Carbon sequestration and the annual net greenhouse gas balance of paddy fields, and their economic-ecological implications within the framework of sustainable development goals 13, 15

1 Introduction

Global climate change, driven by the accumulation of greenhouse gases (GHGs) in the atmosphere, is one of the most pressing environmental challenges facing humanity in the 21st century (David Raj et al., 2024). Greenhouse gas emissions from paddy fields encompass several aspects, including emissions of CH₄ and N₂O, and the uptake of CO₂ by paddy plant canopies through photosynthesis (Wang et al., 2017; Yadav et al., 2004; Kakar et al., 2024). Concentrations of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) in the atmosphere have been rising steadily since the industrial era, driving an increase in global average temperatures that has far-reaching impacts on the world's ecological and hydrological systems, as well as food security (Masson-Delmotte et al., 2021; Vuday Sankar et al., 2018). The agricultural sector accounts for around 23% of total global greenhouse gas emissions and plays a crucial yet paradoxical role: on the one hand, it is a contributor to emissions; on the other, it is an ecosystem with the potential to sequester carbon through biogeochemical processes (Kamyab et al., 2024; Lorenz and Lal, 2018; Power, 2010).

Paddy fields are also one of the largest sources of greenhouse gas emissions in the Asian region, collectively accounting for between 25–36 Tg CH₄ per year globally, equivalent to 10–12% of total global methane emissions (Jena et al., 2023). In Indonesia, variations in CH₄ emissions across paddy fields depend on irrigation regimes, soil types, paddy varieties and land management practices (Ariani et al., 2021). Research by Setyanto et al. (2018) shows that CH₄ emissions from irrigated paddy fields in Indonesia range from 10.2 to 23.4 g CH₄ per growing season, with variations influenced by water conditions, soil type and the use of organic fertilizers. In China, CH₄ emissions from irrigated paddy fields range from 100–200 kg CH₄ ha⁻¹ growing season⁻¹, whilst N₂O emissions are relatively smaller but have a GWP 265 times higher than that of CO₂, meaning their contribution to total greenhouse gas emissions cannot be ignored (Zou et al., 2005). The anaerobic conditions created by waterlogging during the growing season promote methanogenesis, making paddy paddies one of the terrestrial ecosystems with the most dynamic greenhouse gas fluxes (Singh et al., 2018).

Nevertheless, (Saito et al., 2005; Bhattacharyya et al., 2013) have shown that certain paddy fields can act as net carbon sinks when the biomass of the paddy plants absorbs more CO₂ than is released through organic decomposition and other greenhouse gas emissions. In South Asia, the use of paddy field management technologies can reduce CH₄ emissions and generate significant economic benefits through carbon credit mechanisms, whilst also enhancing farmers' food security (Sapkota et al., 2019). It is important to note that findings regarding the potential of paddy fields as carbon sinks generally rely on measurements of Net Primary Productivity (NPP), which is the difference between the carbon fixed by plants through gross primary production (GPP) and the carbon released back into the atmosphere through the plants' own autotrophic respiration (Chapin et al., 2006). NPP is transient in nature as it only represents biological productivity at the plant canopy scale over a single growing season, without taking into account the fate of that carbon after harvest whether it is decomposed by soil microorganisms or re-released into the atmosphere via non-CO₂ emission pathways such as CH₄ and N₂O. In contrast, the Net Ecosystem Carbon Balance (NECB) is a more comprehensive concept, representing the net rate of carbon accumulation or loss at the ecosystem scale, which integrates all carbon exchange pathways over a specific period, typically an annual one, encompassing the Net Ecosystem Exchange (NEE), carbon exports via harvests, non- CO₂ greenhouse gas emissions, and changes in soil organic carbon stocks (Chapin et al., 2006).

The economic valuation of carbon and climate mitigation ecosystem services from paddy fields has attracted considerable attention in international environmental policy discourse, particularly in the context of carbon trading mechanisms and Payments for Ecosystem Services (PES). Several previous studies have quantified the net greenhouse gas balance of paddy fields using the global warming potential (GWP) approach, which allows the conversion of various types of greenhouse gas emissions into carbon dioxide equivalents (CO₂e) over a 100-year period. The total value of global agricultural ecosystem services amounts to thousands of billion United State dollars per year (Costanza et al., 1997, 1998). In the context of greenhouse gas emissions, the social cost of carbon (SCC) serves as a key instrument for assigning a monetary value to greenhouse gas emissions, which can then be integrated into environmental policy (Revesz and Sarinsky, 2022).

In Indonesia, studies on the economic valuation of carbon in the agricultural sector remain relatively limited and have not yet specifically integrated calculations of the annual net greenhouse gas balance with various valuation instruments such as willingness to pay (WTP), marginal mitigation costs, and international carbon market prices. This hinders the evidence-based policy-making required to drive the transition toward a low-emission yet economically viable paddy field farming system. Previous research by Waite et al. emphasized that without a fundamental transformation in agricultural land management, the world will be unable to meet the climate commitments under the Paris Agreement whilst ensuring global food security by 2050. Research by Setyanto et al. (2018) focused specifically on quantifying methane emissions per growing season, but did not link these to crop CO₂ uptake, changes in soil organic carbon stocks, or a comprehensive estimate of the annual net carbon balance. Meanwhile, other studies on the sustainability of rice-farming systems such as the rice-duck integration framework among the Asnaf farming community in Malaysia, generally emphasize the social-institutional adaptation and livelihood resilience of farmers in the face of climate change, rather than the direct biophysical quantification of carbon and greenhouse gas balances (Thavaprakaash, 2026).

This study addresses this gap by integrating NPP measurements into the NECB framework, whilst also examining the economic-ecological implications for achieving SDG 13 and SDG 15. A novel aspect of this study lies in the integration of the climate, ecological and economic dimensions of paddy fields within the SDG framework, which is highly strategic and globally relevant, given its potential to provide valuable insights, particularly for developing countries with similar agricultural characteristics. Integrating greenhouse gas balance analysis and the economic valuation of paddy fields into the framework of the Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 15 (Life on Land), explicitly emphasizes the importance of climate change mitigation actions and the sustainable management of terrestrial ecosystems.

Based on the background and gaps outlined above, this study aims to estimate the net carbon sequestration of paddy field ecosystems, calculate the annual net greenhouse gas balance of paddy fields, and identify the economic-ecological implications for achieving SDG 13 and SDG 15. Thus, this study is expected not only to make a fundamental scientific contribution to the fields of biogeochemistry and environmental economics, but also to serve as a relevant policy reference for governments, international organizations and the global scientific community in their joint efforts to realize a climate-just and sustainable agricultural system.

