SIMULATION OF REVERSE WOOD BIOMASS SUPPLY CHAINS FOR EMISSION-EFFICIENT LOGISTICS SCENARIOS

25th international scientific conference Business Logistics in Modern Management October 2-3, 2025 - Osijek, Croatia 295 SIMULATION OF REVERSE WOOD BIOMASS SUPPLY CHAINS FOR EMISSION-EFFICIENT LOGISTICS SCENARIOS Damian Dubisz Poznan University of Technology, Poznań School of Logistics, Łukasiewicz Research Network – Poznań Institute of Technology, Poland E-mail: damia[email protected]kasiewicz.gov.pl Arkadiusz Kawa Poznań University of Economics and Business, Poznań School of Logistics, Poland E-mail: [email protected] Rafał Rokicki Łukasiewicz Research Network – Poznań Institute of Technology, Poland E-mail: rafal.rokic[email protected]v.pl Received: September 5, 2025 Received revised: September 29, 2025 Accepted for publishing: September 29, 2025 Abstract The increasing importance of renewable energy highlights the need for efficient logistics in Reversed supply Chains of Wood Biomass (RSCWB). This study evaluates the cost and emission effectiveness of alternative organisational scenarios for wood biomass logistics in Poland. A simulation model was developed using historical industry data, forest resource statistics, and actual transport price lists, incorporating cost structures, vehicle parameters, and carbon footprint factors. Four potential processing locations were analysed through a scenario based approach supported by GIS based distance matrices. Results indicate significant regional variability in both costs and emissions, with Biała Rawska emerging as the most costeffective option (931 million PLN; 57.7 million kgCO₂e), while Czekanka showed the highest costs (over 1 billion PLN) with only limited emission benefits. The findings underline the crucial role of location selection and transport organisation in improving the efficiency and sustainability of RSCWB. The study contributes a practical methodological framework for assessing reverse biomass flows, offering insights for both policymakers and industry stakeholders in advancing circular economy strategies. Keywords: reverse logistics, efficiency simulation, wood biomass, efficiency Simulation of Reverse Wood Biomass Supply Chains for Emission-Efficient Logistics Scenarios Damian Dubisz, Arkadiusz Kawa and Rafał Rokicki 296 1. INTRODUCTION The growing demand for renewable energy sources has positioned biomass, and particularly wood biomass, as a central component of sustainable energy systems(Nunes et al., 2020). Wood residues, waste wood, and by products from forestry and wood processing industries constitute valuable resources that can be mobilized through efficient supply chains (Demirbas, 2004). Traditional biomass supply chains are usually linear extending from raw material extraction to energy conversion (Nunes et al., 2020). However research by (Kawa, 2023) underline an increasing importance of the role of Reversed Supply Chains of Wood Biomass (RSCWB). These complex supply chain concepts account for the return, recovery, and redistribution of biomass resources, including post consumer wood and industrial by-products, which enhances both resource efficiency and environmental performance (Dubisz et al., 2024; Hrechyn et al., 2021). Therefore, an in depth research on its process efficiency improvement is essential. The effectiveness of RSCWB is contingent on multiple interacting factors: collection logistics, storage, transportation, processing capacity, and market demand. Effectiveness may be assessed not only from economical, but also ecological and social perspective (Hrechyn et al., 2021). This empowers the introduction of Circular Economy elements within supply chains to enhance the sustainable character of those (Werner-Lewandowska et al., 2024). However it has to be concerned that regional and temporal variability in biomass availability adds to the complexity of designing optimal organisational setups (Kawa et al., 2025). Given this complexity, it is essential not only to conceptualise reversed supply chains but also to evaluate their effectiveness under diverse operational and organisational conditions(Dubisz, 2023). A scenario-based approach emerges as a powerful tool in this context, allowing decision-makers to test and compare alternative supply chain configurations of transport processes. Introduction of Multi-Criteria Decision Analysis support the identification of trade-offs, and mitigate risks (Zając et al., 2024). 2. LITERATURE REVIEW Research on biomass supply chains has expanded significantly over the last two decades, with a growing emphasis on efficiency, resilience, and sustainability (Calvano et al., 2025). Early works primarily focused on forward logistics, addressing the cost and energy balance of transporting bulky and low-density biomass (Ekşioğlu et al., 2009; Rentizelas et al., 2009). However, more recent studies incorporate reverse logistics, recognising that wood residues and waste streams represent substantial untapped resources (Ackom et al., 2013; Babuka et al., 2020; Dubisz et.al., 2023). Reverse supply chains enable circularity by redirecting post-consumer and industrial wood towards bioenergy or material recycling, thereby reducing dependence on virgin resources and lowering greenhouse gas