From Marine Natural Capital Valuation to Fiscal Integrity: A Governance Design for Blue Natural Capital Value at Risk in Indonesia

Abstract

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems.

1. Introduction

The United Nations Sustainable Development Agenda places the protection of marine ecosystems as a core objective through Sustainable Development Goal 14 [1]. For archipelagic states, this objective is not only environmental. Marine ecosystems support revenue bases, coastal livelihoods, food systems, tourism activity, and public spending needs. The fiscal question is therefore whether changes in marine ecosystem conditions can be recognised early enough within planning, fiscal risk, and budget routines. To the best of the authors’ knowledge, the existing literature does not yet offer a governance design that operationally links marine natural capital valuation to fiscal-risk assessment in the state budget cycle, particularly in Indonesia [2]. Without such a mechanism, marine ecological changes that may reduce revenues or increase recovery and adaptation spending remain difficult to trace in fiscal planning documents such as the State Budget Financial Note.

Indonesia is a relevant case due to the scale of its marine territory and the administrative importance of marine resources. The country has 17,504 islands, approximately 108,000 km of coastline, and around 6.4 million km² of waters [3,4,5]. Yet the economic signal is not straightforward. The fisheries sector is a narrow national-accounts category, contributing around 2.5–2.8% of Gross Domestic Product during 2018–2025, while marine transport contributed around 0.28–0.34% over the same period. Taken together, these two consistently observable subsectors averaged about 2.95% of Gross Domestic Product, reaching 2.90% in 2025. This observed series should not be conflated with the broader maritime-sector target in the 2025–2029 National Medium-Term Development Plan (RPJMN), which refers to a broader aggregate that includes fisheries, marine and coastal tourism, sea transport and ports, offshore energy, and the coastal industry [3,6]. The blue economy, in contrast, is not a national-accounts sector but a sustainable development and governance paradigm for managing marine resources.

Figure 1 shows why the fiscal relevance of marine resources cannot be inferred from a single headline indicator. As depicted, Indonesia’s marine and fisheries sector shows broad economic and administrative expansion between 2020 and 2025. Nominal fisheries Gross Domestic Product, fisheries production, marine conservation areas, and Non-Tax State Revenue all increased over the period, while fisheries exports remained broadly positive despite some year-to-year variation. Non-Tax State Revenue refers to government revenue other than taxes and grants, including natural resource levies, service charges, and administrative receipts. These trends indicate sectoral growth and expanding administrative reach, but they also reveal a critical blind spot: headline performance indicators by themselves do not show whether the ecological pressures underlying marine assets have been translated into accountable fiscal-risk information.

This distinction matters because the ecological foundations of marine economic value remain vulnerable. Official and secondary sources report that about 38% of assessed fish stocks are overfished, that mangrove loss may reach up to 52,000 hectares per year, that around one-third of coral reefs are in poor condition, and that plastic pollution causes estimated economic losses of about USD 450 million per year [3,4]. Coral-reef degradation can affect fisheries productivity and marine tourism by reducing habitat quality, fish-stock support, and tourism attractiveness [7,8,9]. Mangrove loss can increase coastal vulnerability and future restoration or disaster-response spending. These ecological changes do not automatically become fiscal events. They become fiscally relevant only when they affect administratively recognised revenue objects, taxable activities, expenditure obligations, fiscal-risk categories, or financing needs. This is the governance gap addressed in this article.

Figure 1. Selected trends in Indonesia’s maritime and fisheries economy. Panel (a) presents the contribution of fisheries and marine transport to the national Gross Domestic Product (GDP) for 2018–2025 [10]. Panel (b) presents selected Ministry of Marine Affairs and Fisheries performance indicators for 2020–2025 [11]. Fisheries Gross Domestic Product and Non-Tax State Revenue values are presented in nominal terms. Figure 1 was prepared with the assistance of ChatGPT (OpenAI, GPT 5.5) for data visualisation and layout refinement, using numerical data supplied by the authors from official sources. The authors verified the underlying data, reviewed the visual output, and approved the final figure.

Figure 1. Selected trends in Indonesia’s maritime and fisheries economy. Panel (a) presents the contribution of fisheries and marine transport to the national Gross Domestic Product (GDP) for 2018–2025 [10]. Panel (b) presents selected Ministry of Marine Affairs and Fisheries performance indicators for 2020–2025 [11]. Fisheries Gross Domestic Product and Non-Tax State Revenue values are presented in nominal terms. Figure 1 was prepared with the assistance of ChatGPT (OpenAI, GPT 5.5) for data visualisation and layout refinement, using numerical data supplied by the authors from official sources. The authors verified the underlying data, reviewed the visual output, and approved the final figure.

Indonesia already has policy and accounting entry points, but these remain incomplete for use in fiscal risk. The 2025–2029 National Medium-Term Development Plan, stipulated through Presidential Regulation No. 12 of 2025, prioritises the blue economy as part of economic transformation and targets an increase in the broader maritime sector’s contribution to Gross Domestic Product from 8.1% to 9.1% by 2029 [6]. The Wealth Accounting and the Valuation of Ecosystem Services programme also supported the development of natural capital accounting in Indonesia, including land and peat accounts, and strengthened the policy relevance of environmental-economic accounting [12]. However, ecosystem-accounting outputs do not inherently translate into fiscal-risk inputs. A separate translation mechanism is required to determine which ecological indicators matter for fiscal exposure, how valuation parameters should be controlled, and how uncertainty should be disclosed before the information is included in budget documents.

International experience confirms that the gap is not the absence of nature-related finance but the absence of a routine fiscal-risk transmission mechanism. The System of Environmental-Economic Accounting provides standardised ecosystem accounts [13], Ocean Accounts extend accounting logic to the marine domain [14], the Marine Natural Capital Risk Register maps asset-level risks and benefits [15], and green public financial management offers an environmental entry point into public finance [16]. Other archipelagic and island contexts also show partial solutions. Seychelles used a sovereign blue bond to support marine protection and fisheries governance [17]. Belize’s 2021 debt-for-nature arrangement linked debt treatment to marine conservation commitments [18]. The Philippines adopted blue-bond guidance to define eligible blue projects [19]. Pacific small island states generally address climate and disaster exposure through aggregate macro-fiscal risk, contingency, and resilience-financing instruments [20]. These mechanisms matter, but they operate mainly at the accounting, risk-mapping, market-finance, or aggregate macro-fiscal level. They do not provide a recurring process for translating ecosystem-condition change into fiscal-exposure signals within the annual budget cycle.

The data used in this article should therefore be read with two limitations in mind. First, the Indonesian economic series used in the Introduction draws on official sectoral and national accounts indicators, but these indicators do not directly measure ecological fiscal exposure. The nominal growth in fisheries Gross Domestic Product and Non-Tax State Revenue should therefore be interpreted as an administrative and economic trend, not as a direct measure of ecological resilience. Second, no standardised cross-country dataset currently quantifies marine ecological fiscal exposure on a comparable basis across archipelagic and island states. The cross-country comparison in this paper is therefore limited to governance mechanisms rather than harmonised fiscal-impact estimates.

This article proposes a governance design for Blue Natural Capital Value at Risk (BNC-VaR), linking changes in marine ecosystem conditions to fiscal-risk readings in the Indonesian state budget cycle. The proposed design is structured as a five-component institutional architecture covering ecosystem data, valuation and parameterisation, fiscal-risk analysis, budgetary implications, and fiscal sustainability. Within this architecture, the Directorate General of State Assets Management serves as a valuation custodian, responsible for maintaining methodological consistency, parameter traceability, and quality assurance across fiscal functions. The term “Value at Risk” is used as an adaptation of public fiscal-risk logic rather than as a conventional market-risk measure. Its conceptual adaptation is clarified in Section 2.5.

