TELKOMNIKA Telecommunication Computing Electronics and Control Vol.
No.
April 2026, pp.
ISSN: 1693-6930.
DOI: 10.
12928/TELKOMNIKA.
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain Imam Santoso1.
Dodyk Pranowo1.
Hendrix Yulis Setyawan1.
Izzum WafiAouddin1.
Naila Maulidina LuAoayya1.
AnnisaAou Choirun2 Department of Agroindustrial Technology.
Faculty of Agricultural Technology.
Universitas Brawijaya.
Malang.
Indonesia Food Engineering Technology Program.
Department of Agricultural Technology.
Politeknik Negeri Jember.
Jember.
Indonesia
Article Info
ABSTRACT
Article history:
IndonesiaAos cocoa sector faces challenges in greenhouse gas emissions and smallholder income volatility.
This study develops a system dynamics model to simulate the interrelationship between carbon emissions and economic performance across the cocoa value chain, identify leverage points, and evaluate alternative policy scenarios.
The model integrates environmental and economic variables into dynamic feedback structures, enabling scenariobased assessment of intervention strategies.
Five scenarios were simulated:
composting cocoa waste increased farmer income by 2% and reduced farmlevel emissions from 0.
43 to 0.
303 kg COCC-eq/kg .
79% total reductio.
biogas conversion raised income by 13.
56% and reduced emissions by 11%.
converting cocoa waste into animal feed slightly increased income by 0.
while cutting emissions by 58.
combining composting with improved transport efficiency reduced emissions by 14%.
and integrating composting, logistics optimization, and government-supported input subsidies yielded the highest performance, with a 13.
50% income increase and a 70% emission These results demonstrate that integrated, system-based interventions can enhance both economic resilience and environmental The system dynamics model provides policymakers and supply chain actors with actionable insights for designing effective, climatealigned strategies in IndonesiaAos cocoa industry.
Received Aug 12, 2025 Revised Dec 21, 2025 Accepted Jan 30, 2026 Keywords:
Carbon emission Cocoa supply chain Intervention scenarios Sustainability System dynamics model This is an open access article under the CC BY-SA license.
Corresponding Author:
Imam Santoso Department of Agroindustrial Technology.
Faculty of Agricultural Technology Universitas Brawijaya Malang, 65145.
Indonesia Email: imamsantoso@ub.
INTRODUCTION
Indonesia is one of the worldAos top cocoa bean producers, behind Cyte dAoIvoire and Ghana, with an annual output of around 550 tons .
Cocoa, like other vital commodities like as palm oil, coffee, tea, and tobacco, makes a considerable contribution to the countryAos foreign exchange revenues .
However.
IndonesiaAos cocoa supply chain is encountering increasingly complex and interconnected issues, notably at the upstream level with smallholder farmers .
, .
These concerns stem from long-standing environmental, social, and economic issues, including deforestation, child labor, and land degradation, as well as persistent farmer poverty and unequal value allocation .
Furthermore, global concerns about climate change and biodiversity loss have increased the need for sustainable practices in all economic sectors, including the cocoa industry .
While the expansion of cocoa planting provides economic opportunity for many tropical countries, it has resulted in significant Journal homepage: http://journal.
id/index.
php/TELKOMNIKA A ISSN: 1693-6930 environmental externalities .
Thus, with rising worldwide demand for chocolate, the cocoa sector must achieve a delicate balance between environmental stewardship and economic viability .
The environmental consequences of cocoa cultivation are widely established, with unsustainable farming practices contributing to soil deterioration, water contamination, and greenhouse gas emissions .
In response, long-term measures to minimize carbon emissions and sustainably manage the cocoa agroindustrial supply chain are increasingly recognized as critical, not only for ecological integrity but also for the long-term profitability of cocoa production systems .
, .
On the side of the economy, the vulnerability of agricultural incomes remains a major concern.
This has resulted in the establishment of programs like the decent income community of practice, which aims to close structural income inequalities and ensure equitable pay for smallholder farmers .
Economic empowerment along the cocoa supply chain is critical to achieving overall sustainability goals.
To address these interconnected concerns, there is a growing interest in system-based techniques that can model and evaluate the dynamic interaction of environmental and economic elements.
Green supply chain management (GSCM) has gained popularity as a conceptual framework for incorporating environmental issues into all stages of the supply chain, including reverse logistics.
In parallel, system dynamics modeling provides a powerful methodological tool for simulating complex systems, allowing policy options to be developed and tested in the face of uncertainty and feedback .
System dynamics has also been positioned in engineering and computer science research as a control-oriented simulation environment that aids decision-making and optimization in complicated networks.
The integration of system dynamics modeling with the GSCM framework is a potential approach to understanding and managing trade-offs in sustainable cocoa production.
This approach enables academics and decision-makers to investigate systemic interconnections, identify leverage points, and evaluate the impact of various interventions on supply chain players .
, .
Such integration is consistent with computational approaches to system optimization and control strategy design, both of which are becoming increasingly important for the development of sustainable industrial systems.
Nonetheless, the existing body of research in this field is minimal.
Most previous research either focuses exclusively on diagnostic assessments of sustainability challenges .
or investigates actor-specific roles in GSCM implementation .
, without integrating these findings into a comprehensive, dynamic systems-based policy model.
This study fills that gap by creating a dynamic systems model based on the GSCM viewpoint that assesses both environmental and economic sustainability in the cocoa supply chain.
The methodological innovation is the integrative application of system dynamics modeling to operationalize GSCM ideas in a comprehensive and adaptive policy simulation framework.
This approach highlights how system-based simulation can be used in engineering and computer science to build successful intervention methods by acting as a computational decision-support and control-oriented modeling tool.
Employing this approach, the study aims to provide concrete recommendations that will assist stakeholders in planning and implementing strategies for a low-carbon and economically resilient cocoa supply chain in Indonesia.
As a result, the goal of this research is to develop a system dynamics model that simulates the interrelationship between carbon emissions and economic performance in the cocoa value chain, identifies leverage points, and evaluates alternative policy scenarios to support the long-term transformation of IndonesiaAos cocoa industry.
METHOD
Data collection This study employs system dynamics to model the cocoa supply chain by integrating key environmental variables .
, carbon emission.
and economic metrics .
, prices and farmer incom.