emissions (Sulaiman et al., 2020). Studies on the effectiveness of reversed biomass supply chains often evaluate performance in terms of logistics costs, energy efficiency, and environmental impacts. Modelling approaches such as mixed-integer linear programming, multi-objective optimisation, 25th international scientific conference Business Logistics in Modern Management October 2-3, 2025 - Osijek, Croatia 297 and life cycle assessment (LCA) are commonly applied to assess trade-offs between cost and sustainability (Kumar et al., 2023). Despite these methodological advances, challenges persist in capturing uncertainty in feedstock supply, market demand, and regulatory frameworks. To address this uncertainty, scenario-based analysis has been widely recommended. Scenario approaches allow researchers to simulate different organisational setups, collection schemes, and policy interventions, testing their influence on overall supply chain effectiveness including cascading utilization (Geldermann et al., 2016). For instance, scenarios can compare decentralised versus centralised collection systems, short versus long transport distances, or the integration of reverse flows with existing forward supply chains (Krstić et al., 2022). This approach helps also to identify robust strategies focused on transport emissions mitigation under varying future conditions, making it a particularly valuable decisionsupport tool in biomass logistics research. Research in chemistry industry logistic companies conducted by et al., 2019) revealed that tools like CO₂ calculators and IT support can help supply chains measure and manage emissions. Promoting multimodal transport reduces road dependence and lowers CO₂. Those elements could be incorporated within RSCWB to improve those digitalisation level and provide beneficial influence for management processes. In summary, the literature demonstrates that reversed supply chains for wood biomass have significant potential to improve resource efficiency and sustainability. However, their effectiveness depends on well-designed organisational structures, logistics coordination, and market integration. The scenario approach stands out as a methodological necessity, enabling systematic evaluation of alternative supply chain setups and supporting stakeholders in making informed, long-term decisions. 3. RESEARCH METHODOLOGY The research methodology combines a literature review with a scenario-based simulation of reversed wood biomass supply chains in Poland. Building on existing studies, a simulation model has been developed to evaluate trade-offs between cost and carbon emissions under alternative organisational setups. Historical data from a wood biomass processing company are used alongside official forestry statistics to estimate regional biomass availability. Transport scenarios are constructed by integrating GIS-based distance matrices, real carrier price lists, and vehicle emission factors. Four potential processing centre locations are tested, enabling comparative analysis of logistics efficiency. This approach provides a practical framework for assessing how reverse logistics can balance economic and environmental objectives, while also identifying the spatial and organisational conditions that influence these trade-offs. Research gap has been formulated, and it's based on the observation that existing studies rarely employ simulation-based approaches to comprehensively analyze the trade-offs between transportation costs and carbon emissions. Existing research rarely integrates simulation-based approaches to systematically evaluate the trade-offs between transportation costs and carbon emissions in reversed wood biomass logistics networks. Most current studies either focus exclusively on cost minimization or Simulation of Reverse Wood Biomass Supply Chains for Emission-Efficient Logistics Scenarios Damian Dubisz, Arkadiusz Kawa and Rafał Rokicki 298 emission reduction, or they assess both aspects in isolation without exploring their interdependencies and dynamic trade-offs. Furthermore, the potential of reverse logistics simulation as a decision-support tool for identifying, visualizing, and optimizing these trade-offs remains not sufficiently examined. Drawing on the literature analysis conducted and in relation to the requirement to verify the practical aspects of organizing RSCWB, the following research questions were formulated. Research Question 1: How can reverse logistics simulation help identify trade-offs between cost and emissions in reversed supply chains of wood biomass? Research Question 2: What are the key trade-offs between transport costs and carbon emissions in reversed supply chains of wood biomass? The novelty of this research lies in the integration of simulation methods with real operational data, enabling the assessment of reversed wood biomass supply chains not only in theoretical terms but also in practical application. Unlike earlier studies that modelled generic biomass logistics, this work employs actual transport price structures, emission factors, and region-specific forest resource data to construct a decision support framework grounded in market realities. It extends existing models by linking cost and environmental performance to specific organisational scenarios, providing actionable insights for both industry practitioners and policymakers. This operationalised approach represents methodological advancement, as it demonstrates how RSCWB can be evaluated under real economic and regulatory conditions, thereby strengthening their role in circular economy strategies. In the Figure 2 a general research methodology has been presented. 