2 Research methods

2.1 Research methodology and design

This study employs a quantitative-descriptive method. This method describes a phenomenon or condition as it is, using numerical data and basic statistics, without testing hypotheses or seeking causal relationships between variables (Thomas and Zubkov, 2023; Lesko et al., 2022). This study is explanatory in nature as there has been no previous research at the study site, or indeed in Indonesia, investigating the annual net greenhouse gas balance of paddy field. Most previous studies have been conducted in forest areas, mangrove forests and along riverbanks (Kusumaningtyas et al., 2022; Siarudin et al., 2021). However, specifically, none have calculated the annual net greenhouse gas balance of paddy fields. Paddy fields are agroecosystems that act as carbon sinks through the photosynthesis of paddy plants. However, at the same time, paddy fields can also be sources of CH₄ and N₂O emissions (Mujiyo et al., 2024; Qian et al., 2023). Therefore, claims regarding carbon mitigation in paddy fields must be based on calculations of the net Global Warming Potential (net GWP) that integrate these three components.

Given that this study does not yet have data on annual changes in soil organic carbon (ΔSOC) collected at regular intervals, it employs the Net Ecosystem Carbon Balance (NECB) framework. The Net Ecosystem Carbon Balance (NECB) is an estimate of carbon sequestration based on the carbon flux balance in the land system, as suggested by Chapin et al. (2006). The analysis was carried out by calculating the carbon fluxes entering and leaving the cultivation system in paddy fields. This approach aims to estimate the Net Greenhouse Gas Balance (NGHGB) annual of paddy fields. The Annual Net Greenhouse Gas Balance is the difference between total greenhouse gas emissions and the amount of carbon successfully sequestered by the cultivation system (Toudert et al., 2018). Furthermore, greenhouse gas emissions in the form of methane (CH₄) and nitrous oxide (N₂O) were converted into carbon dioxide equivalents (CO₂e) using Global Warming Potential (GWP) values in accordance with the IPCC 2019 “Refinement to the 2006 Guidelines for National Greenhouse Gas Inventories” (Chapin et al., 2006). This enables an assessment of the effectiveness of paddy fields in regulating the climate, even though data on changes in soil organic carbon stocks are not yet available.

2.2 Location and time of the research

The research was conducted in Tarik Sub-district, Sidoarjo Regency, East Java Province, Indonesia. The location was selected using a purposive approach based on three strategic considerations. Firstly, Tarik Sub-district is a sub-district in Sidoarjo Regency that still possesses extensive paddy fields, covering an area of 1,332.18 ha. Secondly, this location lies within the Surabaya metropolitan area (the Gerbangkertosusila region) (Firmansyah et al., 2021). The location is adjacent to a peri-urban area, which is a source of carbon emissions, particularly CO₂. Thirdly, Tarik Sub-district is an industrial area, one of the largest of which is Tjiwi Kimia. Industrial areas can contribute significant carbon emissions. Therefore, based on these considerations, this location can serve as a representative of other areas that may have similar conditions. The geographical features of Tarik Sub-district can be seen in Figure 1 below.

The research was carried out by conducting observations on one hectare of productive and strategically located land for research purposes, in accordance with guidance from agricultural extension officers, over the course of two paddy harvesting seasons. Observations began during the first harvesting season in January 2026 and continued during the second harvesting season in June 2026. During the observation period, all data on the land preparation cycle were collected with the assistance of agricultural extension officers at the research site.

2.3 Research data collection procedures and workflow

2.3.1 Ethical and permitting procedures

The research followed the ethical and administrative procedures applicable in Indonesia. In Indonesia, research must comply with research authorization procedures. Research authorization was obtained from the Sidoarjo Regency Agency for National Unity and Politics (BAKESBANGPOL), followed by coordination with the relevant departments, the sub-district government, the Agricultural Extension Center (BPP), as well as farmers' groups and land-owning farmers. Only after all research authorizations had been obtained were the research activities carried out. Consequently, the legal requirements for conducting the research were met in accordance with the applicable procedures.

2.3.2 Research workflow

Research data collection involves compiling data on plant biomass, various carbon inputs and outputs such as organic matter, crop residues, crop yields and other carbon losses, CO₂ exchange (NEE/GPP/ecosystem respiration), and CH₄ and N₂O emissions (Figure 2). Subsequently, these carbon components are used to calculate the Net Ecosystem Carbon Balance (NECB) as the difference between carbon entering and leaving the ecosystem. A positive NECB value indicates that the ecosystem functions as a carbon sink, whilst a negative value indicates a carbon source (Thapa et al., 2019). Subsequently, CH₄ and N₂O emissions are converted to CO₂e using the GWP₁₀₀ value and combined with the CO₂ balance to determine the annual net greenhouse gas balance. In the final stage, an eco-economic assessment of carbon sequestration and greenhouse gas emission mitigation is carried out based on the amount of carbon or CO₂e sequestered or mitigated at the prevailing carbon price. This approach enables a more comprehensive assessment of the ecosystem functions of paddy fields as it considers both the carbon balance and CH₄ and N₂O emissions, which may result in trade-offs between increased soil carbon storage and climate change mitigation benefits. The research data collection workflow is shown in Figure 2.

2.3.3 Research data collection

The research data collection combined primary and secondary data. This technique was used to address the limitations in data collection during field observations. Based on the data obtained from the research site and several data sources supporting the research findings, the research measurement parameters used as a reference in calculating the field research results are presented in Table 1.

Table 1. Summary of research data measurement parameters.

The carbon price refers to the Carbon Tax under Law No. 7 of 2021 on the Harmonization of Tax Regulations; the GWP values for CH₄ and N₂O are dimensionless (ratios) and use the AR6 biogenic values in accordance with the IPCC reporting standards, 2021.

The average weights of fresh and dry straw clumps in Table 1 were obtained by calculating the average of the total sample weights; the same applies to the average weights of fresh and dry roots. As soil respiration measurements had not yet been carried out, this study utilized data from a previous study by Nurhidayati et al. (2023). The results of that study indicated that the respiration rate of paddy fields using a combination of chemical fertilizer and compost was 278.52 mg hr⁻¹m⁻². In that study, the characteristics and land treatments were similar to those at the location of this study. Meanwhile, the carbon pricing data used to calculate the estimated economic value of environmental services in terms of carbon sequestration by paddy fields and the mitigation of greenhouse gas emissions were derived from Law No. 7 of 2021 on the Harmonization of Tax Regulations.

2.3.4 Research data collection process

Biomass sampling was carried out using the purposive sampling method with a destructive sampling technique on paddy clumps. The criteria for sample selection were based on the clumps with the highest and lowest numbers of tillers, thereby obtaining the clumps with the highest and lowest weights. Samples were taken from a one-hectare area of productive land, which was divided into 16 plots to facilitate sampling. From each plot, one sample with the highest weight and one with the lowest weight were taken, resulting in a total of 32 clump samples. The aboveground biomass (AGB) components collected comprised stems, leaves and panicles, whilst the belowground biomass (BGB) consisted of roots. The samples were then weighed fresh in the field using a digital balance accurate to 0.01 kg. To determine the dry weight of the clumps, the samples were dried in an oven at 70 °C until the biomass samples reached a constant weight without damaging the plant tissue (Jimenez-sierra et al., 2021; Huynh et al., 2020).

