Empirical analysis of project–purchaser dynamics in Japan’s blue carbon dioxide removal credit scheme

Empirical analysis of project–purchaser dynamics in Japan’s blue carbon dioxide removal credit scheme | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Empirical analysis of project–purchaser dynamics in Japan’s blue carbon dioxide removal credit scheme Tomohiro Kuwae, Yuka Suzuki, Masanori Furuya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7525613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Carbon dioxide removal (CDR) and associated credit mechanisms are gaining prominence in carbon neutrality strategies, yet empirical evaluations of their sustainability remain limited. Here, we analyzed 61 blue carbon projects and 471 certified transactions under Japan’s J-Blue Credit scheme to examine the characteristics of projects and purchasers and their interrelationships. On average, projects involved 3.2 ± 1.4 co-creators, and transactions were small in volume (2.3 ± 4.2 tCO₂) but high in unit value (~ 400 USD/tCO₂). Approximately 40% of transactions occurred between parties located within the same municipality. Hard-to-abate sectors (e.g., construction, transportation, energy) and companies with explicit decarbonization policies purchased more credits, while the sales sector participated less. Manufacturing companies preferred local projects, whereas service companies preferred innovation-focused projects. Project appeal content, such as co-benefits, significantly influenced purchaser numbers and unit prices, both positively and negatively. These findings demonstrate that multi-stakeholder collaboration, project appeal strategies—including co-benefits—and sector-specific demand critically shape transaction outcomes and market structure, offering insights for designing effective credit schemes and for scaling CDR markets and advancing nature-based solutions globally. Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction To achieve global climate goals, such as the Paris Agreement’s goal to limit the global average temperature increase to below 2°C, not only reductions in CO₂ emissions but also active removal of atmospheric CO₂ are essential 1–3 . A wide range of carbon dioxide removal (CDR) approaches exist, broadly classified into engineered (technology-based) solutions and nature-based solutions 3,4 . Among these, marine CDR encompasses strategies that utilize oceanic processes for atmospheric CO₂ removal 5,6 . A subset of marine CDR is the use of blue carbon ecosystems (e.g., mangroves, salt marshes, seagrass meadows, and macroalgal beds), which absorb CO₂ via photosynthesis and contribute to long-term carbon storage within these ecosystems 7–9 . Several Parties to the United Nations Framework Convention on Climate Change (UNFCCC) now include blue carbon ecosystems in their national greenhouse gas inventories 10 . For example, Japan’s 2023 inventory accounted for CO₂ removals by mangroves, and in 2024, removals by seagrass meadows and macroalgal beds were included for the first time 11 . While direct mitigation by emitters is essential to address climate change 12 , it is not always feasible, particularly in hard-to-abate sectors. In such cases, financial mechanisms that support mitigation elsewhere are necessary to sustain long-term decarbonization efforts 13 . One such mechanism is the carbon market, where carbon credits are bought and sold 14,15 . Carbon markets are broadly categorized into compliance markets, which serve regulatory obligations, and voluntary markets, which allow entities to offset emissions at their discretion. Carbon credits vary by function: some avoid emissions, others reduce them, and some actively remove CO₂ from the atmosphere 16 . Among these, CO₂ removal (CDR) credits are gaining increased attention over avoidance- or reduction-based ones 17 due to concerns over the reliability of avoidance credits 16 , especially regarding additionality 18 . There is also growing recognition that large-scale CDR is essential for achieving net-zero emissions 19 . Nature-based CDR solutions offer a range of co-benefits that contribute to the various Sustainable Development Goals and Global Biodiversity Framework targets 20,21 . However, investment in global decarbonization and biodiversity, including nature-based CDR remains insufficient 15,22 . Moreover, there are implementation barriers, including inadequate policy support and unclear governance, a diversity of land tenure regimes, various regulatory constraints, undefined carbon rights and risks of double counting, limited community benefit-sharing and social acceptance, and high transaction and monitoring costs 5,6,15 . Despite these barriers, the number of nature-based CDR solutions is increasing, particularly in the area of biochar and afforestation/reforestation 23 . Still, their global market share remains limited compared to reduction-based or engineering solutions 17,21,24 . Even prominent CDR technologies like direct air carbon capture and storage face implementation and sustainability challenges 25,26 . Globally, up to 100 blue carbon projects, almost all located in developing countries, have been registered as carbon credits in international registries 27 . However, these estimates are based exclusively on English-language sources, and language barriers limit access to registry data in other regions 28,29 . In 2021, Japan established the J-Blue Credit (JBC) scheme through the Japan Blue Economy Association (JBE), a government-approved Collaborative Innovation Partnership 30 . Although information is available primarily in Japanese, the JBC scheme uniquely provides public access to credit documentation, including project applications and supporting materials, via its official website. This transparency is possible because consent for disclosure was obtained from all parties involved. By 2025, the JBC scheme had certified 61 projects, with an average project area of 21.0 ± 56.9 hectares, 471 transactions, and a total transaction value of 61,805,452 JPY (approximately 420,000 USD) (see Supplementary Information). The effectiveness and viability of carbon credit schemes depend on the alignment between sellers’ and purchasers’ needs and objectives. Accordingly, understanding stakeholder relationships and motivations is essential for tailoring scheme design and sustainability outcomes. However, datasets analyzing both party attributes and their interrelationships are largely unavailable 27,29,31 , partly because bilateral transactions with undisclosed details are common in crediting schemes 32 . Party attributes and relationships vary: for example, credit creators may prioritize higher prices or broader purchaser networks, which require strategies aligned with specific purchaser characteristics. Purchasers, in turn, may select credits based on Environmental, Social, and Governance (ESG) appeal or contributions to sustainability goals. Here, we examine (1) certified project characteristics (participating entities, target vegetation types, appeal content), (2) purchaser characteristics (location, industry, scale), and (3) whether trading outcomes (number of purchasers, unit prices) correlate with project characteristics, using empirical data from the JBC scheme. Although JBC is a domestic initiative, it features a high degree of project and purchaser diversity, and, more importantly, offers a comprehensive and uniquely detailed dataset capturing both credit creators and purchasers. We hypothesize that the motivations and relationships between these parties are fundamental to the development of sustainable nature-based CDR projects. Results Project characteristics Certified projects spanned diverse Japanese locations (Supplementary Fig. 1). Mean project duration was 15.6 ± 14.9 years, with most projects extending ≤ 20 years, although some exceeded 50 years (Supplementary Fig. 2a). The target vegetation was macroalgal beds (49.2%), seaweed farming (18.0%), seagrass meadows (14.8%), tidal flats (1.6%), and multiple ecosystems (16.4%) (Supplementary Fig. 3a). The ecosystem intervention methods were ecosystem creation on substrates (45.9%), ecological restoration (42.6%), and combined approaches (11.5%) (Supplementary Fig. 3b). The mean number of collaborating credit creators per project was 3.2 ± 1.4 (Supplementary Fig. 2b). The most common entities included as credit creators were fishers (86.9%), followed by municipality (67.2%), private company (55.7%), local collective (36.1%), local organization (31.1%), and academic institutions including schools and universities (18.0%) (Supplementary Fig. 4a). The co-benefits/social impacts appealed by credit creators for each project were multiple: economic effects (83.6%), environmental conservation (80.3%), sustainability (75.4%), educational effects (59.0%), multi-stakeholder collaboration (50.8%), and innovation (39.3%) (Supplementary Fig. 4b). Despite these co-benefits appeals, only 11.5% quantified these co-benefits. The credit creators’ trading decisions were divided between public offering (75.4%) and internal utilization or bilateral transactions (24.6%). A network analysis revealed the key variables characterizing the projects: entity (particularly municipality and company participation), appeal content (especially educational effects), trading decision, vegetation (seagrass meadows), and ecosystem intervention methods (Fig. 1 , see Supplementary Fig. 5 and Supplementary Tables 1 and 4, only the statistically significant results were explained below). Municipality participation was significantly associated with higher public offering rates (92.7% versus 40.0% without municipality participation). Company participation was significantly associated with a low public offering rate (55.9% versus 100% without company participation). Local collective participation increased co-creator numbers (4.0 ± 1.4 entities versus 2.7 ± 1.2 without local collective participation). Public offering projects emphasized educational appeals more frequently (71.7%) than non-public offering projects. Projects with public offering, municipality participation, and educational appeal accounted for 45.9% of all projects. Seagrass projects predominantly employed restoration methods (77.8%, Supplementary Fig. 6). Purchaser characteristics The total number of purchasers and total purchase amounts were dominated by the construction, service, manufacturing, transportation, and energy industries (Fig. 2 ). The construction, transportation, and energy industries accounted for very high purchase rates (number of transactions: 44.2%, monetary amount: 44.0%) compared to their share of companies in national industry statistics (ca. 10%) 33 . In contrast, the sales sector (wholesale and retail), which accounts for a much larger share of companies nationwide (ca. 30%) 33 , showed low participation (number of transactions: 7.0%, monetary amount: 5.5%). Management policies prioritized sustainability (30.9%), decarbonization (25.5%), and biodiversity (1.5%). Business size of purchasers was predominantly small and medium enterprises (25th percentile: 50 employees; 50th percentile: 256 employees; 75th percentile: 1528 employees). 