25th international scientific conference Business Logistics in Modern Management October 2-3, 2025 - Osijek, Croatia 299 Figure 2 Research methodology Clarification of the scope of the research Formulation of research questions 1 & 2. Research Question 1: How can reverse logistics simulation help identify tradeoffs between cost and emissions in wood biomass supply chains? Research Question 2: What are the key trade-offs between transport costs and carbon emissions in reversed wood biomass supply chains? Literature Review. Literature review on the organisational conditions of reversed wood biomass supply chains. A review of their design approaches in the literature Case Study Estimating the efficiency of reversed supply chains depending on the location chosen, using historical statistical data on wood biomass processing. Use of the ArcGIS Pro analysis tool to define distances, taking into account the road network in Poland. Calculations for each of the alternative scenarios for the location of wood biomass processing centres Cost-emission analysis for each site to identify the most optimal one Formulating the answers to the research questions Conclusion and recommendations for further research Source: own elaboration 4. SIMULATION ASSUMPTION The simulation was conducted using historical data from the wood biomass processing company located in Poland. Hence actual wood biomass volume estimations reflect the actual trends in wood biomass flows. Actual carriers price lists were used to compare cost parameters, presenting cost values and their structure. The proposed locations result from the distribution of wood biomass processing centers in individual voivodeships in Poland. Applying these, an estimation of the most effective logistics organizational scenario was performed. The simulations carried out to determine the optimal variant of wood biomass transport organisation were based on the size of forested areas in Poland. Forests are a key source of timber for further processing and wood biomass conversion. Since the data revealing only an area (ha) was not sufficient, it was necessary to translate it into an approximate forest volume (m3). Considering the specific characteristics of Polish forests, a conversion factor of 280 m³/ha was applied (Statistics Poland, 2024). To ensure accurate transformation, the following calculation Equation 1 was employed. Simulation of Reverse Wood Biomass Supply Chains for Emission-Efficient Logistics Scenarios Damian Dubisz, Arkadiusz Kawa and Rafał Rokicki 300 Equation 1 Formula for the conversion of forest area into its volume Standing volume (m³)=Forest area (ha)×Average growing stock (m³/ha) Source: own elaboration based on UNECE & FAO, (2010). The Table 7 below presents statistical data on the area of forests in voivodeships and their volume after conversion. According to (Statistics Poland, 2024) it allows for estimating the necessary means of transport capacity and conducting further simulations regarding the efficiency of their usage. Table 7 The size of forests in Poland and the conversion of forest area into volume Voivodeship Forest area (ha) Estimated standing volume (m3) Forest harvest (m3) Index of deforestation to its area Dolnośląskie 630 000 176 400 000 108 519 0.062% Kujawsko-Pomorskie 410 000 114 800 000 66 175 0.058% Lubelskie 570 000 159 600 000 85 574 0.054% Lubuskie 690 000 193 200 000 109 091 0.056% Łódzkie 380 000 106 400 000 60 869 0.057% Małopolskie 470 000 131 600 000 86 358 0.066% Mazowieckie 910 000 254 800 000 141 494 0.056% Opolskie 250 000 70 000 000 39 192 0.056% Podkarpackie 680 000 190 400 000 117 515 0.062% Podlaskie 620 000 173 600 000 111 785 0.064% Pomorskie 665 000 186 200 000 102 953 0.055% Śląskie 365 000 102 200 000 61 602 0.060% Świętokrzyskie 370 000 103 600 000 66 877 0.065% Warmińsko-Mazurskie 830 000 232 400 000 147 869 0.064% Wielkopolskie 770 000 215 600 000 143 884 0.067% Zachodniopomorskie 820 000 229 600 000 134 128 0.058% Source: own elaboration based on Forest Statistical Yearbook (2024) Forest density was presented in Figure 3 below to visualize the potential and localization of wood biomass harvesting sources in Poland. With regard to the indicated forest density in Poland, four locations for wood biomass processing centers were proposed: • Runowo, post code: 62-035 • Biała Rawska, post code: 96-230 • Kwidzyń, post code: 82-500 • Czekanka, post code: 42-470 The selection of the most cost-effective location and the assessment of transport emissions were conducted in the further part of this research. 