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Record W4388634314 · doi:10.1108/msar-09-2023-0047

Shifting geopolitical sands: COP 28 and the new BRICS+

2023· article· en· W4388634314 on OpenAlexaff
Amr El Alfy, Dina El-Bassiouny, Logan Cochrane

Bibliographic record

VenueManagement & Sustainability An Arab Review · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeopoliticsChinaSummitEmerging marketsNegotiationPolitical scienceSustainabilityInternational tradeEconomicsEconomyDevelopment economicsGeographyLawPolitics

Abstract

fetched live from OpenAlex

Purpose The new additions to Brazil, Russia, India, China and South Africa (BRICS) expand into the broader Middle East and North Africa region, adding some of the largest populations and strongest economies of the region to BRICS+. Since the BRICS summit in August 2023, significant media attention has been given to the impacts of these shifting geopolitical sands, from the potential for de-dollarization processes, and the distribution of resource reserves for influencing markets. Conference of the Parties (COP) 28 presents an opportunity for these emerging economies (BRICS+) to assert their role in addressing the global climate crisis and push for more equitable and effective solutions. However, only little has been explored on how the new BRICS+ alignment will influence climate negotiations at COP 28 and the sustainability transition more broadly. This perspective article explores what the changes to BRICS+ mean for COP 28 and the relevance of COP 28 being hosted in a BRICS+ member country. Design/methodology/approach In crafting this perspective paper on BRICS+, the authors' methodology primarily entails a comprehensive review of existing literature, policy documents and academic analyses related to the BRICS+, as well as the examination of official statements, declarations and policy shifts from BRICS+ member countries to gauge their intentions and priorities within this expanded framework. The authors also monitor developments leading up to COP 28 to provide real-time insights into how BRICS+ dynamics shape climate negotiations. Findings The authors' perspective article puts forth a number of insights. First, the BRICS+ member countries are prominent players in global geopolitics. Their involvement in COP 28 could lead to climate negotiations being intertwined with broader geopolitical issues, potentially impacting the pace and direction of climate agreements. Second, COP 28 offers a critical opportunity to bridge the divide between developed and developing nations in the realm of climate action and sustainable development. The BRICS+ countries may, in this COP event, explore options beyond the traditional intergovernmental institutions, which often reflect the influence, hegemony and power dynamics of the Global North. This includes South–South collaboration, bilateral financial support, innovative financing and direct trade. Finally, agendas related to capacity building in this coming event will be a critical component of the global climate change agenda in a way that develops South–South dialogs for climate change adaptation and mitigation. Originality/value The authors' research sheds light on the implications of this expansion for climate negotiations, a critical global concern. It delves into uncharted territory by examining how the BRICS+ alignment may influence climate initiatives, which has not been thoroughly explored in existing literature. This comprehensive perspective fills a critical gap in the current discourse, providing policymakers and scholars with a more holistic understanding of the implications of BRICS+ for the global agenda on sustainability. Moreover, the research offers real-time insights by monitoring developments leading up to and during COP 28, allowing for timely analysis and informed recommendations. This aspect of the research provides immediate value to stakeholders engaged in climate negotiations and international relations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.061
GPT teacher head0.294
Teacher spread0.233 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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