Carbon Footprint Assessment of Recirculating Tank and Pond Systems for African Catfish (Clarias gariepinus) in Sta. Cruz, Laguna, Philippines

Journal of Human Ecology and Sustainability Citation Gipanao, A. U., De Los Reyes, R. B, Saludes, R. B, & Lampayan, R. M. (2025). Carbon Footprint Assessment of Recirculating Tank and Pond Systems for African Catfish (Clarias gariepinus) in Sta. Cruz, Laguna, Philippines. Journal of Human Ecology and Sustainability, 3(1), 5. doi: 10.56237/jhes-iceat2025-002 Corresponding Authors Angel U. Gipanao ([email protected]) Rosa B. De Los Reyes ([email protected]) Academic Editors Perlie P. Velasco Rowena B. Carpio Received: 15 April 2025 Revised: 05 September 2025 Accepted: 12 September 2025 Published: 23 October 2025 ©The Author(s) 2025. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND 4.0) license (https://creativecommons.org/ licenses/by-nc-nd/4.0/). Original Research Article Carbon Footprint Assessment of Recirculating Tank and Pond Systems for African Catfish (Clarias gariepinus) in Sta. Cruz, Laguna, Philippines Angel U. Gipanao 1 , Rosa B. De Los Reyes 1 , Ronaldo B. Saludes 2 , and Rubenito M. Lampayan 1 1Land and Water Resources Engineering Division, Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, College, Laguna, 4031, Philippines 2Agrometeorology, Bio-Structures and Environment Engineering Division, Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, College, Laguna, 4031, Philippines Abstract The carbon footprint of aquaculture in the Philippines remains underexplored, and the data on species-specific emissions across various aquaculture systems are limited. This study evaluates the carbon footprint of African catfish production in recirculating tank and pond systems through a cradle-to-gate approach. Greenhouse gas emissions were quantified for each phase of fish culture, including facility establishment, production, harvesting, packaging, and distribution. This study showed that catfish farming in a recirculating system with a stocking density of 45 k g m−3 and a cropping frequency of 2 cycles per year generated 12.04 k g CO2eq k g LW −1 or 5.01 t on C O2eq yr −1 . In contrast, the pond system with a stocking density of 10 k g m−3 and the same cropping frequency produced an average of 2.80 k g CO2eq k g LW −1 or 17.7 t on C O2eq yr −1 . The production phase was the largest contributor to the overall carbon footprint in both systems, with electricity comprising 75% of emissions in the recirculating tank system and fish feed accounting for 93% in the pond system. Moreover, the scenario used to evaluate the impacts of energy consumption and fingerling delivery logistics on the farm’s overall carbon footprint indicated a reduction in emissions by about 76% by switching to solar energy and utilizing locally produced fingerlings. These findings provide a viable approach to mitigating the environmental impact of African catfish farming in the Philippines. Keywords— African catfish, aquaculture, carbon footprint, greenhouse gas (GHG) 1 1 Introduction The increasing levels of atmospheric carbon dioxide ( CO2 ) have become a major environmental concern, primarily driven by human activities that emit greenhouse gases (GHGs) and contribute to climate change [1,2]. The largest contributors to these emissions include the electricity and heat production sector (24%), industry (21%), agriculture (14%), and transportation (14%), with additional contributions from land use and forest management (11%), and buildings (6.3%) [3]. Although agriculture currently ranks as the third-largest source of greenhouse gas emissions, the increased farm operations, intensified application of fertilizers and pesticides, and energyintensive post-harvest processing, driven by rising global food demand, are poised to amplify its carbon footprint significantly. Carbon footprinting serves as a vital tool for assessing environmental sustainability,guidingpolicy,andsupportingindustryeffortstomeetGHGreductiongoals[4,5]. The carbon footprint represents the total GHG emissions associated with a product, service, or process, typically expressed in carbon dioxide equivalents ( CO2eq ) [6,7]. By identifying the emission sources and the stages at which they occur, carbon footprinting offers insights for designing more sustainable systems. In the Philippines, the Agriculture, Forestry, and Fishing (AFF) sector achieved a total Gross Value Added (GVA) of PhP 2.10 trillion in 2022. Fishing and aquaculture activities recorded a value