2.4 Research data processing

Data processing was carried out in stages within a one-hectare system over the course of 1 year. The first stage yielded dry plant biomass. The dry biomass was then converted into plant carbon and used to calculate NPP. NPP was subsequently integrated with carbon exported via harvest, ecosystem respiration, CH₄ emission, and external organic carbon inputs to obtain NECB. NECB is subsequently converted to a CO₂e basis and combined with CH₄ and N₂O to obtain NGHGB. This structure follows the sequence shown in the research process in Figure 2.

2.4.1 Estimation of paddy field biomass

The biomass of the paddy fields calculated refers to the paddy crops, which are the farmers' primary crop on the land at the study site. Biomass estimation was carried out in stages, starting with the calculation of the total number of clumps, fresh biomass, moisture content, and finally dry biomass. The total number of paddy clumps (N) was calculated based on the cropping pattern used by farmers, the land area and the number of harvest seasons per year, using the following Equation (1):

N = Adr × dc × HS(1)

where, N is the number of paddy plants per hectare; A is the area of land (ha); dr is the distance between rows of plants (cm); dc is the distance between plants within a row (cm); and HS is the number of harvest seasons per year.

Once the number of clumps has been determined, the wet biomass weight is calculated by multiplying the average fresh weight per clump by the number of clumps per hectare. As the NECB requires the separation of plant carbon components, straw and root biomass are calculated separately, whilst dry grain is calculated from harvest or plot yield measurements and is not automatically combined with the straw weight. The weight of straw and root biomass can each be calculated using the following Equation (2):

(WBWstraw = Wstraw × N106) and(WBWroot = Wroot × N106)(2)

where, WBW_straw is the wet biomass weight of straw (t ha⁻¹ yr⁻¹); WBW_root is the wet biomass weight of the roots (t ha⁻¹ yr⁻¹); W_straw is the average fresh weight of straw per clump (g clump⁻¹); W_root is the average fresh weight of roots per clump (g clump⁻¹); N is the total of clumps (clumps ha⁻¹); 10⁶ is the conversion factor from grams to tons.

Fresh biomass cannot be used directly to calculate carbon content because part of its mass consists of water. Therefore, the moisture content is determined from the difference between the fresh weight and the dry weight of the sample. Before calculating the dry weight of the biomass, the moisture content (MC) of the clump is determined. The moisture content can be calculated using the following Equation (3):

MCx = FWx - DWxFWx×100%(3)

where, MC_x is the moisture content of component x (%); FW_x is the fresh weight of sample x (g); DW_x is the dry weight of sample x (g). FW_x and DW_x must be expressed in the same unit of mass, and the MC value is then expressed as a percentage; therefore, in the dry biomass equation, it must be divided by 100.

Dry biomass is obtained by removing the water content from wet biomass. This equation uses MC in decimal form. As MC was previously expressed as a percentage, the MC value is divided by 100 before subtraction. The dry biomass weight (DBW) is calculated using the following Equation (4):

DBWx = WBWx × (1 - (MCx100))(4)

where, DBW_x is the dry biomass weight of component x (t ha⁻¹ yr⁻¹); WBW_x is the wet biomass weight of component x (t ha⁻¹ yr⁻¹); and MC_x is the moisture content of component x (%).

2.4.2 Conversion of dry biomass into carbon

This step is necessary because NPP and NECB are expressed on a carbon mass basis, rather than in terms of dry biomass mass. To this end, dry biomass is converted to carbon mass by multiplying the dry biomass by the carbon fraction (Cf). Paddy and straw are used as components of aboveground biomass (AGB), whilst roots and root rhizodeposition are used as components of belowground biomass (BGB). Consequently, calculations must be made for grain carbon (C_grain), straw carbon (C_straw), root carbon (C_root), and root rhizodeposition (C_rhizo). Carbon absorbed by plants is calculated from dry biomass using a biomass carbon fraction (Cf) of 0.45 (Xun et al., 2010). Meanwhile, carbon entering the soil via root rhizodeposition uses a carbon fraction (Cf) of 0.10 (citation). The conversion of dry biomass to carbon is calculated using the following Equations (5) and (6):

(Cgrain = DBWgrain × Cf) and (Cstraw = DBWstraw × Cf)(5)

where, C_grain is the amount of carbon in the grain (t C ha⁻¹ yr⁻¹); C_straw is the amount of carbon in the straw for the season (t C ha⁻¹ yr⁻¹); DBW_grain is the dry biomass weight of the grain (t ha⁻¹ yr⁻¹); DBW_straw is the dry biomass weight of the straw (t ha⁻¹ yr⁻¹); Cf is the carbon fraction.

(Croot = DBWroot × Cf) and (Crhizo = Croot × Cf)(6)

where, C_root is the root carbon pool (t C ha⁻¹ yr⁻¹); DBW_root is the dry biomass weight of plant roots (t ha⁻¹ yr⁻¹); C_rhizo is the amount of carbon released into the soil via root rhizodeposition (t C ha⁻¹ yr⁻¹); Cf is the carbon fraction.

2.4.3 Net primary productivity (NPP)

Net primary productivity is the rate of biomass accumulation or the rate at which energy is utilized during metabolic processes such as respiration. In the context of this study, net primary productivity is calculated as the total amount of carbon allocated to grain, straw, roots and rhizodeposition. Net primary productivity can be calculated using the following Equation (7).

NPP = Cgrain + Cstraw + Croot + Crhizo(7)

where, NPP is the net primary productivity (t C ha⁻¹ yr⁻¹); C_grain is the amount of carbon in grain (t C ha⁻¹ yr⁻¹); C_straw is the amount of carbon in straw for the season (t C ha⁻¹ yr⁻¹); C_root is the amount of carbon in the roots (t C ha⁻¹ yr⁻¹); C_rhizo is the amount of carbon released into the soil via root rhizodeposition (t C ha⁻¹ yr⁻¹).

2.4.4 Estimates of carbon exports and imports

Carbon export is defined as the accumulation of carbon leaving the paddy field ecosystem. Carbon is released from the paddy field ecosystem during harvesting, ecosystem respiration and CH₄ emissions. During the harvest season, not all biomass carbon leaves the field. The carbon in the paddy is assumed to be removed along with the harvest, whilst the carbon in the straw is not entirely removed from the paddy field ecosystem as some of it is returned to the soil. Carbon released during harvest (C_harvest) and ecosystem respiration (R_eco) can be calculated using Equations (8) and (9) respectively, whilst CH₄ emissions are calculated separately in the subsection on estimating CH₄ emissions from paddy fields.

Charvest = Cgrain + (Cstraw × P)(8)

where, C_harvest is the carbon removed through harvesting (t C ha⁻¹ yr⁻¹); C_straw is the amount of carbon in straw during the growing season (t C ha⁻¹ yr⁻¹); P is the percentage of straw removed from the field (%).