Data were synthesized from diverse primary and secondary sources, spanning 2017 to 2023, to capture recent market and environmental trends.
To ensure methodological rigor, the study utilized purposive sampling for stakeholder interviews and cross-validated data to minimize bias.
Furthermore, sensitivity analysis was conducted to assess model robustness against parameter uncertainties, establishing a valid foundation for policymakers and researchers to design effective sustainability interventions.
Model development process Conceptual model development .
ausal loop diagra.
The development of a system dynamics model seeks to reflect the intricate interrelationships between environmental and economic aspects in the cocoa supply chain.
The model combines crucial variables, mathematical relationships, and feedback loops to describe the systemAos structure and behavior.
Scenario-based simulations are used to investigate prospective interventions, such as sustainable farming methods, market volatility, and policy implementation, and to assess their environmental and economic TELKOMNIKA Telecommun Comput El Control.
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April 2026: 431-451 TELKOMNIKA Telecommun Comput El Control This approach allows for the discovery of leverage points that can boost the overall sustainability of the cocoa supply chain .
Carbon emissions from the cocoa supply chain are not restricted to farming activities.
They are also produced by waste collection, wood burning, chocolate processing, packaging, electricity and water consumption, and transportation.
Farmers are notably associated with significant emission risks during land cultivation, and the processing step contributes through machinery use .
Collectors often oversee distribution and transportation activities, which lead to additional carbon emissions .
These problems highlight the importance of emission reduction methods throughout the cocoa agro-industry, with a focus on production efficiency and ecologically responsible practices.
This study uses a system dynamics modeling approach to assess the connections between carbon emissions, supply chain activities, and overall system performance.
The model incorporates a number of critical characteristics to simulate real-world behavior.
Figure 1 depicts way a causal loop diagram (CLD) is used to display and evaluate the cocoa supply chainAos dynamic feedback structures.
Figure 1.
CLD of the Indonesian cocoa supply chain The CLD, which depicts the key players and relationships in IndonesiaAos sustainable cocoa supply The production system, economy, and environment are the three interconnected domains.
Oe Production system: this component explains how materials move via each supply chain participantAos The flow of supplies from farmers to collectors and ultimately to the chocolate business is highlighted .
Oe Economy: this component describes the flow of expenses and income related to each actorAos actions.
offers information about the supply chainAos financial stability and economic transactions .
Oe Environment: this component focuses on the byproducts that each actorAos supply chain operations It takes into account the effects on the environment, including emissions and trash generated during the stages of cocoa production and processing .
Farmers, middlemen, and the chocolate industry are the three primary supply chain actors that make up the CLD, and they all contribute to the systemAos dynamic behavior.
By examining these variables and their interrelationships, the CLD reveals the feedback loops that govern the systemAos behavior.
The reinforcing loops .
, higher production Ie higher income Ie increased input use Ie higher productivit.
illustrate growth dynamics, while balancing loops .
, increased emissions Ie environmental costs Ie reduced sustainabilit.
capture trade-offs between economic performance and environmental sustainability.
The descriptions and definitions of key variables used in the CLD are presented in Table 1 to provide detailed clarity and facilitate model interpretation.
Quantitative model formulation .
tock-flow diagra.
Building on the previously described CLD, a system dynamics model of the cocoa supply chain was created utilizing a stock-flow diagram (SFD) to capture the flow of materials, emissions, and economic activity within the system.
The SFD model was built on several key assumptions.
First, the model takes into account only three major actors in the cocoa supply chain: farmers, middlemen, and the chocolate processing sector.
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 the farmer level, the model focuses entirely on the conversion of cocoa pods into dry cocoa beans, leaving out deforestation-related activities.
At the intermediary level, only distribution and transportation activities are At the chocolate industry level, the model accounts for cocoa bean processing activities but does not include the transportation of additional raw ingredients or downstream sales distribution.
The model has three interconnected dimensions: production process, economic flow, and environmental impact.
In the economic aspect, only raw material costs and selling prices are taken into account, while other operational expenditures such as labor and maintenance are excluded for simplicity.
Field observations, statistical data from Indonesian statistic (Badan Pusat Statistik / BPS), industry reports, and relevant literature were used to determine parameter values and input data for the SFD, whereas emission coefficients were taken from the intergovernmental panel on climate change (IPCC) .
Figure 2 shows the SFD, which was created using Vensim decision support system (DSS) software.
This Figure 2 depicts the dynamic interactions of critical factors, allowing quantitative simulation and scenario analysis.
Table 2 shows the mathematical equations, parameter definitions, and functional relationships employed in the model.
Table 1.
Definition of key variables in the CLD No.
Variable Cocoa area Cocoa tree numbers Productivity Cocoa production Cocoa bean Farmers price Farmers income Fertilizer used Pesticide used Fertilizer/pesticide emission Cocoa wasted emission Farmers fuel consumption Middleman procurement Middleman income Middleman fuel consumption Middleman distribution emission Cocoa bean inventory Cocoa bean processing Processing waste Chocolate price Chocolate income Electric emission Total emission of cocoa supply Description Total land area used for cocoa cultivation Number of productive cocoa trees Yield of cocoa fruit per tree Total volume of cocoa fruit harvested Dried cocoa bean output after post-harvest Selling price per kilogram of cocoa beans Net income obtained from cocoa sales Amount of fertilizer applied per production cycle Amount of pesticide applied Emissions generated from chemical input usage Emission from unharvested or spoiled cocoa fruit Fuel used for transport and farming activities Volume of cocoa beans purchased from farmers Revenue obtained from reselling cocoa beans Fuel used for transportation and distribution Emissions from transportation and logistics Stock of cocoa beans available for processing Conversion of dried beans into chocolate Waste generated during production Selling price of chocolate products Total revenue from chocolate sales Emission from electricity used in production Aggregated emission from all actors Domain Production Production Production Production Production Actor Farmer Farmer Farmer Farmer Farmer Economy Economy Environment Environment Environment Environment Environment Production/economy Economy Environment Environment Production Production Environment Economy Economy Environment Environment Farmer Farmer Farmer Farmer Farmer Farmer Farmer Middleman Middleman Middleman Middleman Industry Industry Industry Industry Industry Industry Systemwide Figure 2.
