We used hedonic pricing to identify the value of SDGs in biochar carbon credit price, alongside other non-carbon attributes. Hedonic pricing treats goods as a bundle of attributes and estimates the value of each of these attributes separately to find the value of the whole good44. It is a revealed pricing method used to evaluate market values of attributes that otherwise do not have market prices. The method is commonly used in real estate and food markets45, but has also been applied to emissions avoidance credit pricing33,38 and the valuation of renewable energy projects46. In this study, hedonic pricing was applied to an original dataset linking biochar carbon credit prices and their associated SDG claims.
Data
The biochar carbon credit prices were sourced from the CDR.fyi database, which records novel CDR credit transactions reported by voluntary carbon market actors (e.g. marketplaces, buyers, and biochar project developers)11. Our analysis focused on biochar carbon credits, representing nearly half of the dataset. Biochar-backed carbon credits are the most widely produced and delivered type of novel carbon removal credits currently available on the market11. Out of 1716 biochar carbon credit transactions, 173 had recorded credit prices and could be used for analysis. The data was downloaded in August 2024. Our dataset accounts for 10% of the total number of biochar transactions, which represent 3% of the original dataset’s biochar carbon credit transaction volume. However, it represents 40% (N = 31) of the biochar project developers in the overall database (N = 78), which reflected the biochar market at that time. The subset reflects the global distribution of biochar project developers47 and provides a market-representative biochar credit price distribution (Supplementary Methods 1, Figures 8 and 9). Lastly, the dataset shows substantial variation in the number of SDG claims and sustainability pillars (Supplementary Methods 1, Figures 4 to 7).
The SDG claims associated with biochar carbon credits were collected from biochar carbon credit descriptions on marketplace websites, biochar project developers’ websites, or credit registries (Supplementary Data 1). Currently, the promotion of SDGs is left to the discretion of biochar project developers, registries, and marketplaces. Most of the transactions on the CDR.fyi database indicate whether the credits were acquired through a marketplace or directly from biochar project developers. When information on the point of sale was not available, we first checked the SDG descriptions on biochar project developers’ websites and, if those were not available, the registry from which the credit was issued. If the same biochar project developer sold credits on different marketplaces, the SDG descriptions might differ, and thus, the SDGs were recorded per transaction accordingly. Most of the credits’ co-benefits were advertised as SDG icons. Close to a third of transactions had co-benefit descriptions in text format. The textually described co-benefits were mapped onto the SDGs by matching keywords to the SDG descriptions, with two authors performing the mapping independently (full procedure description in Supplementary Methods 2). We could not locate SDGs or co-benefit descriptions for two transactions. Therefore, the dataset used for analysis consisted of 171 transactions that had matched credit prices and SDG descriptions48. The overall relationship between credit prices and SDG claims is shown in Supplementary Methods 1 Fig. 1–3.
The SDG claims associated with each credit constitute the independent variables of interest. In the first research question, the independent variable of interest was the number of SDG claims advertised with a biochar credit. In the second research question, the independent variable of interest was the number of SDG claims per sustainability pillar: environmental, social, and economic. The sustainability pillar framework used in this study was developed by the Stockholm Resilience Centre29. While the framework was initially built to describe SDG interconnections in the food sector, it was later adopted by researchers in a range of social science fields, including corporate social responsibility management49 and sustainable governance50. The social pillar in this framework covers SDG 1, No Poverty; SDG 2, Zero Hunger; SDG 3, Good Health and Well-being; SDG 4, Quality Education; SDG 5, Gender Equality; SDG 7, Affordable and Clean Energy; SDG 11, Sustainable Cities and Communities; and SDG 16, Peace, Justice and Strong Institutions. The environmental pillar is centred on SDG 6, Clean Water and Sanitation; SDG 13, Climate Action; SDG 14, Life Below Water; and SDG 15, Life on Land. Lastly, the economic pillar relates to SDG 8, Economic Growth; SDG 9, Industry, Innovation, and Infrastructure; SDG 10, Reduced Inequalities; and SDG 12, Responsible Consumption and Production.
