A Survey-Based Emission Inventory of Greenhouse Gases Released from Rice Production on Consolidated Land in the Red River Delta of Vietnam

Abstract

In this study, relevant rice cultivation data were collected through a local survey, and the life cycle assessment (LCA) method was employed to quantify greenhouse gas (GHG) emissions from rice production on consolidated land in the Red River Delta (RRD). Systematic sampling was used in face-to-face interviews with 45 rice farming households in a representative commune of Hai Duong province. Specific GHG emissions were significantly higher in the summer crop (averaged at 11.4 t CO₂-eq/ha or 2.2 t CO₂-eq/t grain) than in the spring crop (6.8 t CO₂-eq/ha or 1.2 t CO₂-eq/t grain). Methane was a dominant GHG emitted from paddy fields, contributing 84% of the total emissions of CO₂-eq in the summer crop and 73% in the spring crop. Fertilizer use and N₂O emissions accounted for 9% of emissions in the summer crop and 16% in the spring crop. Energy consumption for machinery and irrigation added a further 4% and 8%, respectively. Annually, as of 2023, the rice production activities in the RRD release 7.3 Tg of CO₂-eq (100 years), a significant contribution to the national GHG emissions. GHG emissions under alternative scenarios of rice straw management were assessed. This study highlights the role of land consolidation in improving water management, which contributes to lowering emissions. Based on the findings, several mitigation measures could be identified, including improved irrigation practices, optimized fertilizer use, and the promotion of sustainable rice straw management practices.

1. Introduction

Sustainable agriculture plays a crucial role in achieving the overall sustainable development goals, as it is responsible for providing food for the growing population around the world. Agriculture is one of the most vulnerable sectors due to climate change. At the same time, it is also a significant source of greenhouse gas (GHG) emissions, contributing 11% to the total global GHG emissions annually [1]. In Vietnam, a country with an agrarian economy, GHG emissions from this sector contribute around 30% of the country’s total CO₂-equivalent emissions [2]. The three largest sources of GHG emissions from agriculture in Vietnam are rice cultivation, livestock, and fertilizer management, with rice cultivation accounting for up to 50% of the sector’s total. With the strong commitment to achieving net-zero GHG emissions by 2050, the Vietnamese government has set a goal to transition rice production to a low-carbon model [3]. It is therefore necessary to quantify GHG emissions from rice production and assess the main factors influencing emissions so that appropriate mitigation solutions can be proposed for achieving this target.

In Vietnam, rice is primarily cultivated in the Red River Delta (RRD) in the north—producing about 5.8 million tons annually (13.3% of the national total production)—and in the Mekong Delta in the south, contributing over half of the total national rice output [4]. Despite being a key rice-producing region, farm areas in the RRD are typically small and fragmented, with average farm sizes being less than 0.25 ha per household and often separated into 5–6 individual plots [5]. Fragmented landholdings reduce the efficiencies of irrigation, pest management, and mechanization [6].

To overcome these limitations, land consolidation policies have been implemented in Vietnam, institutionalized under the 2013 Land Law and promoted through Resolution No. 19-NQ/TW (2012). Land consolidation aims to enlarge farm plots, reduce production costs, improve agricultural productivity, and facilitate mechanization [7]. While several studies have documented the socioeconomic benefits of land consolidation—including improved income [8], farming efficiency [6], and food security [9]—its implications for GHG emissions remain unexplored. Previous studies indicate that GHG emissions vary among rice-producing regions, influenced by climatic conditions and specific cultivation practices—particularly those related to water management, fertilizer application, and straw handling. Moreover, some studies also suggest that GHG emissions tend to be lower in larger rice fields [10,11].

Several studies have been conducted to quantify GHG emissions from rice cultivation in Vietnam, but most of them focused on the Mekong Delta [12] along with some other regions [13]. To date, to the best of the authors’ knowledge, there have been no comprehensive GHG emission inventories specifically conducted to assess the effects of the consolidation of rice fields in the RRD. Such studies should reveal factors affecting GHG emissions at both the national and regional scale, thereby identifying and promoting mitigation solutions for the agricultural sector. While environmental policies increasingly emphasize the dual goals of GHG emissions reductions and economic development, few studies in Vietnam have explicitly connected agricultural GHG emissions to broader environmental–economic frameworks such as the Environmental Kuznets Curve (EKC) hypothesis. The EKC proposes that environmental degradation initially increases with income but later decreases after reaching a certain income threshold. Incorporating this perspective, as highlighted in [14,15], this study situates its findings within the broader context of sustainable economic development. Implementation of emission mitigation measures, e.g., with low-emission rice production technologies and relevant regulations, could help cut down the emission peak (environmental degradation) early in the development pathway, thus contributing to achieving the sustainable development goals.

This study aims to address the data gap by evaluating GHG emissions from consolidated rice fields in the RRD, the main rice production area in the north of Vietnam, using survey-based activity data of agricultural practices and applying the life cycle assessment (LCA) methodology. LCA is a widely accepted approach for quantifying environmental impacts across a product’s entire life cycle—from production and transport to use and disposal [16]—and has been successfully applied in rice production studies across China [10,17,18,19,20], India [21], Thailand [22,23], and Myanmar [24].