of PHP 269.64 billion, or a 12.82% share, following agricultural crops with 46.41% and livestock with 14.25% [8]. In 2023, aquaculture production reached 2.38 million MT in 2023, 34,771.35 MT higher than the volume of production in 2022 [8]. Aquaculture provided the largest share of fisheries production volume at 56.0%, while capture fisheries (including both commercial and municipal sectors) contributed 44.1%. This positions the Philippines as the 12th largest producer of aquaculture globally, and notably, the 4th largest producer of seaweeds. Although aquaculture is widely acknowledged for its potential to enhance food security, it also presents considerable environmental challenges. On a broader scale, its rapid expansion has contributed to ecosystem degradation, including the destruction of wetlands, loss of biodiversity, and the spread of invasive species [9,10]. Moreover, the industry requires significant inputs of land and water, which can lead to inefficient resource utilization and increased environmental stress [9, 11,12]. Aquaculture systems can significantly influence soil and water quality. The application of lime and other soil treatments, while intended to optimize growing conditions, may inadvertently cause [13]. In addition, deteriorating water quality is often linked to nutrient runoff, excessive feed use, and poor waste management practices [14,15,16]. To address these environmental challenges, alongside declining catch in our municipal waters, and ensure an affordable source of protein for Filipinos, adopting sustainable aquaculture practices is essential in the Philippines. Catfish farming is gaining popularity with the global production reaching 1.25 million MT in 2020 [17], with substantial outputs from countries like South Africa, Nigeria, and the Philippines [18, 19]. In the Philippines, the annual production nearly doubled between 2021 and 2023 from 6,574.25 to 12,513.72 MT [20]. Central Luzon and Davao are the highest producing regions, while Laguna in Region IV-A is the top producing province of African catfish. According to BFAR Region 4A, there are 98 identified African catfish farms in Laguna, with the highest concentrations in Calamba (39 farms), Santa Rosa (29 farms), and Sta. Cruz (9 farms) [8]. These farms predominantly operate using pond culture, though tank systems are also employed in more intensive setups [21]. African catfish (Clarias gariepinus) is widely recognized for its rapid growth, high survival rate, and tolerance to varying water conditions. These characteristics make it highly adaptable to both intensive and small-scale operations [22,23,24]. Given these advantages, the expansion of catfish production in the Philippines is highly promising. It presents a good opportunity for sustainable aquaculture, rural livelihood, and national food resilience. While recent research on African catfish has explored areas such as growth performance, nutritional requirements, welfare, and its potential for integration into alternative systems like aquaponGipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 2 ics [25,26], its environmental footprint remains underexplored. This gap highlights the need for further study to support sustainable aquaculture practices. Hence, this study examines the carbon footprint of African catfish cultured in two aquaculture systems: recirculating tank and pond systems. By assessing GHG emissions across the lifecycle of each system, the study aims to identify emission hotspots and propose mitigation strategies to reduce their carbon footprint. This work contributes valuable data toward advancing sustainability in aquaculture, aligning with the United Nations Sustainable Development Goal 13: Take urgent action to combat climate change and its impacts. 2 Methodology 2.1 Site Selection The municipality of Sta. Cruz, located in the province of Laguna, lies along the banks of the Santa Cruz River, which drains into Laguna Lake (Figure 1). In the past, many residents of the municipality cultivated tilapia in pens and cages within the lake. However, as water quality declined, some shifted to developing ponds and tanks to culture a variety of aquaculture species [27,28] — a 0.84hectare farm in Brgy. Pagsawitan cultivates African catfish using both a recirculating tank system and a pond system, making it a suitable site for comparing greenhouse gas emissions between the two culture systems. The farm combines catfish aquaculture with poultry production, including a dedicated bird facility. Figure 