Ecosystem respiration (R_eco) represents the release of carbon in the form of CO₂, primarily resulting from heterotrophic processes and the decomposition of organic matter. Soil respiration can be calculated using the following equation

Reco = Σi (Ra + Rh)(9)

Where, R_eco is the total ecosystem respiration (t C ha⁻¹ yr⁻¹); R_a is the total autotrophic respiration (t C ha⁻¹ yr⁻¹); R_h is the total heterotrophic respiration (t C ha⁻¹ yr⁻¹).

Imported carbon refers to the input of organic materials or compounds originating from outside the field, such as farmyard manure and compost, which increase the organic carbon reserves in paddy fields. Straw originating from the same field and returned to the field is treated as an internal flow and is therefore not counted as imported carbon. In this study, imported carbon from farmyard manure can be estimated using the following Equation (10)

Com = Σi (Mj × Cfj)(10)

where, C_om is the amount of carbon input from external organic matter (t C ha⁻¹ yr⁻¹); M_j is the dry weight of external organic matter type j (t ha⁻¹ yr⁻¹); and Cf_j is the carbon fraction of organic matter type j.

2.4.5 Estimation of CH₄ emissions from paddy fields

CH₄ emissions are strongly influenced by land-use management practices, which give rise to daily land emissions. Daily emissions vary according to the growing season, the water regime on the land, and the addition of organic matter. The scale of organic matter addition to paddy fields can be calculated using the following Equation (11).

SFo = (1 + ROAi× CFOAi)0,59(11)

where, SF_o is scaling factor for the type of organic material; ROA_i is amount of organic material applied (t ha⁻¹); CFOA_i is conversion factor for organic material, generally 0.14 (IPCC, 2006).

The daily emission factor is then used as a reference for determining annual CH₄ emission levels. Daily emission factors are adjusted for the growing season, water patterns and organic matter, and are calculated using the following Equation (12).

EF1 = EFc × SFw × SFp × SFo × SFs,r(12)

where, EF₁ is daily land emission factor (kg CH₄ ha⁻¹ day⁻¹); EF_c is emission factor for continuously flooded paddy without organic matter (kg CH₄ ha⁻¹ day⁻¹); SF_w is scaling factor for water regime during cultivation; SF_p is scaling factor for pre-season water regime; SF_o is scaling factor for organic matter addition; SF_s is scaling factor for soil type; SF_r is scaling factor for paddy variety.

Annual CH₄ emissions are then calculated by multiplying the daily land emission factor (EF₁) by the duration of paddy cultivation per year (d) using the following Equation (13).

ECH4 = EF1 × d(13)

where, ECH₄ is annual CH₄ emissions (kg CH₄ ha⁻¹ yr⁻¹); EF₁ is adjusted daily emission factor (kg CH₄ ha⁻¹ day⁻¹); d is annual paddy cultivation duration (day yr⁻¹).

To convert CH₄ emissions to CO₂ equivalent, the IPCC AR6 100-year Global Warming Potential (GWP₁₀₀) standard is applied, with GWP₁₀₀CH₄ = 27. The conversion is performed using Equation (14).

CO2eCH4= ECH4 × GWP100CH41000(14)

where, CO₂eCH₄ is carbon dioxide equivalent of CH₄ emissions (t CO₂e ha⁻¹ yr⁻¹); ECH₄ is annual CH₄ emissions (kg CH₄ ha⁻¹ yr⁻¹); GWP₁₀₀CH₄ is Global Warming Potential of CH₄; 1,000 is conversion from kilograms to tons.

2.4.6 Estimation of N₂O emissions from paddy fields

N₂O emissions from paddy fields result from the use of fertilizers and organic matter containing nitrogen. The amount of nitrogen applied to the land through the use of fertilizers and organic matter can be determined by calculating the amount of fertilizer and organic matter applied against their nitrogen content. This amount can be calculated using the following Equation (15).

FN = (FUX × Nd × A) + ( FON × Nd × A) + ( FCR × Nd × A)(15)

where, F_N is total nitrogen applied to the land (kg N ha⁻¹ yr⁻¹); FU_X is application of fertilizer type x (kg ha⁻¹ yr⁻¹); Nd is nitrogen dose in the fertilizer (%); FO_N is organic fertilizer application (kg ha⁻¹ yr⁻¹); FC_R is paddy straw application (kg ha⁻¹ yr⁻¹); A is land area (ha).

After calculating the amount of nitrogen applied to the land (F_N), the next step is to calculate the nitrogen content present in N₂O (N₂O-N). N₂O-N is calculated using the specific emission factor for flooded land (EF_1FR) = 0.003 kg⁻¹ N, based on the 2019 IPCC standards. The amount of N₂O–N is calculated using the following Equation (16).

N2ON = FN × EF1FR(16)

where, N₂O–N is nitrogen content in N₂O compound (kg N ha⁻¹ yr⁻¹); F_N is total nitrogen applied (kg N ha⁻¹ yr⁻¹); EF_1FR is specific emission factor for flooded land (N₂O–N kg⁻¹ N).

N₂O–N was then converted to total N₂O emissions using the molecular mass ratio (44/28), as shown in Equation (17).

EN2O = N2ON × 4428(17)

where, EN₂O is N₂O emissions (kg N₂O ha⁻¹ yr⁻¹); N₂O-N is nitrogen content in N₂O compound (kg N ha⁻¹ yr⁻¹); the factor 44/28 = 1.57 is the molecular mass ratio of nitrogen to N₂O.

To convert N₂O emissions into carbon dioxide equivalents (CO₂e), the calculation is based on GWP₁₀₀N₂O = 273, in accordance with the standards set out in the IPCC AR6. The conversion of N₂O to CO₂e is carried out using the following Equation (18).

CO2eN2O = EN2O × GWP100N2O1000(18)

where, CO₂eN₂O is CO₂ equivalent of N₂O emissions (t CO₂e ha⁻¹ yr⁻¹); EN₂O is N₂O emissions (kg N₂O ha⁻¹ yr⁻¹); GWP₁₀₀N₂O is Global Warming Potential of N₂O; 1000 is conversion from kilograms to tons.

2.4.7 Calculation of the net ecosystem carbon balance (NECB)

Net Ecosystem Carbon Balance (NECB) is the total amount of carbon stored in or lost from an ecosystem over a given period. This value indicates whether an ecosystem acts as a carbon sink or a carbon source to the atmosphere (Chapin et al., 2006). Net primary productivity (NPP) represents the carbon allocated to the productive components of plants and rhizodeposition; ecosystem respiration (R_eco) represents the release of carbon in the form of CO₂ through soil respiration; harvested carbon (C_harvest) represents the carbon removed through harvesting; CH₄ represents carbon released in the form of methane, and organic carbon (C_om) represents organic carbon inputs from outside the ecosystem. By adopting the equation from (Chapin et al., 2006), NECB can be calculated using the following Equation (19).