A local household-level survey was conducted in a representative rice plantation commune of Hai Duong province in the RRD to generate data related to activity levels. Using these survey data on rice cultivation, irrigation, and input use, among others, this study provides a bottom–up, field-specific emission inventory of GHG emissions. It also presents RRD regional-scale emission estimates under realistic scenarios of yield and straw management, thereby bridging the gap between localized data and policy-level application. The novelty of this study lies in (i) using local-level, field-specific data from consolidated plots and (ii) estimating regional GHG emissions under realistic straw management and productivity scenarios.

2. Materials and Methods

2.1. Study Area

The RRD covers a total area of nearly 21,278 km² (about 6,4% of the national total area). It comprises two large municipalities of Hanoi Metropolitan Region (HMR), housing the capital, Hanoi, and Hai Phong, together with nine provinces: Vinh Phuc, Bac Ninh, Hai Duong, Hung Yen, Thai Binh, Ha Nam, Nam Dinh, Ninh Binh, and Quang Ninh (Figure 1). This region houses nearly 23.7 million residents as of 2023, with an average population density of 1115 capita/km², which is well above the country’s average of 302 capita/km² [4]. The rural population in the RRD comprised nearly 61 % of its total population in 2023. The RRD climate zone is characterized by a tropical monsoon climate with two distinct seasons: a cold, dry winter and a hot, humid summer with heavy rainfall. The average annual rainfall is 1700 mm, and the average annual temperature ranges from 23 to 24 °C. These climatic conditions are generally favorable for growing rice and other agricultural crops.

The survey was conducted in Quang Nghiep commune, located in the northwest of Tu Ky District, Hai Duong Province, which is a representative rice cultivation area of the RRD (Figure 1). Hai Duong province implemented land consolidation and field reorganization from 2013 to 2015, following Plan 1704/KH-UBND dated 13 September 2013, issued by the Provincial People’s Committee. The surveyed commune covers an area of 368 hectares, comprising three villages with a total of 1632 households. The agricultural land area is 255 hectares, of which rice cultivation accounts for 60% (154 hectares). Rice cultivation in the area has two crop cycles per year. As a result of land consolidation, each household in the commune owns 1–2 plots, as compared to 3–4 plots prior to land consolidation [25].

2.2. Data Collection

Data were collected through structured face-to-face household interviews regarding rice production for the spring and summer crops of 2023. The interviews were conducted from March to April 2024, ensuring that farmers provided accurate retrospective information for both cropping seasons. The survey revealed that rice fields in each village within the commune had relatively uniform soil and irrigation conditions. Random systematic sampling was applied, and 15 households were selected from each village, resulting in a total of 45 surveyed households. As most households owned only 1–2 rice fields, the information provided was relatively consistent and easy to verify. The structured questionnaire covered key aspects of rice production, including yield, rice field area, varieties/growth duration, use of agricultural inputs, and water management practices during the growing season.

Energy consumption data for land preparation and harvesting were collected through interviews with the communal machinery management team, who monitor fuel usage for shared agricultural equipment. Irrigation electricity consumption was estimated based on the maximum allowable electricity consumption for the RRD region [26].

2.3. System Boundary and Functional Unit for LCA

The LCA method was employed to estimate GHG emissions from rice production following the British Standards Institute (BSI) guidelines of PAS 2050:11 [27] in both units, i.e., the emissions per unit area in kg or tons of CO₂-equivalent per hectare (CO₂-eq/ha) and emissions per unit of weight (CO₂-eq/ton of grain). Similar to other studies [10,12,13,28], this study utilized the “Cradle to Farmgate” system boundary to estimate emissions for each cropping season. Within this boundary, production inputs are considered from planting to harvesting for each rice crop cycle.

2.4. Estimation of Greenhouse Gas Emissions

Rice production is the source of both indirect and direct GHG emissions. Indirect emissions come from the activities related to the production of inorganic fertilizers, pesticides, and nylon sheets used for rodent barriers, as well as energy use for land preparation, harvesting, spraying pesticides, and irrigation. In addition, N₂O emissions also stem from atmospheric deposition and the leaching or runoff of nitrogen into the environment. Specifically, the activities releasing direct GHG emissions include the use of diesel for land preparation, direct N₂O emissions from the application of nitrogen-containing fertilizers, and methane emissions during rice cultivation. The latter depend on water management practices and the amount of straw incorporated into the soil after harvest.

GHG emissions from these activities were estimated using the emission factors (EFs) and the activity data. This study used EFs taken from a published and reputable database. Specifically, EFs for inorganic fertilizers (N, P₂O₅, K₂O) were taken from the FAO’s global database on GHG emissions from feed crop production [29], while those for pesticides were adopted from Audsley et al. [30], who classify emissions by product types due to limited data on active ingredients. Emission factors for all relevant inputs are detailed in

Table A1. Greenhouse gas emission factors for agricultural inputs in rice production.

