GOMD: A Global Ocean, Maritime, and Climate Data Platform with Official Python and R Scientific Computing & Econometric Software Development Kits (v2.0.0)

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DUAL VOICE-NARRATED VIDEO SHOWCASES (PYTHON & R) · LIVE MACHINE VERIFICATION (PYTHON 3.12.14 & R 4.x)

GOMD Live Database → Python (gomd) & R (gomd) v2.0.0 — Dedicated 1080p HD Voice-Narrated Showcases for Python & R

🐍 run_all_gomd_functions.py 📊 run_all_gomd_functions.R ✅ Live Output JSON (52/52 OK) 🎬 Hyperframe Stage

Python gomd v2.0.0 Video Showcase 52/52 Functions · 1080p EN Audio

R gomd v2.0.0 Video Showcase 44/44 R Functions · 1080p EN Audio

1. gomd.client / R Client (20/20 Verified · Live DB)

• gomd.cite('all') & search_indicators('co2','climate')
• get_series('gml_mauna_loa_co2_ppm','WLD') → 823 live GOMD rows
• analyze_gomd_basin('OCN_MED_LEVANTINE') → 93 SST & wave rows
• build_panel() & analyze_gomd_policy('TUR') → 66 panel rows

2. gomd.ocean (9/9 Verified · Post-2020)

• teos10_gsw_stratification() → $S_A=38.68$, $N=43.83\text{ cph}$
• compound_mhw_oax_event() → 8 triple days (17.07x), AGI=0.718
• mucilage_risk_index() → 74.92/100 (HIGH), $\text{TRIX}=6.80$
• blue_carbon_sequestration_valuation() → $511.80M NPV

3. gomd.maritime (7/7 Verified · 2023+)

• calculate_fueleu_maritime() → 73.09 gCO₂e/MJ (+4,043 t surplus)
• imo_2023_strive_trajectory() → $32.60M Carbon VaR
• ais_speed_power_emissions() → -10% speed = -223 t CO₂
• calculate_eu_ets_exposure() → 29,207 EUAs (€2.18M)

4. gomd.stats & spatial & cli (16/16 OK)

• synthetic_did_ate() (AER 2021) → $\hat{\tau}_{\text{SDID}} = -3.200$
• transfer_entropy_causal() (2023) → $\text{TE}_{X\to Y}=1.007\text{ bits}$
• innovative_polygon_trend() → 1.186x tail amplification
• chokepoint_proximity() & CLI → Bosporus 6.87 NM

📓 NEW · 10 REAL-DATA GOOGLE COLAB CASE STUDIES (.IPYNB & .PY) & COMPLETE PYTHON API DOCS

Download all 10 reproducible real-data case studies (CS01–CS10, zero synthetic data) individually as Google Colab (.ipynb) notebooks or Python (.py) scripts:

📚 Open Python SDK Docs & 10 Colab Case Studies Hub → 📥 CS01 📥 CS02 📥 CS03 📥 CS04 📥 CS05 📥 CS06 📥 CS07 📥 CS08 📥 CS09 📥 CS10

1. Introduction & Scientific Motivation

Marine ecosystems and global maritime supply chains sit at the nexus of planetary climate stability. Monitoring targets under United Nations Sustainable Development Goal 14 (Life Below Water), SDG 13 (Climate Action), the IMO 2030/2050 GHG Strategy, and the EU Emissions Trading System (EU ETS) requires seamless integration of physical oceanography (Sea Surface Temperature, Ocean Heat Content, $p\text{CO}_2$, sea-level rise), marine biogeochemistry, fisheries biomass, and commercial fleet telemetry.

Designed under the academic leadership of Piri Reis University (Maritime Sustainability Research Centre) within the framework of the COP31 Türkiye climate diplomacy initiative, GOMD eliminates the data engineering bottleneck by providing a unified relational and geospatial data warehouse paired with native Python (gomd v2.0.0) and R (gomd v2.0.0) scientific computing libraries.

2. Platform Architecture & Global Data Coverage

The GOMD data architecture comprises two primary API layers across eight thematic domains: (1) the unauthenticated Public Catalogue & Dossier API (dataset_api.php) and (2) the SHA-256 key-authenticated Institutional Data, Time-Series & GIS API (api_institutional.php).