NECB = NPP-Reco-Charvest-ECH4 + Com(19)

where, NECB is the Net Ecosystem Carbon Balance (t C ha⁻¹ yr⁻¹); NPP is net primary productivity (t C ha⁻¹ yr⁻¹); R_eco is total ecosystem respiration (t C ha⁻¹ yr⁻¹); C_harvest is carbon removed via harvest (t C ha⁻¹ yr⁻¹); ECH₄ is annual CH₄ emissions (kg CH₄ ha⁻¹ yr⁻¹); C_om is the amount of carbon input from external organic matter (t C ha⁻¹ yr⁻¹). Interpretation: NECB > 0 indicates a net carbon surplus or sink, whilst NECB < 0 indicates a net carbon deficit or source.

2.4.8 Calculation of the annual net greenhouse gas balance for paddy fields

The annual net greenhouse gas balance is calculated as the difference between the rate of carbon uptake by paddy field biomass and the total emissions released by the land during its period of use. The annual net greenhouse gas balance of the paddy field ecosystem can be calculated using the following Equation (20).

NGHGBAnnual = (CO2eCH4 + CO2eN2O) - (NECB × 4412)(20)

where, NGHGB_Annual is the annual net greenhouse gas balance from paddy fields (t CO₂e ha⁻¹ yr⁻¹); CO₂eCH₄ is the carbon dioxide equivalent of CH₄ emissions (t CO₂e ha⁻¹ yr⁻¹); CO₂eN₂O is the carbon dioxide equivalent of N₂O emissions (t CO₂e ha⁻¹ yr⁻¹); NECB is the Net Ecosystem Carbon Balance (t C ha⁻¹ yr⁻¹); the factor 44/12 = 3.67 is the ratio of the molecular weight of CO₂ to that of C.

A positive NGHGB_Annual value indicates that the paddy fields is a net source, whilst a negative value indicates that it is a net sink (Li et al., 2021).

2.4.9 Economic-ecological valuation of environmental mitigation

An economic-ecological valuation of the environmental mitigation benefits of carbon sequestration and the annual net greenhouse gas balance of paddy fields was carried out using the carbon tax approach. This study follows the Pigovian Tax system to provide a monetary estimate of the ecosystem services provided by paddy fields in terms of environmental mitigation. A Pigovian tax is a tax levied on economic activities that cause adverse impacts or harm to others and the environment (negative externalities) (Sandmo, 2008). The economic-ecological valuation of paddy fields in terms of carbon sequestration is calculated using the following Equation (4).

EV = NGHGBAnnual × TC(21)

where, EV is the annual economic value of paddy fields in terms of environmental mitigation (Rp ha⁻¹ yr⁻¹); NGHGB_Annual is the annual net greenhouse gas balance of paddy fields (t CO₂e ha⁻¹ yr⁻¹); and TC is the carbon tax (Rp t CO₂e).

3 Research results

3.1 Confidence intervals and sample variability of biomass

Confidence intervals and sample variability were calculated in this study, given that the results largely rely on the measured values of biomass samples. The biomass sample measurements indicated a moderate level of variation among the paddy clumps sampled. From the 32 samples, an average biomass weight of 122.78 g per clump was obtained, with a standard deviation of 18.75 g per clump and a coefficient of variation (CV) of 15.27%. The 95% bootstrap interval (116.569–129.222 g) indicates that the mean estimate is reasonably stable within the calculated interval. From a sampling design perspective, 16 plots were used as observation units and 32 clump samples were used to characterize the biological variation within the plots (see Table 2). Thus, overall, the estimates of paddy biomass at the plot level were sufficiently precise, whilst there was strong biological variation between the sampled clumps.

Table 2. Analysis of confidence intervals and sample variability.

Source: Primary data processed, 2026.

3.2 Estimation of paddy field biomass

Plant clump density forms the basis for all calculations of biomass and carbon produced by paddy fields. Substituting the parameters of land area (A = 1 ha), row spacing (dr = 25 cm), and plant spacing (dc = 25 cm), as well as the number of harvest seasons (HS = 2 yr⁻¹) into Equation (1) yields a paddy plant clump density (N) = ((1 × 104)/(25 × 10² × 25 × 10²)) × 2 = 320,000 clumps ha⁻¹ yr⁻¹. This value falls within the range of paddy clump densities commonly found in lowland irrigated paddy systems on the island of Java using the dense “tegel” cropping pattern.

Based on this clump density, the wet biomass weight (WBW) of straw and roots is calculated using Equation (2). Substituting the average fresh weight of straw per clump (122.78 g clump⁻¹) yields WBW_straw = (122.78 × 320,000)/106 = 39.29 t ha⁻¹ yr⁻¹, whilst substituting the average fresh weight of roots per clump (22.22 g clump⁻¹) yields WBWroot = (22.22 × 320,000)/106 = 7.11 t ha⁻¹ yr⁻¹. A comparison of these two values confirms the pattern of biomass allocation in paddy plants, whereby the above-ground component (straw) is far more dominant than the below-ground component (roots).

Moisture content (MC) was then calculated using Equation (3) to determine the moisture content of the fresh biomass. Substituting the fresh weight and dry weight of the straw per paddy clump yielded MC_straw = ((122.78 – 47.75)/122.78) × 100% = 61.11%. Meanwhile, substituting the fresh weight and dry weight of the roots per clump yields MC_root = ((22.24 – 4.18)/22.24) × 100% = 81.22%. These moisture content values are then substituted into Equation (4) to obtain the dry biomass weight (DBW). The results are DBW_straw = 39.29 × (1 – 0.6111) = 15.28 t ha⁻¹ yr⁻¹ and DBW_root = 7.11 × (1 – 0.8122) = 1.34 t ha⁻¹ yr⁻¹. Meanwhile, the dry grain weight (DBW_grain) was not calculated using (1)–(4), but was instead determined directly from the plot yield measurement of 13.8 t ha⁻¹ yr⁻¹, as presented in Table 1, in accordance with the procedure in sub-section 2.4.1, which separates the measurement of crop yield from the estimation of vegetative biomass.

3.3 Biomass carbon and net primary productivity (NPP)

The conversion of dry biomass to carbon is necessary because NPP and NECB are expressed in units of carbon mass, not biomass mass. Substituting the dry biomass mass of paddy and straw with the biomass carbon fraction (Cf = 0.45) in Equation (5) yields paddy carbon (C_grain) = 13.80 × 0.45 = 6. 21 t C ha⁻¹ yr⁻¹ and straw carbon (C_straw) = 15.28 × 0.45 = 6.88 t C ha⁻¹ yr⁻¹. Furthermore, substituting the dry biomass weight of roots into Equation (6) yields root carbon (C_root) = 1.34 × 0.45 = 0.60 t C ha⁻¹ yr⁻¹, and carbon released into the soil via root rhizodeposition (C_rhizo) = 0.60 × 0.10 = 0.06 t C ha⁻¹ yr⁻¹.