The research question guiding this paper is as follows: how can a governance design enable changes in marine ecosystem conditions to be traced into fiscal-exposure signals relevant to the Indonesian State Budget Financial Note process?

To answer this question, the study uses theory-driven corpus construction, theory synthesis, and a model-paper approach. The analysis follows six linked stages: problem identification, corpus assembly, thematic synthesis, gap-to-requirement mapping, architecture specification, and proposition formulation with internal validation. This design is appropriate because the study does not test a ready-made empirical model. It constructs an institutional architecture from the existing theoretical, accounting, valuation, and fiscal-risk literature, then specifies how the architecture can be evaluated in future empirical work.

The article contributes three elements. First, it provides a gap-mapping matrix that connects the blue economy, marine natural capital valuation, marine accounting, and fiscal-risk literature. Second, it develops five design principles and four evaluable propositions for ecological-to-fiscal translation. Third, it specifies an institutional architecture that can integrate marine ecosystem information into existing fiscal-risk and budgetary routines without creating a parallel administrative infrastructure. The central argument is that fiscal integrity under ecological uncertainty depends not on perfect ecological valuation but on traceable parameters, transparent disclosure of uncertainty, and consistent institutional handoffs from ecosystem data to fiscal risk and budget use.

2. Theoretical Study

This theoretical study positions marine issues as development administration challenges that require interdisciplinary synthesis. As illustrated in Figure 2, the conceptual foundation of the BNC-VaR model spans four functionally distinct literature clusters: the blue economy, marine natural capital valuation, marine accounting and uncertainty, and fiscal risk and budgeting. Rather than reviewing these fields in isolation, this section examines them along a sequential path from ecology to fiscal policy. The progression moves from establishing the policy arena to defining asset and benefit parameters, organising ecological information and uncertainty disclosure, and identifying entry points into public finance.

Figure 2. Logical progression of the four literature clusters towards the Blue Natural Capital-Value at Risk (BNC-VaR) governance architecture. The figure shows how each literature cluster performs a necessary function while leaving an unresolved gap that requires the next analytical step.

Each transition in Figure 2 reveals a specific functional disconnect. Broad blue-economy commitments do not, by themselves, yield usable valuation parameters. Stand-alone valuations require an accounting and reporting framework to remain traceable across decision processes. Marine-accounting outputs, in turn, require a fiscal-risk interface before they can inform budgetary and fiscal-sustainability decisions. The critical analysis in the following subsections identifies these unresolved gaps and explains why a cross-functional governance design is required.

The critical reading of these four clusters is framed by institutional theory and anticipatory governance. Institutional theory, as developed by North [21], explains how formal rules shape actors’ behaviour and reduce transaction costs in public decision chains. It helps explain why ecology-to-fiscal translation requires separated institutional functions, coordination rules, and stable governance mechanisms. However, institutional stability alone does not explain how public institutions should respond to risks that are not yet recognised by existing rules, particularly gradual and cumulative ecological pressures that fall outside conventional fiscal-risk readings [21]. Anticipatory governance addresses this limitation by emphasising foresight, policy integration, actor engagement, and explicit disclosure of knowledge limits and scenario plausibility [22,23,24]. Together, these perspectives provide the analytical lens for reading the four clusters and deriving the design requirements in the synthesis subsection.

2.1. The Blue Economy as an Arena for Development Governance

A bibliometric review of the blue economy shows that this field has grown rapidly since 2017, with a primary focus on marine sustainability, coastal governance, and blue finance [25]. This growth has led to frameworks such as the sustainable ocean economy [26,27] and blue bonds [28], which aim to link marine activities with financing mechanisms. Recent international market guidance has further extended the use of these instruments. The International Capital Market Association (ICMA) Green Bond Principles provide voluntary process guidelines [29], while ICMA’s Sustainable Bonds for Nature: A Practitioner’s Guide and the International Finance Corporation (IFC) Guidelines for Blue Finance extend this guidance to nature-related objectives [30]. The IFC Guidelines for Blue Finance, including the updated Version 2.0, provide guidance for financing blue-economy activities and marine-related objectives [31,32]. However, these frameworks remain largely finance-oriented. They do not explain how fiscal institutions incorporate marine ecological pressures into the state budget cycle. This blind spot matters because governments that plan, finance, or underwrite blue-economy development without monitoring ecological risks may overlook emerging fiscal burdens during budget preparation.

Because the blue economy lacks a single, universally agreed upon operational definition, effective governance requires a pragmatic approach that links the concept to existing policy frameworks and strengthens institutional coherence [33]. Sustainable blue finance also depends on legal and institutional mechanisms that connect stakeholders and align incentives [28]. More broadly, integrating interconnected sustainability issues requires horizontal and vertical coordination, political leadership, and the involvement of social actors so that blue-economy policies do not remain merely normative statements [34].

The blue-economy literature establishes the policy arena for governing marine sustainability and finance. However, it does not provide a mechanism for translating marine ecological pressures into fiscal parameters that the budget system can process. This limitation creates a need for marine natural capital valuation, which explains how ecological conditions can be translated into valuation parameters for fiscal-governance purposes.

2.2. Marine Natural Capital and Valuation as a Basis for Decisions

Consistent decision-making requires a shared understanding of what constitutes a natural resource asset and how it supports the ecosystem services it provides [35]. Building on this premise, this study conceptualises assets not as neutral biophysical entities but as the material basis of the social, economic, and ecological benefits that public policy seeks to safeguard. Integrated approaches caution against reducing valuation solely to economic efficiency, because public decisions must also consider equity and sustainability within interconnected human and natural systems [36]. Valuation in development administration is therefore not simply a calculation technique but a decision-making infrastructure that helps governments assess what is at stake when asset conditions change.

Within coastal and marine environments, public decisions cannot rely on a single economic value, because values and decision contexts vary [37]. The capacity of natural assets to sustain long-term public benefits is also shaped by governance quality [38]. Marine natural capital asset and risk registers provide a way to map relationships among asset status, associated benefits, and risks to ecosystem service provision [15]. Valuations relevant to public finance must therefore explain not only what is valuable, but also what is fiscally exposed when an asset is stressed.

The valuation literature provides the asset and benefit basis for decision-making, but it remains weak as a recurring system for fiscal parameterisation. Valuations are often point-in-time, location-specific, assumption-dependent, and disconnected from downstream fiscal users. This limitation necessitates a recording framework and explicit disclosure of uncertainty, which are addressed in the marine-accounting cluster.

2.3. Marine Accounting, Uncertainty, and the Risk Register

Ecosystem accounting supports coastal and marine governance by increasing transparency, clarifying material dependencies, and linking natural resource stocks and flows to ecosystem services and broader values [39]. The System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) framework provides an integrated statistical basis for organising habitat data, measuring ecosystem services, and linking this information to economic activity [13]. Ocean accounting extends this logic to the marine environment by connecting data on ecosystems, the economy, and human well-being [14,40]. In practice, coupling marine spatial planning with marine accounting can strengthen evidence-based policy decisions [14]. From a development administration perspective, this body of literature provides the state with the information capacity to transform scattered ecological data into policy-relevant information. The National Research and Innovation Agency (BRIN) plays a critical role in providing the scientific evidence base for marine ecosystem-condition indicators, ensuring that the ecological parameters used in BNC-VaR are rooted in the latest scientific standards.