1. The Location of N.T.L Farm in Sta. Cruz, Laguna, Philippines The recirculating tank system (Figure 2) consists of a 2.9m x 2.9m culture tank connected to mechanical and biological filtration units, as well as a sump tank. Wastewater from the culture tank flows by gravity through the filtration units and into the sump tank, where it is then pumped back to the culture tank to ensure water circulation. The pond system, situated adjacent to the recirculating tank, comprises five active ponds and one inactive pond. Three of the ponds measure 7m x 12m, while the remaining ones are larger, measuring 10m x 16m (Figure 3). The typical water depth across all ponds is approximately 1.5 m. The farm relies on electricity supplied by the Manila Electric Company (MERALCO) to operate its equipment, including a 1-hp centrifugal pump, a 3-hp submersible pump, and a 35 W aerator. The Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 3 Figure 2. Flow diagram of the recirculating tank system Figure 3. Examples of pond dimensions: (a) 7 m x 12 m and (b) 10 m x 16 m ponds electricity is utilized for approximately 16 hours a day, while the solar panel supplies power during the remaining 8 hours (typically when solar radiation is at its peak). Although a seven 350 W solar panel has been installed on-site, its potential remains underutilized due to suboptimal placement. For its catfish production, the farm sources fingerlings from Apalit, Pampanga, located approximately 147 kilometers from Laguna. The feed supplies are procured from agricultural stores within Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 4 the municipality of Sta. Cruz. Since its establishment in 2021, the farm has maintained a cropping frequency of two cycles per year, with a stocking density of 45 k gm−3 in recirculating tanks and 10 k gm−3 in ponds. In 2023, approximately 544 kg and 9,407 kg of feeds were given to the fish in the recirculating tank and ponds, respectively. It produced a total of 7,616 kg of African catfish (416 kg from the recirculating tank and 7,200 kg from the ponds), with a feed conversion ratio of 1.31. The harvested fish were distributed across various areas in Luzon, including Batangas and Quezon provinces. Waste generated from the recirculating tank and ponds is repurposed as fertilizer in the nearby rice fields, contributing to sustainable agricultural practices. 2.2 System Boundaries and Parameters The cradle-to-gate system boundary encompasses all processes from facility establishment through production, harvesting, packaging, and distribution to market (Figure 4). It excludes post-market and consumption-related activities. To assess greenhouse gas emissions within this boundary, key parameters considered include the type and quantities of manufacturing materials, fuel consumption, vehicle specifications, and electricity usage. The study adopted the IPCC Guidelines and PAS 2050 in determining the carbon footprint of both aquaculture systems. Data were collected from the farm through a key informant interview using a semi-structured questionnaire. The questionnaire covered a range of topics, including general farm information, operational activities, production metrics, resource inputs, material sourcing and transportation, aquaculture production details, packaging types, and market transfer logistics. Secondary data on emission factors (Table 1) were used to convert activity data into CO2-eq emissions. Table 1. Emission factors considered in this study Category Input Source Fuel used Gasoline (Light Vehicles), Diesel (Light Vehicles), Aviation Gasoline, Gasoline (Motorcycle) [29] Employee, Transportation Vehicle, Type Motorcycle, Tricycle, Jeepney, Taxi/Grab, Car-hatchback, Car-Sedan, CAR-SUV, Pick-up, Bus, Waterborne, Van, Airplane-short haul, Airplane-medium haul, Airplane-long haul, Aircraft [30] Product Transportation, Vehicle Type Rail, HGV and Light Goods Vehicle [31] Manufacturing Concrete, Sand, Steel (rebar), Gravel, PVC pipes, Roof system (steel general), Paint (water borne), Drum (HDPE), Plastics (polyethylene) [32] Fishnet [33] Filter mat (PET), Aquarium Hose (PVC Suspension Polymerized) [34] Aerator [35] Centrifugal Pump [36] Solar Panel [37] Submersible Pond (3hp) [36] Fish Feed [38] Electricity NEG for Luzon-Visayas Grid, NEG of Luzon-Visayas Grid for solar panels [39]] Pond Fertilization Urea, Urea Ammonium Nitrate, Chicken Manure, Lime [38] Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 5 Figure 4. System boundaries for the two