These four carbon components are then substituted into Equation (7) to obtain the net primary productivity (NPP) = 6.21 + 6.88 + 0.60 + 0.06 = 13.75 t C ha⁻¹ yr⁻¹. This NPP value represents the total carbon successfully fixed by paddy plants through photosynthesis and allocated to productive organs and the rhizosphere over two growing seasons in a single year. Proportionally, paddy grains account for 45.16% of NPP, straw accounts for 50.04%, whilst roots and rhizodeposition together account for only 4.80% (Figure 3). These percentages confirm that the majority of the paddy plants' photosynthetic yield is stored in above-ground organs (grains and straw), whilst the contribution of carbon below ground is relatively small.

The NPP value of 13.75 t C ha⁻¹ yr⁻¹ falls within the range of primary productivity for tropical irrigated paddy fields with two harvests per year, which, according to various global studies, is generally reported to range from 8 to 16 t C ha⁻¹ yr⁻¹ (Potter et al., 2012). It is important to emphasize this relatively high NPP value, as at first glance it may give the impression that paddy fields have a large carbon mitigation capacity. However, as outlined in subsections 3.3 and 3.5, the majority of this fixed carbon is not permanently stored within the ecosystem; rather, a portion is removed from the land through harvests. Consequently, NPP cannot be used as the sole indicator of paddy field carbon sequestration capacity and must be supplemented by a comprehensive calculation of the ecosystem's carbon budget.

3.4 Estimates of carbon exports and imports

Carbon outflow (exports) from the paddy field ecosystem in this study was estimated via three pathways: harvest, ecosystem respiration and CH₄ emissions. Meanwhile, carbon inputs were estimated via external organic matter inputs. At harvest, it was assumed that all the carbon in the paddy grains left the field with the harvest, whilst only a portion of the carbon in the straw left, as the remainder was returned to the field as organic matter (in situ). With paddy straw return of 12 t ha⁻¹ yr⁻¹ (Table 1) and a total wet straw biomass production of 39.29 t ha⁻¹ yr⁻¹, the percentage of straw returned to the field was calculated as 30.54%, meaning that 69.46% of the straw was removed from the field (P). Substituting these values into Equation (8) yields the carbon leaving the field via harvest (C_harvest) = 6.21 + (6.88 × 0.6946) = 6.21 + 4.78 = 10.99 t C ha⁻¹ yr⁻¹. This value indicates that all the carbon from the paddy grains and a portion of the carbon from the straw leave the paddy field each year. The practice of farmers at the study site, who tend to remove the majority of the straw from the fields for other purposes such as plantation use and livestock feed, may have significant implications for the sustainability of soil fertility and the long-term organic carbon balance. The low proportion of straw returned to the fields means that the input of fresh organic carbon into the soil is severely limited.

Ecosystem respiration (R_eco) was calculated based on land respiration flux data sourced from previous research on land with characteristics and treatments similar to the study site, amounting to 278.52 mg CO₂ h⁻¹ m² (Nurhidayati et al., 2023). This flux value was integrated over 24 h and 240 days of the growing season per year using Equation (9), yielding a total ecosystem respiration of 16.04 t CO₂ ha⁻¹ yr⁻¹, which is equivalent to R_eco = 16.04 × 0.2727 = 4.37 t C ha⁻¹ yr⁻¹. The value 0.2727 (12/44) is the conversion factor for CO₂ to carbon. This value represents carbon release via autotrophic respiration by plants and heterotrophic respiration by decomposer microorganisms, and constitutes one of the largest components of carbon loss in the paddy fields ecosystem.

In terms of carbon entering the paddy fields, the only external organic input at the study site was 5 t ha⁻¹ of cattle manure in year 1 (Table 1). Substituting this value with the carbon fraction in cattle manure (Cf = 0.46) into Equation (10) yields imported carbon (C_om) = 5 × 0.46 = 2.30 t C ha⁻¹ yr⁻¹. It should be emphasized that straw originating from the same plot and returned to the plot is not counted as imported carbon, but is treated as an internal flow in accordance with the procedure in sub-section 2.4.4, thereby avoiding double counting. The small value of C_om compared with C_harvest and R_eco reflects the minimal use of external organic fertilization at the study site, which is dominated by inorganic fertilization systems.

3.5 Estimation of CH₄ and N₂O emissions

CH₄ emissions were calculated based on the baseline emission factor (EFc) and other factors such as the scale of the water regime, the addition of organic matter, soil type and paddy variety. These factors were adjusted in accordance with the relevant standard reference values from the IPCC (2006) and the Kementrian Lingkungan Hidup (KLH) (2012) that correspond to the characteristics of the study site, namely an intermittent irrigation system (single drainage), the addition of organic matter, alluvial soil, and the Ciherang variety, as presented in Table 3.

Table 3. Components of the daily emission factor (EF₁) and conditions at the study site.

Source: Primary data processed according to IPCC guidelines (2006) and the Ministry of Environment of the Republic of Indonesia (2012).

Substituting the organic matter addition (ROA = 5 t ha⁻¹; CFOA = 0.14) into Equation (11) yields the organic matter scale factor (SFo) = (1 + 2 × 0.14)^0.59 = 1.37. Furthermore, substituting all the factors from Table 4 into Equation (12) yields a daily emission factor (EF1) = 1.61 × 0.46 × 1.00 × 1.37 × 1.00 × 0.55 = 0.56 kg CH₄ ha⁻¹ day⁻¹. With the cultivation period (d) = 240 days yr⁻¹, applying this to Equation (13) yields annual CH₄ emissions (ECH₄) = 0.56 × 240 = 134.40 kg CH₄ ha⁻¹ yr⁻¹. Using GWP100CH₄ = 27, Equation (14) converts this value to CO₂eCH₄ = (134.40 × 27)/1000 = 3.63 t CO₂e ha⁻¹ yr⁻¹. These CH₄ emission values are relatively moderate, owing to the application of an intermittent irrigation regime on the study plots, which can suppress methane emissions compared to continuous flooding.

Table 4. Summary of estimated biomass yields for paddy fields.

Source: Primary data processed, 2026.

^*The weight of dry grain was obtained directly from measurements of harvest yields/yield per unit area (Table 1).

N₂O emissions were calculated based on the amount of nitrogen applied to the land via urea, NPK, farmyard manure and paddy straw (Table 5), with the nitrogen content of each material referring to standard fertilizer nutrient content values (urea 46%, NPK 16%, organic fertilizer 2.9%, straw 0.5%).

Table 5. Fertilizer and organic matter applications and their nitrogen contents.

Source: Primary data processed, 2026.