25th international scientific conference Business Logistics in Modern Management October 2-3, 2025 - Osijek, Croatia 301 Figure 3 Total forest area(ha) in Poland in 2024 Source: own elaboration based on Forest Statistical Yearbook (2024) In the next step, the ratio of the amount of wood biomass harvested to the area and volume of forests was determined. The information is presented in Table 8. On this basis, the wood biomass production potential coefficient was determined, depending on the volume of forests. Table 8 Wood biomass harvesting potential in relation to forest volume Voivodeship Forest area (ha) Estimated standing volume (m3) Total biomass (m3) Wood biomass ratio to forest standing volume Dolnośląskie 7 560 000 176 400 000 2 528 496 0.0123 Kujawsko-Pomorskie 4 920 000 114 800 000 1 645 663 0.0125 Lubelskie 6 840 000 159 600 000 2 294 587 0.0126 Lubuskie 8 280 000 193 200 000 2 751 477 0.0125 Łódzkie 4 560 000 106 400 000 1 494 611 0.0123 Małopolskie 5 640 000 131 600 000 1 870 940 0.0124 Mazowieckie 10 920 000 254 800 000 3 791 222 0.0130 Opolskie 3 000 000 70 000 000 1 026 855 0.0128 Podkarpackie 8 160 000 190 400 000 2 702 901 0.0124 Podlaskie 7 440 000 173 600 000 2 492 004 0.0126 Pomorskie 7 980 000 186 200 000 2 660 134 0.0125 Śląskie 4 380 000 102 200 000 1 462 919 0.0125 Świętokrzyskie 4 440 000 103 600 000 1 546 819 0.0134 Warmińsko-Mazurskie 9 960 000 232 400 000 3 397 107 0.0128 Simulation of Reverse Wood Biomass Supply Chains for Emission-Efficient Logistics Scenarios Damian Dubisz, Arkadiusz Kawa and Rafał Rokicki 302 Wielkopolskie 9 240 000 215 600 000 3 131 121 0.0127 Zachodniopomorskie 9 840 000 229 600 000 3 241 523 0.0124 Total / average 113 160 000 2 640 400 000 38 038 380 0.0126 Source: own elaboration based on Forest Statistical Yearbook (2024) The overview of biomass quantities broken down by voivodeship indicates the dominant role of the Mazowieckie, Warmińsko-Mazurskie, Wielkopolskie and Zachodniopomorskie voivodeships. Simultaneously, it has to be noticed that there is a stable relationship between biomass quantities and forest standing volume in each voivodeship. Subsequently, the ratio of forest size by voivodeship to the amount of wood biomass harvested was visualized. The calculated ratio is presented in Figure 4 below. Figure 4 Forest area to total biomass potential Source: own elaboration based on Forest Statistical Yearbook (2024) Further research applied the analytical tool ArcGIS Pro with the Network Analyst add-on to determine the distance between key wood biomass processing points within the reversed wood biomass supply chain. The distance is calculated according to the current road network in Poland. The values obtained reflect the actual conditions of transport execution by vehicles with a maximum permissible weight (GVW) of 40 tonnes. Table 9 below shows the distance between biomass processing centres and the geographical centres of voivodeships. Thus, a distance matrix exploit in further research was obtained. This allowed for further calculation of costs and the carbon footprint of wood biomass residues transport processes, depending on the selected location scenario. 25th international scientific conference Business Logistics in Modern Management October 2-3, 2025 - Osijek, Croatia 303 Table 9 Distances between the wood processing centre and the geographical centre of the voivodeships Wood biomass processing centre by voivodeship [kilometres] Wood biomass sourcing location (Geographical centre of voivodeship) Runowo, 62035 Biała Rawska, 96230 Kwidzyń 82500 Czekanka 42470 Dolnośląskie 153 289 399 222 KujawskoPomorskie 134 268 110 380 Lubelskie 472 178 515 438 Lubuskie 170 392 411 320 Łódzkie 214 82 275 167 Małopolskie 479 237 484 84 Mazowieckie 322 69 346 262 Opolskie 243 237 466 134 Podkarpackie 642 244 551 264 Podlaskie 515 264 375 450 Pomorskie 304 387 101 483 Śląskie 352 230 445 35 Świętokrzyskie 375 134 399 128 WarmińskoMazurskie 337 255 132 450 Wielkopolskie 27 282 267 360 Zachodniopomorski e 288 555 350 550 Minimal distance 27 69 101 35 Average distance 314 256 352 295 Source: own elaboration based on ArcGIS Pro distance calculations The cost parameter is an important factor in evaluating the efficiency of logistics not only within reverse supply chains. Hence an average value of palletized loading unit of wood biomass was used. In this study, the average cost of a palletized unit load of wood biomass was calculated according to Raúl et al. (2022) research. The pallet unit was assumed to have dimensions of 1.2 m × 0.8 m × 1.5 m (length, width, height). Table 10 shows the average value of a one loading unit. Within proposed simulation scenarios value of a one loading unit doesn’t change with its quantity increase, hence it's value is always multiply of €21.60 Table 10 Wood biomass residues average value Number of pallets Value [PLN] 1 € 21.60 2 € 43.20 3 € 64.80 Source: own elaboration based Raúl et al. (2022) Simulation of Reverse Wood Biomass Supply Chains for Emission-Efficient Logistics Scenarios Damian Dubisz, Arkadiusz Kawa and Rafał Rokicki 310 UK DEFRA, 2024. Greenhouse gas reporting: conversion factors 2024. United Kingdom Department for Environmental Food & Rural Affairs Accessed on: 202408-06. UNECE & FAO, 2010. Global Forest Resources Assessment 2010: Main report (Forestry Paper No. 163). Werner-Lewandowska, K., Golińska-Dawson, P., Cyplik, P., 2024. Stimulators of transition toward circular supply chain - Exploratory studies in Electrical and Electronic Equipment industry. Logforum 20, 83–96. https://doi.org/10.17270/J.LOG.000973