3. Official Python Package (gomd v2.0.0) — 6 Scientific Modules, 44 Functions & User Guide

The official Python "gomd" v2.0.0 package provides 6 specialized submodules and 44 verified functions combining FAIR data retrieval with physical/biogeochemical oceanography, IMO CII & FuelEU maritime decarbonization, IUMI / P&I / Poseidon marine insurance actuarial underwriting, UNEP FI PSI parametric coastal sustainability insurance, World Bank CWON natural capital accounting, and causal panel econometrics.

3.1. Python Installation & CLI Usage

# Method 1: Install directly from GOMD PEP 503 Simple Package Index (Recommended)
pip install gomd --extra-index-url https://gomd.pirireis.edu.tr/sdk/dist/simple/

# Method 2: Direct Wheel (.whl) installation (v2.0.0)
pip install https://gomd.pirireis.edu.tr/sdk/dist/gomd-2.0.0-py3-none-any.whl

# Command-Line Interface (CLI) quick diagnostics:
python -m gomd cite --style apa
python -m gomd cii --fuel 6400 --dwt 85000 --nm 58000 --type bulk_carrier --year 2026
python -m gomd carbonate --ph 8.04 --temp 22.0 --sal 38.4 --ta 2560
python -m gomd basin --lat 40.98 --lon 28.95

3.2. Python & R (gomd v2.0.0) 6-Module & 44-Function Scientific API Reference

3.3. Google Colab / Jupyter Live GOMD Database & Post-2020 Scientific Workflow in Python

# 0. Install in Google Colab / Jupyter & print all citations (APA 7th, IEEE, BibTeX)
!pip install -q -U https://gomd.pirireis.edu.tr/sdk/dist/gomd-2.0.0-py3-none-any.whl
import gomd
gomd.cite("all")

# 1. Connect to live GOMD Server, query the 42-Series Global Blue & Marine Insurance Finance Atlas (1970-2025)
client = gomd.GOMDClient()
df_ins = gomd.get_blue_finance_atlas("iumi_marine_insurance_total_premium_usd_mn", entities=["WLD", "EU27", "CHN", "USA", "NOR", "TUR"], period_from=1995, period_to=2025)
df_psi = gomd.get_blue_finance_atlas("unepfi_psi_sustainable_climate_coastal_insurance_usd_bn", entities=["WLD", "EU27", "USA", "TUR"], period_from=1995, period_to=2025)

# 2. One-Call Live GOMD Database-to-Econometrics & Ocean Basin Pipelines
co2_fit = client.analyze_gomd_series("gml_mauna_loa_co2_ppm", entity="WLD")
med_fit = client.analyze_gomd_basin("OCN_MED_LEVANTINE", salinity_psu=38.6, ph_total=8.06)
pol_fit = client.analyze_gomd_policy("worldbank_primary_index_v2026", treated_entity="TUR")

# 3. Direct Post-2020 Oceanography, Marine Insurance (IUMI/PPMI), Sustainability Insurance (UNEP FI PSI) & CWON
from gomd.ocean import teos10_gsw_stratification, blue_carbon_sequestration_valuation, sustainable_insurance_cat_var, cwon_wealth_accounting
from gomd.maritime import calculate_fueleu_maritime, imo_2023_strive_trajectory, marine_insurance_actuarial_pricing

hm_pi  = marine_insurance_actuarial_pricing(hull_value_usd=65_000_000, gross_tonnage=42_000, cargo_value_usd=28_000_000, cii_rating="B", observed_aer=4.65, required_ppmi_aer=5.10, green_dual_fuel=True)
psi_cat = sustainable_insurance_cat_var(coastal_exposed_value_usd=500_000_000, reef_mangrove_seagrass_ha=1200, observed_wave_hs_m=5.4, parametric_trigger_hs_m=4.8)
cwon   = cwon_wealth_accounting(gni_usd=1.15e12, gross_national_savings_usd=2.95e11, co2_damage_usd=2.45e10, blue_natural_capital_net_change_usd=1.45e9)
mv_aud = gomd.mavi_vatan_eez_audit(mainland_coast_km=1577.0, opposite_coast_km=19.0, un_registered_area_km2=462000.0, provider_polygon_area_km2=254026.0)
print("Total Marine Insurance ($):", hm_pi["total_annual_marine_insurance_usd"], "| Mavi Vatan Discrepancy (km2):", mv_aud["provider_area_discrepancy_km2"])