Substituting the values from Table 5 into Equation (15) gives the total nitrogen applied to the land (FN) = (600 × 0.46 × 1) + (500 × 0.16 × 1) + (5,000 × 0.029 × 1) + (12,000 × 0.005 × 1) = 276 + 80 + 145 + 60 = 561.00 kg N ha⁻¹ yr⁻¹. Using the specific emission factor for flooded land (EF1FR = 0.003), Equation (16) yields the nitrogen content in N₂O (N₂O-N) = 561.00 × 0.003 = 1.68 kg N ha⁻¹ yr⁻¹. Conversion to total N₂O emissions using Equation (17) yields EN₂O = 1.68 × (44/28) = 2.64 kg N₂O ha⁻¹ yr⁻¹. Furthermore, using GWP₁₀₀N₂O = 273, Equation (18) yields CO₂eN₂O = (2.64 × 273)/1000 = 0.72 t CO₂e ha⁻¹ yr⁻¹. In contrast to CH₄ emissions originating from biogeochemical processes in flooded areas, N₂O emissions are dominated by the contribution from the use of fertilizers and organic matter on paddy fields.

As shown in Table 6, total CH₄ and N₂O emissions, converted to CO₂e units, amounted to 4.35 t CO₂e ha⁻¹ yr⁻¹. This figure is relatively moderate compared with various other studies on greenhouse gas emissions from tropical paddy fields, which often report higher figures for continuously flooded irrigation systems (Maris et al., 2016; Wang et al., 2016). This indicates that intermittent water management practices at the study site contribute to the mitigation of direct greenhouse gas emissions from paddy fields. However, as will be discussed in subsection 3.5, these CH₄ and N₂O emission values do not guarantee that paddy fields function as net sinks of greenhouse gases, as they must still be considered within the context of the ecosystem's overall carbon balance.

Table 6. Annual CH₄ and N₂O emissions from paddy fields.

Source: Primary data processed, 2026.

3.6 Net ecosystem carbon balance (NECB) and annual net greenhouse gas balance (NGHGB_*Annual*)

The Net Ecosystem Carbon Balance (NECB) is calculated using Equation (19) by integrating all the carbon flux components estimated in the previous subsections, namely NPP as carbon entering via photosynthesis, as well as R_eco, Harvest, and carbon equivalent to CH₄ emissions (converted to carbon, CCH₄ = ECH₄ × 12/16 = 134.40 × 0.75/1000 = 0.10 t C ha⁻¹ yr⁻¹) as carbon leaving the system, minus C_om as carbon entering from external organic matter. A summary of these components is presented in Table 7.

Table 7. Components of the net ecosystem carbon balance (NECB) for paddy fields.

Source: Primary data processed, 2026.

Substituting the values in Table 7 into Equation (19) yields NECB = 13.75 – 4.37 – 10.99 – 0.10 + 2.30 = 0.59 t C ha⁻¹ yr⁻¹. Referring to the rules for interpreting signs in sub-section 2.4.7, this positive NECB value indicates that the paddy fields at the study site function as a net carbon sink, even though their NPP is relatively high (13.75 t C ha⁻¹ yr⁻¹), as shown in Figure 4.

It is important to emphasize the net carbon fluxes in paddy fields shown in Figure 4 above: claims regarding carbon mitigation in paddy fields cannot be based solely on the amount of carbon fixed by plants (NPP or standing biomass carbon), but must take into account all carbon fluxes leaving the system, particularly through crop exports and ecosystem respiration. In other words, the majority of the carbon fixed by paddy plants is essentially “transit” carbon that is harvested as food, rather than carbon permanently stored within the paddy field ecosystem.

The annual net greenhouse gas balance (NGHGB_Annual) is then calculated using Equation (20), which combines total CH₄ and N₂O emissions on a CO₂e basis with the NECB, which has been converted to CO₂e using a factor of 44/12. Substituting the values yields NGHGB_Annual = (3.63 + 0.72) – (0.59 × 3.67) = 4.35–(2.17) = 2.18 t CO₂e ha⁻¹ yr⁻¹. The NECB value, equivalent to 2.17 t CO₂e ha⁻¹ yr⁻¹, is positive, indicating a carbon surplus in the ecosystem (see Table 8).

Table 8. Annual net GHG balance (NetGHGAnnual) of paddy fields.

Source: Primary data processed, 2026.

The positive NGHGB_Annual value of 2.18 t CO₂e ha⁻¹ yr⁻¹ shown in Figure 5 indicates that the paddy fields at the study site function, on balance, as a net annual source of greenhouse gases. This finding provides important information regarding the common narrative that often portrays paddy fields solely as carbon sinks based on their high photosynthetic capacity. However, this carbon sequestration is subsequently released through ecosystem respiration and emissions resulting from land management practices.

3.7 The economic and ecological value of environmental mitigation

The economic-ecological valuation of paddy fields for environmental mitigation using the carbon tax approach, in Equation (21) yields EV = 2.18 × 30,000 = Rp65,400 ha⁻¹ yr⁻¹, or approximately US$3.67 ha⁻¹ yr⁻¹ (Bank Indonesia exchange rate as at 14 August 2026, Rp17,828 per US$). This EV value represents an estimate of the annual economic liability, which is, in principle, equivalent to the environmental cost that would have to be borne if the greenhouse gas emissions balance from the management of these paddy fields were subject to a carbon tax. This value illustrates the environmental burden arising from the paddy field management system. Consequently, appropriate anticipatory measures are required to reduce the resulting emissions burden. The relationship between paddy field carbon sequestration, the annual net greenhouse gas balance of paddy fields, and the implications for their economic-ecological value is summarized in Figure 6 below.

4 Discussion

4.1 Net carbon sequestration paddy fields

A NECB value of 0.59 t C ha⁻¹ yr⁻¹ indicates that the paddy fields in the study area act as a net carbon sink, albeit by a very narrow margin compared to the gross carbon flux driving it, namely NPP of 13.75 t C ha⁻¹ yr⁻¹. This situation is similar to a study on subtropical paddy systems in China, where the NECB shifted from a slight carbon source of −1.0 Mg C ha⁻¹ yr⁻¹ under the treatment without nitrogen fertilization to a slight carbon sink of +1.1 Mg C ha⁻¹ yr⁻¹ under the treatment with full nitrogen fertilization. The NECB value in this study lies precisely between these two findings, indicating that the carbon sink/source status of paddy fields is not an intrinsic property of the ecosystem, but rather the result of a dynamic balance between organic inputs, crop yields and fertilization intensity, which is highly sensitive to changes in land management practices. The R_eco value in this study, adopted from secondary data Nurhidayati et al. (2023) provides a picture of conditions similar to those in the study area, as it is within the same region of East Java with the same characteristics and land management practices. This R_eco value falls within a range not significantly different from that of other tropical paddy fields worldwide studied by Olsson et al. (2015). The R_eco value provides an indication of the amount of CO₂ released from the ecosystem.