Within the nature-finance domain, measurement, reporting, and verification remain central constraints in connecting natural capital to financial and fiscal decision-making [41]. General sustainability disclosure requirements have been strengthened through standards such as International Financial Reporting Standards Sustainability Disclosure Standard 1 (IFRS S1) [42], while emerging nature-related disclosure frameworks seek to improve the reporting of nature-related risks and dependencies [43]. These developments improve the discipline of disclosure, but they do not, by themselves, provide a layer-by-layer mechanism for translating marine ecosystem conditions, valuation assumptions, and uncertainty into a public fiscal-risk assessment. This limitation reinforces the need for explicit uncertainty disclosure within the BNC-VaR architecture.

A critical reading of marine-accounting practice identifies three structural limitations for fiscal governance. First, uncertainty reporting remains difficult because ocean data are spatially variable, temporally uneven, and often produced across different institutional systems [40,44]. Second, ecosystem accounts do not automatically translate their outputs into parameters that fiscal-risk units can process. Third, accounting and disclosure frameworks do not by themselves provide a fiscal-governance protocol for anticipatory risk disclosure [22,23]. Marine accounting can therefore organise ecological information and improve uncertainty disclosure, but it does not provide a complete transmission path to the fiscal-risk and budget system. This gap leads to the fiscal-risk and budgeting cluster.

2.4. Fiscal Risk, Resilience, and Budget Integration

Environmental shocks, including disasters and climate-related events, can depress growth, reduce revenues, and increase government spending on recovery and adaptation [20,45]. In developing countries, fiscal stress can arise through both physical and transition risks [46]. Adaptive risk management emphasises gradual assessment and sensitivity to high-impact, low-probability events [47,48]. However, these general frameworks have limited explanatory power for the Indonesian marine context. Most fiscal-risk assessments rely on top-down macroeconomic scenarios rather than ecosystem-specific transmission pathways. As a result, fiscal-stress-testing instruments rarely incorporate ecological parameters that are traceable to biophysical data.

Integrating environmental issues into budgeting is also ineffective if they remain treated as a stand-alone function [34,49]. Effective cross-sectoral governance depends on reliable information exchange [50], while successful risk coordination is shaped by formal rules, institutional quality, networks, and bureaucratic hierarchies [51]. Yet this literature does not yet specify a cross-functional architecture capable of connecting ecosystem data providers, valuation nodes, fiscal-risk units, and budget units into a single accountable decision chain.

The fiscal-risk literature shows that ecological stress can affect fiscal variables through exposure pathways, but it does not provide an ecosystem-specific governance design. Without a blue-economy policy arena, ecological fiscal risk lacks a development context. Without valuation, it lacks traceable parameters. Without accounting for and disclosing uncertainty, it relies on opaque assumptions. These four research clusters, therefore, define the architectural requirements synthesised in the next subsection.

2.5. Synthesis of Conceptual Gaps and Design Needs

The four study clusters collectively identify four interdependent gaps. These are the absence of an operational mechanism for translating ecological pressures into fiscal signals, the absence of an institutional parameterisation node linking valuation outputs to fiscal decisions, the absence of adequate uncertainty disclosure for fiscal analysis, and the absence of a cross-functional design connecting ecological data, valuation parameters, fiscal-risk units, and budget documents. Table 1 summarises how these conceptual gaps translate directly into architectural requirements and specific BNC-VaR components.

Table 1. From literature gaps to Blue Natural Capital Value at Risk (BNC-VaR) architectural requirement.

Study Cluster	Conceptual Gap	Architectural Requirement	BNC-VaR Component
Blue Economy	No operational mechanism translates marine ecological pressures into fiscal signals	Validated ecosystem-condition data as the entry point for fiscal translation	Layer 1: Ecosystem Data
Marine Natural Capital Valuation	No institutional parameterisation node links valuation outputs with the fiscal decision chain	Traceable valuation parameters and documented asset assumptions	Layer 2: Valuation and Parameterisation
Marine Accounting and Uncertainty	No adequate uncertainty disclosure framework supports fiscal analysis	Explicit disclosure of data quality, valuation assumptions, sensitivity ranges, and limits of use	Cross-layer uncertainty disclosure across the ecological-to-fiscal chain
Fiscal Risk and Budgeting	No mechanism translates ecological parameters into fiscal-exposure signals	Fiscal exposure scenarios based on ecological parameters and valuation inputs	Layer 3: Fiscal-Risk Analysis
Fiscal Risk and Budgeting	No cross-functional design connects ecological-fiscal signals with budget units and official fiscal documents	Integration of fiscal-exposure signals into budget channels and fiscal-sustainability readings	Layers 4A: Budget Implications, Layer 4B: Fiscal Sustainability

These requirements provide the foundation for the five design principles and four formal propositions developed in this article. They also clarify that BNC-VaR is not a stand-alone environmental-accounting tool but a governance architecture that links the full ecological-to-fiscal chain from ecosystem data and valuation to fiscal-risk analysis, budgetary implications, and medium-term sustainability.

This synthesis also clarifies the adaptation of the term “Value at Risk”. In the financial literature, Value at Risk is a probabilistic loss threshold or quantile-based estimate of potential loss, within a specified time horizon at a given confidence level [52]. In this article, the term is deliberately adapted for public fiscal governance while retaining its core logic: identifying exposure to potential loss. Within the BNC-VaR framework, “value” refers to the parameterised value of marine natural assets, while “at risk” refers to mediated and conditional fiscal exposure arising from changes in marine natural capital. The framework, therefore, operates as an institutional governance instrument rather than a market-based risk measure.

3. Methods

This research is a conceptual article that uses theory-driven corpus construction, theory synthesis, and a model-paper approach within the social sciences tradition. It does not conduct a systematic literature review, a meta-analysis, a bibliometric review, or a PRISMA-based evidence synthesis. The purpose is not to aggregate empirical findings, but to construct a governance architecture by synthesising concepts, mechanisms, and institutional functions from previously separate domains [53,54,55]. This approach is appropriate because the study develops a conceptual model rather than testing causal relationships or generating primary data [56]. In public administration, theory synthesis is well-suited to problems that cut across institutional domains [57], particularly when the object of analysis spans ecology, valuation, public finance, and governance. The development of conceptual models with internal validation through logical consistency checks is also consistent with the public administration modelling tradition [58].

The analytical corpus was constructed purposively and functionally. A source was included when it defined a core construct, explained an ecological-to-fiscal mechanism, provided valuation, accounting, or uncertainty-disclosure logic, identified a fiscal-risk or budget-governance linkage, supplied an operational benchmark, or established a relevant Indonesian institutional mandate. A source was excluded if it was purely descriptive, sector-specific, lacked transferable governance or fiscal relevance, was unrelated to valuation, accounting, uncertainty disclosure, fiscal risk, or public governance, or could not support any design requirement of the proposed architecture. The final corpus comprised 42 documents: 26 peer-reviewed journal articles, 9 technical reports, normative references and international benchmarks, and 7 official Indonesian policy documents. This analytical corpus is distinct from the full reference list, which also contains foundational works and additional sources. Peer-reviewed sources were retrieved primarily from Scopus, Web of Science, and ScienceDirect, covering publications from 2016 to 2026. Older sources were retained only when they provided foundational concepts or official normative references.