SFD of the Indonesian cocoa supply chain TELKOMNIKA Telecommun Comput El Control.
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Variables, parameters, and functional relationships in the cocoa supply chain system dynamics model Variable name Land area Production 04 Ha 800 Tons Productivity rate Production/land area Cocoa plantation Cocoa bean INTEG .
roductiviy rat.
Initial value = 4750 Tons Cocoa plantation production * 0.
27 Ie fraction of total cocoa fruit converted into dried coco.
Cocoa plantation production * 1.
13 Ie average fertilizer uses per ton of production .
13 kg/kg Cocoa supply chain income Pruning and Cocoa plantation production * 0.
0004 Ie average pesticide use per ton of production .
0004 kg/k.
) (Cocoa plantation production * IDR 5.
ertilizer used*IDR 3.
esticide used * IDR .
Cocoa bean * 0.
29 Ie share of cocoa beans that become dried, marketable produc.
IDR 35000 * time Cocoa tree harvest Middleman Farmers supply (Dry cocoa bean * farmers cocoa bean pric.
- farmers cost RANDOM NORMAL .
0, 1700,
1400, 20, .
Dry cocoa bean Supply rate Farmers supply/middleman demand Middleman INTEG .
upply rat.
Initial value = 1000 Middleman cost Farmers cocoa bean price .
iddleman inventory * 1.
Middleman Middleman inventory * 0.
9 Ie fraction of inventory that can be sold to industr.
IDR 37000 * time Fertilizer used Pesticide used Farmers cost Dry cocoa bean Farmers cocoa bean price Farmers income Middleman cocoa bean price Middleman Function of time (Middleman cocoa bean price * middleman suppl.
- middleman cost Variable name Chocolate price Industry selling Industry income Factor emissions (EF)# (Industry selling numbers * chocolate pric.
Chocolate production cost (Farmers income Industry income middleman 13 * land area .
13 Ie 13% of total plantation area requires maintenance annuall.
Oe Combustion EF = 58 kg COCCe/L Oe Fertilizer EF = 0.
2 kg COCCe/kg Oe Landfill EF = 46 kg COCCe/kg Oe Pesticide EF = 0.
262 kg COCCe/kg Oe Fuel EF = 21.
2 kg COCCe/L Oe Aluminum EF = 1.
721 kg COCCe/kg Oe Carboard EF = 0.
46 kg COCCe/kg Oe Electricity EF = 0.
774 kg CO2e/kWh Pruning and maintenance * EF combustion Cocoa husk Cocoa plantation production * 0.
73 Ie cocoa fruit waste fraction .
usk portio.
) Cocoa husk Cocoa husk * landfill EF Fertilizer emission Fertilizer used * fertilizer EF Pesticide active Pesticide emission (Fraction of active chemical in total pesticide mas.
Pesticide used * pesticide active ingredients*pesticide EF 2 Km Middleman distance location Farmer pick up Farmers Farmers emission Middleman pick up capacity Industry distance Middleman Aluminum foil Chocolate industry demand RANDOM NORMAL .
, 500,
250, 2, .
Bean supply rate Middleman supply/chocolate industry Carboard waste Industry INTEG .
ean supply rat.
Initial value = 700 Packaging waste Chocolate Electricity used (Industry inventory .
ndustry inventory * 0.
) * 45
RANDOM NORMAL .
, 500,
250, 5, 201
(Chocolate production * IDR 10.
(Electricity used * IDR 2.
(Industry inventory * IDR 18.
Electricity Chocolate industry emission Total emission Chocolate production cost Function of time IDR 50000 * time RANDOM NORMAL .
0, 3000, 1750, 2, .
907 Tons IF THEN ELSE .
armers supply / pick up capacity Ou 1.
Farmers supply / pick up capacity, .
iddleman distance location * 2/12.
* fuel EF Cocoa husk emission cocoa tree harvest emission farmers transportation emission fertilizer emission pesticide emission 2 unit 1 Km IF THEN ELSE .
iddleman supply / pick up capacity Ou 1, middleman supply / pick up capacity, .
* .
ndustry distance location * 2/12.
* fuel EF 2*chocolate production .
% of total packaging weight made of aluminum 0001 * chocolate production .
01% of total packaging weight in cardboard (Aluminum foil waste * aluminum EF) .
ardboard waste * carbord EF * 0.
9* 0.
01 * 0.
Electricity used * electricity EF Electricity emission packaging waste emission Farmers emission middleman transportation emission chocolate industry emission Notes:
Oe Data without the symbol (#) were obtained from field observations and interviews with actors in the cocoa supply chain.
Oe Data marked with the symbol (#) were sourced from the IPCC .
and Zakcy .
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 Each variable is represented as a time-dependent function, connected by algebraic and integral equations that embody the cumulative and feedback-based aspects of system dynamics modeling.
Each emission component is approximated as the product of activity levels .
, fertilizer use, fuel consumption, and trash generatio.
and the corresponding emission factors shown in Table 2.
The AuIF THEN ELSEAy logic was used to express conditional relationships like transit capacity and distance thresholds.
To determine broader applicability, this modeling approach can be used to other agricultural commodities with similar production-to-processing flows, such as coffee, palm oil, or rubber, by modifying However, its current structure assumes homogeneous agent behavior and static price elasticity, which may restrict its ability to capture regional diversity and market volatility.
Future improvements may include stochastic demand modules or regional land-use change dynamics to improve robustness and Model validity test To ensure the reliability of the developed model, a series of validation tests were conducted.
The validation process includes structural validation and behavioralAestatistical validation, as described below.
Structural model test The structural validation process seeks to ensure that the modelAos structure and causal links correspond to the actual system.
The test involved comparing the logical consistency of feedback loops, variable interactions, and causal directions to recognized theories and field practices.
This verification was conducted using.
literature research on system dynamics modeling, cocoa production systems, and supply chain economics, and .
consultations with cocoa cultivation, industry operations, and sustainable supply chain experts.
Expert assessors analyzed and confirmed the sub-models for cocoa farming, intermediary activities, and chocolate production, verifying the modelAos conceptual soundness and realism.
Behavioral and statistical validation Behavioral validation was carried out by comparing simulation results to historical data from 2017 to 2023 for important variables, specifically cocoa production volume.