4.2 Net carbon sinks and net greenhouse gas sources

A finding that particularly warrants emphasis is the difference in the direction of the values between NECB (positive, carbon sink) and NGHGB_Annual (positive, greenhouse gas source). This finding suggests that paddy fields, whilst capable of acting as carbon sinks, do not necessarily result in a net reduction in greenhouse gas emissions. This is due to the difference in global warming potential (GWP) between the gases: per unit mass, CH₄ and N₂O have global warming potentials equivalent to 27 and 273 times that of CO₂, respectively. Consequently, the mass fluxes of CH₄ and N₂O, which are small in absolute terms (134.40 kg CH₄ and 2.64 kg N₂O ha⁻¹ yr⁻¹), still result in a CO₂e load that is significantly higher than the NECB surplus contribution after conversion to a common basis. N₂O's contribution to total NGHGB was 16.6%, slightly higher than previous studies which reported N₂O's contribution to NGHGB as 12%. This result stems from the trade-off between reduced CH₄ emissions and increased N₂O emissions when paddy fields transition from continuous flooding to an intermittent regime. This finding supports the adoption of intermittent irrigation in Indonesia, given the higher greenhouse effect of N₂O.

The parameter-based carbon balance approach and allometric equations used in this study differ conceptually from the eddy covariance micrometeorological approach commonly used to measure NEE continuously. NEE essentially represents the difference between gross primary productivity (GPP) and ecosystem respiration (R_eco), i.e., the net CO₂ flux between the canopy and the atmosphere, without directly capturing lateral carbon losses through crop exports or non-CO₂ emissions (CH₄ and N₂O) unless these are incorporated via additional measurements. An eddy covariance study in lowland tropical paddy fields in India reported an integrated dry-season NEE of −392 g C m⁻², equivalent to approximately 3.9 t C ha⁻¹ for a single growing season (Bhattacharyya et al., 2013), whilst a study in South Korea reported an average NEE of −4.49 g C m⁻² day⁻¹ in paddy fields. These values are close to the NPP component minus R_eco in this study (13.75 – 4.37 = 9.38 t C ha⁻¹ yr⁻¹).

4.3 Implications for ecological-economic values and the achievement of SDGs 13, 15

The EV value of Rp65,400 ha⁻¹ yr⁻¹ (equivalent to US$3.67 ha⁻¹ yr⁻¹), derived using the Pigovian carbon tax approach, should be interpreted not as the economic benefit of carbon sequestration services, but as an estimate of the economic liability arising from the status of paddy fields as a net source of greenhouse gases, expressed in CO₂e. Although the nominal figure appears small on a per-hectare basis, this finding has a significant impact when aggregated across a larger area of paddy fields; for example, the total area of paddy fields in Tarik Sub-district, covering 1,332.18 hectares, would incur an environmental cost equivalent to Rp87,124,572 yr⁻¹ (± US$4,875.47 yr⁻¹).

With regard to achieving SDG 13 (Climate Action), these findings confirm that mitigation strategies for paddy fields cannot rely solely on the narrative of photosynthetic capacity or carbon sink function; rather, they must explicitly target sources of CH₄ and N₂O emissions, for example by optimizing the rate and timing of nitrogen fertilization to reduce N₂O emissions without compromising productivity, in line with SDG 13.2′s objective to integrate climate considerations into sectoral policies. With regard to the achievement of SDG 15 (Terrestrial Ecosystems), the low proportion of straw returned to the fields and the minimal input of external organic matter indicate a risk of long-term decline in soil quality and soil carbon stocks, which is relevant to the land degradation neutrality target under SDG 15.3. Increasing the proportion of straw returned to the land, as well as other organic inputs, has the potential to increase C_om, strengthen NECB, and gradually build up soil carbon stocks. However, these quantities must be proportionally adjusted to land management practices and irrigation arrangements using a periodic irrigation system to minimize greenhouse gas emissions from these organic inputs.

5 Conclusion

This study examines estimates of net carbon sequestration and the annual net greenhouse gas balance of paddy fields, as well as their economic and ecological implications for achieving SDG 13 and SDG 15. The results show that total annual emissions of CH₄ and N₂O amount to 3.63 t CO₂e ha⁻¹ yr⁻¹ and 0.72 t CO₂e ha⁻¹ yr⁻¹ respectively, resulting in total emissions of 4.35 t CO₂e ha⁻¹ yr⁻¹. The net carbon sink of paddy fields (NECB) was 0.59 t C ha⁻¹, equivalent to 2.17 t CO₂e ha⁻¹ yr⁻¹, with an annual net greenhouse gas balance (NGHGB_Annual) of 2.18 t CO₂e ha⁻¹ yr⁻¹. These results indicate that paddy fields act as a carbon source, implying an environmental carbon tax liability of Rp65,400 ha⁻¹ yr⁻¹. These findings also have implications for achieving SDGs 13 and 15 through the optimisation of fertilizer application rates and timing without compromising productivity, the proportional return of straw and organic matter in line with land management practices, and the use of a periodic irrigation system to minimize greenhouse gas emissions. Consequently, there is a need to integrate climate balance considerations into sustainable land management.

6 Limitations and research recommendations

It should be noted that this valuation has several methodological limitations that require attention in future research. The ecosystem respiration data, CH₄ emission factors (EFc, SFw, SFp, SFs, SFr), and the nitrogen content of fertilizers and organic matter in this study are still based on standard IPCC values and supporting literature, as direct measurements of these parameters have not yet been carried out at the study site. Furthermore, the NECB calculations in this study have not incorporated empirical and periodic data on changes in soil organic carbon stock (ΔSOC); consequently, the NECB estimates obtained are flux-based proxies and require further validation through multi-seasonal monitoring and direct measurements of respiration components, gas emissions, and soil organic carbon dynamics.

From the perspective of research sustainability, further research is recommended to involve repeated seasonal measurements, direct monitoring of greenhouse gas fluxes, and multi-year assessments. From a policy perspective, these findings have practical implications for low-carbon agriculture programmes in Indonesia. Firstly, incentives for farmers to return a larger proportion of straw to the fields have the potential to improve NECB whilst simultaneously reducing NGHGB_Annual, thereby reducing the net emissions burden of paddy fields. Secondly, the continued adoption of intermittent irrigation, as practiced by farmers at the study sites, should be actively encouraged, given its significant contribution to reducing CH₄ emissions compared to continuous flooded irrigation systems. Thirdly, the results of this study underscore the importance of agricultural sector greenhouse gas inventories based on a holistic ecosystem balance approach (NECB and NGHGB), rather than merely estimates of carbon stored at a single point in time, so that mitigation policies and agricultural carbon incentives can be designed more accurately and equitably.

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Footnotes

Edited by: Vijay Singh Meena, ICAR—Mahatma Gandhi Integrated Farming Research Institute, India

Reviewed by: Susanti Withaningsih, Padjadjaran University, Indonesia

Vladimir Tabunshchik, A.O. Kovalevsky Institute of Marine Biological Research, Russia