Emission Sources	Emission Factor (kg CO2-eq per Specified Unit)	References
INDIRECT EMISSIONS
Seed	1.12 kg CO2-eq/kg seed	[48]
Inorganic fertilizer
N	4.00 kg CO2-eq/kg N	[29]
P	1.29 kg CO2-eq/kg P2O5-eq	[29]
K	1.47 kg CO2-eq/kg K2O-eq	[29]
Pesticide
Herbicide	55.44 kg CO2-eq/kg a.i.	[29,30]
Insecticide	44.94 kg CO2-eq/kg a.i.	[29,30]
Fungicide	36.33 kg CO2-eq/kg a.i.	[29,30]
Electricity	0.68 kg CO2-eq/kWh	[40]
Diesel	0.38664 CO2-eq/L diesel	[28]
Plastic sheet	2.51 kg CO2-eq/kg plastic sheet	[49]
N2O
Atmospheric deposition ${(EF}_{N_2{O}_{ATD }})$	0.014 kg N2O-N (kg NH3-N + NOx-N volatilized)−1	[31]
${Frac}_{GASF}$	0.11 kg NH3-N (kg N applied)−1	[31]
Leaching               $\left({EF}_{N_2{O}_{L }}\right)$	0.011 kg N2O-N (kg N leaching/runoff)−1	[31]
${Frac}_{LEACHING}$	0.24 kg N (kg N additions)−1	[31]
DIRECT EMISSIONS
Diesel	2.75 kg CO2-eq/L diesel	[32,50,51]
N2O (during rice cultivation)
Spring rice $\left({EF}_{N_2{O}_{direct}}\right)$	0.0034 kg N2O-N/kg N	[32]
Summer rice ${(EF}_{N_2{O}_{direct}})$	0.0025 kg N2O-N/kg N	[32]
CH4 (during rice cultivation)
Spring crop $\left({EF}_c\right)$	1.61 kg CH4/day/ha	[32]
Summer crop $\left({EF}_c\right)$	3.43 kg CH4/day/ha	[32]

(Appendix A). Activity data—including quantities of fertilizers, fuel, electricity, and other inputs—were obtained from the structured household surveys, as described in Section 3.1. Note that this study calculated the emissions for the base year 2023 in CO₂ equivalents for 100 year-horizon.

Estimation of GHG emissions from agricultural input materials:

GHG emissions from the production of seeds, inorganic fertilizers, pesticides, energy use, and nylon for rodent barriers are calculated using the following formula:

$${GHG}_i=\sum (I_i\times {EF}_i)$$

(1)

where GHG_i is the total GHG emissions (kg CO₂-eq) from the agricultural input i per hectare of rice field; I_i is the quantity of input i; and EF_i is the EF of input i.

• Estimation of N₂O and CH₄ Emissions:

Direct and indirect N₂O emissions from the use of synthetic fertilizers (F_SN) and the incorporation of straw residues (F_CR) were estimated according to IPCC (2019) guidelines [31]. Specifically, Tier 2 emission factors from the Vietnamese Ministry of Agriculture and Rural Development (MARD) [32] were applied for direct N₂O emissions in spring and summer rice seasons in northern Vietnam, while Tier 1 methods were used for indirect N₂O emissions (Appendix A Table A1):

$${GHG}_{N_{2}O}={GHG}_{N_2{O}_{direct}}+{GHG}_{N_2{O}_{indirect}}$$

(2)

$${GHG}_{N_2{O}_{direct}}=(F_{\mathrm{S}\mathrm{N}}+F_{\mathrm{C}\mathrm{R}})\times {EF}_{N_2{O}_{direct}}\times {\displaystyle \frac{44}{28}}\times 265$$

(3)

$${GHG}_{N_2{O}_{indirect}}={GHG}_{N_2{O}_{ATD}}+{GHG}_{N_2{O}_L}$$

(4)

$${GHG}_{N_2{O}_{ATD}}=F_{SN}\times {Frac}_{GASF}\times {EF}_{N_2{O}_{ATD }}\times {\displaystyle \frac{44}{28}}\times 265$$

(5)

$${GHG}_{N_2{O}_L}={(F}_{SN}+F_{CR})\times {Frac}_{LEACHING}\times {EF}_{N_2{O}_{L }}\times {\displaystyle \frac{44}{28}}\times 265$$

(6)

where ${GHG}_{N_2{O}_{direct}}$ and ${GHG}_{N_2{O}_{indirect}}$ are the amounts of direct and indirect N₂O emissions, respectively, from rice cultivation (in kg CO₂-eq/ha) resulting from the use of N (kg N/ha) in synthetic nitrogen fertilizers (F_SN) and crop residues left after harvest (F_CR); ${GHG}_{N_2{O}_{ATD}}$ and ${GHG}_{N_2{O}_L}$ are the amounts of N₂O emissions from atmospheric deposition and leaching/runoff of N; ${EF}_{N_2{O}_{direct}}$ is the direct N₂O EF (kg N₂O-N/kg N); ${EF}_{N_2{O}_{ATD }}$ and ${EF}_{N_2{O}_{L }}$ are the EF for N₂O emissions due to volatilization and leaching/runoff of nitrogen from fertilizers ${EF}_{N_2{O}_{direct}}$ or crop residues; Frac_GASF, Frac_GASM, and Frac_LEACHING are the fraction factors for nitrogen volatilization into the atmosphere from inorganic fertilizers (GASF) and nitrogen leaching/runoff (LEACHING) (detailed in

Table 1. Survey results of for activity data related to rice production in spring and summer rice crops.