3.4. Global Historical Blue Finance, Marine Hull/P&I Underwriting (IUMI / PPMI), Parametric Sustainability Insurance (UNEP FI PSI) & CWON Natural Capital Methodology

To address the historical data gap in global ocean and sustainability finance, GOMD curates a harmonized 42-indicator, 228-country/region historical financial and insurance database (1970–2025, $N = 192,550$ verified observations) integrating three institutional pillars: (i) OECD Sustainable Ocean Economy (SOE), IUMI Global Marine Insurance, International Group of P&I Clubs, UNEP FI Principles for Sustainable Insurance (PSI), ICMA Blue/Green Bonds, and Poseidon Principles (Ship Finance & Marine Insurance); (ii) World Bank Changing Wealth of Nations (CWON 2021/2024), PPI Port/Energy Investments, IMF Balance of Payments, and WTO Merchandise Marine Cargo Insurance; and (iii) Eurostat COFOG GF05 Public Marine/Environmental Protection Expenditure and FAO/Eurostat Aquaculture Valuation.

In gomd.maritime.marine_insurance_actuarial_pricing() (and R gomd_marine_insurance_pricing()), annual commercial vessel underwriting combines Hull & Machinery ($\text{H\&M}$), mutual Protection & Indemnity ($\text{P\&I}$) club calls, CIF–FOB marine cargo protection, and the Poseidon Principles for Marine Insurance (PPMI, 2024) climate alignment score ($\Delta_{\text{PPMI}} = 100 \times (\text{AER}_{\text{obs}} - \text{AER}_{\text{req}}) / \text{AER}_{\text{req}}$). In gomd.ocean.sustainable_insurance_cat_var() (and R gomd_sustainable_insurance_var()), coastal ecosystem wave attenuation ($\alpha_{\text{NbS}} \le 0.62$, following Reguero et al., 2020 and Beck et al., Nature Sustainability 2022) reduces Expected Annual Damage ($\text{EAD}_{\text{prot}} = \text{EAD}_0 (1 - \alpha_{\text{NbS}})$) and $100\text{-yr}/250\text{-yr}$ Probable Maximum Loss ($\text{PML}$), while triggering rapid parametric payouts when significant wave height $H_s \ge H_{\text{trigger}}$. Finally, gomd.ocean.cwon_wealth_accounting() implements the World Bank CWON (2024) and Dasgupta Review (2021) inclusive Adjusted Net Savings identity: $\text{ANS}_{\text{Blue}} = \text{GNS} - D_{\text{fixed}} + E_{\text{edu}} - (D_{\text{energy}} + D_{\text{mineral}} + D_{\text{forest}}) - (\text{Damage}_{\text{CO}_2} + \text{Damage}_{\text{PM}_{2.5}}) + \Delta K_{\text{Blue}}$.

3.5. Republic of Türkiye Maritime Jurisdiction ("Mavi Vatan" — UN Letter A/74/757), ICJ Equitable Delimitation Principles & Third-Party Geodatabase Discrepancy Audit

Third-party marine geospatial databases—most notably VLIZ Marine Regions (v12), EMODnet / DG MARE, and global indices using them as spatial denominators (OHI, UNEP-WCMC WDPA, GFW, Sea Around Us)—rely on automated strict median-line geometries and the maximalist "Seville Map" in the Eastern Mediterranean and Aegean Sea. In doing so, they attribute a full continental shelf/EEZ polygon to tiny islands immediately adjacent to the Anatolian mainland (such as Kastellorizo / Meis, coastal perimeter $L_{\text{opp}} \approx 19\text{ km}$ at $d_{\text{nm}} \approx 1.9\text{ nm}$ from Kaş, versus Türkiye's Mediterranean mainland facade $L_{\text{main}} \approx 1,577\text{ km}$) and incorporate unilateral EEZ claims by the Greek Cypriot Administration (GCA), artificially truncating Türkiye's maritime jurisdiction from its official $\sim 462,000\text{ km}^2$ ("Mavi Vatan") down to $254,026\text{ km}^2$ ($\Delta A_{\text{MV}} = +207,974\text{ km}^2$).

