4. Use and maintenance of Ocean Accounts

Table of Contents

• This section provides guidance relevant to the ongoing maintenance of Ocean Accounts (including the generation of time series), and the use of Ocean Accounts to inform ocean governance. Particular attention is devoted to producing indicators, data sources, policy and governance use cases, research use cases, and enabling factors such as institutional, regulatory, and legal frameworks.

4.1 Indicators for sustainable development

• This section presents a general discussion of the importance of indicators for sustainable development policy, linking to the Summary Indicators table of Section 3.8.

4.1.1 SDG Indicators

• To keep track of progress against the 17 Sustainable Development Goals and 169 associated targets, the Interagency and Expert Group on SDG Indicators (IAEG-SDGs) developed a framework of over 200 indicators, which was adopted by the UN General Assembly in July 2017. Countries are leading on the delivery of the SDGs, on a voluntary basis, and are encouraged to use the framework of globally agreed indicators to report on progress. This will require a significant level of capacity and resources from countries: many indicators do not currently have internationally established methodologies nor available data and/or associated monitoring schemes in place. Countries are encouraged to prioritise and develop their various monitoring schemes over time, in accordance with their national capacities.

• To facilitate the implementation of the global indicator framework, the indicators have been classified into three tiers based on the global availability of methodologies and data (see Table 29 for tier classifications). Tier classifications are reviewed annually based on changes in methodologies and data availability and progress in the development of the indicators (as documented in associated work plans).

Table 29. Tier classification criteria and definitions for SDG indicators.

• Currently, there are few consistent approaches for data collection and reporting for global targets such as the SDGs, or the Aichi Targets of the UN Strategic Plan for Biodiversity (2010-2020). While social and economic data might be collected by National Statistics Offices in the countries, environmental and ecological data are often collected by Non-Governmental Organisations and research institutes at country, regional or even global levels. To support the global reporting process for SDGs, the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) is developing guidelines on data and information flows from national to global levels.

• According to the IAEG-SDGs reporting guidelines, the monitoring data underlying the indicators will be collected and processed at the national level by relevant public and private-sector institutions, and brought together in reporting platforms by the National Statistics Office of the country. From here, the data and information will be transmitted to international agencies, either directly or through regional mechanisms such as the Regional Seas Programmes. The international agencies will then aggregate the country-level data at regional and global levels and submit these aggregates, along with the country data, into the Global SDG Indicators Database, which is maintained by the UN Statistics Division (UNSD). Appendix 6.5 provides an initial link between the SDG indicators and the Ocean Accounts Framework.

• UNEP has developed a provisional Global Manual on Ocean Statistics which focuses on supporting countries in their efforts to track progress against the delivery of SDG14 and the specific indicators under UN Environment custodianship:

  • 14.1.1 Index of Coastal Eutrophication (ICEP) and floating plastic debris density.

  • 14.2.1 Proportion of national exclusive economic zones managed using ecosystem-based approaches.

  • 14.5.1 Coverage of protected areas in relation to marine areas.

4.1.2 Other indicator frameworks

• The Framework for the Development of Environment Statistics (FDES) is a multi-purpose and statistical framework that conceptually defines the scope of environment statistics compatible with other frameworks such as the SEEA and the Driving force-Pressure-State-Impact-Response (DPSIR). It contains six components, 21 sub-components, 60 statistical topics and 458 basic statistics intended as a guide to the collection compilation of environment statistics particularly at the national level. The basic statistics also are organized into three tiers based on the level of relevance, availability and methodological development as follows:

  • Tier I or so-called core set of environment statistics (100 statistics) – high priority and relevance to most countries and have a sound methodological foundation.

  • Tier II (200 statistics) – relevance to most countries but require greater investment of time, resources or methodological development.

  • Tier III (158 statistics) – lower priority or require significant methodological development.

  • The FDES was endorsed by the 44^th session of the Statistical Commission in 2013.

• Ocean Health Index (OHI) measures the state of the world’s oceans in ten categories or “goals”, namely food provision, artisanal fishing opportunities, natural products, carbon storage, coastal protection, tourism and recreation, coastal livelihoods and economies, sense of place, clean waters, and biodiversity. In each goal, four dimensions of status, trend, pressures, and resilience are assessed using globally available, mutually non-exclusive sets of indicators. The OHI is presented in 236 regions including 221 coastal countries/territories and the Antarctic for which the assessment covers inland to one kilometre from the shore and seaward to either three or 200 nautical miles (Exclusive Economic Zone, EEZ), and 15 High Seas areas. The global score is an area-weighted average of the scores of all regions.

• The Global Ocean Observing System (GOOS) is a coordination system of global ocean observations – situ networks, satellite systems, governments, UN agencies and individual scientists – on climate, operational services, and marine ecosystem health. It establishes “Essential Ocean Variables” (EOV’s) as a framework to coordinate efforts, avoid duplication, and set common standards for data collection and dissemination among different ocean observing networks and systems. There are 31 EOV’s and more than 100 sub-variables as of April 2020.