Independent of the purposive corpus assembly, a structured scoping search was conducted strictly to confirm the absence of prior integrated studies and ensure architectural novelty rather than to serve as a systematic retrieval protocol. The search was run using Publish or Perish in April 2026 across the aforementioned databases and timeframe, with three Boolean combinations: “blue natural capital” AND “fiscal risk”, “marine ecosystem” AND “budget governance”, and “VaR” AND “public finance” AND “ecosystem”. This search did not mechanically determine the corpus. Its purpose was to assess whether prior studies had combined marine natural capital valuation, uncertainty disclosure, ecological-to-fiscal translation, fiscal-risk assessment, and budget-cycle relevance within a single institutional governance architecture. The search did not identify a prior study that included this specific combination, although substantial literature exists in each domain.

The BNC-VaR model follows an explicit input–process–output logic. Its inputs are marine ecosystem-condition indicators together with the relevant valuation mandate and fiscal-governance documents. Its process parameterises these inputs into traceable valuation parameters, discloses uncertainty at each step, and translates the parameters into fiscal-exposure scenarios. Its outputs are fiscal-exposure signals, budgetary-implication readings, and fiscal-sustainability readings. The operational details of this logic, including actors, outputs, recipients, and uncertainty disclosure at each layer, are presented in Section 4.1.

The model was constructed through six sequential analytical stages, as summarised in Table 2. Each stage converted a defined input into a documented output under a specific quality-control rule that preserved traceability, internal consistency, and conceptual testability.

Table 2. Six-stage analytical process for BNC-VaR construction.

Stage	Input	Analytical Task	Main Output	Quality-Control Rule
1. Problem identification	Seed literature and policy context	Identify the functional disconnect between marine natural capital valuation and fiscal-risk governance.	Gap statement	The gap must be framed as a functional disconnect, not as a general topic area.
2. Corpus assembly	Domain literature and policy documents	Select sources using functional inclusion and exclusion criteria.	Purposive analytical corpus	Each source must support at least one construct, mechanism, mandate, benchmark, or design requirement.
3. Thematic synthesis	Purposive analytical corpus	Extract constructs, limitations, and unresolved issues across the four study clusters.	Per-cluster construct and gap map	Each source’s contribution and limitation must be recorded in the analytical corpus matrix.
4. Gap-to-requirement mapping	Per-cluster gap map	Convert conceptual gaps into architectural requirements.	Requirement map	A gap is retained only when it constrains ecological-to-fiscal translation.
5. Architecture specification	Requirement map	Specify components, functions, actors, outputs, and relationships in the proposed architecture.	BNC-VaR operating architecture	Each component must be traceable to at least two analytical bases: conceptual, operational, or normative.
6. Proposition formulation and internal validation [59]	Operating architecture and design principles	Formulate propositions and test their logical consistency against the architecture and design principles.	Formal propositions	A proposition is retained only when it is conceptually testable or falsifiable in future empirical research.

Three synthesis instruments supported the six-stage process. The document synthesis matrix mapped each source to its analytical domain, extracted construct, and contribution to model construction. The ecological-to-fiscal chain map specified the logical pathway from changes in ecosystem conditions to valuation parameters and fiscal-exposure signals. The institutional function matrix identified the actors, outputs, and coordination mechanisms associated with each architectural component. The component-level traceability to conceptual, operational, and normative bases is reported in Appendix A Table A1, and the source-level analytical corpus is reported in Appendix B Table A2.

Reliability in this conceptual design rests on transparent inference rather than statistical replication. Beyond the explicit selection criteria and the search boundary set out above, two further safeguards governed inference. Each model component had to draw support from at least two analytical bases. Internal validation also checked the consistency between the identified gaps, architectural requirements, design principles, and formal propositions. Internal falsification was applied by examining whether existing literature or alternative institutional arrangements could challenge the proposed architecture. Where such challenges existed, they were carried into the model as explicit constraints rather than set aside.

This study has three methodological limitations. First, as a purely conceptual architecture, the model requires future empirical testing. Second, the corpus is limited to English-language and Indonesian-language sources available through the selected databases and official repositories, so relevant insights in other languages may be absent. Third, purposive source selection necessarily involves interpretive judgement, which was mitigated by documenting the corpus boundary, the exclusion logic, the synthesis instruments, the quality-control rules, and evidence of traceability. Because one author is affiliated with the Directorate General of State Assets Management, the proposed custodian role should be read as a functionally justified institutional design rather than as an assertion of institutional interest. Alternative institutional placements are considered in Section 4.2.

Another aspect to note is the use of AI-assisted tools in figure preparation. Figure 1 and Figure 4 were prepared or visually refined with the assistance of ChatGPT (OpenAI, GPT-5.5). For Figure 1, the tool was used solely to assist with data visualisation and layout refinement, based on numerical data supplied by the authors from official sources. The authors verified the underlying data, checked the plotted values, reviewed the visual output, and approved the final figure. For Figure 4, the tool was used only to assist the visual preparation of the schematic, including layout refinement, pathway arrangement, and label presentation, based on author-provided conceptual instructions derived from the manuscript’s explanation of ecological-to-fiscal transmission pathways. The tool was not used to generate research data, conduct formal analysis, select references, formulate findings, or determine the model’s interpretation. The authors reviewed, edited, verified, and approved the final figures, including the data labels in Figure 1 and the labels, causal logic, transmission pathways, and analytical meaning in Figure 4. To the best of the authors’ knowledge, the figures do not reproduce any previously published figure, photograph, artwork, logo, third-party dataset visualisation, or copyrighted third-party material.

4. Results and Discussion

4.1. BNC-VaR Operating Architecture and Formal Propositions

The main result of this study is a BNC-VaR governance architecture that embeds marine natural capital valuation into an existing fiscal-risk and budgetary process rather than creating a parallel administrative system. As shown in Figure 3, the model operates through five functional components, namely ecosystem data, valuation and parameterisation, integrated fiscal-risk analysis, budgetary implications, and fiscal sustainability. These components are connected through institutional handovers that convert ecological information into valuation parameters, fiscal-exposure scenarios, budgetary-implication readings, and medium-term fiscal-sustainability readings.

Figure 3. BNC-VaR operating architecture and institutional handovers within existing fiscal-risk and budgetary processes.

Figure 3 is limited to the operating logic of the model. The operational details of each component, including actors, outputs, recipients, timing, uncertainty disclosure, and responsibility boundaries, are specified in Table 3. This separation keeps the figure uncluttered while still presenting a clear operating scheme.

Table 3. Core specifications of the BNC-VaR operating architecture.