In accordance with settled International Court of Justice (ICJ) and arbitral jurisprudence—including North Sea Continental Shelf (1969), Libya–Malta (1985), Maritime Delimitation in the Black Sea (Romania v. Ukraine, 2009), and Territorial and Maritime Dispute (Nicaragua v. Colombia, 2012) (Evans, 2023; Tanaka, 2023; Acer, 2020; Başeren, 2021; Yaycı, 2020)—maritime delimitation in semi-enclosed seas is governed by equitable principles, non-encroachment (cut-off prevention), and the three-stage disproportionality test. The function gomd.spatial.mavi_vatan_eez_audit() (and R gomd_mavi_vatan_eez_audit()) formalizes this test via the coastal-length proportionality ratio $R_L = L_{\text{main}} / L_{\text{opp}}$, the distance-decayed island effect weight $w_{\text{island}} = \min\!\left(1, (L_{\text{opp}}/L_{\text{main}})\exp(-d_{\text{nm}}/12)\right)$, and the disproportionality factor $\mathcal{F}_{\text{disp}} = (A_{\text{opp,claim}} / L_{\text{opp}}) / (A_{\text{prov}} / L_{\text{main}})$, while gomd.get_mavi_vatan_jurisdiction() (R gomd_mavi_vatan_jurisdiction()) serves the authoritative 1986–2026 series anchored in UN Letter A/74/757 (18 March 2020), the 27 November 2019 Türkiye–Libya MoU, and the 21 September 2011 Türkiye–TRNC Continental Shelf Delimitation Agreement.

3.5.1. Zero-Cookie Self-Hosted Scientific Portal Telemetry, Project COUNTER Release 5.1 & Make Data Count Econometrics

Unlike commercial web properties that rely on third-party advertising cookies (e.g., Google AdSense/GA4)—which are frequently blocked by client-side content blockers and cannot capture non-browser programmatic pip/CRAN/WFS requests—GOMD implements a native, zero-cookie, server-side telemetry architecture (platform_analytics_tracker.php & platform_analytics.php) aligned with Project COUNTER Code of Practice Release 5.1 (2023/2025) and the Research Data Alliance (RDA) Make Data Count Standard (Fenner et al., 2019; Cousijn et al., 2019). Daily unique visitors are identified via a privacy-preserving rotating cryptographic digest $h_t = \text{SHA-256}(d_{\text{UTC}} \parallel \text{IP} \parallel \text{UA} \parallel \text{salt})$ compliant with KVKK and GDPR Art. 6(1)(f), with a 30-second double-click deduplication filter. Python gomd.counter_r5_usage_econometrics() (and R gomd_counter_r5_usage_econometrics()) computes Total_Item_Investigations, Unique_Item_Investigations, Total_Item_Requests, Unique_Item_Requests, Conversion Efficiency Ratio ($\text{CER} = R_{\text{unique}} / I_{\text{unique}}$), Shannon Portal Attention Entropy ($H' = -\sum_{i=1}^{S} p_i \log_2 p_i$, Pielou evenness $J' = H' / \log_2 S$), Gini module concentration ($G$), and DAU/MAU researcher stickiness strictly from supplied observations without imputing missing inputs from assumed ratios.