Historical data were sourced from the BPS and industry reports, and simulated results were generated using the established model under baseline Table 3 summarizes the comparative results, demonstrating the tight alignment of simulated and actual production trends.
Model correctness was evaluated quantitatively using the mean absolute percentage error (MAPE) approach.
A model is considered valid if the MAPE is less than 10%.
As shown in Table 3, the calculated MAPE value of 2% demonstrates an outstanding match between simulated and historical data, validating the modelAos capacity to replicate real-world behavior.
Table 3.
Model validation results Cocoa production Data (K.
Simulation (K.
4,750 4,750 4,900 4,908.
5,040
5,067.
5,040
5,226.
5,238
5,384.
5,338
5,543.
5,567
5,702.
MAPE (%) (E.
STDEV (%) (E.
Time .
MAPE (%) Cross-validation was performed by comparing the generated modelAos environmental performance findings to existing life cycle assessment (LCA) studies.
Because one of the modeling features of this study is environmental effect estimation.
LCA was employed as a baseline for emission levels.
According to Miharza et al.
, cocoa cultivation without deforestation generates approximately 2.
18 kg COCC-eq per kg of cocoa beans, while Neira .
reported that chocolate production produces around 2.
82 kg COCC-eq per kg.
The emission findings produced by the system dynamics model are within this range, indicating good agreement and validating the modelAos external validity.
Table 4 presents a detailed comparison of the system dynamics simulation and LCA-based emission values using annual data.
The MAPE result for comparing the simulation and LCA models at the farmer level is 6.
(<10%), with a standard deviation of 0.
01% (<30%).
For the chocolate production stage, the MAPE value is 57%, with a standard deviation of 0.
All results fall well under acceptable error thresholds, indicating that the model has a high level of accuracy and can be deemed valid and dependable for simulation and policy analysis.
TELKOMNIKA Telecommun Comput El Control.
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Model validation based on simulation compared to LCA method Time MAPE (%) STDEV (%) Dry cocoa (K.
Farmer
(Kg COCC) Emission
according to LCA
(Kg COCC) MAPE
(%)
Chocolate (K.
MAPE (%) STDEV (%) Industry (Kg COCC) Emission
according to LCA
(Kg COCC) MAPE
(%)
Sensitivity model In dynamic modeling, sensitivity analysis entails determining how minor adjustments to the modelAos parameters may affect the modelAos output.
In dynamic modeling, which frequently deals with complex systems and time-based interactions, this is especially crucial.
Sensitivity testing aids in determining how resilient the model is to parameter changes and uncertainty .
Since farmer emissions are the most dynamic element of the environmental subsystem and are immediately impacted by production inputs including fertilizer, pesticides, and transportation fuel, they were chosen as the primary variable for sensitivity analysis in this study.
Secondary data was used to validate the emission factor parameter values .
, .
To evaluate how the model might react to different emission situations, three simulated scenarios were created.
The sensitivity analysis supports the modelAos structural robustness and logical consistency under different input intensities.
Figure 3 shows that the Aoextreme highAo scenario, which involves intense use of fertilizers, pesticides, and fuel, leads to much higher emission trajectories than the baseline, exceeding 400,000 Kg COCC by 2040.
Figure 4 confirms baseline stability under normal field conditions.
Figure 5 shows that Aoextreme lowAo conditions, which reflect improved farming practices and lower input intensity, effectively kept emissions below the existing baseline.
In all scenarios, the model demonstrates monotonic behavior with no chaotic oscillations, indicating that the simulation accurately depicts the causal links within IndonesiaAos cocoa supply chain and remains stable even under boundary circumstances.
Figure 3.
Farmers emission on high extreme sensitivity test System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 Figure 4.
Farmers emission on normal condition sensitivity test Figure 5.
Farmers emission on low extreme sensitivity test
RESULTS AND DISCUSSION
Baseline simulation under existing conditions Before adopting scenario-based interventions, a baseline simulation was run to determine the existing trend of carbon emissions in IndonesiaAos coco a supply chain.
Figure 6 depicts expected emissions over the next 16 years, exhibiting a steady increase trend that leads to a growing carbon footprint.
Consistent with Gao et al.
, the carbon footprint of chocolate products includes all stages of the life cycle, from agriculture to processing, distribution, consumption, and end-of-life management, such as reuse or disposal.
As a result, emission reduction at all stages is critical to achieving sustainable production and meeting national climate targets.
Figure 6.
Baseline simulation under existing conditions TELKOMNIKA Telecommun Comput El Control.
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April 2026: 431-451 TELKOMNIKA Telecommun Comput El Control According to the current system, the model calculates carbon emissions at each supply chain level as follows: Cocoa farmers emit 0.
43 kg COCC-eq per kg, whereas middlemen emit 0.
0017 kg COCC-eq per kg.
The chocolate processing sector emits 2.
86 kg COCC-eq per kg.
These findings show that the processing stage accounts for the majority of emissions, emphasizing the importance of industrial processes in determining the overall carbon footprint.
For supply chain participants, this means that interventions focused exclusively on cultivation or distribution may have little impact unless paired with efficiency improvements and emission reduction initiatives at the processing stage.
Unchecked emissions in the cocoa supply chain may result in four critical consequences.
First, climate-induced variations in rainfall and temperature can reduce cocoa productivity and quality.
Second, the overuse of inorganic fertilizers can accelerate soil degradation, affecting long-term land fertility.
Third, biodiversity loss may occur due to deforestation and widespread agrochemical application .
, .
Fourth, socio-economic impacts, including reduced farmer income and potential exposure to carbon taxation in jurisdictions with regulatory frameworks, may arise .
, .
In Indonesia, the cocoa sustainability partnership (CSP) serves as a key multi-stakeholder mechanism to promote sustainability across the cocoa value chain.
The CSP framework emphasizes six strategic blocks: agro-inputs, planting materials, knowledge dissemination, delivery mechanisms, financing, and government participation.
Aligned with these objectives, this study formulates scenario-based interventions targeting three CSP blocks: .
agro-input optimization through the application of compost derived from cocoa cultivation waste, aimed at increasing land productivity and farmer income.
knowledge enhancement to improve farmer skills, technical capacity, and professionalism.
organizational efficiency via upgraded logistics and transportation practices.