4.1.3 Disaster risk indicators

• The Sendai Framework for Disaster Risk Reduction 2015-2030, the successor to the     Framework for Action (HFA) 2005-2015, was adopted at the World Conference on Disaster Risk Reduction held in Sendai, Japan and endorsed by the United Nations General Assembly in 2015. The framework sets out seven measurable targets to monitor progress towards its goal and expected outcome of reducing existing and preventing new disaster risks. The seven targets include:

  • Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared to 2005-2015;

  • Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared to 2005-2015;

  • Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030

  • Substantially reduce disaster damage to critical infrastructure and disruption of basic services, among them health and educational facilities, including through developing their resilience by 2030;

  • Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020;

  • Substantially enhance international cooperation to developing countries through adequate and sustainable support to complement their national actions for implementation of this framework by 2030; and,

  • Substantially increase the availability of and access to multi-hazard early warning systems and disaster risk information and assessments to people by 2030.

• An internationally agreed set of 38 indicators were specifically developed to track progress of the seven global targets. The Sendai framework also contributes to measuring relevant targets and indicators of SDG 1, 11 and 13. The United Nations Office for Disaster Risk Reduction (UNDRR) is mandated to provide support to the implementation, follow-up, and review of the Sendai Framework. Disaster-related statistic framework (DRSF) complements the Sendai framework and SDG indicators by providing measurement and implementation guidance including definitions, classifications, concepts, and methodologies to integrate and harmonize statistics for disaster risk reduction. It proposes a basic range of disaster related statistics covering key statistics before, during and after an emergency event. ESCAP provides secretariat support to the development of the DRSF.

4.1.4 Climate change indicators

• The UNECE CES Task Force on core climate change-related indicators and statistics has updated a set of related key climate change-related statistics using the SEEA and other statistical frameworks for implementation in the European region. The refined set of core climate change-related indicators contains 44 indicators – compared to an initial set of 39 core climate-change related indicators and statistics endorsed by the CES in 2017 – covering five climate change areas namely Drivers (9 indicators), Emissions (9 indicators), Impacts (13 indicators), Mitigation (8 indicators) and Adaptation (5 indicators). It also proposes the inclusion of operational indicators, contextual indicators, and the core climate change-related statistics.

• Out of the 44 indicators, 8 are SDG indicators; 4 are conceptually identical to the Sendai framework; and 25 can be produced from the SEEA-CF and SEEA-EEA. At the global level, UNSD has initiated the development of a global set of climate change statistics and indicators since 2016. The global set will contain a list of climate change statistics/indicators consistent with exiting relevant indicator frameworks, including the UNECE CES set of core climate change-related indicators, and covering five IPCC areas: drivers, impacts, vulnerability, mitigation and adaptation. An initial set has been drafted and piloted by selected countries and international/regional organizations. The global consultation is being planned to be undertaken in mid-2020.

Table X. Number of core climate change-related indicators per area and sub-area. *The set of core indicators intentionally does not break down drivers and emissions according to economic sectors.

4.2 Data sources and platforms for Ocean Accounts

• This section provides a more comprehensive treatment of data sources, building on the more specific guidance provided in Chapter 3. As advised in Chapter 3, there is a broad range of national data that can be exploited to compile ocean accounts, including:

  • existing statistical data such as the SNA, Census and social surveys, ongoing SEEA-CF and SEEA-EEA accounts such as solid waste, land, ecosystem condition, water, energy, environmental activities, ongoing compilations such as environmental compendia using FDES,

  • existing geospatial data, such as national land cover maps,

  • existing administrative data, such as fish catch or mine production statistics.

• These existing data can be repurposed for use in ocean accounts. However, existing data may not be sufficient to compile the accounts that have been designated as priorities. In these cases, compilers may need to explore alternative sources, such as global geospatial and monitoring data. As well, they may need to apply estimation methods, including modelling, to fill gaps or build scenarios of future conditions.

• This section provides insights into ongoing efforts to inventory, integrate and make available data on the ocean including:

  • improving the collection, integration, and applying A digital ecosystem for the environment

  • new developments in Earth observation data

  • ongoing work to develop Essential ocean and ecosystem variables

  • developments in national and international Fisheries data

  • insights into data on Socio-economic conditions

  • a review of Data platforms (that is, large data collections available online)

  • using Modelling to fill data gaps or make estimates about the future, and

  • suggestions for a set of Core ocean statistics selected to be globally applicable and feasible

4.2.1 The case for digital ecosystem for the environment

• In the discussion paper “The Case for a Digital Ecosystem for the Environment” (Jensen & Campbell, 2019), UN Environment makes a compelling case on how data, technology and innovation can transform the way environmental data are collected and managed, and thus can critically enable conditions for better governance.

• As reported by the UN Secretary General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development, without high quality geospatial data, the task of designing, monitoring, and evaluating effective policies to achieve the Sustainable Development Goals (SDGs) is almost impossible. The same concept can be applied to Ocean Accounting, whereby new data management technologies, artificial intelligence, cloud computing and cloud storage of information, together with increased volume of accessible geospatial data, are making it possible to manage, share, process and analyse large volumes of data in near real time as well democratizing access to the data itself.

• The digital ecosystem proposed by UN Environment would comprise of the following four main components: (1) data; (2) infrastructure; (3) algorithms and analytics; and (4) insights and applications. Following this, an Ocean Accounting platform would transform data using an underlying infrastructure combined with algorithms and analytics (i.e. models) into insights and applications that are used by National Statistics Offices and other stakeholders.

• Data: the volume of data currently being generated is so high that we are now accustomed to referring to it as “Big Data”. This term refers to large volumes of data that cannot be processed effectively with traditional applications. Big Data availability is however non-homogenous, as there are a wide variety of different sources (e.g. Earth observation remote sensing and in-situ platforms, citizen science, administrative and financial data, etc.) types (covering different spatial and temporal resolutions), quality, and formats.