Items	Unit	Spring Crop	Summer Crop
		Mean ± 1 SD	Mean ± 1 SD
Plot size	ha	0.17 ± 0.09	0.17 ± 0.09
Rice yield	t/ha	5.75 ± 1.09	5.39 ± 1.00
Seed	kg/ha	63.95 ± 22.69	64.26 ± 23.59
Growing duration	days	118.33 ± 6.22	105.67 ± 6.62
Fertilizer
N	kg/ha	119.21 ± 41.9	120.11 ± 42.87
P2O5	kg/ha	21.27 ± 31.21	21.55 ± 31.12
K2O	kg/ha	54.25 ± 40.17	55.37 ± 40.81
Pesticides
Herbicide	kg a.i./ha	0.20 ± 0.03	0.38 ± 0.06
Insecticide	kg a.i./ha	0.75 ± 0.16	1.02 ± 0.12
Fungicide	kg a.i./ha	0.47 ± 0.33	0.13 ± 0.10
Plastic film	kg/ha	35.98 ± 20.35	35.98 ± 20.35
Diesel	l/ha	87.5 ± 0.00	87.5 ± 0.00
Electricity	kwh/ha	406.03 ± 0.59	374.69 ± 1.37

Note: a.i., active ingredients.

); 44/28 is the conversion factor for the molecular weight ratio of N₂ to N₂O [33]; and 265 is the 100-year global warming potential (GWP) of N₂O relative to CO₂ [34].

A limitation of this study is that the Tier 2 emission factors used to estimate direct N₂O emissions reflect average rice production conditions in northern Vietnam and do not account for variations in irrigation practices, such as continuous flooding versus intermittent drainage, which may influence N₂O emission levels.

Methane emissions during rice cultivation are calculated following IPCC (2019) [31]:

$${GHG}_{{CH}_4}={EF}_d\times t\times 28$$

(7)

where ${GHG}_{{CH}_4}$ is the amount of CH₄ directly emitted during rice cultivation (kg CO₂-eq/ha); EF_d is the daily CH₄ (kg CH₄/ha/day); t is the number of days of the rice-growing period and depends on the season and rice variety; and 28 is the 100-year GWP of CH₄ relative to CO₂ [34].

EF_d is calculated as follows:

$${EF}_d={EF}_c\times {SF}_w\times {SF}_p\times {SF}_o$$

(8)

where EF_c is the baseline EF under continuously flooded fields (kg CH₄/ha/day), varying by crop season to reflect temperature differences. EFc values are based on Tier 2 national emission factors issued by Vietnam’s Ministry of Agriculture and Rural Development ([32]; Table A1). SF_w is the scaling factor for EF_c based on irrigation practices during the cultivation period. In this study, as all rice fields were drained twice per season, SFw = 0.55, following Table 5.12 of the IPCC 2019 Refinement (Volume 4, Chapter 5—Cropland, p. 5.54). SF_p is the scaling factor for the differences in water regime in the pre-season and the cultivation period; in this study, we assessed double cropping of rice in a non-flooded pre-season <180 days. Hence, SF_p = 1. SF_o is the scaling factor for EF_c for the application of organic fertilizers and is calculated using Equation (9).

$${SF}_o={\left(1+\sum _i{ROA}_{i }\times {CFOA}_i\right)}^{0.5}$$

(9)

ROA_i is the application rate of organic amendment i. In this study, farmers did not apply organic fertilizers, so the organic matter added was only from the straw incorporated into the soil after harvest. As all rice fields in the surveyed commune were harvested using combine harvesters, it is assumed that all the straw and stubble were returned to the soil. CFOA_i is the conversion factor for organic matter amendments assumed to be 0.19 for straw incorporation for more than 30 days [31].

The scenario analysis included rice straw burned in the field, with methane (CH₄) and nitrous oxide (N₂O) emissions from straw burning (GHG_B) calculated by applying EFs of 2.7 g/kg for CH₄ and 0.07 g/kg for N₂O to the quantity of crop residues burned, following the IPCC (2006) guidelines.

• Estimating Total GHG Emissions:

The total GHG emissions per unit area (kg CO₂-eq/ha) for a single crop cycle of a rice field are calculated as follows:

$${GHG}_{per unit area}={GHG}_I+{GHG}_{N_{2}O}+{GHG}_{{CH}_4}+{GHG}_B$$

(10)

The total GHG emissions per unit of weight (kg CO₂-eq/kg grain) for a rice field are calculated as follows:

$${GHG}_{per unit weight}={\displaystyle \frac{{GHG}_{per unit area}}{Yield}}$$

(11)

2.5. Data Analysis

The data were processed using Microsoft Excel 365. Descriptive statistics such as mean values and standard deviations were calculated for key variables. An independent samples t-test was used to assess differences in GHG emissions between the spring and summer crops, after verifying the assumptions of normality and equal variances. GHG emissions from the RRD under land consolidation conditions were estimated using official statistical data on average rice yield in 2023, combined with typical regional straw management practices.