aquaculture systems: (a) recirculating tank (b) pond system Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 6 Several limitations should be acknowledged when interpreting the study’s findings. The integrated catfish farming system in Sta. Cruz, Laguna is highly unique, and only one respondent was available for data collection, which restricts the applicability of the results to other farming systems or regions. Transportation distances used in carbon footprint estimations were derived from Google Maps and may not accurately represent actual travel routes. Additionally, emissions from rented construction equipment were excluded due to the lack of fuel consumption data. As a result, the total carbon footprint may be underestimated, and these limitations were considered in the evaluation of the results. 2.3 Methane Emission Estimation The methane was estimated using Equation 1of Zhang et al. [40]: CH4,f l ux =−86.90 l n(DO) + 202.591 (1) where CH4,f l ux is the methane emission of the aquaculture system (gCO2eqm−2yr −1) and DO is the yearly dissolved oxygen measured (mg/L). The methane emissions per square meter served as the emission factor for calculating the methane emissions of the aquaculture systems. This emission factor was multiplied by the area of the deepest part of the recirculating tank and pond system to estimate methane at a uniform depth. 2.4 Carbon Footprint Inventory The aquaculture system’s carbon footprint was estimated using Equation 2, following the approach adopted in previous studies [1]: CF =E F ∗M Q ∗GW P100 ∗conv er si on (2) where CF is the carbon footprint of a material (k gCO2eq) , E F is the material’s emission factor per unit (k gCO2eq (k g −1 ,L−1 ,or KW h−1)) , M Q is the amount of material required to complete the operation (k g,L,KW h) , and GW P100 is the global warming potential of a particular gas using a 100-year average period (based on IPCC’s Sixth Assessment Report in 2021, whereby N2O and CH4were converted into CO2-eq using GWP values of 273 and 27, respectively). Direct emissions from the farm originated primarily from the fuel used in transporting essential items such as fingerlings and fish feed. The carbon footprint of these emissions (CFfuel ) was calculated (per fuel and vehicle types) per year using Equation 3: CFf uel =ÕE FFT ∗Vf uel ∗GW P100 ∗conv er si on (3) where CFf uel is the carbon footprint of the fuel used (k gCO2eq) , E FF T is the material’s emission factor according to the fuel type (k gT J −1), andVf uel is the volume of fuel consumed (L). Intheabsenceofactualfuelmileage, theweightedaverageof light-dutyvehicles,motorvehicles, and air transportation was multiplied by the distance traveled by these vehicles to estimate the fuel used for a single delivery. Note that the light-duty vehicles mentioned are vans, sport utility vehicles, pickup trucks, passenger cars, and light trucks, regardless of wheelbase. Indirect emissions, by contrast, originate from electricity consumption supplied by MERALCO. Monthly electricity bills were used to quantify the total electricity consumed. The Department of Energy’s emission factor [39] for the Luzon-Visayas grid was used to compute carbon emissions. The carbon footprint was determined using Equation 4: CFe=ÕE FLV G ∗ET∗GW P100 ∗conv er si on (4) Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 7 where CFe is the carbon footprint generated by the electricity (k gCO2eq) , E FLV G is the emission factor for the Luzon-Visayas grid (t−CO2MW h−1) , and ET is the total electricity consumed by the farm in 2023 (kW h). Furthermore, the emissions from air and road transportation were estimated using the Emission Inventory Guidebook for shipping operations [31] and the IPCC 2006 Guidelines for Mobile Combustion [29], which serve as a basis for calculating the carbon footprint of air and road transportation. The carbon footprint was determined using Equation 5: CFv ehi cl e =ÕE FV∗DT∗GW P100 (5) where CFv ehi cl e isthecarbonfootprintof the utilizedvehicle type (k gCO2eq) , E FV istheemission factor based on the kind of vehicle utilized for travel (k gGH Gk m−1) , and DT is the distance traveled (k m). To account for the return journey, the distance traveled (DT) was computed by multiplying the distance (in kilometers) between the origin and destination by two, adopted from the previous studies [30,41]. The US EPA [31,42] included distinct emission factors for products and human emissions due to the differences in emission characteristics. The carbon footprint was determined using Equation 6: CFm=ÍE FM∗MQ∗GW P100 ∗conver si on shel f l i f e (6) where CFm is the carbon footprint of the material’s manufacturing process (k gCO2eq) , E FM is the emission factor based on manufacturing activity (k gGH Gk m−1) , and MQ is the weight of the material (k g). 