• Infrastructure: In order to manage this large volume of data a distributed infrastructure is needed which not only guarantees access (cataloguing, discovery, aggregation, navigation) and storage/archiving, but also maximises data sharing, integration and analysis. This can be achieved through cloud-based infrastructures which promote the principles of open accessibility and share standards for data sharing.

• Algorithms and analytics: Data analytics can be defined as the processing of analysing data to provide meaningful insights and information. The process of extraction of relevant information can be automated into processes and algorithms that work over raw data for human consumption. The automated techniques for aggregating large volumes of data, detecting patterns, identifying trends and determining relationships include the adoption of Artificial Intelligence and Machine Learning algorithms.

• Insights and applications: Data needs to be combined, processed and analysed to be transformed into information and ultimately actionable knowledge. End users and stakeholders must be able to understand and apply the information which is provided to them. This implies that information must be applicable, trustworthy, easy to access and simple to comprehend. In order to guarantee this, it is imperative that there is a common thread linking data producers, data managers, infrastructure experts, algorithm developers, application providers to end users and stakeholders.

• In parallel to the concepts elicited by UN Environment for a digital ecosystem, a set of concise and measurable principles have been designed to guide and improve the Findability, Accessibility, Interoperability, and Reusability of digital assets. The FAIR guiding principles for scientific data management and stewardship (Wilkinson et al., 2016) can be considered as a conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.

• Building on these principles, the Secretariat on Group on Earth Observations (GEO) and the GEO Blue Planet initiative, are working on developing an ocean “Knowledge Hub”, an open platform aimed at empowering global experts where co-design, co-production & full reproducibility are key.

• This is particularly relevant for countries and their National Statistics Offices engaged in Ocean Accounting (and monitoring of the Sustainable Development Goals indicators) as it will provide a platform where they can independently access data, algorithms, methodologies to produce the necessary information and actionable knowledge.

4.2.2 Earth observation data

• Earth observations can be defined as the union of diverse data sources, including from satellite, airborne, in-situ platforms, and citizen observatories, which when integrated together, provide a robust basis for understanding the past and present conditions of Earth systems, as well as the interplay between them.^[1] It is therefore the gathering of Earth’s physical, chemical, and biological information from a range of different sources required for improved monitoring and forecasting.

• Earth Observation data can make a substantial contribution in supporting progress towards many of the Sustainable Development Goals (SDG), including those that are more socio‐economic in nature (Andries, et al., 2018). In addition, there is also potential to develop indicators outside the established set of SDG indicators that may be more amenable to the use of EO‐derived data, including Ocean Accounting.

• The use of international (global) space-based earth observations, combined with in-situ and modelling datasets, is key for achieving a solid and reproducible Ocean Accounting framework. This is even more evident as we must consider the transboundary nature of ocean related targets and indicators, specifically for the monitoring and reporting of sea areas which are beyond national (agreed or not) national jurisdiction (i.e. EEZ waters). It is imperative to have a framework, combining space-based Earth Observations together with modelling and in-situ datasets, providing global, regional, and national geospatial ocean products.

• The notion of national data is limited when applied to the ocean and there are a number of “global vs national” issues to be clarified, such as: (1) Who is responsible for the reporting and monitoring in areas beyond national jurisdiction (recognised EEZ's)?; (2) Who should contribute on providing an observational and measurement methodology for indicators which ensures the highest level of consistency and comparability, and; (3) What is the framework for developing transboundary ocean related SDG indicator products at global, regional, national and transboundary level?

• In this context, ocean remote sensing data is invaluable as it provides a consistent, synoptic perspective that can be leveraged in a cost-effective manner by end-users in developing as well as developed nations. Satellite sensors provide insight on physical, biological, biogeochemical, geological, and social related ocean parameters at different spatial resolutions and temporal scales (hourly/daily to multi-annual). They provide rapid, repeated and long-term synoptic observations that inform and complement (in conjunction with in situ measures and modelling/data assimilation activities) a nested global to basin-scale to regional to local ocean observing framework. This represents the end-to-end value chain for ocean observations, going from observations → data → products → information → knowledge for users and the attendant socio-economic benefits.

• Data collected at national level are of critical relevance for global and regional assessments. They feed analyses and modelling of regional seas while providing a validation instrument for regional and global datasets. It is important within this context to highlight that there is currently no clear framework defining: a) who should (can) contribute on providing an integrated observational and measurement methodology; b) how global and regional products can feed into national monitoring and reporting processes, and; c) who should routinely analyse, monitor and report on this indicator at global and regional level.

• Cooperation, at global to local scales and across different sectors, is crucial to achieve long-term sustainable use of our ocean resources. Regional Seas Programmes, Agreements and Conventions can be key to the sustainability of regional coastal and marine ecosystems as they provide a governance and technical/ scientific mechanism for regional cooperation and coordination, aimed at advancing national and transboundary issues.

• What is the relationship between global, regional and national products and how can we ensure that the data required for Ocean Accounting purposes is freely available, consistent, comparable and spatially comprehensive? At the regional level, we could envisage a set of "packages" of products that identify (or approximate) physical or ecological base values or critical thresholds (if known), with a well-defined pathway for their delivery. These regional “actors” can thereafter work with Member States to improve the uptake of Earth Observation data (and related derived products) to be used for monitoring and reporting at national level.