3. Results and Discussion

3.1. Survey Results—Characteristics of Rice Production and Agricultural Inputs

The rice cultivation area for each household in the survey commune has been aggregated into a single plot, with an average area of 0.17 ha (Table 1). Note that not all areas in the RRD have such a consolidated rice field per household. The average RRD plot size is 0.09 ha (three plots per household) [8]. In the midland region of Bac Giang province (not belonging to the RRD), each household has five and nine plots, with an area of about 0.02–0.05 ha per plot [6]. Generally, larger plot sizes make land preparation and water management across the fields more convenient and resource-efficient.

All households interviewed in the commune practiced draining water when the rice stopped tillering to reduce unproductive tillers. Additionally, to facilitate mechanical harvesting, after the rice was nearly heading, irrigation was stopped to allow the fields to dry gradually until harvest. A similar double-drainage application on consolidated land of rice fields may be further assumed for all provinces of the RRD. Note that water drainage practices affect GHG emissions. However, there are no statistics on the extent of water drainage application in rice fields across all areas in Vietnam. In the Mekong Delta, about 35–52% of households practiced alternate wetting and drying during the rice vegetative stage [35]. In the midland region, about 50% of households practiced at least one drainage cycle during the tillering phase [13]. In China, for example, Minh [10] found that rice-growing farmers often practiced multiple water drainages in fields with large plot sizes due to land consolidation, while only draining once mid-season in fields with small plot sizes.

In larger rice fields after consolidation, mechanization is implemented in almost all stages of rice production, i.e., the use of plowing and harrowing machines for land preparation, combine harvesters for harvesting, and electric sprayers for pesticide application. Diesel fuel consumption is 87.5 L per hectare in both seasons. Electricity is consumed to pump irrigation water and to power electric sprayers, amounting to 406.03 kWh/ha for the spring season, higher than the 374.69 kWh/ha for the summer season.

Rice is grown by direct seeding, with a seed rate of about 64 kg/ha for both the spring and summer seasons. The growth period for the spring season is 118 days (from February to May), while for the summer season it is about 106 days (from July to October). The yield of rice in the spring season (5.8 t/ha) tended to be higher than in the summer season (5.4 t/ha). Overall, the yield in both crop seasons was not high and showed significant variation among households (SD = 1 ton/ha). Farmers reported that in 2023, rice was heavily damaged by rats, despite using nylon barriers around the fields and rodenticides.

Farmers only used chemical fertilizers for rice, with application rates almost the same in both seasons (119–120 kg N/ha, 21 kg P₂O₅/ha, and 54–55 kg K₂O/ha). Herbicides and insecticides were used more in the summer season (0.38 and 1.02 kg a.i./ha, respectively) compared to the spring season (0.2 and 0.75 kg a.i./ha). However, fungicides were used more in the spring season (0.47 kg a.i./ha) compared to the summer season (0.13 kg a.i./ha).

Regarding the management of straw after harvest, out of the 45 surveyed households, only 2 households burned the straw, and 3 households collected the straw for feeding cattle or other purposes. This indicates that the amount of rice straw taken away or burned is insignificant, and hence it may be assumed that most of the straw (up to 95%) in this surveyed commune is left in the field and plowed under.

3.2. GHG Emissions from Rice Production

The specific GHG emissions from rice production in the surveyed commune are presented in

Table 2. Total GHG emissions per unit area and per unit of weight in spring and summer crop from surveyed commune.

GHG Emissions	Spring Crop (Mean ± 1 SD)	Summer Crop (Mean ± 1 SD)	p-Value a
t CO2-eq/ha	6.8 ± 0.5	11.4 ± 1.0	0.000
t CO2-eq/t grain	1.2 + 0.2	2.2 ± 0.4	0.000

^a p < 0.05 indicates a significant difference among the means.