71.8% of transactions occurred within the same municipality or prefecture, with 39.7% within the same municipality. A network analysis identified industry sector, location, business size, and decarbonization policy as key purchaser attributes (Fig. 3 , see Supplementary Fig. 7 and Supplementary Tables 2 and 5, only the statistically significant results were explained below). Purchasers selecting local (i.e., same municipality) projects varied by industry: retail (72.7%) and manufacturing (63.9%) showed higher local purchasing rates compared with service (20.0%) and insurance/finance (25.0%) (Supplementary Fig. 8a). Decarbonization policy was more prevalent in the energy (56.3%), construction (39.8%), and transportation (35.4%) industries (Supplementary Fig. 8b). Decarbonization policy increased with business size: small (1–99 employees, 9.5%), medium (100–999 employees, 24.5%), and large (≥ 1000 employees, 44.0%). Project–purchaser relationships and transaction outcomes Purchaser location strongly influenced project selection (Fig. 4 , see Supplementary Fig. 9 and Supplementary Tables 2 and 6, only the statistically significant results were explained below). Local purchasers preferred ecosystem creation methods (77.0%) compared to other ecosystem intervention method (Supplementary Fig. 10a). Non-local purchasers selected the projects including municipality most frequently (69.5%). The service industry favored innovation-focused projects (41.1%) (Supplementary Fig. 10b). The 61 projects with 9,185 tCO₂ were certified during 2021–2025. Of those 61 projects, 46 offered 3,482 tCO₂ through public offerings, of which 1,081 tCO₂ was purchased across 471 transactions. Mean transaction size was 2.3 ± 4.2 tCO₂, and the mean price was 400 USD/tCO₂ when 1 USD was converted into 145.96 JPY. The total allocation method was the most commonly used transaction mechanism (82.4%; see Supplementary Information). The co-benefits/social impacts appealed by credit creators significantly influenced purchaser number and unit price, both positively and negatively (Fig. 5 , see Supplementary Tables 3 and 7, only the statistically significant results were explained below). The appeal of multi-stakeholder collaboration by credit creators significantly increased both purchaser number and unit price, while sustainability appeal significantly decreased both. The appeal of environmental conservation significantly increased purchaser number. Company involvement and seagrass meadow projects significantly increased the unit price, while the appeal of economic benefits, academic involvement, and restoration projects significantly decreased the unit price. Discussion Multi-stakeholder collaboration and sustainability The mean project duration was 15.6 years, and projects averaged 3.2 co-creators with >30% being established by local collectives. These findings suggest the existence of correlations between sustainability and multi-stakeholder collaboration. More than half of the credit creators appealed sustainability and multi-stakeholder collaboration as key features, indicating that they recognized the importance of collaboration for the success of sustainability initiatives 34 . It has long been argued that deep collaboration across a range of disciplines and actors is key for the success of nature-based projects 35 . The same has been shown for forestry projects where community-led initiatives have achieved credit certification 36,37 . However, it has also been shown that stakeholder collaboration between the national government, private companies, and local entities (i.e., public support and political buy-in collaboration) is greater in Japan compared with in the West 29 . In addition, large-scale marine CDR require sustainable technical, financial, and monitoring systems developed through regional partnerships involving universities, non-governmental organizations, businesses, and government organizations 5,6,38 . Thus, enhanced sustainability through long-term activities likely depends on the extent of multi-stakeholder collaboration and the project scales 38,39 . Appeal contents and credit trading decision varied depending on the characteristics of the credit creators (Fig. 1). Namely, municipality involvement was positively associated with the appeal of educational effects and public offering, while private companies appealed innovation and opted not to engage in public offerings (internal credit utilization). This dichotomy reflects the contrasting motivations of the two entities: funding constraints versus emission reduction challenges for municipalities, and for internal credit use over external targets recommended by the Greenhouse Gas Protocol for private companies. To enhance sustainable long-term decarbonization efforts, complementary characteristics could be incorporated. For example, private company–led projects utilizing internal credits could integrate educational programs and diverse multi-stakeholder collaboration to appeal to investors. Municipality-led projects could introduce innovative technologies to attract purchasers, particularly those in the service industry. Co-benefits and spillover effects Credit creators appealed beyond CDR contributions and established benefits including economic returns from fisheries and tourism, environmental conservation including biodiversity, and educational benefits, collectively termed “nature positive benefits” or “co-benefits” (Supplementary Fig. 4b). The local organizations appealed stakeholder diversity and academic institutions appealed educational activities, both of which are aligned with their original entity purpose. While nature-based initiatives purported to contribute to decarbonization and nature positive, only 11.5% explicitly quantified those co-benefits. Quantifying and objectively demonstrating such non-carbon-related benefits remains critical for highlighting the nature-positive contributions of blue carbon initiatives 39 . The mean transaction price was approximately 400 USD per ton of CO₂ (subject to currency exchange), which exceeds the average prices of other credit types 40 . However, the income from credits is often insufficient to cover the costs of implementation and certification, even though the “additionality” requirement specifies that projects must not be financially viable without credit revenue 15,41-43 . This creates an inherent economic risk, despite the requirement's intention to ensure credit quality. Exemplifying sustainability through primary occupation income (e.g., fishery) rather than credit dependence is one way that sustainability initiatives could be enhanced by co-benefits 44,45 . Credit creator spillover appeals significantly influenced purchaser project selection, both positively and negatively (Fig. 5). Appealing to multi-stakeholder collaboration proved effective in increasing purchaser numbers and unit prices, whereas sustainability appeals had unexpectedly negative effects. This result warrants further investigation. One possible explanation is suggested by a previous purchaser motivation survey, which indicated that purchaser tend to “support” new and less-established projects (Supplementary Fig. 11) 46 . Appeals emphasizing economic benefit also had a negative effect on unit price. This indicates that signaling existing economic sustainability through alternative income sources (aquatic products, tourism) may not only reduce purchasers’ motivation to provide additional support, but may also lead to perceptions of lower project "additionality. Nevertheless, credit creators should adopt marketing strategies that align with trading priorities, such as expanding purchaser networks indicated by purchaser number and raising unit price. Project selection by purchasers The purchasers demonstrated several distinct project selection characteristics. Overall, approximately 40% of purchasers selected projects located within the same municipality. Manufacturing companies tended to select local projects, whereas service companies favored innovation-focused projects (Fig. 4). Additionally, credit purchases were higher among hard-to-abate sectors and companies with explicit decarbonization strategies, while participation from the sales sector was relatively low (Fig. 2). While industry-sector carbon credit purchase comparisons lack global empirical analyses, available reports indicate that purchaser willingness to pay varies by location 47 . Manufacturing generally maintains strong local community ties due to its dependence on local labor, land, infrastructure, and companies for procurement, outsourcing, and logistics; conversely, the retail and wholesale industries show lower local