In addition, we considered other non-carbon biochar carbon credit attributes in the analysis. We included a dummy variable for the European Biochar Certification. While it is not a co-benefit certification, it is a globally recognised biochar quality standard and currently the most prominent certification available for biochar and biochar credits51,52. As the biochar market expands, the expectation for certification as a baseline is growing52. Labelling is a common hedonic attribute discussed in the literature, including in studies on carbon credits. It has been shown that certified co-benefits associated with emissions avoidance credits increase credit prices33. We follow previous studies that include a dummy variable for developing country project location and interpret a positive coefficient as an indication of willingness to pay for sustainable development in such contexts38. We examined biochar project location at a continental level38. Lastly, we investigated the price effect of biochar production technologies. There are two broad types of technologies currently in use. The first, industrial technology, uses a closed system with emissions control23. The second, called artisanal, includes pyrolysis methods with kilns that are fired from the top; burning biomass on the top forms a flame curtain or flame cap, protecting the bottom biomass layer from oxygen and turning into ash53. We hypothesized that closed system methods command a price premium over low-technology production approaches because of their standardised process, easier quantification, and the perception of higher-grade biochar54. Control variables include the volume of biochar credits that a biochar project developer has delivered since the beginning of operation, the GDP of the project country, and the volume of the transaction.
The biochar carbon credit prices have a right-skewed distribution with a mean of 171 USD/ton of CO2eq (Supplementary Methods 1 Figure 9; Table 3). On average, each biochar carbon credit is advertised with nearly seven SDG claims, while the range is from one to fourteen SDGs (there are seventeen possible SDGs in total). The social pillar consists of eight SDGs, while the environmental and economic pillars have four SDGs each. SDG 17, Partnerships for the Goals, was included in calculating the total SDG score and excluded from the sustainability pillars analysis, as the chosen SDG framework does not include it in the three sustainability pillars. The environmental pillar has the highest number of SDGs per credit on average (2.42 SDGs per credit). The biochar-producing companies vary by size; the largest project developer produces nearly 1000 times more removal credits than the smallest project developer (Table 3). Most biochar producers employ industrial pyrolysis plants.
Table 3 Descriptives of the variables used in the hedonic pricing model
The most advertised SDGs are environmental: SDG 13, Climate Action, and SDG 15, Life on Land. Next are the social: SDG 2, Zero Hunger, and SDG 1, No Poverty, along with the economic SDGs: SDG 12, Responsible Consumption and Production, and SDG 8, Decent Work and Economic Growth (Fig. 1). The pattern of SDGs advertised by biochar carbon credits corresponds closely to the sustainability impacts typically associated with biochar production. Carbon removal is primarily a climate action measure. Furthermore, since biochar is mainly applied to soil, it contributes to soil improvement and food production. In addition, biochar is primarily produced from plant waste, promoting responsible production and resource use. Lastly, biochar production is an economic action that creates employment and economic growth.
Fig. 1: The number of biochar carbon credits associated with each of the Sustainable Development Goals (SDGs) in the analysis dataset (N=171 credit transactions).
The alternative text for this image may have been generated using AI.
The sustainability pillar grouping follows Stockholm Resilience Centre developed SDG framework29. Blue bars refer to economic pillar SDG claims, yellow bars show social pillar SDGs, and orange bars refer to environmental pillar SDGs. SDG 1 No Poverty, SDG 2 Zero Hunger, SDG 3 Good Health and Well-being, SDG 4 Quality Education, SDG 5 Gender Equality, SDG 6 Clean Water and Sanitation, SDG 7 Affordable and Clean Energy, SDG 8 Decent Work and Economic Growth, SDG 9 Industry, Innovation and Infrastructure, SDG 10 Reduced Inequalities, SDG 11 Sustainable Cities and Communities, SDG 12 Responsible Consumption and Production, SDG 13 Climate Action, SDG 14 Life Below Water, SDG 15 Life on Land, SDG 16 Peace, Justice and Strong Institutions, and SDG 17 Partnerships for the Goals.
We assume that the credit descriptions were made available to the buyer at the time of the credit transaction. The SDG claims were collected in August 2024, while the transactions have been made since 2021. The largest volume of credits was sold in 2024.