. The results show that, owning to a higher EFc (Table A1, Appendix A), the emissions during the summer crop were 11.4 t CO₂-eq/ha (equivalent to 2.2 t CO₂-eq/t grain), significantly higher than those during the spring crop, at 6.8 t CO₂-eq/ha (equivalent to 1.2 t CO₂-eq/t grain) (p < 0.001). These values are lower but still generally comparable to those found for the Mekong Delta (9.7–16.6 t CO₂-eq/ha, equivalent to 1.6–2.7 t CO₂-eq/t grain) [12] and for the Northern Midlands of Vietnam (9.5–15.6 t CO₂-eq/ha, equivalent to 1.3–2.3 t CO₂-eq/t grain) [13]. Our findings are also comparable to those in other rice-growing countries, such as the central [20] and eastern regions [36] of China and the Western Ghats, India [37]. Some studies in Thailand and Myanmar, however, have reported lower GHG emissions, i.e., 3.8–5.2 t CO₂-eq/ha in Phichit, Thailand [38], and 4.6–4.9 t CO₂-eq/ha in Myanmar [24]. The difference in GHG emissions from rice production across studies is explained by differences in the growing season/period, water management, straw management, and the extent of agricultural input. The lower GHG emissions in our case study compared to those in the Mekong Delta and the Northern Midlands of Vietnam are primarily due to lower methane emissions, which are explained by the fact that 100% of the rice fields in the study area adopted double-drainage practices in each crop cycle (discussed further in Section 3.3). Further, the emissions during the summer crop were nearly double those during the spring crop, indicating that reducing the rice cultivation area in the summer crop could help mitigate GHG emissions. This option is even more attractive given the survey results showing a higher yield of the spring crop than the summer crop. It may be considered a relevant measure for GHG emissions reduction while increasing rice production to achieve sustainable development goals, i.e., cutting the peak of emissions sooner in the EKC [14].

3.3. Analysis of GHG Emission Activities Under Current and Alternative Scenarios

3.3.1. Current Emissions

Methane emissions directly from rice paddies during cultivation are the largest contributors to the total GHG emissions from rice production (

Table 3. GHG emissions from different activities in spring and summer crops (kg CO₂-eq/ha).

Items	Spring Crop (Mean ± 1 SD)	Summer Crop (Mean ± 1 SD)
Inputs
Seed	83.1 ± 29.5	83.5 ± 30.7
Fertilizer	584.1 ± 167.9	589.6 ± 172.3
N	476.9 ± 167.6	480.4 ± 171.5
P2O5	27.4 ± 40.3	27.8 ± 40.1
K2O	79.8 ± 59.1	81.4 ± 60
Pesticides	61.9 ± 16.1	71.7 ± 7.3
Herbicide	11 ± 1.8	21.1 ± 3.3
Insecticide	33.7 ± 7.3	45.7 ± 5.5
Fungicide	17.2 ± 11.9	4.8 ± 3.6
Machinery operation	276.8 ± 0.9	276.8 ± 0.9
Irrigation	272.1 ± 0	250.8 ± 0
Plastic film	90.3 ± 51.1	90.3 ± 51.1
Field emissions
CH4	4936.6 ± 417.9	9618.4 ± 1022.1
N2O	506.9 ± 134.7	452.6 ± 119.3
N-fertilizer	376.3 ± 132.3	334.1 ± 119.3
N-straw	130.6 ± 24.3	118.5 ± 22.5

and Figure 2). These emissions account for 84% of the total emissions (9618 kg CO_2-eq/ha) in the summer crop and 73% (4937 kg CO₂-eq/ha) in the spring crop. This finding is similar to those reported in previous studies in Vietnam and the other countries mentioned in Section 3.2. In our study, methane emissions were calculated for different activities using the EFs specific to each crop season, reflecting water management practices and the amount of straw incorporated after harvest (Table A1 and Equation (8)). The higher baseline EFc in the summer season is the main factor causing its higher methane emissions compared to the spring crop, i.e., nearly double.

Large consolidated field sizes and convenient irrigation management allow for the application of double water drainage, i.e., farmers drain the water twice: at the end of the tillering stage and from the heading stage until harvest (25–30 days before harvesting). This practice reduces methane emissions by 45% compared to continuous flooding, i.e., SFw = 0.55 in Equation (8) (0.45 less than the value of 1.0 used for continuous flooding) [31]. As a result, the methane emissions in our study area are considerably lower than in the Northern Midlands and Mekong Delta regions in Vietnam. Specifically, in the Northern Midlands, with about 50% of farmers draining water once per season, methane emissions are 13,917 kg CO₂-eq/ha in the summer crop and 7621 kg CO₂-eq/ha in the spring crop [13]. In the Mekong Delta, with 35% of farmers practicing intermittent flooding [35], the average methane emissions per rice crop is 11,673 kg CO₂-eq/ha (extracted from [12]). This difference highlights how improved water management in consolidated rice fields can effectively reduce total GHG emissions. Moreover, the widespread adoption of such climate-smart practices—facilitated by land consolidation—also reflects the potential alignment of agricultural transitions in Vietnam with the EKC hypothesis, i.e., similar to the shift to more production in the spring crop as discussed above.

The use of fertilizers and N₂O emissions from the field are the next major contributors to total GHG emissions from rice production. These sources account for 9% (1042 kg CO₂-eq/ha) in the summer crop and 16% (1090 kg CO₂-eq/ha) in the spring crop (Figure 2 and Table 3). Among these, the main contribution is from nitrogen fertilizer, accounting for 8% of the total GHG emissions in the summer crop and 15% in the spring crop (Figure 3). The nitrogen application rate in this study is 120 kg/ha, higher than the rate in the Northern Midlands (93–103 kg/ha [13]) and the recommended rate for Hai Duong province (70–80 kg N/ha [39]). Reducing nitrogen use in rice production may be considered a potential option to mitigate GHG emissions.