These interventions provide a system-oriented and evidencebased pathway for reducing emissions while strengthening farmer livelihoods, ensuring alignment with sustainability goals and supporting the long-term resilience of IndonesiaAos cocoa industry.
Scenario development and simulation result Based on the validated system dynamics model, five intervention scenarios were simulated to evaluate their impact on carbon emissions and farmer income in the cocoa supply chain.
The scenarios were designed to integrate circular economy principles and policy interventions, thereby aligning environmental sustainability with socio-economic benefits.
The simulated strategies include: .
utilization of cocoa farming waste as organic compost, .
conversion of cocoa waste into biogas, .
use of cocoa waste as animal feed, .
emission reduction in logistics and processing, and .
reduction of input costs through financial support for fertilizers and pesticides, combined with profitable waste utilization practices.
Scenarios 1-3 emphasize waste valorisation throughout the production stage.
Cocoa pod husk, cocoa mucilage, and cacao bean shell.
The cocoa pod husk, which accounts for 70-80% of fruit weight, is made up of four separate layers .
picarp, mesocarp, sclerotic layer, and endocar.
that are rich in bioactive chemicals.
Cocoa mucilage, a sticky white layer around the beans, contains fermentable sugars and vital minerals, whereas cacao bean shell, which accounts for 10-20% of bean weight, creates the cocoa beanAos protective outer covering and includes antioxidant chemicals .
, .
Valorizing waste by-products as compost, biogas, or animal feed can lessen environmental impact while diversifying revenue streams.
The simulation results investigate the potential for these measures to improve both ecological resilience and farmer welfare.
Scenario 1: utilization of cocoa farming waste as organic compost The simulation results indicate that scenario 1 increases farmer income by 2% while reducing farmlevel emissions from 0.
43 to 0.
303 kg COCC-eq per kg, contributing to a 29.
79% .
%) decline in total supply chain emissions .
ee Figure .
Figure 7.
illustrates the projected increase in farmer income, whereas Figure 7.
shows the corresponding reduction in carbon emissions across the farm level.
These findings demonstrate that composting cocoa by-products into organic fertilizers is both economically and environmentally advantageous.
Economically, the practice reduces reliance on costly synthetic fertilizers, lowering production expenses and strengthening household resilience .
Environmentally, the valorization of cocoa pod husks and cocoa bean shells diverts substantial biomass from uncontrolled decomposition or open burning-major sources of carbon emissions, while simultaneously improving soil fertility .
, .
Detailed simulation data supporting this analysis are provided in Table 5.
Although organic fertilizers may require larger volumes to achieve nutrient balance, their effectiveness has been enhanced through forage-based additives .
, .
They also improve soil microbial activity, cation exchange capacity, and moisture retention, supporting long-term yield improvements .
For farmers, these benefits translate into more sustainable production practices, reduced input costs, and stabilized yields, which collectively improve income security and resilience to market or environmental fluctuations .
, .
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 .
Figure7.
Simulation results of scenario 1: .
farmer income model and .
carbon emission model Table 5.
Comparison simulation of farmer income and emission in existing condition and scenario 1 Time .
Average Farmers income:
existing (IDR) 2,62E 15
2,71E 15
2,80E 14
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,51E 15
3,60E 15
3,69E 15
3,78E 15
3,87E 15
3,96E 15
4,06E 15
4,15E 15
4,24E 14
4,33E 15
4,42E 15
4,51E 14
4,60E 15
4,69E 15
3,22E 15
Difference Percentage Farmers income: scenario 1
(IDR)
2,62E 14
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 14
3,52E 15
3,61E 14
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,29E 15
7,19E 13
Farmers emission: existing (Kg COCC) 2,22E 05 Difference Percentage Farmers emission:
scenario 1 (Kg COCC) 1,56E 05
6,63E 04
From a supply chain perspective, this intervention indirectly benefits middlemen and processors by ensuring a more consistent quality and quantity of cocoa beans, which reduces supply volatility and enhances the predictability of downstream operations.
To support the wider adoption of this practice, scaling the intervention would benefit from farmer training programs, community-level composting facilities, and sustainable certification schemes that reward environmentally friendly practices.
Although composting requires additional labor, representing a minor trade-off in operational efficiency, the long-term benefits clearly outweigh these costs .
These benefits include reduced carbon emissions, improved soil health, and more stable farmer incomes.
Overall, scenario 1 demonstrates that composting as a waste-focused intervention can effectively support the economic, environmental, and social dimensions of sustainability, making it a practical and impactful strategy for promoting sustainable cocoa production in Indonesia.
Scenario 2: conversion of cocoa waste into biogas The simulation results indicate that scenario 2 increases farmer income by approximately 13.
and reduces farm-level emissions from an average of 222,000 kg COCC-eq/year .
to 159,000 kg COCCeq/year, contributing to an 11% decline in total supply chain emissions .
ee Figure .
Figure 8.
illustrates TELKOMNIKA Telecommun Comput El Control.
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April 2026: 431-451 TELKOMNIKA Telecommun Comput El Control the projected increase in farmer income, whereas Figure 8.
shows the corresponding reduction in carbon emissions across the farm level.
These findings demonstrate that anaerobic digestion of cocoa by-products is highly effective in simultaneously mitigating emissions and providing renewable energy for rural Economically, the significant increase in farmer income highlights the attractiveness of this intervention at the community level, while the substantial emission reduction underscores its strong climate mitigation potential.
Detailed simulation data supporting this analysis are provided in Table 6.
Figure 8.
Simulation results of scenario 2: .
farmer income model and .
carbon emission model Table 6.
Comparison simulation of farmer income and emission in existing condition and scenario 2
Farmers (Kg COCC) Average 2,22E 05
Difference Percentage Time
Farmers (Kg COCC) 1,56E 05
6,63E 04
Farmers (Kg COCC) 1,59E 05
6,37E 04
Farmers (IDR) 2,62E 15
2,71E 15
2,80E 14
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,51E 15
3,60E 15
3,69E 15
3,78E 15
3,87E 15
3,96E 15
4,06E 15
4,15E 15
4,24E 14
4,33E 15
4,42E 15
4,51E 14
4,60E 15
4,69E 15
3,22E 15
Difference Percentage Farmers (IDR) 2,62E 14
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 14
3,52E 15
3,61E 14
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,29E 15
7,19E 13
Farmers (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,66E 15
4,37E 14
From a supply chain perspective, the valorization of cocoa pod husk and cacao bean shell through anaerobic digestion ensures efficient use of residues, indirectly benefiting middlemen and processors by stabilizing the quantity and quality of cocoa beans.