resource dependence 48 . Service industry generally prioritizes innovation for maintaining competitiveness and differentiation 49 . As an alternative explanation for the preference of local projects may be evidence that local pride and the value of local relationships are prioritized by purchasers 50 . A previous survey has confirmed that local community support is the primary purchase motivation (Supplementary Fig. 11) 46 , with the purchases presumably part of the sustainability and corporate value creation strategies of the company. In turn, non-local purchasers preferred municipality-involved projects, potentially reflecting trust and reassurance provided by the municipality-involvement in unfamiliar areas (Fig. 3). This selection based on trust and reassurance aligns with our finding that purchasers valued ecosystem restoration over higher-risk ecosystem creation methods. The observed purchaser motivations partly resemble donation behavior, as many purchasers sought to support local or environmental projects; however, unlike straightforward donations, purchasers actively participated as stakeholders, often contributing directly to project implementation. This participation distinguishes carbon credit purchasers from passive donors, as they often seek mutual benefit, knowledge acquisition, or eventual internalization of climate actions, moving beyond a purely philanthropic rationale. Indeed, seven companies that purchased credits also served as credit creators 51 , suggesting progression from financial support through knowledge acquisition to internal implementation, thereby accelerating the decarbonization process. Cultural and geographic context Blue carbon credit projects remain dominated by mangrove projects with limited CDR or macrophyte certifications 15,27,31 . The 61 certified projects, which are almost exclusively seaweed and seagrass initiatives, reflect Japan’s historical use of seaweed as food, long-standing efforts in natural macroalgal bed conservation and creation, more than 320 years of aquaculture practices, and over 60 years of seagrass conservation activities 52-54 . Moreover, high fisher involvement (86.9%) to total projects demonstrates a clear connection between blue carbon decarbonization and fisheries. In the present study, the seagrass projects were characterized by restoration measures, local organization participation, academic stakeholder inclusion, and multi-stakeholder and educational appeal. This characteristic association is consistent with a seagrass restoration project in the United Kingdom, although which lacked the strong leadership and participation of municipalities 55 . Seagrass activities (seed collection, seedling sowing) occur in shallow waters accessible during low tide without specialized equipment, facilitating diverse participation and educational activities. Macroalgal beds in deeper waters require diving equipment, limiting public participation. Thus, habitat-specific characteristics significantly influence project appeal and associated purchasing behavior, and can positively impact purchaser numbers and unit prices through interconnected spillover effects. Conclusion and limitations This study examined credit creator and purchaser dynamics during the initial five-year period of the JBC scheme. Although geographically limited to Japan, the dataset of 61 projects (with a mean duration of 15.6 years and 471 transactions) may help shed light on selected characteristics of sustainable nature-based CDR initiatives, especially in the context of blue carbon ecosystems 29 . By linking project characteristics, purchaser profiles, and trading outcomes, we have presented empirical evidence that stakeholder alignment and project appeal, particularly the co-benefits offered by a project, are central to the performance and sustainability of voluntary credit schemes. Moreover, the findings demonstrate that multi-stakeholder collaboration and the geographical proximity of credit creators and purchasers are critical drivers of transactions. Despite these contributions, several limitations warrant consideration. First, our analysis focused on small-scale projects (21.0 ± 56.9 ha, excluding seaweed farming, which is measured by cultivation rope length rather than land area), which are considerably smaller than the global median size of blue carbon projects (3,000 ha) 27 . Given that social acceptability in local communities can vary with project scale, balancing large-scale carbon removal against systemic co-benefits 4,56 , the limited project size may constrain the generalizability of our results. Second, our study does not fully account for the complex socio-political dynamics, particularly the ways in which public perceptions of CDR shaped by fairness and safety, that often reflect not opposition to the technologies per se but resistance to place-blind implementation perceived as unfair 2,25,34,57 . Future research should extend approaches to larger-scale projects and integrate detailed analyses of community governance and socio-political contexts to enhance finding robustness and applicability. Methods Data collection Project characteristics We compiled data from the JBC scheme, a voluntary carbon credit program managed by the JBE, and focused exclusively on CDR via blue carbon ecosystems. Documents, including project applications and related materials, are publicly available in Japanese (see Supplementary Information) 51,58 . Some case studies on the JBC are available in English 59 . From the credit certification application forms, the following variables were extracted. Target vegetation was classified into five categories: macroalgal beds, seagrass meadows, tidal flats, seaweed farming, and combined approaches. Ecosystem intervention methods were categorized as ecosystem creation on substrates, ecological restoration, or a blended approach. Entities included as credit creators were categorized into fishers, private companies, municipalities, local collectives, academic institutions (including schools and universities), and local organizations. For collectives comprising more than one entity, the number of entities was capped at five, in accordance with the JCB scheme criteria 58 . Co-benefits and social impacts appealed by the credit creators were categorized as economic advantage (e.g., fisheries revenue, tourism), biodiversity and environmental conservation, project sustainability, educational effect, multi-stakeholder collaboration, and technological innovation; these were identified from standardized project summaries published on the JBE website 51 . Geographic and demographic data for each project site was recorded at the municipal level. Population data were retrieved from the corresponding municipal websites. Purchaser characteristics Data for credit purchasers and transaction records were obtained from JBE’s publicly available registry and augmented using detailed datasets maintained internally by JBE. These data were used with informed consent for research purposes. Trading decisions were classified as either transactions through public offerings or internal utilization and bilateral transactions. Due to the absence of verified unit prices in bilateral transactions, only public offerings were analyzed. Business size was categorized based on the number of employees into small (<100 employees), medium (100–999), and large (≥1000), according to corporate websites. Industry sector was determined from company activity descriptions and grouped into nine sectors: construction, manufacturing, wholesale, retail, electricity and energy (including oil and gas), transportation, finance and insurance, service, and other. Sector-level industry representation was compared against national statistics 33 . Management policies were assessed via chief executive office statements from corporate websites, and binary-coded for the presence of three key terms: “decarbonization” (including carbon neutrality), “biodiversity,” and “sustainability.” Geographic alignment between purchasers and credit-generating projects was recorded by identifying whether the purchaser’s head office or branch was located within the same municipality or prefecture as the project site. All transaction amounts were standardized to US dollars using the Federal Reserve exchange rate of 1 USD = 147.15 JPY at the time of analysis. Data Analysis All statistical analyses were performed in R v4.5.0. For preprocessing and variable inclusion, one of the 61 identified projects was excluded due to underrepresented vegetation category (“tidal flats”), which introduced an imbalance into the categorical analyses, resulting in n = 60 valid cases. Assessment of multicollinearity among independent variables was conducted using the variance inflation factor. Variables with a variance inflation factor of >5 were excluded. Distributional assumptions were tested using the Shapiro–Wilk test for continuous variables (project duration, number of co-creators, population). Non-normally distributed variables were analyzed using Spearman’s rank correlation. Association strength between variables was assessed as follows: categorical–categorical: Cramér’s V; categorical–continuous: square root of η² from one-way ANOVA; and continuous–continuous: absolute value of Spearman’s ρ. Associations were considered statistically significant only if both the effect size was >0.3 and the Benjamini–Hochberg false discovery rate–adjusted q-value was <0.05. Network visualization was conducted using the igraph package. Variables were depicted as nodes connected by edges representing significant associations, with force-directed layouts (Fruchterman–Reingold algorithm) used to emphasize structural clusters and central hubs. Multiple factor analysis of mixed data (MFAmix) was conducted using the PCAmixdata package to jointly assess and visualize associations between groups of project and purchaser characteristics. MFAmix supports robust integration of mixed-variable types while preserving within-group variance. Linear mixed-effects modeling was used to assess the influence of project characteristics on two trading priorities (number of purchasers and unit price). The models were fitted using the lmerTest package, with transaction mechanisms entered as a random intercept. In our linear mixed-effects framework, transaction volume was also modeled as a random intercept (Supplementary Fig. 12) to test its potential influence on trading priority. 