Component	Main Actors	Output	Recipient or Use Point	Timing	Uncertainty Disclosure	Responsibility Boundary
Layer 1. Ecosystem Data	MoMAF; MoEF; BPS; BIG; BRIN; local governments	Validated ecosystem indicators and spatial metadata	DGSAM	Annual pre-budget consolidation; update after major ecological shocks	Data quality, spatial coverage, time-series consistency, and measurement limits	Validates ecological conditions only; does not produce valuation parameters or fiscal-risk interpretation.
Layer 2. Valuation and Parameterisation	DGSAM, supported by university-based expert panels	Value parameters, asset assumptions, sensitivity ranges, and QA note	DG-EFS; DG-Budget	Before the formulation of the fiscal-risk scenario in the budget preparation cycle, retained across cycles	Valuation scope, baseline assumptions, sensitivity ranges, limits of use	Acts as State Asset Manager and valuation custodian to set and assure valuation parameters; does not decide budget allocation.
Layer 3. Integrated Fiscal-Risk Analysis	DG-EFS, using DGSAM parameters	Ecological fiscal-exposure scenarios	DG-Budget; DG-FRM; MoF leadership; Financial Note risk process	Financial Note and medium-term fiscal outlook cycle: 3 to 5 years	Causality limits, exposure channels, scenario assumptions, parameter traceability	Interprets exposure across revenue, expenditure, deficit, financing, and sustainability channels; does not produce ecological data or valuation parameters.
Layer 4A. Budgetary Implications	DG-Budget	Budget-relevance notes, fiscal-stress implications, and mitigation priority options	State Budget formulation; budget units; spending units; technical ministries	Annual budget formulation and allocation discussion	Cost range, expenditure sensitivity, priority assumptions, scenario limits	Converts fiscal-risk signals into budgetary options; does not alter ecological data, valuation assumptions, or medium-term fiscal-sustainability readings.
Layer 4B. Fiscal Sustainability	DG-EFS; DG-FRM	Fiscal-resilience and sustainability reading	MoF leadership; medium-term fiscal framework; financing-risk process; development-plan consistency	Medium-term fiscal framework and fiscal-risk reporting cycle	Structural uncertainty, macro-fiscal assumptions, financing sensitivity, medium-term risks	Assesses medium-term fiscal and financing implications; does not control data production, valuation, or annual budget allocation.

Note: MoMAF = Ministry of Marine Affairs and Fisheries; MoEF = Ministry of Environment and Forestry; MoF; Ministry of Finance; BPS = Central Bureau of Statistics; BIG = Geospatial Information Agency; BRIN = National Research and Innovation Agency; DGSAM = Directorate General of State Assets Management; DG-EFS = Directorate General of Economic and Fiscal Strategy; DG-Budget = Directorate General of Budget; DG-FRM = Directorate General of Financing and Risk Management; QA = Quality Assurance.

The five components are derived from the gap-to-requirement logic developed in Table 1. Ecosystem data are required because fiscal analysis cannot begin without a validated ecological entry point. Valuation and parameterisation are required because ecological indicators do not automatically become fiscal parameters. An integrated fiscal-risk analysis is required because valuation parameters must be interpreted through the revenue, expenditure, deficit, financing, and sustainability channels. Budgetary implications and fiscal sustainability are placed at the same level, Layers 4A and 4B, because they are two downstream uses of integrated fiscal-risk analysis rather than a strict hierarchy. The two-way link between them reflects feedback between annual budget relevance and medium-term fiscal resilience. The components draw on established literature on natural capital asset classification and decision-making [35], marine accounting and risk register approaches [15,40], and the blue economy as a cross-sectoral governance paradigm [25]. The synthesis offered here combines these strands into a single ecological-to-fiscal chain designed for fiscal risk and budgetary use.

The architecture is governed by five design principles. Traceability requires each component to map to a distinct analytical function rather than to intuitive relevance. Conditional causality requires the model to treat ecological-to-fiscal links as mediated and context-dependent rather than automatic and to avoid causal claims beyond what the data support. Uncertainty disclosure requires knowledge limits and assumption sensitivity to be stated at each stage. Cross-functional coherence requires risk, budget, and sustainability readings to draw on a common set of parameters. Administrative realism requires the model to operate within existing institutional structures without major restructuring. These principles serve as evaluative standards against which each component and the model as a whole can be assessed.

Building on this architecture, the study formulates four formal propositions. They are framed as claims that future empirical research can evaluate, not as restatements of the model.

P1. In island economies experiencing material marine ecological pressures, ecological fiscal exposure remains hidden when ecological parameters, valuation nodes, and fiscal-risk transmission mechanisms are disconnected.

P2. Effective ecological-to-fiscal translation requires an institutional custodian function that is distinct from ecosystem managers and fiscal-risk users.

P3. Valid ecological-to-fiscal translation depends on explicit uncertainty disclosure across data, parameterisation, fiscal-risk analysis, and budget-use stages.

P4. Traceability, transparency, and consistency can strengthen fiscal integrity even when precise ecological-to-fiscal causal estimates remain unavailable.

Each proposition is bounded. P1 does not hold where a country already has a formal mechanism linking marine ecosystem conditions to fiscal-risk analysis in official budget documents. P2 becomes less institutionally salient where data production, valuation, and fiscal use are performed by a single entity. P3 may require adaptation where ecological data are already highly standardised. P4 is most relevant where precise ecological-to-fiscal causal quantification is not yet feasible.

Table 4 specifies observable indicators, Indonesian data sources, and verification methods for these propositions, distinguishing qualitative from quantitative approaches. The table makes the model evaluable using existing fiscal and administrative records, without requiring a new data bureaucracy.

Table 4. Methodological roadmap for empirical verification of BNC-VaR propositions.

Proposition	Observable Indicators	Indonesian Data Sources	Approach	Verification Method
P1. Hidden ecological fiscal exposure	Marine ecological risk categories; ecosystem-condition references; linkage to revenue, expenditure, deficit, financing, or fiscal sustainability.	Financial Note and State Budget Bill materials, especially the fiscal-risk section; APBN documents; LKPP; sectoral reports from MoMAF, MoEF, BPS, BRIN, and BIG; BPK audit findings on marine revenue or ecosystem-related spending.	Qualitative, supported by descriptive document coding	Code fiscal-risk documents for marine ecological references; compare fiscal disclosure with sectoral ecosystem evidence and audit findings.
P2. Need for a valuation custodian	Valuation protocol; parameter ownership; QA procedure; traceability of assumptions; role separation between data providers, valuation custodian, and fiscal users.	Government valuation regulations; DGSAM valuation assignment files and valuation reports; internal valuation technical guidelines; data handover records; inter-agency coordination minutes or technical notes.	Qualitative, using process tracing and institutional analysis	Trace parameter flow from ecosystem data to valuation outputs and fiscal users; assess whether the custodian function is formally identifiable.
P3. Cross-stage uncertainty disclosure	Data-quality notes; sensitivity ranges; causality limits; scenario assumptions; limits of use; continuity of uncertainty notes across handovers.	One Data Indonesia metadata; ecosystem-data metadata from MoMAF, MoEF, BPS, BRIN and BIG; valuation workpapers; fiscal-risk scenario files; budget-preparation notes; Financial Note supporting materials.	Mixed qualitative and quantitative	Code uncertainty disclosure; compare sensitivity ranges; test whether uncertainty information is retained from data, valuation, fiscal-risk, and budget-use stages.
P4. Fiscal integrity through traceability, transparency, and consistency	Audit trail; cross-cycle parameter consistency; documented methodological changes; clear decision-use boundaries; feedback between annual budget implications and medium-term fiscal sustainability.	DGSAM valuation reports and QA records; DG-EFS fiscal-risk files; DG-Budget budget-formulation notes; DG-FRM financing-risk materials; KEM-PPKF; Financial Note; LKPP; BPK audit reports.	Qualitative, with a possible scoring rubric	Assess traceability and fiscal-integrity criteria; compare parameter consistency across budget cycles; validate findings through expert review.

Note: APBN = State Revenue and Expenditure Budget; LKPP = Central Government Financial Report; BPK = Audit Board of Indonesia; KEM-PPKF = Macroeconomic Framework and Fiscal Policy Principles.

Future empirical research can collect the data required for Table 4 from existing fiscal documents, valuation records, ecosystem-data metadata, audit reports, and budget-preparation materials. The proposed verification strategy, therefore, evaluates whether ecological information can be traced from ecosystem indicators to valuation parameters, fiscal-exposure scenarios, budgetary implications, and fiscal-sustainability readings within Indonesia’s existing fiscal and administrative records.