Empirical Specification
The empirical specification for the first research question is as follows (Eq. 1):
$$log \left({p}_{{it}}\right)= \, log \left({{\beta }_{1}{SDG}}_{{it}}\right)+{\sum }_{k=1}^{6}{\vartheta }_{k}{{LOC}}_{{kit}}+{\beta }_{2}{{EBC}}_{{it}}+{\beta }_{3}{{TECH}}_{{it}} \\ +log \left({\beta }_{4}{{SIZE}\,}_{{it}}^{{dev}}\right)+log ({\beta }_{5}{{SIZE}\,}_{{it}}^{{tran}})+{\theta }_{t}+{\varepsilon }_{{it}}$$
(1)
where \({p}_{i}\) is the biochar carbon credit price and index \(i\) refers to separate credit transactions, \(i=1,\,2,\,\ldots ,\,171\) occurring at time \(t\). \({{SDG}}_{{it}}\) is the number of SDGs associated with credits in a transaction. Credits in one transaction are all sold at one price and have the same number of SDG claims. The remaining non-carbon attributes include \({{LOC}}_{{kit}}\) is the project location (continent) per each transaction where the index \(k\) refers to the continents, \(k={Europe},{North\; America},\,\ldots\), European Biochar Certification dummy, \({{EBC}}_{{it}}\), and biochar production technology dummy, \({{TECH}}_{{it}}\), either industrial (\({TECH}=1\)) or artisanal (\({TECH}=0\)). The control variables include the size of the biochar project developer, \({{SIZE}}_{{it}}^{{dev}}\) and the volume of the transaction, \({{SIZE}}_{{it}}^{{tran}}\). We include fixed effects for a biochar carbon credit transaction announcement date (year), \({\theta }_{t}\). Lastly, \({\varepsilon }_{{it}}\) is an error term assumed to be normally distributed.
The empirical specification for the second research question is as follows (Eq. 2):
$$log \left({p}_{{it}}\right)= \, {\beta }_{1}{{SDG}}_{{it}}^{{En}}+{\beta }_{2}{{SDG}}_{{it}}^{{Ec}}+{\beta }_{3}{{SDG}}_{{it}}^{{Soc}}+{\beta }_{4}{{SDG}}_{{it}}^{{Ec}}\times {{SDG}}_{{it}}^{{Soc}}\\ + {\beta }_{5}{{EBC}}_{{it}}+{\beta }_{6}{{TECH}}_{{it}}+{\beta }_{7}{{DEV}}_{{it}}+{\beta }_{8}{{GDP}}_{{it}}+{\beta }_{9}{{SIZE}\,}_{{it}}^{{dev}} \\ + {\beta }_{10}{{SIZE}\,}_{{it}}^{{tran}}+{\theta }_{t}+{\varepsilon }_{{it}}$$
(2)
where the independent variables of interest are the number of SDG claims per sustainability pillar: environmental, \({{SDG}}_{{it}}^{{En}}\), economic, \({{SDG}}_{{it}}^{{Ec}}\), and social, \({{SDG}}_{{it}}^{{Soc}}\). In the sustainability pillar specification, instead of the continent variable, we include whether the project country is classified as a developing country, \({{DEV}}_{{it}}\), and the country-level GDP, \({{GDP}}_{{it}}\). This change was necessary because of multicollinearity between the continent variable and other covariates, as indicated by pooled OLS regressions using year-demeaned variables. It is known that individual SDGs have synergies and trade-offs55,56, thus we include an interaction term between economic and social sustainability pillars to identify any potential overlap that the sustainability pillar framework might introduce. The other variables are the same as in Eq. 1.
We estimate a fixed-effects panel regression model on an unbalanced panel dataset in R using the within estimator (Product Version 2024. 12.0 + 467). In our model, the within estimator exploits variation over time. We treat SDG independent variables as numerical variables and use a log-log functional form for the first empirical specification (Eq. 1) to estimate the price elasticity with respect to the hedonic variables. The second empirical specification (Eq. 2) is log-linear, as there are multiple credits across the different sustainability pillars that do not have any SDGs in the pillar and natural log of zero is undefined. We use robust standard errors that account for both heteroskedasticity and autocorrelation (HAC), specifically HAC Newey–West standard errors.