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In the voluntary carbon market, buyers will pay for quality. https://web-assets.bcg.com/29/f5/3b36e7cb4ad49df092de00c9792d/bcg-in-the-voluntary-carbon-market-buyers-sept-2023-2.pdf (2023). Gereffi, G., Humphrey, J. & Sturgeon, T. The governance of global value chains. Rev. Int. Political Econ . 12 , 78–104 (2005). United Nations Economic Commission for Europe. Climate Change and Sustainable Development: Policy Recommendations for a Resilient Future . https://unece.org/sites/default/files/2022-01/icp3.pdf (2022). Kavaratzis, M. & Ashworth, G. J. Place marketing: How did we get here and where are we going? J. Place Manag. Dev. 1 , 150–165 (2008). JBE. https://www.blueeconomy.jp Matsuda, O. Recent attempt towards environmental restoration of enclosed coastal seas: Ago Bay restoration project based on the new concept of Sato-Umi. Bull. Fish. Res. Agen. 29 , 9–18 (2010). Duarte, C. M., Bruhn, A. & Krause-Jensen, D. A seaweed aquaculture imperative to meet global sustainability targets. Nat. Sustain. 5 , 185–193 (2022). Duarte, C. M. et al. Carbon burial in sediments below seaweed farms matches that of blue carbon habitats. Nat. Clim. Chang. 15 , 180–187 (2025). Sovacool, B. K. et al. Sociotechnical dynamics of blue carbon management. Environ. Sci. Policy 155 , 103730 (2024). Lezaun, J. Hugging the shore: Tackling marine carbon dioxide removal as a local governance problem. Front. Clim. 3 , 684063 (2021). Buck, H. J. The politics of negative emissions technologies and decarbonization in rural communities. Glob. Sustain. 1 , e2 (2018). J-Blue Credit Guideline. https://www.blueeconomy.jp/wp-content/uploads/jbc2025/20250331_J-BlueCredit_Guidline_v.2.5.pdf Blue Carbon Liaison Council, Case Study on Blue Carbon Initiatives in Japan (2023) https://www.env.go.jp/earth/ondanka/blue-carbon-jp/pdf/materials/03_en_1.pdf Declarations Acknowledgements This study was partly supported by a JSPS KAKENHI (24H01531 to T.K.) and JST Grant Number JPMJPF2206. We thank K. Watanabe and T. Tanaya for their helpful comments on the manuscript, and M. Hori and A. Watanabe for their valuable support regarding the implementation of the J-Blue Credit scheme. Author contributions T.K. secured the funding, conceived and designed the research. T.K., Y.S., and M.F. produced and analyzed the data. T.K. wrote the first draft. T.K., Y.S., and M.F. improved the text and approved the submission. Data availability The original contributions presented in the study are included in the article and Supplementary Information. Further inquiries can be directed to the corresponding author. Code availability The R codes used in the statistical analyses are available on request from the corresponding author. Competing interests Authors declare no competing interests. Additional information Supplementary Information is available for this study at http:// xxx. Additional Declarations There is NO Competing Interest. Supplementary Files supplementarytable083025.xlsx Supplementary tables Supplementaryfinal083025.docx Supplementary documents and figures Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7525613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519520804,"identity":"14a43723-d9ab-45ff-a4a9-94b2168666c0","order_by":0,"name":"Tomohiro Kuwae","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACCTBZgSTC2ECUljNIionTwthGnPkQIDkjx/DDx3l3GHRnNz9/wPDLhoF5NgGd0hI5xpIztz1jMLtzzLCBsS+NgXHOAfxa5CRyN0jzbjvMYHYjAail5zAD44wEglo2//47B6Ql/SNxWqQlcrdJMzaAtOQYNjD8IEKLZM/7b5Y9x57xmN05UzgjsSGNh6BfJI6nJd/4UXNHzux2+4YPH/7YyBkSCjEoOMADjqHENgYewxlE6WA4AI3UPwwM8hLEaRkFo2AUjIKRAwAQJ0nvR5R9DgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3048-3368","institution":"Port and Airport Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Tomohiro","middleName":"","lastName":"Kuwae","suffix":""},{"id":519520805,"identity":"924a0738-0ca3-446c-9314-f141b6716606","order_by":1,"name":"Yuka Suzuki","email":"","orcid":"","institution":"Japan Blue Economy 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1","display":"","copyAsset":false,"role":"figure","size":267292,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram showing the strengths of the associations among the identified project characteristics. Associations between categorical variables were quantified using Cramér’s V, those between categorical and continuous variables using the square root of η² from one-way ANOVA, and those between continuous variables using the absolute value of Spearman’s ρ. Each variable is represented as a node, and associations are shown as edges. The diagram is visualized using the Fruchterman–Reingold force-directed algorithm to highlight structural clusters and central hubs. Edges indicating significant associations (association strength \u0026gt; 0.3 and FDR-adjusted q\u0026lt; 0.05) are depicted in red, with the corresponding association strength labeled. Node color indicates variable category: red for project type (see Supplementary Fig. 6), green for credit creator entity, and blue for co-benefits and social impacts considered appealing by credit creators. Abbreviations: population (municipal population), years (project duration), vegetation (target vegetation), method (ecosystem intervention method), trading (trading decision, public offering versus no public offering), creators (number of co-creators), local_col (local collective), local_org (local organization), academia (academic institution), collaboration (multi-stakeholder collaboration), economy (economic advantage such as fisheries or tourism), and sustainability (project sustainability).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/85eb57305180084ef0df27bc.png"},{"id":94516130,"identity":"7d6f10b4-3331-4461-ad3b-0036f04e1c74","added_by":"auto","created_at":"2025-10-28 16:42:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125840,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of purchasers (left panel) and percentage of total money-based purchase amount of credits (right panel), disaggregated by industry sector.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/a0b1f9efe0a0a33b036d672a.png"},{"id":94516340,"identity":"47a326f0-c4e6-46ec-b3ed-5e098189a785","added_by":"auto","created_at":"2025-10-28 16:43:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50283,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram showing the strength of associations among purchaser characteristics, quantified using Cramér’s V. Significant associations (effect size \u0026gt; 0.3 and FDR-adjusted q \u0026lt; 0.05) are shown as red edges and labeled with strength. The diagram follows the format of Figure 1. Node color indicates variable category: brown for attributes of purchasers and orange for management policy. Abbreviations: size (business size); location (same municipality); industry (sector); decarbon (decarbonization policy).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/4b7eaae79ff076adc1b4c092.png"},{"id":94516264,"identity":"d33f1192-5e12-46ca-86e9-0839087bff98","added_by":"auto","created_at":"2025-10-28 16:43:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145993,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram showing the strength of associations between project (outer circle) and purchaser (inner circle) characteristics. Association strength was defined as in Figure 1. Multiple factor analysis of mixed data was used to analyze interrelationships, preserving the high correlation structure within each group. Significant associations (association strength \u0026gt; 0.3 and FDR-adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are depicted as red lines. Variable definitions and abbreviations are as described in the captions to Figures 1 and 2.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/14c4c2674c5fa515747a86a8.png"},{"id":94516263,"identity":"c4889a08-8c39-405a-9a0e-c6b49a3a1fa3","added_by":"auto","created_at":"2025-10-28 16:43:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76887,"visible":true,"origin":"","legend":"\u003cp\u003eResults of linear mixed-effects models examining the relationship between project characteristics and two trading priorities: number of purchasers (a) and unit price (b). Only variables whose 95% confidence intervals in the forest plots do not intersect with zero are interpreted as having a significant positive or negative effect on the corresponding dependent variable, and are shown as red error bars. Abbreviations: Y, yes; N, no; SG, seagrass meadows; MB, macroalgal beds; vegetation M, multiple vegetation types; method R, restoration; method M, multiple ecosystem intervention methods. Other variable keys are as described in the caption to Figure 1.