Strengthening the Blue Economy in the United Republic of Tanzania through Marine Spatial Planning and Biodiversity Protection
Bibliographic record
Abstract
The United Republic of Tanzania is endowed with a vast marine territory, encompassing a territorial sea of 64,000 square kilometers and an Exclusive Economic Zone of 223,000 square kilometers, alongside a diverse coastline that stretches 1,424 kilometers. This space is characterized by a rich variety of coastal and marine ecosystems, including coral reefs, seagrass beds, mangroves, sandy beaches, rocky shores, and numerous islets, collectively covering approximately 241,500 square kilometers about 20% of Tanzania's total land area. These ecosystems provide essential goods and services that support local communities' livelihoods and cultural practices, hosting remarkable biodiversity, including 150 coral species, 8,000 invertebrate species, 1,000 fish species, 5 marine turtle species, and various other marine organisms.Despite significant progress in conservation, with 33.5% of terrestrial areas and 6.5% of ocean areas under protection, Tanzania faces pressing challenges in conservation critical habitats. To meet international commitments, efforts must intensify to ensure that at least 30% of these habitats are protected by 2030 as per target three of the Kunming Montreal Global Biodiversity Framework.The blue economy resources of Tanzania, which include fisheries, tourism, and aquaculture, are increasingly threatened by climate change and various environmental, social, and economic stressors. These challenges lead to habitat degradation and create uncertainties for coastal communities, ultimately compromising the national economy. In response, the Government of Tanzania is implementing Marine Spatial Planning, a comprehensive public process that analyzes and allocates the spatial and temporal distribution of human activities in marine areas to achieve ecological, economic, and social objectives. A scoping study conducted between July 2022 and June 2023 involved consultations with over 314 stakeholders, focusing on policy, legal, and administrative analyses, as well as a spatial data audit. This study laid a strong foundation for full-scale MSP in Tanzania, revealing eight sectoral scopes for consideration and resulting in 23 recommendations to strengthen the blue economy.Following the scoping phase, Tanzania has made significant strides in marine spatial planning and biodiversity conservation, including the development of National Marine Spatial Planning Guidelines and a Blue Economy Policy with a 10-year strategy. This talk will outline the progress made to date in this East African state, unique challenges, and lessons learned.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".