• Ensuring the sustainable development and responsible conservation of our oceans requires working across national jurisdictions and open sea areas. Global Earth Observation data are fundamental resources that provide physical, biological, chemical, geological and social information on the ocean at different spatial resolutions and temporal scales.

• All data collected, created and curated by Earth observation entities, organisations and programmes is of critical importance. The following three are particularly noteworthy as they cater for the majority of the ocean satellite remote sensing, in-situ and modelling observational datasets and resources:

• Committee on Earth Observation Satellites (CEOS): made up of 55 space agencies from all around the world, exists to ensure the international coordination of satellite Earth observation programs and promotes data exchange to make satellite data available and beneficial to the world. These satellite observations are critical for ocean, coastal and land environmental monitoring, meteorology, disaster response, agriculture and other applications. CEOS organizations currently operate 112 satellites. These satellites and their related systems operate simultaneously and serve both interdisciplinary and international activities; therefore, international discussion and cooperation are critical to their success.

• Global Ocean Observing System (GOOS): A sustained collaborative system of ocean observations, encompassing in situ networks, UN agencies and individual scientists organized around a series of components undertaking requirements assessment, observing implementation and innovation. 

• OceanView: Fostering the development and improvement of operational ocean analysis and forecasting systems worldwide, OceanView defines, monitors and promotes actions aimed at coordinating and integrating research associated with multi-scale and multi-disciplinary ocean analysis and forecasting systems.

• The close cooperation and collaboration with these entities and programmes is key when it comes to the definition of the Earth observation data requirements and needs for Ocean Accounting. Within this context, an initiative like GEO Blue Planet can provide the link between data producers, data managers, infrastructure experts, algorithm developers, application providers and ultimately end users/stakeholders.

4.2.3 “Essential” Ocean and Ecosystem Variables

• One of the recommendations of the OceanObs’09 conference was for international integration and coordination of interdisciplinary ocean observations under a unique and common framework. The Framework on Ocean Observing (FOO, 2012) was implemented under the auspices of the Intergovernmental Oceanographic Commission (IOC) of UNESCO and is coordinated by the Global Ocean Observing System (GOOS). It seeks to meet the need of delivering ocean data to support governance, management, science and other ocean uses. It proposes the coordination and integration of routine and sustained observations of physical, biogeochemical, geological and biological essential ocean variables, or EOVs (Table 30). The EOVs are closely linked to the Essential Climate Variables (ECVs) (Bojinski et al., 2014) which define the observations needed to understand and track the status and trends in climate variability.

• In parallel, the Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed a framework for a set of Essential Biodiversity Variables (EBVs) (Table 31) for use in monitoring programs to understand patterns and changes in Earth's biodiversity (Pereira et al., 2013; Navarro et al., 2018). Within GEO BON, the Marine Biodiversity Observation Network (MBON) frames the EBVs concept for the marine realm (Muller-Karger et al., 2018).

• The ecosystem Essential Ocean Variables (eEOVs) include a set of observable ecological quantities which contribute to the assessment of the ocean ecosystem (Miloslavich et al., 2018). When assessing the condition of the marine ecosystem for the Southern Ocean Observing System, A.J. Constable et al. (2016) identified nine general ecosystem properties to be monitored. These belong to three main areas as follows: (1) Spatial arrangements of taxa: habitat, diversity, spatial distribution of organisms; (2) Food-web structure and function: primary production, ecosystem structure, production, energy transfer, and; (3) Human pressures: regional and global.

• Constable et al., (2016) used nine criteria for assessing the utility and feasibility of the candidate EOVs based on the following concepts: (1) Signal change in ecosystem properties; (2) Contribution to developing and/or applying models investigating change and attribution; (3) Understanding for policy-makers and the public; (4) Alignment with other eEOVs; (5) Ability to be connected to historical datasets ( time-series); (6) Potential to be adapted through time; (7) Can be sampled at space and time scales appropriate to the task; (8) Sufficiently high signal-to-noise ratio, and; (9) Potential for adaptive sampling.

• These multidisciplinary and transdisciplinary efforts categorize specific ocean parameters to be monitored on a continuous basis for addressing the challenge of evaluating the status of our oceans, identify key processes and ultimately determine the sustainability of the ecosystem as a whole, in a synergistic way. Muller-Karger et al. (2018) analyses these efforts and provides a synoptic view for linking the GOOS led effort on EOV and eEOV to the GEO BON EBV proposal. These concepts and criteria are also relevant when evaluating the typology of data sources needed for ocean accounting and evaluating the availability of data at regional and global level. Below are two tables outlining the parameters currently included as EOVs and EBVs.

Table 30. Essential Ocean Variables. Links are to EOV Fact Sheets.

Table 31. Essential Biodiversity Variables

4.2.4 Fisheries data (national)

• Large, industrial, fisheries and smaller scale fisheries are two very different areas with differing international data collection requirements and levels of interest domestically. Extensive data is held on industrial fisheries, including comprehensive stock assessments for many species. Industrial fisheries face transboundary issues where the fish move freely between EEZs and therefore inclusion in Ocean Accounts needs to be carefully thought through. Measurement of small-scale fisheries, reefs and associated ecosystems is challenging. Within the Pacific only a handful of countries have comprehensive vessel registries, with most not collecting comprehensive catch data. Often best available data on small scale and domestic coastal fisheries are from the Household Income and Expenditure Surveys (HIES) which now has a standard fisheries module. As much of the small scale and coastal catches are subsistence or for local sale these do not appear in normal market surveys, export data or structured buying records of businesses. What data exists for small scale fisheries tends to be disparate and held across multiple institutions and access can be hard, or impossible, to get. As a result, measuring year-on-year changes will be challenging and attributing changes more so.