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/1064fa698e55dddf369fd06f.png"},{"id":94551634,"identity":"0c4e09e0-10d4-4ad1-935d-677ee8b9ff38","added_by":"auto","created_at":"2025-10-28 17:50:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/84ce18f0-56bc-4997-acfe-df50d044818b.pdf"},{"id":94516668,"identity":"8223e0b8-93c9-43b0-9eb2-70af7bddf028","added_by":"auto","created_at":"2025-10-28 16:43:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104301,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"supplementarytable083025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/1ff08765833d488609af2b60.xlsx"},{"id":94516192,"identity":"2acb784f-7210-4df9-8f04-7f158dec8bce","added_by":"auto","created_at":"2025-10-28 16:42:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1169699,"visible":true,"origin":"","legend":"Supplementary documents and figures","description":"","filename":"Supplementaryfinal083025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7525613/v1/da3d4e17327e5fcf6a8b42b5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Empirical analysis of project–purchaser dynamics in Japan’s blue carbon dioxide removal credit scheme","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTo achieve global climate goals, such as the Paris Agreement\u0026rsquo;s goal to limit the global average temperature increase to below 2\u0026deg;C, not only reductions in CO₂ emissions but also active removal of atmospheric CO₂ are essential\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. A wide range of carbon dioxide removal (CDR) approaches exist, broadly classified into engineered (technology-based) solutions and nature-based solutions\u003csup\u003e3,4\u003c/sup\u003e. Among these, marine CDR encompasses strategies that utilize oceanic processes for atmospheric CO₂ removal\u003csup\u003e5,6\u003c/sup\u003e. A subset of marine CDR is the use of blue carbon ecosystems (e.g., mangroves, salt marshes, seagrass meadows, and macroalgal beds), which absorb CO₂ via photosynthesis and contribute to long-term carbon storage within these ecosystems\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. Several Parties to the United Nations Framework Convention on Climate Change (UNFCCC) now include blue carbon ecosystems in their national greenhouse gas inventories\u003csup\u003e10\u003c/sup\u003e. For example, Japan\u0026rsquo;s 2023 inventory accounted for CO₂ removals by mangroves, and in 2024, removals by seagrass meadows and macroalgal beds were included for the first time\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile direct mitigation by emitters is essential to address climate change\u003csup\u003e12\u003c/sup\u003e, it is not always feasible, particularly in hard-to-abate sectors. In such cases, financial mechanisms that support mitigation elsewhere are necessary to sustain long-term decarbonization efforts\u003csup\u003e13\u003c/sup\u003e. One such mechanism is the carbon market, where carbon credits are bought and sold\u003csup\u003e14,15\u003c/sup\u003e. Carbon markets are broadly categorized into compliance markets, which serve regulatory obligations, and voluntary markets, which allow entities to offset emissions at their discretion. Carbon credits vary by function: some avoid emissions, others reduce them, and some actively remove CO₂ from the atmosphere\u003csup\u003e16\u003c/sup\u003e. Among these, CO₂ removal (CDR) credits are gaining increased attention over avoidance- or reduction-based ones\u003csup\u003e17\u003c/sup\u003e due to concerns over the reliability of avoidance credits\u003csup\u003e16\u003c/sup\u003e, especially regarding additionality\u003csup\u003e18\u003c/sup\u003e. There is also growing recognition that large-scale CDR is essential for achieving net-zero emissions\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNature-based CDR solutions offer a range of co-benefits that contribute to the various Sustainable Development Goals and Global Biodiversity Framework targets\u003csup\u003e20,21\u003c/sup\u003e. However, investment in global decarbonization and biodiversity, including nature-based CDR remains insufficient\u003csup\u003e15,22\u003c/sup\u003e. Moreover, there are implementation barriers, including inadequate policy support and unclear governance, a diversity of land tenure regimes, various regulatory constraints, undefined carbon rights and risks of double counting, limited community benefit-sharing and social acceptance, and high transaction and monitoring costs\u003csup\u003e5,6,15\u003c/sup\u003e. Despite these barriers, the number of nature-based CDR solutions is increasing, particularly in the area of biochar and afforestation/reforestation\u003csup\u003e23\u003c/sup\u003e. Still, their global market share remains limited compared to reduction-based or engineering solutions\u003csup\u003e17,21,24\u003c/sup\u003e. Even prominent CDR technologies like direct air carbon capture and storage face implementation and sustainability challenges\u003csup\u003e25,26\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlobally, up to 100 blue carbon projects, almost all located in developing countries, have been registered as carbon credits in international registries\u003csup\u003e27\u003c/sup\u003e. However, these estimates are based exclusively on English-language sources, and language barriers limit access to registry data in other regions\u003csup\u003e28,29\u003c/sup\u003e. In 2021, Japan established the J-Blue Credit (JBC) scheme through the Japan Blue Economy Association (JBE), a government-approved Collaborative Innovation Partnership\u003csup\u003e30\u003c/sup\u003e. Although information is available primarily in Japanese, the JBC scheme uniquely provides public access to credit documentation, including project applications and supporting materials, via its official website. This transparency is possible because consent for disclosure was obtained from all parties involved. By 2025, the JBC scheme had certified 61 projects, with an average project area of 21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;56.9 hectares, 471 transactions, and a total transaction value of 61,805,452 JPY (approximately 420,000 USD) (see Supplementary Information).\u003c/p\u003e\u003cp\u003eThe effectiveness and viability of carbon credit schemes depend on the alignment between sellers\u0026rsquo; and purchasers\u0026rsquo; needs and objectives. Accordingly, understanding stakeholder relationships and motivations is essential for tailoring scheme design and sustainability outcomes. However, datasets analyzing both party attributes and their interrelationships are largely unavailable\u003csup\u003e27,29,31\u003c/sup\u003e, partly because bilateral transactions with undisclosed details are common in crediting schemes\u003csup\u003e32\u003c/sup\u003e. Party attributes and relationships vary: for example, credit creators may prioritize higher prices or broader purchaser networks, which require strategies aligned with specific purchaser characteristics. Purchasers, in turn, may select credits based on Environmental, Social, and Governance (ESG) appeal or contributions to sustainability goals.\u003c/p\u003e\u003cp\u003eHere, we examine (1) certified project characteristics (participating entities, target vegetation types, appeal content), (2) purchaser characteristics (location, industry, scale), and (3) whether trading outcomes (number of purchasers, unit prices) correlate with project characteristics, using empirical data from the JBC scheme. Although JBC is a domestic initiative, it features a high degree of project and purchaser diversity, and, more importantly, offers a comprehensive and uniquely detailed dataset capturing both credit creators and purchasers. We hypothesize that the motivations and relationships between these parties are fundamental to the development of sustainable nature-based CDR projects.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eProject characteristics\u003c/h2\u003e\u003cp\u003eCertified projects spanned diverse Japanese locations (Supplementary Fig.\u0026nbsp;1). Mean project duration was 15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9 years, with most projects extending\u0026thinsp;\u0026le;\u0026thinsp;20 years, although some exceeded 50 years (Supplementary Fig.\u0026nbsp;2a). The target vegetation was macroalgal beds (49.2%), seaweed farming (18.0%), seagrass meadows (14.8%), tidal flats (1.6%), and multiple ecosystems (16.4%) (Supplementary Fig.\u0026nbsp;3a). The ecosystem intervention methods were ecosystem creation on substrates (45.9%), ecological restoration (42.6%), and combined approaches (11.5%) (Supplementary Fig.\u0026nbsp;3b). The mean number of collaborating credit creators per project was 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (Supplementary Fig.\u0026nbsp;2b). The most common entities included as credit creators were fishers (86.9%), followed by municipality (67.2%), private company (55.7%), local collective (36.1%), local organization (31.1%), and academic institutions including schools and universities (18.0%) (Supplementary Fig.\u0026nbsp;4a). The co-benefits/social impacts appealed by credit creators for each project were multiple: economic effects (83.6%), environmental conservation (80.3%), sustainability (75.4%), educational effects (59.0%), multi-stakeholder collaboration (50.8%), and innovation (39.3%) (Supplementary Fig.\u0026nbsp;4b). Despite these co-benefits appeals, only 11.5% quantified these co-benefits. The credit creators\u0026rsquo; trading decisions were divided between public offering (75.4%) and internal utilization or bilateral transactions (24.6%).\u003c/p\u003e\u003cp\u003eA network analysis revealed the key variables characterizing the projects: entity (particularly municipality and company participation), appeal content (especially educational effects), trading decision, vegetation (seagrass meadows), and ecosystem intervention methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, see Supplementary Fig.