2.5 Construction Phase Thecarbonfootprintcalculationforconstructionmaterials (CFc) involvedaccountingforemissions from manufacturing activities, transportation, and fuel use. The carbon footprint was determined using Equation 7. CFc=ÕC Fm+C Ff uel +CFv(7) 2.6 Production Phase The carbon footprint of the production phase (CFp) included the emissions from manufacturing activities, transportation, and fuel consumption. Electricity consumption was accounted for based on the energy consumed by the equipment in both systems. Methane emissions were also considered using Zhang’s empirical equation [40]. The carbon footprint was determined using Equation 8 CFp=ÕC Fm+C Ff uel +CFv+CH4,f l ux +C Fe(8) 2.7 Harvesting and Packaging Phase The carbon footprints during the harvesting and packaging phase (CFhp ) included the emissions from manufacturing activities, transportation, and fuel consumption. The carbon footprint was determined using Equation 9. CFhp =ÕCFm+C Ff uel +CFv(9) Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 8 2.8 Distribution to the Market Phase The carbon footprint during the distribution to the market (CFd t m ) was based on vehicle specifications, distance traveled, and fuel consumption. The carbon footprint was determined using Equation 10: CFd t m =ÕC Ff uel +CFv(10) 2.9 Total Carbon Footprint The annual carbon footprint of growing catfish, which is the sum of carbon footprints obtained from various phases, was computed using the Equations 11 and 12: CFt ot al ,annual =CFc+CFp+C Fhp +C Fd t m (11) CFl ivew ei ght =C Ft ot al ,annual F unct i onal uni t (12) where CFt ot al ,annual is the annual carbon footprint of the catfish (k gCO2eq) , CFl ivew ei ght is the annual carbon footprint of the catfish (k gCO2eq) , and F unct i onal uni t is the weight of the catfish produced in a year (k g LW ). 2.10 Scenario Analysis To determine the reduction of GHG emissions hotspots, three scenarios were developed based on the PAS 2050 list of common opportunities for emission reduction actions [43] and the availability of data from the farmer. 2.10.1 Scenario 1: Solar Energy as a Source of Power This scenario followed the recommended actions outlined in PAS 2050 for reducing energy-related emissions by generating renewable energy on-site and utilizing it to power operations. Two cases were examined: (A) the combined use of electricity (16 hours per day) and solar energy (8 hours per day) throughout the year, and (B) the exclusive reliance on solar energy for 24 hours per day throughout the year. The study, however, did not incorporate the solar irradiance data specific to the region, which may have compromised the accuracy of the estimated carbon footprint reduction from solar energy utilization. 2.10.2 Scenario 2: Delivery and Mobility of the Fingerlings Four locations with varying distances from the aquaculture farm (Table 2) were considered as suppliers of fingerlings. These locations are known suppliers of high-quality fingerlings in the Philippines. Table 2. Proposed sources of fingerlings across Luzon Gipanao et al. (2025) |Journal of Human Ecology and Sustainability 3(1), 5 9 Statements and Declarations Funding Information The researcher was a recipient of a thesis grant through the DOST-SEI Scholarship. Acknowledgment The authors acknowledge the support provided by the personnel of NTL Fish Farm during the conduct of water quality assessments. Appreciation is also extended to individuals who facilitated access to the study site and shared relevant technical insights that contributed to the completion of this research. Competing Interest The authors declare no competing interests related to this study. Ethical Considerations In compliance with the Data Privacy Act of 2012 (Republic Act No. 10173), the information collected during the study was used solely for research purposes and remains confidential. Data Availability The data in this study is available upon request from the authors. Disclosure of the Use of Artificial Intelligence During the preparation of this work, the author used Grammarly and ChatGPT to check grammatical errors and critique the sentence as concisely as possible. 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