3.5.2. Source-Grounded Data Assistant: Retrieval-Augmented Generation (RAG), Whitelisted Tool Calling & Numeric Grounding Audit

Rather than fine-tuning parametric weights on observational tables—which risks memorizing stale values and fabricating unverified numbers—the GOMD Data Assistant (data_assistant.php, data_assistant_api.php, data_assistant_core.php) implements a non-parametric Retrieval-Augmented Generation (RAG) and Reasoning-and-Acting (ReAct) tool-calling architecture (Lewis et al., 2020; Yao et al., 2023; Schick et al., 2023). Catalogue metadata across open indicators, ocean evidence series, and scientific sources are indexed in a MySQL InnoDB FULLTEXT table (gomd_rag_documents), strictly excluding commercial/licensed feeds (clarksons, lseg, refinitiv, bloomberg, spglobal, ihs_markit, kpler, vortexa, lloyds_list) and personal user tables. The language model is never granted free-form SQL execution; it can invoke at most four whitelisted read-only PHP tools per turn: search_catalog, describe_indicator, get_series, and series_stats (which computes OLS slope, Theil–Sen median slope $\hat{\beta}_{\text{Sen}} = \text{median}_{iAttributable to Identified Sources (AIS) framework (Rashkin et al., 2023), every numeric token in the generated response is audited against the numeric pool $V$ returned by the tools within a decimal-precision rounding tolerance $|x - v| \le 0.5 \times 10^{-d}$; any unmatched number is flagged in unverified_numbers. Both SDKs expose ask_assistant() / gomd_ask_assistant() and the local verifier audit_numeric_grounding() / gomd_audit_numeric_grounding().

3.6. 10 Reproducible Real-Data Scientific Case Studies (CS01–CS10) & Empirical Findings

To guarantee strict empirical reproducibility and eliminate any synthetic data dependencies, all 10 official Google Colab case studies (CS01–CS10) and the master data mining suite (sdk/examples/gomd_multivariate_ocean_data_mining.py) stream live observational time series and multi-country panels directly from https://gomd.pirireis.edu.tr via GOMDClient.get_series(), get_blue_finance_atlas(), and get_mavi_vatan_jurisdiction(). Table 3 summarizes the empirical results verified against the live GOMD database.

4. Official R Package (gomd v2.0.0) — 46-Function Documentation & User Guide

Built for R and RStudio in strict accordance with CRAN packaging standards, the R "gomd" v2.0.0 package exports 46 scientific and live-server functions and includes a native "inst/CITATION" definition supporting R's built-in citation("gomd") and gomd_cite("all") commands.

4.1. R Installation

# Method 1: Install directly from v2.0.0 source tarball URL (Recommended for RStudio / Posit Cloud / Colab R)
install.packages("https://gomd.pirireis.edu.tr/sdk/dist/gomd_2.0.0.tar.gz", repos = NULL, type = "source")

# Method 2: Install directly from the GOMD CRAN-compliant repository
install.packages("gomd", repos = c("https://gomd.pirireis.edu.tr/sdk/dist", "https://cloud.r-project.org"), type = "source")

4.2. Live GOMD Database & Post-2020 Multi-Module Scientific Workflow in R

library(gomd)

# 1. Display official citation in RStudio (native R citation system + helper)
citation("gomd")
gomd_cite("all")

# 2. Query the 42-Indicator Global Blue & Marine Insurance Finance Atlas (1970-2025) & build panels
df_ins  <- gomd_blue_finance_atlas("iumi_marine_insurance_total_premium_usd_mn", entities = c("WLD", "EU27", "CHN", "USA", "NOR", "TUR"))
df_psi  <- gomd_blue_finance_atlas("unepfi_psi_sustainable_climate_coastal_insurance_usd_bn", entities = c("WLD", "EU27", "USA", "TUR"))
panel_r <- gomd_build_panel(c("wb_co2_total", "wb_port_traffic"), 2010, 2024)

# 3. Run one-call live GOMD series, ocean basin & Synthetic DiD policy pipelines in R
res_co2 <- gomd_analyze_series("gml_mauna_loa_co2_ppm", entity = "WLD")
res_med <- gomd_analyze_basin("OCN_MED_LEVANTINE", salinity_psu = 38.6, ph_total = 8.06)
res_pol <- gomd_analyze_policy("worldbank_primary_index_v2026", treated_entity = "TUR")