While water regime and N input were explicitly considered, soil pH and texture—factors known to influence N₂O and CH₄ emissions—were not included. The Tier 2 emission factors used reflect average regional conditions and do not differentiate by soil properties, which is a limitation. Field-specific data may improve future estimates.

Energy use for machinery operation and irrigation also contributes significantly to GHG emissions, accounting for 4% (527.6 kg CO₂-eq/ha) in the summer crop and 8% (548.9 kg CO₂-eq/ha) in the spring crop. These values are slightly lower than those reported for the Northern Midlands [13], which were 3.6% (564 kg CO₂-eq/ha) for the summer crop and 6.4% (601 kg CO₂-eq/ha) for the spring crop, despite higher mechanization levels, particularly during harvesting. Note that we used the national electricity grid emission factor of 0.68 kg CO₂-eq/kWh [40], which is lower than the 0.86 kg CO₂-eq/kWh used in a previous study in the Northern Midlands region [13]. This difference is due to the annual updates of grid emission factors reflecting changes in the national energy mix. Our study applied the most recent emission factor published in 2022, whereas the earlier study used the 2019 value. This variation in emission factor may partially explain the lower energy-related GHG emissions observed in our results. The remaining contributions from pesticides, rodent-proof nylon, and seeds are negligible, each accounting for only 1%.

3.3.2. Emissions Under Alternative Straw Management Scenarios

Methane and N₂O emissions are strongly influenced by the amount of straw incorporated into the soil after harvest. In the surveyed commune, favorable conditions for mechanization have enabled 100% of households to use combine harvesters. Consequently, most of the straw is left in the field and incorporated into the soil through plowing for decomposition, a practice that significantly contributes to both methane and N₂O emissions.

To assess the potential for emissions reduction, an alternative scenario was evaluated in which straw is not incorporated into the soil. Under this scenario, methane emissions were reduced by approximately 40%, from 9618 to 5582 kg CO₂-eq/ha in the summer crop and from 4937 to 2934 kg CO₂-eq/ha in the spring crop. N₂O emissions also decreased by about 26%, from 507 to 376 kg CO₂-eq/ha in the spring and from 453 to 334 kg CO₂-eq/ha in the summer crop. As a result, the total GHG emissions declined by 31% in the spring crop (from 6812 to 4679 kg CO₂-eq/ha) and by 36% in the summer crop (from 11,434 to 7279 kg CO₂-eq/ha), as shown in

Table 4. GHG emissions (CH₄, N₂O, and total) from rice fields under various straw management scenarios in spring and summer crops (kg CO₂-eq/ha).

Items	Spring Crop		Summer Crop
	With Straw Incorporation	Without Straw Incorporation	With Straw Incorporation	Without Straw Incorporation
CH4	4936.6 ± 417.9	2934.0 ± 154.1	9618.4 ± 1022.1	5581.5 ± 349.8
N2O	506.7 ± 134.7	376.3 ± 132.3	452.6 ± 119.3	334.1 ± 119.3
Total GHG emissions a	6811.7 ± 505.7	4678.5 ± 311.0	11,433.9 ± 1014.2	7278.5 ± 383.2

^a Total GHG emissions include CH₄, N₂O, and other emission sources associated with rice production activities (e.g., fertilizer application, machinery operation, and irrigation).

. These results emphasize the critical role of straw management in reducing emissions from rice production.

Nevertheless, open-field burning of straw—a common practice in many regions—leads to the release of not only GHGs but also short-lived climate pollutants (SLCPs), such as black carbon, and various toxic air pollutants, including fine particulate matter and hazardous gases [41,42]. Alternative approaches to straw disposal, such as off-site utilization for composting, mushroom cultivation, or biofuel production, offer effective emission reduction pathways. However, these alternatives are often labor-intensive and less attractive to farmers [43], likely due to the absence of viable business models and weak supply chain infrastructure. Recently, microbial products designed to accelerate straw decomposition in the field have been introduced in several provinces of Vietnam. These technologies present a promising solution, as they reduce methane emissions while retaining organic nutrients in the soil for subsequent cropping seasons [44].

3.4. Estimation of GHG Emissions from Rice Production in RRD

To explore the implications of land consolidation and broader mitigation opportunities, we estimated GHG emissions from rice production in the RRD under typical conditions for 2023. The RRD, located in the lower reaches of the Red River and Thai Binh River in northern Vietnam, encompasses 11 provinces, as detailed in Figure 1. The region covers a total natural area of 2127.8 thousand hectares, including 771 thousand hectares of agricultural land. Rice is cultivated in two cropping seasons annually. In the spring season, 477 thousand hectares are used for rice cultivation, with an average yield of 6.7 tons per hectare, while in the summer season, 460 thousand hectares are used with average crop yields of 5.7 tons per hectare [4]. These yields are notably higher than those recorded in the surveyed commune, where average yields were 5.75 and 5.39 tons per hectare for the spring and summer–autumn seasons, respectively (Table 1).