4.2. Ecological-to-Fiscal Transmission and Institutional Interpretation

The ecological-to-fiscal transmission logic rests on a simple but consequential distinction. Marine ecosystem degradation is not, in itself, a fiscal event. It becomes fiscally relevant only when ecological change affects an administratively recognised revenue base, an expenditure obligation, a fiscal-risk category, or a financing need. The analytical problem is therefore not whether ecosystems decline but whether the effects of that decline can be recognised, valued, transmitted, and used within the fiscal decision chain. This places ecological information inside existing fiscal-risk, budgetary, and medium-term fiscal planning routines rather than outside them.

Figure 4 illustrates the transmission logic through three stylised maritime-economy pathways. Its purpose is not to assert automatic fiscal effects but to identify where ecological change must be parameterised before it can be read as fiscal exposure. In the fisheries pathway, declining fish-stock biomass becomes fiscally relevant only when measurable changes in stock status, capture volume, production value, or licensing objects narrow the Non-Tax State Revenue base. In the coral-reef pathway, degradation may affect fiscal exposure through tourism-service channels, where live coral cover, habitat quality, tourism attractiveness, and associated service-flow value mediate potential central and local tax receipts. In the mangrove pathway, ecosystem loss operates mainly through the expenditure channel, as increased coastal vulnerability may raise spending on prevention, recovery, restoration, and disaster response [3,5]. The same structure can be adapted to other maritime or environmental contexts by replacing the ecosystem indicator, valuation parameter, fiscal channel, and responsible institution while preserving the sequence of measurement, parameterisation, fiscal interpretation, and budgetary use.

Figure 4. Illustrative ecological-to-fiscal transmission pathways. This figure was generated with the assistance of ChatGPT (OpenAI, GPT-5.5) based on author-provided conceptual instructions and was reviewed and approved by the authors.

These pathways show that ecological fiscal exposure is mediated rather than automatic. Fiscal relevance depends not only on ecological severity but also on administrative recognition. A severe stock decline can remain fiscally invisible if it is not reflected in reported catch, licensing objects, or fiscal-risk documentation, while a moderate reef deterioration can become fiscally visible once it affects taxable tourism activity or rehabilitation needs. Figure 4 does not claim that ecological decline automatically produces a specific deficit or financing burden. It identifies where causal claims must be tested, because each pathway requires evidence linking the ecological parameter to a production or service effect, that effect to a revenue or expenditure object, and that object to a fiscal outcome, through links that may be delayed, indirect, or conditional on administrative capacity. Crucially, this mediated transmission must also account for confounding macroeconomic variables. A decline in marine non-tax revenue or tourism taxes may stem from exogenous economic shocks, such as demand contraction or price volatility, rather than purely from ecosystem degradation. The framework acknowledges this attribution problem without claiming perfect econometric causality.

To avoid a false sense of precision, BNC-VaR treats valuation outputs as decision-support boundary information rather than deterministic estimates of fiscal loss. Within Layer 3, numerical values are interpreted through sensitivity ranges, documented assumptions, and conditional causality notes to reduce the risk of conflating ecological signals with broader economic noise. The resulting “Value at Risk” therefore functions as a heuristic signal of anticipatory fiscal exposure, not as an exact accounting deficit. This design is reinforced by separating the valuation-custody function from the fiscal-interpretation function. The valuation custodian maintains parameter consistency and quality assurance, while fiscal-risk units translate these parameters into aggregate macro-fiscal exposures. This separation requires budget decision-makers to account for ecological uncertainty in fiscal-risk assessment, rather than treating valuation figures as unquestionable facts.

Institutional uncertainty is part of the transmission problem. Even where ecological and valuation evidence exist, transmission may be weakened by unclear mandates, fragmented data standards, limited data sharing, or differing evidentiary thresholds between technical and fiscal institutions. This administrative-recognition layer is not always robust in practice. Audit findings on licensing-related marine non-tax revenue administration in Indonesia indicate weaknesses in internal controls, compliance, data collection, reference-price setting, and information-system support across fisheries resources, marine-space use, and small-island utilisation. In particular, the Audit Board noted that the Ministry’s information system had not yet supported the identification of all non-tax revenue objects related to capture-fisheries resources, and that the fisheries-vessel database had not been fully synchronised with the vessel database of the Ministry of Transportation. These findings show that administrative-recognition capacity is a present constraint on translating marine resource use into accountable fiscal information [60]. Therefore, actual fiscal exposure strictly depends on whether the fiscal system has a traceable pathway for receiving, validating, interpreting, and using ecological information.

No single organisation controls the whole ecological-to-fiscal chain. Positioning Directorate General of State Assets Management (DGSAM) as the valuation custodian is consistent with its state asset management and government valuation functions. In the BNC-VaR architecture, this role does not require DGSAM to produce ecological data or determine fiscal policy. Its function is to maintain valuation-parameter consistency, document assumptions, apply quality-assurance procedures, and provide traceable parameter handovers to fiscal-risk users. For marine natural capital valuation, this custodian role should be supported by structured scientific and methodological assurance. Technical agencies and research institutions provide ecological evidence, while BRIN, universities, and independent experts support the peer review of parameter protocols, disclosure of uncertainties, and assessment of causality assumptions. This arrangement strengthens methodological credibility without turning academic actors into implementing agencies or replacing the authority of fiscal institutions. The handover points between these functions are analytically decisive because they determine whether ecological information remains isolated within sectoral boundaries or enters the state’s fiscal architecture.

This reframes fiscal integrity as a property of the translation process. The relevant question is not whether ecological fiscal impacts can already be estimated with numerical precision but whether the fiscal system can demonstrate how an ecological signal was identified, translated, interpreted under uncertainty, and used. Fiscal integrity in this sense rests on three operational conditions: traceability of parameters back to ecological data, transparency about assumptions and uncertainty, and consistency of methods across budget cycles. Without these conditions, ecological information may enter policy discussion as a general concern, but it cannot function as accountable fiscal information.

4.3. Comparative Position, Novelty, and Boundary Conditions

The contribution of this study lies in the interface between existing approaches rather than in the invention of a new stand-alone concept. Blue economy, natural capital valuation, ecosystem accounting, green public financial management, fiscal stress testing, and nature-based financing instruments each address part of the problem. Their limitation is functional rather than conceptual. They do not, by themselves, connect the full chain from ecological measurement to budgetary use within a single traceable decision process. The novelty of BNC-VaR is therefore located at the governance-design level. It specifies how outputs from ecological and accounting systems can be translated into fiscal-exposure readings that are usable in existing budget and fiscal-risk processes.

This positioning is consistent with Indonesia’s existing experience in natural capital accounting. The Wealth Accounting and the Valuation of Ecosystem Services (WAVES) supported strengthening of the Integrated System of Environmental-Economic Accounts (SISNERLING) shows that SEEA-based natural capital accounting can provide structured evidence on natural capital, support inter-ministerial data coordination, and inform planning and policy dialogue, including the RPJMN, Nationally Determined Contribution (NDC) strategic planning, and long-term development vision [12]. It also shows that the Ministry of Finance had already been engaged in policy dialogue on the fiscal potential of natural resources, although that engagement had not yet produced a routine fiscal-risk transmission mechanism [12]. The remaining gap is therefore not the absence of natural capital accounting or fiscal-institutional awareness. The gap lies in the absence of a routine governance mechanism that converts ecosystem-condition change into parameterised fiscal-exposure information for budget-cycle fiscal-risk disclosure.