The digestate by-product can be applied as a nutrient-rich fertilizer, reducing dependence on synthetic inputs and improving soil fertility .
Policy-wise, adoption is constrained by the high initial capital costs of anaerobic digestion systems, which may limit individual farmer System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 Therefore, cooperative ownership models or collective schemes are recommended to ensure accessibility and scalability across cocoa-producing regions.
From an environmental perspective, converting cocoa pod husk and cacao bean shell into biogas through anaerobic digestion presents a promising pathway for renewable energy generation.
IndonesiaAos high cocoa production generates approximately 1680-2088 kilotons of cacao bean shell and cocoa pod husk annually, providing abundant biomass feedstock for this process .
The biochemical composition of these residues, rich in proteins, fats, and carbohydrates, makes them suitable substrates for methane-producing microbes, thereby facilitating efficient biogas yields .
The biogas produced can substitute up to 20% of household cooking gas demand .
, while the digestate by-product serves as a nutrient-rich fertilizer, reducing reliance on synthetic inputs.
Although the implementation of anaerobic digestion systems requires substantial initial investment, representing a trade-off between upfront cost and operational sustainability, the long-term benefits, including emission reduction, renewable energy generation, improved soil fertility, and increased farmer income, clearly outweigh these challenges .
Overall, scenario 2 demonstrates that cocoa waste valorization via biogas production can advance the economic, environmental, and social dimensions of sustainability, offering a practical and impactful strategy for promoting sustainable cocoa production in Indonesia.
Scenario 3: conversion of cocoa waste into feed-stock The simulation results indicate that scenario 3 reduces farm-level emissions from an average of 222,000 kg COCC-eq per year .
to 64,700 kg COCC-eq per year, corresponding to a 58.
6% reduction, while increasing farmer income marginally from 3.
22 y 10a to 3.
23 y 10a, or about 0.
23% per production cycle .
ee Figure .
Figure 9.
illustrates the projected increase in farmer income, whereas Figure 9.
shows the corresponding reduction in carbon emissions across the farm level.
These results suggest that, although scenario 3 does not provide large-scale income gains, it is highly effective in reducing emissions and supporting integrated farming systems through feed substitution.
Detailed simulation data supporting this analysis are provided in Table 7.
Figure 9.
Simulation results of scenario 3: .
farmer income model and .
carbon emission model Strategically, this scenario focuses on converting cocoa pod husk and cacao bean shell into livestock feed, which complements other waste valorization strategies, such as biogas production .
From a supply chain perspective, the feed-stock pathway diversifies the outlets for cocoa residues, indirectly enhancing resilience and circularity of the cocoa supply chain.
By substituting conventional feed, it also reduces costs for livestock farmers and mitigates the environmental burden associated with waste disposal.
From an environmental perspective, scenario 3 achieves substantial emission reduction at the farm level, although the impact on farmer income is modest.
The implementation requires affordable detoxification techniques to ensure feed safety and nutritional quality .
While this represents a trade-off between economic gain and environmental benefit, the scenario contributes to sustainability by promoting circular use of cocoa waste and integrating crop-livestock systems.
Overall, scenario 3 demonstrates that cocoa waste valorization through feed-stock production provides a complementary pathway alongside composting and biogas production, advancing environmental and social sustainability, even if the direct economic benefit is limited.
Detailed simulation data supporting this analysis are provided in Table 6.
TELKOMNIKA Telecommun Comput El Control.
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Comparison simulation of farmer income and emission in existing condition and scenario 3
Farmers (Kg COCC) Average 2,22E 05
Difference Percentage Time
Farmers (Kg COCC) 1,56E 05
6,63E 04
Farmers (Kg COCC) 1,59E 05
6,37E 04
Farmers (Kg COCC) 6,47E 04
-9,14E 04
Farmers existing (IDR) 2,62E 15
2,71E 15
2,80E 14
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,51E 15
3,60E 15
3,69E 15
3,78E 15
3,87E 15
3,96E 15
4,06E 15
4,15E 15
4,24E 14
4,33E 15
4,42E 15
4,51E 14
4,60E 15
4,69E 15
3,22E 15
Difference Percentage Farmers (IDR) 2,62E 14
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 14
3,52E 15
3,61E 14
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,29E 15
7,19E 13
Farmers (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,66E 15
4,37E 14
Farmers (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 14
3,79E 14
3,88E 14
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,23E 15
7,49E 12
Scenario 4: emission reduction at the collector and processing stages, particularly in logistics and The simulation results indicate that scenario 4 delivers substantial environmental gains by combining the waste valorization measures introduced in scenario 3 with targeted logistics improvements at the collector level.
Specifically, replacing transportation fleets older than ten years with fuel-efficient alternatives enhances fuel efficiency by 61% and achieves a 65.
26% reduction in total supply chain emissions compared to the baseline .
ee Figure .
While the magnitude of emission reduction is similar to scenario 3, the inclusion of logistics optimization improves operational efficiency, reduces fuel costs, and strengthens midstream sustainability performance within the cocoa supply chain.
Detailed simulation data supporting this analysis are provided in Table 8.
Figure 10.
Simulation of scenario 4 for the carbon emission model System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 From a technical perspective, logistics inefficiencies have long been a persistent emission hotspot.
Collectors and processors often rely on vehicles over a decade old, with degraded combustion performance caused by worn injectors, piston rings, and catalytic converters.
These mechanical inefficiencies result in excessive COCC emissions, incomplete combustion, and higher operating costs .
By transitioning to newer vehicle models compliant with stricter emission standards, environmental performance is enhanced while reducing total transportation expenses.
The simulation outcomes thus confirm that technological modernization within logistics can create dual benefits, improving both climate performance and economic viability.
Table 8.
Comparison simulation of total emission in existing condition and scenario 4 Time .