\u0026nbsp;5 and Supplementary Tables\u0026nbsp;1 and 4, only the statistically significant results were explained below). Municipality participation was significantly associated with higher public offering rates (92.7% versus 40.0% without municipality participation). Company participation was significantly associated with a low public offering rate (55.9% versus 100% without company participation). Local collective participation increased co-creator numbers (4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 entities versus 2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 without local collective participation). Public offering projects emphasized educational appeals more frequently (71.7%) than non-public offering projects. Projects with public offering, municipality participation, and educational appeal accounted for 45.9% of all projects. Seagrass projects predominantly employed restoration methods (77.8%, Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePurchaser characteristics\u003c/h3\u003e\n\u003cp\u003eThe total number of purchasers and total purchase amounts were dominated by the construction, service, manufacturing, transportation, and energy industries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The construction, transportation, and energy industries accounted for very high purchase rates (number of transactions: 44.2%, monetary amount: 44.0%) compared to their share of companies in national industry statistics (ca. 10%)\u003csup\u003e33\u003c/sup\u003e. In contrast, the sales sector (wholesale and retail), which accounts for a much larger share of companies nationwide (ca. 30%)\u003csup\u003e33\u003c/sup\u003e, showed low participation (number of transactions: 7.0%, monetary amount: 5.5%). Management policies prioritized sustainability (30.9%), decarbonization (25.5%), and biodiversity (1.5%). Business size of purchasers was predominantly small and medium enterprises (25th percentile: 50 employees; 50th percentile: 256 employees; 75th percentile: 1528 employees). 71.8% of transactions occurred within the same municipality or prefecture, with 39.7% within the same municipality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA network analysis identified industry sector, location, business size, and decarbonization policy as key purchaser attributes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, see Supplementary Fig.\u0026nbsp;7 and Supplementary Tables\u0026nbsp;2 and 5, only the statistically significant results were explained below). Purchasers selecting local (i.e., same municipality) projects varied by industry: retail (72.7%) and manufacturing (63.9%) showed higher local purchasing rates compared with service (20.0%) and insurance/finance (25.0%) (Supplementary Fig.\u0026nbsp;8a). Decarbonization policy was more prevalent in the energy (56.3%), construction (39.8%), and transportation (35.4%) industries (Supplementary Fig.\u0026nbsp;8b). Decarbonization policy increased with business size: small (1\u0026ndash;99 employees, 9.5%), medium (100\u0026ndash;999 employees, 24.5%), and large (\u0026ge;\u0026thinsp;1000 employees, 44.0%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eProject–purchaser relationships and transaction outcomes\u003c/h3\u003e\n\u003cp\u003ePurchaser location strongly influenced project selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, see Supplementary Fig.\u0026nbsp;9 and Supplementary Tables\u0026nbsp;2 and 6, only the statistically significant results were explained below). Local purchasers preferred ecosystem creation methods (77.0%) compared to other ecosystem intervention method (Supplementary Fig.\u0026nbsp;10a). Non-local purchasers selected the projects including municipality most frequently (69.5%). The service industry favored innovation-focused projects (41.1%) (Supplementary Fig.\u0026nbsp;10b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 61 projects with 9,185 tCO₂ were certified during 2021\u0026ndash;2025. Of those 61 projects, 46 offered 3,482 tCO₂ through public offerings, of which 1,081 tCO₂ was purchased across 471 transactions. Mean transaction size was 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 tCO₂, and the mean price was 400 USD/tCO₂ when 1 USD was converted into 145.96 JPY. The total allocation method was the most commonly used transaction mechanism (82.4%; see Supplementary Information). The co-benefits/social impacts appealed by credit creators significantly influenced purchaser number and unit price, both positively and negatively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, see Supplementary Tables\u0026nbsp;3 and 7, only the statistically significant results were explained below). The appeal of multi-stakeholder collaboration by credit creators significantly increased both purchaser number and unit price, while sustainability appeal significantly decreased both. The appeal of environmental conservation significantly increased purchaser number. Company involvement and seagrass meadow projects significantly increased the unit price, while the appeal of economic benefits, academic involvement, and restoration projects significantly decreased the unit price.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMulti-stakeholder collaboration and sustainability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean project duration was 15.6 years, and projects averaged 3.2 co-creators with \u0026gt;30% being established by local collectives. These findings suggest the existence of correlations between sustainability and multi-stakeholder collaboration. More than half of the credit creators appealed sustainability and multi-stakeholder collaboration as key features, indicating that they recognized the importance of collaboration for the success of sustainability initiatives\u003csup\u003e34\u003c/sup\u003e. It has long been argued that deep collaboration across a range of disciplines and actors is key for the success of nature-based projects\u003csup\u003e35\u003c/sup\u003e. The same has been shown for forestry projects where community-led initiatives have achieved credit certification\u003csup\u003e36,37\u003c/sup\u003e. However, it has also been shown that stakeholder collaboration between the national government, private companies, and local entities (i.e., public support and political buy-in collaboration) is greater in Japan compared with in the West\u003csup\u003e29\u003c/sup\u003e. In addition, large-scale marine CDR require sustainable technical, financial, and monitoring systems developed through regional partnerships involving universities, non-governmental organizations, businesses, and government organizations\u003csup\u003e5,6,38\u003c/sup\u003e. Thus, enhanced sustainability through long-term activities likely depends on the extent of multi-stakeholder collaboration and the project scales\u003csup\u003e38,39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAppeal contents and credit trading decision varied depending on the characteristics of the credit creators (Fig. 1). Namely, municipality involvement was positively associated with the appeal of educational effects and public offering, while private companies appealed innovation and opted not to engage in public offerings (internal credit utilization). This dichotomy reflects the contrasting motivations of the two entities: funding constraints versus emission reduction challenges for municipalities, and for internal credit use over external targets recommended by the Greenhouse Gas Protocol for private companies. To enhance sustainable long-term decarbonization efforts, complementary characteristics could be incorporated. For example, private company\u0026ndash;led projects utilizing internal credits could integrate educational programs and diverse multi-stakeholder collaboration to appeal to investors. Municipality-led projects could introduce innovative technologies to attract purchasers, particularly those in the service industry.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-benefits and spillover effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCredit creators appealed beyond CDR contributions and established benefits including economic returns from fisheries and tourism, environmental conservation including biodiversity, and educational benefits, collectively termed \u0026ldquo;nature positive benefits\u0026rdquo; or \u0026ldquo;co-benefits\u0026rdquo; (Supplementary Fig. 4b). The local organizations appealed stakeholder diversity and academic institutions appealed educational activities, both of which are aligned with their original entity purpose. While\u0026nbsp;nature-based initiatives purported to contribute to decarbonization and nature positive, only 11.5% explicitly quantified those co-benefits. Quantifying and objectively demonstrating such non-carbon-related benefits remains critical for highlighting the nature-positive contributions of blue carbon initiatives\u003csup\u003e39\u003c/sup\u003e. The mean transaction price was approximately 400 USD per ton of CO₂ (subject to currency exchange), which exceeds the average prices of other credit types\u003csup\u003e40\u003c/sup\u003e. However, the income from credits is often insufficient to cover the costs of implementation and certification, even though the \u0026ldquo;additionality\u0026rdquo; requirement specifies that projects must not be financially viable without credit revenue\u003csup\u003e15,41-43\u003c/sup\u003e. This creates an inherent economic risk, despite the requirement\u0026apos;s intention to ensure credit quality. Exemplifying sustainability through primary occupation income (e.g., fishery) rather than credit dependence is one way that sustainability initiatives could be enhanced by co-benefits\u003csup\u003e44,45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCredit creator spillover appeals significantly influenced purchaser project selection, both positively and negatively (Fig. 5). Appealing to multi-stakeholder collaboration proved effective in increasing purchaser numbers and unit prices, whereas sustainability appeals had unexpectedly negative effects. This result warrants further investigation. One possible explanation is suggested by a previous purchaser motivation survey, which indicated that purchaser tend to \u0026ldquo;support\u0026rdquo; new and less-established projects (Supplementary Fig. 11)\u003csup\u003e46\u003c/sup\u003e. Appeals emphasizing economic benefit also had a negative effect on unit price. This indicates that signaling existing economic sustainability through alternative income sources (aquatic products, tourism) may not only reduce purchasers\u0026rsquo; motivation to provide additional support, but may also lead to perceptions of lower project \u0026quot;additionality.