The comparison below uses a functional stopping point as the organising criterion. Rather than treating existing approaches as competitors, it asks what each approach produces, where its output enters public-finance decision-making, and where it stops before becoming a recurrent fiscal-risk reading within the budget cycle. Table 5 benchmarks BNC-VaR against relevant accounting, risk, public-finance, and blue-finance approaches by combining application scenarios, operational costs, departmental adaptability, fiscal-risk limitations, and the specific interface added by BNC-VaR.

Table 5. Comparative positioning of BNC-VaR against relevant accounting, fiscal, and blue-finance approaches.

Approach	Application Scenario	Relative Operational Cost	Departmental Adaptability	Limitation for Fiscal-Risk Disclosure	BNC-VaR Positioning
System of Environmental-Economic Accounting (SEEA) Ecosystem Accounting	National accounts of ecosystem extent, condition, services, and asset value	High, due to statistical, spatial, biophysical, and monetary accounting requirements	Strong for statistical and environmental-accounting agencies, less direct for fiscal-risk units	Produces ecosystem accounts but does not translate ecosystem change into budget-cycle exposure	Uses ecosystem accounts as upstream evidence for valuation, custody, and fiscal-risk translation
Ocean Accounts	Marine accounts linking ecosystems, ocean uses, economic activity, and social benefits	High, due to ecological, spatial, socio-economic, and marine-use integration	Strong for marine planning, statistics, and ocean-policy institutions, less embedded in fiscal-disclosure routines	Reports marine condition and use, but lacks a routine pathway to fiscal-risk statements	Converts marine-accounting outputs into parameterised fiscal-exposure readings
Marine Natural Capital Risk Register	Asset-level mapping of marine risks, benefits, and dependencies	Moderate, requiring risk identification, validation, and periodic updating	Strong for environmental and resource-management bodies, weaker for budget and fiscal-risk institutions	Maps risks and dependencies but does not link them to revenue, expenditure, deficit, or financing analysis	Links risk register logic to valuation parameters, uncertainty disclosure, and fiscal-use points
Green public financial management	Environmental integration into planning, budgeting, tagging, reporting, and oversight	Moderate, because it builds on existing public-finance systems	Strong for finance ministries and budget institutions, adaptable across environmental issues	Provides budget entry points but lacks marine-specific parameters and valuation assurance	Adds marine-specific parameterisation and traceable ecological-to-fiscal translation within budget routines
Fiscal stress testing	Simulation of shocks to revenue, expenditure, deficit, debt, and financing needs	High, due to macro-fiscal modelling, scenarios and data requirements	Strong for fiscal strategy, macroeconomic, debt, and financing units; weaker for ecological data producers	Captures aggregate shocks but lacks traceable ecosystem-condition inputs and valuation parameters	Provides ecologically grounded exposure parameters for fiscal-stress logic
Value-at-Risk approaches in the blue economy	Financial-risk estimation from sustainability pressures for investors, portfolios, or markets	Moderate to high, depending on financial, market, and exposure data quality	Strong for investors, regulators, and financial institutions; weaker for public-budget institutions	Estimates financial exposure but lacks public-sector handover and budget-cycle mechanisms	Adapts risk logic to public fiscal governance rather than market portfolio management
Green and blue finance instruments	Capital mobilisation or pricing linked to environmental and blue-economy commitments	Moderate to high; depends on transaction design, verification, monitoring, reporting, and investor requirements	Strong for finance ministries, debt offices, regulators, development banks, and issuers	Links nature to finance or debt terms, but does not produce internal fiscal-risk readings from ecosystem condition	Provides fiscal-exposure readings that can inform but not replace financing instruments

The comparison shows that BNC-VaR is not a substitute for these existing frameworks. It is a downstream governance processor. Its function is to receive ecological, accounting, risk, or valuation outputs and convert them into fiscal-exposure information that can be interpreted by fiscal-strategy, budget, and fiscal-sustainability units. This position falls between a comprehensive ecosystem accounting system and a single valuation exercise. It occupies the institutional interface between ecological evidence and fiscal decision-making.

This positioning also clarifies the relationship with nature and blue-finance instruments. As outlined above, these instruments already link environmental commitments to sovereign finance, investor confidence, market eligibility, and debt terms [17,18,19,29,30,32,41]. Their fiscal relevance is therefore recognised. However, they operate primarily at the financing, issuance, guidance, or transaction-design node. They do not themselves determine how a decline in fish-stock biomass, coral-reef condition, or mangrove protection should be converted into a recurrent fiscal-risk signal within a state budget process. BNC-VaR addresses a different node: the in-cycle interpretation of ecosystem condition as fiscal exposure before, or independent of, any financing transaction.

The applicable context is therefore specific. BNC-VaR is most relevant for archipelagic, coastal, or marine-resource-dependent economies that meet three conditions. First, marine degradation has material revenue or expenditure implications. Second, fiscal functions are institutionally separated across data, valuation, risk, budget, and financing units. Third, the government already has a disclosure vehicle, such as a Financial Note, a fiscal-risk statement, a medium-term fiscal framework, or a budget risk report. The ecological context is also bounded. The framework is most applicable where ecosystem change can be linked to an identifiable fiscal channel, such as fisheries revenue, tourism-related taxation, coastal-protection expenditure, rehabilitation spending, disaster-response costs, or financing pressure.

The framework has objective limitations. It depends on the quality, continuity, and spatial resolution of upstream ecosystem data. It requires a legally recognisable valuation-custodian function, because without such authority, parameter consistency across budget cycles cannot be maintained. It adds limited value in highly integrated fiscal systems where data production, valuation, risk analysis, and budget allocation are already performed within one institution. It also identifies fiscal exposure, but by itself, it does not prevent ecological degradation or finance restoration. Finally, the operational-cost comparison in Table 5 is qualitative because no published costing exists for implementing these frameworks in Indonesia on a comparable basis.

These limits do not weaken the contribution. They define its proper scope. The framework should be evaluated not as a universal replacement for existing approaches but as a boundary mechanism that connects their outputs to fiscal governance. Future empirical research can therefore examine which institutional conditions most affect the translation of ecological to fiscal outcomes: the strength of the custodian mandate, the quality of ecosystem metadata, the stability of interdepartmental handovers, the disclosure of uncertainty, and the consistency of valuation parameters across budget cycles.

4.4. Implementation Roadmap and Future Empirical Agenda

The implementation question is administrative rather than conceptual. The preceding sections have specified the operating architecture, the ecological-to-fiscal transmission logic, and the comparative position of BNC-VaR. From a practical perspective, its most immediate entry point is the preparation of the Financial Note, where it can complement existing fiscal-risk analysis by adding ecological exposure signals without replacing established fiscal mechanisms. The National Medium-Term Development Plan 2025 to 2029 prioritises the blue economy as part of economic transformation [6], while Minister of Finance Regulation No. 99 of 2024 provides an initial regulatory basis by including marine natural resources among objects of valuation [61]. These entry points open a pathway for integrating ecological dimensions into fiscal planning, but they do not, by themselves, resolve data fragmentation, weak institutional handovers, or uncertainty disclosure.

Table 6 presents the roadmap in compact form, targeting four administrative failure points: fragmented ecosystem data, discontinuity of valuation parameters, methodological uncertainty, and the absence of a routine budget-cycle entry point. The detailed actor mapping, legal bases, governance instruments, and reference benchmarks are provided in Appendix C Table A3. This separation keeps the main text focused on implementation logic while preserving the operational detail needed for administrative feasibility assessment.