• Recent research suggest species abundance (fish stocks) can be estimated from data-poor fish stock assessments, where a review of methods suggests a Bayesian hierarchical framework is the most feasible approach [1]. Further research suggests developing relative abundance indices based on spatially detailed fisher catch and effort data [2].

• Current fisheries accounting approaches have recognized the differing needs of commercial and small-scale fisheries from a data collection and account maintenance perspective. Nationally-based efforts to develop fisheries accounts have performed pilot studies to focus on either commercial or small-scale fisheries, directed either by a government or international entity (non-profit, NGO, UN partnerships). Accounting pilot studies on commercial fisheries include:

  • Natural Captial Accounting and Valuation of Ecosystem Services (NCAVES) project in China, relating fishing intensity to the loss of ecosystem services and net effects on marine GDP in 2018

  • Phillipines use of a satellite accounting approach determined in 2018 the fishing sector contributed the largest share of the Philippine ocean economy at 29% [3] which is instrumental in guiding the monitoring and assessment of ocean-related economic targets set in the 2017-2022 Philippine Development Plan (PDP).

  • Canada’s Department of Fisheries and Oceans (DFO) collaboration with Statistics Canada to apply to the ocean accounts framework in a pilot project to harmonize key ocean-related data, include commerical fisheries contribution to marine GDP.

• Small-Scale Fisheries Account Development examples include:

  • Fiji Bureau of Statistics compilation and annual publication of subsistence, informal, and small-scale aquaculture GVA as part of its GDP compilation.

  • Environmental Defense Fund’s (EDF) work on community-level fisheries in Baja California, Mexico since 2015 to create satellite fishery accounts at remote fishing villages.

  • International Institute for Environment and Development (IIED) development of a toolkit for small scale fisheries in Costa Rica in 2019 which provides the framework to mainstream values of small scale fisheries in national accounts.

4.2.5 Fisheries data (intergovernmental)

• Key sources and initiatives: Coordinating Working Party on Fishery Statistics (CWP); FISHCODE STF – Strategy for Improving Information on Status and Trends of Capture Fisheries; Aquatic Sciences and Fisheries Abstracts (ASFA); Fisheries and Resources Monitoring System (FIRMS); Fisheries Global Information System (FIGIS); FAO FishFinder, the Species Identification and Data Programme; GLOBEFISH – Analysis and information on world fish trade; Global Record of Fishing Vessels, Refrigerated Transport Vessels and Supply Vessels; FishStatJ – The FAO Fisheries and Aquaculture Department uses a software for fishery statistical time series. In November 2017 a new version of FishStatJ was released. This version can access the information on “Fisheries Commodities Production and Trade 1976-2015.”; Global Record of Fishing Vessels, Refrigerated Transport Vessels and Supply Vessels; The 2017 Global Record on Voluntary Guidelines for Catch Documentation Schemes

• There are a number of international instruments meant to regulate fisheries and prevent or at least deter Illegal, Unreported and Unregulated Fishing or IUU fishing at the global, regional and national levels. At the international level, these standards are found in: 1) The 1982 United Nations Convention on the Law of the Sea (UNCLOS/ LOSC); The 1993 FAO Agreement to Promote Compliance with International Conservation and Management Measures by Fishing Vessels on the High Seas (the 1993 Compliance Agreement; 3) The 1995 UN fish Stocks Agreement; The 1995 Code of Conduct for Responsible Fisheries; The 2001 International Plan of Action to Prevent, Deter, and Eliminate Illegal, Unreported and Unregulated Fishing (IPOA-IUU); 6)   The 2005 Rome Declaration on Illegal, Unreported and Unregulated Fishing; The 2009 Agreement on Port State Measures to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated Fishing, 31/12/2016 FAO, Revised edition; The 2014 Voluntary Guidelines for Flag State Performance; and FAO Voluntary Guidelines for Catch Documentation Schemes. 7) Regional Fishing Management Organizations (RFMOs) are pivotal in facilitating intergovernmental cooperation in high-seas areas, in managing and monitoring major deep-sea fisheries.

4.2.6 Socio-Economic conditions

• In addition to environmental (state of the ocean) and fisheries related ecosystem datasets, Andries et. al. (2018) have demonstrated the increasing opportunity of Earth Observation data to complement or even replace traditional ground‐based methods of collecting environmental and socio‐economic data. Examples include, indicators of economic growth (Henderson, et al., 2011), socio‐economic activities (Chen & Nordhaus, 2011), urbanisation impacts on the environment (Ma et al., 2012), daytime and night-time fishing activities (Waluda et al, 2004; Straka et al., 2015).

• One important element related to economic activity is maritime transport and associated port operations. Over 80 % of world merchandise trade by volume is being carried by sea and maritime transport remains the backbone supporting international trade. Maritime traffic is monitored at national and regional level through an International Maritime Organisation (IMO) regulation which requires Automated Identification System (AIS) to be fitted aboard all ships of 300 gross tonnage and upwards engaged on international voyages, cargo ships of 500 gross tonnage and upwards not engaged on international voyages and all passenger ships irrespective of size. The regulation requires that the exchange of AIS shall include the ship's identity, type, position, course, speed, navigational status and other safety-related information - automatically to appropriately equipped shore stations, other ships and aircraft. This data is used to monitor and track vessels globally as AIS signals can be detected by both shore stations and by satellite.