\u0026nbsp;Nevertheless, credit creators should adopt marketing strategies that align with trading priorities, such as expanding purchaser networks indicated by purchaser number and raising unit price.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject selection by purchasers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purchasers demonstrated several distinct project selection characteristics. Overall, approximately 40% of purchasers selected projects located within the same municipality. Manufacturing companies tended to select local projects, whereas service companies favored innovation-focused projects (Fig. 4). Additionally, credit purchases were higher among hard-to-abate sectors and companies with explicit decarbonization strategies, while participation from the sales sector was relatively low (Fig. 2). While industry-sector carbon credit purchase comparisons lack global empirical analyses, available reports indicate that purchaser willingness to pay varies by location\u003csup\u003e47\u003c/sup\u003e. Manufacturing generally maintains strong local community ties due to its dependence on local labor, land, infrastructure, and companies for procurement, outsourcing, and logistics; conversely, the retail and wholesale industries show lower local resource dependence\u003csup\u003e48\u003c/sup\u003e. Service industry generally prioritizes innovation for maintaining competitiveness and differentiation\u003csup\u003e49\u003c/sup\u003e. As an alternative explanation for the preference of local projects may be evidence that local pride and the value of local relationships are prioritized by purchasers\u003csup\u003e50\u003c/sup\u003e. A previous survey has confirmed that local community support is the primary purchase motivation (Supplementary Fig. 11)\u003csup\u003e46\u003c/sup\u003e, with the purchases presumably part of the sustainability and corporate value creation strategies of the company. In turn, non-local purchasers preferred municipality-involved projects, potentially reflecting trust and reassurance provided by the municipality-involvement in unfamiliar areas (Fig. 3). This selection based on trust and reassurance aligns with our finding that purchasers valued ecosystem restoration over higher-risk ecosystem creation methods.\u003c/p\u003e\n\u003cp\u003eThe observed purchaser motivations partly resemble donation behavior, as many purchasers sought to support local or environmental projects; however, unlike straightforward donations, purchasers actively participated as stakeholders, often contributing directly to project implementation. This participation distinguishes carbon credit purchasers from passive donors, as they often seek mutual benefit, knowledge acquisition, or eventual internalization of climate actions, moving beyond a purely philanthropic rationale. Indeed, seven companies that purchased credits also served as credit creators\u003csup\u003e51\u003c/sup\u003e, suggesting progression from financial support through knowledge acquisition to internal implementation, thereby accelerating the decarbonization process.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCultural and geographic context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlue carbon credit projects remain dominated by mangrove projects with limited CDR or macrophyte certifications\u003csup\u003e15,27,31\u003c/sup\u003e. The 61 certified projects, which are almost exclusively seaweed and seagrass initiatives, reflect Japan\u0026rsquo;s historical use of seaweed as food, long-standing efforts in natural macroalgal bed conservation and creation, more than 320 years of aquaculture practices, and over 60 years of seagrass conservation activities\u003csup\u003e52-54\u003c/sup\u003e. Moreover, high fisher involvement (86.9%) to total projects demonstrates a clear connection between blue carbon decarbonization and fisheries. In the present study, the seagrass projects were characterized by restoration measures, local organization participation, academic stakeholder inclusion, and multi-stakeholder and educational appeal. This characteristic association is consistent with a seagrass restoration project in the United Kingdom, although which lacked the strong leadership and participation of municipalities\u003csup\u003e55\u003c/sup\u003e. Seagrass activities (seed collection, seedling sowing) occur in shallow waters accessible during low tide without specialized equipment, facilitating diverse participation and educational activities. Macroalgal beds in deeper waters require diving equipment, limiting public participation. Thus, habitat-specific characteristics significantly influence project appeal and associated purchasing behavior, and can positively impact purchaser numbers and unit prices through interconnected spillover effects.\u003c/p\u003e"},{"header":"Conclusion and limitations","content":"\u003cp\u003eThis study examined credit creator and purchaser dynamics during the initial five-year period of the JBC scheme. Although geographically limited to Japan, the dataset of 61 projects (with a mean duration of 15.6 years and 471 transactions) may help shed light on selected characteristics of sustainable nature-based CDR initiatives, especially in the context of blue carbon ecosystems\u003csup\u003e29\u003c/sup\u003e. By linking project characteristics, purchaser profiles, and trading outcomes, we have presented empirical evidence that stakeholder alignment and project appeal, particularly the co-benefits offered by a project, are central to the performance and sustainability of voluntary credit schemes. Moreover, the findings demonstrate that multi-stakeholder collaboration and the geographical proximity of credit creators and purchasers are critical drivers of transactions. Despite these contributions, several limitations warrant consideration. First, our analysis focused on small-scale projects (21.0 \u0026plusmn; 56.9 ha, excluding seaweed farming, which is measured by cultivation rope length rather than land area), which are considerably smaller than the global median size of blue carbon projects (3,000 ha)\u003csup\u003e27\u003c/sup\u003e. Given that social acceptability in local communities can vary with project scale, balancing large-scale carbon removal against systemic co-benefits\u003csup\u003e4,56\u003c/sup\u003e, the limited project size may constrain the generalizability of our results. Second, our study does not fully account for the complex socio-political dynamics, particularly the ways in which public perceptions of CDR shaped by fairness and safety, that often reflect not opposition to the technologies per se but resistance to place-blind implementation perceived as unfair\u003csup\u003e2,25,34,57\u003c/sup\u003e. Future research should extend approaches to larger-scale projects and integrate detailed analyses of community governance and socio-political contexts to enhance finding robustness and applicability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compiled data from the JBC scheme, a voluntary carbon credit program managed by the JBE, and focused exclusively on CDR via blue carbon ecosystems. Documents, including project applications and related materials, are publicly available in Japanese (see Supplementary Information)\u003csup\u003e51,58\u003c/sup\u003e. Some case studies on the JBC are available in English\u003csup\u003e59\u003c/sup\u003e. From the credit certification application forms, the following variables were extracted. Target vegetation was classified into five categories: macroalgal beds, seagrass meadows, tidal flats, seaweed farming, and combined approaches. Ecosystem intervention methods were categorized as ecosystem creation on substrates, ecological restoration, or a blended approach. Entities included as credit creators were categorized into fishers, private companies, municipalities, local collectives, academic institutions (including schools and universities), and local organizations. For collectives comprising more than one entity, the number of entities was capped at five, in accordance with the JCB scheme criteria\u003csup\u003e58\u003c/sup\u003e. Co-benefits and social impacts appealed by the credit creators were categorized as economic advantage (e.g., fisheries revenue, tourism), biodiversity and environmental conservation, project sustainability, educational effect, multi-stakeholder collaboration, and technological innovation; these were identified from standardized project summaries published on the JBE website\u003csup\u003e51\u003c/sup\u003e. Geographic and demographic data for each project site was recorded at the municipal level. Population data were retrieved from the corresponding municipal websites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurchaser characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for credit purchasers and transaction records were obtained from JBE\u0026rsquo;s publicly available registry and augmented using detailed datasets maintained internally by JBE. These data were used with informed consent for research purposes. Trading decisions were classified as either transactions through public offerings or internal utilization and bilateral transactions. Due to the absence of verified unit prices in bilateral transactions, only public offerings were analyzed. Business size was categorized based on the number of employees into small (\u0026lt;100 employees), medium (100\u0026ndash;999), and large (\u0026ge;1000), according to corporate websites. Industry sector was determined from company activity descriptions and grouped into nine sectors: construction, manufacturing, wholesale, retail, electricity and energy (including oil and gas), transportation, finance and insurance, service, and other. Sector-level industry representation was compared against national statistics\u003csup\u003e33\u003c/sup\u003e. Management policies were assessed via chief executive office statements from corporate websites, and binary-coded for the presence of three key terms: \u0026ldquo;decarbonization\u0026rdquo; (including carbon neutrality), \u0026ldquo;biodiversity,\u0026rdquo; and \u0026ldquo;sustainability.