Average Total emission:
existing (Kg COCC) Difference Percentage Total emission:
scenario 1 (Kg COCC) 1,28E 05
31E 05
1,35E 05
1,39E 05
1,43E 05
1,47E 05
1,51E 05
1,54E 05
1,58E 05
1,62E 05
1,66E 05
1,70E 05
1,74E 05
1,77E 05
1,81E 05
1,85E 05
1,89E 05
1,93E 05
1,97E 05
2,00E 05
2,04E 05
2,08E 05
2,12E 05
2,16E 05
Total emission:
scenario 2 (Kg COCC) 1,29E 05
33E 05
1,37E 05
1,41E 05
1,45E 05
1,49E 05
1,53E 05
1,57E 05
1,61E 05
1,65E 05
1,68E 05
1,72E 05
1,76E 05
1,80E 05
1,84E 05
1,88E 05
1,92E 05
1,96E 05
2,00E 05
2,03E 05
2,07E 05
2,11E 05
2,15E 05
2,19E 05
Total emission:
scenario 3 (Kg COCC) 6,33E 04
6,50E 04
6,67E 04
6,84E 04
7,00E 04
7,17E 04
7,34E 04
7,51E 04
7,68E 04
7,85E 04
8,02E 04
8,18E 04
8,35E 04
8,52E 04
8,69E 04
8,86E 04
9,03E 04
9,20E 04
9,36E 04
9,53E 04
9,70E 04
9,87E 04
1,00E 05
1,02E 05
Total emission:
scenario 4 (Kg COCC) The combined strategy underscores the importance of addressing upstream waste valorization and downstream logistics modernization simultaneously.
As noted by Savino et al.
, transportation-related strategies play a pivotal role in emission reduction across agricultural supply chains, where optimizing vehicle operation and routing contributes directly to both sustainability and profitability.
In this scenario, collector-level modernization not only reduces logistics-based emissions but also improves supply chain coordination and delivery reliability, indirectly supporting farmers through more predictable product flows.
From a sustainability perspective, scenario 4 exemplifies a systemic intervention that enhances both environmental and economic dimensions.
Quantitatively, the 65.
26% reduction in total emissions corresponds to a significant mitigation potential equivalent to over 155,000 kg COCC-eq annually, while the 61% increase in fleet fuel efficiency lowers transportation energy intensity per unit of cocoa delivered.
These improvements support the decarbonization targets of IndonesiaAos agro-industrial sector and contribute to green logistics transformation in rural commodity chains.
While this approach demonstrates strong technical and environmental performance, its adoption requires coordinated policy measures.
The high upfront investment needed for fleet renewal and compliance with emission standards may deter small-scale collectors.
Therefore, public-private partnership schemes, such as subsidized credit for new vehicles .
, logistics cooperatives, or carbon offset incentives, should be developed to enhance accessibility and ensure equitable participation.
Overall, scenario 4 reflects a pragmatic balance between economic efficiency and environmental responsibility, where integrated actions across the production and distribution stages generate synergistic sustainability gains.
By pairing waste utilization with cleaner transportation systems, the cocoa sector can simultaneously enhance profit margins, reduce environmental footprints, and strengthen long-term supply chain resilience.
TELKOMNIKA Telecommun Comput El Control.
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April 2026: 431-451 TELKOMNIKA Telecommun Comput El Control Scenario 5: reduction of input costs through financial support for fertilizers and pesticides, integrated with one of the most profitable waste utilization strategies Simulation results indicate that scenario 5 generates the highest overall performance among all interventions tested.
The combination of government-financed fertilizer and pesticide subsidies with profitable waste valorization strategies results in a 13.
50% increase in farmer income, from an average of 22y10a IDR under existing conditions to 3.
65y10a IDR under this scenario.
Concurrently, total emissions along the cocoa supply chain decline from 0.
43 kg COCC-eq per kg of beans to 0.
13 kg COCC-eq per kg, representing a 70% reduction compared to baseline conditions .
ee Figure .
These findings highlight that institutional financial support, when paired with sustainable practices, can effectively align economic resilience and environmental sustainability within the cocoa sector.
Detailed simulation data supporting this analysis are provided in Tables 9 and 10.
Figure 11.
Simulation of scenario 5 for the farmer income model Table 9.
Comparison simulation of total income in existing condition and scenario 5 Time .
Average Farmers income:
existing (IDR) 2,62E 15
2,71E 15
2,80E 14
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,51E 15
3,60E 15
3,69E 15
3,78E 15
3,87E 15
3,96E 15
4,06E 15
4,15E 15
4,24E 14
4,33E 15
4,42E 15
4,51E 14
4,60E 15
4,69E 15
3,22E 15
Difference Percentage Farmers income:
scenario 1(IDR) 2,62E 14
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 14
3,52E 15
3,61E 14
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,29E 15
7,19E 13
Farmers income:
scenario 2 (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,66E 15
4,37E 14
Farmers income:
scenario 3 (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 14
3,79E 14
3,88E 14
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,23E 15
7,49E 12
Farmers income:
scenario 4 (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 14
3,79E 14
3,88E 14
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,23E 15
7,49E 12
Farmers income:
scenario 5 (IDR) 2,62E 15
2,71E 15
2,80E 15
2,89E 15
2,98E 15
3,07E 15
3,16E 15
3,25E 15
3,34E 15
3,43E 15
3,52E 15
3,61E 15
3,70E 15
3,79E 15
3,88E 15
3,97E 15
4,06E 15
4,15E 15
4,24E 15
4,33E 15
4,42E 15
4,51E 15
4,60E 15
4,69E 15
3,65E 15
4,36E 14
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930 Table 10.
Comparison simulation of total emission in existing condition and scenario 5 Time .
Carbon Emission per kg Farmers (Kg COCC) Farmers (Kg COCC) Farmers (Kg COCC) Farmers (Kg COCC) Farmers (Kg COCC) Dry cocoa bean existing (Kg COCC) Farmers (Kg COCC) Fertilizer subsidies are a well-established policy instrument to enhance productivity and improve smallholder welfare .
In smallholder cocoa systems, labor is primarily family-based and thus not a direct cash expenditure, making input costs .
articularly fertilizers and pesticide.
the dominant financial burden.
By subsidizing these inputs, governments can directly reduce production costs, thereby improve household income stability and enable farmers to reinvest in productivity-enhancing and environmentally sustainable practices such as waste valorization.
In turn, higher profitability encourages adoption of circular initiatives that transform cocoa pod husks or shells into useful by-products, further contributing to emission mitigation and nutrient cycling at the farm level.