• Vessels engaged in fisheries activities also need to report their locations. The vessel monitoring system (VMS) is a satellite-based monitoring system which at regular intervals provides data to the fisheries authorities on the location, course and speed of vessels. AIS and VMS real-time, historical and traffic density data, are key elements for evaluating the maritime transport component within an Ocean Accounting framework. Further to this, the environmental signature of the maritime transport community on the ocean ecosystem can be monitored through Earth observation. One such example is the monitoring and reporting of oil spills from vessels. 

4.2.7 Data platforms

• Many countries still have problems when accessing data (IAEA, 2014). Data can be scarce, or not available in a timely fashion, or too complex to discover and access.

• However, the volume of data available is constantly increasing. For example, the daily volume of data from the EU’s Copernicus Earth Observation programme Sentinel satellites is estimated to be approximately 20 Terabytes per day (Esch, et al., 2018).

• The term “Big data” is commonly used to describe the sheer amount of data collected by sensors, however data can be big in different ways: data volume, variety of form, velocity of processing, veracity of uncertainty (Lynch, 2008). Due to these considerable increases, the challenge has been for the last years to develop solutions which “bring the user to the data instead of the data to the user”. This is made possible by technological advances in cloud technologies, the development of data cube technologies, the availability of Analysis Ready Datasets (ARD) and ultimately the development of web-based platforms providing access to these services. As part of this effort, the Group of Earth Observation is developing a concept of “Knowledge Hub” which applies a zero download model and ultimately empowers global experts to use Earth Observation data (satellite remote sensing, in-situ and modelling) to create reusable and shareable knowledge.

• Requirements for spatial resolutions and temporal sampling vary for different data types. For example, some ecosystem geospatial parameters do not need to be measured every year due to their multi-annual longevity, while others have seasonal and inter-annual variation related to their processes and hence may need to be estimated on a monthly or annual basis. There are multiple platforms nowadays available where to gather Earth Observation datasets and information. While it is not the objective of this Technical Guidance document to provide a fully comprehensive list of all available data platforms, this section will provide a number of references for use.

• The scores of different data platforms vary from online search and download portals to processing and analytical tools. Data availability ranges from in-situ point measurements to raster products based on satellite datasets, from local to global spatial coverage and from real-time to historical climatologies. In addition, datasets offered by platforms vary from general applications down to specific local applications.

• Pendleton et al. (2019) argue that although many ocean data platforms exist, we lack an understanding and regular monitoring of the biological and human dimensions of the ocean. Many habitats, including the deep sea, ocean trenches, ice-bound waters, methane seeps, and even coral reefs remain poorly studied at the global scale. Costello et al. (2010) show that geographic gaps in biodiversity data are particularly acute for many parts of the global ocean including coastal areas of the Indian Ocean, the southern and eastern Mediterranean Sea, polar seas, and much of the South American coastal ocean.

• According to Arzberger et al. (2004), Chavan and Ingwersen (2009), Costello (2009), Kim and Zhang (2015) and Ferguson et al. (2014) online platforms are often discipline-specific or application specific, creating barriers to discovery and integration. In addition, in many cases data are easily dissociated from the people who helped create and curate them, rendering communication between users and producers challenging.

• As previously mentioned, many different data platforms exist, each providing access to different types and levels of Earth observation data. We are however observing an important shift whereby users do not need to shift and download large volumes of data anymore for processing and provide access to the data and to the analytical algorithms directly on the cloud. This can potentially decrease the barriers for users in both developed and developing countries.

• Examples of online platforms include Geo-Wiki, Google Earth Engine (GEE), the different Copernicus Data Information and Access Services (DIAS), Earth Server2, Digital Earth Australia and many more.

• There is nevertheless to date no established optimal data platform implementation and best practice for applications in the ocean and coastal domain. For Ocean Accounting purposes this is even more true as there is the need to integrate distinct geospatial observations from diverse ecosystem domains, extrapolate this observational knowledge to include the full 3-d ocean (i.e. also including below the sea surface) and combine with socio-economic information, all under the appropriate statistical framework.

• The ‘Geo-Wiki’ and the ‘Openforis Collect Earth’ initiatives are examples of platforms which use earth observation and citizen science to conduct research and provide data, tools and services to perform fast, accurate and cost-effective assessments. They however have thus far only been used for land-based applications.

• Some areas where online data platforms have been used to support the development of monitoring and management applications in the ocean and coastal domain have been for mangrove monitoring and conservation and coral reef mapping.

• While it is not the objective of this Guidance document to provide a comprehensive list of available data platforms, Appendix 6.1 provides many references. As well, Appendix 6.1 contains a summary of ESCAP’s Global Ocean Data Inventory, which is available online^[4], and is classified according to the components of the ocean accounts framework. Efforts are ongoing to link with work with IOC-UNESCO’s ODISCat.

[Figure on Data Platforms under development]

4.2.8 Modelling

• In the past, measured data and modelled data relevant to the ocean are often used for different purposes and by different communities. However, an emerging approach is to consider measured datasets and modelled data within the same information infrastructure. That is, models can fill in gaps by estimating data from what has been observed in other locations or periods. Similarly, measured data can be used as additional input to models. Together, they can support the development of future scenarios.