\u0026rdquo; Geographic alignment between purchasers and credit-generating projects was recorded by identifying whether the purchaser\u0026rsquo;s head office or branch was located within the same municipality or prefecture as the project site. All transaction amounts were standardized to US dollars using the Federal Reserve exchange rate of 1 USD = 147.15 JPY at the time of analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed in R v4.5.0. For preprocessing and variable inclusion, one of the 61 identified projects was excluded due to underrepresented vegetation category (\u0026ldquo;tidal flats\u0026rdquo;), which introduced an imbalance into the categorical analyses, resulting in \u003cem\u003en\u003c/em\u003e = 60 valid cases. Assessment of multicollinearity among independent variables was conducted using the variance inflation factor. Variables with a variance inflation factor of \u0026gt;5 were excluded. Distributional assumptions were tested using the Shapiro\u0026ndash;Wilk test for continuous variables (project duration, number of co-creators, population). Non-normally distributed variables were analyzed using Spearman\u0026rsquo;s rank correlation. Association strength between variables was assessed as follows: categorical\u0026ndash;categorical: Cram\u0026eacute;r\u0026rsquo;s V; categorical\u0026ndash;continuous: square root of \u0026eta;\u0026sup2; from one-way ANOVA; and continuous\u0026ndash;continuous: absolute value of Spearman\u0026rsquo;s \u0026rho;. Associations were considered statistically significant only if both the effect size was \u0026gt;0.3 and the Benjamini\u0026ndash;Hochberg false discovery rate\u0026ndash;adjusted q-value was \u0026lt;0.05. Network visualization was conducted using the igraph package. Variables were depicted as nodes connected by edges representing significant associations, with force-directed layouts (Fruchterman\u0026ndash;Reingold algorithm) used to emphasize structural clusters and central hubs. Multiple factor analysis of mixed data (MFAmix) was conducted using the PCAmixdata package to jointly assess and visualize associations between groups of project and purchaser characteristics. MFAmix supports robust integration of mixed-variable types while preserving within-group variance. Linear mixed-effects modeling was used to assess the influence of project characteristics on two trading priorities (number of purchasers and unit price). The models were fitted using the lmerTest package, with transaction mechanisms entered as a random intercept. In our linear mixed-effects framework, transaction volume was also modeled as a random intercept (Supplementary Fig. 12) to test its potential influence on trading priority. Because the direction and magnitude of the principal coefficients remained consistent in models excluding transaction volume as a random intercept, we report the more parsimonious model without transaction volume, which exhibited fewer statistically significant coefficients (Fig. 5), thereby reducing the risk of over-interpretation. Effect sizes were standardized as t-values (i.e., estimate / standard error), and a 95% confidence interval excluding zero was considered evidence of a statistically significant effect.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBabiker, M., Berndes, G., Blok, K. et al. Cross-sectoral perspectives. In \u003cem\u003eClimate\u003c/em\u003e \u003cem\u003eChange\u003c/em\u003e \u003cem\u003e2022:\u003c/em\u003e \u003cem\u003eMitigation\u003c/em\u003e \u003cem\u003eof\u003c/em\u003e \u003cem\u003eClimate\u003c/em\u003e \u003cem\u003eChange.\u003c/em\u003e \u003cem\u003eContribution\u003c/em\u003e \u003cem\u003eof\u003c/em\u003e \u003cem\u003eWorking\u003c/em\u003e \u003cem\u003eGroup\u003c/em\u003e \u003cem\u003eIII\u003c/em\u003e \u003cem\u003eto\u003c/em\u003e \u003cem\u003ethe\u003c/em\u003e \u003cem\u003eSixth\u003c/em\u003e \u003cem\u003eAssessment\u003c/em\u003e \u003cem\u003eReport\u003c/em\u003e \u003cem\u003eof\u003c/em\u003e \u003cem\u003ethe\u003c/em\u003e \u003cem\u003eIntergovernmental\u003c/em\u003e \u003cem\u003ePanel\u003c/em\u003e \u003cem\u003eon\u003c/em\u003e \u003cem\u003eClimate\u003c/em\u003e \u003cem\u003eChange\u003c/em\u003e (eds. Shukla, P. R. et al.) (Cambridge Univ. Press, 2022).\u003c/li\u003e\n \u003cli\u003eSovacool, B. K., Baum, C. M. \u0026amp; Low, S. Sociotechnical dynamics of carbon removal. \u003cem\u003eJoule\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 57\u0026ndash;82 (2023).\u003c/li\u003e\n \u003cli\u003eSmith, S. M. et al. (eds.) \u003cem\u003eThe\u003c/em\u003e \u003cem\u003eState\u003c/em\u003e \u003cem\u003eof\u003c/em\u003e \u003cem\u003eCarbon\u003c/em\u003e \u003cem\u003eDioxide\u003c/em\u003e \u003cem\u003eRemoval\u003c/em\u003e \u003cem\u003e2024\u003c/em\u003e \u003cem\u003e\u0026ndash;\u003c/em\u003e \u003cem\u003e2nd\u003c/em\u003e \u003cem\u003eEdition\u003c/em\u003e. DOI 10.17605/OSF.IO/F85QJ (2024).\u003c/li\u003e\n \u003cli\u003eLow, S., Baum, C. M. \u0026amp; Sovacool, B. K. Rethinking net-zero: Hard vs. soft alternatives. \u003cem\u003eGlob.\u003c/em\u003e \u003cem\u003eEnviron.\u003c/em\u003e \u003cem\u003eChange\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 102530 (2022).\u003c/li\u003e\n \u003cli\u003eDoney, S. C., Wolfe W. H., McKee, D. C., Fuhrman, J. G. 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The politics of negative emissions technologies and decarbonization in rural communities. \u003cem\u003eGlob.\u003c/em\u003e \u003cem\u003eSustain.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, e2 (2018).\u003c/li\u003e\n \u003cli\u003eJ-Blue Credit Guideline. https://www.blueeconomy.jp/wp-content/uploads/jbc2025/20250331_J-BlueCredit_Guidline_v.2.5.pdf\u003c/li\u003e\n \u003cli\u003eBlue Carbon Liaison Council, \u003cem\u003eCase\u003c/em\u003e \u003cem\u003eStudy\u003c/em\u003e \u003cem\u003eon\u003c/em\u003e \u003cem\u003eBlue\u003c/em\u003e \u003cem\u003eCarbon\u003c/em\u003e \u003cem\u003eInitiatives\u003c/em\u003e \u003cem\u003ein\u003c/em\u003e \u003cem\u003eJapan\u003c/em\u003e (2023) https://www.env.go.jp/earth/ondanka/blue-carbon-jp/pdf/materials/03_en_1.pdf\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partly supported by a JSPS KAKENHI (24H01531 to T.K.) and JST Grant Number JPMJPF2206. We thank K. Watanabe and T. Tanaya for their helpful comments on the manuscript, and M. Hori and A. Watanabe for their valuable support regarding the implementation of the J-Blue Credit scheme.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.K. secured the funding, conceived and designed the research. T.K., Y.S., and M.F. produced and analyzed the data. T.K. wrote the first draft. T.K., Y.S., and M.F. improved the text and approved the submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article and Supplementary Information. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R codes used in the statistical analyses are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Information is available for this study at http:// xxx.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7525613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7525613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCarbon dioxide removal (CDR) and associated credit mechanisms are gaining prominence in carbon neutrality strategies, yet empirical evaluations of their sustainability remain limited. Here, we analyzed 61 blue carbon projects and 471 certified transactions under Japan\u0026rsquo;s J-Blue Credit scheme to examine the characteristics of projects and purchasers and their interrelationships. On average, projects involved 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 co-creators, and transactions were small in volume (2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 tCO₂) but high in unit value (~\u0026thinsp;400 USD/tCO₂). Approximately 40% of transactions occurred between parties located within the same municipality. Hard-to-abate sectors (e.g., construction, transportation, energy) and companies with explicit decarbonization policies purchased more credits, while the sales sector participated less. Manufacturing companies preferred local projects, whereas service companies preferred innovation-focused projects. Project appeal content, such as co-benefits, significantly influenced purchaser numbers and unit prices, both positively and negatively. These findings demonstrate that multi-stakeholder collaboration, project appeal strategies\u0026mdash;including co-benefits\u0026mdash;and sector-specific demand critically shape transaction outcomes and market structure, offering insights for designing effective credit schemes and for scaling CDR markets and advancing nature-based solutions globally.\u003c/p\u003e","manuscriptTitle":"Empirical analysis of project–purchaser dynamics in Japan’s blue carbon dioxide removal credit scheme","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 20:20:20","doi":"10.21203/rs.3.rs-7525613/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f03bc548-f971-4b0f-b589-55180339637e","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55214561,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"},{"id":55214562,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2025-10-27T20:20:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 20:20:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7525613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7525613","identity":"rs-7525613","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}