# 4. Direct Post-2020 Oceanography, Marine/Sustainability Insurance, CWON, Mavi Vatan Audit & FuelEU Maritime in R
gsw    <- gomd_teos10_gsw(salinity_psu = 38.6, temp_c = 15.2, pressure_dbar = 120, lat = 36.5)
bc     <- gomd_blue_carbon_valuation(seagrass_ha = 12500, saltmarsh_ha = 4200, mangrove_ha = 800, scc_usd_per_tco2 = 185)
hm_pi  <- gomd_marine_insurance_pricing(hull_value_usd = 65000000, gross_tonnage = 42000, cii_rating = "B", green_dual_fuel = TRUE)
psi    <- gomd_sustainable_insurance_var(coastal_exposed_value_usd = 500000000, reef_mangrove_seagrass_ha = 1200)
cwon   <- gomd_cwon_wealth_accounting(gni_usd = 1.15e12, gross_national_savings_usd = 2.95e11)
mv_aud <- gomd_mavi_vatan_eez_audit(mainland_coast_km = 1577.0, opposite_coast_km = 19.0, un_registered_area_km2 = 462000.0)

5. Data Governance, Maritime Jurisdiction (Blue Homeland) Disclaimer, Licensing & Mandatory Citation Policy

The Python and R gomd software packages are released under the open-source MIT License. All underlying observational series carried by GOMD preserve the original licence terms and cryptographic SHA-256 lineage of their respective upstream providers (e.g., CC BY 4.0 for World Bank WDI/CWON, Copernicus Marine, FAO FishStatJ, Eurostat, IUMI, UNEP FI, and UN SDG indicators).

Scientific Use & Republic of Türkiye Maritime Jurisdiction ("Mavi Vatan") Disclaimer: External international datasets (including VLIZ Marine Regions, EMODnet / DG MARE, UNEP-WCMC WDPA, Ocean Health Index, EMSA, Copernicus Marine, Global Fishing Watch, and Sea Around Us) are integrated into GOMD strictly for scientific and academic research purposes. Default provider maritime polygons reflecting automatic median-line geometries, the maximalist "Seville Map", full continental shelf/EEZ attribution to Kastellorizo (Meis), or unilateral EEZ claims by the Greek Cypriot Administration (GCA) carry zero legal validity or recognition for the Republic of Türkiye. Regarding Türkiye's Exclusive Economic Zone (EEZ) and Continental Shelf ("Mavi Vatan" — $\sim 462,000\text{ km}^2$) and TRNC maritime rights ($\sim 9,100\text{ km}^2$), official boundaries registered with the United Nations (UN Letter A/74/757 of 18 March 2020, 27 November 2019 Türkiye–Libya MoU, 21 September 2011 Türkiye–TRNC Continental Shelf Delimitation Agreement, Black Sea EEZ agreements, and SHODB / DEHUKAM official charts) prevail exclusively.

Mandatory Citation Requirement: To ensure scientific reproducibility and credit institutional curation, authors publishing academic papers, doctoral/master's theses, ESG disclosures, or policy briefs that utilize GOMD data or its Python/R SDKs must include the primary GOMD reference citation shown in the citation box above, alongside the upstream data provider returned in the lineage field of the API response.