Table 6. Compact implementation roadmap for BNC-VaR in Indonesia.

Phase	Administrative Constraint	Targeted Governance Action	Operational Output
Phase 1. Data governance alignment	Marine ecosystem data remain fragmented across technical, statistical, geospatial, and local-government institutions, with inconsistent metadata, spatial resolution, and temporal frequency	Align existing ecosystem data flows within the One Data Indonesia framework [62] without creating a new repository; define minimum metadata requirements for spatial coverage, temporal consistency, and confidence level	Standardised marine ecosystem-condition indicators and metadata protocol for valuation use
Phase 2. Custodian and parameter protocol	No institutional function maintains consistency in valuation parameters, version control, and quality assurance across budget cycles	Formalise a valuation-custodian function within the government-valuation framework; establish a version-controlled parameter register and a documented handover protocol for fiscal-risk users	Marine valuation parameter register, quality-assurance record, and parameter handover protocol
Phase 3. Controlled pilot and uncertainty disclosure	Ecological-to-fiscal translation remains methodologically uncertain and lacks a standard disclosure protocol	Conduct a controlled pilot in one marine sector or ecosystem; apply layer-specific uncertainty disclosure covering data quality, sensitivity ranges, causality limits, and scenario assumptions	Pilot BNC-VaR report, uncertainty-disclosure protocol, and reviewed fiscal-exposure scenario
Phase 4. Budget-cycle integration and scaling	Marine ecological fiscal exposure is not yet routinely integrated into fiscal-risk disclosure, medium-term fiscal planning, or budget relevance review	Embed pilot outputs into existing fiscal documents and routines, including the Fiscal-Risk Statement, medium-term fiscal framework, and budget-relevance review; expand gradually based on pilot evidence	Ecological fiscal-exposure note or annex, medium-term ecological-risk scenario, and periodic traceability, transparency, and consistency review

Note: The detailed actor mapping, legal bases, governance instruments, and operational outputs underlying this compact roadmap are provided in Appendix C Table A3. The roadmap connects existing data, valuation, fiscal-risk, budget, financing, and accountability functions and does not establish a parallel administrative infrastructure.

The sequencing in Table 6 is deliberately incremental. Fiscal use should not precede minimum data compatibility, and budget-cycle integration should not precede parameter ownership, uncertainty disclosure, and pilot testing. Bypassing these stages risks creating a false sense of fiscal relevance, allowing ecological information to enter policy documents without the traceability, comparability, and uncertainty controls required for accountable fiscal-risk disclosure.

This staged pathway is necessary because the main implementation risk is not technical complexity alone. It is the possibility that ecological information will move through institutions without stable metadata, accountable parameter ownership, or clear decision-use boundaries. Siloed data between ministries can constrain the information exchange required for integrated fiscal analysis [51]. Early implementation should therefore prioritise three safeguards before any fiscal-risk disclosure is made: common metadata standards, a documented parameter register, and an assurance forum, supported by BRIN’s scientific leadership and university-based independent peer review, that reviews traceability, uncertainty, and cross-cycle consistency.

Domestic experience reinforces this caution. The WAVES programme in Indonesia demonstrated the importance of inter-agency coordination, reliable data, and regulatory support for natural capital accounting [12]. Its lesson for this framework is not that ecosystem accounting is insufficient but that accounting outputs do not automatically become fiscal-risk inputs. A translation mechanism is still required to determine which ecological indicators matter for fiscal exposure, how valuation parameters are controlled, and how uncertainty is disclosed before the information is incorporated into fiscal documents.

The future empirical agenda follows directly from the four implementation safeguards embedded in the roadmap. To reduce the risk of empirical overextension, future studies should begin with strict analytical bounding rather than attempting a nationwide assessment of all marine ecosystems and fiscal channels simultaneously. Initial testing should focus on one administratively identifiable ecosystem–fiscal linkage, such as capture-fisheries licensing and non-tax revenue in a selected fisheries management area, reef-based tourism and local tax exposure in a defined marine tourism destination, or mangrove loss and rehabilitation spending in a specific coastal district. Within such bounded settings, research can systematically test the four safeguards. First, it can examine whether stronger ecosystem metadata improves parameter reliability, thereby addressing the data-fragmentation problem in Phase 1. Second, it can assess whether a version-controlled custodian register improves cross-cycle comparability, which corresponds to the parameter-continuity safeguard in Phase 2. Third, it can evaluate whether uncertainty-disclosure protocols are retained during institutional handovers, directly testing the methodological safeguard introduced in Phase 3. Fourth, it can examine whether ecological fiscal-exposure notes improve the quality of fiscal-risk disclosure, which tests the budget-cycle integration objective in Phase 4. Because these questions are expressed in terms of institutional functions rather than country-specific agencies, they can later be examined comparatively across other archipelagic, coastal, or resource-dependent economies. Improvements in ecosystem-data quality, marine spatial planning, and marine accounting could then support a semi-quantitative version of the framework without altering its core role as an institutional governance design [14,63].

5. Conclusions

This article has addressed a persistent disconnect in public administration. Marine ecological degradation is increasingly recognised as an economic and fiscal concern, yet it rarely enters the state budget cycle as a recurring, parameterised fiscal-risk signal. To address this gap, the study developed the BNC-VaR architecture. Rather than adding a parallel environmental-accounting system, BNC-VaR is designed as an institutional boundary mechanism that translates validated ecosystem data into traceable valuation parameters, discloses uncertainty at each stage, and channels the result into existing fiscal-risk, budgetary, and fiscal-sustainability routines. Its central claim is conceptual. Fiscal integrity under ecological uncertainty depends less on perfect ecological valuation than on traceable parameters, transparent disclosure of uncertainty, and consistent institutional handovers.

The framework’s value in archipelagic and coastal states rests on administrative realism as much as on technical precision. The Indonesian analysis indicates that credible ecological-to-fiscal translation requires clear institutional handovers, a formal valuation custodian, and disciplined disclosure of uncertainty. Without these safeguards, implementation could create a false sense of fiscal relevance, allowing ecological data to enter policy documents without the accountability controls that sound public financial management requires. The framework, therefore, identifies fiscal exposure and the institutional conditions for its recognition. It does not, by itself, produce precise ecosystem valuations, halt degradation, or predict specific fiscal outcomes.

These considerations are especially relevant for the Global South. Many developing coastal and archipelagic economies combine a high structural reliance on marine natural capital for revenue and livelihoods with limited fiscal space to absorb ecological and climate shocks. In such economies, unmonitored marine degradation can represent not only an environmental loss but also a latent, largely unrecorded fiscal exposure. By making the translation of ecological stress into fiscal exposure explicit and traceable, BNC-VaR offers an institutional basis for bringing ecological risk into medium-term fiscal planning earlier than current routines allow. Because the architecture is defined by institutional functions rather than by country-specific bodies, other governments can map the same sequence onto their own data, valuation, fiscal risk, and budget institutions.

As a conceptual contribution, the framework is offered as a testable foundation rather than a finished instrument. Its four propositions and five design principles are intended for empirical evaluation in future work, drawing on existing fiscal documents, valuation records, and audit findings. Subject to that evaluation, integrating marine natural capital into fiscal governance is less an environmental accounting exercise than a means of strengthening valuation assurance, fiscal integrity, and public accountability amid accelerating ecological change.