge of poverty in cocoa farming communities. Using the Q methodology, we engaged over 50 stakeholders from various levels (international policy experts, cocoa sector stakeholders in Ghana, and cocoa farmers) to understand how LID is perceived, including its potential to transform the rural poverty complex embedded in Ghana’s cocoa supply chain. While the LID is lauded for increasing producer price across the board, our findings indicate that the lack of regard for farmer diversity (i.e., tenure rights, sharecroppers, and caretakers), farm size, and land management strategies (agroforestry versus clearing forest to establish farms) risks undermining the ability of this pricing mechanism to reduce farmer poverty as a way to foster sustainability in the sector. Further, the LID is siloed from on-going sustainability governance efforts in the sector, such as zero deforestation cocoa. If the LID is delivered to farmers across the board without any quid pro quo for how cocoa is produced, the policy’s unintended consequences may include increasing deforestation in the short term, while lowering the world market price of cocoa in the long term as cocoa supply increases. We conclude with policy implications on why different perspectives matter in managing sustainability trade-offs in deforestation frontiers. This study provides important insights for understanding how to achieve multiple sustainability goals together.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.348
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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