Post-harvest rice straw management in the RRD is highly diverse. Based on previous studies, it is estimated that approximately 25% of straw is openly burned in the field, 30% is incorporated into the soil, and 45% is collected for off-site use [45,46]. These proportions were applied in this study to estimate greenhouse gas emissions from rice production across the RRD. In applying these estimates, we followed common assumptions used in previous research [12]: when straw is collected for off-site use, it is assumed that 5% remains in the field and is incorporated into the soil; similarly, under open-field burning practices, 20% of the straw is considered incorporated into the soil, while the remaining 80% is burned. These assumptions were used to reflect practical field conditions and to calculate the associated CH₄ and N₂O emissions under different straw management regimes.

Table 5. Total GHG emissions per unit area and per unit of weight in spring and summer crops on consolidated land in RRD.

GHGs	Spring Crop	Summer Crop
Specific emissions
t CO2-eq/ha	5.8	9.4
t CO2-eq/t grain	0.9	1.7
Total grain production in 2023
Million tons	3.2	2.6
Total GHG emissions, Tg
GHG per crop	2.9	4.4
Total GHG of both crops	7.3

presents estimates of GHG emissions associated with consolidated farmland in the RRD under typical production conditions in 2023 (including yield levels and straw management practices) during both rice cropping seasons. The results show that emissions in the spring season are 5.8 t CO₂-eq per hectare and 0.9 t CO₂-eq per ton of grain, which are lower than those in the summer–autumn season (9.4 t CO₂-eq/ha and 1.7 t CO₂-eq/t grain, respectively). These findings suggest that the RRD has lower GHG emissions compared to the surveyed commune, likely due to higher rice productivity and a lower proportion of straw incorporation into the soil. However, when assessing GHG emissions, it is necessary to consider SLCFs to obtain a more comprehensive assessment of emissions and the associated climate and environment effects. This incorporation is recommended for future studies.

Using official rice production statistics [4], total GHG emissions from rice cultivation in the Red River Delta (RRD) were estimated and are presented in Table 5. These emissions represent approximately 10.5% of Vietnam’s total agricultural CO₂ emissions in 2021, which were reported at 69.6 Tg [47]. This regional estimate highlights the significant contribution of rice production in the RRD to national emissions and underscores the need for mitigation measures, particularly through improved productivity and better straw management practices. Future studies should also incorporate emissions from short-lived climate forcers (SLCFs), such as black carbon and organic carbon, as well as other air pollutants including fine particulate matter and toxic gases, to provide a more comprehensive assessment of environmental and climate impacts.

4. Conclusions

GHG emissions from rice production under conditions of consolidated land in the surveyed commune of the RRD for the summer crop were 11.4 t CO₂-eq/ha (equivalent to 2.2 t CO₂-eq/t grain), significantly higher than those for the spring crop, at 6.8 t CO₂-eq/ha (equivalent to 1.2 t CO₂-eq/t grain). Methane emissions directly from rice paddies during cultivation were the largest contributors, totaling 9618 kg CO₂-eq/ha in the summer, accounting for 84% of the total emissions in that period, and 4937 kg CO₂-eq/ha in the spring, accounting for 73% in that period. The use of fertilizers and N₂O emissions from the field are the next major contributors, collectively accounting for 9% and 16% of the total in the summer crop and the spring crop, respectively. Energy use for machinery operations and irrigation also contributes significantly, i.e., 4% and 8% of the total in the summer and spring crops, respectively.

The specific GHG emissions in the surveyed commune are lower than those in the Mekong Delta and Northern Midlands of Vietnam mainly due to lower methane emissions, which can be partly explained by the application of double-drainage practices in rice cultivation. Land consolidation efforts resulted in larger rice fields and are thus proven to enhance efficient water management, an important factor in reducing GHG emissions. Better straw management practices and more rational fertilizer use should be adopted to further reduce GHG emissions. The fact that GHG emissions in the summer crop are nearly double those in the spring season indicates that reducing the rice cultivation area in the summer crop will also contribute to reducing GHG emissions.

GHG emissions were calculated for rice production in the entire RRD based on the surveyed results but accounting for the difference in straw management practices. As of 2023, rice production in the RRD releases 7.3 Tg of GHG in CO₂-eq per year (100-year horizon), a significant contribution to the total national GHG emissions. The lower specific GHG emissions in the RRD compared to the surveyed commune are due to less straw incorporated into the soil for degradation.

While this study provides valuable insights, its findings contain several limitations. The survey was conducted only with a single commune, and emissions were calculated for only one production year, thus limiting the spatial and temporal generalizability of the results. To overcome these limitations, future surveys should be conducted in different areas of the RRD. The emission trend should be analyzed, especially to reflect the impacts of national low-emission development strategies promoting climate-smart rice production. In addition, several emission factors—particularly those related to fertilizers and pesticides—were obtained from the literature. Local measurements of emission factors are required to fully capture the local agricultural practices and conditions of the region. Future research should therefore adopt multi-site, multi-year designs and prioritize the development of locally calibrated emission factors. Such efforts are essential to improve the accuracy of emission estimates and to support the advancement of climate-smart rice production in alignment with national low-emission development strategies.