• The SEEA-EEA Expert Forum (UNSD 2015) suggested a review of ecosystem services models with the intent of better understanding opportunities for applying them for official statistics. A review was initiated, but not completed (Bordt, Jackson and Ivanov, 2015). The SEEA-EEA Technical Recommendations (United Nations, 2017) include a brief review of some ecosystem services-related biophysical models.

• The term “modelling” for the purposes of this paper is intended to include any quantitative or qualitative approach used in the absence of measured data. This would include estimation, interpolation, projection and scenario approaches.

• Other than estimating or projecting the provision of ecosystem services, models have also been developed to estimate fish stock dynamics, economic production/consumption, ocean and climate dynamics and potential impacts from natural disasters.

• As with the ecosystem services-related models reviewed, it is expected that other models and the accounting approach could be mutually reinforcing: (a) estimating accounts data where data are unavailable and (b) using accounts data and classifications in models. Projecting future conditions are generally out of the scope of the SEEA itself, but the calculation of asset values depends on assumptions about the future stream of services. It has been suggested that to accomplish this, a baseline future scenario would be required. For example, estimating a future stream of services based on expected changes in the extent and condition of the stock. Table 32 and Figure 18 illustrate potential linkages between modelling approaches and Ocean Accounts.

• Better linking accounts with models is one approach to linking individual models together. For example, models focussing on stocks could be linked to models on production and consumption if concepts and classifications were aligned.

• Options to be explored include (a) using modelling approaches to estimate missing data in accounts, (b) using accounts to provide data to models, (c) using scenario approaches to estimate future conditions, and (d) other projection approaches.

Table 32. Illustrative contributions of modelling to Ocean Accounts. *Source: Bordt et al, 2015. Note: Numbers refer to Figure 18. The number zero (0) refers to components not systematically treated in SEEA-EEA.

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Figure 18. Components of SEEA-EEA amenable to modelling/estimation

4.2.9 Core ocean statistics

• The previous sections describe and use a range of data at various levels. In this section, we sort through these and suggest a set of 30 Core Ocean Statistics that are relevant to most countries and most countries should be able to collect them.

• Each data collection has its unique specifications of how the data are collected, aggregated and presented. One principle of the core set is that the compiler of ocean accounts should be able to consult various agencies and academic experts and ask if data are available for a certain statistic. The descriptions need to be sufficiently precise to communicate what is required without being so precise that it is never coincides with what is available. We apply the terminology for “variables”, “indicators” and “index” of the SEEA^[5] in terms of levels of aggregation:

  • Variables are any quantitative measure reflecting a phenomenon of interest. Variables may measure individual characteristics and are often direct measures, such as temperature or number of individuals of a species;

  • Indicators are variables with a normative interpretation associated…with a view to informing policy and decisions. Indicators are often the results of comparison with a reference condition, such as temperature above seasonal average, or number of individuals in a species compared to 10 years ago;

  • An index is a (thematically) aggregated indicator, which represents relatively broad aspects of the studied system in a single number. Temperature is combined with other data on timing to create indices of growing season length. Populations of several species may be combined into a biodiversity index.

• For the purposed of this guidance, “statistics” may be any variable, indicator or index or information about them, such as mean, median, maximum, etc. Another consideration is that indicator frameworks may be referring to variables, indicators, and indices without distinguishing among them. The lists of core ocean statistics begin with a general description of 30 kinds of statistics that should be included in a national core set. These are neither variables, indicators, nor indices, but general topics that should be considered. We then provide specifications for more detailed statistics for specific ocean ecosystem types. This description is still rather generic in that for any statistic, there may be a choice of several measurements and analytical methods to produce them. Such details are beyond the scope of this guidance and would not be globally applicable.

• Given the breadth of the ocean accounts, a discussion of how a statistic fits would help searching for it and fitting it into the appropriate accounts. Since this section introduces new concepts, the discussion in Appendix 6.9 provides an explanation of how some “stressors” can be treated in the ocean accounts. Table 30x presents a list of “core ocean statistics” which should form the basis of an ocean account. These statistics are organized by Ecosystem Condition (Scientific-generated data and Resources) and Ecosystem Services (Regulation, Provisioning, Cultural, and Governance). These statistics can be sourced from both public and private sectors and the organization and availability of these data will depend on national ecosystem accounting practices. The statistics provided in Table 30x here are only a baseline suggestion and will require a feedback loop of development in early stages of implementation, especially when considering the context within a specific ecosystem type To provide a starting basis for ocean account building, ET-specific tables have been created in Appendix 6.1 which inform on the variables most informative for each ecosystem type. These variables have been sourced from the primary literature and identified as informative to Ecosystem Condition and Services categories.

• Locations in Technical Guidance Manual which can be referenced when defining Economic, Social, Governance, and Biophysical Indicators part of Table 30x core statistics.