From a supply chain perspective, this scenario creates a cascade of benefits.
Increased farmer profitability strengthens upstream resilience, allowing farmers to maintain consistent supply volumes and quality standards demanded by processors and exporters.
Simultaneously, the lower input costs stabilize farm-gate prices, reducing volatility across the midstream network of collectors and processors.
These findings are consistent with broader evidence showing that targeted subsidies and institutional incentives can significantly improve recycling rates, stakeholder engagement, and socioeconomic welfare, particularly when tailored to local conditions .
Studies also indicate that integrated policy packages that combine subsidies, penalties, and education are more effective than single instruments in overcoming adoption barriers and sustaining behavioral change .
For instance, combined measures in construction waste management have successfully increased contractor participation in recycling initiatives, while in agriculture, subsidies to support waste recovery and organic fertilizer production have accelerated circular economy transitions and reduced environmental pollution .
Nevertheless, while subsidies can catalyze adoption and short-term benefits, policy design must also consider long-term financial sustainability and the risk of dependency.
Overall, scenario 5 demonstrates that environmental reform in agriculture is most effective when supported by robust government intervention and incentive-aligned policies that link waste valorization with improved farmer welfare.
However, scenario 5 also involves trade-offs.
While subsidies stimulate short-term gains and accelerate adoption, they carry fiscal risks and potential dependency if not supported by a clear phase-out or cost-sharing mechanism.
Hence, a sustainable policy design should include graduated subsidy structures, capacity-building programs, and institutional monitoring systems to maintain alignment between financial incentives and environmental performance.
Integrating such measures into national cocoa sustainability frameworks ensures that farmer welfare improvements are not achieved at the expense of long-term fiscal TELKOMNIKA Telecommun Comput El Control.
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April 2026: 431-451 TELKOMNIKA Telecommun Comput El Control Overall, scenario 5 demonstrates that combining financial policy intervention with technological waste utilization offers the most balanced and synergistic pathway toward a sustainable cocoa supply chain.
The scenario effectively strengthens farmer welfare, improves circular resource use, and achieves substantial decarbonization across production and distribution segments, providing a model for integrated sustainability policy in IndonesiaAos agroindustrial sector.
Uncertainty analysis and comparison with target emission reductions A sensitivity analysis was conducted on key input parameters, such as fertilizer use, cocoa pod husk generation, and transportation efficiency, to assess the robustness of the model outcomes.
The results indicate that variations in farmer emissions and production parameters produce consistent and logical responses throughout the system without destabilizing other variables.
This demonstrates that the model is stable and capable of accurately representing the causal relationships within IndonesiaAos cocoa supply chain.
When compared with the sustainable cocoa production program (SCPP) targets in Indonesia, which aim to reduce cocoa plantation greenhouse gas emissions by 30% over five years while increasing carbon sequestration, the simulated interventions, such as waste valorization, input subsidies, and logistics optimization, show the potential to meet or even exceed these national targets under favorable implementation conditions.
These findings highlight that policy recommendations should consider both the expected impacts and the inherent uncertainties in the system.
Interventions like cooperative biogas adoption, fertilizer subsidies, or transportation upgrades can be designed with phased implementation or buffer mechanisms to account for variability in outcomes.
By framing the simulation results alongside national targets and uncertainty considerations, the model provides a practical and risk-informed guide for sustainable cocoa supply chain management in Indonesia.
Recommendation for stakeholders The simulation identifies actionable strategies to optimize IndonesiaAos cocoa supply chain across economic and environmental dimensions.
Key interventions include promoting waste-to-biogas conversion and organic fertilizers via cooperatives, alongside improved price transmission mechanisms to stabilize farmer income.
To operationalize this, the study advocates integrating system dynamics with information and communication technology (ICT) platforms for real-time monitoring, using optimization models for efficient subsidy allocation, and adopting green logistics.
However, success depends on addressing socio-economic therefore, incentive programs must prioritize smallholders to bridge capital and infrastructure gaps.
Ultimately, this integrated decision-support framework aligns supply chain reforms with IndonesiaAos climate and development goals, offering a scalable pathway for sustainable agriculture.
CONCLUSION
This study employed a system dynamics model to evaluate the trade-offs between carbon emissions and economic performance in IndonesiaAos cocoa supply chain.
Simulations of five scenarios revealed distinct impacts: scenario 1 .
rganic compos.
increased income by 2% and reduced total emissions by 29.
arm-level decreasing from 0.
43 to 0.
303 kg COCC-eq/k.
scenario 2 .
boosted income by 13.
with an 11% emission cut.
scenario 3 .
ocoa fee.
achieved a 58.
6% emission reduction but only marginal income growth .
23%).
and scenario 4 .
ogistics/processin.
reduced emissions by 65.
Scenario 5, integrating subsidies with waste utilization, emerged as the optimal intervention, raising income by 13.
and slashing total emissions by 70% .
43 to 0.
13 kg COCC-eq/k.
These findings confirm that integrating institutional support with sustainable practices is the most effective strategy, validating system dynamics as a robust decision-support tool for balancing farmer welfare and environmental sustainability.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial support provided through a research grant from the Institute for Research and Community Service (DRPM).
Universitas Brawijaya.
FUNDING INFORMATION
This work was supported by a research grant under the AuHibah Guru Besar Tahun 2023Ay program provided by the Institute for Research and Community Service (DRPM).
Universitas Brawijaya, under Grant No.
9/UN10.
F10/PT.
03/2023.
System dynamics control simulation for sustainability of IndonesiaAos cocoa supply chain (Imam Santos.
A ISSN: 1693-6930
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author Imam Santoso Dodyk Pranowo Hendrix Yulis Setyawan Izzum WafiAouddin Naila Maulidina LuAoayya AnnisaAou Choirun C : Conceptualization M : Methodology So : Software Va : Validation Fo : Formal analysis ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue I : Investigation R : Resources D : Data Curation O : Writing - Original Draft E : Writing - Review & Editing ue ue ue Vi : Visualization Su : Supervision P : Project administration Fu : Funding acquisition CONFLICT OF INTEREST STATEMENT Authors state no conflict of interest.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author.
Imam Santoso (IMS), upon reasonable request.
REFERENCES