6. Validation & Performance Benchmarks

7. References — Including Post-2020 Frontier Literature

1. Acer, Y. (2020). The Aegean Maritime Disputes and International Law (Updated ed.). Routledge / DEHUKAM Maritime Law Series.
2. Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic Difference-in-Differences. American Economic Review, 111(12), 4088–4118. https://doi.org/10.1257/aer.20190159
3. Bakker, D. C. E., et al. (2016). A multi-decade record of high-quality $f\text{CO}_2$ data in version 3 of the Surface Ocean $\text{CO}_2$ Atlas (SOCAT). Earth System Science Data, 8(2), 383–413. https://doi.org/10.5194/essd-8-383-2016
4. Başeren, S. H. (2021). Doğu Akdeniz’de Deniz Yetki Alanları Sınırlandırması ve Türkiye’nin Hakları. Ankara: TÜDAV / DEHUKAM Yayınları.
5. Beck, M. W., Heck, N., Narayan, S., Menéndez, P., Torres-Ortega, S., Losada, I. J., ... & Reguero, B. G. (2022). Return on investment for mangrove and reef flood protection. Nature Sustainability, 5(7), 618–626. https://doi.org/10.1038/s41893-022-00873-0
6. Bertram, C., Quaas, M., Reusch, T. B., Vafeidis, A. T., Wolff, C., & Rickels, W. (2021). The blue carbon wealth of nations. Nature Climate Change, 11(8), 704–709. https://doi.org/10.1038/s41558-021-01089-4
7. Burger, F. A., Terhaar, J., & Frölicher, T. L. (2022). Compound marine heatwaves and ocean acidity extremes. Nature, 608(7922), 327–332. https://doi.org/10.1038/s41586-022-04992-4
8. Cheng, L., Abraham, J., Hausfather, Z., & Trenberth, K. E. (2020). How fast are the oceans warming? Science, 363(6423), 128–129. https://doi.org/10.1126/science.aav7619
9. Clarke, T. M., Wabnitz, C. C., Striegel, S., Frölicher, T. L., Reygondeau, G., & Cheung, W. W. (2021). Aerobic growth index (AGI): An index to understand the impacts of ocean warming and deoxygenation on marine ectotherms. Progress in Oceanography, 195, 102588. https://doi.org/10.1016/j.pocean.2021.102588
10. Dasgupta, P. (2021). The Economics of Biodiversity: The Dasgupta Review. London: HM Treasury.
11. European Union. (2023). Regulation (EU) 2023/1805 of the European Parliament and of the Council of 13 September 2023 on the use of renewable and low-carbon fuels in maritime transport (FuelEU Maritime). Official Journal of the European Union, L 234, 48–100.
12. Evans, M. D. (2023). Relevant Circumstances and Equitable Maritime Delimitation in Enclosed and Semi-Enclosed Seas. International & Comparative Law Quarterly, 72(2), 311–342.
13. Friedlingstein, P., et al. (2023). Global Carbon Budget 2023. Earth System Science Data, 15(12), 5301–5369. https://doi.org/10.5194/essd-15-5301-2023
14. Gruber, N., Boyd, P. W., Frölicher, T. L., & Vogt, M. (2021). Biogeochemical extremes and compound events in the ocean. Science, 374(6566), eabc9852. https://doi.org/10.1126/science.abc9852
15. Hersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
16. International Court of Justice (ICJ). (2009). Maritime Delimitation in the Black Sea (Romania v. Ukraine), Judgment, I.C.J. Reports 2009, p. 61; & Territorial and Maritime Dispute (Nicaragua v. Colombia), Judgment, I.C.J. Reports 2012, p. 624.
17. IMO. (2020). Fourth IMO Greenhouse Gas Study 2020. London: International Maritime Organization.
18. IMO. (2023). Resolution MEPC.377(80): 2023 IMO Strategy on Reduction of GHG Emissions from Ships. Adopted 7 July 2023. London: International Maritime Organization.
19. International Union of Marine Insurance (IUMI). (2025). IUMI Global Marine Insurance Report: Hull, Cargo, Offshore Energy & Protection and Indemnity Statistics (1995–2025). Hamburg: IUMI.
20. Lauvset, S. K., et al. (2022). GLODAPv2.2022: the latest version of the global interior ocean biogeochemical data product. Earth System Science Data, 14(12), 5543–5572. https://doi.org/10.5194/essd-14-5543-2022
21. Macreadie, P. I., Costa, M. D., Atwood, T. B., Friess, D. A., Kelleway, J. J., Kennedy, H., ... & Duarte, C. M. (2021). Blue carbon as a natural climate solution. Nature Reviews Earth & Environment, 2(12), 826–839. https://doi.org/10.1038/s43017-021-00224-1
22. Poseidon Principles for Marine Insurance (PPMI). (2024). A Global Framework for Assessing and Disclosing the Climate Alignment of Marine Insurance Portfolios. Copenhagen: Global Maritime Forum.
23. Reguero, B. G., Beck, M. W., Schmid, D., Stadmueller, D., Raepple, J., Schüssele, S., & Pfliegner, K. (2020). Financing coastal resilience by combining nature-based risk reduction with insurance. Ecological Economics, 169, 106487. https://doi.org/10.1016/j.ecolecon.2019.106487
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