  • Economic: Potential examples of economic statistics includes:

    • Section 2.3.1 Defining Environmental Assets

    • Section 2.3.5 Monetary asset accounts

    • Figure 3: Flows to the economy (supply/use of energy, water, materials)

    • Classification of Ocean Assets

    • Section 2.9 Ocean Wealth (SEEA and SNA balance sheet)

    • Table 14 ISIC Codes

  • Social: Potential examples of social statistics:

    • Classification of Ocean Ecosystem Services

    • Table 8 Provisioning, Regulating, and Cultural

    • Table 9 Ecosystem Services

    • Fisheries Income (Split between commercial/artisanal)

    • Sustainable Fishing/Harvesting Practices

    • Tourism Income

    • Measures of Economic Activity

  • Governance: Potential examples of governance statistics

    • Monetary flow (supply and use accounts)

    • Table 9: Government expenditures on ocean management and protection/enforcement. Pollutant loading.

    • Definition of governance

    • Table 16-18: Governance Tables

    • Assessing Governance

    • Appendix 6.4 Potential FDES topics and statistics applicable to Ocean Accounts

  • Biophysical: Statistics which inform on biophysical processes can be sourced from already existing publicly available datasets (see Data Platforms), active government-funded research, or private research endeavoured for the specific purpose of ocean account building.

• It is important to note the biophysical statistics listed in Table 30x are a general guide for all marine ecosystem types. The relative informative power of these biophysical statistics will change depending on the ecosystem type (ET; see Classification of Ocean Ecosystem Services). To better inform the user on which statistics to prioritize for a given ET (Coral Reefs, Mangroves, Kelp Forests, Estuaries and Salt Marshes, Sediment, and Open Ocean) within the defined BSU (basic spatial unit; see the spatial data infrastructure for Ocean Accounts), see Appendix 6.10: Core Ocean Statistics for Key Ecosystem Types.

Table 30x. Core Ocean Statistics (in progress)

4.3 Policy and governance use cases for Ocean Accounts

• Ocean Accounts are one of a range of different information products that can be used to support policy-making and other government decision-making related to oceans. These can be distinguished from one other in terms of an “information pyramid” that differentiates between the level of information presented, and by the functions an information product supports in government decision-making (see Figure 19 below). The pyramid classifies information products into four groups in a hierarchical structure with each layer feeding the layers above. Data and statistics are the foundation of the pyramid and support the operation of an ocean accounting system. Indicators are produced from the accounts, which can be aggregated to produce key indicators. Indicators can be source both directly from data and statistics, and from the accounts.

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Figure 19 Relationship between Ocean Accounts and other information products.

• Within a comprehensive information system for government decision-making, Ocean Accounts (and all accounts) provide an intermediate structure that connects higher level information (indicators) with lower level information (basic data and statistics) in a coherent framework. Consequently, they support analysis and decision-making in a wide variety of policy and governance use cases. A non-exhaustive selection of these use cases is explained further below, organised into the following categories:

  • Strategic and planning decisions: including those associated with marine spatial planning, and formulation of strategic development plans for the ocean economy. 

  • Regulatory decisions: including granting of permits and licenses for marine activities, in accordance with relevant spatial and development plans or other policy objectives.

  • Operational and management decisions: including integrated coastal zone management, ecosystem-based management, management of marine protected areas, other forms of local marine area-based management, and disaster risk response.

  • Finance and investment decisions: including fiscal policies and programmatic investment related to oceans, including funding for administrative capacity concerning oceans.

  • Technical advice and reporting: including cost-benefit assessment, environmental impact assessments, progress reporting against agreed commitments, and supporting the delivery of decision-making in the above categories.

4.3.1 Strategic and planning decisions

• Decision-making about the ocean is increasingly informed by a range of laws, policies, and processes designed to pursue defined strategic objectives, and/or plan use of ocean space in an integrated manner. Prevalent features of ocean policy and governance in this context include:

• Strategic development plans for the ocean economy, including the proliferating range of national “Blue Economy”, “Ocean Economy” and “Blue Growth” plans that establish multi-sectoral development objectives and targets aligned with diverse guiding principles. A regionally representative list of examples includes the European Union’s Blue Growth Strategy, South Africa’s Operation Phakisa Oceans Economy strategy, Fiji’s National Ocean Policy, and Chapter 41 of China’s 13^th Five-Year Plan for Economic and Social Development focusing on “widening space” for the Blue Economy.

• Marine spatial planning (MSP): is commonly defined as a public process of analysing and allocating the spatial and temporal distribution of human activities in marine areas to achieve ecological, economic and social objectives that are usually specified through a political process. Diverse MSP approaches are implemented by at least 70 countries across all major regions.

• Ocean Accounts can perform several support functions for strategic and planning decisions that may justify a decision to invest effort and resources to compile them. By virtue of their holistic and integrated structure, Ocean Accounts can be used as a basis for analysing the economic relevance of the ocean’s environmental assets, the environmental implications of ocean-based economic activity, and wide a range of other relationships that impact on the ability of countries to achieve sustainable development. This analysis supports the identification and evaluation of policy response options, in terms of their impacts on assets (environmental, social, economic) that underpin development, and on the flows of services and benefits from these assets.

• More specifically, the Ocean Accounts Framework provides a basis for compiling three broad domains of aggregate indicators that are directly relevant to performance monitoring of ocean development strategy:

  • Ocean product, focusing on the economic outputs of human activity regarding the ocean, with monetary components aggregating to ocean Gross Domestic Product or net domestic product (NDP),

  • Benefits received by nationals from the ocean, including physical measures of ecosystem services, and monetary measures of ocean income that can be aggregated to net national income (NNI) and gross national income (GNI). Income measures can be (and benefit from being) disaggregated to show the importance of the ocean for different segments of the population, for example women, indigenous peoples, and other marginalised groups.